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Room 108", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Adaptation and Identification with Improved Transient Performance and Accelerated Convergence", "paperCount": 5 }, { "code": "FrA09", "anchor": "fra09", "type": "Regular Session", "room": "Convention Hall - Room 109", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Physics Informed and Grey Box Model Identification I", "paperCount": 6 }, { "code": "FrA10", "anchor": "fra10", "type": "Open Invited Track Session", "room": "Convention Hall - Room 110", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Recent Advances in Iterative Learning and Repetitive Control I", "paperCount": 6 }, { "code": "FrA13", "anchor": "fra13", "type": "Regular Session", "room": "Exhibition Center 1 - Room 211", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Applications of Optimal Control", "paperCount": 6 }, { "code": "FrA14", "anchor": "fra14", "type": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 212", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Multi-Objective Optimization Techniques in Control Systems Engineering", "paperCount": 6 }, { "code": "FrA16", "anchor": "fra16", "type": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Modeling, Simulation and Control of Distributed Parameter Systems IV", "paperCount": 6 }, { "code": "FrA17", "anchor": "fra17", "type": "Invited Session", "room": "Exhibition Center 1 - Room 215", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Simulation Modeling, Machine Learning and Optimization Algorithms to Support Decision Making in Production, Logistics, and Supply Chain Management", "paperCount": 6 }, { "code": "FrA19", "anchor": "fra19", "type": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Large-Scale Complex Systems: Analysis and Control IV", "paperCount": 6 }, { "code": "FrA20", "anchor": "fra20", "type": "Invited Session", "room": "Exhibition Center 1 - Room 218", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "20 Years Smart Factory – Lessons Learned and Future Challenges", "paperCount": 5 }, { "code": "FrA21", "anchor": "fra21", "type": "Regular Session", "room": "Exhibition Center 1 - Room 311", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "JO-CEP: Wind Power and Control", "paperCount": 6 }, { "code": "FrA22", "anchor": "fra22", "type": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Modeling and Diagnostics of the Respiratory System I", "paperCount": 5 }, { "code": "FrA23", "anchor": "fra23", "type": "Invited Session", "room": "Exhibition Center 1 - Room 313", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Machine Learning for Process Control & Optimization", "paperCount": 6 }, { "code": "FrA24", "anchor": "fra24", "type": "Regular Session", "room": "Exhibition Center 1 - Room 314", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Process Monitoring, Fault Detection and Diagnosis", "paperCount": 5 }, { "code": "FrA25", "anchor": "fra25", "type": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Biomedical and Medical Imaging, Image Processing, Visualization", "paperCount": 6 }, { "code": "FrA26", "anchor": "fra26", "type": "Invited Session", "room": "Exhibition Center 1 - Room 316", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Advances in Optimal Control, Learning, Data-Driven Control and Decision-Making for Vehicle Autonomy", "paperCount": 6 }, { "code": "FrA27", "anchor": "fra27", "type": "Regular Session", "room": "Exhibition Center 1 - Room 317", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Trajectory Tracking and Path Following for AVs", "paperCount": 6 }, { "code": "FrA28", "anchor": "fra28", "type": "Regular Session", "room": "Exhibition Center 2 - Room 121", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "JO-JSC: Biomedical System Modeling, Identification, and Simulation", "paperCount": 6 }, { "code": "FrA30", "anchor": "fra30", "type": "Regular Session", "room": "Exhibition Center 2 - Room 123", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "JO: Monitoring, Performance Assessment, and Fault Detection in Control Systems", "paperCount": 6 }, { "code": "FrA31", "anchor": "fra31", "type": "Demonstration Session", "room": "Exhibition Center 2 - Room 124", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Demonstration: Control Systems and Applications", "paperCount": 5 }, { "code": "FrA32", "anchor": "fra32", "type": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 321", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Smart Materials Based Mechatronic Systems and Structures: From Innovative Design to Control", "paperCount": 6 }, { "code": "FrA33", "anchor": "fra33", "type": "Regular Session", "room": "Exhibition Center 2 - Room 322", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "JO-MECH: Mechatronic System Estimation and Control I", "paperCount": 6 }, { "code": "FrA34", "anchor": "fra34", "type": "Regular Session", "room": "Exhibition Center 2 - Room 323", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Mechatronics for Robotic Systems", "paperCount": 6 }, { "code": "FrB01", "anchor": "frb01", "type": "Regular Session", "room": "Convention Hall - Room 101", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "JO: Optimal Control and Optimization", "paperCount": 5 }, { "code": "FrB02", "anchor": "frb02", "type": "Regular Session", "room": "Convention Hall - Room 102", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "JO: Controller Synthesis", "paperCount": 6 }, { "code": "FrB03", "anchor": "frb03", "type": "Regular Session", "room": "Convention Hall - Room 103", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Applications of FAS Theory in Aircraft and Unmanned Aerial Vehicles", "paperCount": 5 }, { "code": "FrB05", "anchor": "frb05", "type": "Regular Session", "room": "Convention Hall - Room 105", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "LB: Distributed and Networked Systems", "paperCount": 8 }, { "code": "FrB06", "anchor": "frb06", "type": "Regular Session", "room": "Convention Hall - Room 106", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "LB: Machine Learning and Robotics", "paperCount": 8 }, { "code": "FrB07", "anchor": "frb07", "type": "Invited Session", "room": "Convention Hall - Room 107", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Advances in Distributed Control and Estimation for Complex Networked Systems", "paperCount": 6 }, { "code": "FrB08", "anchor": "frb08", "type": "Invited Session", "room": "Convention Hall - Room 108", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Interdisciplinary Advances in Stochastic/Nonlinear Systems Identification: Methods, Theory, and Applications to Judicial Sentencing Modeling", "paperCount": 6 }, { "code": "FrB09", "anchor": "frb09", "type": "Regular Session", "room": "Convention Hall - Room 109", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Physics Informed and Grey Box Model Identification II", "paperCount": 5 }, { "code": "FrB10", "anchor": "frb10", "type": "Open Invited Track Session", "room": "Convention Hall - Room 110", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Recent Advances in Iterative Learning and Repetitive Control II", "paperCount": 6 }, { "code": "FrB13", "anchor": "frb13", "type": "Regular Session", "room": "Exhibition Center 1 - Room 211", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "JO-EAAI: Applications of Optimal Control", "paperCount": 6 }, { "code": "FrB14", "anchor": "frb14", "type": "Regular Session", "room": "Exhibition Center 1 - Room 212", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Safety Critical Control", "paperCount": 6 }, { "code": "FrB15", "anchor": "frb15", "type": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 213", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Fractional Order Differentiation in Modeling and Control", "paperCount": 6 }, { "code": "FrB16", "anchor": "frb16", "type": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Modeling, Simulation and Control of Distributed Parameter Systems V", "paperCount": 6 }, { "code": "FrB17", "anchor": "frb17", "type": "Invited Session", "room": "Exhibition Center 1 - Room 215", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "A Key for Sustainable Longevity: Integrating Physical Asset Management and Obsolescence Management", "paperCount": 6 }, { "code": "FrB19", "anchor": "frb19", "type": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Large-Scale Complex Systems: Analysis and Control V", "paperCount": 6 }, { "code": "FrB20", "anchor": "frb20", "type": "Invited Session", "room": "Exhibition Center 1 - Room 218", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Challenges in Reconfigurable, Flexible or Agile Manufacturing Systems", "paperCount": 6 }, { "code": "FrB21", "anchor": "frb21", "type": "Regular Session", "room": "Exhibition Center 1 - Room 311", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "JO-CEP: Power Systems and Control", "paperCount": 6 }, { "code": "FrB22", "anchor": "frb22", "type": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Modeling and Diagnostics of the Respiratory System II", "paperCount": 6 }, { "code": "FrB23", "anchor": "frb23", "type": "Invited Session", "room": "Exhibition Center 1 - Room 313", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Modeling and Optimization in Bioprocesses", "paperCount": 5 }, { "code": "FrB24", "anchor": "frb24", "type": "Regular Session", "room": "Exhibition Center 1 - Room 314", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "AI Methods for FDI/FTC", "paperCount": 6 }, { "code": "FrB25", "anchor": "frb25", "type": "Regular Session", "room": "Exhibition Center 1 - Room 315", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Biomedical System Modeling, Identification, and Simulation", "paperCount": 6 }, { "code": "FrB26", "anchor": "frb26", "type": "Invited Session", "room": "Exhibition Center 1 - Room 316", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Perception and Situational Awareness for Autonomous Ships", "paperCount": 6 }, { "code": "FrB27", "anchor": "frb27", "type": "Regular Session", "room": "Exhibition Center 1 - Room 317", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Mission Planning and Decision Making for AVs", "paperCount": 6 }, { "code": "FrB28", "anchor": "frb28", "type": "Regular Session", "room": "Exhibition Center 2 - Room 121", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "JO-JSC: Healthcare Management, Disease Control, Critical Care", "paperCount": 6 }, { "code": "FrB30", "anchor": "frb30", "type": "Regular Session", "room": "Exhibition Center 2 - Room 123", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "JO-CEP: Smart Buildings and Building Automation", "paperCount": 5 }, { "code": "FrB31", "anchor": "frb31", "type": "Demonstration Session", "room": "Exhibition Center 2 - Room 124", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Demonstration: Robotics and Autonomous Systems", "paperCount": 6 }, { "code": "FrB32", "anchor": "frb32", "type": "Invited Session", "room": "Exhibition Center 2 - Room 321", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Medical and Rehabilitation Robotics", "paperCount": 6 }, { "code": "FrB33", "anchor": "frb33", "type": "Regular Session", "room": "Exhibition Center 2 - Room 322", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "JO-MECH: Mechatronic System Estimation and Control II", "paperCount": 5 }, { "code": "FrB34", "anchor": "frb34", "type": "Regular Session", "room": "Exhibition Center 2 - Room 323", "day": "Friday", "date": "August 28, 2026", "source": "Downloads/IFAC WC 2026 Program _ Friday August 28, 2026.html", "title": "Social Robotics and Ethics", "paperCount": 5 } ], "papers": [ { "id": "Mo-MoM00.1", "code": "MoM00.1", "title": "Good Old Fashioned Engineering Can Close the 100, 000 Year \"Data Gap\" in Robotics", "day": "Monday", "date": "August 24, 2026", "time": "08:30-09:30", "sessionCode": "MoM00", "sessionTitle": "Good Old Fashioned Engineering Can Close the 100, 000 Year \"Data Gap\" in Robotics", "sessionType": "Plenary Session", "room": "Auditorium", "authors": [ { "name": "Goldberg, Ken", "affiliation": "University of California, Berkeley" } ], "keywords": [ "AI-powered robotics", "Robotic learning and adaptation" ], "abstract": "Large models based on internet-scale data can now pass the Turing Test for intelligence. In this sense, data has \"solved\" language and many analogously claim that data has solved speech recognition and computer vision. Will data also solve robotics and automation, allowing general-purpose humanoid robots to achieve human-level performance? Using commonly accepted metrics for converting word and image tokens into time, the amount of internet-scale data used to train contemporary large vision language models (VLMs) is on the order of 100,000 years. I'll review 3 ways researchers are pursuing to close this gap, and a 4th approach, where data is collected as real robots operate in real commercial environments – which requires bootstrapping with AI and \"good old-fashioned engineering\" to create robots with real return on investment that will be adopted by industry. Such robots can create a \"data flywheel\" to increase performance and enable new functionality, accelerating the timeline to achieve reliable, general-purpose robots.", "url": "" }, { "id": "Mo-MoA01.1", "code": "MoA01.1", "title": "A Scenario Approach to the Robustness of Nonconvex–Nonconcave Minimax Problems", "day": "Monday", "date": "August 24, 2026", "time": "09:50-09:55", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Peng, Huan", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Chen, Guanpu", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Cyber security networked control", "Resilient networked control systems" ], "abstract": "This paper investigates probabilistic robustness of nonconvex–nonconcave minimax problems via the scenario approach. Specifically, under convex strategy sets for all players, inspired by recent advances in scenario optimization, we first establish a probabilistic robustness guarantee for an ε-stationary point, overcoming the dependence on the non-degeneracy assumption by proving the monotonicity of the stationary residual in the number of scenarios. Furthermore, in the presence of nonconvex strategy sets, we reveal the fundamental difficulty of obtaining a tight theoretical bound based on this recent framework. Consequently, we establish a relaxed, yet rigorously valid, probabilistic bound for a global minimax point. A numerical experiment corroborates our theoretical findings.", "url": "" }, { "id": "Mo-MoA01.2", "code": "MoA01.2", "title": "Model-Free Optimal Capturing Strategy for Multi-Agent Pursuit-Evasion Differential Games Via Reinforcement Learning", "day": "Monday", "date": "August 24, 2026", "time": "09:55-10:00", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Shi, Ran", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Zhang, Hai-Tao", "affiliation": "Huazhong (Central China) Univeristy of ScienceandTechnology" }, { "name": "Li, Jialuo", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Ding, Jianing", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Liu, Xiaohua", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Yuan, Bowen", "affiliation": "Huazhong University of Science and Technology" } ], "keywords": [ "Multi-agent systems" ], "abstract": "This paper investigates a multi-agent pursuit-evasion (MPE) differential game problem subject to unknown dynamics and external disturbances, where the pursuers seek to intercept the escaping evaders. The core theoretical challenge lies in determining the optimal capturing strategy for this complex game scenario. To address this, a target-selection algorithm is first introduced for pursuers, decomposing the collective MPE differential game into multiple single-pursuer-single-evader (SPSE) sub-games. Subsequently, a zero-sum differential game framework is established to derive the associated optimal game strategies. Sufficient conditions are derived to guarantee the capturability of the associated closed-loop game system. Furthermore, a data-driven reinforcement learning (RL) algorithm is developed for the online learning of the optimal game protocol. Finally, numerical simulations are conducted to validate the effectiveness of the proposed game strategy.", "url": "" }, { "id": "Mo-MoA01.3", "code": "MoA01.3", "title": "From String to Mesh Stability of Nonlinear Multi-Agent Systems in Discrete-Time (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:00-10:05", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Duarte Vargas, Leonardo", "affiliation": "L2S - Université Paris-Saclay" }, { "name": "Iovine, Alessio", "affiliation": "CNRS, CentraleSupélec" }, { "name": "Mattioni, Mattia", "affiliation": "Università Degli Studi Di Roma La Sapienza" }, { "name": "Stoica, Cristina", "affiliation": "CentraleSupélec, Université Paris-Saclay" } ], "keywords": [ "Multi-agent systems" ], "abstract": "This paper provides a new scalable verification test to ensure that disturbances do not amplify along the interconnection of a multi-agent system composed of heterogeneous agents in discrete-time. The proposed Mesh Stability extends the concept of String Stability to networks with general topology. The developed theoretical approaches are illustrated with a simulation example of a vehicle platoon in a ring road.", "url": "" }, { "id": "Mo-MoA01.4", "code": "MoA01.4", "title": "Spatio-Temporal Reconnection for Multi-Robot Networks Using Adaptive Prescribed-Time CBFs", "day": "Monday", "date": "August 24, 2026", "time": "10:05-10:10", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Liu, Hao", "affiliation": "University of Illinois Chicago" }, { "name": "Yang, Yupeng", "affiliation": "University of North Carolina at Charlotte" }, { "name": "Zhang, Yanze", "affiliation": "University of Illinois at Chicago" }, { "name": "Luo, Wenhao", "affiliation": "University of Illinois Chicago" } ], "keywords": [ "Multi-agent systems", "Adaptive control of multi-agent systems", "Control of networks" ], "abstract": "In multi-robot systems, maintaining persistent communication graph connectivity is often overly restrictive, especially when robots have limited communication ranges but operate in large environments. Instead, allowing robots to temporarily disconnect and later reconnect is often more desirable for efficient task execution while still ensuring timely information sharing across the team. In this paper, we propose an adaptive prescribed-time control barrier function (adaptive PT-CBF) framework that enables robots to temporarily disconnect and re-enter the communication range within an adjustable and feasible prescribed time. Moreover, we introduce a reconnection triggering mechanism that jointly considers task execution and reconnection urgency, thereby providing a principled way to decide when reconnection should occur. Theoretical analysis justifies convergence to the satisfying reconnection within a prescribed finite time. Experimental results validate the performance of our proposed adaptive PT-CBF with improved task efficiency and satisfying reconnections.", "url": "" }, { "id": "Mo-MoA01.5", "code": "MoA01.5", "title": "Conformism–Individualism Trade-Offs in LQG Graphon MFG with Control Mean Field Costs", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:15", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Huang, Ziqi", "affiliation": "McGill University" }, { "name": "Caines, Peter E.", "affiliation": "McGill Univ" } ], "keywords": [ "Multi-agent systems", "Control of networks" ], "abstract": "Limitations on the power or resources available to individual agents frequently arise in real-world games. To model such situations, this work studies a class of Linear Quadratic Gaussian Graphon Mean Field Games (LQG–GMFG) whose cost functional incorporates quadratic penalties on deviations from both an agent’s privately desired control and its local control mean field. These penalties represent two distinct motivations: individualism (acting on private preferences) and conformism (avoiding the higher resource costs incurred when acting differently from others). Separate state and control mean-field consistency conditions are imposed, and conditions for the existence of solutions are given. Using spectral decomposition, an explicit value function is obtained for the infinite-horizon, exponentially discounted stationary case, and numerical simulations reveal a trade-off between conformism and individualism.", "url": "" }, { "id": "Mo-MoA01.6", "code": "MoA01.6", "title": "Compliant Topology Design in Affine Formation Control Via Stress-Energy Minimization", "day": "Monday", "date": "August 24, 2026", "time": "10:15-10:20", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Wang, Yumeng", "affiliation": "Beijing Institute of Technology" }, { "name": "Yang, Qingkai", "affiliation": "Beijing Institute of Technology" }, { "name": "Chen, Wei", "affiliation": "Beijing Institute of Technology" }, { "name": "Fang, Hao", "affiliation": "Beijing Institute of Technology" } ], "keywords": [ "Multi-agent systems", "Control over networks", "Distributed control and estimation" ], "abstract": "Affine formation control provides an efficient framework for global maneuvers, but it is challenged by local, non-affine deformations. Such deformations induce high internal stress within conventionally rigid interaction topologies, leading to increased control effort. Inspired by structural mechanics, this paper proposes a compliant topology design method by introducing the concept of stress-energy. Specifically, we formulate two l1-regularized semidefinite programs to obtain optimal stress matrices that exhibit omnidirectional and task-specific compliance, respectively. Comparative simulations validate the superiority of our proposed topology construction schemes in reducing control cost and enhancing deformability.", "url": "" }, { "id": "Mo-MoA01.7", "code": "MoA01.7", "title": "An Individual-Delay-Reflected Generalized Consensus Analysis for Multi-Agent Systems with Heterogeneous Time-Varying Delays", "day": "Monday", "date": "August 24, 2026", "time": "10:20-10:25", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Lee, Hye Jin", "affiliation": "POSTECH" }, { "name": "Lee, Ho Sub", "affiliation": "POSTECH" }, { "name": "Lee, Hae Seong", "affiliation": "POSTECH" }, { "name": "Park, PooGyeon", "affiliation": "Pohang Univ. of Sci. & Tech" } ], "keywords": [ "Multi-agent systems", "Control under communication constraints", "Consensus" ], "abstract": "In multi-agent systems, heterogeneous time delays exist for all agents because of the difference in communication environments. Therefore, the consensus analysis of a system considering a homogeneous time-varying delay among all agents results in conservatism. In this study, an individual-delay-reflected generalized consensus is proposed for multi-agent systems with heterogeneous time-varying delays with various bounds. To reflect heterogeneous time-varying delays, the proposed Lyapunov–Krasovskii functional is constructed by dividing the integral term into intervals containing heterogeneous delays and considering augmented vectors with delay states and integral states. Furthermore, by adding zero equality conditions, conservatism is reduced. N-dependent generalized integral inequality is used to allow the user to adjust the computational complexity. Numerical examples demonstrate a reduction in conservatism with the proposed consensus criterion.", "url": "" }, { "id": "Mo-MoA01.8", "code": "MoA01.8", "title": "A Scalable L2-Gain Using a Matrix-Weighed Adjacency Matrix", "day": "Monday", "date": "August 24, 2026", "time": "10:25-10:30", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Axelson-Fisk, Magnus", "affiliation": "Technische Universität Berlin" }, { "name": "Knorn, Steffi", "affiliation": "TU Berlin" } ], "keywords": [ "Multi-agent systems", "Distributed control and estimation" ], "abstract": "We study multi-agent systems composed of linear agents interconnected through state coupling and subject to external disturbances. Considering a broad class of network topologies without imposing structural restrictions, we describe the overall system dynamics using a matrix-weighted adjacency matrix. Building on conditions that guarantee a bounded L2 gain for a given network, we derive sufficient conditions under which an entire family of networks achieves a scalable L2 gain, i.e., a performance bound that remains independent of network size. These results provide a systematic framework for assessing robustness and scalability in dynamically varying multi-agent networks with MIMO agents. The results are illustrated by a numerical example.", "url": "" }, { "id": "Mo-MoA01.9", "code": "MoA01.9", "title": "Distributed Safety-Aware Affine Formation Generation and Control for Multi-Agent Systems", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:35", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Zhao, Xinyue", "affiliation": "Beijing Insititute of Technology" }, { "name": "Yang, Qingkai", "affiliation": "Beijing Institute of Technology" }, { "name": "Huang, Hailong", "affiliation": "The Hong Kong Polytechnic University" }, { "name": "Feng, Shuai", "affiliation": "Nanjing University of Science and Technology" }, { "name": "Fang, Hao", "affiliation": "Beijing Institute of Technology" } ], "keywords": [ "Multi-agent systems", "Distributed control and estimation", "Consensus" ], "abstract": "Most formation control methods emphasize controller design while overlooking reference formation generation, which is crucial for collaborative performance and safety. This paper proposes a safety-aware formation generation and control framework that enables flexible multi-agent maneuvering in complex environments with dual-layer safety guarantees. First, we introduce parameter-level control barrier function (CBF) that imposes safety directly in the affine-parameter space, ensuring the generated reference formation is inherently collision-free. Then, a distributed consensus algorithm is proposed to drive all agents to consensus on common affine parameters, yielding coherent formation deformations. Finally, a standard agent-level CBF-based quadratic program is employed as a backend controller to track the safe reference trajectories. Simulations in cluttered environments validate the effectiveness of the approach.", "url": "" }, { "id": "Mo-MoA01.10", "code": "MoA01.10", "title": "Dynamic Consensus of Multi-Agent Systems with Distributed Collision Avoidance and Adaptive Performance Constraints", "day": "Monday", "date": "August 24, 2026", "time": "10:35-10:40", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Rüger, Marcel", "affiliation": "Universität Kassel" }, { "name": "Stursberg, Olaf", "affiliation": "University of Kassel" } ], "keywords": [ "Multi-agent systems", "Distributed control and estimation", "Consensus" ], "abstract": "This paper proposes a decentralized control framework for collision-free trajectory tracking in homogeneous multi-agent systems with actuation constraints. Building on the concept of adaptive performance functions known for single agents, the method enables each agent to autonomously regulate its transient tracking performance in response to local interactions and control saturation. The core contributions are a dynamic consensus-based reference generation mechanism and a relevance-based selection of potential collision partners using a prediction of the closest approach. A modified flexible performance law ensures that tracking performance is preserved even when avoidance or saturation temporarily dominate the control action. A Lyapunov-based analysis guarantees invariance of the performance envelope and boundedness of all closed-loop signals. Simulation results with interacting agents in a three dimensional space demonstrate collision-free motion and convergence.", "url": "" }, { "id": "Mo-MoA01.11", "code": "MoA01.11", "title": "Distributed Stabilization of Heterogeneous Multi-Agent Systems: A Lyapunov Approach", "day": "Monday", "date": "August 24, 2026", "time": "10:40-10:45", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Ma, Yuxin", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Li, Xianwei", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Li, Shaoyuan", "affiliation": "Shanghai Jiao Tong Univ" } ], "keywords": [ "Multi-agent systems", "Distributed control and estimation", "Control of networks" ], "abstract": "This paper addresses the problem of distributed stabilization for heterogeneous linear multi-agent systems (MASs). It is assumed that all agents use relative state/output information, while only a subset can utilize absolute measurements. We present a Lyapunov-based approach, proposing both state- and output-feedback protocols. Under the standard stabilizability and detectability assumptions, it is shown that the proposed protocols ensure distributed asymptotic stabilization if the directed augmented communication graph contains a spanning tree. The effectiveness of the proposed approach is demonstrated through a simulation example, which verifies the ability of the proposed control strategy to stabilize heterogeneous linear MASs under the specified conditions.", "url": "" }, { "id": "Mo-MoA01.12", "code": "MoA01.12", "title": "Distributed Multi-Target Enclosing Control Framework for a Split and Merge Task", "day": "Monday", "date": "August 24, 2026", "time": "10:45-10:50", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "García-Lechuz Sierra, Juan", "affiliation": "University of Zaragoza" }, { "name": "Aragues, Rosario", "affiliation": "Universidad De Zaragoza" }, { "name": "Lopez-Nicolas, Gonzalo", "affiliation": "Universidad De Zaragoza" } ], "keywords": [ "Multi-agent systems", "Distributed control and estimation", "Control of networks" ], "abstract": "This paper studies the problem of cooperative multi-target enclosing. More specifically, we propose a distributed control framework to address the case where it is necessary to split or merge the team of agents as the distance between target groups increases or decreases, respectively. We first present a multi-target enclosing control law combining an affine formation control law with distance-based control terms to adjust formations around targets. Then, a novel weight matrix design is proposed for affine formation control of regular polygons. The distributed nature of this weight design method allows agents to locally compute the weights so that they can reorganize in subgroups or merge while ensuring convergence. Stability analysis of the proposed weight design method is included, as well as a numerical simulation using the proposed enclosing control to illustrate the splitting and merging task.", "url": "" }, { "id": "Mo-MoA01.13", "code": "MoA01.13", "title": "The Distance-Based Formation Controller Design for Multi-Agent Systems in Port-Hamiltonian Form", "day": "Monday", "date": "August 24, 2026", "time": "10:50-10:55", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Zhao, Jingyi", "affiliation": "Dalian University of Technology" }, { "name": "Wu, Yongxin", "affiliation": "Université Marie Et Louis Pasteur" }, { "name": "Garcia de Marina, Hector", "affiliation": "Universidad De Granada" }, { "name": "Wu, Yuhu", "affiliation": "Dalian University of Technology" }, { "name": "Le Gorrec, Yann", "affiliation": "FEMTO-ST, SupMicroTech Besançon" } ], "keywords": [ "Multi-agent systems", "Distributed control and estimation", "Control over networks" ], "abstract": "Based on the practical scenario where collisions in formation control may lead to agent damage, this paper investigates the integrated problem of distance-based formation control and collision avoidance for multi-agent systems governed by port-Hamiltonian dynamics. A foundational step involves constructing a signed incidence matrix, which, by design, corresponds to a directed acyclic graph and possesses the full column rank property. To overcome the prevalent issue of local minima in traditional artificial potential fields, a novel design utilizing attraction-only potentials is introduced, with collision avoidance rigorously enforced by safety barriers. This framework leads to a unified controller that concurrently manages velocity tracking, target formation acquisition, and inter-agent safety. The stability of the resulting closed-loop system is guaranteed through LaSalle's invariance principle. Numerical simulations demonstrate the validity and effectiveness of the proposed control strategy.", "url": "" }, { "id": "Mo-MoA01.14", "code": "MoA01.14", "title": "Hierarchical Cooperative Perception for Large-Scale Swarm Herding under Sensing Constraints", "day": "Monday", "date": "August 24, 2026", "time": "10:55-11:00", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Zhu, Haonan", "affiliation": "Beihang University" }, { "name": "Chen, Zilu", "affiliation": "Beihang University" }, { "name": "Han, Liang", "affiliation": "Beihang University" } ], "keywords": [ "Multi-agent systems", "Distributed control and estimation", "Control under communication constraints" ], "abstract": "The cooperative herding of high-entropy, non-cooperative swarms is a critical yet challenging problem in multi-agent control. However, existing macroscopic theories often rely on idealized global state availability, leading to perceptual fragmentation when applied under physical sensing constraints. To bridge this gap, we propose a Hierarchical Cooperative Perception (HCP) architecture. By coupling sparse informed observers with dense local actuators, HCP reconstructs non-local potential fields to overcome sensing blind spots without global communication. We derive a macroscopic flux balance analysis grounded in non-reciprocal field theory to establish rigorous stability conditions. Validated through large-scale simulations and high-fidelity PyBullet experiments with hundreds of quadrotors, the approach achieves an 80% higher containment rate than baseline methods. Crucially, the macroscopic formulation renders control complexity invariant to population size, ensuring scalability to massive swarms beyond hardware limits.", "url": "" }, { "id": "Mo-MoA01.15", "code": "MoA01.15", "title": "Multi-Agent Object Transportation Via Distributed-Optimization-Based Reference Force Design", "day": "Monday", "date": "August 24, 2026", "time": "11:00-11:05", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Sugawara, Taiga", "affiliation": "The University of Osaka" }, { "name": "Sakurama, Kazunori", "affiliation": "The University of Osaka" } ], "keywords": [ "Multi-agent systems", "Distributed optimization", "Consensus" ], "abstract": "This paper proposes a distributed control framework for cooperative object transportation by multi-agent systems. Reference forces are computed through a constrained optimization that incorporates grasping and avoiding undesired rotation. To ensure scalability, the optimization is solved using a distributed algorithm in which each agent updates its reference force through local computation and limited neighbor-to-neighbor communication. Numerical simulations demonstrate that the proposed method maintains grasping and achieves a desired reference of the object's velocity, enabling flexible and scalable cooperative transport.", "url": "" }, { "id": "Mo-MoA01.16", "code": "MoA01.16", "title": "Barrier-Certified Multi-Agent Ergodic Coverage Over Complex Surfaces", "day": "Monday", "date": "August 24, 2026", "time": "11:05-11:10", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Aminzadeh, Ali", "affiliation": "Tampere University" }, { "name": "Gusrialdi, Azwirman", "affiliation": "Tampere University" } ], "keywords": [ "Multi-agent systems", "Distributed optimization", "Control under communication constraints" ], "abstract": "This paper presents a barrier-certified multi-agent ergodic coverage framework for safe and efficient exploration over complex non-Euclidean surfaces. We address the challenge of extending surface ergodic exploration to distributed multi-agent systems (MASs), where globally coupled ergodic statistics must be estimated cooperatively while satisfying safety and communication constraints. Building on the Laplace–Beltrami (LB) eigenbasis, we formulate a distributed ergodic coverage problem on meshable surfaces that enables cooperative exploration with respect to a desired inspection density. Safety is enforced through a unified set of control barrier functions (CBFs) guaranteeing inter-agent collision avoidance, distance-based connectivity, line-of-sight (LOS) preservation, and minimum surface clearance, leading to geometry-dependent couplings. A distributed consensus mechanism enables cooperative estimation of global ergodic statistics without centralized coordination, while maintaining performance and improving scalability. The framework is validated in a simulated 3D wind turbine inspection scenario.", "url": "" }, { "id": "Mo-MoA01.17", "code": "MoA01.17", "title": "Distributed Algorithms for Coopetition in Multi-Agent Systems", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:15", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Du, Hongbo", "affiliation": "Beijing Institute of Technology" }, { "name": "Yu, Hao", "affiliation": "Beijing Institute of Technology" }, { "name": "Liu, Shenyu", "affiliation": "Beijing Institute of Technology" }, { "name": "Shi, Dawei", "affiliation": "Beijing Institute of Technology" }, { "name": "Gao, Bo", "affiliation": "Beijing Institute of Graphic Communication" } ], "keywords": [ "Multi-agent systems", "Distributed optimization", "Distributed control and estimation" ], "abstract": "This paper studied a distributed coopetition problem for multi-agent systems (MASs), where the state of each agent reflects the extent of its contributions in a task. There are two key components in the considered coopetition problem: collaborative tasks and competitive constraints. The former necessitates a cumulative (weighted) contribution from all agents to achieve a desired outcome, while the latter comes from the competition among agents: no single agent exerts significantly more effort than the others (considering the respective weights). First, the proposed coopetition problem is transformed into an equivalent constrained optimization problem. then, a distributed algorithm for solving the coopetition problem is provided from the Karush-Kuhn-Tucker (KKT) conditions of the optimization problem. Subsequently, it is proved that the algorithm can ensure the states of agents to converge to one of its equilibria, which are the necessary and sufficient condition to the coopetition problem. Finally, an example is simulated to illustrate the effectiveness of the theoretical results.", "url": "" }, { "id": "Mo-MoA01.18", "code": "MoA01.18", "title": "Multi-Robot Adaptive Pursuit Via Dynamic Clustering and Assignment Optimization", "day": "Monday", "date": "August 24, 2026", "time": "11:15-11:20", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Wang, Ziteng", "affiliation": "Zhejiang University" }, { "name": "Gu, Dingning", "affiliation": "Zhejiang University" }, { "name": "You, Feng", "affiliation": "Zhejiang University" }, { "name": "Li, Xinyue", "affiliation": "Zhejiang University" }, { "name": "Sheng, Kaiyuan", "affiliation": "Zhejiang University" }, { "name": "Liu, Hanchuan", "affiliation": "Zhejiang University" }, { "name": "Hong, Chenhui", "affiliation": "Zhejiang University" }, { "name": "Lin, Yinglian", "affiliation": "Deepwater Engineering Construction Center, CNOOC Shenzhen Branch, Shenzhen" }, { "name": "Xiong, Rong", "affiliation": "Zhejiang University" }, { "name": "Zheng, Xingwen", "affiliation": "Zhejiang University" } ], "keywords": [ "Multi-agent systems", "Distributed optimization", "Distributed control and estimation" ], "abstract": "This paper addresses multi-robot pursuit failures caused by evader clustering, which typically leads to formation overlap and trajectory conflicts. We propose a Dynamic Adaptive Hunting (DAH) framework that replaces static assignments with a real-time dynamic clustering mechanism based on evader spatial distribution. To enhance efficiency, an intra-cluster optimization strategy refines target assignments to suppress trajectory crossings and mitigate the long-tail effect, thereby accelerating overall convergence. At the execution layer, an Artificial Potential Field (APF) controller provides goal-directed guidance with effective collision avoidance. Simulations across varying swarm scales confirm that DAH significantly reduces capture time and travel distance compared to non-optimized baselines, validating its efficacy and scalability in complex, dynamic scenarios.", "url": "" }, { "id": "Mo-MoA01.19", "code": "MoA01.19", "title": "Safe TSY Null-Space Deep Reinforcement Learning for Bearing-Rigid Quadrotor Formations", "day": "Monday", "date": "August 24, 2026", "time": "11:20-11:25", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Aliyari, Morteza", "affiliation": "Department of Electrical Engineering, National Taiwan University" }, { "name": "Tsai, Cheng-Huan", "affiliation": "National Taiwan University" }, { "name": "Lin, Tsung-Kai", "affiliation": "National Taiwan University" }, { "name": "Wang, En-Rong", "affiliation": "National Taiwan University" }, { "name": "Chiang, Ming-Li", "affiliation": "National Taiwan University" }, { "name": "Fu, Li-Chen", "affiliation": "National Taiwan Univ" } ], "keywords": [ "Multi-agent systems", "Distributed reinforcement learning", "Consensus and reinforcement learning control" ], "abstract": "This paper presents a safe multi-agent deep reinforcement learning framework for cooperative quadrotor formation flight based on bearing rigidity. A team of UAVs is required to navigate cluttered environments while preserving a desired formation shape and avoiding collisions. A rigidity-based bearing controller guarantees convergence to the desired shape up to global translation, uniform scaling and coordinated yaw (TSY). On top of this analytic layer, we embed a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) architecture whose actors operate only in the TSY null-space, so learning affects the group motion but cannot inject formation distortion. Safety is enforced by a zeroing control barrier function (CBF) quadratic program that filters the nominal control into a safe joint velocity. Unlike conventional safe RL, we differentiate through the CBF–QP and train the centralized critic and decentralized actors on the executed safe actions, eliminating the train–test mismatch between nominal and filtered policies. Simulations in Gazebo with a bearing-rigid three–quadrotor formation show that the proposed method achieves higher success rate, faster and more consistent convergence, and significantly lower formation error than an RL+CBF baseline that acts in the full joint action space.", "url": "" }, { "id": "Mo-MoA01.20", "code": "MoA01.20", "title": "A Learning-Based Communication Framework for Multi-Agent Pursuit-Evasion Game", "day": "Monday", "date": "August 24, 2026", "time": "11:25-11:30", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Chen, Ke", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Peng, Xiangyang", "affiliation": "Tianjin University" }, { "name": "Gong, Youmin", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Yuan, Qiufan", "affiliation": "Shanghai Institute of Aerospace System Engineering" }, { "name": "Ma, Guangfu", "affiliation": "Harbin Institute of Technology" }, { "name": "Mei, Jie", "affiliation": "Harbin Institute of Technology, Shenzhen" } ], "keywords": [ "Multi-agent systems", "Learning methods for control", "Distributed reinforcement learning" ], "abstract": "In multi-agent Pursuit-Evasion (PE) scenarios, effective communication among pursuers is essential for successful coordination and capture efficiency. Traditional PE algorithms often face limitations due to fixed communication structures and inadequate adaptability to dynamic environments. To address these challenges, this study introduces a learning-based communication framework specifically designed for multi-target PE tasks. We enhance the existing Target-oriented Multi-Agent Communication and Cooperation (ToM2C) framework for multi-target PE scenarios by integrating an intensity-based filtering mechanism in place of its original Graph Neural Network (GNN) module. This filtering mechanism enables selective communication among pursuers based on confidence in target assignment predictions. Simulation results demonstrate significant improvements in both capture success rates and communication efficiency. Physical experiments validate sim-to-real transferability, confirming the effectiveness of the proposed approach.", "url": "" }, { "id": "Mo-MoA01.21", "code": "MoA01.21", "title": "Wasserstein Distributionally Robust Nash Equilibrium Seeking with Heterogeneous Data: A Lagrangian Approach", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:35", "sessionCode": "MoA01", "sessionTitle": "Shotgun: Multi-Agent and Networked Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Wang, Zifan", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Pantazis, George", "affiliation": "TU Delft" }, { "name": "Grammatico, Sergio", "affiliation": "Delft Univ. of Tech" }, { "name": "Zavlanos, Michael M.", "affiliation": "Duke University" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Multi-agent systems", "Randomized algorithms in stochastic systems" ], "abstract": "We study a class of distributionally robust games where agents are allowed to heterogeneously choose their risk aversion with respect to distributional shifts of the uncertainty. In our formulation, heterogeneous Wasserstein ball constraints on each distribution are enforced through a penalty function leveraging a Lagrangian formulation. We then formulate the distributionally robust game as a variational inequality problem, and show that under certain assumptions the original seemingly infinite-dimensional Nash equilibrium problem is equivalent to a multi-agent but finite-dimensional variational inequality problem with a strongly monotone mapping. Due to the inner maximization problem, it is however still challenging to calculate a distributionally robust Nash equilibrium. To this end, we design an approximate Nash equilibrium seeking algorithm and prove convergence of the average regret to a quantity that diminishes with the number of iterations, thus learning the desired equilibrium up to an a priori specified accuracy. Numerical simulations corroborate our theoretical findings.", "url": "" }, { "id": "Mo-MoA02.1", "code": "MoA02.1", "title": "MsCoFFe: A Multi-Stage Composite Feature Enhancement FramEwork for UAV Tiny Object Detection in Road Monitoring of Smart City", "day": "Monday", "date": "August 24, 2026", "time": "09:50-09:55", "sessionCode": "MoA02", "sessionTitle": "Shotgun: Automatic Control and Systems Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Wang, Ya", "affiliation": "Hangzhou Normal University" }, { "name": "Yao, Le", "affiliation": "Hangzhou Normal University" }, { "name": "Zhu, Zheren", "affiliation": "Hangzhou Normal University" }, { "name": "Yang, Zeyu", "affiliation": "Huzhou Normal University" }, { "name": "Wang, Jiayu", "affiliation": "Beihang University" }, { "name": "Jiang, Xiaoyu", "affiliation": "Beihang University" } ], "keywords": [ "AI for smart cities", "Low-altitude economy", "Cyber-physical urban systems" ], "abstract": "Object detection from Unmanned Aerial Vehicles (UAVs) is pivotal for the road monitoring task of smart city but faces severe challenges due to the prevalence of tiny objects. These targets suffer from spatial information decay, high-frequency feature submergence, and pixel misalignment within Deep Neural Networks (DNNs). To address these systemic bottlenecks, this paper proposes a Multi-stage Composite Feature enhancement FramEwork (MsCoFFe) for the current popular deep learning based UAV vision models. Unlike specific model patches, MsCoFFe is a general and plug-and-play framework designed to reinforce feature fidelity and alignment. It introduces the Feature Complementary Mapping (FCM) and Multi-Kernel Perception (MKP) modules in the backbone to preserve spatial details and enable multi-scale perception. Furthermore, it incorporates High-Frequency Perception (HFP) and Spatial Dependency Perception (SDP) modules in the neck network to amplify weak target signals and dynamically correct pixel shifts via cross-attention. The case study on the VisDrone2019 dataset demonstrate that integrating MsCoFFe into state-of-the-art deep learning object detectors, such as RT-DETR and DEIM, significantly improves detection robustness. Notably, the proposed MsCoFFe increases the AP50 of the DEIM model by 6.8%, validating its effectiveness in complex aerial surveillance scenarios with tiny objects.", "url": "" }, { "id": "Mo-MoA02.2", "code": "MoA02.2", "title": "DmmD: Dual mmWave Radar Drone Detection System for Urban Emergency", "day": "Monday", "date": "August 24, 2026", "time": "09:55-10:00", "sessionCode": "MoA02", "sessionTitle": "Shotgun: Automatic Control and Systems Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Li, Shenglei", "affiliation": "Waseda University" } ], "keywords": [ "AI for smart cities", "Smart city control and optimization", "Cyber-physical urban systems" ], "abstract": "Millimeter-wave radar is attractive for urban emergency response because it remains operative in darkness and visual obscurants, yet existing drone-detection systems trade 3D spatial resolution against temporal continuity. We present DmmD, a dual-mmWave-radar framework that combines a Multi-View Doppler Rectification Layer with an STC-Net based on 3D ConvLSTM. MVDRL aligns Doppler features from orthogonal views using geometric priors before fusion. Experiments on a synchronized dual-IWR6843 platform achieve 97.10 % AP, improve AP1 over CubeDN, and reduce mean localization error to 0.52 m. Barrier tests further show less than 1% point-cloud density reduction through visually opaque materials.", "url": "" }, { "id": "Mo-MoA02.3", "code": "MoA02.3", "title": "Simultaneous Implementability Problem for Multi-Dimensional Systems in the Behavioral Framework (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:00-10:05", "sessionCode": "MoA02", "sessionTitle": "Shotgun: Automatic Control and Systems Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Ishii, Rei", "affiliation": "The University of Electro-Communications" }, { "name": "Kaneko, Osamu", "affiliation": "The University of Electro-Communications" } ], "keywords": [ "Analytic design", "Linear systems", "Control of complex systems" ], "abstract": "In the behavioral approach to systems and control, a system is characterized by the set of the trajectories, which is referred to as the behavior. Using this approach enables us to obtain solutions that are completely independent of mathematical expressions and to discuss them in a set-theoretical context. As considered in the standard control theory, one fundamental problem is whether a given control specification can be implemented for a particular plant. This issue has also been studied within the behavioral approach. In cases where the dynamics of a plant varies, it becomes important to determine the extent of acceptable changes. We formalized this problem as the simultaneous implementability problem, this means to consider what is a condition under which a single specification can be realized by using a single controller for two different plants. In this paper, we adopt an set-theoretical approach to examine the simultaneous implementability problem in the behavioral approach.", "url": "" }, { "id": "Mo-MoA02.4", "code": "MoA02.4", "title": "Real-Time Classification of Tyre Models in High-Performance Vehicles: Comparing Model-Based and Learning-Based Approaches (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:05-10:10", "sessionCode": "MoA02", "sessionTitle": "Shotgun: Automatic Control and Systems Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Milani, Sabrina", "affiliation": "Politecnico Di Milano" }, { "name": "Leoni, Jessica", "affiliation": "Politecnico Di Milano" }, { "name": "Corno, Matteo", "affiliation": "Politecnico Di Milano" }, { "name": "D'Avico, Luca", "affiliation": "Politecnico Di Milano" }, { "name": "Tanelli, Mara", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Automotive system identification and modelling", "Modeling, supervision, control and diagnosis of automotive systems", "AI and learning-based control for automotive systems" ], "abstract": "Automatic real-time tyre identification is crucial for improving vehicle performance, safety, and efficiency. This capability is valuable in racing applications, where it can support consistency checks and strategic decisions, and even more relevant in urban and aftermarket scenarios, where tyre information is often unavailable, and vehicle control systems could benefit from real-time adaptation. Despite its relevance, the literature mainly focuses on tyre usage monitoring. Furthermore, these approaches also reveal a trade-off between practicality and interpretability: model-based methods provide physically meaningful results but often require measurements that are rarely available in real-world vehicles, whereas machine learning methods exploit accessible vehicle signals and achieve high predictive performance, typically at the expense of interpretability. To address this gap, this paper presents and compares two real-time tyre classification strategies: a model-based method designed to rely on accessible vehicle measurements, and an interpretable learning-based approach. Their performance is assessed in both simulation and real-world experiments. While both methods achieve optimal performance in simulation, real-world variability and noise reduce the accuracy of the model-based approach. In contrast, the learning-based classifier maintains an F1-score of 96.5%, proving to be a practical and interpretable solution for real-time tyre recognition.", "url": "" }, { "id": "Mo-MoA02.5", "code": "MoA02.5", "title": "Structure of Human–Automation Trust in the Japanese Cultural Context: Cross-Cultural Validation of Affect-Based and Cognition-Based Initial Trust", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:15", "sessionCode": "MoA02", "sessionTitle": "Shotgun: Automatic Control and Systems Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Cui, Zixin", "affiliation": "University of Tsukuba" }, { "name": "Zhou, Huiping", "affiliation": "University of Tsukuba" }, { "name": "Itoh, Makoto", "affiliation": "University of Tsukuba" } ], "keywords": [ "Cognitive and emotional control/AI systems, arts and control", "Cross-cultural aspects of engineering", "Human-centric automation/AI Systems, and human agency" ], "abstract": "Japanese culture places significant emphasis on emotionality alongside intellectual and logical aspects. This study examined the structure of initial trust in automation within the Japanese cultural context. Through exploratory and confirmatory factor analyses across three AI-enabled automation systems, the two-dimensional structure of initial trust, comprising cognition-based and affect-based initial trust, was supported. This finding is consistent with that observed in the Chinese context, although the specific items retained for each dimension were only partially aligned with those in the original Chinese scale. These results highlight the importance of distinguishing between cognition-based and affect-based trust in assessing initial trust in automation within both Chinese and Japanese cultural settings. Designers and practitioners should explicitly account for these two dimensions in the initial trust management of automation systems, thereby ensuring greater conceptual clarity and more accurate measurement.", "url": "" }, { "id": "Mo-MoA02.6", "code": "MoA02.6", "title": "An Interactive Virtual Training System for Twelve-Phase Rectifier Generators in Control Engineering Education (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:15-10:20", "sessionCode": "MoA02", "sessionTitle": "Shotgun: Automatic Control and Systems Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Zhou, Xingwei", "affiliation": "Wuhan University" }, { "name": "Hu, Wenshan", "affiliation": "Wuhan University" }, { "name": "Lei, Zhongcheng", "affiliation": "Wuhan University" } ], "keywords": [ "Control education laboratories", "Industry-academia collaboration in control education", "Internet based control education" ], "abstract": "This paper presents an interactive virtual training system for the fault diagnosis and operation of twelve-phase rectifier generators, addressing the high cost and risks of physical training in control engineering education. Developed with Unity3D and Vue.js, the system enables principle learning, operational procedures, and fault injection in a simulated environment. A dedicated assessment module automatically evaluates trainee performance. The platform provides a safe, flexible, and effective tool for enhancing practical understanding and troubleshooting skills of complex marine electrical systems, demonstrating the significant value of virtual simulation technology in modern control education.", "url": "" }, { "id": "Mo-MoA02.7", "code": "MoA02.7", "title": "Human Skill Evaluation with Multi-Objective Optimization in Context of Unknown Intentions (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:20-10:25", "sessionCode": "MoA02", "sessionTitle": "Shotgun: Automatic Control and Systems Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Speidel, Piet", "affiliation": "Robert Bosch GmbH" }, { "name": "Hilsch, Michael", "affiliation": "Robert Bosch GmbH" }, { "name": "Alt, Benedikt", "affiliation": "Robert Bosch GmbH" }, { "name": "Schildbach, Georg", "affiliation": "University of Luebeck" } ], "keywords": [ "Cyber-physical and human systems (CPHS)", "Human-centric automation/AI Systems, and human agency", "System dynamics and control in CPHS" ], "abstract": "This paper introduces a novel human skill evaluation framework that leverages multiobjective optimization to address the limitations of assessing human proficiency in dynamic, complex systems with unknown intentions. Previous methods struggle with multi-objective tasks, offer limited interpretability, or require extensive data. Our framework quantifies human skill by measuring the Euclidean distance from a human’s Key Performance Indicator (KPI) vector to the surface of Pareto optimal solutions. We explore various intention assumptions by selecting different points on the Pareto Front and evaluate their impact on skill assessment using manual parking maneuver simulations and demonstrate the framework’s real-time computability. The results highlight the influence of intention assumptions on skill evaluation and demonstrate the potential for a robust, interpretable, and adaptable approach for quantifying human skill.", "url": "" }, { "id": "Mo-MoA02.8", "code": "MoA02.8", "title": "A Generalized Nash Equilibrium-Seeking Scheme for Trauma Resuscitation", "day": "Monday", "date": "August 24, 2026", "time": "10:25-10:30", "sessionCode": "MoA02", "sessionTitle": "Shotgun: Automatic Control and Systems Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Ekpo, Promise", "affiliation": "Cornell Tech" }, { "name": "Taylor, Angelique", "affiliation": "Cornell Tech" }, { "name": "Molu, Lekan", "affiliation": "Molux Labs" } ], "keywords": [ "Cyber-physical and human systems (CPHS)", "Social computing", "Game theories" ], "abstract": "Trauma resuscitation is a clinical process for treating life-threatening physiological disorders in safety-critical environments, driven by the experience of healthcare workers (HCWs). Designing and optimizing quantifiable metrics that accurately capture HCW decisions may augment current resuscitation procedures with the potential to improve patient outcomes. This motivates our socio-technical formulation of trauma resuscitation as a distributed generalized Nash equilibrium (GNE)-seeking game with coupled inequality constraints. This method is optimized over a time-varying communication graph. We introduce novel insights from clinical experience to model HCWs behavior. This work facilitates the best possible resuscitation outcome given HCWs’ workloads, schedules, competencies, and limited resources.", "url": "" }, { "id": "Mo-MoA02.9", "code": "MoA02.9", "title": "Towards Population Models of Human Control with Covariate Effects", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:35", "sessionCode": "MoA02", "sessionTitle": "Shotgun: Automatic Control and Systems Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Aguilar-López, José M.", "affiliation": "University of Seville" }, { "name": "Mosquera, Elena", "affiliation": "Universidad De Sevilla" }, { "name": "Hatanaka, Takeshi", "affiliation": "Institute of Science Tokyo" }, { "name": "Maestre, Jose M.", "affiliation": "University of Seville" } ], "keywords": [ "Cyber-physical and human systems (CPHS)", "System dynamics and control in CPHS", "Human-centric automation/AI Systems, and human agency" ], "abstract": "Human operators play critical roles in cyber--physical systems, yet control--theoretic models typically treat inter--subject variability as noise rather than as systematic patterns linked to individual characteristics. This article introduces a population mixed--effects framework for modeling human sensorimotor control that explicitly relates controller parameters to demographic and experiential covariates. Closed--loop identification experiments were conducted with 66 participants performing a single--axis target acquisition task, with the human modeled as a SISO controller and the plant as a kinematic integrator. Comparing PI, PID, and second--order structures, we find that the second--order model with a real zero consistently outperforms PI/PID, and that video game experience emerges as a particularly strong predictor of controller performance, with experienced players exhibiting faster response dynamics and improved tracking accuracy.", "url": "" }, { "id": "Mo-MoA02.10", "code": "MoA02.10", "title": "Stochastic Energy Management of Hydrogen-Based Geo-Distributed Data Centers", "day": "Monday", "date": "August 24, 2026", "time": "10:35-10:40", "sessionCode": "MoA02", "sessionTitle": "Shotgun: Automatic Control and Systems Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Chen, Mengxiao", "affiliation": "Xi'an Jiaotong University" }, { "name": "Cao, Xiaoyu", "affiliation": "Xi'an Jiaotong University" }, { "name": "Sun, Xunhang", "affiliation": "Xi'an Jiaotong University" }, { "name": "Tian, Zhaoming", "affiliation": "Xi'an Jiaotong University" }, { "name": "Li, Miaomiao", "affiliation": "Xi’an Jiaotong University" }, { "name": "Dong, Yuchen", "affiliation": "Xi'an Jiaotong University" }, { "name": "Guan, Xiaohong", "affiliation": "Xi'an Jiaotong University" } ], "keywords": [ "Data centers and cloud computing", "Decision making under uncertainty" ], "abstract": "Integrating on-site renewable energy (RE) generation into data centers (DCs) offers a promising pathway toward energy sustainability. However, the inherent intermittency, volatility, and uncertainty of RE may expose DC energy systems to substantial risks of supply–demand imbalance. To address this challenge, this paper develops a stochastic energy management method for hydrogen-based geo-distributed data centers (HBGDCs). A remaining-time bucket mechanism is proposed to explicitly capture the temporal flexibility of DC workloads by dynamically tracking diminishing processing windows. Moreover, to handle forecast errors in renewable generation and workload arrivals, a receding-horizon scheduling framework is designed, in which a scenario-based two-stage stochastic optimization model is integrated. Numerical studies on a typical HBGDC system show that the proposed approach consistently improves operational efficiency under both normal and adversarial conditions, while being highly tolerant to forecast errors.", "url": "" }, { "id": "Mo-MoA02.11", "code": "MoA02.11", "title": "Dynamic Coalition Game-Based Task Allocation for Multi-Spacecraft Systems with Threat-Adaptive Weights", "day": "Monday", "date": "August 24, 2026", "time": "10:40-10:45", "sessionCode": "MoA02", "sessionTitle": "Shotgun: Automatic Control and Systems Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Yu, Changping", "affiliation": "Beihang University (BUAA)" }, { "name": "Liu, Yang", "affiliation": "Beihang University, Beijing, P.R.China" }, { "name": "Zheng, Zewei", "affiliation": "Beihang University" }, { "name": "Zhang, Jia'ming", "affiliation": "Beihang University" } ], "keywords": [ "Decision making under uncertainty" ], "abstract": "This paper proposes a dynamic coalition game-theoretic framework for multispacecraft cooperative task allocation in adversarial environments with uncertain target priorities. The key innovation is an augmented time-varying characteristic function that integrates mission beneffts, execution costs, and transition penalties, with threat-adaptive weight mechanisms. We introduce an intelligence conffdence metric that dynamically evolves through observation, enabling adaptive target prioritization. The Shapley value allocation mechanism ensures fairness and stability while a utility maximization formulation with individual rationality constraints prevents coalition deviations. The system dynamically adjusts task assignments in response to changing threat levels, ensuring consistent performance over time by explicitly accounting for the costs of switching tasks and reorganizing teams.", "url": "" }, { "id": "Mo-MoA02.12", "code": "MoA02.12", "title": "Reinforcement Learning Framework Using Optimal Control and Control Barrier Functions for Reach-Avoid Games with Exclusion Zones (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:45-10:50", "sessionCode": "MoA02", "sessionTitle": "Shotgun: Automatic Control and Systems Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Santos Franco, Daniel", "affiliation": "Queen's University" }, { "name": "Rabbath, Camille Alain", "affiliation": "Queen's University" }, { "name": "Givigi, Sidney", "affiliation": "Queen's University" } ], "keywords": [ "Differential or dynamic games", "Applications of optimal control", "Control barrier functions and state space constraints" ], "abstract": "We study the reach-avoid problem, where a pursuer aims to capture an evader, targeting a target plane in three-dimensional space (3D) while avoiding exclusion zones. As there is no optimal control for situations involving exclusion zones, we propose using Reinforcement Learning (RL) to generalize the optimal control from scenarios without exclusion zones to those that include them. To guarantee that the pursuer does not enter the exclusion zones, we use Control Barrier Functions (CBF) as both a safety filter and as a measure of reward for the pursuer. We demonstrate the necessity of each proposed component within the framework by conducting an ablation study. Furthermore, the efficacy of the framework is validated through simulation against optimal control with CBF.", "url": "" }, { "id": "Mo-MoA02.13", "code": "MoA02.13", "title": "Human-Centric Peer-To-Peer Federated Learning with Trusted Data Sharing for Skill Transfer in Industry 5.0 (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-10:55", "sessionCode": "MoA02", "sessionTitle": "Shotgun: Automatic Control and Systems Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Jazi, Mahran", "affiliation": "Tel Aviv University" }, { "name": "Ben-Gal, Irad", "affiliation": "Tel Aviv University" } ], "keywords": [ "Human-centric automation/AI Systems, and human agency", "Decentralized economics/ecosystems (DeEco)" ], "abstract": "Industry~5.0 is reshaping smart manufacturing toward human-centric production, where operators collaborate with AI systems and networked machines. In such environments, workstations, teams, and operators face different tasks and conditions, resulting in non-identically distributed (non-IID) data and heterogeneous expertise. These factors challenge centralized AI deployment and raise privacy, scalability, and robustness concerns. This paper proposes a human-centric peer-to-peer federated learning (P2P-FL) framework for collaborative skill transfer in Industry~5.0. Each worker or production cell is represented by an edge device that trains a local decision-support model and exchanges model parameters with socially or organizationally connected peers over a decentralized graph. To mitigate non-IID effects while preserving privacy and autonomy, we introduce trusted data sharing, where peers share only a small, controlled fraction of local data with selected neighbors. Using MNIST, CIFAR-10, CIFAR-100, and an industrial NEU surface-defect dataset with synthetic non-IID worker profiles, we compare FedAvg, FedProx, and P2P-FL with trusted sharing levels of 20% and 40%. Results show that modest sharing significantly improves final accuracy and macro-level performance while reducing client performance disparities. The findings highlight implications for human--AI collaboration, workforce upskilling, and AI assistants in Industry~5.0 smart manufacturing.", "url": "" }, { "id": "Mo-MoA02.14", "code": "MoA02.14", "title": "Style-Invariant sEMG Recognition for Human–Robot Interaction (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:55-11:00", "sessionCode": "MoA02", "sessionTitle": "Shotgun: Automatic Control and Systems Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Cho, Hyeong Rae", "affiliation": "Korea Institute of Robotics & Technology Convergence" }, { "name": "Jang, Sunho", "affiliation": "Korea Institute of Robotics and Technology Convergence" }, { "name": "Hong, Hyung Gil", "affiliation": "Korea Institute of Robotics Technology Convergence" }, { "name": "Yun, Haeyong", "affiliation": "Kiro" }, { "name": "Cho, YongJun", "affiliation": "Korea Institute of Robotics Technology Convergence" } ], "keywords": [ "Human-robot interaction", "Medical and rehabilitation robotics", "AI-powered robotics" ], "abstract": "Surface electromyography (sEMG) is increasingly used in wearable human–robot interaction systems; however, inter-subject variability limits reliable transfer of gesture intent across users. This paper presents a style-invariant learning framework that enhances subject-independent sEMG-based gesture recognition without requiring subject identity labels. The method employs Instance Selective Whitening (ISW) for self-supervised pre-training to suppress subject-specific style from feature covariance, followed by supervised fine-tuning for gesture classification. Experiments on Ninapro DB1, DB2, and DB4 show improved accuracy and reduced cross-subject performance variance. The results suggest the potential of the proposed framework for adaptive sEMG-driven wearable HRI systems, while real-time robotic validation remains an important direction for future work.", "url": "" }, { "id": "Mo-MoA02.15", "code": "MoA02.15", "title": "Using a Smartphone-Based Brake Testing Application and Real Vehicle Data in Automotive Engineering Education (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:00-11:05", "sessionCode": "MoA02", "sessionTitle": "Shotgun: Automatic Control and Systems Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Tapak, Peter", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Kocúr, Michal", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Matej, Juraj", "affiliation": "Research and Development Department, TESTEK, A.s., Vajnorská 137, 831 04 Bratislava, Slovakia" } ], "keywords": [ "Industry-academia collaboration in control education", "Control education laboratories", "Control engineering curricula" ], "abstract": "This paper presents the integration of a smartphone-based brake testing application, originally developed for periodic technical inspections (PTI) and expert practice, into an undergraduate course on vehicle motion. The TESTEK mobile application records vehicle acceleration using the internal sensors of Android devices and evaluates braking performance in accordance with UN ECE regulations, providing the mean fully developed deceleration (MFDD) and related indicators. The same application family has been deployed at all PTI stations in the Slovak Republic and has been validated against certified decelerometers, which makes its results suitable both for regulatory use and for education. We describe how real braking tests recorded by this application are reused in the subject Processes of Vehicle Motion as the basis for a kinematics assignment in which students analyse acceleration, velocity, distance and MFDD, and identify individual phases of the braking process. The assignment combines numerical integration, signal preprocessing and interpretation of results in the context of legislation. The proposed approach requires only low-cost hardware (a smartphone and, when needed, a generic OBD interface) yet provides students with authentic, industry-grade data and tools. We outline the course context, the design of the laboratory task, implementation experience and qualitative observations, and discuss planned extensions towards remote laboratories.", "url": "" }, { "id": "Mo-MoA02.16", "code": "MoA02.16", "title": "Conceptual Questions on Stability, Structure, and Equilibria in State-Space LTI Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:05-11:10", "sessionCode": "MoA02", "sessionTitle": "Shotgun: Automatic Control and Systems Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Goubej, Martin", "affiliation": "University of West Bohemia" }, { "name": "Varagnolo, Damiano", "affiliation": "NTNU - Norwegian University of Science and Technology" } ], "keywords": [ "Repositories for control education", "Control education learning analytics", "Control engineering curricula" ], "abstract": "We present a small collection of conceptual multiple-choice questions (MCQs) on continuous-time LTI systems, designed for a second-year bachelor course on fundamentals of automatic control or dynamical systems. The questions target four recurrent misconceptions: (i) confusing internal (equilibrium) stability with external (BIBO) stability; (ii) believing that poles on the imaginary axis automatically imply bounded trajectories, irrespective of Jordan structure; (iii) assuming that repeated eigenvalues in state-space realizations necessarily cause loss of controllability or observability; and (iv) overlooking that equilibria and working points are solutions of linear algebraic equations whose existence and uniqueness depend on the column space and null space of the system matrix. The exercises are intended primarily as pen-and-paper MCQs (no calculators or computer algebra required), suitable for in-class formative assessment, written examinations, or as prompts for short oral discussions. The prerequisite learning outcomes (PLOs) include being able to solve linear systems of equations, compute eigenvalues and eigenvectors (and in some questions Jordan blocks), and interpret state-space models and BIBO stability. The assessed intended learning outcomes (ILOs) focus on distinguishing different notions of stability, relating boundedness to Jordan structure, diagnosing controllability/observability from input/output directions, and determining existence and uniqueness of equilibria. Annotated solutions explicitly address the targeted misconceptions and can be used as self-study material by students or as a discussion guide for instructors.", "url": "" }, { "id": "Mo-MoA02.17", "code": "MoA02.17", "title": "The Missing Variable: Socio-Technical Alignment in Risk Evaluation", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:15", "sessionCode": "MoA02", "sessionTitle": "Shotgun: Automatic Control and Systems Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Flehmig, Niclas", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Lundteigen, Mary Ann", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Yin, Shen", "affiliation": "Norwegian University of Science and Technology" } ], "keywords": [ "Safety-critical and resilient systems", "Human-centric automation/AI Systems, and human agency", "Regulation, policy, and legal issues in control/AI" ], "abstract": "This paper addresses a critical gap in the risk assessment of AI-enabled safety-critical systems. While these systems, where AI systems assist human operators, function as complex socio-technical systems, existing risk evaluation methods fail to account for the associated complex interaction between human, technical, and organizational components. Through a comparative analysis of system attributes from both socio-technical and AI-enabled systems and a review of current risk evaluation methods, we confirm the absence of explicit socio-technical considerations in standard risk expressions. To bridge this gap, we introduce a novel socio-technical alignment ( STA ) variable designed to be integrated into the traditional risk equation. This variable estimates the degree of harmonious interaction between the AI systems, human operators, and organizational processes. A case study on an AI-enabled liquid hydrogen ( LH 2 ) bunkering system demonstrates the variable's relevance. By comparing a naive and a safeguarded system design, we illustrate how the STA -augmented expression captures socio-technical safety implications that traditional risk evaluation overlooks, providing a more system-theoretic basis for risk evaluation.", "url": "" }, { "id": "Mo-MoA02.18", "code": "MoA02.18", "title": "Output Consensus for Matrix-Weighted Heterogeneous Linear Multi-Agent Systems under Distributed DoS Attacks", "day": "Monday", "date": "August 24, 2026", "time": "11:15-11:20", "sessionCode": "MoA02", "sessionTitle": "Shotgun: Automatic Control and Systems Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Zhou, Siwen", "affiliation": "Beihang University" }, { "name": "Liu, Yang", "affiliation": "Beihang University, Beijing, P.R.China" }, { "name": "Li, Wenling", "affiliation": "Beihang University" } ], "keywords": [ "Social networks for smart cities", "Smart city security and resilience", "Cyber-physical urban systems" ], "abstract": "This paper investigates the resilient output consensus control for heterogeneous linear multi-agent systems (MASs) under matrix-weighted networks subject to denial-of-service (DoS) attacks. Matrix-valued interaction weights are employed to characterize the interdependencies among the multidimensional agent states. Differing from prior work on synchronous attacks, a more general scenario is considered where attacks independently and randomly disrupt individual interaction links, modeled by a Markov switching process. First, a fully distributed resilient estimator is proposed, enabling followers to estimate the leader state even under DoS attacks. Based on the estimator, a distributed control protocol is then developed to guarantee asymptotic output tracking in the mean-square sense for all followers. Finally, numerical simulations are conducted to validate the effectiveness of the proposed protocol.", "url": "" }, { "id": "Mo-MoA03.1", "code": "MoA03.1", "title": "Safe Reinforcement Learning for Building Thermal Control under Hardware Constraints", "day": "Monday", "date": "August 24, 2026", "time": "09:50-09:55", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Montazeri`, Mina", "affiliation": "Empa" }, { "name": "Künzli, Stefan", "affiliation": "Empa" }, { "name": "Remlinger, Carl", "affiliation": "SDSC" }, { "name": "Heer, Philipp", "affiliation": "Empa, Urban Energy Systems" } ], "keywords": [ "Demand response", "Big data and machine learning applied to smart cities", "Smart buildings and building automation" ], "abstract": "Reinforcement learning (RL) offers a data-driven alternative to model-based control for building heating systems. However, most existing approaches focus solely on energy efficiency and thermal comfort, overlooking actuator degradation caused by frequent valve switching. This paper presents an RL-based control framework that jointly optimizes energy consumption, occupant comfort, and actuator longevity. Using a physically consistent neural network model trained on real data from the UMAR unit at the NEST building in Dübendorf, Switzerland, two RL algorithms—A2C and PPO—are evaluated under varying switching-penalty strategies and a smooth policy architecture (LipsNet). Results show that a PPO controller with a temperature-dependent switching penalty reduces valve cycles ten-fold while increasing energy use by only 7%. The LipsNet network further achieves comparable energy efficiency with four times fewer switching events. These findings demonstrate that incorporating hardware-aware constraints into RL training can extend actuator lifespan without compromising overall system performance.", "url": "" }, { "id": "Mo-MoA03.2", "code": "MoA03.2", "title": "Smarter Than Throttling: DVFS and Flow Control for Efficiency-Driven CPU Cooling", "day": "Monday", "date": "August 24, 2026", "time": "09:55-10:00", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Zheng, Jianwen", "affiliation": "Politecnico Di Milano" }, { "name": "Dionigi, Federico", "affiliation": "Politecnico Di Milano" }, { "name": "Terraneo, Federico", "affiliation": "Politecnico Di Milano" }, { "name": "Leva, Alberto", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Energy management systems", "Control and management of energy systems" ], "abstract": "Thermal and performance control in modern CPUs faces a fundamental trade-off: maintaining thermal safety via DVFS (i.e., reducing frequency) limits performance, while overcooling wastes energy. We propose a cascade-like thermal management scheme that acts coordinately on frequency and coolant flow: the former counteracts millisecond-scale load variations to keep the chip safe, while the latter adapts heat removal on a slower time frame to reduce overcooling and associated energy waste. We also present a tuning strategy for the scheme, demonstrate its potential through simulations, and discuss technical viability in realistic settings such as data centres.", "url": "" }, { "id": "Mo-MoA03.3", "code": "MoA03.3", "title": "An OPF-Based Analysis of LMP Formation and Congestion Surplus under LCC HVDC Minimum Transfer Requirements", "day": "Monday", "date": "August 24, 2026", "time": "10:00-10:05", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Kim, Ki-Hyun", "affiliation": "Konkuk University" }, { "name": "Roh, Jae-Hyung", "affiliation": "Konkuk University" }, { "name": "Park, Jong-Bae", "affiliation": "Konkuk University" } ], "keywords": [ "Energy market", "Electrical transmission systems", "Energy management systems" ], "abstract": "This study investigates the effect of the minimum transfer requirement of Line-Commutated Converter (LCC) HVDC systems on nodal price formation and congestion surplus in electricity markets. To systematically examine this characteristic, a two-bus DC Optimal Power Flow (OPF) model is proposed that explicitly incorporates both minimum and maximum power transfer limits. Because thyristor-valve-based LCC HVDC systems require a minimum level of power transfer through the converter, this operational characteristic imposes an asymmetric lower-bound constraint on power flow that does not arise in conventional AC transmission systems. Analytical results derived from the Lagrangian formulation demonstrate that when the minimum transfer requirement becomes binding, this lower-bound constraint directly influences the nodal price difference between regions. Consequently, even when power flows in the forward direction, the price differential may be reversed, giving rise to negative congestion surplus. These findings indicate that the minimum transfer requirement can materially affect nodal prices and market settlement outcomes. Simulation results corroborate the analytical findings, confirming that the minimum transfer requirement can cause congestion surplus to become negative under specific load conditions — an outcome that does not arise in a standard transmission-line model without this constraint. These results suggest that the operational characteristics of LCC HVDC may introduce variability into the settlement revenues of Financial Transmission Rights (FTRs). Accordingly, FTR market participants may benefit from explicitly accounting for the minimum transfer requirement when formulating bidding and hedging strategies, as it can alter both the direction and magnitude of nodal price differences and congestion surplus.", "url": "" }, { "id": "Mo-MoA03.4", "code": "MoA03.4", "title": "Explainable Artificial Intelligence for Improving Probabilistic Deep Learning in Grid-Scale Load Forecasting", "day": "Monday", "date": "August 24, 2026", "time": "10:05-10:10", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "van Zyl, C", "affiliation": "University of Pretoria" }, { "name": "Ye, Xianming", "affiliation": "University of Pretoria" }, { "name": "Raj, Naidoo", "affiliation": "University of Pretoria" }, { "name": "Zhu, Bing", "affiliation": "Beihang University" } ], "keywords": [ "Forecasting of power supply and demand", "Energy management systems", "Energy market" ], "abstract": "Probabilistic load forecasting is required when operators must plan for both expected demand and forecast uncertainty. However, feature selection remains difficult for deep probabilistic models because their outputs describe lower and upper quantiles rather than a single point forecast. This study evaluates whether explainable artificial intelligence (XAI) attributions of model-implied predictive spread can support feature selection in probabilistic load forecasting. A Quantile CNN-LSTM is trained on ISO New England load, weather, market, and calendar data to produce 24-hour-ahead 90% prediction intervals. The lower and upper quantile forecasts are transformed into two explanation targets: an interval midpoint, representing demand magnitude, and an interval width, representing predictive spread. SHAP and Permutation Feature Importance (PFI) are used to rank features for each target. The rankings are tested through recursive feature ablation, tracking forecast error, interval width, and prediction-interval coverage. SHAP-based mean and width rankings, and PFI-based mean rankings, improve forecast accuracy by approximately 14–16% and move empirical coverage closer to the nominal 90% level. PFI-based width rankings do not provide the same benefit. Width-based feature selection did not outperform mean-based selection because the same demand and weather variables dominate both targets. The main contribution is therefore diagnostic: width attributions show whether features that drive demand magnitude also drive the model’s predictive spread, enabling feature selection to be evaluated directly from probabilistic model outputs rather than from a separate point-forecasting model.", "url": "" }, { "id": "Mo-MoA03.5", "code": "MoA03.5", "title": "Monotonicity Analysis of Interval-Optimal Operation Plans for Thermal Power Generation and Inter-Area Power Transmission in Electric Power Networks", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:15", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Kojima, Yuga", "affiliation": "Tokyo University of Marine Science and Technology" }, { "name": "Koike, Masakazu", "affiliation": "Tokyo University of Marine Science and Technology" }, { "name": "Ishizaki, Takayuki", "affiliation": "Tokyo Institute of Technology" }, { "name": "Ramdani, Nacim", "affiliation": "Université D'Orléans" } ], "keywords": [ "Forecasting of power supply and demand", "Energy management systems", "Power plant control" ], "abstract": "本研究は広域電力ネットワークの日先運用最適化手法を提案します。太陽光発電(PV)の発電と需要は信頼区間を持つ純電力需要予測プロファイルとして扱われ、需要予測プロファイルに対する運用パラメータの単調性を用いて、区間の内のいかなる実現にも最適性を維持する運用範囲を導出します。オペレーターには、各エリアにおける熱発電、バッテリー貯蔵、エリア間電力転送の上限と下限が提供されており、需要が信頼区間内でどのように振る舞っても最適な運用計画範囲を特定できます。本論文では、面積が少ないにもかかわらず電力網の構造を検討し、運用電力の単調性解析を行います。", "url": "" }, { "id": "Mo-MoA03.6", "code": "MoA03.6", "title": "Machine Learning Topology Filtering and Parameter Identification of Power Networks", "day": "Monday", "date": "August 24, 2026", "time": "10:15-10:20", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Ouali, Rabah", "affiliation": "Ecole Centrale De Lille" }, { "name": "Dieulot, Jean-Yves", "affiliation": "Polytech Lille" }, { "name": "Legry, Martin", "affiliation": "Arts Et Métiers ParisTech" }, { "name": "Yim, Pascal", "affiliation": "Ecole Centrale De Lille" }, { "name": "Guillaud, Xavier", "affiliation": "L2EP, Ecole Centrale De Lille, France" }, { "name": "Colas, Frédéric", "affiliation": "ENSAM" } ], "keywords": [ "Power electronics", "Electrical transmission systems" ], "abstract": "This paper presents a methodology for retrieving the impedance parameters of subsystems within a radial power grid from global impedance measurements. The first stage involves filtering the contribution of topological parameters (e.g., connection cables) through a denoising autoencoder. Several network architectures were investigated and compared, including multilayer perceptrons, convolutional neural networks, and recurrent networks for both encoder and decoder structures. In the second stage, the parameters of the subsystems were identified by incorporating the relative proportion of each subsystem within the network into the machine learning algorithm. The proposed method was validated on a case study involving a wind farm equipped with power converters, where the identified parameters achieved an accuracy of up to 5%. The most effective configuration employed a multiplicative operation on the admittance feature map vectors. This study represents an initial step toward the development of aggregated power grid models derived solely from external measurements.", "url": "" }, { "id": "Mo-MoA03.7", "code": "MoA03.7", "title": "Backstepping Control with Prescribed Error Bounds and Fixed-Time Convergence for DC Microgrids with Constant Power Loads", "day": "Monday", "date": "August 24, 2026", "time": "10:20-10:25", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Gao, Yiming", "affiliation": "University of Alberta" }, { "name": "Shu, Zhan", "affiliation": "University of Alberta" }, { "name": "Li, Yunwei", "affiliation": "University of Alberta" } ], "keywords": [ "Power electronics", "Power systems stability", "Energy management systems" ], "abstract": "This paper proposes an improved observer-based backstepping control scheme for DC microgrids with constant power loads (CPLs). A prescribed-performance function (PPF) is employed to restrict the tracking error within predefined bounds, while an enhanced fixed-time control achieves a smaller settling-time bound. In addition, a sliding-mode disturbance observer (FT-SMDO) is developed to estimate the time-varying power flow of uncertain CPLs. To ensure optimal estimation performance and eliminate manual gain tuning, the Grey Wolf Optimizer (GWO) is utilized to automatically tune the FT-SMDO parameters. Simulation results demonstrate that the proposed method achieves faster voltage recovery, improved robustness, and superior overall performance compared with existing controllers.", "url": "" }, { "id": "Mo-MoA03.8", "code": "MoA03.8", "title": "Mechanical Analogy for Power System Dynamics with Park’s Synchronous Machine Models", "day": "Monday", "date": "August 24, 2026", "time": "10:25-10:30", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Nishino, Taku", "affiliation": "Tokyo Institute of Technology" }, { "name": "Koizumi, Jigen", "affiliation": "Institute of Science Tokyo" }, { "name": "Terao, Kentaro", "affiliation": "Institute of Science Tokyo" }, { "name": "Ishizaki, Takayuki", "affiliation": "Tokyo Institute of Technology" } ], "keywords": [ "Power systems stability" ], "abstract": "This paper proposes a mechanical analogy to provide an intuitive understanding of power system dynamics, especially for novices. Our approach is applicable to multi-machine systems and incorporates the high-fidelity Park's model. We demonstrate a comprehensive mapping where all state variables of the power system, including generators and loads, correspond to states in the analogy. This framework facilitates the understanding of complex nonlinear dynamics and is validated by establishing its rigorous correspondence with the system's energy function.", "url": "" }, { "id": "Mo-MoA03.9", "code": "MoA03.9", "title": "Homotopic Policy Iteration for Linear Zero-Sum Games: Application to Load Frequency Control", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:35", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Ning, Yongkai", "affiliation": "Northwestern Polytechnical University" }, { "name": "Hu, Junhao", "affiliation": "AVIC Chengdu Aircraft Design & Research Institute" }, { "name": "Wang, Zhong", "affiliation": "Northwestern Polytechnical University" }, { "name": "Li, Yan", "affiliation": "Northwestern Polytechnical University" } ], "keywords": [ "Power systems stability", "Distributed optimization for smart grids", "Power plant control" ], "abstract": "Load Frequency Control (LFC) is crucial for maintaining power system stability by restoring nominal frequency and balancing inter-area power flows after disturbances.The control‑disturbance interaction can be modeled as a linear zero-sum game within the H_infty control framework. While the Simultaneous Policy Update Algorithm (SPUA) has offered higher computational efficiency than the traditional double-loop method for linear zero-sum games, it relies on the Newton–Kantorovich conditions for convergence, making it highly dependent on specific initial conditions that are difficult to verify, especially in model-free settings.This paper employs a homotopy-based single-loop policy iteration method for solving linear zero-sum games arising in LFC. The method only requires an initial stabilizing controller, obtained through an iterative homotopy procedure, and avoids the need for system dynamics or a predefined initial matrix. As a result, it offers improved computational efficiency and reliable convergence. Simulation studies on a single-area power system demonstrate the method’s robustness and accuracy compared with SPUA approach.", "url": "" }, { "id": "Mo-MoA03.10", "code": "MoA03.10", "title": "Transient Stability Analysis of Inverter-Based Power Systems Based on Energy Function Convexity", "day": "Monday", "date": "August 24, 2026", "time": "10:35-10:40", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Terao, Kentaro", "affiliation": "Institute of Science Tokyo" }, { "name": "Nishino, Taku", "affiliation": "Tokyo Institute of Technology" }, { "name": "Ishizaki, Takayuki", "affiliation": "Tokyo Institute of Technology" } ], "keywords": [ "Power systems stability", "Electrical transmission systems" ], "abstract": "This paper performs a numerical analysis of transient stability in power systems using the convexity of the energy function and an analogy with mass-spring-damper systems. The Hessian of the energy function and its eigenvalues are interpreted as the spring constant matrix and spring strength, respectively. Numerical results demonstrate that increasing the spring constant matrix through parameter tuning of the VSG model enhances the system's transient stability. Furthermore, a positive correlation exists between the critical clearing time (CCT) during a ground fault and the stiffness. Using the analogy with the physical system, an intuitive interpretation is provided for the mechanism by which stronger springs increase CCT.", "url": "" }, { "id": "Mo-MoA03.11", "code": "MoA03.11", "title": "From the ISS Property to Boundedness of Power Networks with Multiple Synchronous Generators and DERs Using Bounded Integral Control", "day": "Monday", "date": "August 24, 2026", "time": "10:40-10:45", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Alexandridis, Theodosis", "affiliation": "University of Patras" }, { "name": "Michos, Grigoris", "affiliation": "University of Patras" }, { "name": "Konstantopoulos, George", "affiliation": "University of Patras" } ], "keywords": [ "Power systems stability", "Electrical transmission systems", "Control and management of energy systems" ], "abstract": "We derive the nonlinear dynamical model of an AC power system consisting of multiple Synchronous Generators (SGs) and Distributed Energy Resources (DERs) interfaced with the grid by DC/AC power converters, in a generic meshed network topology that also incorporates the dynamical phenomena of the lines and the loads. In particular, the high-order nonlinear model is used for the SGs, while the converter units of the DERs are considered to operate in grid-forming mode, leading to dynamical modelling in the local rotating frame of each Generating Unit (GU), i.e. each SG and DER; thus facilitating the application of decentralised controllers. Based on the port-Hamiltonian nonlinear dynamical structure obtained for the complete power system, input-to-state stability (ISS) is analytically proven for the first time, as far as the authors know, when taking into account both SGs and grid-forming DC/AC converters in the power system model, considering also the sixth-order nonlinear model for the SGs. Furthermore, bounded integral controllers are designed for each GU that guarantee boundedness of the closed-loop system solution, without requiring any knowledge of system parameters, while additionally satisfying desired input constraints. A 4-bus power network is simulated to validate the ISS and boundedness properties of the developed dynamical model, as well as the input constraint satisfaction provided by the controllers.", "url": "" }, { "id": "Mo-MoA03.12", "code": "MoA03.12", "title": "Active Power Limiting Control for Angle Stability Enhancement of Grid-Forming Inverters", "day": "Monday", "date": "August 24, 2026", "time": "10:45-10:50", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Liu, Yiwei", "affiliation": "Chinese University of Hong Kong, Shenzhen" }, { "name": "Yang, Luwei", "affiliation": "Shenzhen Research Institute of Big Data" }, { "name": "Shunbo, Lei", "affiliation": "School of Science and Engineering, Chinese University of Hong Kong, Shenzhen 518172" } ], "keywords": [ "Power systems stability", "Power electronics" ], "abstract": "Maintaining phase-angle stability is crucial for grid-forming inverters in renewable-dominated power systems, particularly under severe disturbances and low short-circuit strength. To enhance stability resilience, the paper proposes a safety filter that shapes the active-power reference to keep the inverter–grid phase difference within a safe margin, thereby mitigating overcurrent and loss-of-synchronism risks. In contrast to traditional current-limiting or mode-switching methods, the proposed safety filter is implemented via a control barrier function and acts as a lightweight modification of the active-power reference while preserving the nominal control architecture during normal operation. Analytical results derived on a reduced-order model establish formal safety guarantees under bounded grid-angle jumps. Extensive reduced-order Monte Carlo simulations across diverse short-circuit scenarios validate reliable angle-margin preservation and the associated safety-intervention trade-off.", "url": "" }, { "id": "Mo-MoA03.13", "code": "MoA03.13", "title": "Multi-Frequency Stability Assessment of a Grid-Connected Converter Using Takagi-Sugeno Framework", "day": "Monday", "date": "August 24, 2026", "time": "10:50-10:55", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Rezai, Laila", "affiliation": "HTW Berlin, University of Applied Sciences, Control Systems Group" }, { "name": "Schulte, Horst", "affiliation": "HTW Berlin" } ], "keywords": [ "Power systems stability", "Power electronics", "Power plant control" ], "abstract": "This paper proposes a unified framework for modeling and large-signal stability analysis of grid-connected inverters. It demonstrates how the Takagi-Sugeno (TS) framework provides a rigorous theoretical foundation by representing three-phase inverter systems con- nected to the grid as a state- and input-dependent weighted combination of linear models. This paper details modeling and stability analysis, with particular emphasis on input-to-state stability (ISS), a structural requirement for inverter systems in which grid voltage fluctuations are uncontrollable inputs. To address the practical requirement of fully describing the inverter system’s operating range as defined by grid code specifications, this work presents a modeling method accompanied by LMI-based stability analysis in the large-signal domain—not merely the small-signal range as commonly found in the literature.", "url": "" }, { "id": "Mo-MoA03.14", "code": "MoA03.14", "title": "Power Management for DC Microgrids with Partially Uncontrollable Storage (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:55-11:00", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Oliani, Igor", "affiliation": "UFABC" }, { "name": "Lunardi, Angelo", "affiliation": "L2S, CentraleSupélec, CNRS, University Paris-Saclay" }, { "name": "Alfeu, Sguarezi", "affiliation": "Universidade Federal ABC CECS" }, { "name": "Iovine, Alessio", "affiliation": "CNRS, CentraleSupélec" } ], "keywords": [ "Control and management of energy systems", "Energy management systems", "Control and optimization for sustainability and energy systems" ], "abstract": "This paper addresses secondary-layer power management in DC microgrids with hybrid storage configurations, including partially uncontrollable fast devices such as supercapacitors. Unlike conventional approaches, we consider scenarios where fast storage outputs are dictated by primary-layer dynamics, while slower storage units track secondary-layer references. We propose a practical strategy that prevents the state-of-charge of uncontrollable devices from reaching extreme levels by temporarily operating them as energy buffers and introducing a control-mode signal to coordinate DC-bus stabilization and power tracking. The approach is implemented via Model Predictive Control, and simulations demonstrate that it ensures long-term microgrid stability while enhancing robustness and operational flexibility.", "url": "" }, { "id": "Mo-MoA03.15", "code": "MoA03.15", "title": "Bilevel GA–MILP Optimization of Greenhouse Temperature Setpoints and Multi-Energy Scheduling (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:00-11:05", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "González Morales, Rubén Avelino", "affiliation": "Universidad De Almería" }, { "name": "García-Mañas, Francisco", "affiliation": "University of Almería" }, { "name": "Rodríguez-Díaz, Francisco", "affiliation": "University of Almería" }, { "name": "Quijano, Nicanor", "affiliation": "Universidad De Los Andes" }, { "name": "Lopez-Jimenez, Jorge", "affiliation": "Universidad De Los Andes" }, { "name": "Becerra-Terón, Antonio", "affiliation": "University of Almería" } ], "keywords": [ "Energy management systems", "Forecasting of power supply and demand" ], "abstract": "Optimizing greenhouse temperature to balance crop productivity and energy efficiency is a major challenge in protected agriculture. This work introduces an optimization framework that integrates climate, crop growth, and Energy Hub modeling. A bilevel GA–MILP (genetic algorithm - mixed integer linear programming) strategy is applied: the GA maximizes profit by calculating heating and cooling setpoints for adequate crop growth, while the MILP focuses on minimizing operational costs by energy scheduling. A simulated case study based on a Mediterranean greenhouse was used to evaluate the approach, achieving up to 43% cost savings compared to manually setting the temperature setpoints. Although this comes with a 8% reduction in revenue, the overall profit increases by 5%, representing a modest economic gain but a significant contribution to the sustainability of food production.", "url": "" }, { "id": "Mo-MoA03.16", "code": "MoA03.16", "title": "High-Fidelity Simulation and Control of a Centrifugally-Stiffened Airborne Wind Energy System (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:05-11:10", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Waibel, Johannes", "affiliation": "EPFL" }, { "name": "Brouillon, Jean-Sébastien", "affiliation": "ETHZ" }, { "name": "Jones, Colin, N", "affiliation": "EPFL" } ], "keywords": [ "Wind power", "Control and management of energy systems" ], "abstract": "Multi-kite Airborne Wind Energy systems harvest wind energy through several kites and tethers. While they are predicted to yield significantly higher power output than single-kite systems, they are also considered more complex, and practical real-world designs have yet to appear. We propose a novel multi-kite system in which the kites are constrained to orbit each other by tethers connecting their inner wingtips. The centrifugal stiffening in this arrangement results in a quasi-rigid rotor that transmits mechanical power to the ground-based generator by pulling out a Y-shaped tether. Such a system is modeled with high fidelity and controlled with simple means. This shows that the proposed architecture is less complex than commonly thought and has important advantages over previously proposed single-kite systems.", "url": "" }, { "id": "Mo-MoA03.17", "code": "MoA03.17", "title": "Trajectory Control and Trim of Tethered Aircraft Using Motion Primitives (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:15", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Vinha, Sérgio", "affiliation": "University of Porto, Faculty of Engineering" }, { "name": "Fernandes, Gabriel M.", "affiliation": "University of Porto, Faculty of Engineering" }, { "name": "Fernandes, Manuel C. R .M.", "affiliation": "Universidade Do Porto" }, { "name": "Fontes, Fernando A. C. C.", "affiliation": "Universidade Do Porto" } ], "keywords": [ "Control and optimization for sustainability and energy systems", "Wind power" ], "abstract": "This paper investigates trajectory control of tethered aircraft flying on circular paths by exploiting motion primitives defined on a spherical surface. Using the motion primitives, we derive a longitudinal model of the aircraft and characterise the trim conditions required to maintain steady flight on a prescribed primitive. These trim conditions are then used as a feedforward law around which simple feedback controllers are designed. The simulation results show that combining trim-based feedforward and low-complexity feedback achieves accurate path-following and speed regulation, illustrating the potential of motion-primitive-based models for the guidance and control of tethered aircraft in airborne wind energy applications.", "url": "" }, { "id": "Mo-MoA03.18", "code": "MoA03.18", "title": "Improving Hydrogen Purity Production in High-Pressure Alkaline Electrolyzers Using Quadratic Dynamic Matrix Control", "day": "Monday", "date": "August 24, 2026", "time": "11:15-11:20", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Aguirre, Omar", "affiliation": "Universidad San Francisco De Quito" }, { "name": "Uribe, Jorge", "affiliation": "Universidad San Francisco De Quito" }, { "name": "Camacho, Oscar", "affiliation": "Universidad San Francisco De Quito" }, { "name": "Ocampo-Martinez, Carlos", "affiliation": "Universitat Politecnica De Catalunya (UPC)" } ], "keywords": [ "Hydrogen systems for energy generation and storage", "Control and management of energy systems", "Energy storage systems" ], "abstract": "This work proposes a constrained quadratic dynamic matrix control (QDMC) strategy to reduce hydrogen–oxygen cross-contamination in high-pressure alkaline electrolyzers, thus improving the purity of the supplied gases. To reduce gas contamination, the controller adjusts the opening of the two outlet valves based on the system pressure and the difference in liquid level between the two gas separation chambers. A quadratic dynamic matrix controller (QDMC) with constraints and multiple inputs and outputs (MIMO) is developed. The behavior of the closed-loop system under the proposed controller was assessed through simulation, employing a 25-state high-fidelity non-linear model. The simulation results show a hydrogen purity below 0.35% O2 under all scenarios.", "url": "" }, { "id": "Mo-MoA03.19", "code": "MoA03.19", "title": "Operational Scheduling of PEM Electrolyzers Using Grid Electricity and Renewables under Carbon-Intensity Constraints", "day": "Monday", "date": "August 24, 2026", "time": "11:20-11:25", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Hamed, Lina", "affiliation": "McMaster University" }, { "name": "Dalle Ave, Giancarlo", "affiliation": "McMaster University" }, { "name": "Swartz, Christopher L.E.", "affiliation": "McMaster University" } ], "keywords": [ "Hydrogen systems for energy generation and storage", "Control and optimization for sustainability and energy systems", "Demand response" ], "abstract": "Green hydrogen production using Proton Exchange Membrane (PEM) electrolyzers can support the decarbonization of hard-to-electrify sectors. PEM electrolyzer systems can operate either off-grid using only renewable energy or in a grid-connected configuration that supplements renewables with grid electricity. While grid-connected operation improves flexibility and continuity of operation, the carbon intensity (CI) of the hydrogen produced depends on the time-varying emissions associated with the bulk grid. The economic performance of grid-connected systems also depends on how well operation is aligned with low electricity price periods, which requires short-term forecasting. This study develops a rolling horizon optimization (RHO) framework that incorporates updated SARIMA-based electricity price forecasts, renewable availability, and CI limits. A mixed-integer linear programming (MILP) model determines electrolyzer loading, compression, and storage decisions. Several representative operating days with different grid CI levels are examined. Without CI limits, production shifts toward low-price periods, resulting in average CI values between 3.8 and 6.6 kg CO₂e/kg H₂, depending on the CI of the grid electricity used. When CI limits are imposed, grid-only operation cannot satisfy the threshold on high CI days, whereas renewable availability enables low CI production near 1.2–1.6 kg CO₂e/kg H₂. On low CI days, constrained and unconstrained outcomes have negligible differences. These results show that meeting carbon-intensity requirements while maintaining economic performance requires scheduling strategies that account for both price variability and renewable availability.", "url": "" }, { "id": "Mo-MoA03.20", "code": "MoA03.20", "title": "Comparative Exergy and Techno-Economic Analysis of Hydrogen Storage Systems Integrated with LNG Cold Energy", "day": "Monday", "date": "August 24, 2026", "time": "11:25-11:30", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Ko, Jin", "affiliation": "Yonsei University" }, { "name": "Byun, Juyoung", "affiliation": "Yonsei University" }, { "name": "Song, Kyongmin", "affiliation": "Yonsei University" }, { "name": "Kim, Junghwan", "affiliation": "Yonsei University" } ], "keywords": [ "Hydrogen systems for energy generation and storage", "Process modeling, identification, and estimation techniques", "Energy storage systems" ], "abstract": "Integrating liquefied natural gas (LNG) cold energy into hydrogen systems offers an opportunity to reduce cooling loads and improve process efficiency, yet its system-level benefits across production and storage stages remain underexplored. To address this gap, four hydrogen supply configurations combining two production routes (SMR and ATR) with two storage pathways (LOHC and NH3) were modeled, and exergy and techno-economic analyses were performed with and without LNG cold-energy integration. LNG cold energy reduced cooling and pre-conditioning demands in the storage section, providing moderate improvements in exergy efficiency and operating costs across all cases. LOHC-based systems achieved the highest efficiencies (91–92%) and the lowest levelized hydrogen costs (1.99–2.38 /kg), with the SMR–LOHC configuration exhibiting the most favorable performance. In contrast, NH3-based systems showed lower efficiencies (81–83%) and higher costs (3.26–3.68 /kg) due to additional energy demands associated with high-pressure synthesis and multi-stage compression. This study offers a quantitative assessment of LNG cold-energy use across both production and storage stages and demonstrates its potential to enhance the efficiency and economic viability of LNG-based hydrogen systems, while clarifying system-level trade-offs between LOHC and NH3 storage routes.", "url": "" }, { "id": "Mo-MoA03.21", "code": "MoA03.21", "title": "Estimators for Hydropower Plant Efficiency Based on Physical Models", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:35", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Alonso, Augustin", "affiliation": "Gipsa-Lab" }, { "name": "Robert, Gerard", "affiliation": "EDF - Hydro Engineering Centre" }, { "name": "Besancon, Gildas", "affiliation": "Grenoble INP - UGA" } ], "keywords": [ "Hydropower" ], "abstract": "Monitoring the energy efficiency of hydropower units is critical for production optimisation and predictive maintenance, but direct measurement through thermodynamic tests is costly and seldom performed. Continuous estimation from standard operational data is therefore desirable, yet challenging due to the absence of direct net head instrumentation and to flow-dependent non-stationary noise on industrial sensors. This paper proposes a ``grey-box'' methodology in which three physics-based dynamic models for the net head (Pressure-Based, Surge-Tank-Based, and Upstream-Reservoir-Based) are coupled with Adaptive Cubature Kalman Filters (ACKF) and Smoothers (ACRTSS). Process-noise non-stationarity is handled by a sliding-window variance estimator applied directly on the noisy input signals. Validation on a high-head plant with real industrial data shows that the proposed dynamic smoother reduces the RMSE against thermodynamic references by approximately 30% and improves temporal stability by a factor of five compared with filtered static methods.", "url": "" }, { "id": "Mo-MoA03.22", "code": "MoA03.22", "title": "Automatic Power Control Method for Start-Up Stage of High-Temperature Gas-Cooled Reactor", "day": "Monday", "date": "August 24, 2026", "time": "11:35-11:40", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Shen, Pengyu", "affiliation": "Tsinghua University" }, { "name": "Zhu, Yunlong", "affiliation": "Tsinghua University" }, { "name": "Zhang, Jinming", "affiliation": "Tsinghua University" }, { "name": "Zhonghua, Cheng", "affiliation": "INET, Tsinghua University" }, { "name": "Xiong, Huasheng", "affiliation": "Tsinghua University" }, { "name": "Dong, Zhe", "affiliation": "Tsinghua University" }, { "name": "Huang, Xiaojin", "affiliation": "Tsinghua University" } ], "keywords": [ "Nuclear power", "Power plant control" ], "abstract": "To mitigate the high operator workload and operational risks associated with manual control rod operation during the start-up stage of High-Temperature Gas-Cooled Reactors (HTGRs), this paper proposes an automated power control method. The start-up process is divided into two power ranges: 0–30% and 30–50% of Rated Full Power (RFP). The operation of control rod is automated by presetting parameters such as the operation sequence, position limits, step size, and interval time. In the 0–30% RFP stage, the flow rates of the primary circuit coolant and the secondary circuit coolant are fixed. In the 30–50% RFP stage, a linear ramp-up strategy for feedwater flow rate is implemented to effectively suppress the excessively steam temperature and ensure a stable steam temperature increase, while primary helium flow rate remains unchanged. Simulation results demonstrate that the proposed method achieves stable power increase and confirms its control performance and operational safety. Furthermore, this study analyzes the influence of negative temperature feedback on reactor power and examines the stabilizing effect of feedwater regulation on steam temperature. The findings provide the practical insights for the automatic control of start-up stage of HTGRs.", "url": "" }, { "id": "Mo-MoA03.23", "code": "MoA03.23", "title": "Model Predictive Control of Thermo-Hydraulic Systems Using Primal Decomposition", "day": "Monday", "date": "August 24, 2026", "time": "11:40-11:45", "sessionCode": "MoA03", "sessionTitle": "Shotgun: Power and Energy Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Vieth, Jonathan", "affiliation": "Hamburg University of Technology" }, { "name": "Eichler, Annika", "affiliation": "DESY" }, { "name": "Speerforck, Arne", "affiliation": "Hamburg University of Technology" } ], "keywords": [ "Thermal systems modelling", "Control and optimization for sustainability and energy systems", "Energy management systems" ], "abstract": "Decarbonizing the global energy supply requires more efficient heating and cooling systems. Model predictive control enhances the operation of cooling and heating systems but depends on accurate system models, often based on control volumes. We present an automated framework including time discretization to generate model predictive controllers for such models. To ensure scalability, a primal decomposition exploiting the model structure is applied. The approach is validated on an underground heating system with varying numbers of states, demonstrating the primal decomposition’s advantage regarding scalability.", "url": "" }, { "id": "Mo-MoA04.1", "code": "MoA04.1", "title": "Bearing-Only Solution to the Fermat-Weber Location Problem for Unicycle Agent", "day": "Monday", "date": "August 24, 2026", "time": "09:50-09:55", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Cheah, Hong Liang", "affiliation": "UNSW" }, { "name": "Deghat, Mohammad", "affiliation": "University of New South Wales" }, { "name": "Guivant, Jose", "affiliation": "UNSW Australia" } ], "keywords": [ "Guidance, navigation and control for AVs", "Automatic control, optimization, real-time operations in transportation", "Control architectures in automotive control" ], "abstract": "This paper addresses bearing-only algorithms for solving the Fermat-Weber Location Problem (FWLP) with a unicycle agent. Unlike existing FWLP solutions for single- or double-integrator agents, our approach accounts for the nonholonomic constraints of wheeled robots. We first develop a bearing-only control law for the case with stationary beacons. Next, we consider saturated control inputs and propose a corresponding bearing-only control law. Finally, we address moving beacons with constant velocities and develop a control law that enables the unicycle agent to track the moving Fermat–Weber point. Both simulations and experiments are provided to demonstrate the effectiveness of the proposed methods.", "url": "" }, { "id": "Mo-MoA04.2", "code": "MoA04.2", "title": "Vehicle-Following Model Predictive Control for Platooning on Curved Roads Guaranteeing String Stability", "day": "Monday", "date": "August 24, 2026", "time": "09:55-10:00", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Zhang, Qihang", "affiliation": "University of Groningen" }, { "name": "Qiu, Meng", "affiliation": "Suzhou University of Technology" }, { "name": "Cao, Ming", "affiliation": "University of Groningen" } ], "keywords": [ "Multi-vehicle systems", "Intelligent transportation systems", "Trajectory tracking and path following for AVs" ], "abstract": "Cutting-corner behavior and loss of string stability are two principal concerns on platoon performance over curved roads. Because vehicle following governs how a platoon responds to curvature, it directly determines the significance of cutting-corner effects. Inspired by Newell’s car-following model, we propose a curved-road following method that uses the predecessor’s time-delayed state as the reference for each follower, enabling accurate tracking while avoiding cutting-corner behavior. Building on this method, we design a model predictive control (MPC) scheme that avoids cutting corners while maintaining the desired inter-vehicle spacing. With appropriately selected controller parameters, the closed-loop platoon preserves string stability. Simulation results validate the proposed following method and show that the MPC controller both prevents cutting-corner behavior and preserves string stability along the platoon.", "url": "" }, { "id": "Mo-MoA04.3", "code": "MoA04.3", "title": "Fixed-Time Control for the Roll Channel of Dual-Spin Projectiles with Canards", "day": "Monday", "date": "August 24, 2026", "time": "10:00-10:05", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Tang, Li", "affiliation": "Beijing Information Science and Technology University" }, { "name": "Fan, Junfang", "affiliation": "Beijing Information Science and Technology University" }, { "name": "Ge, Jiahao", "affiliation": "Beijing Information Science and Technology University" }, { "name": "Zhang, Donghao", "affiliation": "Beijing Information Science and Technology University" }, { "name": "Li, Jingtao", "affiliation": "Beijing Institute of Spacecraft System Engineering" } ], "keywords": [ "Nonlinear adaptive control", "Neural and fuzzy adaptive control", "Learning methods for control" ], "abstract": "To address the control challenges posed by the strong nonlinearity and parameter uncertainty in the roll channel of canard-guided dual-spin projectiles, a fixed-time tracking control method based on radial basis function neural networks is proposed. Initially, a seven-degree-of-freedom coupled rigid-body dynamics model for the dual-spin projectile was developed, treating aerodynamic parameter uncertainties as lumped disturbances. The model was then decoupled into roll channel and pitch/yaw channel dynamics subsystems using time-scale separation. Radial basis function neural networks were employed to precisely approximate the model uncertainties. Moreover, filters were introduced to compute the virtual derivatives, effectively preventing the common issue of \"derivative explosion\" in traditional control systems. The designed controller integrates roll angle tracking error feedback with lumped disturbance estimation feedforward, aiming to achieve fixed-time convergence and enhance the system's convergence speed and robustness, thereby ensuring precise roll angle tracking control. Using the Lyapunov method, the uniform ultimate bounded stability of the closed-loop system was demonstrated. Simulation results indicate that under conditions of aerodynamic parameter perturbation with a frequency of 1000 Hz and amplitude deviation of ±30%, the method can achieve an average roll angle tracking error of no more than 0.1 degrees, exhibiting excellent maneuver command tracking precision and robustness.", "url": "" }, { "id": "Mo-MoA04.4", "code": "MoA04.4", "title": "Adaptive Control with Directional Forgetting for Uncertain Euler-Lagrange Systems", "day": "Monday", "date": "August 24, 2026", "time": "10:05-10:10", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Manchola, Miguel", "affiliation": "Syracuse University" }, { "name": "Rubino, Nicholas", "affiliation": "Syracuse University" }, { "name": "Duenas, Victor", "affiliation": "Syracuse University" } ], "keywords": [ "Nonlinear adaptive control", "Nonlinear system identification", "Learning methods for control" ], "abstract": "Adaptive control has been extensively used to estimate constant unknown parameters in uncertain nonlinear dynamical systems and to exploit those estimates to improve tracking performance. Memory regressor extension (MRE) methods leverage accumulated input–output data to relax excitation requirements, with full-data MRE integrating the entire history of regressors to drive parameter updates. Alternatively, forgetting-based MRE introduces selective data discounting to retain the benefits of stored information while improving robustness to disturbances. Forgetting-based estimation methods achieve this by constructing an information matrix (IM), i.e., an integral regressor matrix whose stored data is strategically discounted to accommodate changes in the dynamics. Traditional exponential forgetting applies a uniform decay across the entire regressor space, which can cause estimator windup under poor persistence of excitation (PE), where the IM becomes positive semi-definite, and the parameter estimates deteriorate over time. In contrast, directional forgetting (DF) discounts data only along the subspaces spanned by new information in the regressor. Although existing DF approaches, including orthogonal and oblique projection methods, successfully prevent estimator windup, they are often limited to first-order dynamics, assume exact knowledge of the system, and fail to address closed-loop tracking, limiting their applicability. This paper develops a nonlinear adaptive control scheme that incorporates oblique DF into a closed-loop design for uncertain Euler–Lagrange systems, achieving both kinematic tracking and parameter estimation. Integral data-driven regressors and input vectors are used to avoid computing second-order derivatives. A Lyapunov-based analysis establishes global exponential convergence of both tracking and parameter estimation errors under the PE condition. Numerical simulations of a two-degree-of-freedom robotic system validate the developed method, demonstrating satisfactory tracking performance and reliable estimation of constant unknown parameters.", "url": "" }, { "id": "Mo-MoA04.5", "code": "MoA04.5", "title": "Adaptive Backstepping Fault-Tolerant Control for Large-Scale Time-Delay Systems with Input Saturations", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:15", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Zhang, Jiao-Yang", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Fan, Huijin", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Liu, Lei", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Wang, Bo", "affiliation": "Huazhong University of Science and Techonology" } ], "keywords": [ "Nonlinear adaptive control", "Stochastic adaptive control" ], "abstract": "This article investigates the adaptive backstepping fault-tolerant control (FTC) problem for uncertain large-scale time-delay systems subject to input saturations. By establishing a technical lemma, the growth assumption imposed on the delayed interactions is successfully removed. Then, an adaptive FTC scheme is presented, which is capable of accommodating the stochastic intermittent failures of multiple saturated actuators. With the aid of a Lyapunov-Krasovskii functional, it is proven that all the closed-loop signals remain globally ultimately bounded in probability. Also, it is established that the tracking error can be reduced by tuning design parameters in a explicitly manner.", "url": "" }, { "id": "Mo-MoA04.6", "code": "MoA04.6", "title": "Multitask Recognition of Types and Operating States of Underwater Engines Based on Mel Spectrogram Decomposition in a GRU-With-Attention Model", "day": "Monday", "date": "August 24, 2026", "time": "10:15-10:20", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Albuquerque, Luis Paulo", "affiliation": "Universidade Federal Do Rio De Janeiro - UFRJ" }, { "name": "Monteiro Guedes, Pedro Henrique", "affiliation": "Rio De Janeiro State University" } ], "keywords": [ "Perception and filtering in marine systems", "Sensors and actuators in marine systems", "Decision and support in marine systems" ], "abstract": "This work addresses multitask recognition of the active engine (M1–M5) and its operating state from underwater audio. We compare four shared feature-extraction networks, here termed backbones, namely BiLSTM+attention, GRU+attention, a temporal Transformer, and ResNet-50 on spectrograms, all coupled to conditional state heads. Preprocessing uses 0.5 s windows of 64-bin log-mel spectrograms, z-score normalization, and light augmentation (random gain, Gaussian noise, and SpecAugment). Experiments are conducted on the single-engine subset of Wolfset, with evaluation at segment and file levels. Among the reference models, GRU and Transformer reach file-level F1 of 1.00 for engine and up to 0.68 for state. Motivated by these results, we propose a sub-spectrogram GRU variant; with B=8, it yields the best trade-off (mean F1 = 0.800; file-F1: engine = 1.00, state = 0.74). Removing augmentation substantially degrades state recognition (file-F1 0.74→0.47). On a Tesla T4 GPU, end-to-end inference over a complete file under the adopted windowing required 83–113 s with memory usage < 225 MB, supporting batch or near-online monitoring rather than strict real-time deployment.", "url": "" }, { "id": "Mo-MoA04.7", "code": "MoA04.7", "title": "Non-Linear Model Predictive Control of Vessel Energy Systems", "day": "Monday", "date": "August 24, 2026", "time": "10:20-10:25", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Löffler, Charlotte", "affiliation": "Delft University of Technology" }, { "name": "Kopka, Timon", "affiliation": "Delft University of Technology" }, { "name": "Geertsma, Rinze", "affiliation": "Delft University of Technology" }, { "name": "Polinder, Henk", "affiliation": "Delft Univ. of Technology" }, { "name": "Coraddu, Andrea", "affiliation": "Delft University of Technology" } ], "keywords": [ "Power and propulsion in marine systems", "Modelling, identification and control in marine systems", "Marine renewable energy systems" ], "abstract": "Ship electrification is a major enabler for zero-emission shipping and the use of alternative fuels and power sources. However, they contribute to higher complexity of energy systems, which leads to suboptimal operation for conventional rule-based control. Alternatively, advanced control can take the available knowledge about the vessel and its operation into account. This paper presents a nonlinear multi-objective Model Predictive Control approach for a hybrid-electric vessel energy system to enhance energy efficiency. In a simulation study, the controller shows the potential to reduce fuel consumption by 2.5 %.", "url": "" }, { "id": "Mo-MoA04.8", "code": "MoA04.8", "title": "Railway Infrastructure Monitoring: From Diagnosis to Prescriptive Maintenance Bottlenecks", "day": "Monday", "date": "August 24, 2026", "time": "10:25-10:30", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Bounouh, Aziz", "affiliation": "IMS" }, { "name": "Melchior, Pierre", "affiliation": "Université De Bordeaux - Bordeaux INP/ENSEIRB-MATMECA" }, { "name": "Chevrié, Mathieu", "affiliation": "IMS Laboratory" }, { "name": "Airimitoaie, Tudor-Bogdan", "affiliation": "Univ. Bordeaux" } ], "keywords": [ "Rail transportation modelling and control systems", "Planning, management and security in transportation", "Modeling and simulation of transportation systems" ], "abstract": "This paper provides a control-engineering reading of railway infrastructure monitoring, formally stating the underlying maintenance problem as a partially observed sequential decision problem and reviewing, through this lens, the available observables, the methodological pipelines, and the bottlenecks that prevent closing the loop in practice. While modern sensors achieve sufficient observability, the integration of heterogeneous data into a closed prescriptive loop remains fragmented. We identify three structural challenges: multi-scale temporal fusion, the performance-explainability trade-off, and the lack of longitudinal benchmarks for sequential decision-making. On this basis, we outline a roadmap toward hybrid supervision systems combining physics-based estimators, probabilistic prognosis and constrained decision policies.", "url": "" }, { "id": "Mo-MoA04.9", "code": "MoA04.9", "title": "Velocity Tracking for Autonomous Railway-Based Urbanloop Pods by Contraction", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:35", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Wang, Weihao", "affiliation": "Université De Lorraine" }, { "name": "Kreiss, Jérémie", "affiliation": "Université De Lorraine" }, { "name": "Lorenzetti, Pietro", "affiliation": "CRAN, CNRS, Université De Lorraine" }, { "name": "Licitra, Letizia", "affiliation": "Urbanloop SAS" }, { "name": "Lefebvre, Gaëtan", "affiliation": "Alstom" }, { "name": "Postoyan, Romain", "affiliation": "CRAN, CNRS, Université De Lorraine" } ], "keywords": [ "Rail transportation modelling and control systems", "Trajectory tracking and path following for AVs", "Autonomous vehicles" ], "abstract": "We present a model-based methodology to synthesize velocity controllers for individual Urbanloop pods, which are autonomous railway-based vehicles. They are designed for energy-efficient, low-cost, rapid, and seamless urban transport. First, we derive a physics-based pod dynamical model and rigorously reveal that it exhibits two time scales. We then leverage singular perturbation methods combined with recent contraction theory tools to design the controller, guaranteeing that the pod velocity tracks the given reference velocity profile. This controller combines a contractive output-feedback component with a reference-inducing feedforward term. We prove that the trajectories of the original, full-order model exponentially converge to the reference trajectory up to an error proportional to the time-scale separation parameter. Finally, numerical simulations illustrate the relevance of the approach.", "url": "" }, { "id": "Mo-MoA04.10", "code": "MoA04.10", "title": "Safety Control of Self-Organized Swarm Coordination under Obstacles and Adversaries", "day": "Monday", "date": "August 24, 2026", "time": "10:35-10:40", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Li, Jiacheng", "affiliation": "University of Macau" }, { "name": "Zhiyuan, Zhang", "affiliation": "The Department of Electromechanical Engineering, University of Macau" }, { "name": "Liu, Jason J. R.", "affiliation": "University of Macau" }, { "name": "Kishida, Masako", "affiliation": "University of Tsukuba" } ], "keywords": [ "Resilient networked control systems", "Cyber security networked control", "Consensus" ], "abstract": "This paper addresses the safety control problem of a self-organized swarm in environments with obstacles and adversaries. To mitigate adversarial impacts, a reputation mechanism is introduced for both leaderless and virtual-leader scenarios to quantify mutual trust among agents. This mechanism integrates local behavioral assessments with neighbors' reputations, allowing agents with low reputations to be regarded as potentially malicious. Such malicious agents are then isolated through communication weight adjustments at the cyber layer and repulsive potential fields at the physical layer. The distributed safety control laws are designed to ensure self-organizing characteristics and collision-free maneuvers. Simulation results demonstrate that the proposed approach effectively preserves self-organized swarm behavior and guarantees safety despite the coexistence of obstacles and adversaries.", "url": "" }, { "id": "Mo-MoA04.11", "code": "MoA04.11", "title": "Planetary Terrain Datasets and Benchmarks for Rover Path Planning", "day": "Monday", "date": "August 24, 2026", "time": "10:40-10:45", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Chancán, Marvin", "affiliation": "Luleå University of Technology" }, { "name": "Banerjee, Avijit", "affiliation": "Luleå University of Technology" }, { "name": "Nikolakopoulos, George", "affiliation": "Luleå University of Technology" } ], "keywords": [ "Space exploration and transportation" ], "abstract": "Planetary rover exploration is attracting renewed interest with several upcoming space missions to the Moon and Mars. However, a substantial amount of data from prior missions remain underutilized for path planning and autonomous navigation research. As a result, there is a lack of space mission-based planetary datasets, standardized benchmarks, and evaluation protocols. In this paper, we take a step towards coordinating these three research directions in the context of planetary rover path planning. We propose two large planetary datasets, MarsPlanBench and MoonPlanBench, derived from high-resolution digital terrain images of Mars and the Moon. In addition, we set up classic and learned path planning algorithms, in a unified framework, and evaluate them on our proposed datasets using a popular path planning benchmark. Through comprehensive experiments, we report new insights on the performance of representative planning algorithms on planetary terrains, for the first time to the best of our knowledge. Our results show that classic methods can achieve up to 100% global path planning success rates on average across challenging terrains such as Moon's north and south poles. Conversely, learning-based models, although showing promising results in less complex environments, still struggle to generalize to planetary domains. Code and datasets available at: https://github.com/mchancan/PlanetaryPathBench.", "url": "" }, { "id": "Mo-MoA04.12", "code": "MoA04.12", "title": "Leveraging Resonant Orbits with Venus for Low-Energy Multiple Asteroid Flyby Missions", "day": "Monday", "date": "August 24, 2026", "time": "10:45-10:50", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Zubko, Vladislav", "affiliation": "Space Research Institute of the Russian Academy of Sciences" }, { "name": "Chernenko, Olga", "affiliation": "Space Research Institute (IKI) of the Russian Academy of Sciences (RAS)" }, { "name": "Pupkov, Maxim", "affiliation": "Space Research Institute (IKI) of the Russian Academy of Sciences (RAS)" } ], "keywords": [ "Space exploration and transportation", "Aerospace mission control and operations", "Guidance, navigation and control of aircraft and spacecraft" ], "abstract": "This paper presents an optimization-based framework for designing multiple asteroid flyby missions in the inner Solar System. The core of the methodology leverages Venus gravity assists to place the spacecraft on controlled resonant orbits, enabling the construction of complex flyby sequences. We formulate the trajectory design as a two-stage optimization problem: first, a geometric pre-selection identifies candidate asteroids based on resonant orbit manifolds; second, a global-local optimization technique minimizes the total velocity increment (Delta v) while satisfying constraints on gravity-assist turn angles and launch energy. Numerical results demonstrate the method’s efficacy, generating fuel-efficient tours from a 2029 launch that include up to seven asteroid flybys with a launch Delta v under 3.6 km/s. The proposed approach demonstrates that resonant flyby sequences are highly competitive with direct transfers, often reducing propellant require", "url": "" }, { "id": "Mo-MoA04.13", "code": "MoA04.13", "title": "Safe and Efficient Optimization-Based Trajectory Planning Using Conformal Prediction", "day": "Monday", "date": "August 24, 2026", "time": "10:50-10:55", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Dimou, Emmanouil", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Börve, Erik", "affiliation": "Chalmers University of Technology" }, { "name": "Kanellopoulos, Aris", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Murgovski, Nikolce", "affiliation": "Chalmers University of Technology" } ], "keywords": [ "Trajectory and path planning for AVs", "Autonomous vehicles" ], "abstract": "The problem of trajectory planning in stochastic, dynamic environments is inves tigated, with an emphasis on formulating efficient collision avoidance constraints. Black-box predictors provide an estimate of the stochastic obstacles’ state and the uncertainty of this estimate is quantified off-line via the statistical tool of Conformal Prediction. The resulting quantification is combined with elements of convex geometry, leading to the construction of the unsafe sets, regions which the obstacles, admitting polytopic representations, may occupy. The unsafe sets preserve the properties of compactness and convexity. Thus the safety constraints involving them and an agent with polytopic representation, may be efficiently formulated utilizing the Hyperplane Separation Theorem. The proposed optimization-based trajectory planning algorithm provides probabilistic collision avoidance and recursive feasibility guarantees, over finite time horizon, via progressive tightening of the unsafe sets. Its efficacy is demonstrated in the context of autonomous parking scenarios.", "url": "" }, { "id": "Mo-MoA04.14", "code": "MoA04.14", "title": "Trajectory Planning for Non-Communicating Mobile Robots Using Inverse Optimal Control", "day": "Monday", "date": "August 24, 2026", "time": "10:55-11:00", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Majer, Nina", "affiliation": "FZI Research Center for Information Technology" }, { "name": "Epple, Yannick", "affiliation": "Karlsruher Institut Für Technologie (KIT), FZI Forschungszentrum Informatik" }, { "name": "Ye, Xin", "affiliation": "FZI Research Center for Information Technology" }, { "name": "Schwab, Stefan", "affiliation": "FZI - Research Center for Information Technology" }, { "name": "Hohmann, Soeren", "affiliation": "KIT" } ], "keywords": [ "Trajectory and path planning for AVs", "Autonomous vehicles", "Cooperative navigation" ], "abstract": "To enable an efficient interaction of non-communicating mobile robots in collision avoidance scenarios, we present a novel combined trajectory planning and prediction algorithm. Inverse optimal control is used to estimate unknown goal states of all robots based on observed past trajectories. Each robot also takes the perspective of other robots in considering self-prediction and solves a joint prediction problem using the estimated goal states. The resulting predictions are then considered for planning. Simulation results of scenarios with 2-8 robots show that the median of the durations until all vehicles reach their goals is 9.8 % faster compared to planning with constant acceleration based estimated goal states. Moreover, the proposed approach never leads to the solver being unable to find a solution to the planning or prediction problem.", "url": "" }, { "id": "Mo-MoA04.15", "code": "MoA04.15", "title": "AUG: A Closed-Form Adaptive Understeer Gradient Lateral Controller for Autonomous Racing", "day": "Monday", "date": "August 24, 2026", "time": "11:00-11:05", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Chang, Seokyung", "affiliation": "Hanyang University" }, { "name": "Jo, Kichun", "affiliation": "Hanyang University" } ], "keywords": [ "Trajectory tracking and path following for AVs", "Guidance, navigation and control for AVs", "Autonomous vehicles" ], "abstract": "Autonomous racing provides a valuable testbed for evaluating controllers in high-speed, traction-limit conditions. On scaled platforms, however, limited sensing and computation restrict the use of Model Predictive Control, motivating lightweight controllers that still capture nonlinear tire effects. This paper proposes the Adaptive Understeer Gradient (AUG) controller, a closed-form steering law that converts L1 guidance-based desired lateral acceleration into steering command while adaptively reflecting tire nonlinearity. It requires only a few parameters, no lookup tables, and can be tuned in real-time. Experiments in simulation and real-world F1TENTH racing show that AUG significantly reduces cross-track error and lap time compared to Pure Pursuit, while requiring far less tuning effort than existing dynamics-aware controllers. The code is available at: https://github.com/skcworld/controller.", "url": "" }, { "id": "Mo-MoA04.16", "code": "MoA04.16", "title": "The Path Following Evaluation Metric IAX: A Toolbox for Fair Comparison across Controllers, Craft and Conditions", "day": "Monday", "date": "August 24, 2026", "time": "11:05-11:10", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Tufte, Andreas Gudahl", "affiliation": "NTNU" }, { "name": "Rambech, Alexander Brevad", "affiliation": "Oslo Metropolitan University" } ], "keywords": [ "Trajectory tracking and path following for AVs", "Guidance, navigation and control for AVs", "Marine system guidance, navigation and control" ], "abstract": "Path following should be evaluated along the path, not in time. We present a metric for comparison of path following using the line integral of the absolute value of the cross-track error along the desired track. The metric, which we term IAX, and its variants, ensure fair comparison regardless of the speed of progression along the path. We demonstrate in two cases that IAX is beneficial over the integral of absolute error (IAE) for such scenarios, and also provides a spatial interpretation in the plot. A toolbox is provided for ease of calculation of the proposed metric.", "url": "" }, { "id": "Mo-MoA04.17", "code": "MoA04.17", "title": "Multi-Dock Unit-Load Warehouse Design: A Systematic Survey", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:15", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Biswas, Sanchita", "affiliation": "S.P. Jain Institute of Management & Research (SPJIMR)" }, { "name": "Rao, Subir", "affiliation": "SPJIMR" } ], "keywords": [ "Transportation logistics" ], "abstract": "This systematic survey reviews the design and operational efficiency of unit-load warehouses utilizing multiple pickup and deposit (P/D) points. We analyze the evolution of facility layouts from traditional parallel aisles to non-traditional configurations, including Fishbone and Flying-V designs, specifically within multi-dock environments. The study categorizes literature based on storage policies, command cycles, and dock arrangements to evaluate their collective impact on travel distance. By synthesizing findings on optimal dock placement, this paper identifies critical research gaps and provides design guidelines for maximizing performance in modern logistics facilities.", "url": "" }, { "id": "Mo-MoA04.18", "code": "MoA04.18", "title": "Intrusive Uncertainty Quantification for Control Systems with Timing Effects and Parametric Uncertainties", "day": "Monday", "date": "August 24, 2026", "time": "11:15-11:20", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Vandamme, Antoine", "affiliation": "Robert Bosch GmbH" }, { "name": "Gallant, Melanie", "affiliation": "Robert Bosch GmbH" }, { "name": "Mark, Christoph", "affiliation": "Robert Bosch GmbH" }, { "name": "von Keler, Johannes", "affiliation": "Robert Bosch GmbH" }, { "name": "Beermann, Laura", "affiliation": "Robert Bosch GmbH" }, { "name": "Schmidt, Kevin", "affiliation": "Robert Bosch GmbH" } ], "keywords": [ "Uncertain systems", "Linear parameter-varying systems", "Linear time-delay systems" ], "abstract": "Modern control design for dynamical systems must account for system uncertainties, including both static and dynamic ones. The primary challenge is to develop computationally efficient methods that can reliably capture the resulting stochastic system behavior. This paper proposes a novel and efficient uncertainty quantification method to represent a stochastic dynamical system through its mean and covariance trajectories. The approach models dynamic disturbances as a Gaussian Process, which is then reformulated as a Stochastic Differential Equation (SDE) to avoid the high computational cost of traditional Karhunen-Loève expansions. By combining this SDE representation with a surrogate model based on intrusive polynomial chaos expansion, we can analytically derive the mean and covariance dynamics for the system. This allows for a fast and accurate propagation of both static (parametric and timing) and dynamic uncertainties through the system model, making it suitable for advanced control design and online applications like model predictive control. The approach is illustrated by an application from longitudinal vehicle motion control.", "url": "" }, { "id": "Mo-MoA04.19", "code": "MoA04.19", "title": "Regenerative Braking Controller Design for Passenger Comfort in Electrical Vehicles", "day": "Monday", "date": "August 24, 2026", "time": "11:20-11:25", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Kim, Minseo", "affiliation": "Kookmin University" }, { "name": "Chang, H.J.", "affiliation": "Kookmin University" } ], "keywords": [ "Electric and solar vehicles", "Adaptive and robust control of automotive systems", "Modeling, supervision, control and diagnosis of automotive systems" ], "abstract": "Electric vehicles (EVs) incorporate regenerative braking mechanisms that enhance energy efficiency through the recovery of kinetic energy; however, rapid torque response of electric motors and transients during brake blending can increase longitudinal jerk and degrade passenger comfort. This paper proposes a comfort-aware brake blending strategy using an adaptive neuro fuzzy inference system that outputs a regenerative braking ratio (Z in [0,1]) derived from vehicle speed, battery SOC, and a motion- sickness indicator based on the Motion- Sickness Dose Value (MSDV). The controller was trained in MATLAB/Simulink using simulated braking scenarios, with training targets designed to balance SOC recovery and MSDV growth. A case of deceleration from 80~km/h to 40~km/h demonstrated that the proposed controller reduced MSDV to a greater extent than PID and fuzzy control while preserving regenerative energy recovery. These preliminary results suggest that dynamically adjusting regenerative braking intensity based on input variable changes enables efficient energy recovery while effectively suppressing passenger motion sickness.", "url": "" }, { "id": "Mo-MoA04.20", "code": "MoA04.20", "title": "Polynomial Chaos Approximation for Worst-Case Transient Performance of Linear Systems", "day": "Monday", "date": "August 24, 2026", "time": "11:25-11:30", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Izquierdo Serra, Mario", "affiliation": "Airbus Defence and Space GmbH" }, { "name": "Martin, Maurice", "affiliation": "Airbus Defence and Space GmbH" }, { "name": "Delchambre, Simon", "affiliation": "Airbus Defence and Space GmbH" }, { "name": "Winkler, Stefan", "affiliation": "Airbus DS" }, { "name": "Pfifer, Harald", "affiliation": "Technische Universität Dresden" } ], "keywords": [ "Uncertain systems", "Probabilistic robustness" ], "abstract": "The goal of this paper is to approximate the worst-case transient performance of uncertain linear time-invariant systems, subject to both L2-bounded input signals and known disturbances, e.g., reference tracking commands. System uncertainties are described through real-valued random variables with a known probability distribution. The worst-case performance analysis is formulated as a parametric Riccati differential equation, which is approximately solved using polynomial chaos expansion. The objective is to estimate a bound on the Euclidean norm of the system output at a given time. The effectiveness of the approach is demonstrated on the example of a spacecraft attitude and orbit control system.", "url": "" }, { "id": "Mo-MoA04.21", "code": "MoA04.21", "title": "Python-Based Confidence-Aware Vision Control Integration", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:35", "sessionCode": "MoA04", "sessionTitle": "Shotgun: Transportation Systems and Control I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Jung, Ju-Young", "affiliation": "School of Electrical Engineering, Kookmin University" }, { "name": "Chang, H.J.", "affiliation": "Kookmin University" } ], "keywords": [ "High-performance motion control systems", "Robot perception and sensing", "Aerial, field, and marine robotics" ], "abstract": "This study proposes a confidence-aware vision-based control architecture that directly generates control inputs from visual errors. These visual errors are processed using a Python-based image processing pipeline. Typically, vision-based gimbal control delivers geometric detection results directly to the controller. However, in real-world environments, the reliability of visual information varies drastically over time, owing to changes in illumination, occlusions, or frame losses. In this study, OpenCV-based region of interest extraction, combined with a lightweight Tiny-CNN model,is used to estimate the confidence of detection results. The estimated confidence is incorporated into the control input via a soft-gating mechanism. The proposed structure continuously adjusts the input magnitude in undetected or low-confidence intervals, minimizing discontinuous transmission of control signals. The visual error and confidence data generated in the Python environment are integrated with a MATLAB/Simulink control system via a file-based interface. The controller is implemented in two configurations: (i) an ANFIS-based parallel compensation structure, and (ii) a pure ANFIS structure. The experimental results demonstrate that the proposed confidence-aware input modulation reduces response deviations under disturbance conditions and improves input stability.", "url": "" }, { "id": "Mo-MoA05.1", "code": "MoA05.1", "title": "Agency and Control in Human-AI Knowledge Work: Large Language Models within Qualitative Research", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:05", "sessionCode": "MoA05", "sessionTitle": "LB: Human Machine Cooperation and Digital Twins", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Rapp, Amon", "affiliation": "University of Turin" }, { "name": "Fiore, Enea", "affiliation": "University of Turin" }, { "name": "Mancosu, Moreno", "affiliation": "University of Turin" }, { "name": "Tirassa, Maurizio", "affiliation": "University of Turin" } ], "keywords": [ "Human-centric automation/AI Systems, and human agency" ], "abstract": "Human-AI collaboration raises important questions about agency and control, particularly as Large Language Models (LLMs) are increasingly integrated into knowledge work. This late-breaking work paper reports preliminary findings from a qualitative study investigating qualitative researchers’ perceptions of LLMs in their research practices, particularly focusing on how LLMs’ agency affects their acceptance of the technology. Results indicate that delegating core analytical work to LLMs raises concerns about the loss of control over the research process. The findings suggest that LLM design should prioritize flexible, user-controlled support, rather than the complete automation of tasks that are considered central to research practices.", "url": "" }, { "id": "Mo-MoA05.2", "code": "MoA05.2", "title": "Development of a Digital Twin of a 6-Axis Robot in Unity for Further Usage in AI Data Generation Und Mixed-Reality Applications", "day": "Monday", "date": "August 24, 2026", "time": "10:05-10:20", "sessionCode": "MoA05", "sessionTitle": "LB: Human Machine Cooperation and Digital Twins", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Wittenberg, Carsten", "affiliation": "Heilbronn University" }, { "name": "Gleinser, Manuel", "affiliation": "Heilbronn University OAS" } ], "keywords": [ "Human-robot interaction", "Human AI integration", "AI-powered robotics" ], "abstract": "The digital twin of a 6-axis robot created in this project will serve as a basis for future research projects, particularly in the areas of machine learning, the explainability of AI, and the use of innovative human-technology interactions like Mixed Reality. The suitability of Unity as a platform for the holistic implementation of digital twins will also be explored. A physically representative model of the real twin will be created and communication between the twins will be implemented. The motivation for this is that Unity provides all the necessary tools to create a 3D visualization of the robot and its environment and also provides means for integrating complex programs via linked C# scripts. For simulation purposes, the digital twin will be given independent control. Unlike many existing applications, the robot will not only be driven by simple movement equations that shift components, but also by motor forces in its joints. The physics engine included in Unity will be used for this purpose without additional modules. In addition, the digital twin should be able to track the movements of its real-world counterpart through communication.", "url": "" }, { "id": "Mo-MoA05.3", "code": "MoA05.3", "title": "Establishing a Dynamic Multimodal HRI Dataset for Engagement Analysis with a Humanoid Robot", "day": "Monday", "date": "August 24, 2026", "time": "10:20-10:35", "sessionCode": "MoA05", "sessionTitle": "LB: Human Machine Cooperation and Digital Twins", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Kim, Boowan", "affiliation": "Department of Information and Telecommunication Engineering, Incheon National University" }, { "name": "Jo, Wonse", "affiliation": "Incheon National University" } ], "keywords": [ "Human-robot interaction", "Human machine cooperation & integration", "Robot perception and sensing" ], "abstract": "This paper presents an experimental design for constructing a multimodal dataset to analyze user engagement in human–robot interaction (HRI). Prior studies have mainly relied on observable behavioral cues, with limited frameworks integrating physiological signals. We therefore propose a structured data-collection protocol to build a multimodal dataset that includes wearable physiological signals, behavioral data, and self-report measures under different levels of task complexity defined in this experiment.", "url": "" }, { "id": "Mo-MoA05.4", "code": "MoA05.4", "title": "Human-In-The Loop or AI-In-The-Loop? Human-AI Alignment Is All We Need", "day": "Monday", "date": "August 24, 2026", "time": "10:35-10:50", "sessionCode": "MoA05", "sessionTitle": "LB: Human Machine Cooperation and Digital Twins", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Arellano, Giovanna Martinez", "affiliation": "University of Nottingham" } ], "keywords": [ "Industrial artificial intelligence", "Human-technology integration in manufacturing", "Cyber-physical production systems" ], "abstract": "As we strive to redefine future manufacturing systems that embrace Artificial Intelligence, we have started to think about ethical and sustainable ways to integrate this technology to ensure it benefits the human. Human-centric Industrial AI has started to take different shapes in the scientific community, but questions on what we mean by having the human do the creative tasks is not yet well defined. This paper makes an attempt to define what human augmentation means through a Human-Industrial AI Alignment framework, providing concrete ideas of how it could be implemented and discusses the feasibility of human creativity in an AI-powered automated environment.", "url": "" }, { "id": "Mo-MoA05.5", "code": "MoA05.5", "title": "Toward Specification-Based Validation of Digital System of Units for Smart Manufacturing: Proposing a Mathematical Logic Approach", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:05", "sessionCode": "MoA05", "sessionTitle": "LB: Human Machine Cooperation and Digital Twins", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Okamoto, Junichi", "affiliation": "National Institute of Advanced Industrial Science and Technology (AIST)" }, { "name": "Shirono, Katsuhiro", "affiliation": "National Institute of Advanced Industrial Science and Technology" } ], "keywords": [ "Maintenance engineering, management and services", "Cyber-physical production systems", "Smart production and logistics in manufacturing" ], "abstract": "The digitalization of manufacturing systems driven by Industry 4.0 has increased importance of the unambiguous exchange of measurement data. As measurement data are expected to be processed more frequently by machines and algorithms, the Digital (International) System of Units (D‑SI) was proposed as a machine‑readable, SI‑based metadata scheme for describing measurement data. Given the central role of time‑series measurement data in manufacturing systems, this study proposes a specification‑based validation framework for D‑SI‑compliant measurement data. In the proposed framework, specifications are written as mathematical logical expressions, and validation is performed by evaluating whether the data satisfies these expressions. To illustrate this approach, we formalize three representative and fundamental types of specifications—unit consistency, occurrence of a specific measurement value, and temporal relationships between physical quantities.", "url": "" }, { "id": "Mo-MoA05.6", "code": "MoA05.6", "title": "Robust Control and Stochastic Stability Analysis of Industry 5.0 Digital Twins under Model Uncertainty", "day": "Monday", "date": "August 24, 2026", "time": "11:05-11:20", "sessionCode": "MoA05", "sessionTitle": "LB: Human Machine Cooperation and Digital Twins", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Khademi, Mohammad", "affiliation": "University of Genoa" }, { "name": "Revetria, Roberto", "affiliation": "University of Genoa" } ], "keywords": [ "Manufacturing plant simulation, control and optimization", "Industry X.0 for production and logistics", "Maintenance engineering, management and services" ], "abstract": "Industry 5.0 requires prescriptive Digital Twins (DTs) that exhibit stochastic stability against model uncertainty. This paper investigates the stochastic stability of an optimized operational control policy under a rigorous stress-test protocol where reliability parameters are subjected to ±20% model uncertainty. We formalize a \"Stochastic Predictive Threshold Controller\" (Deterministic Substitution) to absorb stochastic variance through managed preventive stops. Validated via N=30 Monte Carlo replications, results demonstrate a robust mean profit of €3,011.99 and a 100% service level despite high entropy. Crucially, we show that sustainability-driven heuristics discover stability margins that protect system bottlenecks, providing the statistical assurance required for deploying prescriptive DTs in volatile industrial networks.", "url": "" }, { "id": "Mo-MoA05.7", "code": "MoA05.7", "title": "A Preliminary Study on Automated Syringe Disposal Using Bimanual Manipulation", "day": "Monday", "date": "August 24, 2026", "time": "11:20-11:35", "sessionCode": "MoA05", "sessionTitle": "LB: Human Machine Cooperation and Digital Twins", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Shim, Jae Woo", "affiliation": "Seoul National University" }, { "name": "Kang, Seongjoon", "affiliation": "Seoul National University" }, { "name": "Lee, Jong Hyeon", "affiliation": "Seoul National University" }, { "name": "Jeon, Byoungjun", "affiliation": "Seoul National University Hospital" }, { "name": "Baek, Changhoon", "affiliation": "Seoul National University Hospital" }, { "name": "Cho, Minwoo", "affiliation": "Seoul National University Hospital" }, { "name": "Kim, Sungwan", "affiliation": "Seoul National University, Seoul" } ], "keywords": [ "Teleoperation", "Human-robot interaction", "Robotic learning and adaptation" ], "abstract": "This study investigates the feasibility of automating syringe disposal using a bimanual robotic system to minimize the manual handling of contaminated sharps. Using an imitation learning policy trained on 50 demonstration episodes, the system achieved a 76% end-to-end success rate. Analysis identified the needle detachment phase as the primary bottleneck due to alignment errors, while other stages remained highly reliable. These results demonstrate the potential for robotic automation in medical waste management using limited data. Furthermore, this research provides a foundation for ensuring the safety of healthcare professionals and preventing secondary infections through the development of robust robotic disposal systems.", "url": "" }, { "id": "Mo-MoA06.1", "code": "MoA06.1", "title": "Data-Driven Disturbance Decoupling with Arbitrary Pole Placement", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA06", "sessionTitle": "Data-Driven Control Theory I", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Li, Bohan", "affiliation": "Imperial College London" }, { "name": "Mao, Junyu", "affiliation": "Imperial College London" }, { "name": "Scarciotti, Giordano", "affiliation": "Imperial College London" } ], "keywords": [ "Data-driven control theory" ], "abstract": "In this work, we present a novel data-driven formulation to solve the pole placement problem with or without the additional requirement of disturbance decoupling. The proposed approach is based on determining a data-driven solution to constrained Sylvester equations. Exploiting these equations, we also study the effect of process noise and develop an approach to obtain a pole placement design that is robust to such noise. Finally, the proposed methods are illustrated by means of a numerical example.", "url": "" }, { "id": "Mo-MoA06.2", "code": "MoA06.2", "title": "Data-Driven Feedforward Control with Guaranteed Worst Tracking Error Using Finite Reference Sets", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA06", "sessionTitle": "Data-Driven Control Theory I", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Ochiai, Yuki", "affiliation": "University of Tsukuba" }, { "name": "Nguyen-Van, Triet", "affiliation": "University of Tsukuba" }, { "name": "Kawai, Shin", "affiliation": "University of Tsukuba" } ], "keywords": [ "Data-driven control theory" ], "abstract": "This paper proposes a data-driven design method for a fixed-structure finite impulse response feedforward controller for an unknown stable discrete-time SISO LTI plant, assuming that only finite-length input-output data are available. The proposed method explicitly provides an upper bound on the finite-horizon worst-case tracking error over a prescribed finite set of representative reference signals. Using a Hankel-matrix-based trajectory representation derived from Willems' fundamental lemma, input-output trajectories consistent with the finite data are represented, and the minimization of the worst-case tracking-error bound over the prescribed reference set is formulated as a second-order cone program. This enables an offline one-shot design of a feedforward controller with a certified error bound for the prescribed finite reference set, without explicit model identification. Numerical simulations demonstrate that the proposed method constructs a feedforward controller with a finite-data-based error bound and achieves favorable reference-tracking performance.", "url": "" }, { "id": "Mo-MoA06.3", "code": "MoA06.3", "title": "Data-Driven Predictive Control of Nonlinear Systems Via Koopman Embedding", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA06", "sessionTitle": "Data-Driven Control Theory I", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Oh, Seungbeen", "affiliation": "RPTU University Kaiserslautern-Landau" }, { "name": "Mishra, Vikas Kumar", "affiliation": "RPTU" }, { "name": "Bajcinca, Naim", "affiliation": "University of Kaiserslautern" } ], "keywords": [ "Data-driven control theory" ], "abstract": "We propose a data-driven predictive control scheme for nonlinear systems with closed-loop stability guarantees. Using tools from Koopman operator theory and behavioral systems theory, we construct a data-driven lifted representation that enables the use of linear predictive control techniques for nonlinear dynamics. We show that, under suitable assumptions, exponential stability of the closed-loop lifted system implies exponential stability of the closed-loop nonlinear system. To address noisy measurements, the approach incorporates regularization and truncated singular value decompositions of the data matrices. Numerical experiments illustrate that the proposed framework achieves reliable trajectory tracking even in noisy settings and performs consistently across Hankel, Page, and Trajectory data matrices.", "url": "" }, { "id": "Mo-MoA06.4", "code": "MoA06.4", "title": "Maglev Module Control Via Regularized Data-Enabled Policy Optimization", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA06", "sessionTitle": "Data-Driven Control Theory I", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Zhang, Tianbo", "affiliation": "Beijing Jiaotong University" }, { "name": "Zhao, Feiran", "affiliation": "ETH Zurich" }, { "name": "Shen, Dong", "affiliation": "Renmin University of China" }, { "name": "You, Keyou", "affiliation": "Tsinghua University" }, { "name": "Bao, Zeyu", "affiliation": "Beijing Jiaotong University" }, { "name": "Jiang, Wei", "affiliation": "Beijing Jiaotong University" }, { "name": "Jian, Wang", "affiliation": "Beijing Jiaotong University" }, { "name": "Cai, Baigen", "affiliation": "Beijing Jiaotong University" } ], "keywords": [ "Data-driven control theory" ], "abstract": "Maglev trains, a leading technology for next-generation rail transit, offer a promising path to higher operating speeds. However, model-based control methods are challenged by model inaccuracies, while conventional PID control requires extensive parameter tuning. This paper proposes a direct data-driven strategy that bypasses modelling process. Specifically, the data-enabled policy optimization method is designed to use real-time train operation data to learn and adjust the control policy, thereby ensuring train adaptivity and stability. To account for mechanical and electromagnetic variations in the maglev modules, a regularization term is incorporated to the cost function, thereby rendering it robust to complex disturbances, even within a constrained adjustment space. Furthermore, the algorithm can be implemented recursively and hence is computationally efficient. Simulations demonstrate the effectiveness of the proposed approach.", "url": "" }, { "id": "Mo-MoA06.5", "code": "MoA06.5", "title": "Adaptive Sampling Using Variational Autoencoder and Reinforcement Learning", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA06", "sessionTitle": "Data-Driven Control Theory I", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Rasheed, Adil", "affiliation": "Norwegian University of Science and Technology (NTNU)" }, { "name": "Shahly, Mikael Aleksander Jansen", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Aftab, Muhammad Faisal", "affiliation": "University of Agder (UiA)" } ], "keywords": [ "Data-driven control theory", "Active learning and experiment design", "Consensus and reinforcement learning control" ], "abstract": "Compressed sensing enables sparse sampling but relies on generic bases and random measurements, limiting efficiency and reconstruction quality. Optimal sensor placement uses historcal data to design tailored sampling patterns, yet its fixed, linear bases cannot adapt to nonlinear or sample-specific variations. Generative model-based compressed sensing improves reconstruction using deep generative priors but still employs suboptimal random sampling. We propose an adaptive sparse sensing framework that couples a variational autoencoder prior with reinforcement learning to select measurements sequentially. Experiments show that this approach outperforms CS, OSP, and Generative model-based reconstruction from sparse measurements.", "url": "" }, { "id": "Mo-MoA06.6", "code": "MoA06.6", "title": "Data-Driven Adaptive Output Regulation of Unknown Linear Systems", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA06", "sessionTitle": "Data-Driven Control Theory I", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Liu, Shangkun", "affiliation": "Zhejiang University" }, { "name": "Wang, Lei", "affiliation": "Zhejiang University" }, { "name": "Yi, Bowen", "affiliation": "Polytechnique Montréal" } ], "keywords": [ "Data-driven control theory", "Adaptive observer design", "Learning methods for control" ], "abstract": "This paper investigates the linear output regulation problem with both the exosystem and the plant fully unknown. A data-driven regulator is proposed to achieve asymptotic regulation and closed-loop stability without performing model identification. The method constructs a nominal approximate internal model and filters of input and outputs, thereby yielding a stabilizable cascaded nominal system whose states are available. For this nominal system, a stabilizing law is derived from an offline dataset that has been acquired from the plant during experiments, such that the system states exponentially converge to a subspace. An identifier in discrete-time is, then, implemented to correct the internal model and update the stabilizing law; as a result, the regulation error can be steered to zero asymptotically under some persistent excitation conditions.", "url": "" }, { "id": "Mo-MoA07.1", "code": "MoA07.1", "title": "Distributed 3D Source Seeking Via SO(3) Geometric Control of Robot Swarms (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA07", "sessionTitle": "Advances in Rigidity Theory, Multi-Agent Formations, and Distributed Localization", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Bautista Villar, Jesús", "affiliation": "University of Granada" }, { "name": "Garcia de Marina, Hector", "affiliation": "Universidad De Granada" } ], "keywords": [ "Multi-agent systems", "Resilient networked control systems", "Distributed control and estimation" ], "abstract": "This paper presents a geometric control framework on the Lie group SO(3) for 3D source-seeking by robots with first-order attitude dynamics and constant translational speed. By working directly on SO(3), the approach avoids Euler-angle singularities and quaternion ambiguities, providing a unique, intrinsic representation of orientation. We design a proportional feed-forward controller that ensures exponential alignment of each agent to an estimated ascending direction toward a 3D scalar field source. The controller adapts to bounded unknown variations and preserves well-posed swarm formations. Numerical simulations demonstrate the effectiveness of the method, with all code provided open source for reproducibility.", "url": "" }, { "id": "Mo-MoA07.2", "code": "MoA07.2", "title": "The Geometry of Hidden Modes in Distance-Based Formation Control (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA07", "sessionTitle": "Advances in Rigidity Theory, Multi-Agent Formations, and Distributed Localization", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Goldgraber Casspi, Solomon", "affiliation": "Technion - Israel Institute of Tech" }, { "name": "Zelazo, Daniel", "affiliation": "Technion - Israel Institute of Technology" } ], "keywords": [ "Multi-agent systems", "Control of networks", "Control over networks" ], "abstract": "This paper presents a geometric input-output analysis of hidden modes in distance-based formation control. We study the linearized dynamics under a gradient control law to characterize the system's structural limitations and their dynamic consequences. Our main contribution is a unified geometric framework for uncontrollable modes. We first prove that uncontrollable rigid-body modes are exactly characterized by a global rotational subspace, mathcal{R}_i. To generalize this, we introduce the local rotational subspace, mathcal{T}_i, which establishes a strict geometric bound on the hidden deformations for minimally connected actuators (i.e., when the actuator node has as many neighbors as the dimension of the space). Finally, we demonstrate the dynamic implications of this structure by proving that the system's ability to recover its shape is determined by an input's alignment with the local component of the standard rotational rigid-body mode, directly linking the geometry of hidden modes to disturbance rejection. We illustrate our results with a case study.", "url": "" }, { "id": "Mo-MoA07.3", "code": "MoA07.3", "title": "Control Barrier Function-Based Bearing-Only Formation Tracking of Mobile Agents with Obstacle Avoidance (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA07", "sessionTitle": "Advances in Rigidity Theory, Multi-Agent Formations, and Distributed Localization", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Tran, Quoc Van", "affiliation": "Hanoi University of Science and Technology" }, { "name": "Lee, Changyu", "affiliation": "Kongju National University" }, { "name": "Yuan, Xin", "affiliation": "The University of Adelaide" }, { "name": "Ahn, Hyo-Sung", "affiliation": "Gwangju Institute of Science and Technology (GIST)" } ], "keywords": [ "Control of networks", "Distributed control and estimation", "Multi-agent systems" ], "abstract": "Bearing-only formation tracking control scheme based on control barrier functions (CBFs) for nonholonomic agents with static obstacle avoidance is investigated in this work. A relative-degree one CBFs is constructed for safety guarantee on the motion of control points, ahead of the centers, of the agents in a bearing-only formation maneuvering. The safety condition is then enforced as inequality constraints in a quadratic programming (QP) formulation that modifies the nominal formation control flows of the agents in a minimal way to avoid the obstacles. The optimal solution to the QP can be computed in closed-form and has an intuitive geometrical interpretation. Under the formation tracking control scheme, the formation system is shown to be uniformly stable and converges to the target formation asymptotically while obstacle avoidance is ensured.", "url": "" }, { "id": "Mo-MoA07.4", "code": "MoA07.4", "title": "Distributed Rigid Formability of Angle-Based Multiagent Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA07", "sessionTitle": "Advances in Rigidity Theory, Multi-Agent Formations, and Distributed Localization", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Li, Wenyou", "affiliation": "Southern University of Science and Technology" }, { "name": "Chen, Liangming", "affiliation": "Southern University of Science and Technology (SUSTech)" }, { "name": "Lin, Zhiyun", "affiliation": "Southern University of Science and Technology" } ], "keywords": [ "Multi-agent systems", "Control of networks" ], "abstract": "Achieving desired formations is a fundamental and important task for multiagent systems in engineering practice. This task generally involves two sequential stages: formation shape design and formation controller design. In most existing studies, the desired formation is predetermined and assumed to be graphically rigid, which inherently implies a centralized formation shape design. Therefore, it is still an important but challenging problem for each agent to check in a distributed manner whether it forms a rigid formation with its neighbors. To address this challenge, we propose an interesting concept, rigid formability, to check those subsets forming rigid sub-formations in a large-scale multiagent system. As a starting point, this work focuses on checking the rigid formability of angle-constrained multiagent systems in a distributed manner. Numerical simulations are performed to confirm the validity of the theoretical results.", "url": "" }, { "id": "Mo-MoA07.5", "code": "MoA07.5", "title": "Error Analysis of Target Localization Using a Sensitivity-Based Approach (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA07", "sessionTitle": "Advances in Rigidity Theory, Multi-Agent Formations, and Distributed Localization", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Zhu, Xiaolin", "affiliation": "Southern University of Science and Technology" }, { "name": "Lu, Shengyu", "affiliation": "The University of Hong Kong" }, { "name": "Chen, Liangming", "affiliation": "Southern University of Science and Technology (SUSTech)" }, { "name": "Cui, Jinqiang", "affiliation": "Pengcheng Laboratory" } ], "keywords": [ "Multi-agent systems", "Distributed control and estimation", "Statistical analysis" ], "abstract": "When localizing a target through two cooperative agents using distance and angle measurements, although the relative position of the target with respect to the agents can be directly obtained through their triangular geometry, the localization error may vary significantly when measurement noise exists. Motivated by this, this paper aims to investigate the sensitivity of the target localization error when the target and agents are in different geometric configurations. Firstly, this paper presents a geometric analysis, revealing the impact of deviations in various measurement quantities on the localization error of the target. Secondly, sensitivity analysis tools are utilized to conduct global and local sensitivity analyses on the relative position of the target with respect to the agents. The results of the sensitivity analysis indicate that the target’s relative position is less sensitive to the measurement noise of quantities with lower sensitivity indices. Furthermore, the differential-based sensitivity analysis provides a foundation for minimizing the localization error. Specifically, the localization error bound depends on the geometric configuration of the two agents and the target, as well as noise parameters. Finally, simulation results validate the effectiveness of the proposed method.", "url": "" }, { "id": "Mo-MoA07.6", "code": "MoA07.6", "title": "Symmetry-Based Formation Control on Cycle Graphs Using Dihedral Point Groups (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA07", "sessionTitle": "Advances in Rigidity Theory, Multi-Agent Formations, and Distributed Localization", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Martinez, Zamir", "affiliation": "Technion - Israel Institute of Technology" }, { "name": "Zelazo, Daniel", "affiliation": "Technion - Israel Institute of Technology" } ], "keywords": [ "Multi-agent systems", "Control of networks", "Control under communication constraints" ], "abstract": "This work develops a symmetry-based framework for formation control on cycle graphs using Dihedral point-group constraints. We show that enforcing inter-agent reflection symmetries, together with anchoring a single designated agent to its prescribed mirror axis, is sufficient to realize every C_{nv}-symmetric configuration using only n-1 communication links. The resulting control laws have a matrix-weighted Laplacian structure and guarantee exponential convergence to the desired symmetric configuration. Furthermore, we extend the method to enable coordinated maneuvers along a time-varying reference trajectory. Simulation results are provided to support the theoretical analysis.", "url": "" }, { "id": "Mo-MoA08.1", "code": "MoA08.1", "title": "Convex Computation of Regions of Attraction from Data Using Sums-Of-Squares Programming (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA08", "sessionTitle": "JO-JSC: Learning and Adaptive Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Khattabi, Oumayma", "affiliation": "CentraleSupélec" }, { "name": "Tacchi, Matteo", "affiliation": "Univ. Grenoble Alpes, CNRS, Grenoble INP (Institute of Engineering Univ. Grenoble Alpes), GIPSA-Lab" }, { "name": "Olaru, Sorin", "affiliation": "CentraleSupelec" } ], "keywords": [ "Data-driven control theory", "Learning methods for control" ], "abstract": "This paper focuses on the analysis of the Region of Attraction (RoA) for unknown autonomous dynamical systems. A data-driven approach based on the moment-Sum-of-Squares hierarchy is proposed, enabling novel RoA outer approximations despite reduced information on the dynamics. The main contribution consists of bypassing the system model and, hence, the recurring constraint on its polynomial structure. Numerical experiments showcase the influence of data on learned approximating sets, highlighting the potential of this method.", "url": "" }, { "id": "Mo-MoA08.2", "code": "MoA08.2", "title": "Model-Free Optimization and Control of Rigid Body Dynamics: An Extremum Seeking for Vibrational Stabilization Approach (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA08", "sessionTitle": "JO-JSC: Learning and Adaptive Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Palanikumar, Rohan", "affiliation": "University of Cincinnati" }, { "name": "Elgohary, Ahmed", "affiliation": "University of Cincinnati" }, { "name": "Martini, Simone", "affiliation": "University of Cincinnati" }, { "name": "Eisa, Sameh", "affiliation": "University of Cincinnati" } ], "keywords": [ "Extremum seeking and model free adaptive control", "Nonlinear adaptive control" ], "abstract": "In this paper, we introduce a model-free, real-time, dynamic optimization and control method for a class of rigid body dynamics. Our method is based on a recent extremum seeking control for vibrational stabilization (ESC-VS) approach that is applicable to a class of second-order mechanical systems. The new ESC-VS method is able to stabilize a rigid body dynamic system about the optimal state of an objective function that can be unknown expression-wise, but assessable through measurements; the ESC-VS is operable by using only one perturbation/vibrational signal. We demonstrate the effectiveness and the applicability of our ESC-VS approach via three rigid-body systems: (1) satellite attitude dynamics, (2) quadcopter attitude dynamics, and (3) acceleration-controlled unicycle dynamics. The results illustrate the ability of our ESC-VS to operate successfully as a new methodology of optimization and control for rigid body dynamics.", "url": "" }, { "id": "Mo-MoA08.3", "code": "MoA08.3", "title": "Model-Free Source Seeking of Exponentially Convergent Unicycle: Theoretical and Robotic Experimental Results (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA08", "sessionTitle": "JO-JSC: Learning and Adaptive Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Palanikumar, Rohan", "affiliation": "University of Cincinnati" }, { "name": "Elgohary, Ahmed", "affiliation": "University of Cincinnati" }, { "name": "Grushkovskaya, Victoria", "affiliation": "Alpen-Adria University of Klagenfurt" }, { "name": "Eisa, Sameh", "affiliation": "University of Cincinnati" } ], "keywords": [ "Extremum seeking and model free adaptive control", "Nonlinear adaptive control" ], "abstract": "This paper introduces a novel model-free, real-time unicycle based source seeking design. This design autonomously steers the unicycle dynamic system towards the extremum point of an objective function or physical/scalar signal that is unknown expression-wise, but accessible via measurements. A key contribution of this paper is that the introduced design converges exponentially to the extremum point of objective functions (or scalar signals) that behave locally like a higher-degree power function (e.g., fourth-degree polynomial function) as opposed to locally quadratic objective functions, the usual case in literature. We provide theoretical results and design characterization, supported by a variety of simulation results that demonstrate the robustness of the proposed design, including cases with different initial conditions and measurement delays/noise. Also, for the first time in the literature, we provide experimental robotic results that demonstrate the effectiveness of the proposed design and its exponential convergence ability. These experimental results confirm that the proposed exponentially convergent extremum seeking design can be practically realized on a physical robotic platform under real-world sensing and actuation constraints.", "url": "" }, { "id": "Mo-MoA08.4", "code": "MoA08.4", "title": "The Waterbed Effect on Quasiperiodic Disturbance Observer: Avoidance of Sensitivity Tradeoff with Time Delays (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA08", "sessionTitle": "JO-JSC: Learning and Adaptive Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Muramatsu, Hisayoshi", "affiliation": "Hiroshima University" } ], "keywords": [ "Iterative and repetitive learning control" ], "abstract": "In linear time-invariant systems, the sensitivity function to disturbances is designed under a sensitivity tradeoff known as the waterbed effect. To compensate for a quasiperiodic disturbance, a quasiperiodic disturbance observer using time delays was proposed. Its sensitivity function avoids the sensitivity tradeoff, achieving wideband harmonic suppression without amplifying aperiodic disturbances or shifting harmonic suppression frequencies. However, its open-loop transfer function is not rational and does not satisfy the assumptions of existing Bode sensitivity integrals due to its time delays. This paper provides Bode-like sensitivity integrals for the quasiperiodic disturbance observer in both continuous-time and discrete-time representations and clarifies the avoided sensitivity tradeoff with time delays.", "url": "" }, { "id": "Mo-MoA08.5", "code": "MoA08.5", "title": "Taming Non-Linearity with Local Data-Driven Predictive Control (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA08", "sessionTitle": "JO-JSC: Learning and Adaptive Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Pasini, Lorenzo", "affiliation": "University of Padua" }, { "name": "Bruschetta, Mattia", "affiliation": "University of Padova" }, { "name": "Chiuso, Alessandro", "affiliation": "University of Padova" } ], "keywords": [ "Learning methods for control", "Data-driven control theory" ], "abstract": "Model Predictive Control (MPC) relies on dynamical models to compute optimal control actions in a receding-horizon fashion. When accurate models are unavailable or expensive to obtain, data-driven predictive control (DDPC) offers a viable alternative for optimal control synthesis. While the linear case is now relatively well understood, both the theoretical foundations and practical implementations for nonlinear systems remain less developed. In this paper, we proposed an effective approach toward closing this gap by combining locally linear data-driven approximations that, when iteratively refined, yield a sequential data-driven quadratic programming algorithm for nonlinear DDPC. The proposed DDPC framework is validated through numerical simulations on an inverted pendulum benchmark.", "url": "" }, { "id": "Mo-MoA08.6", "code": "MoA08.6", "title": "Compatible Realisation of Control and Identification of Direct Adaptive Control Via Probing Signal Auto-Elimination (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA08", "sessionTitle": "JO-JSC: Learning and Adaptive Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Takakura, Akira", "affiliation": "Keio University" }, { "name": "Yokoyama, Takashi", "affiliation": "Keio University" }, { "name": "Nozaki, Takahiro", "affiliation": "Keio University" }, { "name": "Adachi, Shuichi", "affiliation": "Keio University" }, { "name": "Ohmori, Hiromitsu", "affiliation": "Keio University" } ], "keywords": [ "Time/parameter varying system identification", "Model reference adaptive control" ], "abstract": "Model Reference Adaptive Control Systems (MRACS) offer excellent responsiveness but often fail to achieve parameter convergence. While conventional methods inject probing signals to ensure convergence, they typically degrade control performance. This study proposes a control error-based auto-elimination scheme that adaptively regulates the probing signal without predefined timing. This approach enables parameter identification during transient phases and automatically suppresses the signal once tracking is achieved. Furthermore, it allows for signal re-injection upon detecting performance degradation, realizing a compatible balance between identification and control. Simulations under varying plant conditions confirm the effectiveness of the proposed structure.", "url": "" }, { "id": "Mo-MoA09.1", "code": "MoA09.1", "title": "Adaptive Online Optimization for Microgrids with Renewable Energy", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA09", "sessionTitle": "Linear System Identification I", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "van Weerelt, Wouter J. A.", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Fontan, Angela", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Bastianello, Nicola", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Linear system identification", "Control of networks" ], "abstract": "In this paper we propose a novel adaptive online optimization algorithm tailored to the management of microgrids with high renewable energy penetration, which can be formulated as a constrained, online optimization problem. The proposed algorithm is characterized by a control-based design that applies the internal model principle, and a system identification routine tasked with identifying such internal model. In addition, in order to ensure the constraints are verified, we integrate a projection onto the constraint set. We showcase promising numerical results for the microgrid use case, highlighting in particular the enhanced adaptability of the proposed algorithm to changes in the internal model. The performance of the proposed algorithm is shown to outperform state-of-the-art alternative in the long-term, ensuring efficient management of the grid.", "url": "" }, { "id": "Mo-MoA09.2", "code": "MoA09.2", "title": "Quantifying Human Ankle–Hip Torque Strategies under Small Perturbations", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA09", "sessionTitle": "Linear System Identification I", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Sung, Jiyoon", "affiliation": "Gwangju Institute of Science and Technology" }, { "name": "Cho, Kwonseung", "affiliation": "Gwangju Institute of Science and Technology" }, { "name": "Hur, Pilwon", "affiliation": "Gwangju Institute of Science and Technology" } ], "keywords": [ "Linear system identification" ], "abstract": "Following an external perturbation, humans recover balance through a coordinated response of the ankle and hip joints, but the underlying joint-torque strategy that produces this motion cannot be measured non-invasively. The objective of this study is to quantify participant specific ankle–hip torque response strategies under small impulsive backward perturbations. Using measured ankle and hip joint angles and the perturbation force, ankle, and hip joint torque trajectories were estimated by applying direct collocation to a sagittal-plane double inverted pendulum model. Functional principal component analysis (FPCA) was then applied to the reconstructed torque trajectories to extract dominant temporal torque patterns and to summarize between-participant differences in a low-dimensional score space. As a quantitative baseline, standard inverse dynamics computed from the measured kinematics with butterworth filtering was implemented and compared with direct collocation using open-loop forward-replay error; the dynamics residual on the measured trajectory is reported in the appendix. For nine healthy participants, the first two principal components explained most of the between-participant variability and corresponded to the initial response magnitude and the late-phase residual regulation. Together with the baseline comparison, the FPCA scores provide a compact descriptor of individual balance and a basis for future clustering and personalized design of wearable balance-assistive devices.", "url": "" }, { "id": "Mo-MoA09.3", "code": "MoA09.3", "title": "Self-Identifying Internal Model-Based Online Optimization", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA09", "sessionTitle": "Linear System Identification I", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "van Weerelt, Wouter J. A.", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Zhang, Lantian", "affiliation": "KTH" }, { "name": "Zhang, Silun", "affiliation": "KTH" }, { "name": "Bastianello, Nicola", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Linear system identification", "Data-driven control theory" ], "abstract": "In this paper, we propose a novel online optimization algorithm built by combining ideas from control theory and system identification. The foundation of our algorithm is a control-based design that makes use of the internal model of the online problem. Since such prior knowledge of this internal model might not be available in practice, we incorporate an identification routine that learns this model on the fly. The algorithm is designed starting from quadratic online problems but can be applied to general problems. For quadratic cases, we characterize the asymptotic convergence to the optimal solution trajectory. We compare the proposed algorithm with existing approaches, and demonstrate how the identification routine ensures its adaptability to changes in the underlying internal model. Numerical results also indicate strong performance beyond the quadratic setting.", "url": "" }, { "id": "Mo-MoA09.4", "code": "MoA09.4", "title": "Inverse Discrete-Time Finite-Horizon LQR under Noise Corrupted Optimal Control", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA09", "sessionTitle": "Linear System Identification I", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Fan, Yali", "affiliation": "Nanyang Technological University" }, { "name": "Liu, Wenjie", "affiliation": "Nanyang Technological University, Singapore" }, { "name": "Li, Yibei", "affiliation": "Chinese Academy of Sciences" }, { "name": "Xie, Lihua", "affiliation": "Nanyang Technological University" } ], "keywords": [ "Linear system identification", "Data-driven control theory" ], "abstract": "The identifiability and solution of the inverse optimal control (IOC) for linear quadratic regulators (LQR) has been widely investigated. Most of the existing works rely on demonstrations under optimal control, whereas suboptimal ones have rarely been considered. In this paper, we study the IOC problem for discrete-time finite-horizon (DTFH) LQR under the optimal control corrupted by an additive noise. We prove that the IOC problem remains well-posed through establishing the corresponding model identifiability. An approximate solution is obtained by a single-level statistically consistent estimator using second-moment to minimize the dynamic mismatch. The second-moment approach reduces the computational complexity of the residual function compared with the commonly used risk function approach. This estimator does not require access to the input sequence or prior knowledge of the noise covariance. Its effectiveness is verified through numerical examples.", "url": "" }, { "id": "Mo-MoA09.5", "code": "MoA09.5", "title": "D-Optimized Sampling Design for System Identification", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA09", "sessionTitle": "Linear System Identification I", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Dozzi, Enrico", "affiliation": "Eindhoven University of Technology" }, { "name": "Oomen, Tom", "affiliation": "Eindhoven University of Technology" }, { "name": "González, Rodrigo A.", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Linear system identification" ], "abstract": "Traditional system identification with multisine inputs relies on uniform sampling and periodic excitation to preserve Fourier orthogonality and avoid spectral leakage, limiting its use in scenarios with irregular sampling or nonperiodic inputs. This work investigates continuous-time system identification under nonperiodic multisine excitation and nonuniform sampling. We develop a nonparametric frequency response function estimator suited to such conditions and design irregular sampling schemes that enhance the informativeness of measurements and reduce spectral leakage. The proposed sampling scheme improve the statistical accuracy of system identification in settings where periodic excitation is impractical.", "url": "" }, { "id": "Mo-MoA09.6", "code": "MoA09.6", "title": "Continuous-Time Closed-Loop System Identification Using Parallel PI Controller and Reference Prefiltering: Colored Noise Case", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA09", "sessionTitle": "Linear System Identification I", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Vignaud, Jamy", "affiliation": "Universtié De Bordeaux" }, { "name": "Victor, Stephane", "affiliation": "Univ. Bordeaux" }, { "name": "Knevez, Jean-Yves", "affiliation": "Université De Bordeaux, I2M" }, { "name": "Cahuc, Olivier", "affiliation": "Université De Bordeaux, I2M" }, { "name": "Verlet, Philippe", "affiliation": "VLM Robotics" } ], "keywords": [ "Linear system identification", "Data-driven control theory" ], "abstract": "Accurate spindle modeling is essential for the control and optimization of power consumption in machining processes. This paper addresses closed-loop continuous-time system identification for spindle speed control in machine-tool applications featuring a parallel PI controller and a reference prefilter. Two algorithms are proposed: a simplified instrumental-variable method assuming white measurement noise, and an extended version that accounts for colored noise through optimal whitening. Monte Carlo simulations on a benchmark process demonstrate that the colored-noise method yields more accurate and consistent parameter estimates, particularly under low signal-to-noise ratios.", "url": "" }, { "id": "Mo-MoA10.1", "code": "MoA10.1", "title": "Chance-Constrained Neural MPC under Uncontrollable Agents Via Sequential Convex Programming (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA10", "sessionTitle": "JO-NAHS: Control of Hybrid and Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Wang, Shuqi", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Feng, Mingyang", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Chen, Yu", "affiliation": "Shanghai Jiao Tong Univ" }, { "name": "Gao, Yue", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Yin, Xiang", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Model predictive control of hybrid systems", "Reachability analysis, verification and abstraction of hybrid systems", "Stochastic hybrid systems" ], "abstract": "This work presents a safe motion planning framework that addresses the challenge of ensuring probabilistic safety guarantees when the behavior of uncontrollable agents depends on both their own state and the state of the controllable system. We model the uncontrollable agent dynamics as random variables drawn from unknown state-dependent distributions, which are learned from offline data using neural networks. To provide probabilistic guarantees on pre- diction errors, we employ split conformal prediction to construct region-specific, time-dependent uncertainty bounds. These uncertainty bounds are integrated into a model predictive control formulation, resulting in a chance-constrained optimal control problem that ensures safety with high probability over the planning horizon. To solve this non-convex, discontinuous optimization problem, we propose a two-loop iterative sequential convex programming algorithm. The inner loop solves convexified subproblems with fixed error bounds, while the outer loop refines these bounds based on updated control sequences. We establish convergence guarantees under mild regularity conditions and demonstrate that the algorithm returns solutions satisfying the KKT optimality conditions. The framework is validated in a high-fidelity autonomous driving simula- tor with interactive pedestrians. Experimental results demonstrate that our approach achieves superior safety and efficiency compared to baseline methods, with success rates exceeding 99.5 percent while maintaining higher average speeds in multi-pedestrian scenarios.", "url": "" }, { "id": "Mo-MoA10.2", "code": "MoA10.2", "title": "Seamless Hybrid Prescribed-Time Switching Control for Multi-Agent Systems with Smooth Transitions (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA10", "sessionTitle": "JO-NAHS: Control of Hybrid and Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "D'Alfonso, Luigi", "affiliation": "University of Calabria, UNICAL" }, { "name": "Merzi, Mehmet Alp", "affiliation": "University of Calabria" }, { "name": "Fedele, Giuseppe", "affiliation": "Università Della Calabria" } ], "keywords": [ "Multi-agent systems", "Hybrid and switched systems modeling", "Consensus" ], "abstract": "This paper proposes a novel framework for multi-agent systems that ensures seamless switching among sequential models while guaranteeing exact, prescribed-time convergence. By employing continuous, time-varying weighting functions within a composite control law, the strategy interpolates between regimes to maintain command input continuity. The synchronization logic utilizes LaSalle’s invariance principle via hybrid time-domain transformations, avoiding discontinuous feedback and Lyapunov decay inequalities. Theoretical analysis and numerical simulations confirm that integrating bio-inspired potentials with temporal compression achieves smooth, scalable, and finite-horizon coordination under frequent model transitions.", "url": "" }, { "id": "Mo-MoA10.3", "code": "MoA10.3", "title": "Fault Detection for Singular System with Predicted Disturbance Based on Zonotope Set-Membership Estimation (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA10", "sessionTitle": "JO-NAHS: Control of Hybrid and Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Chen, Jiafeihong", "affiliation": "College of Intelligent Systems Science and Engineering, Harbin Engineering University" }, { "name": "Feng, Zhiguang", "affiliation": "Harbin Engineering University" } ], "keywords": [ "Reachability analysis, verification and abstraction of hybrid systems", "Fault detection and diagnosis" ], "abstract": "This paper proposes a fault detection strategy for singular system subject to unknown disturbance. Based on the neural network and unknown input observer, the predicted disturbance is partially decoupled, and the rest is attenuated by solving the optimization problem. According to the set-membership technique, outer approximation zonotope for the irregular intersection between iterative update set and measured strip of neural network output weight is calculated to facilitate the generation of the desired threshold, and further becomes more tight by Frobenius norm minimization technique. A numerical example is provided to testify the effectiveness.", "url": "" }, { "id": "Mo-MoA10.4", "code": "MoA10.4", "title": "Sum-Of-Squares Certificates for Almost-Sure Reachability of Stochastic Polynomial Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA10", "sessionTitle": "JO-NAHS: Control of Hybrid and Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Bahari Kordabad, Arash", "affiliation": "Max Planck Institute for Software Systems: MPI SWS" }, { "name": "Majumdar, Rupak", "affiliation": "Max Planck Institute for Software Systems and University of California at Los Angeles" }, { "name": "Soudjani, Sadegh", "affiliation": "Max Planck Institute for Software Systems" } ], "keywords": [ "Reachability analysis, verification and abstraction of hybrid systems", "Markov decision process", "Stability and stabilization of hybrid systems" ], "abstract": "In this paper, we present a computational approach to certify almost sure reachability for discrete-time polynomial stochastic systems by turning drift–variant criteria into sum-of-squares (SOS) programs solved with standard semidefinite solvers. Specifically, we provide an SOS method based on two complementary certificates: (i) a drift certificate that enforces a radially unbounded function to be non-increasing in expectation outside a compact set of states; and (ii) a variant certificate that guarantees a one-step decrease with positive probability and ensures the target contains its nonpositive sublevel set. We transform these conditions to SOS constraints. For the variant condition, we enforce a robust decrease over a parameterized disturbance ball with nonzero probability and encode the constraints via an S-procedure with polynomial multipliers. The resulting bilinearities are handled by an alternating scheme that alternates between optimizing multipliers and updating the variant and radius until a positive slack is obtained. Two case studies illustrate the workflow and certifies almost-sure reachability.", "url": "" }, { "id": "Mo-MoA10.5", "code": "MoA10.5", "title": "Stability and Ultimate Boundedness of Observer-Based Sampled-Data Nonlinear Switched Systems with Dwell-Time (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA10", "sessionTitle": "JO-NAHS: Control of Hybrid and Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Katz, Rami", "affiliation": "Tel Aviv University" }, { "name": "Russo, Antonio", "affiliation": "Università Degli Studi Di Bergamo" }, { "name": "Incremona, Gian Paolo", "affiliation": "Politecnico Di Milano" }, { "name": "Colaneri, Patrizio", "affiliation": "Politecnico Di Milano" }, { "name": "Giordano, Giulia", "affiliation": "Università Degli Studi Di Trento" } ], "keywords": [ "Stability and stabilization of hybrid systems", "Hybrid and switched systems modeling" ], "abstract": "We address the problem of stability and ultimate boundedness of nominally linear-affine switched systems with uncertain Lipschitz nonlinearities under dwell-time constraints. In particular, we propose a sampled-data switching law based on a state observer and on Lyapunov-Metzler inequalities, accounting for the sampled-data output measurements. We derive time-dependent LMI conditions for global asymptotic stability – or, in the presence of switching affine terms, ultimate boundedness – of the closed-loop system, and we provide an estimate of the average quadratic cost and a bound on its maximum deviation from the actual cost. We also discuss the feasibility of the derived LMIs; in particular, we show how to incorporate the observer gains into the matrix inequalities, provide equivalent reduced-order LMI conditions, and prove that the LMIs can be made time-independent through discretisation on a finite grid. Numerical examples illustrate our theoretical results and their efficacy.", "url": "" }, { "id": "Mo-MoA10.6", "code": "MoA10.6", "title": "Robust Multi-Agent Safety Via Tube-Based Tightened Exponential Barrier Functions (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA10", "sessionTitle": "JO-NAHS: Control of Hybrid and Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Koulong, Armel", "affiliation": "University of Alabama" }, { "name": "Pakniyat, Ali", "affiliation": "Multi-Modal Multi-Agent Control (M³AC) Lab" } ], "keywords": [ "Multi-agent systems", "Consensus", "Distributed control and estimation" ], "abstract": "This paper presents a constructive framework for synthesizing provably safe controllers for nonlinear multi-agent systems subject to bounded disturbances. The methodology applies to systems representable in Brunovsky canonical form, accommodating arbitrary-order dynamics in multi-dimensional spaces. The central contribution is constraint tightening method that formally couples robust error feedback with nominal trajectory planning. The key insight is that the design of an ancillary feedback law, which confines state errors to a robust positively invariant (RPI) tube, simultaneously provides the information needed to ensure the safety of the nominal plan. The geometry of the resulting RPI tube is leveraged via its support function to derive state-dependent safety margins. These margins systematically tighten the high relative-degree exponential control barrier function (eCBF) constraints on the nominal planner, guaranteeing that any nominal trajectory satisfying the tightened constraints yields a provably safe trajectory for the true, disturbed system. The planner is implemented within a distributed Model Predictive Control (MPC) scheme that optimizes performance while inheriting the robust safety guarantees.", "url": "" }, { "id": "Mo-MoA11.1", "code": "MoA11.1", "title": "An Exercise on Feedforward-Feedback Control (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA11", "sessionTitle": "Control Engineering Exercises", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 201", "authors": [ { "name": "Laurini, Mattia", "affiliation": "Università Degli Studi Di Parma" }, { "name": "Piazzi, Aurelio", "affiliation": "University of Parma" } ], "keywords": [ "Control engineering curricula" ], "abstract": "The position regulation of a mass sliding on a rectilinear guide is addressed by two feedforward-feedback control strategies: a set-point filtered two-degree-of-freedom (2-DOF) scheme and a plant inversion scheme. The design of these control systems complies with an amplitude constraint on the force applied to the mass and the requirement of no overshoot or oscillations in the mass motion. A numerical example compares the performances of the two schemes, evidencing the advantages of the plant inversion control. The exercise may be suitable for teaching in an undergraduate first course in automatic control.", "url": "" }, { "id": "Mo-MoA11.2", "code": "MoA11.2", "title": "Control Exercises - Investigation of Stability in Continuous Linear Control Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA11", "sessionTitle": "Control Engineering Exercises", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 201", "authors": [ { "name": "Bars, Ruth", "affiliation": "Budapest University of Technology and Economics" }, { "name": "Keviczky, Laszlo", "affiliation": "HUN-REN Institute for Computer Science and Automation, Hungarian Research Network" }, { "name": "Sik, Dávid", "affiliation": "Budapest University of Technology and Economics" } ], "keywords": [ "Control engineering curricula", "Repositories for control education" ], "abstract": "Closed loop control systems are based on negative feedback. The output of the plant to be controlled is measured, and this value is compared to the reference signal. The obtained error signal provides the input signal to a controller which creates the actuating signal to the input of the plant. The output signal should reach the required value or follow the shape of the reference signal after deceasing of the transients. The control system should track the reference signal and also eliminate the effect of the disturbances and of the uncertainties. Stability is a basic issue in feedback control systems. This means that the transients should decrease and the steady state should be reached. The dead time in the process, delayed action strongly influences stability. Ensuring stability in case of controlling an oscillating or an unstable process (e.g. inverted pendulum) is also a challenge. The intended learning outcome is understanding the problem of stability, illustrating the problem analysing the proportional control of a pure dead time process and the ability to use different methods learned in the theoretical material to evaluate stability. Stability can be analysed based on the location of the poles in the closed loop system, or in the frequency domain using the Nyquist stability criterion. Several examples are given to check stability conditions of a continuous control system using different methods. The aim of the exercises is to give a deeper understanding of stability and expertise in using stability investigation methods.", "url": "" }, { "id": "Mo-MoA11.3", "code": "MoA11.3", "title": "Exercise on ADRC As a Unified Platform for Control Education (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA11", "sessionTitle": "Control Engineering Exercises", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 201", "authors": [ { "name": "Schiavo, Michele", "affiliation": "University of Brescia" }, { "name": "Visioli, Antonio", "affiliation": "University of Brescia" } ], "keywords": [ "Repositories for control education" ], "abstract": "This paper presents an exercise constituted by a sequence of open-ended, design-and-explain questions that guide students through the theoretical design and practical simulation of an Active Disturbance Rejection Control (ADRC) for a pendulum. The exercise is structured as a theoretical ``paper-and-pencil'' part followed by a practical part performed in MATLAB/Simulink. The exercise requires students to leverage preexisting knowledge of system modeling, state-space representation, and observer design. The resource is intended for undergraduate or master's students in control engineering who have completed an introductory automatic control course, aiming to bridge the gap between theoretical concepts and practical implementation.", "url": "" }, { "id": "Mo-MoA11.4", "code": "MoA11.4", "title": "Exercises on the Fibonacci Sequence and Internal Model Control for B.Sc.-Level Courses (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA11", "sessionTitle": "Control Engineering Exercises", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 201", "authors": [ { "name": "Mendoza Lopetegui, José Joaquín", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Repositories for control education", "Control education laboratories", "Continuing control education" ], "abstract": "This paper presents two exercises in control systems theory targeted towards learners taking a B.Sc.-level course on the topic. The first exercise is designed to serve as a motivating example of the ubiquity of control theory and its usefulness in solving problems in seemingly unrelated contexts. The famous Fibonacci sequence serves as a vehicle for analyzing homogeneous linear recurrence relations with constant coefficients. The second exercise is intended to illustrate a common misconception regarding the application of Internal Model Control principles for robust regulation, namely, achieving zero steady-state error in a Linear Time Invariant setting. Basic tools covered in an introductory course on control systems are employed throughout.", "url": "" }, { "id": "Mo-MoA11.5", "code": "MoA11.5", "title": "Conceptual Questions on the Non-Equivalence between Convergence and Simple Stability Properties of Equilibria (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA11", "sessionTitle": "Control Engineering Exercises", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 201", "authors": [ { "name": "Ferrante, Augusto", "affiliation": "University of Padova" }, { "name": "Varagnolo, Damiano", "affiliation": "NTNU - Norwegian University of Science and Technology" } ], "keywords": [ "Repositories for control education", "Control education learning analytics", "Control engineering curricula" ], "abstract": "This paper presents a collection of conceptual stability questions for continuous and discrete time systems, both in open-ended and multiple choice questions formats, designed to target typical misconceptions regarding simple (a.k.a. marginal) stability and convergence properties of equilibria, and the distinction between local and global behaviors. The intended aids are pen-and-paper reasoning and schematic phase-portrait intuition. The target audience is advanced BSc or early MSc students with basic knowledge of dynamical systems and equilibrium notions. Prerequisite LOs (PLOs): - Recall definitions of equilibrium, simple (marginal) stability, asymptotic stability, and convergence. - Manipulate and reason about nonlinear continuous- and discrete-time dynamical systems. - Interpret trajectories and qualitative behavior using phase-portrait arguments. Intended LOs (ILOs): - Distinguish convergence from simple stability in nonlinear systems. - Diagnose violations of the delta--varepsilon based definition of simple stability. - Understand how switching, saturation, or history-dependent dynamics may affect equilibrium properties.", "url": "" }, { "id": "Mo-MoA11.6", "code": "MoA11.6", "title": "Three Control Design Questions for Students and Lecturers (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA11", "sessionTitle": "Control Engineering Exercises", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 201", "authors": [ { "name": "Canevi, Mehmet", "affiliation": "Nigde Omer Halisdemir University" }, { "name": "Dincel, Emre", "affiliation": "Istanbul Technical University" }, { "name": "Ustoglu, Ilker", "affiliation": "Istanbul Technical University" }, { "name": "Söylemez, Mehmet Turan", "affiliation": "Istanbul Tecnical University" } ], "keywords": [ "Repositories for control education", "Internet based control education" ], "abstract": "This paper presents three exam questions in control design, selected from many years of teaching both undergraduate and graduate control engineering courses. The first question demonstrates a numerical approach in which controller zeros and residue poles are placed outside the dominant pole region to meet time-domain performance specifications. Although the idea is numerical, the question is intentionally structured so that it can be solved algebraically with pen and paper. The second question illustrates the use of the Separation Principle in designing a Luenberger observer together with state feedback, highlighting the complications that arise when unobserv- able modes appear in the observer design. The final question is taken from an industrial setting: a pasteurization process modeled as a time-delay plant with an unknown delay. The problem focuses on applying Ziegler–Nichols tuning when only limited information about the plant is available.", "url": "" }, { "id": "Mo-MoA12.1", "code": "MoA12.1", "title": "Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems Via Deep Reinforcement Learning (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA12", "sessionTitle": "Control and Optimization for Smart Cities I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 205", "authors": [ { "name": "Scarpel, Edoardo", "affiliation": "University of Padua" }, { "name": "Pettena, Alberto", "affiliation": "University of Padova" }, { "name": "Cederle, Matteo", "affiliation": "University of Padova" }, { "name": "Chiariotti, Federico", "affiliation": "University of Padova" }, { "name": "Fabris, Marco", "affiliation": "University of Padova" }, { "name": "Susto, Gian Antonio", "affiliation": "University of Padova" } ], "keywords": [ "Decision making under uncertainty", "Smart city control and optimization", "AI for smart cities" ], "abstract": "This paper proposes a fully dynamic Deep Reinforcement Learning (DRL) method for rebalancing dockless bike-sharing systems, overcoming the limitations of periodic, system-wide interventions. We model the service through a graph-based simulator and cast rebalancing as a Markov decision process. A DRL agent routes a single truck in real time, executing localized pick-up, drop-off, and charging actions guided by spatiotemporal criticality scores. Experiments on real-world data show significant reductions in availability failures with a minimal fleet size, while limiting spatial inequality and mobility deserts. Our approach demonstrates the value of learning-based rebalancing for efficient and reliable shared micromobility.", "url": "" }, { "id": "Mo-MoA12.2", "code": "MoA12.2", "title": "A Reinforcement Learning–Based Stackelberg Game for Demand Response between Power Systems and Data Centers (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA12", "sessionTitle": "Control and Optimization for Smart Cities I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 205", "authors": [ { "name": "Zhou, Hanchen", "affiliation": "Tsinghua University" }, { "name": "Jia, Qing-Shan", "affiliation": "Tsinghua University" }, { "name": "Sun, Xunhang", "affiliation": "Xi'an Jiaotong University" }, { "name": "Basar, Tamer", "affiliation": "Univ. of Illinois Urbana-Champaign" } ], "keywords": [ "Data centers and cloud computing", "AI for smart cities", "Decision making under uncertainty" ], "abstract": "This paper investigates the coordination between power systems and data centers in uncertain environments. First, the interaction is formulated as a Stackelberg game where the retailer operator from the power systems sets dynamic electricity prices and the data center operator responds through workload scheduling and battery operations. A realistic data center model is developed to capture task-level temporal flexibility and to enable workload shifting subject to deadline and capacity constraints. Second, a reinforcement learning–based solution framework is developed in which two agents are trained for the leader and the follower. The follower agent is pre-trained offline to learn the optimal demand response under general pricing policies, and the learned follower is subsequently used to train the leader agent to derive an optimal pricing strategy. Finally, numerical experiments demonstrate that the proposed method is shown to achieve the highest renewable energy utilization while maintaining competitive net revenue compared with time-of-use (TOU) and rule-based pricing policies. Overall, the results indicate that the learning-based Stackelberg game framework effectively leverages renewable generation patterns and data center flexibility to enhance system-level performance.", "url": "" }, { "id": "Mo-MoA12.3", "code": "MoA12.3", "title": "Balancing Efficiency and Fairness in Traffic Light Control through Deep Reinforcement Learning (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA12", "sessionTitle": "Control and Optimization for Smart Cities I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 205", "authors": [ { "name": "Cederle, Matteo", "affiliation": "University of Padova" }, { "name": "Scatto, Giacomo", "affiliation": "University of Padova" }, { "name": "Susto, Gian Antonio", "affiliation": "University of Padova" } ], "keywords": [ "Smart city control and optimization", "Decision making under uncertainty", "AI for smart cities" ], "abstract": "Urban traffic congestion presents a significant challenge for modern cities, which impacts mobility and sustainability. Traditional traffic light control systems often fail to adapt to dynamic conditions, leading to inefficiencies. This paper proposes a novel deep reinforcement learning agent for traffic light control that addresses this limitation by explicitly integrating fairness considerations for both vehicular and pedestrian traffic. Unlike prior work, our approach dynamically balances these flows based on real-time demand, moving beyond systems focused solely on vehicles. Experimental results demonstrate that our agent effectively reduces congestion while ensuring equitable service for both the categories of road users. This research contributes to a practical and adaptable solution for intelligent traffic management within the framework of smart cities, paving the way for more efficient and inclusive urban mobility.", "url": "" }, { "id": "Mo-MoA12.4", "code": "MoA12.4", "title": "Distributed State Estimation for Microgrid Systems with Unreliable Measurements (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA12", "sessionTitle": "Control and Optimization for Smart Cities I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 205", "authors": [ { "name": "Ma, Kai", "affiliation": "Yanshan University" }, { "name": "Wang, Yuyin", "affiliation": "Yanshan University" }, { "name": "Li, Hui", "affiliation": "Yanshan University" }, { "name": "Yang, Jie", "affiliation": "Yanshan University" } ], "keywords": [ "Smart city control and optimization", "Cyber-physical urban systems", "Urban energy distribution systems" ], "abstract": "As a crucial component of energy supply in smart cities, microgrid systems require accurate state estimation to support efficient decision-making and control in smart city energy systems. Although deploying numerous sensors enhances estimation reliability, it also increases the system's susceptibility to unreliable measurements caused by unknown disturbances and random link failures in communication networks. To address these challenges, this paper investigates the distributed state estimation problem for microgrid systems subject to both measurement outliers and random communication link failures. The proposed distributed state estimation algorithm comprises two key components: first, a variational Bayesian-based local state estimator that adaptively mitigates external disturbances; second, a robust consensus-based fusion mechanism that effectively integrates local estimates from neighboring sensors while maintaining estimation performance under random link failures.", "url": "" }, { "id": "Mo-MoA12.5", "code": "MoA12.5", "title": "Passivity-Based Hierarchical Frequency Control for Nonlinear Power Systems with Grid-Forming Inverters (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA12", "sessionTitle": "Control and Optimization for Smart Cities I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 205", "authors": [ { "name": "Kang, Heng", "affiliation": "Keio University" }, { "name": "Namerikawa, Toru", "affiliation": "Keio University" } ], "keywords": [ "Smart city control and optimization", "Urban energy distribution systems", "Cyber-physical urban systems" ], "abstract": "This paper investigates a passivity-based hierarchical frequency control framework for frequency regulation and system-wide stability in heterogeneous nonlinear power systems. The power network consists of synchronous generators (SGs) with turbine/governor dynamics, virtual synchronous generators (VSGs), and droop-controlled inverters (DCIs) interconnected through nonlinear power flows. Since such systems generally lack strict passivity due to heterogeneous dynamics and relative degrees, we employ the passivity-short concept to capture their input–output behavior in a unified manner. Based on this framework, we design a hierarchical control strategy comprising local controllers to stabilize the overall system and a distributed control law using limited communication for global cooperation. The proposed method guarantees overall system stability and enhances the frequency regulation, as demonstrated by simulation results.", "url": "" }, { "id": "Mo-MoA12.6", "code": "MoA12.6", "title": "AI and AR-Supported Triage Assistance for Mass-Casualty Incidents in Cyber-Physical Urban Systems: The TARIS Concept (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA12", "sessionTitle": "Control and Optimization for Smart Cities I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 205", "authors": [ { "name": "Schwarz, Christoph Stephan", "affiliation": "Universität Der Bundeswehr München" }, { "name": "Nistor, Marian Sorin", "affiliation": "Universität Der Bundeswehr München" }, { "name": "Pickl, Stefan", "affiliation": "Universität Der Bundeswehr München" } ], "keywords": [ "Cyber-physical urban systems", "Human-centric automation/AI Systems, and human agency", "Decision making under uncertainty" ], "abstract": "Emergency medical services (EMS) are part of the critical infrastructure of cyber-physical urban systems (CPUS), and mass casualty incidents (MCIs) place particular strain on prehospital triage. This paper introduces TARIS (Triage Assistance using Real-time Intelligence & Support), a conceptual assistance system that combines artificial intelligence (AI) and augmented reality (AR) for triage support in such events. Following a design science process, TARIS links wearable sensors, AR-based guidance and AI-generated triage suggestions in a modular cloud–fog architecture spanning physical and cyber layers. Qualitative expert interviews suggest that practitioners see potential to reduce workload, support decision consistency and improve information flow along the rescue chain.", "url": "" }, { "id": "Mo-MoA13.1", "code": "MoA13.1", "title": "Model Predictive Control for Setpoint Tracking with Reference Preview", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA13", "sessionTitle": "Model Predictive Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Jin, Jiawei", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Lu, Renzhi", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Zhang, Fan", "affiliation": "Sun Yat-Sen University" }, { "name": "Raimondo, Davide Martino", "affiliation": "Università Degli Studi Di Trieste" }, { "name": "Wan, Yiming", "affiliation": "Huazhong University of Science and Technology" } ], "keywords": [ "Model predictive control" ], "abstract": "Model Predictive Control (MPC) is widely used for constrained setpoint tracking problems. Due to lack of anticipation of future setpoint changes, tracking MPC often suffers from transient performance degradation or loss of feasibility under significant setpoint variations. This paper proposes a reference-preview MPC that improves transient tracking performance while guaranteeing persistent feasibility under arbitrary setpoint variations. The controller explicitly incorporates a finite-horizon preview of future setpoint values into the optimization problem, and introduces a time-varying artificial reference sequence as additional decision variables. A maximal tracking admissible invariant set is constructed from a parameterization of all feasible steady states and then employed as the terminal constraint, eliminating the need for online terminal set recomputation. We prove recursive feasibility and asymptotic stability of the closedloop system. A simulation example demonstrates that the proposed approach achieves faster, anticipatory responses compared with existing tracking MPC methods that do not utilize future setpoint knowledge.", "url": "" }, { "id": "Mo-MoA13.2", "code": "MoA13.2", "title": "Closed-Loop Performance of MPC for Tracking Periodic References", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA13", "sessionTitle": "Model Predictive Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Ehmann, Nadine", "affiliation": "University of Stuttgart" }, { "name": "Allgower, Frank", "affiliation": "University of Stuttgart" } ], "keywords": [ "Model predictive control" ], "abstract": "Model Predictive Control (MPC) for tracking provides a powerful framework for tracking potentially unreachable and time-varying references while ensuring recursive feasibility, stability and convergence to the best reachable reference through the introduction of an artificial reference. In this work, we extend existing results in two ways. First, we address not only setpoint tracking but tracking of periodic references and make use of an formulation that does not require terminal ingredients, thereby reducing design effort. Second, we provide rigorous bounds on the closed-loop performance and show that for suitable parameters the MPC for tracking input becomes optimal as the prediction horizon tends to infinity.", "url": "" }, { "id": "Mo-MoA13.3", "code": "MoA13.3", "title": "Efficient Uniform Feasible-Set Sampling for Approximate Linear MPC", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA13", "sessionTitle": "Model Predictive Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Milios, Elias Lido Celestino", "affiliation": "ETH Zurich, Robert Bosch GmbH" }, { "name": "Berkel, Felix", "affiliation": "Robert Bosch GmbH" }, { "name": "Gruber, Felix", "affiliation": "Robert Bosch GmbH" }, { "name": "Zeilinger, Melanie N.", "affiliation": "ETH Zurich" }, { "name": "Wabersich, Kim Peter", "affiliation": "Robert Bosch GmbH" } ], "keywords": [ "Model predictive control", "Design methods for data-based control", "Linear systems" ], "abstract": "Model Predictive Control (MPC) offers safe and near-optimal control but suffers from high computational costs. Approximate MPC (AMPC) mitigates this by learning a cheaper surrogate policy, typically by training a neural network on state-MPC input pairs. Generating training data is a major bottleneck, requiring solving the MPC for numerous states sampled from its feasible set. Since this feasible set is implicitly defined and unknown, efficient sampling is nontrivial but crucial. We propose the linear MPC Hit-and-Run (LMPC-HR) sampler for linear MPC with polyhedral constraints. We identify the feasible set boundaries along search directions, a crucial step within HR, by formulating the problem as a convex linear program, replacing expensive iterative searches with a single optimization step. A numerical study demonstrates that LMPC-HR reduces the computational cost of generating uniformly distributed samples from the feasible set by an order of magnitude compared to standard baselines.", "url": "" }, { "id": "Mo-MoA13.4", "code": "MoA13.4", "title": "Disturbance-Adaptive Model Predictive Control for Bounded Average Constraint Violations", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA13", "sessionTitle": "Model Predictive Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Shi, Jicheng", "affiliation": "EPFL" }, { "name": "Jones, Colin, N", "affiliation": "EPFL" } ], "keywords": [ "Model predictive control", "Probabilistic robustness", "Stochastic optimal control problems" ], "abstract": "This paper considers stochastic linear time-invariant systems subject to time-averaged state-constraint violation bounds, without assuming knowledge of the disturbance distribution. We propose a disturbance-adaptive model predictive control (DAD-MPC) framework, which adjusts the confidence level and the induced disturbance set based on measured constraint violations. Using a robust invariance method, DAD-MPC ensures recursive feasibility and guarantees robust or asymptotic bounds on the average violation rate. Additionally, the bounds remain valid even with an inaccurate disturbance model, enabling the use of data-driven disturbance quantification methods such as conformal prediction. Simulation results demonstrate that the proposed approach reduces closed-loop cumulative cost compared to state-of-the-art methods across different target violation rates, while satisfying average violation bounds.", "url": "" }, { "id": "Mo-MoA13.5", "code": "MoA13.5", "title": "Safe Adaptive-Sampling Control Via Robust M-Step Hold Model Predictive Control", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA13", "sessionTitle": "Model Predictive Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Schutz, Spencer", "affiliation": "UC Berkeley" }, { "name": "Vallon, Charlott", "affiliation": "University of California, Berkeley" }, { "name": "Borrelli, Francesco", "affiliation": "University of California" } ], "keywords": [ "Model predictive control", "Sampled-data/digital control", "Linear systems" ], "abstract": "In adaptive-sampling control, the control frequency can be adjusted during task execution. Ensuring that these changes do not jeopardize the safety of the system being controlled requires attention. We introduce robust M-step hold model predictive control (MPC) to address this. Our formulation provides robust constraint satisfaction for an uncertain discrete-time system model subject to an adaptable multi-step input hold (\"M-step hold\"). We show how to ensure recursive feasibility of the MPC utilizing M-step hold extensions of robust invariance, and demonstrate how to enable safe adaptive-sampling control via the online selection of M. We evaluate the utility of the robust M-step hold MPC formulation in a cruise control example.", "url": "" }, { "id": "Mo-MoA13.6", "code": "MoA13.6", "title": "Differentiator-Based Learning and Model Predictive Control of Nonlinear Systems on the Fly", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA13", "sessionTitle": "Model Predictive Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Yap, Wendy", "affiliation": "Uppsala University" }, { "name": "Verginis, Christos", "affiliation": "Uppsala University" } ], "keywords": [ "Model predictive control", "Uncertain systems", "Data-driven robust control" ], "abstract": "We develop a learning-based control algorithm for constrained trajectory tracking by nonlinear systems with unknown dynamics by using data obtained on the fly. The algorithm consists of three steps: first, it uses an observer-based differentiator to estimate the systems’ state derivatives; second, it uses limited information on the systems dynamics (such as Lipschitz constants in a given set) and state measurements obtained on the fly, i.e., from a single system trajectory, to compute in real-time a set-based over-approximation of the unknown dynamic terms; this over-approximation leads to a differential-inclusion-based dynamic approximation that is updated on the fly as new state measurements are obtained. Third, it uses the overapproximation in a Nonlinear Model Predictive Control procedure to achieve tracking of a predefined trajectory while complying with state and input constraints. We provide theoretical guarantees and simulations results on two underactuated systems.", "url": "" }, { "id": "Mo-MoA14.1", "code": "MoA14.1", "title": "Online Policy Iteration for Adaptive Linear Quadratic Regulator under Stochastic Disturbances", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA14", "sessionTitle": "Learning for Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Virgiani, Vina Putri", "affiliation": "Tokyo Metropolitan University" }, { "name": "Masuda, Shiro", "affiliation": "Tokyo Metropolitan University" } ], "keywords": [ "Adaptive control design", "Learning methods for optimal control", "Linear systems" ], "abstract": "The study presents an online Policy Iteration (PI) algorithm for the adaptive Linear Quadratic Regulator (LQR) problem under stochastic disturbances. The proposed method recursively estimates the value function using an Instrumental Variable (IV) technique from real-time input–output data, eliminating the requirement for pre-collected data. In the subsequent policy improvement step, the state feedback gains are updated via a gradient descent law with a properly chosen step size, enabling continuous online adaptation while ensuring smooth convergence and a monotonic decrease of the value function toward the optimal value. The main theoretical contributions include the convergence of the proposed online PI algorithm and the analysis of the convergence properties of the parameter estimation. For further verification, numerical simulations are presented to demonstrate the effectiveness and practical applicability of the method without requiring full knowledge of the system models under stochastic disturbances.", "url": "" }, { "id": "Mo-MoA14.2", "code": "MoA14.2", "title": "Adaptive Reinforcement Learning Control of Pure Feedback Nonlinear Plants", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA14", "sessionTitle": "Learning for Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Medvedev, Mikhail", "affiliation": "Taganrog Technological Institute of SouthernFederalUniversity" }, { "name": "Gaiduk, Anatoliy", "affiliation": "Taganrog Institute of Technology of Southern FederalUniversity" }, { "name": "Pshikhopov, Vyacheslav", "affiliation": "Institute of Robotic and Control, Taganrog" }, { "name": "Medvedev, Ilya", "affiliation": "Southern Federal University" } ], "keywords": [ "Adaptive control design", "Learning methods for optimal control", "Uncertain systems" ], "abstract": "This article presents a method for adaptive control of pure feedback nonlinear plants. Control is designed based on the Actor-Critic reinforcement learning method. A modification of the Actor-Critic algorithm is proposed, which lacks the feature of zeroing control. An additional coefficient has been introduced into the tuning algorithm to stabilize the control parameters. Adaptive control is approximated by radial base functions network. The design of control is carried out in stages, based on the structure of the control object. The convergence analysis of the control system and numerical studies using the example of vessel trim control are carried out.", "url": "" }, { "id": "Mo-MoA14.3", "code": "MoA14.3", "title": "Uncertainty-Aware Clustered Federated Identification for Controller Design: Application to Lateral Dynamics and Yaw-Rate Tracking", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA14", "sessionTitle": "Learning for Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Weber, Jakob", "affiliation": "AIT Austrian Institute of Technology" }, { "name": "Gurtner, Markus", "affiliation": "Austrian Institute of Technology GmbH" }, { "name": "Trachte, Adrian", "affiliation": "Robert Bosch GmbH" }, { "name": "Kugi, Andreas", "affiliation": "TU Wien" } ], "keywords": [ "Design methods for data-based control", "Learning methods for optimal control", "Robust controller synthesis" ], "abstract": "Many fleet-control scenarios require consistent task performance across clients with heterogeneous dynamics and limited local data. We propose an uncertainty-aware clustered federated identification-to-control pipeline. Each client performs online exponentially weighted recursive least-squares identification during task execution and uploads parameter and covariance estimates. The server clusters clients in parameter space using a Mahalanobis distance, computes precision-weighted cluster models, and designs a two-degree-of-freedom controller per cluster. On a lateral vehicle dynamics benchmark for yaw-rate tracking during lane changes, with heterogeneity from speed and payload, the method recovers latent regimes and achieves near-nominal fleet-wide tracking. Compared with nominal, FedAvg-based, and Euclidean-clustering baselines, uncertainty-aware clustering yields more consistent tracking across regimes at modest communication and computation cost.", "url": "" }, { "id": "Mo-MoA14.4", "code": "MoA14.4", "title": "Transferring Probability Density Functions for Big Data-Driven Predictive Control of Nonlinear Processes", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA14", "sessionTitle": "Learning for Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Han, Shuangyu", "affiliation": "University of New South Wales" }, { "name": "Yan, Yitao", "affiliation": "University of New South Wales" }, { "name": "Bao, Jie", "affiliation": "The University of New South Wales" }, { "name": "Huang, Biao", "affiliation": "Univ. of Alberta" } ], "keywords": [ "Design methods for data-based control", "Nonlinearity learning from data" ], "abstract": "We present a big data-driven predictive control approach in the behavioural systems framework to control nonlinear processes in an operation region where only limited data are available. To control the nonlinear system behaviour in the operation region (target) that has a limited number of data trajectories, we utilise the large number of data trajectories in another operation region (source), under the assumption of the existence of a bijective and differentiable mapping of data trajectories between the two regions. The proposed approach consists of three steps. The first step aims to transfer the probability density function of the data trajectory from a source operation region to another target operation region. The second step is to approximate local linear sub-behaviours in the target operation region based on the transferred probability density function. The last step is to utilise the local linear sub-behaviours for online big data-driven predictive control. The proposed approach is illustrated by an example of controlling a vanadium flow battery.", "url": "" }, { "id": "Mo-MoA14.5", "code": "MoA14.5", "title": "Model-Free Control of a Nonlinear Three Tank System Using Reservoir Computing", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA14", "sessionTitle": "Learning for Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Thielke, Marcel", "affiliation": "Karlsruhe Institute of Technology" }, { "name": "Meurer, Thomas", "affiliation": "Karlsruhe Institute of Technology (KIT)" } ], "keywords": [ "Design methods for data-based control", "Nonlinearity learning from data", "Data-driven robust control" ], "abstract": "Reservoir computing is used to develop a model-free inversion-based controller for a nonlinear system. For this, an echo state network is considered as reservoir that is trained to learn the output-to-input map of the system from training data. This results in a model-free (nonlinear) tracking controller by driving the trained reservoir with the measured output and a desired reference trajectory. To address training errors and to enhance the robustness of the model-free control approach, an adaptation mechanism is proposed to adjust the output layer of the reservoir online. In addition, the local asymptotic stability of the closed-loop control system is analyzed. Finally, the control approach is validated using experimental data and is implemented to control a nonlinear three tank system. The obtained experimental results clearly confirm the performance of the model-free control concept.", "url": "" }, { "id": "Mo-MoA14.6", "code": "MoA14.6", "title": "Prescribed-Time Control of Fully-Actuated Nonlinear Systems Using Deep Koopman Operator", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA14", "sessionTitle": "Learning for Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Lee, Yeonseo", "affiliation": "Seoul National University" }, { "name": "Park, Hyeongjun", "affiliation": "Seoul National University" } ], "keywords": [ "Design methods for data-based control", "Stability of nonlinear systems", "Nonlinearity learning from data" ], "abstract": "Driving a system to its target within a prescribed time is essential in time-critical operations where the convergence time is a hard mission constraint. Exact convergence at the prescribed time requires unbounded gains incompatible with actuator bounds, and existing designs require analytical or restrictive structural knowledge of the dynamics. This paper proposes a data-driven prescribed-time (PT) control framework for mechanically fully actuated nonlinear systems with unknown dynamics. A deep Koopman operator lifts the system into a linear time-invariant representation, on which a bounded feedback derived from a parametric Lyapunov equation regulates the physical state, with a robust term absorbing the matched closure error of the lift, achieving practical PT convergence. The design admits a closed-form ellipsoidal domain of attraction determined by the actuator bound, initial state, and prescribed time. Numerical simulations on close-range rendezvous with an eccentric chief orbit confirm convergence within the prescribed time without violating the actuator bound.", "url": "" }, { "id": "Mo-MoA15.1", "code": "MoA15.1", "title": "Dissipativity and Absolute Stability Analysis of Systems with a Clockwise Preisach Operator", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA15", "sessionTitle": "Stability of Interconnected Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Keulen, Jurrien", "affiliation": "University of Groningen" }, { "name": "Jayawardhana, Bayu", "affiliation": "University of Groningen" } ], "keywords": [ "Stability of nonlinear systems", "Interconnected nonlinear systems" ], "abstract": "In this paper we present dissipativity analysis of clockwise Preisach hysteresis operators, which is subsequently used to establish the absolute stability of an LTI system feedback interconnected with such hysteresis element. First, we show the construction of a storage function for clockwise Preisach hysteresis operators that satisfies the clockwise dissipativity inequality, where the supply rate is given by ydot u with (y,u) be the output and input of the hysteresis operators, respectively. Correspondingly, we show that a negative feedback interconnection of a negative imaginary system with a clockwise Preisach operator is absolutely stable.", "url": "" }, { "id": "Mo-MoA15.2", "code": "MoA15.2", "title": "Stability of Continuous-Time Systems with Input-Saturation Using Slab-Defined Piecewise-Quadratic Lyapunov Functions", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA15", "sessionTitle": "Stability of Interconnected Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Béchu, Maxime", "affiliation": "Université Paris Saclay, Laboratoire Des Signaux Et Systèmes" }, { "name": "Rodriguez-Ayerbe, Pedro", "affiliation": "Supelec" }, { "name": "Valmorbida, Giorgio", "affiliation": "L2S, CentraleSupelec" } ], "keywords": [ "Saturation and discontinuity", "Stability of nonlinear systems", "Convex optimization" ], "abstract": "This paper presents a global and local stability analysis of continuous-time systems with saturation or deadzone nonlinearities. The approach uses an implicit ramp-based model and Piecewise Quadratic Lyapunov Functions (PWQ LF). A key contribution is the enhancing of PWQ LF partition using fictitious ramp signals. Good assumptions about these ramps allow us to write properties that lead to less conservatism in the Linear Matrix Inequalities (LMIs), ensuring PWQ non-negativity and estimating the region of attraction using Lyapunov’s theory. Numerical results demonstrate the effectiveness of the method.", "url": "" }, { "id": "Mo-MoA15.3", "code": "MoA15.3", "title": "Flexibility Propagation in Lyapunov Function Synthesis to Design Positivizing Stabilizers for Cyclic Networks Via Asymmetric Dissipativity", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA15", "sessionTitle": "Stability of Interconnected Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Ito, Hiroshi", "affiliation": "Kyushu Institute of Technology" } ], "keywords": [ "Stability of nonlinear systems", "Lyapunov methods", "Interconnected nonlinear systems" ], "abstract": "This paper pursues dissipativity-based control design for positivizing and stabilizing dynamical systems with respect to freely specified interior equilibria in positive state spaces. Recently, the use of asymmetrically scaled sectorial (ASSEC) supply rates was proposed to construct a single Lyapunov function establishing positivization and stabilization simultaneously. In contrast to symmetric dissipativity, such as Lp gain, passivity, and input-to-state stability, uncommon denominators arising from ASSEC supply rates not only deny the standard technique to justify weighted summation of the supply rates but also require cascade and feedback connections to be treated separately. This paper proposes a mechanism of flexibility propagation in Lyapunov function synthesis for cycle networks. The flexibility is useful to confirm propagation of a single extra decay across a network to form an unbounded total decay. The flexibility also unifies cascade and feedback Lyapunov function formulas, and establishes the continuity of the Lyapunov function with respect to network connection strength. Its usefulness and effectiveness are illustrated by a gradient descent controller design example.", "url": "" }, { "id": "Mo-MoA15.4", "code": "MoA15.4", "title": "Stabilization of Interconnected Singularly Perturbed Switched Affine Systems Coupled by a Slow LTI System", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA15", "sessionTitle": "Stability of Interconnected Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "de Souza, Ryan P. C.", "affiliation": "Ampere, Centrale Lyon" }, { "name": "Kader, Zohra", "affiliation": "ENSEEIHT-LAPLACE" }, { "name": "Caux, Stéphane", "affiliation": "INPT - LAPLACE - University of Toulouse" } ], "keywords": [ "Nonlinear control of switched & hybrid systems", "Switching stability and control", "Lyapunov methods" ], "abstract": "Over the past few decades, several techniques have been presented for the stabilization control of Switched Affine Systems (SASs), most of which are based on the solution of Linear Matrix Inequalities (LMIs), making them numerically efficient. In the case of interconnected SASs though, the performance of the numerical solvers can become severely degraded, specially in the case of large-scale systems composed of multiple SASs. In addition, some systems exhibit two distinct timescales (fast and slow) and they are usually referred to as singularly perturbed systems, for which analysis and control design suffer from poor conditioning. In this paper, we propose a constructive control design method for the stabilization of an interconnection of heterogeneous SPSASs overcoming the aforementioned difficulties. We focus on the case where the SPSASs are coupled by an LTI system that is slow compared to the fast dynamics of the SPSASs, examples of which can be found in power electronic applications. The proposed controller is decentralized and independent of the singular perturbation parameter.", "url": "" }, { "id": "Mo-MoA15.5", "code": "MoA15.5", "title": "Online Parameter Estimation for Output-Coupled Nonlinear Systems Using Exact Differentiation", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA15", "sessionTitle": "Stability of Interconnected Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Lundt, Torben Niklas", "affiliation": "University of Hohenheim" }, { "name": "Schaum, Alexander", "affiliation": "University of Hohenheim" } ], "keywords": [ "Nonlinear observers and filters", "Interconnected nonlinear systems", "Sliding mode control" ], "abstract": "The problem of online parameter estimation for output-coupled nonlinear systems is discussed using exact differentiators to determine the time derivatives of the available measurements. For this purpose a class of nonlinear systems is considered that can be brought into a regressor form using the associated observability map. First, the case of a single system is addressed and sufficient conditions for the parameter estimation algorithm are established using Lyapunov's direct method. This is showcased for the Van der Pol oscillator. Then, the case of output-coupled systems is considered and a generalization of the first result is provided. This second result is then used for the parameter estimation of coupled Kuramoto oscillators, leading to improved convergence conditions in comparison to the recent study in Lundt et al. (2025). All case example are accompanied with numerical simulation results for discussion of the potential of the proposed approach.", "url": "" }, { "id": "Mo-MoA15.6", "code": "MoA15.6", "title": "Network Synchronization Via Dynamic Perturbations", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA15", "sessionTitle": "Stability of Interconnected Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Pena Ramirez, Jonatan", "affiliation": "CICESE" }, { "name": "Cuesta-Garcia, Jose Ricardo", "affiliation": "CICESE Research Center" }, { "name": "Stolwijk, Twan Mathijs", "affiliation": "University of Technology Eindhoven" }, { "name": "Fey, Rob H.B.", "affiliation": "PO Box 513, Eindhoven University of Technology" } ], "keywords": [ "Interconnected nonlinear systems", "Control of complex systems", "Adaptive control design" ], "abstract": "While investigating the onset of synchronization in networks of oscillatory systems, a rather intuitive assumption is to consider that all the oscillators are identical. However, there exist oscillatory systems that seem to defy this ideal scenario, in such way that the oscillators only achieve synchronization when they are heterogeneous. Here, we will exploit this counter-intuitive direction. In particular, we will consider a family of phase-amplitude oscillators, for which identical synchronization can only be observed when there is certain amount of heterogeneity in the oscillators. In our approach, this heterogeneity is introduced by adding dynamic disturbances to the nodes. Specifically, each disturbance is generated by a first-order linear system which is ad hoc designed such that the disturbance vanishes once the desired synchronous behavior has been reached. In the absence of this heterogeneity, the network of homogeneous oscillators cannot be synchronized. We consider two scenarios: the case where every node in the network is perturbed and the case where the perturbations are applied to only a few nodes. In both cases, we conduct a stability analysis of the synchronous solution and the obtained results are illustrated via numerical simulations. Ultimately, the results presented here suggest that our approach successfully induces synchronization by temporarily introducing heterogeneity in the network.", "url": "" }, { "id": "Mo-MoA16.1", "code": "MoA16.1", "title": "Quadratic Repulsiveness for Continuous-Time Polytopic LPV Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA16", "sessionTitle": "LMIs and S-Variable Approach in Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Rotondo, Damiano", "affiliation": "Universitetet I Stavanger" }, { "name": "Cristofaro, Andrea", "affiliation": "Sapienza University of Rome" } ], "keywords": [ "Linear parameter-varying systems", "Control barrier functions and state space constraints", "Robust linear matrix inequalities" ], "abstract": "This paper proposes the extension of the recently introduced concept of quadratic repulsiveness to polytopic linear parameter varying (LPV) systems. The novel idea of the quadratic repulsiveness is the interest in driving the state of the system out of a certain undesired region of the space, described as the super-level set of a prescribed, fixed a priori sign-indefinite quadratic function. With this goal in mind, the problem of designing stabilizing and repulsive gain-scheduled controllers is cast in terms of matrix inequalities (LMIs) that can be treated with existing solvers. The proposed approach is illustrated with the case-study of an electrical circuit.", "url": "" }, { "id": "Mo-MoA16.2", "code": "MoA16.2", "title": "Sample-Based Synthesis of Gain-Scheduled Controllers for Descriptor Systems Characterized by Random Polytopes (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA16", "sessionTitle": "LMIs and S-Variable Approach in Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Hosoe, Yohei", "affiliation": "Kyoto University" }, { "name": "Kamidaira, Ayumu", "affiliation": "University" }, { "name": "Peaucelle, Dimitri", "affiliation": "LAAS-CNRS" }, { "name": "Hagiwara, Tomomichi", "affiliation": "Kyoto Univ" } ], "keywords": [ "Linear parameter-varying systems", "Control of uncertain LPV systems", "Switching linear systems" ], "abstract": "This paper is concerned with robust stability analysis and gain-scheduled state feedback synthesis for discrete-time descriptor systems whose coefficient matrices are characterized by random polytopes. Since the derived inequality conditions include random variables, a sample-based method of using them is proposed, which is illustrated with numerical examples.", "url": "" }, { "id": "Mo-MoA16.3", "code": "MoA16.3", "title": "S-Variable Approach for Generic Convex Polyhedra (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA16", "sessionTitle": "LMIs and S-Variable Approach in Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Peaucelle, Dimitri", "affiliation": "LAAS-CNRS" }, { "name": "Callegari, Sara", "affiliation": "LAAS-CNRS, Université De Toulouse, INSA" }, { "name": "Ebihara, Yoshio", "affiliation": "Kyushu University" }, { "name": "Hosoe, Yohei", "affiliation": "Kyoto University" }, { "name": "Sato, Masayuki", "affiliation": "Kyushu Institute of Technology" } ], "keywords": [ "Uncertain systems", "Convex optimization", "Sum-of-squares" ], "abstract": "For long the S-variable approach has proved its efficiency for addressing robustness issues with respect to matrix-valued bounded, polytopic uncertainties. The results are extended in this paper to generic convex polyhedra thus allowing robustness analysis with respect to unbounded uncertainties. Results apply to rationnaly-dependent models with respect to such polyhedral uncertainties. Moreover the S-variable approach is proved to be enhanced thanks to a new affine rows-decoupled descriptor modeling of such systems. Examples of application of these results are a reinterpretation of the KYP-lemma as well as a reformulation of the links between sum-of-squares approach and the S-variable approach.", "url": "" }, { "id": "Mo-MoA16.4", "code": "MoA16.4", "title": "Covariance Stabilization for a Class of Stochastic Discrete-Time Linear Systems Using the S-Variable Approach (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA16", "sessionTitle": "LMIs and S-Variable Approach in Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Moussa, Kaouther", "affiliation": "INSA Hauts-De-France, LAMIH" }, { "name": "Peaucelle, Dimitri", "affiliation": "LAAS-CNRS" } ], "keywords": [ "Uncertain systems", "Robust linear matrix inequalities", "Lyapunov methods" ], "abstract": "This paper deals with the problem of covariance stabilization for a class of linear stochastic discrete-time systems in the Stochastic Model Predictive Control (SMPC) framework. The considered systems are affected by independent and identically distributed (i.i.d.) additive and parametric stochastic uncertainties (potentially unbounded), in addition to polytopic deterministic uncertainties bounding the mean of the state and input parameters. The design conditions presented in this paper are formulated as Linear Matrix Inequalities (LMIs), using the S-variable approach in order to reduce the potential conservatism. These conditions are derived using a deterministic exact characterization of the covariance dynamics, the latter involves bilinear terms in the control gain. A technique to linearize such dynamics is presented, it results in a descriptor representation allowing to derive sufficient conditions for the design of a covariance-stabilizing controller. The derived condition is first compared with a known necessary and sufficient stability condition for systems without deterministic uncertainties and additive stochastic noise. Although more conservative, the proposed condition is more numerically tractable, with an LMI size scaling as O(n^2) instead of O(n^3). Then, the same condition is used to design controllers that are robust to both deterministic and stochastic uncertainties. Several numerical examples are presented for comparison and illustration.", "url": "" }, { "id": "Mo-MoA16.5", "code": "MoA16.5", "title": "Homogeneous Rational Lyapunov Functions for Stability Analysis of Continuous-Time Takagi-Sugeno Fuzzy Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA16", "sessionTitle": "LMIs and S-Variable Approach in Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Quilles-Marinho, Yara", "affiliation": "Korea Advanced Institute of Science and Technology" }, { "name": "Lee, Donghwan", "affiliation": "Korea Advanced Institute of Science and Technology" }, { "name": "Oliveira, Ricardo C. L. F.", "affiliation": "University of Campinas" }, { "name": "Peres, Pedro L. D.", "affiliation": "Universidade Estadual De Campinas" } ], "keywords": [ "Lyapunov methods", "Stability of nonlinear systems", "Robust linear matrix inequalities" ], "abstract": "This paper proposes a homogeneous rational Lyapunov function for exponential stability analysis of continuous-time Takagi-Sugeno fuzzy systems. The numerator and denominator are homogeneous polynomials with independent degrees, generalizing homogeneous polynomial Lyapunov functions and providing additional flexibility. Stability conditions are derived in terms of matrix inequalities and solved through an LMI-based iterative algorithm that jointly optimizes the numerator, denominator, and exponential decay rate. Unlike related rational formulations, both numerator and denominator matrices remain decision variables. Numerical examples show that the proposed approach reduces conservatism and improves certified decay rates compared with existing conditions based on polynomial Lyapunov matrices.", "url": "" }, { "id": "Mo-MoA16.6", "code": "MoA16.6", "title": "An LMI-Based Approach for Explicit Computation of Stabilizing Output Feedback Gains Using Polyhedral Lyapunov Functions (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA16", "sessionTitle": "LMIs and S-Variable Approach in Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Oliveira, Ricardo C. L. F.", "affiliation": "University of Campinas" }, { "name": "Ernesto, Jackson G.", "affiliation": "Federal University of Santa Catarina" }, { "name": "Castelan, Eugenio B.", "affiliation": "Univ. Federal De Santa Catarina" }, { "name": "Peres, Pedro L. D.", "affiliation": "Universidade Estadual De Campinas" } ], "keywords": [ "Control of uncertain LPV systems", "Robust controller synthesis", "Robust linear matrix inequalities" ], "abstract": "This paper proposes an iterative linear matrix inequality (LMI) based approach to compute, directly as variables of the problem, output feedback control gains for discrete-time linear parameter varying systems and the corresponding polyhedral Lyapunov function with a prescribed level of complexity. For that, the (necessary and sufficient) conditions from the literature are manipulated through the double application of Finsler's lemma, generating a new equivalent formulation where the control gains and other meaningful decision variables appear explicitly in the conditions. These new conditions are solved through a locally convergent LMI-based iterative algorithm. Numerical examples illustrate the results.", "url": "" }, { "id": "Mo-MoA17.1", "code": "MoA17.1", "title": "Trajectory Tracking Control for Pendubot Swing-Up: A Combined Time-Reversal and Time-Delay Estimation Approach", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA17", "sessionTitle": "Nonlinear Tracking Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Liu, Wenyu", "affiliation": "Southeast University" }, { "name": "Xin, Xin", "affiliation": "Southeast University" }, { "name": "Liu, Yannian", "affiliation": "Southeast University" } ], "keywords": [ "Lagrangian and Hamiltonian systems", "Disturbance rejection and input-to-state stability", "Output regulation and tracking" ], "abstract": "This paper presents a robust control framework for Pendubot swing-up and stabilization. To avoid computationally expensive optimization, a reference trajectory is generated via the time-reversal of a SD-controlled swing-down motion. Since physical friction breaks the dynamic symmetry required by this approach, a time-delay control law is designed to achieve robust finite-horizon tracking of the active joint. By introducing a virtual inertia, the controller estimates and compensates for the actuated-channel lumped disturbance, including active-joint friction, coupling effects, and model mismatch entering the first joint dynamics, without explicit regression models. Input-to-state stability of the active-joint tracking error dynamics is analytically established. Finally, a switched linear quadratic regulator provides local stabilization near the upright equilibrium. Numerical simulations validate the method's robustness against active-joint friction and a representative inertial parameter perturbation.", "url": "" }, { "id": "Mo-MoA17.2", "code": "MoA17.2", "title": "A New Almost Global Velocity-Free Geometric Attitude Tracking Control for Rigid Body on SO(3)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA17", "sessionTitle": "Nonlinear Tracking Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Ye, Yaobang", "affiliation": "Beihang University" }, { "name": "Zuo, Zongyu", "affiliation": "Beijing University of Aeronautics and Astronautics" } ], "keywords": [ "Application of nonlinear analysis and design", "Controller constraints and structure" ], "abstract": "This paper addresses the attitude tracking control problem for a rigid body with attitude measurements only, when angular velocity measurements are not available. To circumvent the complexity and ambiguity associated with alternative attitude representations like Euler angles or quaternions, the attitude dynamics and the proposed control system are globally represented on special orthogonal groups. Through the design of an auxiliary variable, an efficient and practical angular velocity-free control strategy with a simple structure is proposed, which achieves asymptotic tracking of an attitude command without requiring explicit knowledge of angular velocity signals.", "url": "" }, { "id": "Mo-MoA17.3", "code": "MoA17.3", "title": "Decoupling Feedback Control of Cathode Gas Conditioning in FC Testbeds", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA17", "sessionTitle": "Nonlinear Tracking Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Markler, Christoph", "affiliation": "TU Wien" }, { "name": "Köppel, Dominik", "affiliation": "TU Wien" }, { "name": "Hametner, Christoph", "affiliation": "TU Wien" }, { "name": "Jakubek, Stefan M.", "affiliation": "Technical Univ. of Vienna/Austria" } ], "keywords": [ "Output feedback nonlinear control", "Application of nonlinear analysis and design", "Output regulation and tracking" ], "abstract": "To support the precise and repeatable evaluation of fuel cell systems, testbed gas conditioning units must accurately reproduce dynamic conditions with minimal interference between flow, pressure, and temperature. This work addresses the intrinsic coupling of these variables by introducing a model- and flatness-based feedback control scheme for the gas conditioning subsystem of a commercial fuel cell test bed. The approach enables purposeful actuation of balance-of-plant components to generate desired gas conditions while minimizing cross-coupling in the remaining states. The resulting decoupled behavior is demonstrated in simulations, highlighting the method’s potential to enhance diagnostic capability and accelerate experimental development of fuel cell systems.", "url": "" }, { "id": "Mo-MoA17.4", "code": "MoA17.4", "title": "Further Results on Input Disturbance Rejection for Strict-Feedforward Systems", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA17", "sessionTitle": "Nonlinear Tracking Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Sun, Jiu-Cheng", "affiliation": "South China University of Technology" }, { "name": "Xu, Dabo", "affiliation": "South China University of Technology" } ], "keywords": [ "Output regulation and tracking", "Disturbance rejection and input-to-state stability", "Application of nonlinear analysis and design" ], "abstract": "This paper revisits the input disturbance rejection problem for a class of strict-feedforward nonlinear systems in the presence of uncertain exosystems. To address the challenges arising from exosystem uncertainties, a nonlinear internal model is introduced and incorporated into the regulator design. Unlike most existing approaches, which typically assume known exosystems and employ linear internal models, the proposed method explicitly accounts for parameter uncertainties through the construction of an augmented system embedding the nonlinear internal model. Based on this augmented formulation, a robust stabilizing controller is systematically developed. It thus provides a solution for rejecting disturbances generated by uncertain exosystems in strict-feedforward nonlinear systems using bounded controls.", "url": "" }, { "id": "Mo-MoA17.5", "code": "MoA17.5", "title": "Coordinated Path Following Control for Quadrotor Based on Enhanced Extended State Observer", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA17", "sessionTitle": "Nonlinear Tracking Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Kim, Stanislav", "affiliation": "ITMO University" }, { "name": "Pyrkin, Anton", "affiliation": "ITMO University" }, { "name": "Borisov, Oleg", "affiliation": "ITMO University" }, { "name": "Wang, Sen", "affiliation": "ITMO University" }, { "name": "Bobtsov, Alexey", "affiliation": "ITMO University" } ], "keywords": [ "Output feedback nonlinear control", "Nonlinear observers and filters", "Application of nonlinear analysis and design" ], "abstract": "The coordinated path following problem for a quadrotor UAV is addressed. Unlike trajectory tracking, the coordinated approach decouples geometric convergence to a spatial curve from the timing law governing the speed along the path. The control synthesis relies on dynamic extension to achieve uniform relative degree, followed by transformation to a cascade normal form. An extended state observer reconstructs the unmeasured velocity components and estimates lumped disturbances from position measurements only. The resulting output feedback law ensures semi-global asymptotic stability with bounded tracking error. Simulation results for straight-line and helical paths con rm the theoretical predictions.", "url": "" }, { "id": "Mo-MoA17.6", "code": "MoA17.6", "title": "Design of Smooth Reference Trajectory Generators Considering Dynamic Constraints", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA17", "sessionTitle": "Nonlinear Tracking Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Kokunko, Julia", "affiliation": "VA Trapeznikov Institute of Control Sciences RAS" }, { "name": "Krasnova, Svetlana", "affiliation": "ICS" } ], "keywords": [ "Output regulation and tracking", "Control barrier functions and state space constraints", "Disturbance rejection and input-to-state stability" ], "abstract": "A multi-block tracking differentiator for smoothing support trajectories, considering specified constraints on dynamic features, is designed via the block control principle. This is a canonical autonomous system with corrective actions in the form of nested sigmoids. A system of double inequalities has been formalized for tuning the constant correction gains, at which the differentiator output tracks the support non-smooth trajectory with some accuracy and generates a reference trajectory, while the remaining variables generate smooth bounded derivatives of the corresponding orders. Mechanisms for varying the gains have been developed to improve the accuracy of the support trajectory approximation without losing smoothness and violating the specified constraints. The results of the numerical simulation are presented.", "url": "" }, { "id": "Mo-MoA18.1", "code": "MoA18.1", "title": "Low-Rank Federated Adaptation for IoT-Enabled Manufacturing System Health Monitoring - a TEP Case Study (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA18", "sessionTitle": "Artificial Intelligence and Digital Twins for Next-Generation Prognostics and Health Management in Smart Manufacturing I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Nguyen, Duc An", "affiliation": "University of Technology Tarbes Occitanie Pyrénées (UTTOP)" }, { "name": "Deng, Weikun", "affiliation": "Université De Technologie Tarbes Occitanie Pyrénées - UTTOP" }, { "name": "Trinh, Trung", "affiliation": "Department of Process Technology, SINTEF Industry" }, { "name": "Nguyen, Thi Phuong Khanh", "affiliation": "University of Technologie Tarbes Occitanie Pyrénées" }, { "name": "Medjaher, Kamal", "affiliation": "University of Technology Tarbes Occitanie Pyrénées (UTTOP)" } ], "keywords": [ "Manufacturing prognostics and health management", "Industrial artificial intelligence" ], "abstract": "This paper presents Fed-LoRA, a lightweight federated learning framework for real-time monitoring in IoT-enabled connected manufacturing. The system integrates an edge–cloud architecture with event-driven model updates and external LoRA adapters, enabling safe, traceable, and resource-efficient adaptation. Edge devices perform continuous Health indicator (HI) construction, prognostics, and closed-loop control, supported by an MQTT pipeline with low latency and reliable streaming across QoS levels. Updates are triggered only when an HI-conflict detector flags inconsistent degradation trends, sending privacy-preserving summaries to the cloud. A few-sample, few-epoch federated update then trains only LoRA adapters while keeping the base model frozen, reducing computation and communication by over 90%. A validation-gated aggregation releases updates only when accuracy improves. Experiments on the Tennessee Eastman Process show 70–80% reductions in RUL prediction errors, more coherent HI behavior, and elimination of premature triggers, while requiring just 0.63% of the sample exposure of a 200-epoch centralized baseline.", "url": "" }, { "id": "Mo-MoA18.2", "code": "MoA18.2", "title": "An Improved Probability-Calibrated Domain-Adversarial Neural Network for Bearing Fault Diagnosis (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA18", "sessionTitle": "Artificial Intelligence and Digital Twins for Next-Generation Prognostics and Health Management in Smart Manufacturing I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Yin, Xiaojing", "affiliation": "Changchun University of Technology" }, { "name": "Jiang, Cheng", "affiliation": "Changchun University of Technology" }, { "name": "Xi, Xiaopeng", "affiliation": "Seoul National University" }, { "name": "Peng, Shouxin", "affiliation": "Changchun University of Technology" }, { "name": "Nie, Chuang", "affiliation": "Changchun University of Technology" }, { "name": "Yubo, Shao", "affiliation": "Changchun University of Technology" } ], "keywords": [ "Manufacturing prognostics and health management", "Industrial artificial intelligence" ], "abstract": "In recent years, transfer learning has been widely adopted in bearing fault diagnosis owing to its ability to transfer knowledge across different operating conditions. However, under significant variations in operating conditions, such as cross-load scenarios, the distribution of fault samples in the target domain often undergoes substantial shifts. As a result, models may achieve acceptable overall accuracy while still struggling to reliably discriminate critical fault categories. To address this issue, a probability-calibrated domain-adversarial neural network (PC-DANN) is proposed in this paper for cross-load bearing fault diagnosis. When the target domain lacks labels, labeled samples from the source domain are exploited to jointly drive feature extraction and adversarial domain alignment, enabling PC-DANN to learn transferable fault features. Simultaneously, a probability calibration module is introduced at the classifier output to perform class-wise temperature scaling and bias correction on the class logits, alleviating prediction overconfidence and class-dependent logit bias under cross-load transfer. With this design, diagnostic accuracy and probability calibration in the target domain under diverse operating conditions are simultaneously improved. Cross-load unsupervised transfer experiments are conducted on a publicly available bearing dataset, and the results demonstrate that PC-DANN outperforms competing methods across multiple evaluation metrics, thereby validating its effectiveness and advantages in scenarios involving cross-load conditions and variations in the fault-class distribution.", "url": "" }, { "id": "Mo-MoA18.3", "code": "MoA18.3", "title": "An Intelligent Two-Stage Remaining Useful Life Prediction Method Integrating Health Indicator Construction with Informer (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA18", "sessionTitle": "Artificial Intelligence and Digital Twins for Next-Generation Prognostics and Health Management in Smart Manufacturing I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Zhong, Maiying", "affiliation": "Shandong University of Science and Technology" }, { "name": "Zhang, Xin", "affiliation": "Shandong University of Science and Technology" }, { "name": "Xi, Xiaopeng", "affiliation": "Seoul National University" } ], "keywords": [ "Manufacturing prognostics and health management" ], "abstract": "The prediction of remaining useful life (RUL) is the key to prognostics and health management. Regarding the issue of incomplete representation of the health status in RUL prediction, an intelligent two-stage prediction method is proposed. In the first stage, health indicator (HI) cluster is constructed to represent the health status considering the changing trend of the extracted feature data. Informer is trained to obtain the HI model. In the second stage, the identified degradation points, derived through spectral clustering, are applied to a gated recurrent unit (GRU) based model for predicting the RUL. The results show that the proposed method achieves high accuracy, outperforming some traditional methods represented by convolutional neural network (CNN) and long short-term memory (LSTM) methods.", "url": "" }, { "id": "Mo-MoA18.4", "code": "MoA18.4", "title": "A Dual-View Contrastive Learning Framework for Industrial Anomaly Detection", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA18", "sessionTitle": "Artificial Intelligence and Digital Twins for Next-Generation Prognostics and Health Management in Smart Manufacturing I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Yao, Bohan", "affiliation": "China University of Petroleum (East China)" }, { "name": "Deng, Xiaogang", "affiliation": "China University of Petroleum" }, { "name": "Wang, Ping", "affiliation": "China University of Petroleum" }, { "name": "Ji, Hongquan", "affiliation": "Shandong University of Science and Technology" } ], "keywords": [ "Manufacturing prognostics and health management", "Industrial artificial intelligence" ], "abstract": "Most industrial anomaly detectors rely on a single view, focusing solely on temporal sequences or variable structures, which limits their ability to capture comprehensive dependencies. In this paper, we propose a dual-view framework jointly modeling temporal sequence and variable-relation graph, so dependencies unfolding in time and across variables are both represented. Contrastive learning in each view pulls together semantically consistent instances while pushing apart others, shaping discriminative representations without labels. Moreover, A cross-view alignment loss enforces semantic consistency between two views, enabling coherent fusion for scoring. Experiments on the Tennessee Eastman process demonstrate consistent improvements over a set of methods.", "url": "" }, { "id": "Mo-MoA18.5", "code": "MoA18.5", "title": "Prognostics and Health Management Beyond Tangible Assets: A Systematic Mapping Study (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA18", "sessionTitle": "Artificial Intelligence and Digital Twins for Next-Generation Prognostics and Health Management in Smart Manufacturing I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Chapelin, Julien", "affiliation": "ACESI GROUP" }, { "name": "Rose, Bertrand", "affiliation": "Université De Strasbourg" } ], "keywords": [ "Manufacturing prognostics and health management", "Data-driven and AI-based modelling of production and logistics", "Industrial artificial intelligence" ], "abstract": "Prognostics and health management has proven effective for predicting failures and optimising maintenance in tangible assets. However, the growing reliance on software-driven, cloud-based and interconnected infrastructures extends reliability challenges to intangible assets such as applications, configurations and data flows. These assets degrade through mechanisms that differ fundamentally from physical deterioration and are not fully addressed in traditional prognostic approaches. To clarify how predictive maintenance is studied across this heterogeneous landscape, this paper conducts a Systematic Mapping Study covering work on intangible assets and hybrid systems that integrate tangible and intangible assets interactions. The results show that while prognostic methods for tangible assets remain a crucial foundation, the literature provides limited coverage of degradation processes affecting intangible assets and offers almost no approaches for predictive maintenance in hybrid systems where tangible and intangible components interact and mutually influence each other’s degradation. Existing contributions also remain fragmented across disciplinary boundaries, preventing the emergence of a coherent cross-domain understanding. Based on the mapping, the paper discusses a cross-layer perspective intended to support future development of a unified framework for predictive maintenance applicable to tangible, intangible and hybrid assets.", "url": "" }, { "id": "Mo-MoA19.1", "code": "MoA19.1", "title": "Optimal Cabin Cooling Control for BEV Using Neural ODE-Based Surrogate Model (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA19", "sessionTitle": "Advances in AI-Powered Automotive Control and Diagnostic Technologies", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Buck, Simon", "affiliation": "Robert Bosch GmbH" }, { "name": "Alt, Benedikt", "affiliation": "Robert Bosch GmbH" }, { "name": "Aka, Julius", "affiliation": "Augsburg University" }, { "name": "Mikelsons, Lars", "affiliation": "Augsburg University" } ], "keywords": [ "AI and learning-based control for automotive systems", "Electric and solar vehicles", "Nonlinear and optimal automotive control" ], "abstract": "Developing predictive control strategies is a time-consuming and expert-intense process, particularly for complex nonlinear systems. Classical predictive control approaches use first-principle models, which require significant effort for derivation, parameterization, and validation. To mitigate these challenges, this work suggests the use of Neural Ordinary Differential Equations (NODEs) for fast and precise data-driven modeling. This approach provides a method for the automatic and easy design of models for predictive controllers, thereby meeting the industry's need for faster development, increased energy efficiency, and less effort. We demonstrate the efficiency and efficacy of this methodology in the application of fast and energy-optimal cabin cooling for a recent Battery Electric Vehicle (BEV) thermal system topology.", "url": "" }, { "id": "Mo-MoA19.2", "code": "MoA19.2", "title": "Experimental Demonstration of Safe and Automated In-Cylinder Pressure Shaping Using Constrained Extremum Seeking (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA19", "sessionTitle": "Advances in AI-Powered Automotive Control and Diagnostic Technologies", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Versmissen, Mats", "affiliation": "Eindhoven University of Technolgoy" }, { "name": "Vlaswinkel, Maarten", "affiliation": "Eindhoven University of Technology" }, { "name": "Willems, Frank", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "AI and learning-based control for automotive systems", "Engine and powertrain modeling and control", "Adaptive and robust control of automotive systems" ], "abstract": "To support decarbonization of transport, research is focusing on advanced high efficient combustion concepts running on low-carbon fuels. Optimizing the performance of these complex engines over a wide range of operating conditions results in exploded control calibration efforts for traditional calibration methods. In this paper, an automated engine calibration framework based on constrained Extremum Seeking (ES) control is proposed to effectively reduce calibration times. Contrary to previous research that optimizes an efficiency metric, the proposed ES algorithm directly shapes the entire in-cylinder pressure trace by decomposing it into Principal Components (PCs) and controlling the associated weights towards their optimal reference, defined by an Ideal Thermodynamic cycle. This method finds an optimal trade-off between controllability of the PC weights and optimality, while explicitly addressing combustion safety constraints using a novel gradient based projection method. The proposed ES controller was successfully implemented for fuel path calibration on a single cylinder engine running in dual-fuel (diesel-gasoline) mode, demonstrating optimality and convergence within 4 minutes and validating the handling of an arbitrary maximum in-cylinder pressure constraint. This work highlights the practical viability of automated calibration methods through a model-free in-cylinder pressure shaping approach.", "url": "" }, { "id": "Mo-MoA19.3", "code": "MoA19.3", "title": "Experimental Demonstration of Time-Efficient Auto-Calibration of a Vehicle Thermal Management System Using Safe Reinforcement Learning (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA19", "sessionTitle": "Advances in AI-Powered Automotive Control and Diagnostic Technologies", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Garg, Prasoon", "affiliation": "DAF Trucks NV" }, { "name": "Silvas, Emilia", "affiliation": "Netherlands Organisation for Applied Scientific Research" }, { "name": "Willems, Frank", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "AI and learning-based control for automotive systems", "Hybrid, electric and alternative drive vehicles", "Modeling, supervision, control and diagnosis of automotive systems" ], "abstract": "Future automotive powertrains are becoming increasingly complex, leading to exploding time and cost demands for calibration using conventional methods. This paper presents a Reinforcement Learning (RL)-based control strategy for a battery electric vehicle thermal system with safety constraints. A novel exploration approach combines an online Gaussian Process Regression model with a reciprocal Control Barrier Function to ensure safe, information-efficient learning. Validated on a vehicle test bench, the method autonomously optimizes heat pump steady-state operation under varying ambient conditions. The approach achieves heat pump efficiency within 2% of the optimum and reduces calibration time by 69% compared to conventional map-based methods.", "url": "" }, { "id": "Mo-MoA19.4", "code": "MoA19.4", "title": "ChatMPC for Human-Interactive Autonomous Driving (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA19", "sessionTitle": "Advances in AI-Powered Automotive Control and Diagnostic Technologies", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Miyaoka, Yuya", "affiliation": "Keio University" }, { "name": "Inoue, Masaki", "affiliation": "Keio University" } ], "keywords": [ "AI and learning-based control for automotive systems", "Learning and adaptation in autonomous vehicles", "Cooperative navigation" ], "abstract": "Autonomous vehicles require accurate environmental sensing through onboard sensors, and their safety is compromised when those sensors fail to detect critical safety hazards. To address this issue, we present a control framework where a passenger acts as a cooperative safety monitor. Using natural language reports, such as ``there is an obstacle in front!'', a language understanding structure interprets the undetected safety issue and instantly generates a new virtual safety constraint in the vehicle's Model Predictive Control (MPC). This extension allows the vehicle to safely and in real-time avoid dangers not recognized by its own sensors. We also implement the framework in an autonomous vehicle with the CARLA simulator to demonstrate its effectiveness.", "url": "" }, { "id": "Mo-MoA19.5", "code": "MoA19.5", "title": "Unsupervised Driving Regime Discovery Using Interpretable TCN Transformer Autoencoders with Spectral Clustering (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA19", "sessionTitle": "Advances in AI-Powered Automotive Control and Diagnostic Technologies", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Yasami, Amirreza", "affiliation": "University of Alberta" }, { "name": "Tofigh, Mohamadali", "affiliation": "University of Alberta" }, { "name": "Shahbakhti, Mahdi", "affiliation": "University of Alberta" }, { "name": "Koch, Charles Robert", "affiliation": "University of Alberta" } ], "keywords": [ "Artificial intelligence in transportation", "Information processing and decision support in transportation", "Modeling and simulation of transportation systems" ], "abstract": "Understanding and quantifying driving behavior is essential for enhancing fuel efficiency, reducing emissions, and improving fleet-level operational efficiency. This paper presents an interpretable deep learning framework for unsupervised driving regime discovery, the Temporal Convolutional Transformer with Spectral Clustering (TCTSC). The model integrates a Temporal Convolutional Network (TCN) for short-term vehicle dynamics, a Transformer encoder for long-range temporal dependencies, and spectral regularization with a differentiable K-Means module for compact and balanced clustering. The framework is trained and validated on large-scale Controller Area Network (CAN) data from Edmonton Transit Service diesel buses operating under diverse traffic and weather conditions. TCTSC achieves over 75% lower reconstruction errors than DenseAE and more than 85% lower than PCA, preserving both steady-state and transient behaviors. Outcome-grounded evaluation using fuel consumption shows that TCTSC explains 59.7% of the variance (R^2), achieves a Spearman correlation of 0.55, and attains the lowest MAE and RMSE among all models. The identified regimes, idling, deceleration, efficient cruising, high-load cruising, and aggressive acceleration, provide interpretable, physically meaningful insights into real-world bus operations and a robust foundation for data-driven eco-driving analysis and driver assessment.", "url": "" }, { "id": "Mo-MoA19.6", "code": "MoA19.6", "title": "Detection of Structural Changes in Vehicles Using MIMO Local Rational Model (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA19", "sessionTitle": "Advances in AI-Powered Automotive Control and Diagnostic Technologies", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Lindstrom, Filip", "affiliation": "Linköping University" }, { "name": "Nord, Anders", "affiliation": "Volvo Cars Coorporation" }, { "name": "Wirje, Anders", "affiliation": "Volvo Cars" }, { "name": "Sjögren, Anders", "affiliation": "Volvo Cars" }, { "name": "Frisk, Erik", "affiliation": "Linköping University" }, { "name": "Jung, Daniel", "affiliation": "Linköping University" } ], "keywords": [ "Modeling, supervision, control and diagnosis of automotive systems", "Automotive system identification and modelling", "Vehicle dynamic systems" ], "abstract": "This study presents a robust method for detecting structural faults in vehicles using nonparametric frequency-domain identification. The approach uses a MIMO Local Rational Model to estimate transfer functions from accelerometer data. Physical experiments were conducted on a vehicle at various road surfaces, speeds, and induced fault conditions. Fault-specific features are extracted and classified using L1-regularized logistic regression. The proposed method achieved high detection accuracy in the experiments while being robust to changes in input excitation, demonstrating strong generalization performance.", "url": "" }, { "id": "Mo-MoA20.1", "code": "MoA20.1", "title": "Trends in Offshore Renewable Energy Fault Detection and Isolation (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA20", "sessionTitle": "FDI and FTC Strategies for Offshore Renewable Energy Applications", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Peña-Sanchez, Yerai", "affiliation": "Mondragon University" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "Simani, Silvio", "affiliation": "University of Ferrara" } ], "keywords": [ "Applications of FDI/FTC", "Fault detection and isolation methods", "Wind power" ], "abstract": "This paper provides a structured review of fault detection and isolation (FDI) methods across the offshore renewable energy (ORE) landscape, covering fixed-bottom offshore wind, floating offshore wind, wave energy converters, and tidal current turbines. For each technology, the study identifies the dominant fault types and target subsystems, analyzes the prevailing modeling approaches (model-based, data-driven, or hybrid), and assesses the current level of validation maturity, ranging from numerical simulation to field deployment. The analysis reveals a distinct correlation between the technology readiness level of each sector and its FDI focus: mature technologies prioritize grid-side reliability, while emerging sectors focus on structural integrity and primary conversion survivability. Finally, a cross-technology comparative analysis is presented, highlighting transferable methodologies, identifying the validation gap in data-driven strategies, and outlining emerging research priorities to enhance the resilience of future ORE systems.", "url": "" }, { "id": "Mo-MoA20.2", "code": "MoA20.2", "title": "Leveraging Sliding Mode FDI for Excitation Force Estimation in Wave Energy (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA20", "sessionTitle": "FDI and FTC Strategies for Offshore Renewable Energy Applications", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Papini, Guglielmo", "affiliation": "Politecnico Di Torino" }, { "name": "Faedo, Nicolás", "affiliation": "Politecnico Di Torino" } ], "keywords": [ "Applications of FDI/FTC", "Control and management of energy systems", "Fault detection and isolation methods" ], "abstract": "The growing concern over climate change is driving the energy production research into renewable energy sources. Among them, wave energy stands out as a key player in complementing its sister renewables, i.e. solar and wind. Nonetheless, high commercialisation costs are hindering its widespread adoption. One of the key tools to reduce costs is constituted by energy-maximising optimal control strategies, which require accurate estimators of the excitation force for reaching energy generation optimality. In the state-of-the-art of excitation force estimation, there is a lack of algorithms capable of simultaneously handling modelling uncertainties, which are typical of experimental applications, without introducing additional assumptions about the nature of the waves, e.g. an explicit model of the excitation force itself. To fill this gap, this study proposes an excitation force estimator based on the implementation of a sliding mode observer, adapted from the field of fault diagnosis. The estimator, whose design is based on an identified model of the WaveStar prototype, is tested on experimental data collected under irregular sea state conditions to demonstrate its effectiveness in estimating the wave excitation force within a realistic framework.", "url": "" }, { "id": "Mo-MoA20.3", "code": "MoA20.3", "title": "On the Use of Excitation Force Estimates for Fault Diagnosis in Wave Energy Converters (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA20", "sessionTitle": "FDI and FTC Strategies for Offshore Renewable Energy Applications", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Gonzalez-Esculpi, Alejandro", "affiliation": "Maynooth University" }, { "name": "Peña-Sanchez, Yerai", "affiliation": "Mondragon University" }, { "name": "Ringwood, John", "affiliation": "Maynooth University" } ], "keywords": [ "Fault detection and isolation methods", "Applications of FDI/FTC" ], "abstract": "Real-time knowledge of the excitation force (EF) that drives wave energy converters (WECs) has an important role in optimizing, and monitoring, the performance of these systems. corr{Since EF cannot be directly measured, estimation methods proposed in the literature are mainly based on (E1) observation of the WEC dynamics, or (E2) wave elevation measurements}. The accuracy of both types of estimators involves several factors, such as the complexity of the wave field and the physical structure of the WEC. This paper focuses on evaluating the potential of combining diverse EF estimates for fault diagnosis (FD) in WECs, since E1-type estimates are typically affected by changes in the system dynamics (faults), while E2-type estimates remain unaffected. The main contributions are the formulation of a fault detectability condition and the proposal of a scheme for detecting faults by processing different EF estimates. Numerical simulations of the WEC operation, in the presence of faults and parameter deviation in the WEC model, given a realistic sea wave profile, are performed to validate the proposed scheme.", "url": "" }, { "id": "Mo-MoA20.4", "code": "MoA20.4", "title": "A Switching NARX Scheme for Sensor and Actuator Fault Detection in a Floating Offshore Wind Farm Benchmark (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA20", "sessionTitle": "FDI and FTC Strategies for Offshore Renewable Energy Applications", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Peña-Sanchez, Yerai", "affiliation": "Mondragon University" }, { "name": "Penalba, Markel", "affiliation": "Mondragon University" }, { "name": "García Violini, Demián", "affiliation": "Universidad Nacional De Quilmes" }, { "name": "Azpilgain-Maiza, Irati", "affiliation": "Mondragon University" }, { "name": "Nava, Vincenzo", "affiliation": "Basque Center for Applied Mathematics" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" } ], "keywords": [ "Applications of FDI/FTC", "Fault detection and isolation methods", "Wind power" ], "abstract": "Effective fault detection and identification (FDI) is essential to reduce operation and maintenance costs in floating offshore wind turbines (FOWTs). To address this, the present paper considers a FOWT benchmark in the literature, proposing a Switching NARX scheme that alternates between a stable one-step predictor and a sensitive closed-loop simulator. The proposed FDI scheme incorporates a \"backtracking\" validation process, and a genetic algorithm is employed to automatically optimize the decision thresholds and logic parameters, maximizing the F1-score. Results demonstrate that this data-driven strategy provides robust detection with negligible computational cost, successfully identifying pitch sensor faults under realistic turbulent conditions.", "url": "" }, { "id": "Mo-MoA20.5", "code": "MoA20.5", "title": "Health-Aware Economic MPC of Wind Turbines Integrating Fuzzy Neural Networks and LPV Modeling (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA20", "sessionTitle": "FDI and FTC Strategies for Offshore Renewable Energy Applications", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Abduljaleel, Shafaq", "affiliation": "UPC" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "Dankir, Sara", "affiliation": "Institut De Robòtica I Informàtica Industrial (CSIC-UPC), Carrer Llorens Artigas, 4-6, 08028 Barcelona, Spain , TED: AEEP, FPL, Ab" } ], "keywords": [ "AI methods for FDI/FTC", "Wind power" ], "abstract": "By incorporating Fuzzy Neural Networks for on-the-fly damage estimation within a Linear Parameter-Varying framework, this paper proposes a health-aware Economic Model Predictive Control strategy for wind turbines. The proposed controller simultaneously optimises the economic rewards for turbine operation with the control of structural health, within the structural fatigue constraints of the blades. This is done to counterbalance the powering of blades, which is the primary cause of fatigue. A simulation with a 5 MW turbine model shows a 25% reduction in cumulative blade stress, along with a 15% increase in energy efficiency for this method, in comparison to standard PID control. This confirms the turbine maintains sustainable operation.", "url": "" }, { "id": "Mo-MoA20.6", "code": "MoA20.6", "title": "Multi-Agent Reinforcement Learning for Resilient, Distributed Energy Management in Large-Scale Smart Grids", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA20", "sessionTitle": "FDI and FTC Strategies for Offshore Renewable Energy Applications", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Okreghe, Christian Oghoverhuvwu", "affiliation": "University College London" } ], "keywords": [ "Multi-agent systems", "Distributed reinforcement learning", "Control of networks" ], "abstract": "Modern smart grids face critical challenges in coordinating distributed energy resources, demand response, and networked microgrids in a resilient, decentralized manner. This paper proposes a multi-agent reinforcement learning (MARL) framework for large-scale smart grid energy management, emphasizing decentralized optimization and control-theoretic rigor. The problem is formulated as a cooperative Markov game. A centralized-training, decentralized-execution (CTDE) MARL algorithm with actor–critic networks, attention mechanisms and prioritized experience replay is developed. Lyapunov-based safe RL ensures stability and safety throughout learning and deployment. Adversarial training and federated learning further enhance robustness and privacy. Simulation on a multi-microgrid test system shows an 18% cost reduction over stochastic programming, smoother power exchange, and no load shedding under attack.", "url": "" }, { "id": "Mo-MoA21.1", "code": "MoA21.1", "title": "Online Nonlinear Optimisation of DC Microgrids with Boost Converters (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA21", "sessionTitle": "Stability Analysis and Control of Converter-Dominated Power Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Ferguson, Joel", "affiliation": "Maynooth University" }, { "name": "Ahmed, Saeed", "affiliation": "Faculty of Science and Engineering, University of Groningen" } ], "keywords": [ "Distributed optimization for smart grids", "Control and management of energy systems", "Power systems stability" ], "abstract": "We propose a method for online optimisation and control of DC microgrids with boost converters. In these systems, the control input is the switching duty cycle associated with each grid node. This leads to a bilinear term in the corresponding optimisation problem, which prevents the use of linear optimisation methods. To address this, we employ a recently proposed switched systems approach for online nonlinear optimisation of the microgrid, while guaranteeing convergence of the overall network. The effectiveness of the method is demonstrated through numerical simulation.", "url": "" }, { "id": "Mo-MoA21.2", "code": "MoA21.2", "title": "Reactive Power Control at the TSO-DSO Interface with Reserves for Dynamic Voltage Support (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA21", "sessionTitle": "Stability Analysis and Control of Converter-Dominated Power Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Zettl, Irina", "affiliation": "IAEW at RWTH Aachen University" }, { "name": "Meier, Luca", "affiliation": "IAEW at RWTH Aachen University" }, { "name": "Klein-Helmkamp, Florian", "affiliation": "RWTH Aachen University, Institute for High Voltage Equipment and Grids, Digitalization and Energy Economics" }, { "name": "Ulbig, Andreas", "affiliation": "RWTH Aachen University" } ], "keywords": [ "Electrical distribution systems", "Electrical transmission systems", "Power systems stability" ], "abstract": "The utilization of volatile energy sources will lead to challenges regarding voltage control in the future. Decentralized, inverter-based resources are capable of reactive power control, but are mainly installed in distribution grids. Integrating these resources in the voltage control of the transmission system presents a viable alternative to installing more compensating devices but requires coordination between distribution and transmission system operators. Such a control scheme must be able to account for operational limits in the distribution grid. This paper proposes a model predictive control (MPC)-based coordination scheme for the reactive power exchange at the point of common coupling between distribution and transmission systems that additionally accounts for reactive power reserves for dynamic voltage support (DVS) in distribution grids. The results show, that the coordination scheme effectively manages the reactive power exchange even while withholding reserves for DVS.", "url": "" }, { "id": "Mo-MoA21.3", "code": "MoA21.3", "title": "Power-Hardware-In-The-Loop Implementation of Online Inertia Estimation in Microgrids Using 5G Communication (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA21", "sessionTitle": "Stability Analysis and Control of Converter-Dominated Power Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Cortés-Martínez, Rolando", "affiliation": "Brandenburg University of Technology Cottbus-Senftenberg" }, { "name": "Mathew, Riya", "affiliation": "Brandenburg University of Technology Cottbus-Senftenberg (BTU C-S)" }, { "name": "Zurita-Bustamante, Eric William", "affiliation": "Brandenburg University of Technology Cottbus–Senftenberg" }, { "name": "Schiffer, Johannes", "affiliation": "Brandenburg University of Technology Cottbus-Senftenberg" } ], "keywords": [ "Electrical distribution systems", "Power systems stability", "Real time simulators for energy systems" ], "abstract": "Power system inertia reflects the grid’s capability to resist rapid frequency changes triggered by disturbances. The transition of electrical power systems generation from conventional fossil-fueled synchronous generators to converter-based renewable energy resources substantially reduces the inherent overall system inertia, which in turn impacts system stability and robustness. Therefore, in future power systems architectures, such as microgrids, it is vital to monitor the system inertia in real time. In this paper, we develop a Power-Hardware-in-the-Loop (PHiL) implementation of an online distributed inertia estimator for microgrids based on the consensus + innovations algorithm, which was originally developed for multi-area inertia estimation in synchronous generator-dominated power systems. Moreover, we show how the estimator performs under an actual 5G mobile communication architecture and compare the performance with that of a wired local area network, confirming its reliability and accuracy for real-time inertia estimation.", "url": "" }, { "id": "Mo-MoA21.4", "code": "MoA21.4", "title": "Smart Bidirectional Charging of EVs for Vehicle-To-Grid Capability: A Robust Data-Driven Control Strategy (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA21", "sessionTitle": "Stability Analysis and Control of Converter-Dominated Power Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Shi, Yanyan", "affiliation": "The University of Manchester" }, { "name": "Abd Wahid, Siti Sufiah", "affiliation": "University of Manchester" }, { "name": "Xu, Yiqiao", "affiliation": "University of Manchester" }, { "name": "Parisio, Alessandra", "affiliation": "The University of Manchester" } ], "keywords": [ "Energy management systems", "Electric vehicles integration in energy networks", "Electric vehicles and charging stations" ], "abstract": "The integration of photovoltaics (PV) with vehicle-to-grid (V2G)-capable electric vehicles (EVs) in residential microgrids offers a promising path toward sustainable energy man agement, yet faces challenges from the intermittent nature of PV generation and the stochastic, nonlinear dynamics of EVs. Existing results often rely on simplified EV models with constant charging/discharging efficiencies, limiting their practical efficacy, while high-fidelity modeling remains computationally prohibitive. To overcome these limitations, this paper proposes a robust data-driven control strategy based on zonotopic predictive control (ZPC) and mixed-integer programming (MIP) for coordinating multiple EVs in a PV-powered residential microgrid. The proposed strategy introduces multiple sets of binary variables to prevent simultaneous charg ing/discharging of multiple EVs and employs a matrix-zonotope recursion for over-approximated reachable-set computation, thereby ensuring robust constraint satisfaction under unknown but bounded disturbances. Numerical simulations demonstrate that the proposed strategy effectively enhances PV self-consumption and reduces grid electricity costs, lowering the average PV curtailment rate from 8.13% to 3.26% while maintaining operational reliability.", "url": "" }, { "id": "Mo-MoA21.5", "code": "MoA21.5", "title": "Online Finite-Time Optimization for Frequency Regulation in Virtual Power Plants with Experimental Validation (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA21", "sessionTitle": "Stability Analysis and Control of Converter-Dominated Power Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Mathew, Riya", "affiliation": "Brandenburg University of Technology Cottbus-Senftenberg (BTU C-S)" }, { "name": "Mercado Uribe, José Ángel", "affiliation": "Brandenburg University of Technology" }, { "name": "Texis-Loaiza, Oscar", "affiliation": "Brandenburg University of Technology Cottbus - Senftenberg" }, { "name": "Schiffer, Johannes", "affiliation": "Brandenburg University of Technology Cottbus-Senftenberg" } ], "keywords": [ "Power plant control", "Power systems stability", "Distributed optimization for smart grids" ], "abstract": "With the growing penetration of converter-interfaced renewable energy resources, virtual power plants (VPPs) have become an effective framework for coordinating heterogeneous distributed energy resources (DERs). To provide ancillary services in a power system, a VPP must deliver a time-varying power response, such as active power adjustments driven by system frequency deviations. The optimal allocation of the individual DER contributions to the overall VPP response can be cast as a time-varying constrained optimization problem. To address this problem, we employ a finite-time primal-dual gradient descent (FT-PDGD) algorithm, whose convergence to the optimal time-varying trajectory is formally established through a Lyapunov-based analysis. Experimental validation on a Power Hardware-in-the-Loop testbed demonstrates the real-time performance of the proposed FT-PDGD. Furthermore, we compare its performance with two existing FT-PDGD variants from the literature and the standard PDGD algorithm.", "url": "" }, { "id": "Mo-MoA21.6", "code": "MoA21.6", "title": "Power System Inertia Contribution and DC Voltage Stability of HVDC-Links of Offshore Wind Farms by Limited Grid-Forming Control (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA21", "sessionTitle": "Stability Analysis and Control of Converter-Dominated Power Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Alaya, Oussama", "affiliation": "University of Stuttgart" }, { "name": "Achenbach, David", "affiliation": "University of Stuttgart" }, { "name": "Lens, Hendrik", "affiliation": "University of Stuttgart" } ], "keywords": [ "Power systems stability", "Wind power", "Electrical transmission systems" ], "abstract": "The decline of inertia in modern power systems jeopardizes frequency stability. Offshore wind farms (OWFs) connected via high-voltage direct current (HVDC) links can mitigate this by providing virtual inertia, but inertia provision conflicts with DC-voltage stability of the link. This paper proposes a modular, communication-free control scheme that trades off both aspects. Onshore, a grid-forming converter with virtual synchronous machine behaviour is combined with PID DC-voltage control and a limited grid-forming loop. Offshore, a DC-voltage-to-frequency law interacts with a grid-following OWF with frequency-sensitive mode control operated without curtailment. Linearised Laplace-domain analysis clarifies impacts on inertia and damping. Time-domain simulations confirm improved frequency support, bounded DC-voltage deviations, and fast post-fault power realignment.", "url": "" }, { "id": "Mo-MoA22.1", "code": "MoA22.1", "title": "Integrating Automation into Sustainable Manufacturing: An Analytical Perspective (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA22", "sessionTitle": "Toward Human-Centric Intelligent Manufacturing: Advances and Challenges I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Patalas-Maliszewska, Justyna", "affiliation": "University of Zielona Góra" }, { "name": "Łosyk, Hanna", "affiliation": "University of Zielona Góra" }, { "name": "Kadbhane, Snehal Vasant", "affiliation": "K.K. Wagh Institute of Engineering Education & Research" }, { "name": "Bocewicz, Grzegorz", "affiliation": "Koszalin University of Technology" }, { "name": "Klos, Slawomir", "affiliation": "University of Zielona Gora, Faculty of Mechanical Engineering" } ], "keywords": [ "Sustainable and circular supply chain and production", "Sustainable and circular manufacturing systems", "Data-driven and AI-based modelling of production and logistics" ], "abstract": "The relationship between the implementation of Industry 4.0 and 5.0 (I4.0/I5.0) technologies in manufacturing enterprises and the enhancement of their sustainability levels presents a significant challenge today. The primary issue is understanding how the effects of production automation contribute to the achievement of the Sustainable Development Goals (SDGs) within manufacturing contexts and support the realization of their core principles. This study, based on empirical survey data collected from 200 European automotive manufacturing companies from western Poland, analyzes the impact of adopting I4.0/I5.0 technologies to automate manufacturing processes and increase the sustainability level of production. Specifically, our research aims to assess the effects of enhanced automation on changes in Key Performance Indicators (KPIs) related to the implementation of selected SDGs in production. The focus is on ensuring: sustainable water management (SDG 6), efficient energy use (SDG 7), sustainable consumption and production patterns (SDG 12), and actions to mitigate climate change (SDG 13). The study highlights that the implementation of Digital Twins and collaborative robots for process automation in the automotive industry contributes to achieving SDG12 and SDG13, particularly by reducing generated waste and decreasing gas emissions. Furthermore, the research results provide recommendations for managers regarding the expected impact of integrating automation into sustainable production (SP).", "url": "" }, { "id": "Mo-MoA22.2", "code": "MoA22.2", "title": "Machine Learning-Based Prediction of Grinding Defects in Polymer Flowmeter Bodies: A Model Comparison Study (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA22", "sessionTitle": "Toward Human-Centric Intelligent Manufacturing: Advances and Challenges I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Antosz, Katarzyna", "affiliation": "Rzeszow University of Technology" }, { "name": "Jasiulewicz-Kaczmarek, Malgorzata", "affiliation": "Poznan University of Technology" }, { "name": "Piechowski, Mariusz", "affiliation": "WSB Merito University" }, { "name": "Smutnicki, Czeslaw", "affiliation": "Wroclaw University of Science and Technology" }, { "name": "Husár, Jozef", "affiliation": "Technical University of Košice" } ], "keywords": [ "Intelligent manufacturing systems", "Industrial artificial intelligence", "Data-driven and AI-based modelling of production and logistics" ], "abstract": "This study investigates the use of supervised machine learning models to predict grinding‑induced quality defects in polymer flowmeter body manufacturing. The empirical data set comprises approximately 2,000 parts produced on an industrial grinding line and described by nine process parameters capturing the thermal state of the workpiece, the kinematics of the operation and the condition of the grinding tool (X1–X9). Four defect categories are considered—indentations and scratches (D1), cracks (D2), surface irregularities and waviness (D3) and dimensional deviations of critical features (D4)—which form the target variable in a multiclass classification task. Four classifiers are analysed: a bilayer neural network (BNN), a decision tree (DT), a medium neural network (MNN) and a fine Gaussian support vector machine (SVM). The models are compared using accuracy, error rate, macro‑averaged precision, recall and F1‑score, as well as confusion matrices and ROC curves. The SVM achieves the best overall performance (99.6% accuracy, 0.4% error rate), closely followed by the MNN (99.4% accuracy). To interpret the SVM decisions, a Shapley‑value analysis is performed. It reveals that surface temperature (X1) and grinding wheel speed (X8) are the most influential parameters, followed by cutting speed (X6), feed rate (X4) and grinding time (X9), while coolant type (X2) and cooling rate (X3) play a minor role. The results demonstrate that data‑driven models can provide reliable defect predictions and actionable insight into the key drivers of grinding quality.", "url": "" }, { "id": "Mo-MoA22.3", "code": "MoA22.3", "title": "A Rule-Based and Data-Driven Approach to Enhancing Energy Efficiency in Production: A Case Study from the Metal Industry (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA22", "sessionTitle": "Toward Human-Centric Intelligent Manufacturing: Advances and Challenges I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Roznowski, Marek", "affiliation": "RM Proinvest" }, { "name": "Patalas-Maliszewska, Justyna", "affiliation": "University of Zielona Góra" } ], "keywords": [ "Sustainable and circular manufacturing systems", "Smart production and logistics in manufacturing", "Human-technology integration in manufacturing" ], "abstract": "Improving energy efficiency in production is currently one of the key challenges in sustainable production (SP) from environmental, economic, and social perspectives. Both the parameters of production processes and the buildings in which production takes place impact energy efficiency, especially in the context of achieving the 7 Sustainable Development Goal (SDG7) aimed at ensuring access to sustainable and modern energy. The appropriate selection and integration of technologies for buildings and mechanical systems to save energy in production remain a gap in the field of SP. Therefore, this study aims to develop an approach consisting of: (1) parameters describing the selected production processes and environmental conditions, (2) indicators determining energy efficiency, (3) technologies and data acquisition, and (4) a rule-based approach. An innovative rule-based and data driven approach has been developed to simulate changes in energy management in production, based on the integration of alternative energy sources, variability in production processes, and infrastructure constraints. As a case study, a small and medium-sized enterprise (SME) from the metal industry has been selected. The research results enable the identification of scenarios for the necessary actions to reduce energy consumption in production. Furthermore, it demonstrates that the proposed approach (scenarios) is a useful tool for supporting decision-making by production managers in the context of introducing changes in work organization and energy resource management, with the aim of improving energy management efficiency in production and enhancing the level of SP.", "url": "" }, { "id": "Mo-MoA22.4", "code": "MoA22.4", "title": "Energy-Efficient UAV–UGV Coordination for Stock Taking in Cold Storage: A Metaheuristic Approach (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA22", "sessionTitle": "Toward Human-Centric Intelligent Manufacturing: Advances and Challenges I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Wewalage, Anuda", "affiliation": "University of Moratuwa" }, { "name": "Thibbotuwawa, Amila", "affiliation": "University of Moratuwa" }, { "name": "Weerasinghe, Kasuni Vimasha", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Nielsen, Izabela", "affiliation": "Aalborg University" }, { "name": "Bocewicz, Grzegorz", "affiliation": "Koszalin University of Technology" }, { "name": "Nielsen, Peter", "affiliation": "Aalborg University" } ], "keywords": [ "Logistics and warehouse management", "Supply chain and logistics engineering, simulation and optimization", "Simulation and optimization in production, operations and services" ], "abstract": "Cold storage environments pose challenges for both workers and UAVs, as freezing temperatures accelerate battery depletion and reduce efficiency. This study introduces a three-dimensional Energy-Aware Drone Routing Problem that integrates UAVs with UGVs, using the latter as a mobile base for stocktaking. We compare Ant Colony Optimization (ACO) with a Traveling Salesperson Problem (TSP) approach implemented via Google OR-Tools. Results show TSP offers faster computation, while ACO reduces energy consumption by approximately 12%, making it the preferred method for cold storage operations. Future work will scale the model to larger warehouses and refine energy estimates using real-world data.", "url": "" }, { "id": "Mo-MoA22.5", "code": "MoA22.5", "title": "Enhancing the Feasibility of Service Missions under Travel Time Uncertainty (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA22", "sessionTitle": "Toward Human-Centric Intelligent Manufacturing: Advances and Challenges I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Radzki, Grzegorz", "affiliation": "Koszalin University of Technology" }, { "name": "Bocewicz, Grzegorz", "affiliation": "Koszalin University of Technology" }, { "name": "Jasiulewicz-Kaczmarek, Malgorzata", "affiliation": "Poznan University of Technology" }, { "name": "Nielsen, Izabela", "affiliation": "Aalborg University" }, { "name": "Banaszak, Zbigniew", "affiliation": "Koszalin University of Technology" } ], "keywords": [ "Simulation and optimization in production, operations and services", "Digital supply chain and production", "Supply network dynamics and control" ], "abstract": "This paper addresses the problem of fast feasibility assessment for service delivery missions under travel time uncertainty. A preliminary feasibility assessment of the service mission plan, which synchronizes transport operations and service activities within pre-determined time periods, taking into account travel time uncertainties caused by traffic jams, accidents or other disruptions, allows for the elimination of variants that require time-consuming calculations but do not guarantee implementation. To enable a formal feasibility evaluation, the paper introduces a graph-based declarative model of time-window structure, where each feasible mission corresponds to a graph coloring with a number of colors not exceeding the available vehicle fleet size. The analysis is extended to uncertain travel times modeled as intervals, leading to a set of graphs representing possible mission realizations. The proposed approach provides a necessary feasibility condition that can be verified efficiently before the full search of a service plan. Experimental results confirm the applicability of the method for preliminary feasibility assessment in dynamic service delivery environments subject to travel time uncertainty.", "url": "" }, { "id": "Mo-MoA22.6", "code": "MoA22.6", "title": "Operationalizing Human Digital Twins: A Retrospective Synthesis (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA22", "sessionTitle": "Toward Human-Centric Intelligent Manufacturing: Advances and Challenges I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Sharotry, Abhimanyu", "affiliation": "Texas State University" }, { "name": "Jimenez, Jesus", "affiliation": "Texas State University" }, { "name": "Mendez, Francis", "affiliation": "Texas State University" } ], "keywords": [ "Human-centered production and logistics", "Viable and resilient supply chain and production" ], "abstract": "Work-related musculoskeletal disorders in manual material handling (MMH) persist due to static-threshold safety systems that ignore individual variability. Industry 5.0 calls for human-centric, adaptive solutions. This paper presents a retrospective synthesis of empirically investigated Human Digital Twin (HDT) components developed across multiple studies. The Micro-Twin enables marker-less human action recognition; the Meso-Twin models individualized fatigue baselines; the Holographic Twin fuses physiological, biomechanical, and cognitive signals for real-time feedback; the Synthetic Bridge provides computational kinematic simulation; and the Distal Shadow enforces edge-based safety interlocks. Together, these modules advance ergonomic monitoring toward adaptive, integrated systems supporting workforce resilience and inclusive manufacturing.", "url": "" }, { "id": "Mo-MoA23.1", "code": "MoA23.1", "title": "Latency-Optimized Secure Data Processing for CPS Based on Efficient Scalar-Ciphertext Multiplication (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA23", "sessionTitle": "Encrypted Control and Optimization I", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Kim, Sin", "affiliation": "Hanyang University" }, { "name": "Lee, Seunghwan", "affiliation": "Hanyang University, WaLLLnut" }, { "name": "Shin, Dong-Joon", "affiliation": "Hanyang University, WaLLLnut" } ], "keywords": [ "Cyber physical systems", "Safety and security in networked control", "Control software architecture" ], "abstract": "Cyber-physical systems (CPS) are increasingly utilized in various applications including smart factories, autonomous driving, and healthcare, where secure processing of sensitive data is critical. Fully homomorphic encryption (FHE), which has emerged as a core technology for enhancing CPS security, enables computation over encrypted data, preventing exposure of sensor outputs, state estimates, and control inputs to potential adversaries. However, the practical deployment of FHE in control systems faces significant challenges due to computational overhead, especially from encrypted multiplication, resulting in high latency. In this work, we propose an efficient scalar-ciphertext multiplication method that employs primitive gates together with a variant of the non-adjacent form (NAF), thereby significantly reducing the computational cost. Simulation results show that the proposed method reduces the scalar-ciphertext multiplication latency by up to 63.9% compared to conventional FHE-based schemes.", "url": "" }, { "id": "Mo-MoA23.2", "code": "MoA23.2", "title": "ARX-Implementation of Encrypted Nonlinear Dynamic Controllers Using Observer Form (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA23", "sessionTitle": "Encrypted Control and Optimization I", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Hong, Deuksun", "affiliation": "Seoul National University" }, { "name": "Song, Donghyeon", "affiliation": "Seoul National University" }, { "name": "Jeong, Mingyu", "affiliation": "Seoul National University of Science and Technology" }, { "name": "Kim, Junsoo", "affiliation": "Seoul National University of Science and Technology" } ], "keywords": [ "Safety and security in networked control" ], "abstract": "While computation-enabled cryptosystems applied to control systems have improved security and privacy, a major issue is that the number of recursive operations on encrypted data is limited to a finite number of times in most cases, especially where fast computation is required. To allow for nonlinear dynamic control under this constraint, a method for representing a state-space system model as an auto-regressive model with exogenous inputs (ARX model) is proposed. With the input as well as the output of the plant encrypted and transmitted to the controller, the reformulated ARX form can compute each output using only a finite number of operations, from its several previous inputs and outputs. Existence of a stable observer for the controller is a key condition for the proposed representation. The representation replaces the controller with an observer form and applies a method similar to finite-impulse-response approximation. It is verified that the approximation error and its effect can be made arbitrarily small by an appropriate choice of a parameter, under stability of the observer and the closed-loop system. Simulation results demonstrate the effectiveness of the proposed method.", "url": "" }, { "id": "Mo-MoA23.3", "code": "MoA23.3", "title": "Sensor Attack Detection Method for Encrypted State Observers (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA23", "sessionTitle": "Encrypted Control and Optimization I", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Jang, Yeongjun", "affiliation": "Seoul National University" }, { "name": "Lee, Sangwon", "affiliation": "Department of Electrical and Information Engineering, Seoul National University of Science and Technology" }, { "name": "Kim, Junsoo", "affiliation": "Seoul National University of Science and Technology" } ], "keywords": [ "Safety and security in networked control", "Cyber physical systems" ], "abstract": "This paper proposes an encrypted state observer that is capable of detecting sensor attacks without decryption. We first design a state observer that operates over a finite field of integers with the modular arithmetic. The observer generates a residue signal that, under sparse attack and sensing redundancy conditions, indicates the presence of attacks. Then, we develop a homomorphic encryption scheme that enables the observer to operate over encrypted data while automatically disclosing the residue signal. Unlike our previous work restricted to single-input single-output systems, the proposed scheme is applicable to general multi-input multi-output systems. Given that the disclosed residue signal remains below a prescribed threshold, the full state can be recovered as an encrypted message.", "url": "" }, { "id": "Mo-MoA23.4", "code": "MoA23.4", "title": "Experimental Examination of Secure Two-Party Controller Computation (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA23", "sessionTitle": "Encrypted Control and Optimization I", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Teranishi, Kaoru", "affiliation": "The University of Osaka" }, { "name": "Suh, Jihoon", "affiliation": "University of Texas at Austin" }, { "name": "Tanaka, Takashi", "affiliation": "Purdue University" } ], "keywords": [ "Safety and security in networked control", "Cyber physical systems", "IT/OT-security in automation systems" ], "abstract": "A secure two-party computation protocol for running dynamic controllers over secret sharing has recently been proposed. Unlike encrypted control schemes based on homomorphic encryption, this protocol enables operating dynamic controllers for an infinite time horizon without controller-state decryption, controller-state reset, or input re-encryption. However, the two-party setting introduces additional online communication between the computing parties, which may hinder real-time feasibility. In this study, we demonstrate the feasibility of the protocol through implementation on a commercial cloud platform with an inverted pendulum testbed. Experimental results show that the proposed protocol successfully stabilized the pendulum despite the online communication overhead.", "url": "" }, { "id": "Mo-MoA23.5", "code": "MoA23.5", "title": "On the (non-)resilience of Encrypted Controllers to Covert Attacks (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA23", "sessionTitle": "Encrypted Control and Optimization I", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Binfet, Philipp", "affiliation": "TU Dortmund University" }, { "name": "Adamek, Janis", "affiliation": "TU Dortmund University" }, { "name": "Schulze Darup, Moritz", "affiliation": "TU Dortmund University" } ], "keywords": [ "Safety and security in networked control", "Cyber physical systems", "Virtualized and cloud-based control architectures" ], "abstract": "The security of networked control systems (NCS) is receiving increasing attention from both cyber-security and system-theoretic perspectives. The former focuses on classical IT security goals such as confidentiality, integrity, and availability of process data, while the latter investigates tailored attacks (and detection schemes), including covert and zero-dynamics attacks. Confidentiality in control systems can, for instance, be achieved by securely outsourcing the evaluation of the controller to third-party platforms, such as cloud services. The underlying technology enabling such secure computation often is homomorphic encryption (HE). Recent works in encrypted control have proposed modifications to underlying HE schemes to achieve not only confidentiality but also resilience to certain types of integrity attacks. While extensions in this direction are desirable in principle, we show that the integrity problem in encrypted control cannot be solved by public-key HE schemes alone due to their inherent malleability. In other words, the same homomorphisms that enable encrypted control % in the first place can be leveraged not only constructively but also destructively. More precisely, we demonstrate that NCS are vulnerable to covert attacks, even when encrypted control is employed. Remarkably, this remains possible without knowledge of an unencrypted model. Yet, resilience to such attacks can still be achieved through complementary techniques. We present an approach based on verifiable computation that integrates with modern homomorphic cryptosystems and is asymptotically secure while incurring no communication overhead.", "url": "" }, { "id": "Mo-MoA24.1", "code": "MoA24.1", "title": "Perception and Path Planning System for Autonomous Roadside Mulching (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA24", "sessionTitle": "Automatic Control in Mobile Agricultural Robotics", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Knuutinen, Jere", "affiliation": "Aalto University, ELEC School" }, { "name": "Backman, Juha", "affiliation": "Natural Resources Institute Finland" }, { "name": "Linkolehto, Raimo", "affiliation": "Natural Resources Institute Finland" }, { "name": "Visala, Arto", "affiliation": "Aalto University, ELEC School" } ], "keywords": [ "Sensing and perception in agriculture", "Agricultural robotics", "Positioning and navigation in agriculture and forestry" ], "abstract": "Autonomous roadside mulching requires perception and planning systems capable of reasoning about the 3D structure of the environment, which consists of vegetation and roadside objects such as poles, traffic signs, and guardrails. This paper addresses the problem by investigating and applying Normal Distribution Transform (NDT)–based traversability estimation for perception. In addition, two one-class support vector machines (SVMs) are trained to separately estimate the road surface and traversable regions in the roadside area. For planning, a path planner based on dynamic programming is proposed that uses traversability estimates and the road edge to select a collision-safe Cartesian path for a roadside mulcher. The effectiveness of the proposed system is demonstrated in real Finnish roadside conditions. Preliminary results show that the system can reliably detect objects such as utility poles in roadside environments and generate Cartesian paths that avoid collisions. In future work, the resulting Cartesian paths will be integrated into an automatic nonlinear model predictive control (NMPC) framework for full autonomous operation.", "url": "" }, { "id": "Mo-MoA24.2", "code": "MoA24.2", "title": "Multi-Reference Path Tracking Control for an Agricultural Tractor with Nonlinear Model Predictive Control (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA24", "sessionTitle": "Automatic Control in Mobile Agricultural Robotics", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Moll, Marcel", "affiliation": "Technical University of Munich" }, { "name": "Oksanen, Timo", "affiliation": "Technical University of Munich" } ], "keywords": [ "Positioning and navigation in agriculture and forestry", "Agricultural robotics", "Control in precision agriculture" ], "abstract": "Guiding a tractor along a predefined reference path is a key component of precision agriculture. This study develops a path tracking controller based on Nonlinear Model Predictive Control, which incorporates multiple segments of a piecewise-linear reference path directly into the objective function. In addition, methods for selecting viable reference segments from the full path are presented. The control system is evaluated during a field test with a tractor controlled via the Tractor Implement Management steering interface. The NMPC solver converged on average after 3.45 ms and tracked the curved reference path with a mean absolute cross-track error of 6.1 cm.", "url": "" }, { "id": "Mo-MoA24.3", "code": "MoA24.3", "title": "Cartesian Control of Road Mulching Machine with Task Scaling (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA24", "sessionTitle": "Automatic Control in Mobile Agricultural Robotics", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Pitkenin, Aleksanteri", "affiliation": "Aalto University" }, { "name": "Knuutinen, Jere", "affiliation": "Aalto University, ELEC School" }, { "name": "Backman, Juha", "affiliation": "Natural Resources Institute Finland" }, { "name": "Visala, Arto", "affiliation": "Aalto University, ELEC School" } ], "keywords": [ "Modeling and estimation in agriculture", "Agricultural robotics", "Control in precision agriculture" ], "abstract": "This paper presents a mathematical model and an intuitive Cartesian joystick controller for a tractor-mounted hydraulic boom mulcher used in roadside vegetation management. The boom is modeled as a four-degree-of-freedom manipulator with revolute joints actuated by hydraulic cylinders. The kinematic model combines Denavit--Hartenberg parameters, geometric constraints, and analytical and numerical mappings between cylinder lengths and joint angles. Cylinder dynamics are represented by first-order models with range limits. A Jacobian-based resolved-rate motion controller maps Cartesian joystick commands to joint velocities, which are converted to cylinder velocities for simulation. Cylinder limits are respected using constraint-aware task scaling to ensure smooth motion near limits. The resulting virtual prototype, implemented in MATLAB and RViz with ROS 2, enables intuitive end-effector control and provides a foundation for future nonlinear model predictive control aimed at reducing operator workload and improving safety in roadside vegetation management.", "url": "" }, { "id": "Mo-MoA24.4", "code": "MoA24.4", "title": "Corner Cases: Headland Coverage Path Planning for Autonomous Driving in Arable Farming (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA24", "sessionTitle": "Automatic Control in Mobile Agricultural Robotics", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Soitinaho, Riikka", "affiliation": "Technical University of Munich" }, { "name": "Oksanen, Timo", "affiliation": "Technical University of Munich" } ], "keywords": [ "Agricultural robotics", "Positioning and navigation in agriculture and forestry" ], "abstract": "This paper presents a new method for headland coverage path planning for arable fields. Several earlier approaches suggest covering the headland with nested polygons and smooth turns, however, covering the field corners entirely requires manoeuvres with reversing. In the new method, the polygon corners are modified to allow a reversing turn. A comparison to two other methods considering gap, overlap, and crossing the field boundary shows an improvement in the coverage result especially in field corners of around 90 degrees, and 240 degrees and above. Applicability of the new method is shown with several examples of real polygonal field maps.", "url": "" }, { "id": "Mo-MoA24.5", "code": "MoA24.5", "title": "Neural Distance-Guided Path Integral Control for Tractor-Trailer Navigation", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA24", "sessionTitle": "Automatic Control in Mobile Agricultural Robotics", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Wei, Peng", "affiliation": "University of California, Davis" }, { "name": "Peng, Chen", "affiliation": "Zhejiang University" }, { "name": "Vougioukas, Stavros", "affiliation": "University of California, Davis" } ], "keywords": [ "Agricultural robotics", "Positioning and navigation in agriculture and forestry" ], "abstract": "Autonomous and safe navigation of tractor–trailer systems requires accurate, real-time collision avoidance and dynamically feasible control, particularly in cluttered and complex agricultural environments. This is challenging due to their articulated, deformable geometries and nonlinear dynamics. Traditional methods oversimplify vehicle geometry or rely on precomputed distance fields that assume a known map, limiting their applicability in dynamic, partially unknown environments. To address these limitations, we propose a geometric neural encoder that provides fast and accurate distance estimates between the full tractor–trailer body and raw LiDAR perception, enabling real-time, map-free geometric reasoning. These learned distances are integrated into a Model Predictive Path Integral (MPPI) controller, allowing the system to incorporate true articulated geometry directly into its cost evaluation and enabling more responsive navigation in challenging agricultural settings. Simulation results demonstrate that the proposed framework generates dynamically feasible and safe trajectories for navigating tractor–trailer systems in cluttered and complex environments.", "url": "" }, { "id": "Mo-MoA24.6", "code": "MoA24.6", "title": "Crop-Row Following in Grass Fields Using Deep Learning Detection, Sliding Mode Control and Fuzzy Velocity Modulation", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA24", "sessionTitle": "Automatic Control in Mobile Agricultural Robotics", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Ferreira da Costa, Igor", "affiliation": "Norwegian University of Life Sciences" }, { "name": "Trier, Erik Lykke", "affiliation": "Norwegian University of Life Sciences" }, { "name": "Candea Leite, Antonio", "affiliation": "Norwegian University of Life Sciences" } ], "keywords": [ "Agricultural robotics", "Computer vision in agriculture", "Positioning and navigation in agriculture and forestry" ], "abstract": "Improving the nitrogen use efficiency (NUE) of perennial ryegrass is an important sustainability goal for temperate agriculture. While autonomous ground robots can facilitate phenotyping in large field trials, navigation in unstructured grass fields remains a challenge for standard vision-based systems. This work presents an integrated navigation pipeline featuring an improved deep-learning line detection model and a robust image-based visual servoing (rIBVS). A new line modeling approach and a fuzzy-based velocity modulation module that adjusts forward speed to prevent line loss are proposed. The system was evaluated using a custom simulator and a Gazebo 3D environment built from real-world UAV field scans. Results demonstrate that the fuzzy modulation reduces overshoots and prevents navigation failure in high-error scenarios. The velocity modulation enabled a 40% increase in maximum allowable operational speed while maintaining real-time CPU performance (10 Hz) on a low-power CPU.", "url": "" }, { "id": "Mo-MoA25.1", "code": "MoA25.1", "title": "Hybrid Modeling for Personalized Glucose Regulation: Combining Physiological and Predictor-Based Subspace Identification (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA25", "sessionTitle": "Engineering Diabetes Technologies I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Ahmadasas, Mohammad", "affiliation": "Illinois Institute of Technology" }, { "name": "Rashid, Mudassir", "affiliation": "Illinois Institute of Technology" }, { "name": "Cinar, Ali", "affiliation": "Illinois Inst. of Tech" } ], "keywords": [ "Biomedical system modeling, identification, and simulation", "Artificial pancreas or organs", "Control of physiological and clinical variables" ], "abstract": "Accurate modeling of glucose–insulin dynamics is essential for personalized fully-automated insulin delivery (fAID) in people with type 1 diabetes. This work presents a hybrid modeling framework that integrates a mechanistic physiological model with a data-driven model identified using predictor-based subspace identification (PBSID). The PBSID model is trained on patient blood-glucose data to capture short-term temporal dynamics, while the physiological model provides interpretability and ensures stability. The prediction accuracies of both models are evaluated over a past horizon to compute adaptive weighting coefficients that define their contribution to the hybrid model employed in the model predictive controller (MPC). This adaptive structure enables the fAID system controller to dynamically balance the predictions of the physiological and data-driven models based on recent performance. Closed-loop simulations with virtual patients demonstrate that the proposed hybrid model improves glucose prediction accuracy, increases time-in-range of glucose levels (92%) and enhances controller robustness against intra- and inter-subject variability, laying the foundation for intelligent, self-adapting fAID systems for personalized glucose regulation in people with diabetes.", "url": "" }, { "id": "Mo-MoA25.2", "code": "MoA25.2", "title": "Real-Time Estimation of Glucose Rate of Appearance and Changes in Insulin Action without Meal Announcements Using a Time-Varying Kalman Filter (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA25", "sessionTitle": "Engineering Diabetes Technologies I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Moscoso-Vásquez, Marcela", "affiliation": "University of Virginia" }, { "name": "Fabris, Chiara", "affiliation": "University of Virginia" }, { "name": "Breton, Marc D", "affiliation": "University of Virginia" } ], "keywords": [ "Biomedical system modeling, identification, and simulation", "Artificial pancreas or organs", "Control of physiological and clinical variables" ], "abstract": "Intra- and inter-individual variability in insulin sensitivity, along with the absence of meal announcements, pose major challenges for state estimation in closed-loop insulin delivery. This work introduces a non-recursive state estimator for glucose–insulin dynamics that adaptively modulates process noise through a likelihood-driven switching parameter informed solely by CGM and insulin histories. Leveraging a measurement of the probability of prandial disturbances, and a retroactive correction mechanism that backfills the switching parameter to compensate for delays inherent to CGM-based responses, the method estimates changes on insulin action and glucose rate of appearance. Validation was conducted across matching-model in-silico experiments, nonlinear simulations with the UVA-Padova Type 1 Diabetes Simulator, and clinical data. Across all scenarios, the estimator accurately captured imposed variations in insulin sensitivity and meal absorption dynamics during meals with differing absorption kinetics. These findings support the proposed approach as a robust state-estimation framework for model-based closed-loop insulin delivery systems.", "url": "" }, { "id": "Mo-MoA25.3", "code": "MoA25.3", "title": "The Driver-Blindness Phenomenon: Why Deep Sequence Models Default to Autocorrelation in Blood Glucose Forecasting (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA25", "sessionTitle": "Engineering Diabetes Technologies I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Shakeri, Heman", "affiliation": "University of Virginia" } ], "keywords": [ "Artificial pancreas or organs", "Biomedical signal measurement and processing", "Decision support and control in medicine" ], "abstract": "Deep sequence models for blood glucose forecasting consistently fail to leverage clinically informative drivers—insulin, meals, and activity—despite well-understood physiological mechanisms. We term this Driver-Blindness and formalize it via Delta_{text{drivers}}, the performance gain of multivariate models over matched univariate baselines. Across the literature, Delta_{text{drivers}} is typically near zero. We attribute this to three interacting factors: architectural biases favoring autocorrelation (C1), data fidelity gaps that render drivers noisy and confounded (C2), and physiological heterogeneity that undermines population-level models (C3). We synthesize strategies that partially mitigate Driver-Blindness—including physiological feature encoders, causal regularization, and personalization—and recommend that future work routinely report Delta_{text{drivers}} to prevent driver-blind models from being considered state-of-the-art.", "url": "" }, { "id": "Mo-MoA25.4", "code": "MoA25.4", "title": "Control-Oriented Model Reduction for Type 1 Diabetes: A Moment-Based Approach (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA25", "sessionTitle": "Engineering Diabetes Technologies I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Saggese, Arian", "affiliation": "Institute of Research in Electronics, Control, and Signal Processing-LEICI - National University of La Plata" }, { "name": "Faedo, Nicolás", "affiliation": "Politecnico Di Torino" }, { "name": "Fushimi, Emilia", "affiliation": "Instituto LEICI, Facultad De Ingeniería, UNLP-CONICET" }, { "name": "Garelli, Fabricio", "affiliation": "University of La Plata" } ], "keywords": [ "Artificial pancreas or organs", "Biomedical system modeling, identification, and simulation" ], "abstract": "In the development of technologies for the treatment of Type 1 Diabetes (T1D), detailed physiological models that describe glucose–insulin dynamics are utilized for in silico validation, while low-order models are commonly used for controller design. However, existing low-order models do not guarantee accurate temporal or frequency behaviour. This work introduces a moment-based reduction framework tailored to control-oriented applications in T1D. Building on a linearisation of the Dalla Man model, we establish the stability properties required for moment matching and derive families of reduced models that preserve steady-state responses and remain accurate within the operating bandwidth. We further propose a non-convex optimisation method to refine the parameters used to construct the reduced-order model, with the aim of improving the response outside the selected matching points. When benchmarked against standard control-oriented models, the optimised reduced models consistently achieve lower errors relative to the linearised Dalla Man model.", "url": "" }, { "id": "Mo-MoA25.5", "code": "MoA25.5", "title": "Reproducing and Optimizing GLP-1RA Therapy with Automated Insulin Delivery in Adults with Type 1 Diabetes: An In-Silico Study (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA25", "sessionTitle": "Engineering Diabetes Technologies I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Lv, Dayu", "affiliation": "University of Virginia" }, { "name": "El Fathi, Anas", "affiliation": "University of Virginia" }, { "name": "Kovatchev, Boris", "affiliation": "University of Virginia" }, { "name": "Viral, Shah", "affiliation": "Indiana University" } ], "keywords": [ "Artificial pancreas or organs", "Decision support and control in medicine", "Biomedical system modeling, identification, and simulation" ], "abstract": "This study evaluates semaglutide as an adjunct to Automated Insulin Delivery (AID) using the UVA/Padova T1D Simulator. We reproduced outcomes from a 26-week clinical trial in adults with Type 1 Diabetes and obesity. Subsequently, we optimized controller parameters (Carbohydrate Ratio, Correction Factor, Basal Rate) to minimize the Glycemia Risk Index while ensuring hypoglycemia did not increase. The simulation successfully reproduced the therapeutic effects of semaglutide observed in the clinical data. Optimized Hybrid Closed-Loop (HCL) settings increased Time in Range (TIR) from 69.3% to 77.1%. Crucially, optimized Fully Closed-Loop (FCL) with semaglutide achieved 69.2% TIR, comparable to standard HCL, whereas FCL without semaglutide remained suboptimal (55.3% TIR). These findings suggest that adjunct semaglutide with optimized therapy parameters may enable viable, burden-free fully closed-loop systems.", "url": "" }, { "id": "Mo-MoA25.6", "code": "MoA25.6", "title": "Hybrid (Physiological Model, Neural Network) Glucose Dynamics Estimation (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA25", "sessionTitle": "Engineering Diabetes Technologies I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Siket, Máté", "affiliation": "Obuda University" }, { "name": "Rashid, Mudassir", "affiliation": "Illinois Institute of Technology" }, { "name": "Cinar, Ali", "affiliation": "Illinois Inst. of Tech" } ], "keywords": [ "Artificial pancreas or organs", "Biomedical system modeling, identification, and simulation" ], "abstract": "Accurate and efficient estimation of blood glucose dynamics from real-world data is a challenging, but crucial task for the development fully automated insulin delivery systems. We proposed a hybrid (physiological model and neural network) architecture that provides a novel way of dealing with multiple difficulties in glucose dynamics estimation. Our proposed method aims to address inter-, intra-patient variability, meal carbohydrate estimation, and glucose prediction without the need of optimization or retraining. It uses an encoder-decoder neural network to estimate the initial condition and parameters of the physiological model (ordinary differential equation), a generative adversarial network to estimate carbohydrate intakes, and a physiological model to estimate, and predict blood glucose concentrations. The method was trained and evaluated on real-world glucose monitoring and insulin pump data. Our proposed hybrid approach achieved similar performance to a state-of-the-art digital twin methodology; on the testing dataset, it achieved more stable predictions, lower mean absolute errors in the prediction window, and orders of magnitude faster execution time -- without meal announcement.", "url": "" }, { "id": "Mo-MoA26.1", "code": "MoA26.1", "title": "Concurrent Learning-Based Adaptive Online Identification of Battery Thermal Dynamics (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA26", "sessionTitle": "Thermal Management of Electrified Vehicles I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Jang, Seunghun", "affiliation": "Korean Adavanced Institute of Science and Technology" }, { "name": "Park, Changeun", "affiliation": "KAIST" }, { "name": "Choi, Kyunghwan", "affiliation": "Korea Advanced Institute of Science and Technology" } ], "keywords": [ "Automotive system identification and modelling", "AI and learning-based control for automotive systems" ], "abstract": "This paper presents an adaptive online identification framework for battery thermal modeling in a battery thermal management (BTM) system. A lumped thermal model identified offline from experimental data provides a nominal thermal model; however, mismatches under varying operating conditions reduce long-horizon prediction accuracy and degrade model predictive control (MPC) performance in BTM system. To address this issue, an adaptive observer is presented for online estimation of unknown thermal dynamics, providing guaranteed parameter convergence and bounded estimation errors. Furthermore, the adaptation law is extended using a Concurrent Learning (CL) framework to improve parameter convergence without relying on persistent excitation (PE), incorporating both current and stored data. Simulation results obtained in the MATLAB/Simulink environment of the IFAC 2026 benchmark problem present that the proposed CL-based approach enhances the prediction accuracy of the estimated thermal model in long-horizon rollout predictions based on future information derived from the driving profile, achieving up to a 32.3% reduction in RMSE compared with the offline model over long prediction horizons.", "url": "" }, { "id": "Mo-MoA26.2", "code": "MoA26.2", "title": "Reinforcement Learning for Control Design in Thermal Management of Battery Electric Vehicles (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA26", "sessionTitle": "Thermal Management of Electrified Vehicles I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Li, Yichen", "affiliation": "Nanjing University of Science and Technology" }, { "name": "Zhao, Yan", "affiliation": "Nanyang Technology University" }, { "name": "Kao, Yonggui", "affiliation": "HIT" }, { "name": "Wu, Junli", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Electric and solar vehicles" ], "abstract": "This paper investigates the control design problem in the thermal management system of a battery electric vehicle (BEV). The control objective is to minimize the total energy consumption while satisfying the temperature constraints for the cabin, the battery, and the motor during driving. One of the reinforcement learning algorithms, i.e., Q-learning, is first used to find the optimal control strategy. Based on the given simulation model, the results are obtained. It is shown that the algorithm is effective; however, the training process is time-consuming and the resulting system performance is not yet satisfactory. These issues will be addressed in future work.", "url": "" }, { "id": "Mo-MoA26.3", "code": "MoA26.3", "title": "Benchmark Problem for Thermal Management Strategy Design of Connected Battery Electric Vehicles (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA26", "sessionTitle": "Thermal Management of Electrified Vehicles I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Xu, Fuguo", "affiliation": "Dalian University of Technology" }, { "name": "Zhang, Jiangyan", "affiliation": "Dalian Minzu University" }, { "name": "Song, Kang", "affiliation": "Tianjin University" }, { "name": "Shen, Tielong", "affiliation": "Dalian University of Technology" }, { "name": "Suzuki, Kunihiko", "affiliation": "Hitachi Astemo, Ltd" }, { "name": "Kako, Junichi", "affiliation": "Toyota Motor Corporation" }, { "name": "Kim, Jinsung", "affiliation": "Hyundai Motor Company" } ], "keywords": [ "Engine and powertrain modeling and control", "Electric and solar vehicles", "Modeling and simulation of transportation systems" ], "abstract": "This benchmark proposes a thermal management problem for battery electric vehicles (BEVs) that considers both energy consumption minimization, temperature control of electric devices, and cabin comfort. The vehicle is assumed to be running in a connected environment with real-time vehicle-to-everything (V2X) data available. The benchmark problem formulation is provided, and a simulation platform for BEVs with V2X data is provided to the challengers. The purpose of this benchmark problem is to provide a platform for students and early-career researchers to tackle the challenges of on-board thermal management strategies in electric vehicles, and to exchange cutting-edge research results in automotive system control and optimization. The organizing team consists of industry experts and academic researchers with backgrounds in control engineering.", "url": "" }, { "id": "Mo-MoA26.4", "code": "MoA26.4", "title": "Benchmark Problem for Thermal Management Strategy of Connected Battery Electrical Vehicles (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA26", "sessionTitle": "Thermal Management of Electrified Vehicles I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Hou, Shengyan", "affiliation": "Jilin University" }, { "name": "Ma, Qian", "affiliation": "Jilin University" }, { "name": "Zhang, Haoyang", "affiliation": "Jilin University" }, { "name": "Wang, Yilin", "affiliation": "Jilin University" }, { "name": "Jia, Zhihuan", "affiliation": "Jilin University" }, { "name": "Ma, Yan", "affiliation": "Jilin University" }, { "name": "Gao, Jinwu", "affiliation": "Jilin University" } ], "keywords": [ "Hybrid, electric and alternative drive vehicles", "Nonlinear and optimal automotive control", "AI and learning-based control for automotive systems" ], "abstract": "An effective thermal management strategy (TMS) plays a crucial role in ensuring the operational safety and energy efficiency of battery electric vehicles (BEVs), while also enhancing thermal comfort within the cabin. To address the slow thermodynamic response of batteries and increased complexity in integrated systems, a multi-layer nonlinear model predictive control strategy was developed. This strategy utilizes Intelligent Transportation System information to optimize energy consumption within the integrated system. The upper and lower controllers coordinate through long- and short-term velocity predictions, respectively, resolving issues such as multi-layer control, rapid implementation, and reference trajectory tracking.", "url": "" }, { "id": "Mo-MoA26.5", "code": "MoA26.5", "title": "Intelligent Predictive Thermal Management Control Algorithm for Battery Electric Vehicles Based on V2X Information (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA26", "sessionTitle": "Thermal Management of Electrified Vehicles I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Lin, Shibo", "affiliation": "Dalian University of Technology" }, { "name": "Tan, Xuelin", "affiliation": "Dalian University of Technology" }, { "name": "Ouyang, Haojie", "affiliation": "Dalian University of Technology" }, { "name": "Yang, Dongjie", "affiliation": "Ningbo University of Technology" }, { "name": "Kang, Mingxin", "affiliation": "Northeastern University" }, { "name": "Wu, Yuhu", "affiliation": "Dalian University of Technology" } ], "keywords": [ "Intelligent transportation systems", "Electric and solar vehicles", "Vehicle dynamic systems" ], "abstract": "This paper proposes an intelligent predictive thermal management strategy based on V2X information for the thermal management problem of connected battery electric vehicles. The research focuses on multi-objective optimization, simultaneously considering energy consumption minimization, thermal safety constraints of electrical components, and cabin comfort. In the connected environment, real-time traffic information obtained via V2X is utilized to predict future vehicle speed trends, and the future thermal loads of the battery and motor are estimated by combining the predicted speed profile with thermodynamic models. On this basis, a Model Predictive Control (MPC) strategy is designed to coordinately regulate the actuators of the cooling system, achieving the minimization of total energy consumption of the thermal management system while satisfying temperature constraints. The proposed strategy is validated using the benchmark simulation platform, aiming to explore the potential of intelligent connected technology in the field of electric vehicle thermal management.", "url": "" }, { "id": "Mo-MoA27.1", "code": "MoA27.1", "title": "Visualization-Based Comparative Analysis of Bow-Stern Elevator AUV Steering Strategies for Multi-Mission Scenarios (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA27", "sessionTitle": "Marine Robotics: Sailing into the Future of Waterborne Autonomous Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Wang, Andong", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Zhang, Jialei", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Yifan, Liu", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Guo, Heng", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Han, Rui", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Xiang, Xianbo", "affiliation": "Huazhong University of Science and Technology" } ], "keywords": [ "Marine robotics", "Autonomous marine systems and vehicles", "Decision and support in marine systems" ], "abstract": "This paper presents a visualization-based framework for selecting steering strategies for bow-stern elevator Autonomous Underwater Vehicles (AUVs). Five performance indices are established to comprehensively evaluate AUV motion performance across diverse mission scenarios. Based on these indices, an evaluation system is constructed to quantitatively assess the effectiveness of various steering strategies. A visual analysis is then conducted using a Voronoi diagram based on the UMAP method, enabling rapid and intuitive strategy selection. Based on the lake trial experimental data, the optimal and worst steering strategies were selected for different mission scenarios.", "url": "" }, { "id": "Mo-MoA27.2", "code": "MoA27.2", "title": "Spatially Aware Value Fusion in Decomposed Reward Architectures for Marine Manipulation (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA27", "sessionTitle": "Marine Robotics: Sailing into the Future of Waterborne Autonomous Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Sivtsov, Vladimir", "affiliation": "University of Zagreb Faculty of Electrical Engineering and Computing" }, { "name": "Alaran, Muslim", "affiliation": "University of Zagreb Faculty of Electrical Engineering and Computing" }, { "name": "Shkolnik, Daniil", "affiliation": "University of Zagreb Faculty of Electrical Engineering and Computing" }, { "name": "Papanikolaou, Athanasios", "affiliation": "University of Zagreb Faculty of Electrical Engineering and Computing" }, { "name": "Markovic, Ivan", "affiliation": "University of Zagreb Faculty of Electrical Engineering and Computing" }, { "name": "Zereik, Enrica", "affiliation": "Cnr - Inm" }, { "name": "Petrovic, Ivan", "affiliation": "University of Zagreb" }, { "name": "Bonsignorio, Fabio", "affiliation": "University of Zagreb" } ], "keywords": [ "AI and embodied-AI in marine systems", "Marine robotics", "Autonomous marine systems and vehicles" ], "abstract": "Deep reinforcement learning has shown great potential in marine robotics due to its ability to automatically obtain control policies for various tasks. However, there are still challenges, especially in complex scenarios where wave-induced motions affect the robotic system, making manipulation significantly more difficult. In this work, we present a new value function balancing method for end-to-end learning of control policies. The method is based on adaptive changes of the coefficients of terms of the value function in decomposed reward architectures. We validated the method on marine manipulation problems in simulation and by real-world validation and have shown that it achieves better performance than existing algorithms in the presence of waves of various magnitudes.", "url": "" }, { "id": "Mo-MoA27.3", "code": "MoA27.3", "title": "Acoustic-Based Guidance for Automatic Docking of Holonomic AUVs (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA27", "sessionTitle": "Marine Robotics: Sailing into the Future of Waterborne Autonomous Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Regalo, Ravi", "affiliation": "Instituto Superior Técnico, Universidade De Lisboa" }, { "name": "Cabecinhas, David", "affiliation": "Instituto Superior Tecnico" }, { "name": "Pascoal, Antonio M.", "affiliation": "Ist-Id, Vat 509830072" } ], "keywords": [ "Marine system guidance, navigation and control", "Perception and filtering in marine systems", "Marine robotics" ], "abstract": "This paper describes a system to automatically dock an AUV onto a docking station without precise knowledge of the position and orientation of the latter, in the presence of unknown ocean currents, using a fully acoustic sensing architecture. The system relies on a pair of Ultrashort Baseline sensors, one onboard the vehicle and one installed on a seabed-resident docking station, enabling operation in low-visibility environments where cameras are ineffective. Relative orientation is estimated by a nonlinear complementary filter on SO(3), while an Extended Kalman Filter provides relative position, supplying pose estimates to a geometric controller on SE(3) that executes the docking manoeuvre. The complete system is implemented in a dedicated software suite and validated in simulation and water trials.", "url": "" }, { "id": "Mo-MoA27.4", "code": "MoA27.4", "title": "A Software Suite for the Development and Implementation of Cooperative Motion Planning and Control Systems Using Bézier Curves and Networked Control Techniques (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA27", "sessionTitle": "Marine Robotics: Sailing into the Future of Waterborne Autonomous Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Monteiro, Gorka", "affiliation": "Instituto Superior Técnico, Universidade De Lisboa" }, { "name": "Sabetghadam, Bahareh", "affiliation": "Institute Superior Tecnico" }, { "name": "Cunha, Rita", "affiliation": "Instituto Superior Técnico, Universidade De Lisboa" }, { "name": "Pascoal, Antonio M.", "affiliation": "Ist-Id, Vat 509830072" }, { "name": "Cabecinhas, David", "affiliation": "Instituto Superior Tecnico" } ], "keywords": [ "Marine system guidance, navigation and control", "Trajectory and path planning for AVs", "Trajectory tracking and path following for AVs" ], "abstract": "This paper focuses on motion planning and control of marine vehicles both from a conceptual and practical standpoint. A software suite for cooperative systems design, analysis, and real-time implementation that builds upon trajectory planning and networked cooperative control techniques is described. Firstly, the problem of trajectory planning is cast in the form of an optimal control problem, exploiting the use of Bézier curves, taking explicitly into account: i) temporal specifications and /or energy expenditure, ii) vehicle dynamical constraints, iii) vehicle-obstacle avoidance, and iv) inter-vehicle temporal or spatial deconfliction objectives. The obtained time-parametrized trajectories are then re-parameterized, yielding spatial paths (with desired speed profiles along them) to be followed cooperatively by resorting to networked path following control techniques. The resulting software suite allows for seamless integration in a large class of autonomous marine vehicles. The paper includes the results of field tests in a protected water area.", "url": "" }, { "id": "Mo-MoA27.5", "code": "MoA27.5", "title": "Transforming Point Cloud to Simulation: A Pipeline for 3D Environment Integration", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA27", "sessionTitle": "Marine Robotics: Sailing into the Future of Waterborne Autonomous Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Tripathy, Aparajita", "affiliation": "Oulu University of Applied Sciences" }, { "name": "Lamponen, Aki", "affiliation": "Oulu University of Applied Sciences" }, { "name": "Säkkinen, Jukka", "affiliation": "Oulu University of Applied Sciences" }, { "name": "van Deventer, Jan", "affiliation": "Luleå University of Technology" } ], "keywords": [ "Modeling and simulation of transportation systems" ], "abstract": "Testing and validation of heavy machinery and off-road vehicles are time-consuming and costly in physical field tests. To address these challenges, Vehicle-in-the-Loop (VIL) testing emerges as a safe, cost-friendly, and repeatable approach for evaluating vehicle performance. One of the important components in VIL testing is the creation of 3D environments that can accurately capture the terrain geometry and surface characteristics. However, generating such 3D environments from raw point cloud data and managing multiple environment models is a complex and labor-intensive process. To address these challenges, we propose a pipeline framework that can generate simulation-ready environment models from raw point cloud data by utilizing freeware software such as CloudCompare and Blender. The framework also integrates the 3D models with Mevea, a real-time simulation platform for VIL testing of off-road heavy machinery. In addition, the framework introduces a modular environment management interface that enables seamless switching between multiple preconfigured environments using Mevea's assembly activation and deactivation approach. Our main contribution is to provide a high-fidelity and flexible 3D environment integration pipeline for off-road vehicle simulation, which helps in reducing manual configuration efforts and offers a repeatable, scalable, and user-friendly mechanism for handling multiple environments during virtual testing of off-road vehicles.", "url": "" }, { "id": "Mo-MoA28.1", "code": "MoA28.1", "title": "Learning Cross-Domain Latent Control Policies (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA28", "sessionTitle": "JO-CEP: Guidance, Navigation and Control of Aircraft and Spacecraft", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Zhang, Congxi", "affiliation": "Beijing Institute of Control Engineering" }, { "name": "Xie, Yongchun", "affiliation": "Beijing Institute of Control Engineering" } ], "keywords": [ "AI for aircraft and spacecraft navigation, guidance and control", "AI and learning-based control for automotive systems" ], "abstract": "In unstructured space environments, intelligent spacecraft need to have the ability for autonomous planning and control based on high-dimensional information. Currently, learning based end-to-end control policies perform well in the domain they have been trained. However, if the relationship between latent variables and observation variables differs between the domain of the expert-controlled agent (source domain) and the domain of the target agent (target domain), the learned control policies often fail to achieve cross-domain transfer. To solve this problem, this paper presents an identifiable representation learning method for latent controllers, which ensure a certain equivalence of the representations as well as the controller models in different domains. Based on the identifiable latent controller, we proposes a cross-domain latent control policy learning method. For given tasks, the learned latent control policy can ensure that the state of the target agent reaches the latent space desired state planned in the expert control process. This paper provides a new idea for cross-domain learning of end-to-end control policies and improves the generalization ability of the learned control policies. The results have potential to enhance the end-to-end control capability of intelligent spacecraft with safety guarantee.", "url": "" }, { "id": "Mo-MoA28.2", "code": "MoA28.2", "title": "LightDefogGS: Lightweight 3D Fog Removal through Gaussian Splatting and Gradient-Boosted Filtering (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA28", "sessionTitle": "JO-CEP: Guidance, Navigation and Control of Aircraft and Spacecraft", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Tao, Siyuan", "affiliation": "The University of Osaka" }, { "name": "Minami, Yuki", "affiliation": "University of Hyogo" }, { "name": "Ishikawa, Masato", "affiliation": "Osaka University" } ], "keywords": [ "AI for aircraft and spacecraft navigation, guidance and control", "AI and learning-based control for automotive systems", "Robotic vision for AVs" ], "abstract": "Recently, Gaussian Splatting–based fog removal methods have shown strong potential for recovering degraded information that single-image approaches struggle to restore. These methods focus on optimizing the reconstruction process to enhance visual clarity under adverse weather conditions. However, their performance still degrades in challenging, dense fog conditions. To address this issue, we propose LightDefogGS, a lightweight 3D fog removal framework that models fog as removable volumetric particles within Gaussian Splat representations. LightDefogGS reconstructs 3D point clouds from multi-view images and employs a feedforward pipeline with a LightGBM-based classifier to separate fog from scene elements. This enables more accurate fog removal while reducing computational cost compared to existing methods.", "url": "" }, { "id": "Mo-MoA28.3", "code": "MoA28.3", "title": "Identification of Nonlinear Sloshing Dynamics Using Operational Manoeuvres (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA28", "sessionTitle": "JO-CEP: Guidance, Navigation and Control of Aircraft and Spacecraft", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Burgin, Emily", "affiliation": "Technische Universität Dresden" }, { "name": "Thiele, Frederik", "affiliation": "Technische Universität Dresden" }, { "name": "Dehombreux, Charles", "affiliation": "Airbus Defence and Space" }, { "name": "Garnier, Benoit", "affiliation": "Airbus Defence and Space" }, { "name": "Manuel-Juanpere, Xavier", "affiliation": "Airbus Defence and Space" }, { "name": "Pfifer, Harald", "affiliation": "Technische Universität Dresden" } ], "keywords": [ "Flight dynamics modelling and identification", "Guidance, navigation and control of aircraft and spacecraft", "Space exploration and transportation" ], "abstract": "Complex space systems exhibit non-linear dynamics that are unmodelled during the design phase. This can cause detrimental effects on performance after launch. On-board identification of these dynamics can be used to maximise operational performance and mitigate risk. This paper proposes an algorithm that identifies the non-linear dynamics of fuel sloshing using only on-board measurements acquired during normal operation. No additional excitation manoeuvres are used, thus conserving propellant and maintaining the mission timeline. The method uses an l1-regularised linear regression to determine the governing equations of the dynamics. The algorithm is demonstrated on a communication satellite with a dual-tank architecture and a chemical propulsion system. Real flight data, provided by Airbus Defence and Space, demonstrate the algorithm’s applicability to industry-grade systems.", "url": "" }, { "id": "Mo-MoA28.4", "code": "MoA28.4", "title": "Hierarchical Estimation of Uncertainties in Multi-Rotors: A Yaw-Motion Study Using Test Bench (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA28", "sessionTitle": "JO-CEP: Guidance, Navigation and Control of Aircraft and Spacecraft", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Tran, G. Q. Bao", "affiliation": "University of Illinois Urbana-Champaign" }, { "name": "Nguyen, Binh Minh", "affiliation": "The University of Tokyo" } ], "keywords": [ "Condition monitoring and maintenance of aerospace systems", "Flight dynamics modelling and identification", "Kalman filtering techniques in automotive control" ], "abstract": "This is a shortened version of our article (Tran and Nguyen, Control Engineering Practice, 2026) of the same title. We design observers to estimate yaw uncertainties in a multi-rotor system. The considered experimental test bench consists of a drone body driven by two motor–propeller units, each affected by disturbances and possible faults. Based on an observability analysis, we construct a nonlinear observer to estimate the drag coefficient, disturbances, and actuator angular velocities from motor current measurements. We also derive sufficient conditions and propose a Kalman-like observer to estimate loss-of-effectiveness coefficients and yaw disturbances, using yaw rate measurements and actuator-level estimates. Simulations and experiments demonstrate the effectiveness and feasibility of the approach.", "url": "" }, { "id": "Mo-MoA28.5", "code": "MoA28.5", "title": "BIM-AKF: A Hybrid Approach to Robust INS Vertical Channel Stabilization (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA28", "sessionTitle": "JO-CEP: Guidance, Navigation and Control of Aircraft and Spacecraft", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Manhães Gabriel de Brito Cavalcanti, Vinícius", "affiliation": "Instituto Federal Fluminense" }, { "name": "Caputo Durão, Carlos Renato", "affiliation": "Federal University of Lavras (UFLA)" }, { "name": "Villalobos Hernandez, Guillermo Esau", "affiliation": "Technology Innovation Institute" }, { "name": "Korimi, Maheedhar", "affiliation": "Technology Innovation Institute" }, { "name": "Nguyen, Hung", "affiliation": "Instituto Superior Técnico (NIF: 501507930)" }, { "name": "Oliveira E Silva, Felipe", "affiliation": "Federal University of Lavras" } ], "keywords": [ "Flight dynamics modelling and identification", "Guidance, navigation and control of aircraft and spacecraft" ], "abstract": "Vertical Channel (VC) instability poses a critical challenge in Inertial Navigation Systems (INSs), particularly for aerial applications. This work proposes BIM-AKF, a novel hybrid architecture that integrates Baro-Inertial Mechanization (BIM) as a pre-stabilizer for the INS VC, with an Augmented Kalman Filter (AKF) that models only the residual VC errors after BIM correction. Unlike standard Kalman filters, which embed the full unstable INS VC dynamics, BIM-AKF leverages either Auto-Correlation Function (ACF) or Allan Variance (AV) analysis of BIM-only outputs to optimally parameterize residual error propagation in statespace. The framework seamlessly supports Additional Aiding Sensors (AASs), e.g., Global Navigation Satellite Systems (GNSSs). Real Unmanned Aerial Vehicle (UAV) flights with intentional GNSS outages demonstrate that the proposed BIM-AKF outperforms existing methods, establishing a new benchmark in robust, multi-sensor navigation.", "url": "" }, { "id": "Mo-MoA29.1", "code": "MoA29.1", "title": "Situation-Aware Interactive MPC Switching for Autonomous Driving", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA29", "sessionTitle": "Learning and Adaptation in Autonomous Vehicles", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Qi, Shuhao", "affiliation": "Eindhoven University of Technology" }, { "name": "Aori, Qiling", "affiliation": "Eindhoven University of Technology" }, { "name": "Zhang, Luyao", "affiliation": "TU Delft" }, { "name": "Lazar, Mircea", "affiliation": "Eindhoven Univ. of Technology" }, { "name": "Haesaert, Sofie", "affiliation": "TU Eindhoven" } ], "keywords": [ "Autonomous vehicles", "Learning and adaptation in autonomous vehicles", "Cooperative navigation" ], "abstract": "Autonomous driving in interactive traffic scenarios remains challenging because of the mutual influence among vehicles and the inherent uncertainty of surrounding agents. Several model predictive control (MPC) formulations have been proposed to address this challenge, each adopting a different model of inter-agent interaction. While higher-fidelity interaction models enable more intelligent behavior, they incur substantially greater computational cost. Since strong interactions arise only occasionally in real traffic, a practical strategy for balancing performance and computational overhead is to invoke an appropriate controller based on situational demands. To this end, we first conduct a comparative study to assess and hierarchize the interactive capabilities of different MPC formulations. Building on this hierarchy, we then develop a neural network-based classifier for situation-aware switching among these controllers. We demonstrate that, by invoking the most advanced interactive MPC only in rare but critical situations and relying on a basic MPC in the majority of situations, situation-aware switching substantially improves overall performance while significantly reducing computational load.", "url": "" }, { "id": "Mo-MoA29.2", "code": "MoA29.2", "title": "Data-Driven Reachable-Set-Based Vulnerability Analysis of ADS Controllers against Parameter Tampering Attacks", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA29", "sessionTitle": "Learning and Adaptation in Autonomous Vehicles", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Ye, Zi", "affiliation": "Zhejiang University" }, { "name": "Yucheng, Ruan", "affiliation": "Zhejiang University" }, { "name": "Zhao, Chengcheng", "affiliation": "Zhejiang University" } ], "keywords": [ "Autonomous vehicles", "Learning and adaptation in autonomous vehicles", "Multi-vehicle systems" ], "abstract": "Automated driving systems (ADS) are complex closed-loop cyber-physical systems whose behavior depends critically on controllers and their calibrated parameters. Tampering with such parameters can therefore induce unsafe maneuvers and serious safety hazards, making it important to analyze the vulnerability of ADS controllers against parameter-tampering attacks. However, such analysis is challenging for production-like ADS because their closed-loop dynamics are difficult to model analytically. To address this challenge, we formalize a parameter-tampering attack model for ADS controllers and develop a data-driven vulnerability analysis tool based on scenario-optimization-based reachable-set approximation. The tool enables risk-bounded safety verification without requiring tractable white-box dynamics. Furthermore, we conduct a single-lane car-following case study on an OpenPilot–MetaDrive co-simulator and identify safe intervals for longitudinal controller gains against parameter tampering attacks.", "url": "" }, { "id": "Mo-MoA29.3", "code": "MoA29.3", "title": "CleanNet: Modular DRL Framework for Autonomous Urban Sanitation", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA29", "sessionTitle": "Learning and Adaptation in Autonomous Vehicles", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Zhang, Runxi", "affiliation": "Tongji University" }, { "name": "Cai, Ziheng", "affiliation": "Cowa Robot" }, { "name": "Li, Wenhao", "affiliation": "Tongji University" }, { "name": "Liao, Wenlong", "affiliation": "COWAROBOT" }, { "name": "Jin, Bo", "affiliation": "Tongji University" } ], "keywords": [ "Learning and adaptation in autonomous vehicles", "Autonomous vehicles" ], "abstract": "This paper proposes a modular deep reinforcement learning framework for autonomous sanitation vehicles operating in complex urban environments. Addressing the dual challenges of precise curb-following under nonlinear vehicle dynamics and robust obstacle avoidance, the architecture employs a modular design. A high-level decision module dynamically switches between specialized sub-policies: Proximal Policy Optimization (PPO) for precision sweeping operations and Generative Adversarial Imitation Learning (GAIL) for complex intersection traversal. Furthermore, we bridge the sim-to-real gap via a learned GRU model capturing temporal hysteresis and a feature redistribution mechanism ensuring domain-invariant perception. Experimental results demonstrate that our framework achieves higher tracking accuracy and robustness compared to established search-based (Hybrid A*) and optimization-based (EM Planner) baselines in real-world deployments.", "url": "" }, { "id": "Mo-MoA29.4", "code": "MoA29.4", "title": "Control of Mixed-Autonomy Traffic Via Autonomous Vehicles with Lane-Changing Behavior", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA29", "sessionTitle": "Learning and Adaptation in Autonomous Vehicles", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Pei, Shuwei", "affiliation": "University of Groningen" }, { "name": "Sayin, Muhammed Omer", "affiliation": "Bilkent University" }, { "name": "Ahmed, Saeed", "affiliation": "Faculty of Science and Engineering, University of Groningen" } ], "keywords": [ "Learning and adaptation in autonomous vehicles", "Intelligent transportation systems", "Multi-vehicle systems" ], "abstract": "Recent work (Yan et al., 2023) showed that a single autonomous vehicle (AV) can stabilize stop-and-go oscillations on a double-lane ring road, assuming human drivers do not change lanes. We discovered that extending this paradigm to a single AV trained with human lane-changing is insufficient: it still fails to stabilize traffic flow. Motivated by this, we study a double-lane ring road with human lane-changing and propose a rule-based, pair-aligned control strategy via two AVs that synchronizes their motion across lanes. This controller suppresses human lane-changing, couples the two lanes into a single virtual lane, and mitigates oscillations. Its structure is inspired by cooperative reinforcement learning (RL) experiments, where two AVs repeatedly learned to form a cross-lane paired configuration. In simulations, our controller increases stabilized average speed by 7.4% compared to two AVs equipped with a single-lane stabilization controller (Yan et al., 2023).", "url": "" }, { "id": "Mo-MoA29.5", "code": "MoA29.5", "title": "Distributed Formation Control Via Hop-Optimized BFS-Guided Cooperative Learning of Mobile Robots", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA29", "sessionTitle": "Learning and Adaptation in Autonomous Vehicles", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Chen, Yihang", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Liang, Hongjing", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Liu, Chang", "affiliation": "University of Electronic Science and Technology of China" } ], "keywords": [ "Multi-vehicle systems", "Learning and adaptation in autonomous vehicles", "Trajectory tracking and path following for AVs" ], "abstract": "This paper presents a distributed formation control framework for multiple mobile robots subject to external disturbances and prescribed performance constraints. The framework introduces an improved dual-stage fixed-time performance function converging sequentially to a baseline trajectory and a final bound at designated times, effectively accommodating large initial errors. To optimize network communication, a distributed hop-optimized breadth-first search algorithm establishes an optimal subgraph based on minimum hops and maximum signal strength, reducing communication burden while ensuring reliability. Furthermore, an error-based cooperative learning controller strictly confines system errors within predefined boundaries. Its cooperative learning law leverages error-weighted estimates from neighbors to enhance adaptive estimation accuracy against unknown disturbances. Finally, numerical simulations and hardware experiments demonstrate the proposed framework's effectiveness.", "url": "" }, { "id": "Mo-MoA29.6", "code": "MoA29.6", "title": "Curriculum Learning-Enhanced RL Trajectory Planning for Radar Avoidance", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA29", "sessionTitle": "Learning and Adaptation in Autonomous Vehicles", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Guoquan, Tang", "affiliation": "Beijing Institute of Technology" }, { "name": "Li, Zhuo", "affiliation": "Beijing Institute of Technology" }, { "name": "Wu, Chu-ge", "affiliation": "Beijing Institute of Technology" }, { "name": "Wang, Jingjing", "affiliation": "Beijing University of Technology" }, { "name": "Sun, Jian", "affiliation": "Beijing Institute of Technology" } ], "keywords": [ "Trajectory and path planning for AVs", "Autonomous vehicles", "Learning and adaptation in autonomous vehicles" ], "abstract": "This work investigates a trajectory planning problem for autonomous vehicles to avoid being detected by a number of radars and to reach a desired position as soon as possible. We formulate the problem as a time-optimal control problem with the constraints of the vehicle's cumulative probability of being detected and its dynamical model. Given the non-convex characteristic and functional constraints inherent to the problem, it is challenging to derive a globally optimal solution. To overcome it, we employ the framework of reinforcement learning (RL) to train an adaptive trajectory planning policy respective to scenarios with different numbers of the radars. Furthermore, we incorporate the idea of curriculum learning by gradually increasing the complexity of radars' detection, and to enhance the efficiency of training samples. Finally, simulations are conducted to compare the curriculum learning-enhanced RL policy and a nonlinear programming numerical solver, which demonstrate the effectiveness and superiority of the proposed method.", "url": "" }, { "id": "Mo-MoA30.1", "code": "MoA30.1", "title": "Review of Digital Technologies Aimed at Changing the Lifestyle of Young People Aged 15-25 with the Goal of Primary Cancer Prevention: Literature Review and Research Agenda (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA30", "sessionTitle": "Digital Technologies for Healthy Ageing and Social Inclusion", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Pivka, Jurij", "affiliation": "Alma Mater Europaea Https: //en.almamater.si" }, { "name": "Šabeder, Renata", "affiliation": "University Alma Mater Europaea Slovenia" }, { "name": "Bogataj, David", "affiliation": "Alma Mater Europaea University" } ], "keywords": [ "Diversity and inclusion in digital culture", "Digital culture", "Control and automation to improve social and political stability" ], "abstract": "Non-communicable diseases, especially cancer, are the leading cause of premature mortality worldwide. The key period for shaping behavioural patterns that influence cancer risk is adolescence and early adulthood. The proliferation of digital technologies has enabled new, flexible and cost-effective ways to deliver behaviour change interventions to this population. This review article critically assesses different types of digital interventions – such as text messages, mobile applications, online e-learning platforms, photo-aging applications, and wearable devices—and their effectiveness in promoting cancer-preventive behaviors among young people aged 15 to 25. It discusses their behavioral mechanisms, advantages, limitations, and design suggestions. Despite promising findings, many interventions suffer from a rapid decline in engagement and a lack of long-term evidence of effectiveness. The article concludes by highlighting the importance of using theoretical frameworks, involving young people in the design process, and the need for long-term studies to strengthen the research agenda in this area.", "url": "" }, { "id": "Mo-MoA30.2", "code": "MoA30.2", "title": "Urban Facility Management for Livable and Inclusive Public Spaces Supporting Older Adults in Smart Cities (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA30", "sessionTitle": "Digital Technologies for Healthy Ageing and Social Inclusion", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Mirjalali, Azam", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Gohari, Savis", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Temeljotov Salaj, Alenka", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Johansen, Agnar", "affiliation": "Norwegian University of Science and Technology" } ], "keywords": [ "Smart city design and planning", "Cost-effective operation and maintenance", "Social networks for smart cities" ], "abstract": "Population ageing and expanding digitalization have made ensuring inclusive public spaces increasingly complex. This paper examines how Urban Facility Management (Urban FM) supports age-friendly environments in smart urban districts through a case study of Treklang in Bærum municipality, Norway. Based on document analysis, interviews, field observations, and stakeholder discussions, the study finds that Urban FM functions as the socio-technical integrator mediating between Age-Friendly City objectives and Smart City systems across strategic, tactical, and operational levels. Findings indicate that inclusive smart-ageing districts depend less on technological sophistication and more on sustained Urban FM capacity to coordinate governance, technology, and everyday operations.", "url": "" }, { "id": "Mo-MoA30.3", "code": "MoA30.3", "title": "Digital Support for Ageing Populations: A Community-Based Approach to Mobility and Social Inclusion (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA30", "sessionTitle": "Digital Technologies for Healthy Ageing and Social Inclusion", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Adhikari, Aashish", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Diaconu, Mara-Gabriela", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Johansen, Agnar", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Temeljotov Salaj, Alenka", "affiliation": "Norwegian University of Science and Technology" } ], "keywords": [ "Smart city design and planning", "Social transportation and social energy", "Smart city security and resilience" ], "abstract": "Despite global commitments such as the UN’s initiative on the rights of older people and the Sustainable Development Goal (SDGs) highlighting challenges about mobility, housing, and social participation, older adults continue to face social isolation, fragmented services, and increasing technological barriers. Winter conditions in Norway intensify these challenges, contributing to reduced mobility, fall-related accidents, and declining overall well-being. This study explores how digital platforms can support safer mobility and social inclusion for older adults. Through an Experts in Teamwork course at NTNU, a platform mockup “VandreVenner” (English: Walking Buddies) was developed, to connect older adults with student volunteers who accompany them on walks. Surveys conducted among seniors and students in Trondheim indicate strong interest and willingness to participate, suggesting scalable potential for enhancing mobility, safety, and inclusion.", "url": "" }, { "id": "Mo-MoA30.4", "code": "MoA30.4", "title": "The Impact of VR Exercises on Physical Function and Fall Prevention in Older Adults: Literature Review and Research Agenda (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA30", "sessionTitle": "Digital Technologies for Healthy Ageing and Social Inclusion", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Šantek-Zlatar, Gordana", "affiliation": "University Alma Mater Europaea , Maribor" }, { "name": "Friščić, Marina", "affiliation": "Alma Mater Europaea Maribor Slovenia" }, { "name": "Bogataj, David", "affiliation": "Alma Mater Europaea University" } ], "keywords": [ "Social computing", "Digital culture", "Cyber-physical and human systems (CPHS)" ], "abstract": "The quality of life of older adults depends on physical function, independence in self-care activities, psychological well-being, and the prevention of injuries and falls, which represent an important public health and eldercare challenge. There are various exercise programs that can reduce the risk of falls, but long-term sustainability in exercise programs is limited by monotony, weakened motivation, and insufficient adaptation of exercise for the elderly population. A new approach to exercising older adults using virtual reality (VR) is promising because it allows for guided, motivating, interactive exercises with the possibility of feedback on progress. This paper reviews the literature and research that deals with the impact of VR exercises on physical function and fall prevention in older adults. The literature was searched in the PubMed and Web of Science databases, and 14 scientific papers were selected for analysis according to scientific quality and thematic rating. The reviewed research shows that VR exercises, in addition to improving balance, mobility, lower extremity strength, and greater functional capacity, increase motivation and safety in movement, and contribute to reducing the fear of falling and injuries. They may also contribute to reducing fall incidents and injuries, which represents an important finding for fall-prevention practice. From the perspective of automation and control, VR exercises can be understood as a technological system that connects human movement with sensor-based feedback, task adaptation and interaction between users and technology. The paper identifies research gaps related to the usability and application of VR exercises in the home environment, particularly long-term effectiveness, protocol standardization and accessibility. Future research should focus on longitudinal studies with the development of VR systems for fall prevention with age-appropriate VR exercise equipment and standardized protocols in this area. Keywords: VR exercise, fall prevention, older adults, human-in-the-loop systems, quality of life", "url": "" }, { "id": "Mo-MoA30.5", "code": "MoA30.5", "title": "Group-Based Physical Activity As a Catalyst for Social Interaction, Psychological Well-Being, and Functional Health in Long-Term Care: A Comprehensive Narrative Review (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA30", "sessionTitle": "Digital Technologies for Healthy Ageing and Social Inclusion", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Končan Marinček, Mojca", "affiliation": "Alma Mater Europaea" }, { "name": "Bogataj, David", "affiliation": "Alma Mater Europaea University" } ], "keywords": [ "Social networks for smart cities" ], "abstract": "Social isolation and loneliness are critical public health concerns among institutionalized older adults. Group-based physical activity has emerged as a promising intervention capable of simultaneously improving physical function, psychosocial well-being, and social engagement in long-term care (LTC) environments. This narrative review synthesizes evidence from 20 peer-reviewed studies published between 2015 and 2025 to evaluate how physiotherapist-led group exercise influences social connectedness, emotional health, and group cohesion. Findings indicate that structured group activity enhances communication, interpersonal trust, positive affect, motivation, and sense of belonging, while reducing loneliness and depressive symptoms. Mechanisms include rhythmic synchrony, peer encouragement, shared goals, and the mediating role of the physiotherapist as a facilitator of supportive group climates. Implications for physiotherapy practice and future research directions are provided. A PRISMA-informed methodology guided study selection. A literature quality appraisal and thematic analysis accompany the review", "url": "" }, { "id": "Mo-MoA30.6", "code": "MoA30.6", "title": "Exploring the Relationship between Technological Advancement, Technostress, and Aging in Urban Settings: Literature Review (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA30", "sessionTitle": "Digital Technologies for Healthy Ageing and Social Inclusion", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Rotovnik Omerzu, Ana", "affiliation": "Alma Mater Europea" }, { "name": "Horvat Grilanc, Sanja", "affiliation": "Alma Mater Europaea University" }, { "name": "Mežnarec-Novosel, Suzanna", "affiliation": "University Alma Mater Europaea" } ], "keywords": [ "Social networks for smart cities" ], "abstract": "Introduction Population aging increases reliance on digital technologies in urban environments, where older adults often face technostress, technological anxiety and digital exclusion. Methods A literature review was conducted across APA PsycARTICLES, PubMed and Web of Science (2020–2025). Twenty-seven eligible studies were thematically analysed across three domains. Results Technological anxiety still remains a major barrier to technology adoption, shaped by digital skills, confidence and contextual demands. At the same time, well-designed digital solutions, from communication tools to health and rehabilitation technologies, can reduce loneliness, support emotional wellbeing and enhance social connectedness. Smart-city infrastructures show mixed effects, acting as stressors or supportive environments depending on usability and accessibility. Conclusion Technology benefits older adults only when it is accessible, adaptable and supported. Age-friendly digitalisation and inclusive urban design are essential to reduce stress, prevent exclusion and strengthen wellbeing, providing a basis for strategies and policies that promote digital inclusion among older adults.", "url": "" }, { "id": "Mo-MoA32.1", "code": "MoA32.1", "title": "CASTLE: Concurrent Attention-Based Student-Teacher Learning with Exteroceptive Perception", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA32", "sessionTitle": "Humanoid and Legged Robots", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Kim, Minjae", "affiliation": "Postech" }, { "name": "Han, Soohee", "affiliation": "Pohang University of Science and Technology" } ], "keywords": [ "AI-powered robotics", "Robotic learning and adaptation", "Humanoid and legged robots" ], "abstract": "Robust perceptive locomotion in unstructured environments demands tightly integrated proprioceptive and exteroceptive sensing. Recently, Reinforcement Learning (RL) for locomotion has increasingly adopted a student-teacher learning framework in which a teacher is trained with privileged information and its behavior is later distilled into a separate student policy. However, this decoupling adds suboptimality and deployment overhead. We present CASTLE (Concurrent Attention-based Student–Teacher Learning with Exteroceptive Perception), which trains the student and teacher simultaneously to produce a single deployable policy. This joint training enables efficient and robust locomotion in simulation, resulting in reliable, high-speed traversability across diverse and challenging terrains.", "url": "" }, { "id": "Mo-MoA32.2", "code": "MoA32.2", "title": "Global Path Planner with Multi-Model Switching", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA32", "sessionTitle": "Humanoid and Legged Robots", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Gori, Pietro", "affiliation": "University of Pisa" }, { "name": "Iotti, Francesco", "affiliation": "University of Pisa" }, { "name": "Zelenay, Eduard", "affiliation": "Slovak University of Technology" }, { "name": "Marko, Rastislav", "affiliation": "Panza Robotics" }, { "name": "Pierallini, Michele", "affiliation": "University of Pisa" }, { "name": "Angelini, Franco", "affiliation": "University of Pisa" }, { "name": "Pannocchia, Gabriele", "affiliation": "University of Pisa" }, { "name": "Garabini, Manolo", "affiliation": "University of Pisa" } ], "keywords": [ "Autonomous navigation", "Task and motion planning", "Humanoid and legged robots" ], "abstract": "This work enhances global path planning via a pure‑pursuit controller with multi‑model kinematic switching that sustains plan fidelity across diverse terrains. The system includes a traversability graph for terrain analysis, a Heading-Aware A* algorithm generating feasible paths, and a multi-model Pure Pursuit controller for dynamic tracking. A core innovation is adaptive kinematic modeling, enabling real-time switching between kinematic models based on terrain features and robot states. This adaptability optimizes path efficiency and energy use in challenging scenarios. We validate the approach in simulation on different platforms, namely, Artaban quadruped and X3 quadrotor drone, showcasing improved performance, robustness, and adaptability over standard baselines.", "url": "" }, { "id": "Mo-MoA32.3", "code": "MoA32.3", "title": "Depth-Attentive Quadrupedal Locomotion Via Proprioception-Guided Cross Attention", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA32", "sessionTitle": "Humanoid and Legged Robots", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Kim, Mincheol", "affiliation": "Korea Advanced Institute of Science and Technology (KAIST)" }, { "name": "Song, Gyuhyeun", "affiliation": "University of Seoul" }, { "name": "Noh, Sitae", "affiliation": "Hongik University" }, { "name": "Park, Cheolmin", "affiliation": "KAIST" }, { "name": "Myung, Hyun", "affiliation": "KAIST" } ], "keywords": [ "Humanoid and legged robots", "AI-powered robotics", "Robotic learning and adaptation" ], "abstract": "Quadruped robots struggle to perform highly dynamic behaviors due to the kinematic limitations of rigid-body designs. We introduce a biomimetic two-DoF flexible spine that expands mobility but requires predictive terrain awareness beyond proprioception-only control. To address this, we propose proprioception-guided visual attention (PGVA), which uses proprioception as a query to extract task-relevant information from depth images. PGVA resolves sensor-fusion ambiguity and enhances perception efficiency. Integrated with the flexible spine, our approach enables agile, terrain-aware locomotion across challenging environments.", "url": "" }, { "id": "Mo-MoA32.4", "code": "MoA32.4", "title": "Realizing Four Walking Modes in an Almost Linear Biped Robot Via Unified Modeling and Control", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA32", "sessionTitle": "Humanoid and Legged Robots", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "He, Yuetong", "affiliation": "JAIST" }, { "name": "Sedoguchi, Taiki", "affiliation": "Japan Advanced Institute of Science and Technology" }, { "name": "Asano, Fumihiko", "affiliation": "Japan Advanced Institute of Science and Technology" } ], "keywords": [ "Humanoid and legged robots", "Degree of automation", "Variable autonomy" ], "abstract": "This paper presents a unified modeling and control framework that enables a single legged robot with knee joints to realize four distinct gaits: biological–human, biological–bird, wheel–human, and wheel–bird. All modes share an almost linear dynamic model and a common control law, differing only in nominal configuration parameters and the swing-leg rotation direction. Simulations demonstrate stable limit-cycle walking in all modes and show that smooth gait transitions can be achieved by adjusting only a few parameters.", "url": "" }, { "id": "Mo-MoA32.5", "code": "MoA32.5", "title": "A Compliant Ankle-Actuated Compass Walker with Triggering Timing Control", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA32", "sessionTitle": "Humanoid and Legged Robots", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Kerimoglu, Deniz", "affiliation": "Georgia Institute of Technology" }, { "name": "Uyanik, Ismail", "affiliation": "Hacettepe University" } ], "keywords": [ "Humanoid and legged robots", "Mechatronic system estimation, identification, control", "Biomedical and biomimetic mechatronic systems" ], "abstract": "Passive dynamic walkers are widely adopted as a mathematical model to represent biped walking. The stable locomotion of these models is limited to tilted surfaces, requiring gravitational energy. Various techniques, such as actuation through the ankle and hip joints, have been proposed to extend the applicability of these models to level ground and rough terrain with improved locomotion efficiency. However, most of these techniques rely on impulsive energy injection schemes and torsional springs, which are quite challenging to implement in a physical platform. Here, a new model is proposed, named triggering controlled ankle actuated compass gait (TC-AACG), which allows non-instantaneous compliant ankle pushoff. The proposed technique can be implemented in physical platforms via series elastic actuators (SEAs). Our systematic examination shows that the proposed approach extends the locomotion capabilities of a biped model compared to impulsive ankle pushoff approach. We provide extensive simulation analysis investigating the locomotion speed, mechanical cost of transport, and basin of attraction of the proposed model.", "url": "" }, { "id": "Mo-MoA32.6", "code": "MoA32.6", "title": "Self-Triggered MPC Framework for Balance Recovery of Biped Robots with Guarantee of Recursive Feasibility", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA32", "sessionTitle": "Humanoid and Legged Robots", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Kim, Junsoo", "affiliation": "University of Seoul" }, { "name": "Park, Gyunghoon", "affiliation": "University of Seoul" }, { "name": "Tazaki, Yuichi", "affiliation": "Kobe University" } ], "keywords": [ "Humanoid and legged robots", "Mechatronic system estimation, identification, control", "Task and motion planning" ], "abstract": "This paper addresses push recovery problem for a biped robot in the sagittal plane in the presence of instantaneous external force. In particular, we aim to present an unified framework for simultaneously planning of not only future trajectories of zero moment point (ZMP) and center of mass (CoM) but also future footprints. For this purpose, a self-triggered model predictive control (ST-MPC) comes into the picture, which computes an optimal control problem only at triggering moments that are associated with the stepping time. The present ST-MPC-based approach has a merit of theoretically guaranteeing recursive feasibility and thus ability of balance recovery, for which the terminal set and cost of the ST-MPC needs to be properly selected. We verify the validity of the ST-MPC with both theoretical result and simulation.", "url": "" }, { "id": "Mo-MoA33.1", "code": "MoA33.1", "title": "A DDQN-Based Lane-Changing Decision-Making Method for Mixed Vehicle Flow in Bottleneck Areas (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA33", "sessionTitle": "Cooperative Control for Intelligent Connected Vehicles and Transportation", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Tan, Yufeng", "affiliation": "Chongqing University of Posts and Telecommunications" }, { "name": "Zhao, Hang", "affiliation": "Chongqing University of Posts and Telecommunications" }, { "name": "Li, Yongfu", "affiliation": "Chongqing University of Posts and Telecommunications" } ], "keywords": [ "Social transportation and social energy", "Smart city control and optimization", "Decision making under uncertainty" ], "abstract": "Effective lane-changing decision methods can alleviate congestion in bottleneck areas. In the field of connected and autonomous vehicles (CAVs), deep reinforcement learning (DRL) presents a novel approach to solving this problem, leveraging its superior perception and decision-making capabilities. However, existing DRL-based lane-changing decision methods are confined to the microscopic level, focusing on optimizing the traffic efficiency of individual vehicles while neglecting the overall coordination of traffic flow in bottleneck areas. To this end, this study proposes a DRL-based lane-changing decision method integrating macro and micro perspectives. Specifically, a traffic wave velocity model is incorporated to perceive macro-level congestion propagation. Additionally, a composite reward function is designed to improve overall traffic efficiency while also accounting for individual vehicle speed benefits and driving safety. Experimental results demonstrate that the proposed lane-changing decision method can increase mean travel speed by up to 27.6% and reduce maximum queue length by up to 722 meters.", "url": "" }, { "id": "Mo-MoA33.2", "code": "MoA33.2", "title": "Attention-Based Multi-Agent Control for Autonomous Veshicles Platooning in Mixed Traffic with Enhanced MAPPO and Predictive Rewards (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA33", "sessionTitle": "Cooperative Control for Intelligent Connected Vehicles and Transportation", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Yang, Yang", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Zhang, Yifan", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Xu, Yunwen", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Li, Dewei", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Decision making under uncertainty", "Smart city control and optimization", "AI for smart cities" ], "abstract": "Highway mixed traffic scenarios composed of autonomous vehicles (AVs) and human-driven vehicles (HDVs) face prominent challenges in vehicle formation control, along with insufficient robustness against HDV behavior randomness. To address these issues, this paper proposes a multi-agent formation control method based on an improved Multi-Agent Proximal Policy Optimization (MAPPO). A core innovation is the integration of learnable AV embeddings and an attention structure, which enables centralized training and distributed decision-making for multiple AVs to achieve collaborative longitudinal-lateral control. To adapt to the multiobjective formation requirements and tackle HDV randomness, a multi-dimensional reward function covering formation consistency, travel efficiency, and safety is constructed, integrating HDV real-time trajectory prediction to generate future rewards. Comparative experiments on the simulation platform show that the proposed method improves formation efficiency and significantly reduces computational latency while ensuring safety, verifying its effectiveness and engineering application potential in mixed traffic scenarios.", "url": "" }, { "id": "Mo-MoA33.3", "code": "MoA33.3", "title": "Robust DMPC for Multi-Agent Consensus Over General Directed Graphs: An Inverse Optimal Consensus Approach (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA33", "sessionTitle": "Cooperative Control for Intelligent Connected Vehicles and Transportation", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Zhang, Wencong", "affiliation": "Zhejiang University of Technology" }, { "name": "He, De-feng", "affiliation": "Zhejiang University of Technology" }, { "name": "Shi, Yang", "affiliation": "University of Victoria" } ], "keywords": [ "Mentoring in control engineering", "Decision making under uncertainty" ], "abstract": "This paper addresses the consensus problem of linear multi-agent systems over a general directed communication graph and develops a robust distributed model predictive control (DMPC) strategy. First, a pre-designed consensus protocol is developed for unconstrained systems based on inverse optimal control theory to achieve leader-following consensus. Building upon this protocol, a robust DMPC algorithm is designed to handle neighboring prediction errors. Moreover, the recursive feasibility of the proposed DMPC algorithm is established, and the closed-loop system is guaranteed to asymptotically achieve leader-following consensus. Finally, simulations are presented to demonstrate the effectiveness of the proposed strategy.", "url": "" }, { "id": "Mo-MoA33.4", "code": "MoA33.4", "title": "Delay-Dependent Dissipative Analysis of Load Frequency Control for Power Systems with Wind Power and Electric Vehicles", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA33", "sessionTitle": "Cooperative Control for Intelligent Connected Vehicles and Transportation", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Huang, HongJian", "affiliation": "China University of Geosciences (Wuhan)" }, { "name": "Wang, Hong-Zhang", "affiliation": "China University of Geosciences, Wuhan" }, { "name": "Yuan, Zhe-Li", "affiliation": "China University of Geosciences" }, { "name": "Zhang, Chuan-Ke", "affiliation": "China University of Geosciences" }, { "name": "Dong, Kai-Feng", "affiliation": "China University of Geosciences" }, { "name": "Jin, Fang", "affiliation": "China University of Geosciences" } ], "keywords": [ "Cyber-physical urban systems", "Social transportation and social energy", "Urban energy distribution systems" ], "abstract": "This paper is concerned with the delay-dependent dissipative analysis of load frequency control (LFC) with wind power and electric vehicles (EVs). Firstly, a structure of LFC equiped with a time delay PI controller is proposed, incorporating EVs participating in frequency regulation and wind power output. Secondly, a less conservative dissipative analysis criterion of LFC related time delay is obtained by utilizing a matrix-injection method, such that the dissipative index and time-varying delay boundary can be calculated. Finally, the anti-interference ability of proposed model is analysis and the larger time delay boundary values are obtained. Thus results are verified by a case study.", "url": "" }, { "id": "Mo-MoA33.5", "code": "MoA33.5", "title": "Large-Scale EV/FCEV Charging Hub Coordination: A Scalable Hierarchical Reinforcement Learning Method", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA33", "sessionTitle": "Cooperative Control for Intelligent Connected Vehicles and Transportation", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Tian, Zhaoming", "affiliation": "Xi'an Jiaotong University" }, { "name": "Cao, Xiaoyu", "affiliation": "Xi'an Jiaotong University" }, { "name": "Guan, Xiaohong", "affiliation": "Xi'an Jiaotong University" } ], "keywords": [ "Social transportation and social energy", "AI for smart cities", "Smart city control and optimization" ], "abstract": "Hybrid charging hubs that jointly serve Electric Vehicles (EVs) and Fuel Cell Electric Vehicles (FCEVs) have emerged as an economically promising option for fueling next-generation FCEV fleets. However, coordinating them at scale is computationally prohibitive, as complex cross-hub traffic interactions induce an extremely high-dimensional decision space. To address this challenge, we propose a hierarchical reinforcement learning (HRL) method for large-scale hub coordination. Leveraging the natural system hierarchy, we model the problem as a leaderfollower Markov Decision Process (LF-MDP) and introduce the Goal-Explicit Reward Problem (GERP). GERP theoretically decouples the learning processes of the leader and followers, enabling efficient parallel training. We further implement a priority-based intra-hub allocation scheme to make the coordination problem compatible with GERP and thus enabling fully decoupled policy learning. Case studies demonstrate that our method successfully scales to large-scale scenario while maintaining high convergence and control performance.", "url": "" }, { "id": "Mo-MoA33.6", "code": "MoA33.6", "title": "Accelerating Time-Optimal Trajectory Planning for Connected and Automated Vehicles with Graph Neural Networks", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA33", "sessionTitle": "Cooperative Control for Intelligent Connected Vehicles and Transportation", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Le, Viet-Anh", "affiliation": "University of Pennsylvania" }, { "name": "Malikopoulos, Andreas", "affiliation": "Cornell University" } ], "keywords": [ "Automatic control, optimization, real-time operations in transportation", "AI and learning-based control for automotive systems", "Trajectory and path planning for AVs" ], "abstract": "In this paper, we present a learning-based framework that accelerates time- and energy-optimal trajectory planning for connected and automated vehicles (CAVs) using graph neural networks (GNNs). We formulate the multi-agent coordination problem encountered in traffic scenarios as a cooperative trajectory planning problem that minimizes travel time, subject to motion primitives derived from energy-optimal solutions. The performance of this framework can be further improved through replanning at each time step, enabling the system to incorporate newly observed information.To achieve real-time execution, we employ a graph isomorphism network with edge features (GINEConv) to learn the solutions of the time-optimal trajectory planning problem from offline-generated data. The trained model produces online predictions that serve as warm-starts for numerical optimization, thereby enabling rapid computation of minimal exit times and the associated feasible trajectories. This learning-to-warm-start approach substantially reduces computation time while preserving the control performance of the time- and energy-optimal trajectory planning framework.", "url": "" }, { "id": "Mo-MoA34.1", "code": "MoA34.1", "title": "Optimal Control Synthesis of Closed-Loop Recommendation Systems Over Social Networks (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA34", "sessionTitle": "Cyber-Physical-Human Systems: From Individual Empowerment to Societal Impact I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Mariano, Simone", "affiliation": "GIPSA-Lab, CNRS, Grenoble" }, { "name": "Frasca, Paolo", "affiliation": "CNRS, GIPSA-Lab, Grenoble" } ], "keywords": [ "Social networks and opinion dynamics", "System dynamics and control in CPHS", "Responsible automation" ], "abstract": "This paper addresses the problem of designing recommendation systems for social networks and e-commerce platforms from a control-theoretic perspective. We treat the design of recommendation systems as a state-feedback infinite-horizon optimal control problem with a performance index that (i) rewards alignment/engagement, (ii) penalizes polarization and large deviations from an uncontrolled baseline, and (iii) regularizes exposure across neighboring users. The recommendation entries are fed to the platform users, who are assumed to follow a networked, multi-topic, continuous-time opinion dynamics. We show that the designed control yields a stabilizing recommendation system under simple algebraic/spectral conditions on the weights that encode the platform’s preference for engagement, stability of preferences, polarization, and cross-user diversity. Conversely, we show that when ill-posed weights are selected in the optimal control problem (namely, when engagement is excessively rewarded), the closed-loop system can exhibit destabilizing, pathological behaviors that conflict with the design objectives.", "url": "" }, { "id": "Mo-MoA34.2", "code": "MoA34.2", "title": "Trust in the Friedkin-Johnsen Model: Incentives under Partial Information (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA34", "sessionTitle": "Cyber-Physical-Human Systems: From Individual Empowerment to Societal Impact I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Grünter, Philipp", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Fontan, Angela", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Social networks and opinion dynamics" ], "abstract": "Designing incentives in social networks is a challenging problem, including the limited availability of resources for influencing the adoption of desired behaviors and the need to account for the level of trust that individual agents place in policy-makers. The challenge is further exacerbated when the underlying network structure and social dynamics are only partially known. Existing work typically assumes perfect knowledge of the network, which is rarely available in practice. We extend the Friedkin-Johnsen opinion dynamics model by incorporating a trust mechanism and study incentive design under partial information. Our results show that effective incentives can be designed even under partial information, and that targeted interventions consistently outperform naive broadcasting strategies under the same budget constraints. These findings highlight the importance of trust-aware and network-sensitive interventions in real-world policy design.", "url": "" }, { "id": "Mo-MoA34.3", "code": "MoA34.3", "title": "On a Co-Evolving Opinion-Leadership Model in Social Networks (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA34", "sessionTitle": "Cyber-Physical-Human Systems: From Individual Empowerment to Societal Impact I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Alutto, Martina", "affiliation": "Politecnico Di Torino" }, { "name": "Zino, Lorenzo", "affiliation": "Politecnico Di Torino" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Fontan, Angela", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Social networks and opinion dynamics" ], "abstract": "Leadership in social groups often emerges dynamically from interactions and opinion exchange. Empirical evidence suggests that individuals with strong opinions tend to gain influence, while maintaining alignment with the social context is crucial for sustained leadership. Motivated by the social psychology literature that supports these empirical observations, we propose a novel dynamical system in which opinions and leadership co-evolve within a social network. Our model extends the Friedkin-Johnsen framework by making susceptibility to peer influence time-dependent, turning it into the leadership variable. Leadership strengthens when an agent holds strong yet socially aligned opinions, and declines when such alignment is lost, capturing the trade-off between conviction and social acceptance. We formally analyze the coupled dynamics, establishing sufficient conditions for convergence to a non-trivial equilibrium, and examining two time-scale separation regimes reflecting scenarios where opinion and leadership evolve at different speeds.", "url": "" }, { "id": "Mo-MoA34.4", "code": "MoA34.4", "title": "Fairness-Aware Design of Nudging Policies under Stochasticity and Prejudices (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA34", "sessionTitle": "Cyber-Physical-Human Systems: From Individual Empowerment to Societal Impact I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Piccinin, Lisa", "affiliation": "Politecnico Di Milano" }, { "name": "Quaresmini, Camilla", "affiliation": "Politecnico Di Milano" }, { "name": "Vitale, Edoardo", "affiliation": "Politecnico Di Milano" }, { "name": "Tanelli, Mara", "affiliation": "Politecnico Di Milano" }, { "name": "Breschi, Valentina", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Social networks and opinion dynamics" ], "abstract": "In this work, we present an injustice-aware innovation-diffusion model that extends the Generalized Linear Threshold framework to capture the stochastic nature of adoption shaped by inequalities. Because incentive policies can inadvertently amplify these inequalities, we build on this model to design a fair Model Predictive Control (MPC) scheme that incorporates equality and equity objectives for incentive allocation. Simulations using real mobility-habit data show that injustice reduces overall adoption, while equality smooths the distribution of incentives, and equity reduces disparities in final outcomes. These results show that including fairness ensures effective diffusion without exacerbating existing social inequities.", "url": "" }, { "id": "Mo-MoA34.5", "code": "MoA34.5", "title": "On a Coupled Adoption-Opinion Framework for Competing Innovations (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA34", "sessionTitle": "Cyber-Physical-Human Systems: From Individual Empowerment to Societal Impact I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Alutto, Martina", "affiliation": "Politecnico Di Torino" }, { "name": "Dabbene, Fabrizio", "affiliation": "CNR" }, { "name": "Fontan, Angela", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Ravazzi, Chiara", "affiliation": "National Research Council of Italy (CNR)" } ], "keywords": [ "Social networks and opinion dynamics" ], "abstract": "In this paper, we propose a two-layer adoption-opinion model to study the diffusion of two competing technologies within a population whose opinions evolve under social influence and adoption-driven feedback. After adopting one technology, individuals may become dissatisfied and switch to the alternative. We prove the existence and uniqueness of the adoption-diffused equilibrium, showing that both technologies coexist and no monopoly scenario can arise. Numerical simulations show that while opinions shape the equilibrium adoption levels, the relative market share between the two technologies depends solely on their user-experience. As a consequence, interventions that symmetrically boost opinions or adoption can disproportionately favor the higher-quality technology, illustrating how symmetric control actions may generate asymmetric outcomes.", "url": "" }, { "id": "Mo-MoA34.6", "code": "MoA34.6", "title": "Susceptibility Optimization and the Wisdom of Crowds in Influence Networks (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA34", "sessionTitle": "Cyber-Physical-Human Systems: From Individual Empowerment to Societal Impact I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Tian, Ye", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Sheng, Anzhi", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Fontan, Angela", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Wang, Long", "affiliation": "Peking Univ" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Social networks and opinion dynamics", "Human-centric automation/AI Systems, and human agency", "Cyber-physical and human systems (CPHS)" ], "abstract": "This paper studies how to maximize the wisdom of crowds in social networks by shaping individual susceptibilities. The problem is formulated as a nonlinear, nonconvex optimization over the Friedkin-Johnsen opinion dynamics, in which individuals' susceptibilities are tuned to minimize the collective estimation bias. Exploiting the intrinsic social power structure of the opinion dynamics, we propose the sequential social power method, which first computes an optimal social power allocation via a quadratic program, and then recovers all susceptibility vectors that yield this allocation. We prove that, if the influence network is strongly connected, these optimal susceptibility vectors can be obtained in closed form using the Laplacian pseudoinverse and the dominant left eigenvector of the influence matrix. Numerical simulations and application examples illustrate our theoretical results.", "url": "" }, { "id": "Mo-MoA35.1", "code": "MoA35.1", "title": "GenAI in Institutions of Higher Education from Values and Good Practices to a Systematic Change Management? (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA35", "sessionTitle": "Human‑Centric AI: Ethics, Leadership, and Systemic Transformation", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Doyle-Kent, Mary", "affiliation": "South East Technological University" }, { "name": "Dr. Jesse, Norbert", "affiliation": "Dataciders GmbH" }, { "name": "Farrell, Hazel", "affiliation": "SETU" }, { "name": "O'Neill, Brenda", "affiliation": "South East Technological University, Waterford" }, { "name": "Organ, John", "affiliation": "South East Technological University, Ireland" } ], "keywords": [ "Generative AI in control education", "Control engineering curricula", "Digital culture" ], "abstract": "Generative artificial intelligence (GenAI) represents a profoundly disruptive technological development, presenting both significant opportunities and complex challenges for companies and higher education institutions (HEIs). HEIs, and their faculties in particular, carry a growing responsibility to critically evaluate the role of GenAI within educational contexts. This evaluation operates across two key dimensions: first, GenAI as an operational and managerial tool; and second, GenAI-related knowledge and competencies as essential components of contemporary education and graduate employability. While research follows its own rules, the implications for the modernization of curricula development are far from being clear. The authors outline the disruptive impact of GenAI for companies’ and HEIs and assess guidelines for GenAI at Irish and German HEIs. Finally, they draw conclusions for further analysis.", "url": "" }, { "id": "Mo-MoA35.2", "code": "MoA35.2", "title": "AI Problems and Threats: Human Rights and Human Centred Solutions (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA35", "sessionTitle": "Human‑Centric AI: Ethics, Leadership, and Systemic Transformation", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "O'Neill, Brenda", "affiliation": "South East Technological University, Waterford" }, { "name": "Hersh, Marion A.", "affiliation": "University of Glasgow" } ], "keywords": [ "Human-centric automation/AI Systems, and human agency", "Engineering ethics in control and AI", "Diversity and inclusion in digital culture" ], "abstract": "Artificial intelligence (AI) increasingly affects all aspects of society. It includes the use of algorithms on big data and AI tool forms such as agentic and generative AI. The EU AI Act and Convention were introduced recently - in 2024. Data bias disadvantages already marginalised groups, and there may be limited ability to contest (unjust) AI decisions. This results in ‘slow violence’, the slow grinding attrition of human rights. There has been an increase in the form of technologies which use AI e.g., types of smart doorbell and dashboard footage which effect privacy. On a larger scale democracy too is affected by AI as it brings a strong element of power to those who create it and to those who excel in its use as countries race to become leaders in this area. Use of AI involves high energy consumption which threatens moves to net zero. Education on AI and its risks, ethics and regulation tied to human rights are required for the protection of society. Most potential solutions are ‘top down’ approaches. For instance, The EU AI Act and Convention were introduced in 2024. More women and members of marginalised groups need to engage in research and decision making in relation to AI. This paper suggests a ‘moratorium’ on AI use in the short term to enable a just transition so that the planet and all of its inhabitants can benefit. This paper speaks to key controversies and debates in the interaction between automation and control fields, and AI and society.", "url": "" }, { "id": "Mo-MoA35.3", "code": "MoA35.3", "title": "Exploring the Role of Intelligent Control System Automation in Achieving Leadership Objectives within an Irish Financial Institution (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA35", "sessionTitle": "Human‑Centric AI: Ethics, Leadership, and Systemic Transformation", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Byrne, Darren", "affiliation": "South East Technological University" }, { "name": "Tuite, Aisling", "affiliation": "South East Technological University" }, { "name": "Organ, John", "affiliation": "South East Technological University, Ireland" } ], "keywords": [ "Industrial and service applications of AI and intelligent automation", "Control and automation to improve social and political stability", "Engineering ethics in control and AI" ], "abstract": "Amid pervasive global uncertainty and recent disruptions to international stability, control systems have become increasingly central to shaping the trajectory of global development. This paper examines how leadership approaches in the Irish financial sector respond to such uncertainty by fostering ethical innovation, embracing human-centred systems and intelligent control system automation, and promoting inclusive socio-technical transformation. The research design combines insider research, auto-ethnography, and reflexive practices to enable rigorous analysis of rich interview and ethnographic data. Findings highlight how technical professionals in Ireland’s financial industry deploy intelligent control system automation to advance leadership objectives pertaining to risk management, particularly in maintaining stability for the critical interface between banking institutions and their customers. This includes ensuring the reliability of digital platforms (such as online forms), by assessing the potential risks of control system failure and the subsequent impact from an end-user perspective, the financial institution in question and wider society. Ultimately, this paper demonstrates how the adoption of human-centred systems and intelligent control system automation contributes to the achievement of leadership objectives by sustaining stability within the complex digital ecosystem of a leading Irish financial institution.", "url": "" }, { "id": "Mo-MoA35.4", "code": "MoA35.4", "title": "Systems Leadership for Organisational Transformation in the Age of Artificial Intelligence – Aligning ENRICHER, Control Science and Automation Engineering (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA35", "sessionTitle": "Human‑Centric AI: Ethics, Leadership, and Systemic Transformation", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "O'Neill, Brenda", "affiliation": "South East Technological University, Waterford" }, { "name": "Stapleton, Larry", "affiliation": "Knewfutures Consulting" }, { "name": "Carew, Peter J.", "affiliation": "South East Technological University" } ], "keywords": [ "Human-centric automation/AI Systems, and human agency", "Responsible automation", "Digital culture" ], "abstract": "Drawing on principles from control and automation science—particularly those associated with complexity theory such as diversity, connectivity, interdependence, adaptability, and emergence—the paper argues for a leadership style which is based upon a human-centred approach to AI transformation. It introduces the ENRICHER methodology as a framework for operationalising digital leadership in advanced intelligent human-machine systems. ENRICHER embodies values-driven development, contextual insights, co-evolutionary development, and semantic expressiveness. It offers a structured yet adaptive methodology that enables leaders and their teams to navigate the complexities associated with intelligent systems-driven organisational transformations. Aligning ENRICHER with systems leadership the paper offers a new lens for understanding intelligent systems deployment as a dynamic, participatory process requiring ethical reflexivity, organisational hospitality and continuous, ongoing reconfiguration and adaptation. The paper invites control and automation science practitioners and theorists to engage with leadership as a systemic function in the age of AI-enabled automation and control.", "url": "" }, { "id": "Mo-MoA35.5", "code": "MoA35.5", "title": "Vibe Coding for Smart Urban Futures - a Conceptual and Participatory Approach to Human-Centred AI in a Living Lab Context (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA35", "sessionTitle": "Human‑Centric AI: Ethics, Leadership, and Systemic Transformation", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Organ, John", "affiliation": "South East Technological University, Ireland" }, { "name": "Clancy, Michelle", "affiliation": "South East Technological University" } ], "keywords": [ "AI for smart cities", "Engineering ethics in control and AI", "Agent & AI technology for business and economy" ], "abstract": "This research presents an interdisciplinary intervention exploring the creative and ethical potential of large language models (LLMs) within the early development of a Smart Urban Living Lab led by local government in an Irish microcity. Integrating expertise in collaborative innovation (AHSS) and computer science (STEM), the project pilots vibe coding, an emerging human-in-the-loop approach that leverages natural language prompts to centre emotional contexts, user intent, and civic participation. Through a co-designed hackathon, the project engages regional stakeholders, including policymakers, educators, technologists and citizens in prototyping AI-enabled concepts that address real-world challenges in smart health, sustainable transport and digital inclusion. The hackathon serves as both a testbed and an action research intervention, enabling interdisciplinary knowledge exchange, socially responsive AI, human-centred design and participatory innovation. It generates actionable steps to embed ethical, inclusive, and emotionally attuned AI practices in emerging smart urban systems.", "url": "" }, { "id": "Mo-MoA35.6", "code": "MoA35.6", "title": "Using Synthetic GenAI Content in Immersive Digital Cultural Heritage Spaces and Control Systems: An Ethical Analysis and Guidelines (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA35", "sessionTitle": "Human‑Centric AI: Ethics, Leadership, and Systemic Transformation", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Murphy, Cian", "affiliation": "South East Technological University Waterford" }, { "name": "Carew, Peter J.", "affiliation": "South East Technological University" }, { "name": "Stapleton, Larry", "affiliation": "Knewfutures Consulting" } ], "keywords": [ "Digital culture", "Diversity and inclusion in digital culture", "Human-centric automation/AI Systems, and human agency" ], "abstract": "The user journey in Cultural Heritage spaces such as museums has evolved significantly from the traditional model of physical displays in designated locations to a more digitised and immersive experience. Artificial Intelligence (AI) and immersive technologies such as Virtual Reality (VR) can allow attendees to experience a collection from any location in an online environment. Generative Artificial Intelligence (GenAI) which can produce new information such as text or images by processing complex data offers a new opportunity in this context. GenAI can help to convey the story behind an artefact for example with supplementary material to enhance the knowledge and awareness regarding its place within society. However, this must be approached with an ethical mindset to ensure cultural sensitivity, accuracy and respect. The primary objective of this research is to act as a source of guidance for the ethical use of GenAI content within cultural heritage spaces and control systems.", "url": "" }, { "id": "Mo-MoA36.1", "code": "MoA36.1", "title": "Control-Oriented Design Framework for Heat Pump Based TMS of EV (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA36", "sessionTitle": "JO-CEP: Energy Management Systems and Control I", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Ahn, Changsun", "affiliation": "Pusan National University" }, { "name": "Sun, Jing", "affiliation": "Univ of Michigan" } ], "keywords": [ "Electric and solar vehicles", "Modeling, supervision, control and diagnosis of automotive systems", "Modeling and simulation of transportation systems" ], "abstract": "Efficient thermal management is essential for the performance, safety, reliability, and energy efficiency of electric vehicles, yet designing heat pump–based systems remains challenging due to coupled thermal dynamics, multiple heat sources, and diverse operational demands. This paper introduces a control-oriented, graph-based framework for modeling, analyzing, and optimizing heat pump-based EV thermal management systems. By representing components and heat-transfer pathways as nodes and edges, the proposed framework enables modular, reconfigurable modeling and rapid evaluation of alternative architectures. A case study optimizing TMS designs for multiple U.S. climate conditions demonstrates the framework’s ability to capture the influence of regional thermal demands on system configuration, component sizing, and energy consumption. The proposed framework offers a computationally efficient and versatile tool for design, configuration, and controller co-optimization, supporting climate-adaptive, next-generation EV thermal management strategies.", "url": "" }, { "id": "Mo-MoA36.2", "code": "MoA36.2", "title": "Dynamic Optimization for Real-Time Charging of Electric Vehicles (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA36", "sessionTitle": "JO-CEP: Energy Management Systems and Control I", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Ambrosino, Luca", "affiliation": "Politecnico Di Torino" }, { "name": "Nguyen Manh, Khai", "affiliation": "VinUniversity" }, { "name": "Zorgati, Riadh", "affiliation": "Electricité De France" }, { "name": "Nguyen-Ngoc, Doanh", "affiliation": "VinUniversity" }, { "name": "El Ghaoui, Laurent M.", "affiliation": "Univ. of California at Berkeley" }, { "name": "Calafiore, Giuseppe", "affiliation": "Politecnico Di Torino" } ], "keywords": [ "Electric vehicles and charging stations", "Electric vehicles integration in energy networks" ], "abstract": "The growing adoption of electric vehicles (EVs) presents significant challenges for energy management, grid stability, and user satisfaction in charging stations. Traditional approaches to EV charging often fail to adapt to the dynamic and uncertain nature of vehicle arrivals, energy demand, and electricity prices. This paper proposes a novel dynamic optimization framework based on a sliding-horizon approach for real-time EV charging manage\u0002ment. The framework integrates probabilistic modeling with optimization techniques to address uncertainties in vehicle arrival times, energy demand, and electricity prices. By using a linear programming model that continuously adapts to real-time data, the system ensures efficient power allocation, reduces operational costs, and maximizes customer satisfaction. Simulation results suggest that the proposed dynamic optimization approach may outperform traditional First-In-First-Served (FIFS) approaches and provides a flexible solution for optimizing charging station operations, paving the way for a more efficient and sustainable EV charging infrastructure.", "url": "" }, { "id": "Mo-MoA36.3", "code": "MoA36.3", "title": "A Quadratic Programming-Based Optimization Strategy for Mitigating SoC Imbalance in Microgrid EMS (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA36", "sessionTitle": "JO-CEP: Energy Management Systems and Control I", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Lee, Chang dae", "affiliation": "The Seoul National University of Science and Technology University (SeoulTech)" }, { "name": "Shin, Jinsu", "affiliation": "Seoul National University of Science and Technology" }, { "name": "Lee, Young Il", "affiliation": "Seoul National Univ of Science and Technology" } ], "keywords": [ "Electrical distribution systems", "Control and management of energy systems", "Multi-energy networks" ], "abstract": "Energy Management Systems (EMS) are essential for optimizing microgrid operations by coordinating distributed energy resources such as photovoltaic (PV) systems and energy storage systems (ESS). However, a significant gap remains between simulation-based control and real-world operation, where the State of Charge (SoC) of ESS often experiences abrupt jumps and long-term drifts due to current integration errors and sensor bias. These disturbances reduce control stability and lead to infeasible and suboptimal dispatch decisions, particularly in microgrids operating multiple distributed ESS banks. To address this issue, this paper proposes a quadratic programming (QP)-based low-step optimization method that introduces an SoCbalancing term into the objective function. The proposed controller mitigates SoC imbalance among multiple ESS units and improves post-jump recovery during real-time EMS operation. Simulation results confirm that the method enhances SoC convergence, maintains constraint feasibility, and improves overall operational efficiency. Experimental validation conducted in the SeoulTech microgrid further demonstrates that the proposed strategy ensures stable and coordinated SoC behavior across distributed ESS units during real-world recalibration events. The results collectively indicate that the proposed framework effectively bridges the gap between theoretical optimization and practical field control in microgrids operating multiple distributed ESS banks.", "url": "" }, { "id": "Mo-MoA36.4", "code": "MoA36.4", "title": "An Embedded Accelerated Decentralized Optimization Algorithm with Application to Energy Communities (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA36", "sessionTitle": "JO-CEP: Energy Management Systems and Control I", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Ferro, Giulio", "affiliation": "Università Degli Studi Di Genova" }, { "name": "Grammatico, Sergio", "affiliation": "Delft Univ. of Tech" }, { "name": "Parodi, Luca", "affiliation": "University of Genoa" }, { "name": "Rahimi Baghbadorani, Reza", "affiliation": "Erasmus University Rotterdam" }, { "name": "Robba, Michela", "affiliation": "University of Genova" } ], "keywords": [ "Energy communities", "Control and management of energy systems", "Distributed optimization and control for smart cities" ], "abstract": "Renewable Energy Communities (RECs) enable local energy sharing, reduce grid dependency, and support the energy transition. This work proposes an embedded-oriented Energy Community Management framework that maximizes shared energy while minimizing individual costs, increasing economic benefits. The architecture uses bilevel programming, decoupled via a reformulation of the objective and subproblems with KKT conditions. Optimization employs a modified ADMM algorithm with Nesterov acceleration for faster convergence. Implemented on low-power microcontrollers (ODROID-N2L and H3+), the framework demonstrates real-time feasibility and highlights the potential of lightweight, decentralized REC management.", "url": "" }, { "id": "Mo-MoA36.5", "code": "MoA36.5", "title": "Formulation of MILP-Based Models to Assess the Techno-Economic Impact of District Heating Electrification in Energy Communities (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA36", "sessionTitle": "JO-CEP: Energy Management Systems and Control I", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Ahmed, Hussain", "affiliation": "Tampere University" }, { "name": "Vilkko, Matti Kalervo", "affiliation": "Tampere University" } ], "keywords": [ "Energy communities", "Energy management systems", "Control and optimization for sustainability and energy systems" ], "abstract": "Transitioning district heating in energy communities (ECs) from fossil-fuel to electrification requires advanced analytical tools for long-term assessment and informed decision-making. This paper proposes two novel mixed-integer linear programming models for a Finnish EC with diverse power-generating and storage units, with gas boiler and electric boiler configurations to promote sector-coupling. Both models are simulated over a year using operational data to compare operating costs, carbon emissions, and reliance on local renewable electricity generation. Results show that the electric boilers configuration significantly reduces emissions, lowers costs, and improves local renewable energy utilization, highlighting the benefits of electrifying ECs for future sustainability.", "url": "" }, { "id": "Mo-MoA36.6", "code": "MoA36.6", "title": "Optimal Energy Scheduling in Multi-Vector Microgrids with Renewables and Hydrogen-Based Components (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA36", "sessionTitle": "JO-CEP: Energy Management Systems and Control I", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Annaswamy, Anuradha", "affiliation": "Massachusetts Inst. of Tech" }, { "name": "Casella, Virginia", "affiliation": "University of Genova" }, { "name": "Ennassiri, Yassine", "affiliation": "University of Genoa" }, { "name": "Ferro, Giulio", "affiliation": "Università Degli Studi Di Genova" }, { "name": "Lee, Kwang Y.", "affiliation": "Baylor University" }, { "name": "Parodi, Luca", "affiliation": "University of Genoa" }, { "name": "Robba, Michela", "affiliation": "University of Genova" } ], "keywords": [ "Energy management systems", "Hydrogen systems for energy generation and storage", "Control and management of energy systems" ], "abstract": "The need for decarbonization, efficiency, and resilience drives the transition towards multi-vector energy systems. This paper presents a hydrogen-based microgrids model integrating electricity, thermal, and hydrogen components for optimization and management. The model considers operational constraints of hydrogen systems, such as manufacturer-temperature limits, to mitigate degradation and ensure component durability. The solution to the optimisation problem is obtained using an enhanced Augmented Lagrangian Method with a switching matrix for selective constraint enforcement. A case study confirms effectiveness for large-scale applications and in field implementation, optimising with a 45.3 seconds computation time and a convergence gap of 10^{-6} %.", "url": "" }, { "id": "Mo-MoA37.1", "code": "MoA37.1", "title": "State-Dependent Stochastic DoS Attacks on Event-Triggered Cyber-Physical Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "09:50-10:10", "sessionCode": "MoA37", "sessionTitle": "Resilience Control of Urban Power Distribution Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Wu, Lang", "affiliation": "Central South University" }, { "name": "Liu, Fang", "affiliation": "Central South University" }, { "name": "Liu, Qianyi", "affiliation": "Central South University" } ], "keywords": [ "Safety-critical and resilient systems", "Cyber-physical and human systems (CPHS)" ], "abstract": "Event-triggered mechanisms are widely used to alleviate communication burdens in resource-constrained cyber-physical systems (CPSs). However, the existing denial-of-service (DoS) attacks against them are largely limited to time-dependent (e.g., periodic or Bernoullidistributed) models, which cannot exploit the sparsity of event-triggered data, thus limiting their attack efficiency. Therefore, a novel state-dependent stochastic DoS attack strategy is proposed in this paper. First, by analyzing the difference of historical triggered data, the attacker can evaluate and disrupt the transmission of critical data packets without any prior knowledge of the system model or trigger thresholds. Furthermore, to guarantee the practical attack feasibility, a constraint of maximum continuous attack rate (MCAR) is introduced, and an online optimization algorithm for attack parameters is designed. Finally, comparative experiments demonstrate the effectiveness and superiority of the proposed attack strategy.", "url": "" }, { "id": "Mo-MoA37.2", "code": "MoA37.2", "title": "A Review on Resilience Enhancement of Multi-Network Coupled Urban Distribution Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:10-10:30", "sessionCode": "MoA37", "sessionTitle": "Resilience Control of Urban Power Distribution Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Feng, Xu", "affiliation": "Hunan University" }, { "name": "Li, Yong", "affiliation": "Hunan University" }, { "name": "Liu, Jiayan", "affiliation": "Hunan University" }, { "name": "Zhan, Yuxuan", "affiliation": "Hunan University" } ], "keywords": [ "Smart city security and resilience", "Urban energy distribution systems", "Cyber-physical urban systems" ], "abstract": "With the advancement of resilient power grid initiatives, the interdependence among urban transportation, information, and power distribution networks has become increasingly pronounced, with their cascading effects emerging as a critical determinant of distribution network resilience. From a multi-network coupling perspective, this paper begins by outlining the key technological framework for enhancing resilience in multi-network coupled distribution systems. Subsequently, resilience enhancement measures for transportation-distribution and cyber-distribution coupled systems are summarized from both static and dynamic perspectives. Finally, the limitations of existing research are analyzed, and future directions are discussed.", "url": "" }, { "id": "Mo-MoA37.3", "code": "MoA37.3", "title": "Distribution Network Topology Identification Based on a Bayesian Dual-Aggregation Graph Neural Network (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:30-10:50", "sessionCode": "MoA37", "sessionTitle": "Resilience Control of Urban Power Distribution Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Chen, Chun", "affiliation": "Changsha University of Science and Technology" }, { "name": "Xiao, Ziliang", "affiliation": "Changsha University of Science and Technology" }, { "name": "Han, Ziang", "affiliation": "Changsha University of Science and Technology" }, { "name": "Cao, Yijia", "affiliation": "Changsha University of Science and Technology" }, { "name": "Wang, Weiyu", "affiliation": "Changsha University of Science and Technology" }, { "name": "An, Yi", "affiliation": "State Grid Jiangxi Electric Power Research Institute" } ], "keywords": [ "Urban energy distribution systems", "Smart city control and optimization", "AI for smart cities" ], "abstract": "Distribution networks with high penetration of distributed energy resources experience frequent topology changes, while sparse smart-meter data in non-Phasor Measurement Unit (PMU) environments make topology identification difficult. A Bayesian Dual-Aggregation Graph Neural Network (BD-GNN) is proposed, which uses single-timeframe node voltage magnitudes to infer line connection status. A graph-attention/EdgeConv dual aggregation captures global and local structural features, and a Bayesian neural network quantifies parameter uncertainty and improves robustness. Tests on modified IEEE 33- and 123-bus systems show that BD-GNN achieves highly accurate, noise-resilient topology identification and outperforms existing machine- and deep-learning baselines.", "url": "" }, { "id": "Mo-MoA37.4", "code": "MoA37.4", "title": "A T-Connection DC Distribution Network Bipolar Fault Protection Method Based on Differential-Flow Frequency-Domain Bandwidth (I)", "day": "Monday", "date": "August 24, 2026", "time": "10:50-11:10", "sessionCode": "MoA37", "sessionTitle": "Resilience Control of Urban Power Distribution Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Yan, Shiqi", "affiliation": "Beijing Jiaotong University" }, { "name": "Du, Xiaotong", "affiliation": "Beijing Jiaotong University" }, { "name": "Wang, Haoyue", "affiliation": "Beijing Jiaotong University" }, { "name": "Li, Meng", "affiliation": "Beijing Jiaotong University" }, { "name": "He, Jinghan", "affiliation": "Beijing Jiaotong University" } ], "keywords": [ "Smart city security and resilience" ], "abstract": "In flexible DC distribution networks, distributed power sources often adopt the T-connection method for grid connection. When a fault occurs, the instantaneous discharge of the T-connection branch capacitor can cause a sudden change in differential current, which can easily lead to maloperation of traditional differential protection. To ensure the safe and reliable operation of DC distribution systems with T-connection branches, this paper proposes a bipolar fault protection method for T-connected DC distribution networks based on the frequency domain bandwidth of differential current. Firstly, the DC distribution line is decoupled in modulus. Secondly, a one-mode network is selected for complex frequency domain transformation to analyze the relationship between the frequency domain differential current expression and bandwidth for both internal and external faults. Finally, a fault identification criterion is constructed based on the relationship between the fault frequency domain differential current expression and bandwidth. The research results show that the proposed protection method can achieve quick action for full-line faults, possesses reliability for both internal and external faults, can withstand 20dB white noise and a synchronization error of 40μs, and has a protection trip time within 3ms. For faults with a 50Ω transition resistance, the protection can still operate correctly.", "url": "" }, { "id": "Mo-MoA37.5", "code": "MoA37.5", "title": "Active Distribution Network Expansion Planning Method Based on DCGAN-Generated Scenarios (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:10-11:30", "sessionCode": "MoA37", "sessionTitle": "Resilience Control of Urban Power Distribution Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Wang, Shengyuan", "affiliation": "Tianjin University" }, { "name": "Fengzhang, Luo", "affiliation": "Tianjin University" }, { "name": "Jing, Xu", "affiliation": "State Grid Tianjin Electric Power Company" }, { "name": "Lukun, Ge", "affiliation": "State Grid Tianjin Electric Power Company" } ], "keywords": [ "Cost-effective operation and maintenance" ], "abstract": "To address the challenges of uncertainty and flexibility in distribution network planning under high penetration of wind and solar power, this paper proposes a scenario-driven active distribution network (ADN) expansion planning method based on a deep convolutional generative adversarial network (DCGAN). The proposed method first employs DCGAN to extract features and learn the joint distribution of historical wind speed and solar irradiance data, generating representative multidimensional power output scenarios. Typical scenarios are then selected using K-means clustering to comprehensively capture the stochastic characteristics of renewable energy generation. On this basis, an ADN expansion planning model is established, which jointly optimizes battery energy storage system-soft open points(E-SOP), distributed generation , battery energy storage system , line expansion, and substation capacity. The objective is to minimize the annualized total cost while balancing the economic efficiency, flexibility, and reliability of system investment and operation. Case studies on a 54-bus distribution system demonstrate that the proposed method outperforms traditional deterministic planning approaches by effectively reducing annual costs and enhancing renewable energy accommodation as well as system robustness.", "url": "" }, { "id": "Mo-MoA37.6", "code": "MoA37.6", "title": "Optimal Scheduling of Multiple Integrated Energy Systems in Electricity-Carbon-Green Certificate Coupling Market (I)", "day": "Monday", "date": "August 24, 2026", "time": "11:30-11:50", "sessionCode": "MoA37", "sessionTitle": "Resilience Control of Urban Power Distribution Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Wang, Yahui", "affiliation": "Xiangtan University" }, { "name": "Hu, Jingyu", "affiliation": "Xiangtan University" }, { "name": "Zhu, Hongzhang", "affiliation": "State Key Laboratory of Disaster Prevention & Reduction for Power Grid, Changsha 410000, China" }, { "name": "Wang, Yixiao", "affiliation": "Central South University" }, { "name": "Yi, Lingzhi", "affiliation": "Xiangtan University" } ], "keywords": [ "Social transportation and social energy", "Smart city control and optimization", "System dynamics and control in CPHS" ], "abstract": "With the advancement of the dual-carbon goals, multiple integrated energy systems (IES) face increasingly stringent carbon emission constraints, an unquantified environmental value of renewable energy, and fragmented resource allocation across individual systems. To address these challenges, this paper develops a Stackelberg game model between a Multi-IES Operator (MIEO) and multiple IES within a coupled electricity, carbon, and green certificate market framework. In the upper layer, the MIEO dynamically adjusts internal green certificate allocation ratios and electricity price signals based on IES feedback on power purchase/sale volumes and green certificate transactions. In the lower layer, each IES independently optimizes energy dispatch, carbon quota utilization, and green certificate trading decisions under privacy-preserving conditions. Through internal green certificate trading and the mutual recognition mechanism between the green certificate and carbon markets, it achieves low-cost carbon offsetting and resource sharing. Simulation results demonstrate that this approach quantifies and enhances the environmental value of new energy sources, thereby increasing the transaction rate of green certificates while reducing overall system costs. This validates the economic viability and low-carbon advantages of the proposed strategy.", "url": "" }, { "id": "Mo-MoB01.1", "code": "MoB01.1", "title": "Value-Based Connections in MPC and RL for Learning-Based Control (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:10-14:10", "sessionCode": "MoB01", "sessionTitle": "Learning-Based Control in Value Space: Bridging Reinforcement Learning and Differentiable Predictive Control", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Banker, Thomas", "affiliation": "University of California Berkeley" }, { "name": "Lawrence, Nathan P.", "affiliation": "University of California, Berkeley" }, { "name": "Mesbah, Ali", "affiliation": "University of California, Berkeley" } ], "keywords": [ "Adaptive control design" ], "abstract": "Model predictive control (MPC) and reinforcement learning (RL) share the common objective of optimal decision-making under uncertainty. While both originate from dynamic programming, they diverge in their assumptions and computational approximations. MPC exploits predictive models to optimize decisions online, enabling safe and robust control under constraints. RL, in contrast, learns control policies through trial and error, often using function approximation to generalize near-optimal decisions across diverse situations. The complementary strengths of MPC and RL have motivated the development of RL-MPC frameworks that combine learning and optimization for data-driven control. This tutorial talk presents a value-based perspective on integrating RL and MPC, using value functions as a unifying connection between the two paradigms. We outline the advantages and practical challenges of RL-MPC integration, motivating two recent frameworks: MPCritic and soft MPCritic. These frameworks illustrate how tractable integration can be achieved by reconciling the approximations inherent to RL and MPC.", "url": "" }, { "id": "Mo-MoB01.2", "code": "MoB01.2", "title": "Differentiable Predictive Control: From Offline Pre-Training to Safe Online Deployment (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:10-15:10", "sessionCode": "MoB01", "sessionTitle": "Learning-Based Control in Value Space: Bridging Reinforcement Learning and Differentiable Predictive Control", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Drgona, Jan", "affiliation": "Pacific Northwest National Laboratory" } ], "keywords": [ "Adaptive control design" ], "abstract": "", "url": "" }, { "id": "Mo-MoB02.1", "code": "MoB02.1", "title": "Headland Turning Path Planning towards Coverage Path Planning for a Robotic Vehicle with a Towed Implement in Orchards", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:15", "sessionCode": "MoB02", "sessionTitle": "Shotgun: Biological and Social Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Yamasaki, Yoshitomo", "affiliation": "Hokkaido University" }, { "name": "Noguchi, Noboru", "affiliation": "Hokkaido University" } ], "keywords": [ "Agricultural robotics", "Control in precision agriculture", "Positioning and navigation in agriculture and forestry" ], "abstract": "We proposed turning path planning towards coverage path planning (CPP) for a robotic vehicle towing an agricultural implement. We developed an extended turning path model based on two arcs and a straight segment, considering the turning radius difference. Feasible combinations of turning paths were then verified by simulating the trajectories of the robotic vehicle and the towed implement. The robotic vehicle followed the proposed turning path within 0.10 m on average for both the vehicle and the implement. The proposed method to generate the feasible turning table provided a clue to practical CPP.", "url": "" }, { "id": "Mo-MoB02.2", "code": "MoB02.2", "title": "An Adaptive Control Architecture for Slope and Terrain Compensation in Autonomous Navigation in Mediterranean Greenhouses", "day": "Monday", "date": "August 24, 2026", "time": "13:15-13:20", "sessionCode": "MoB02", "sessionTitle": "Shotgun: Biological and Social Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Cañadas-Aránega, Fernando", "affiliation": "University of Almeria" }, { "name": "Wollherr, Dirk", "affiliation": "Technical University of Munich" }, { "name": "Guzman, Jose Luis", "affiliation": "University of Almeria" }, { "name": "Moreno, Jose Carlos", "affiliation": "University of Almeria" }, { "name": "Blanco, Jose Luis", "affiliation": "Universidad De Almeria" } ], "keywords": [ "Automatic control in greenhouses", "Agricultural robotics", "Positioning and navigation in agriculture and forestry" ], "abstract": "The ability to move stably over terrain with varying slopes and textures is essential for mobile agricultural robots operating in complex and dynamic environments such as greenhouses, where small terrain irregularities can lead to significant navigation errors. This article presents a novel terrain-adaptation strategy based on the carried payload, ensuring accurate and robust trajectory tracking. The proposed approach is based on: (i) the experimental characterization of the most common types of greenhouse soil, concrete, compacted sand, and gravel, and (ii) the direct measurement of terrain slope using the IMU, in order to estimate the force with which this angle affects the motor input. Based on this information, a cascade trajectory-tracking scheme has been designed, consisting of a model-based predictive controller (MPC) in the outer loop and a PI controller in the inner loop. The system incorporates an adaptive feedforward control through gain scheduling approach, capable of adjusting to disturbances caused by variations in slope and terrain type. Simulation results demonstrate that the differential-drive robot achieves a significant improvement both in error indices and in control signal efficiency, highlighting the effectiveness and robustness of the proposed approach.", "url": "" }, { "id": "Mo-MoB02.3", "code": "MoB02.3", "title": "Tokenized Coordination Framework with Verifiable State for AAM Manufacturing", "day": "Monday", "date": "August 24, 2026", "time": "13:20-13:25", "sessionCode": "MoB02", "sessionTitle": "Shotgun: Biological and Social Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Habbachi, Salwa", "affiliation": "Macau University of Science and Technology" }, { "name": "Rouabah, Younes", "affiliation": "Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Macao 999078," }, { "name": "Goh, Craymon", "affiliation": "Curge Advance Sdn. Bhd., and the Machinery and Engineering Industries Federation (MEIF), Kuala Lumpur 50470, Malaysia" }, { "name": "Zheng, Jademont", "affiliation": "Aterdrip Investment Limited, Hong Kong 999077, China" }, { "name": "Ma, Siji", "affiliation": "Macau University of Science and Technology" }, { "name": "Ding, Wendy", "affiliation": "Obuda University" }, { "name": "Wang, Fei-Yue", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Kovacs, Levente", "affiliation": "Obuda University" } ], "keywords": [ "Blockchain intelligence", "Financial systems", "Decentralized economics/ecosystems (DeEco)" ], "abstract": "The manufacturing process of Advanced Air Mobility (AAM) faces continuous funding challenges which result in longer production times because of concealed system problems, poor coordination, and unmonitored accountability. The paper presents a system framework which combines a Verifiable State Layer with a Token-Driven Coordination Layer to create a single state representation system that supports programmable financial operations, incentive programs, settlement processes, and governance mechanisms. The system uses state assets to represent engineering events which produce immediate feedback for delay detection and parameter adjustment through token dynamics. The research uses thermal-test delay, software rollback, and propulsion failure simulations to demonstrate enhanced liquidity stability, risk exposure, and coordination performance which will serve as a foundation for developing future AAM manufacturing systems.", "url": "" }, { "id": "Mo-MoB02.4", "code": "MoB02.4", "title": "A Methodology for Designing Blockchain Architectures in Logistics: An Application to Intra-Hub Physical Internet Operations", "day": "Monday", "date": "August 24, 2026", "time": "13:25-13:30", "sessionCode": "MoB02", "sessionTitle": "Shotgun: Biological and Social Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Sassi, Hayder", "affiliation": "Univ. Polytechnique Hauts-De-France, LAMIH" }, { "name": "Perez, Monica-Juliana", "affiliation": "Université Polytechnique Hauts-De-France - LAMIH UMR CNRS 8201" }, { "name": "Trentesaux, Damien", "affiliation": "LAMIH UMR CNRS 8201, SurferLab, University of Valenciennes and Hainaut-Cambresis" }, { "name": "Idel Mahjoub, Yassine", "affiliation": "Université Polytechnique Haut-De-France" } ], "keywords": [ "Blockchain intelligence", "Industrial and service applications of AI and intelligent automation" ], "abstract": "This paper introduces a general methodology for designing and assessing blockchain architectures in logistics systems. The objective is not to promote a specific platform but to provide a structured process that clarifies how architectural decisions—such as asset modelling, event representation, metadata strategies, smart-contract roles and governance configurations—shape the performance, cost, confidentiality and informational value of blockchain-enabled solutions. The methodology is illustrated through an intra-hub Physical Internet (PI or pi) case, where a discrete-event simulation is coupled with a blockchain layer used to certify handling events. In this application, pi-containers are instantiated as digital assets and intra-hub areas as logistical wallets, enabling the analysis of alternative blockchain configurations under controlled operational conditions. The prototype shows the feasibility of integrating blockchain as a non-intrusive certification layer while offering a testbed for scenario-based comparison. The contribution is methodological and exploratory: it formalizes a design workflow, defines relevant evaluation indicators and establishes a foundation for future quantitative assessment of blockchain architectures in logistics and other cyber-physical domains. Future work will execute full simulation campaigns and extend the methodology to additional application areas.", "url": "" }, { "id": "Mo-MoB02.5", "code": "MoB02.5", "title": "A Control-Theoretic Framework for Financial Trend Identification Using Multi-Sensor Observations and POMDP Decision Making under Partial Observability", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:35", "sessionCode": "MoB02", "sessionTitle": "Shotgun: Biological and Social Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Ghanbarpour, Alireza", "affiliation": "Post Doctoral Researcher" }, { "name": "Ghanbarpour, Alireza", "affiliation": "Post Doctoral Researcher" }, { "name": "Tomizuka, Masayoshi", "affiliation": "Univ of California, Berkeley" } ], "keywords": [ "Business and financial analytics", "Financial systems", "Econometric models and methods" ], "abstract": "Financial markets are dynamic stochastic systems in which essential variables—such as regime direction, liquidity conditions, and volatility structure—are not directly observable. This partial observability creates a decision-making problem analogous to that of autonomous robotic agents operating with limited and noisy sensors. Motivated by this analogy, this paper develops a mathematically rigorous framework that models market trend identification and trading as a Partially Observable Markov Decision Process (POMDP). The proposed approach integrates multi-sensor financial perception through (i) a Support Vector Machine–based regime classifier constructed from multi-scale EMA and stochastic features, and (ii) a structural geometric indicator (EMMAi) that delineates dynamic support, resistance, and trend-confirmation zones. These sensors constitute a heterogeneous observation set analogous to multi-modal robotic perception modules, enabling complementary and noise-resilient information about the latent market state. A full POMDP formulation is derived, specifying the hidden regime space, stochastic transition dynamics, sensor-driven observation model, Bayesian belief-state update, and an action space consisting of directional trading decisions. The belief state provides a probabilistic estimate of the latent market trend and serves as the sufficient statistic for policy computation. Building on tools from optimal control under uncertainty, we compute a risk-aware trading policy via value-based POMDP methods augmented with constraints on drawdown, tail-risk, and action stability—analogous to safety constraints in autonomous robotics. Experimental results on equity index data demonstrate that (i) belief-state estimation substantially improves regime detection relative to direct signal-driven methods, (ii) multi-sensor fusion reduces observational noise and enhances stability, and (iii) the resulting POMDP controller achieves superior risk-adjusted performance and robustness under uncertainty. The proposed formulation introduces a principled control-theoretic foundation for autonomous decision making in financial systems and illustrates the deep methodological parallels between robotics in uncertain environments an", "url": "" }, { "id": "Mo-MoB02.6", "code": "MoB02.6", "title": "Heterogeneous Learning Mechanisms in Zero-Sum Games: From Best-Iterate to Last-Iterate Convergence", "day": "Monday", "date": "August 24, 2026", "time": "13:35-13:40", "sessionCode": "MoB02", "sessionTitle": "Shotgun: Biological and Social Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Guo, Xinxiang", "affiliation": "Chinese Academy of Sciences" }, { "name": "Zhang, Junyue", "affiliation": "University of Chinese Academy of Sciences" }, { "name": "Mu, Yifen", "affiliation": "Academy of Mathematics and Systems Science, Chinese Academy of Sciences" }, { "name": "Wang, Xiao", "affiliation": "Shanghai University of Finance and Economics" }, { "name": "Panageas, Ioannis", "affiliation": "UC Irvine" } ], "keywords": [ "Computational economics" ], "abstract": "Heterogeneous learning has recently emerged as a promising approach for computing Nash equilibria, yet its last-iterate convergence remains unclear. This paper establishes convergence results in zero-sum games under three dynamics: (1) mirror descent (MD) versus best response; (2) MD versus smoothed best response (SBR); and (3) Tikhonov-regularized MD versus SBR. We prove best-iterate convergence, unilateral last-iterate convergence, and bilateral last-iterate convergence, respectively. These heterogeneous dynamics each offer distinct advantages in computing equilibria and exploiting opponents. Simulations further highlight the significant impact of heterogeneous learning on game dynamics.", "url": "" }, { "id": "Mo-MoB02.7", "code": "MoB02.7", "title": "Tracking and Counting of Mulch-Occluded Cotton Seedling Based on RT-DETRv2 and CAMEL", "day": "Monday", "date": "August 24, 2026", "time": "13:40-13:45", "sessionCode": "MoB02", "sessionTitle": "Shotgun: Biological and Social Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Yang, Yaoyu", "affiliation": "Zhejiang University" }, { "name": "Chang, Fangle", "affiliation": "Zhejiang University" }, { "name": "Yang, Jiahong", "affiliation": "Zhejiang University" }, { "name": "Meng, Ziyang", "affiliation": "Shandong University of Technology" }, { "name": "Xie, Lei", "affiliation": "Zhejiang University" }, { "name": "Su, Hongye", "affiliation": "Zhejiang University" } ], "keywords": [ "Computer vision in agriculture", "Control in precision agriculture" ], "abstract": "Precision agriculture relies heavily on accurate seedling stand counts for yield prediction and crop management. However, automated counting in plastic-mulched cotton fields remains challenging because seedlings are frequently occluded by mulch, affected by specular reflections, and visually similar to one another. To address these limitations, this paper proposes a multi-object tracking (MOT) and counting framework. We first adopt RT-DETRv2 as the core detector to obtain accurate seedling locations in complex field imagery. We then adapt CAMEL, an association module for Context-Aware Multi-Cue ExpLoitation, to replace heuristic matching with a learnable association process. CAMEL uses a Temporal Encoder (TE) to model motion history and a Group-Aware Feature Fusion Encoder (GAFFE) to integrate spatial and appearance cues for improved identity discrimination under occlusion. Finally, a virtual-line counting strategy is used to reduce overcounting caused by trajectory fragmentation. Experimental results show that RT-DETRv2 achieves 67.25 FPS and an mAP@0.5 of 0.987. Compared with DeepSORT and ByteTrack, the CAMEL-based tracker achieves 70.6 HOTA, 85.1 MOTA, 77.8 IDF1, and fewer identity switches. Counting performance is evaluated against manual counts across five video segments, achieving an average counting precision (ACP) of 88.84% and an R2 of 0.95. These results indicate that the proposed framework can support real-time monitoring of cotton seedling emergence under mulch-covered field conditions.", "url": "" }, { "id": "Mo-MoB02.8", "code": "MoB02.8", "title": "A Feedforward Compensation Scheme for Multiple Inputs in Propofol Anesthesia", "day": "Monday", "date": "August 24, 2026", "time": "13:45-13:50", "sessionCode": "MoB02", "sessionTitle": "Shotgun: Biological and Social Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Jakubowski, Damian", "affiliation": "Wrocław University of Science and Technology" }, { "name": "Pawlowski, Andrzej", "affiliation": "Wroclaw University of Science and Technology" } ], "keywords": [ "Control of physiological and clinical variables", "Pharmacokinetics, tracer kinetic modelling and drug delivery", "Biomedical system modeling, identification, and simulation" ], "abstract": "In this work a control scheme for multiple input signal sources for the anaesthesia process is introduced and analysed. The proposed scenario considers the situation where propofol can be manually adjusted in the presence of the feedback control that is designed to keep the Bispectral Index Scale (BIS) at the desired level. The proposed feedforward compensation scheme is integrated within a Model Predictive Control (MPC) technique that allows one to consider the effect of the manually introduced drug in computation of control signal. In this way, it is possible to handle the external input signal that disturbs the controller actions. When this additional input signal is not considered during computation of control action by feedback controller it could lead to significant performance losses or even unstable behaviour due to improper constraints management. The conceived system is tested through a simulation study that evaluates a possible clinical situation to highlight the performance and advantages of the analysed control approach. The results obtained indicate that the proposed architecture has significant potential in practical clinical applications to improve patient safety as well as to extend the versatility of interventions requiring total intravenous anesthesia where an automatic control system for drug delivery can be used.", "url": "" }, { "id": "Mo-MoB02.9", "code": "MoB02.9", "title": "A Knowledge Asset Protocol for Compute-Driven Publishing Ecosystems", "day": "Monday", "date": "August 24, 2026", "time": "13:50-13:55", "sessionCode": "MoB02", "sessionTitle": "Shotgun: Biological and Social Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Ding, Wendy", "affiliation": "Obuda University" }, { "name": "Wang, Fei-Yue", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Ma, Siji", "affiliation": "Macau University of Science and Technology" }, { "name": "Liang, Xiaolong", "affiliation": "Chinese Academy of Sciences" }, { "name": "Tian, Yong-Lin", "affiliation": "State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijin" }, { "name": "Ge, Jingwei", "affiliation": "University Research and Innovation Center, Obuda University, Budapest H-1034, Hungary" }, { "name": "Kovacs, Levente", "affiliation": "Obuda University" } ], "keywords": [ "Decentralized economics/ecosystems (DeEco)", "Blockchain intelligence", "Econometric models and methods" ], "abstract": "The existing publishing ecosystem fails to support modern AI operations, as these systems require knowledge that is machine-readable, executable, and composable. The combination of blockchain technology with smart-contract systems enables the creation of verifiable assets which can execute automatically and settle transactions through automated processes. This study offers a Knowledge Asset Protocol (KAP) as a method to transform scholarly content into executable on-chain assets that incorporate verification functions, payment systems, and programmatic governance mechanisms. The paper outlines the protocol’s core properties and architecture and demonstrates its applicability. By unifying technical, economic, and governance layers, KAP provides foundational infrastructure for compute-driven publishing ecosystems.", "url": "" }, { "id": "Mo-MoB02.10", "code": "MoB02.10", "title": "Solvability of the Output Corridor Control Problem by Pulse-Modulated Feedback (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:55-14:00", "sessionCode": "MoB02", "sessionTitle": "Shotgun: Biological and Social Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Medvedev, Alexander", "affiliation": "Uppsala University" }, { "name": "Proskurnikov, Anton V.", "affiliation": "Politecnico Di Torino" } ], "keywords": [ "Dynamics and control of biologically motivated nonlinear systems", "Biomedical system modeling, identification, and simulation", "Control of physiological and clinical variables" ], "abstract": "The problem of maintaining the output of a positive time-invariant single-input single-output system within a predefined corridor of values is treated. For third-order plants possessing a certain structure, it is proven that the problem is always solvable under stationary conditions by means of pulse-modulated feedback. The obtained result is utilized to assess the feasibility of patient-specific pharmacokinetic-pharmacodynamic models with respect to patient safety. A population of Wiener models capturing the dynamics of a neuromuscular blockade agent is studied to investigate whether or not they can be driven into the desired output corridor by clinically acceptable sequential drug doses (boluses). It is demonstrated that low values of a parameter in the nonlinear pharmacodynamic part lie behind the detected model infeasibility.", "url": "" }, { "id": "Mo-MoB02.11", "code": "MoB02.11", "title": "A Decentralized Financial Model for Knowledge Payment-Based Publishing", "day": "Monday", "date": "August 24, 2026", "time": "14:00-14:05", "sessionCode": "MoB02", "sessionTitle": "Shotgun: Biological and Social Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Jiang, Tai", "affiliation": "Macau University of Science and Technology" }, { "name": "Cao, Shuyu", "affiliation": "Institute 706 the Second Academy" }, { "name": "Lin, Fei", "affiliation": "Macau University of Science and Technology" }, { "name": "Wang, Fei-Yue", "affiliation": "Institute of Automation, Chinese Academy of Sciences" } ], "keywords": [ "Financial systems", "Blockchain intelligence", "Business and financial analytics" ], "abstract": "This paper presents JournalDAO, a decentralized knowledge payment-based publishing system integrating blockchain authorization, decentralized finance (DeFi), and tokenized incentives for decentralized science (DeSci). Unlike conventional models where reading is restricted behind paywalls or made free through OA fees, JournalDAO keeps access open while requiring on-chain purchase authorization for citation or other academic and commercial uses. Each purchase distributes revenue to all token holders including authors, reviewers, and publishers according to their token shares, and also adds the purchaser to the holder set. Authors receive incremental tokens as evidence of increasing scholarly recognition, whereas publishers and reviewers retain fixed allocations. The resulting token dilution induces diminishing marginal returns and a transparent break-even structure that rewards early identification of valuable research and makes manipulative self-purchases economically infeasible. Through analytical derivation and case studies, the paper demonstrates how parameter choices shape revenue dynamics, break-even thresholds, and holder distributions. The results indicate that JournalDAO provides a sustainable and tamper-resistant mechanism for compensating intellectual contributions while preserving openness and academic integrity.", "url": "" }, { "id": "Mo-MoB02.12", "code": "MoB02.12", "title": "A Gradient-Based Distributed Algorithm for Triopoly Advertising Competition Game Over Interconnected Market Systems", "day": "Monday", "date": "August 24, 2026", "time": "14:05-14:10", "sessionCode": "MoB02", "sessionTitle": "Shotgun: Biological and Social Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Jiang, Kaichen", "affiliation": "Dalian University of Technology" }, { "name": "Yue, Mingda", "affiliation": "School of Control Science and Engineering, Dalian University of Technology" }, { "name": "Varga, Balint", "affiliation": "Karlsruhe Institute of Technology (KIT), Campus South" }, { "name": "Wu, Yuhu", "affiliation": "Dalian University of Technology" }, { "name": "Wang, Junsong", "affiliation": "Dalian University of Technology" }, { "name": "Wang, Kaiyu", "affiliation": "Dalian University of Technology" } ], "keywords": [ "Game theories", "Econometric models and methods", "Social networks and opinion dynamics" ], "abstract": "This paper investigates a triopoly advertising competition problem over interconnected market systems using a noncooperative game framework that effectively captures the strategic interactions and conflicting objectives among the three firms. By taking both the targeted advertising efforts of the firms and the continuous co-evolution of consumer opinions across market systems via social network interactions into consideration, we build a noncooperative game model with nonlinear cost functions to analyze the optimal advertising strategy of each firm. To address the challenge of limited information exchange among firms, we design an estimation mechanism for each firm to estimate the current strategy profile and propose a gradient-based distributed algorithm to seek the Nash equilibrium of the game. Finally, numerical simulations are provided for verifying the developed results.", "url": "" }, { "id": "Mo-MoB02.13", "code": "MoB02.13", "title": "Policy Design for Games on Multiplex Networks Via Graph Limits", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:15", "sessionCode": "MoB02", "sessionTitle": "Shotgun: Biological and Social Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Petrov, Ilya", "affiliation": "Institute of Control Sciences of RAS and HSE University" } ], "keywords": [ "Game theories", "Social networks and opinion dynamics", "Computational economics" ], "abstract": "We study strategic interactions in multiplex networks, where the same agents interact through several types of links. The resulting network games are difficult to analyze directly when the number of agents is large and when actions on different layers interact. We consider a linear--quadratic game with within-layer spillovers and cross-activity interactions, and specialize graphon games framework to constant graph functions on each layer. This yields a representative-agent system in the layer averages, which approximates large sampled network games and keeps the dependence on layer densities and game parameters explicit. The reduction provides a finite-dimensional basis for studying equilibrium responses to incentives and structural changes from control and optimization perspectives.", "url": "" }, { "id": "Mo-MoB02.14", "code": "MoB02.14", "title": "Sampled Data Closed-Loop Controller of a Pressure-Driven Filtration Device with Dead Volume", "day": "Monday", "date": "August 24, 2026", "time": "14:15-14:20", "sessionCode": "MoB02", "sessionTitle": "Shotgun: Biological and Social Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Vincendon, Michael", "affiliation": "Mines Paris - PSL" }, { "name": "Petit, Nicolas", "affiliation": "MINES Paris, PSL University" } ], "keywords": [ "Medical devices, systems and solutions", "Biomedical system modeling, identification, and simulation", "Dynamics and control of biologically motivated nonlinear systems" ], "abstract": "The paper considers a microfluidic device used to filtrate particles in a suspension. The input under consideration is the input pressure, and the output of interest is the particle concentration in one of the two branches. Closed-loop control of this system has been theoretically studied in continuous-time, stressing the complexity induced by a dead volume causing an input varying delay of hydraulic type. To account for instrumentation limitations, we consider a sampled-based control strategy. We recast the control problem as a discrete-time nonlinear two-states dynamics. A closed-loop controller is proposed which is tested experimentally. Exponential convergence in closed-loop to reachable setpoints is obtained.", "url": "" }, { "id": "Mo-MoB02.15", "code": "MoB02.15", "title": "Reference-Model-Based Control Including Human Torque Estimation for Cable-Driven Rehabilitation System (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:20-14:25", "sessionCode": "MoB02", "sessionTitle": "Shotgun: Biological and Social Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Ortiz Gutierrez, Nery Uriel", "affiliation": "Université Polytechnique Hauts-De-France" }, { "name": "Guerra, Thierry Marie", "affiliation": "Polytechnic University Hauts-De-France Valenciennes" }, { "name": "Peixoto, Márcia Luciana da Costa", "affiliation": "Université Polytechnique Hauts-De-France" }, { "name": "Pessim, Paulo Sergio Pereira", "affiliation": "Universite Polytechnique Hauts-De-France" }, { "name": "Dequidt, Antoine", "affiliation": "Université De Valenciennes Et Du Hainaut-Cambrésis" }, { "name": "Delprat, Sebastien", "affiliation": "Université Polytechnique Haut De France" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "Paganelli, Sébastien", "affiliation": "University of Valenciennes Et Du Hainaut Cambrésis" } ], "keywords": [ "Rehabilitation engineering and healthcare delivery", "Medical devices, systems and solutions" ], "abstract": "This paper presents a reference-model-based control strategy for human-interactive rehabilitation devices designed to ensure robust assistance during movement. The proposed approach combines feedforward and feedback actions to control the nonlinear system along physiotherapist-defined trajectories. The human torque, which represents the patient’s contribution to movement, is estimated in real-time using a Proportional-Integral Observer. This real-time estimation allows the system to adjust the level of assistance according to the user’s capabilities. Experimental validation in a prototype demonstrates the effectiveness of the proposed approach.", "url": "" }, { "id": "Mo-MoB02.16", "code": "MoB02.16", "title": "Concept of a Sensor Test Environment for Dusty Agricultural Conditions", "day": "Monday", "date": "August 24, 2026", "time": "14:25-14:30", "sessionCode": "MoB02", "sessionTitle": "Shotgun: Biological and Social Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Buckel, Peter", "affiliation": "Technical University of Munich" }, { "name": "Hermann, Johannes", "affiliation": "DHBW Ravensburg" }, { "name": "Wollmann, Jonas", "affiliation": "DHBW Ravensburg" }, { "name": "Dietmüller, Thomas", "affiliation": "DHBW Ravensburg" }, { "name": "Oksanen, Timo", "affiliation": "Technical University of Munich" } ], "keywords": [ "Sensing and perception in agriculture", "Computer vision in agriculture", "Agricultural robotics" ], "abstract": "Dust in agriculture presents a significant challenge for autonomous agricultural machinery. Dust can impair the performance of sensors and algorithms. This work, therefore, presents a concept for a proving ground consisting of an indoor and outdoor area. The indoor area comprises a laboratory test bench where dust circulates in a closed system and a test hall where life-size objects can be placed. The outdoor area features dedicated test setups that enable reproducible data to be recorded with and without dust during real-world agriculture work. The proving ground and the setups are visualized in 3D.", "url": "" }, { "id": "Mo-MoB02.17", "code": "MoB02.17", "title": "Field-Scale Soil Moisture Mapping from UAV Multispectral-Thermal Data with Augmentation and Reference Correction", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:35", "sessionCode": "MoB02", "sessionTitle": "Shotgun: Biological and Social Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Adamgye, Christian", "affiliation": "University of Alberta" }, { "name": "Agyeman, Bernard", "affiliation": "University of Alberta" }, { "name": "Bo, Song", "affiliation": "University of Alberta" }, { "name": "Liu, Jinfeng", "affiliation": "University of Alberta" } ], "keywords": [ "Sensing and perception in agriculture", "Modeling and estimation in agriculture" ], "abstract": "Efficient irrigation requires accurate field-scale soil moisture estimates. This work develops a UAV sensor fusion approach that combines multispectral and thermal imagery with in-field soil moisture sensors to improve estimation accuracy. This approach has an offline training phase and an online bias-correction phase. In offline training, 296 paired samples (multispectral/thermal features and in-field soil moisture sensor readings) are augmented via quadratic interpolation and denoised with principal component analysis (PCA). A neural network trained on the augmented, PCA-transformed data reduces normalized root mean squared error (NRMSE) from 0.271 to 0.226 compared with training without augmentation and PCA. During online deployment, a reference-sensor bias correction compensates for drift in environmental and field conditions, reducing NRMSE from 0.3267 to 0.1668 while preserving spatial gradients. These results demonstrate that combining augmentation, PCA, and reference-sensor feedback with UAV multispectral-thermal data substantially improves field-scale soil moisture estimation.", "url": "" }, { "id": "Mo-MoB02.18", "code": "MoB02.18", "title": "Steering Opinion through Dynamic Stackelberg Optimization", "day": "Monday", "date": "August 24, 2026", "time": "14:35-14:40", "sessionCode": "MoB02", "sessionTitle": "Shotgun: Biological and Social Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Rastgoftar, Hossein", "affiliation": "University of Arizona" } ], "keywords": [ "Social computing", "System dynamics and control in CPHS", "Social networks and opinion dynamics" ], "abstract": "This paper employs the Friedkin–Johnsen (FJ) model to describe the evolution of opinions in a social network composed of regular and stubborn agents. In the adopted framework, stubborn agents represent influential entities whose opinions are not directly shaped by their neighbors, whereas regular agents update their opinions as a convex combination of their neighbors’ opinions and their own initial beliefs. The goal is to steer the population toward a common reference opinion while respecting the intrinsic preferences of all agents. Without loss of generality, the origin is selected as the desired consensus point by shifting the opinion space, so that any target opinion profile can be mapped to zero. The steering problem is formulated as a finite-horizon Stackelberg game between the stubborn (leader) and regular (follower) subgroups, where stubborn agents strategically adjust their opinions and regular agents adapt their openness to external influence. The decision variables are the stubborn agents’ opinion adjustments and the regular agents’ bounded openness parameters, which jointly determine the nonlinear network dynamics. We propose a bi-level solution scheme that integrates quadratic programming for the followers and dynamic programming for the leaders, and computes the corresponding Stackelberg strategies through forward–backward propagation. Numerical simulations illustrate how the proposed architecture drives the network toward the desired consensus while limiting the magnitude of stubborn opinion change and regular agents’ openness.", "url": "" }, { "id": "Mo-MoB02.19", "code": "MoB02.19", "title": "EEG-fNIRS Fusion through Spatial-Temporal Alignment for Cognitive Task (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:40-14:45", "sessionCode": "MoB02", "sessionTitle": "Shotgun: Biological and Social Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Feng, Qixuan", "affiliation": "Qingdao University" }, { "name": "Xue, Binqiang", "affiliation": "Qingdao University" }, { "name": "Liu, Yinhua", "affiliation": "Qingdao University" }, { "name": "Kang, Min-Kyoung", "affiliation": "Pusan National University" }, { "name": "Hong, Keum-Shik", "affiliation": "Pusan National University" } ], "keywords": [ "Biomedical signal measurement and processing" ], "abstract": "Cognitive tasks are an important application area in brain-computer interfaces (BCI). Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are commonly used to monitor brain activity. EEG has high temporal resolution and can capture instantaneous brain electrical activities, while fNIRS provides higher spatial resolution and can reflect changes in brain blood flow. Due to the differences in time and space between the two, how to effectively integrate these two signals to improve the accuracy of cognitive tasks has become an important challenge. This paper proposes a fusion method based on spatio-temporal alignment, by optimizing the alignment and fusion process of EEG and fNIRS signals, to overcome the problems of signal asynchrony and noise interference, thereby improving the recognition effect of cognitive tasks. This method can effectively integrate the temporal information of EEG and the spatial information of fNIRS, providing a more comprehensive representation of cognitive states. Experimental results show that compared with traditional methods, the proposed fusion method significantly improves the performance of cognitive tasks. This research provides a new solution for the effective integration of EEG and fNIRS in cognitive tasks and demonstrates the potential of multimodal brain imaging technology in BCI applications.", "url": "" }, { "id": "Mo-MoB03.1", "code": "MoB03.1", "title": "Isodamping Tuning of PIDA Controllers", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:15", "sessionCode": "MoB03", "sessionTitle": "Shotgun: Process and Power Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Campregher, Francesco", "affiliation": "University of Brescia" }, { "name": "Visioli, Antonio", "affiliation": "University of Brescia" } ], "keywords": [ "Advanced process control" ], "abstract": "In this paper we present a tuning methodology for Proportional-Integral-Derivative-Acceleration (PIDA) controllers, also known as Proportional-Integral-Double-Derivative controllers (PIDD or PIDD2). In particular, the parameters are optimized to achieve the isodamping property at the gain crossover frequency, that is, a flat phase so that the same overshoot is achieved in the set-point response also in case of process gain variations. Simulation results demonstrate that the additional acceleration action allows the user to significantly improve the performance with respect to PID controllers so that PIDA controllers can be a valid alternative to fractional-order PID (FOPID) controllers for which the isodamping tuning is typically used.", "url": "" }, { "id": "Mo-MoB03.2", "code": "MoB03.2", "title": "Design of a Robust H∞ Mixed Sensitivity Temperature Controller for a Steel Slab Reheating Furnace", "day": "Monday", "date": "August 24, 2026", "time": "13:15-13:20", "sessionCode": "MoB03", "sessionTitle": "Shotgun: Process and Power Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Rivas-Perez, Raul", "affiliation": "Havana Technological University" }, { "name": "Sotomayor Moriano, Javier", "affiliation": "Pontificia Universidad Católica Del Perú" }, { "name": "Pérez Zuñiga, Gustavo", "affiliation": "Pontifical Catholic University of Peru" }, { "name": "Feliu-Batlle, Vicente", "affiliation": "Univ of Castilla-La Mancha. CIF: Q-1368009E" } ], "keywords": [ "Advanced process control", "MMM process modeling, identification, and estimation techniques" ], "abstract": "Robust temperature control in the soaking zone of a steel slab reheating furnace is addressed. A dynamic model of the nominal process is obtained using a system identification technique based on real-time data, resulting in a second-order model. A robust H∞ mixed-sensitivity temperature controller is then designed. Simulations of the control system are carried out using the designed robust controller and a conventional PI controller. A comparative analysis of the simulation results highlights the superior performance of the proposed H∞ controller.", "url": "" }, { "id": "Mo-MoB03.3", "code": "MoB03.3", "title": "Cascade Model Predictive Control of Air Handling-Unit for Building Temperature Regulation", "day": "Monday", "date": "August 24, 2026", "time": "13:20-13:25", "sessionCode": "MoB03", "sessionTitle": "Shotgun: Process and Power Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Wang, Liuping", "affiliation": "RMIT University" }, { "name": "Guan, Robin", "affiliation": "RMIT University" }, { "name": "Meegahapola, Lasantha", "affiliation": "RMIT University" } ], "keywords": [ "Advanced process control", "Model-predictive and optimization-based control in chemical processes", "Industrial applications of process control" ], "abstract": "Heating Ventilation and Air Conditioning systems have been one of the most energy intensive units in buildings. How to regulate and optimize these systems for reducing energy consumptions while maintaining occupant's comfort level provides a great opportunity in the area of building automation and power grid support. This paper presents an experimental study on the air-handling-unit, which is the fundamental building block of a heating ventilation and air conditioning system. The focus is to address the problems of severe nonlinearity, large time delay and the combination of these two factors. Choosing discrete-time model predictive control as the vehicle for the control system design and implementation, the experimental study shows that a cascade model predictive control system with a dual sampling rate is an effective approach to solve the difficult control problems in a typical heating ventilation and air conditioning system.", "url": "" }, { "id": "Mo-MoB03.4", "code": "MoB03.4", "title": "A Rapid-Prototype MPC Tool Based on gPROMS Platform", "day": "Monday", "date": "August 24, 2026", "time": "13:25-13:30", "sessionCode": "MoB03", "sessionTitle": "Shotgun: Process and Power Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Wu, Liang", "affiliation": "Johns Hopkins University" } ], "keywords": [ "Advanced process control", "Model-predictive and optimization-based control in chemical processes", "Industrial applications of process control" ], "abstract": "This paper presents a rapid-prototyping Model Predictive Control (MPC) tool built on the gPROMS platform, supporting the entire MPC design workflow. The gPROMS-MPC tool can not only directly interact with a first-principle-based gPROMS model for closed-loop simulations but also utilizes its mathematical information to derive simplified control-oriented models, basically via linearization techniques. It can inherit the interpretability of the first-principle-based gPROMS model, unlike the PAROC framework, in which the control-oriented models are obtained from black-box system identification based on gPROMS simulation data. The gPROMS-MPC tool allows users to choose when to linearize, such as at each sampling time (successive linearization) or at some specific points to obtain one or multiple good linear models. The gPROMS-MPC tool implements our previous construction-free CDAL and the online parametric active-set qpOASES algorithms to solve sparse or condensed MPC problem formulations, respectively, for possible successive linearization or high state-dimension cases. Our CDAL algorithm is also matrix-free and library-free, thus supporting embedded C-code generation. After many example validations of the tool, here we only show one example to investigate the performance of different MPC schemes.", "url": "" }, { "id": "Mo-MoB03.5", "code": "MoB03.5", "title": "Sparse State Feedback Control for Industrial Applications", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:35", "sessionCode": "MoB03", "sessionTitle": "Shotgun: Process and Power Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Gurpegui, Alba", "affiliation": "Lund University" }, { "name": "Norlund, Frida", "affiliation": "Lund University" }, { "name": "Soltesz, Kristian", "affiliation": "Lund University" }, { "name": "Rantzer, Anders", "affiliation": "Lund Univ" } ], "keywords": [ "Advanced process control", "Model-predictive and optimization-based control in chemical processes", "Industrial applications of process control" ], "abstract": "We present an optimization-based methodology for designing sparse state-feedback controllers for industrial applications that are suited for linear control, and demonstrate the framework by designing a level controller for an industrial rougher flotation bank at the Aitik mine. In contrast to the dense linear-quadratic (LQ) controller gains currently operating at the concentrator, our approach enforces a sparsity pattern that is consistent with the interaction structure of the flotation bank and accounts for the worst-case expected inflow disturbances during tuning, while optimizing controller performance through the Integral Absolute Error (IAE) index. The non-zero elements of the sparse gain matrices are optimized using a coordinate search algorithm that handles bound constraints and preserves closed-loop stability. The resulting sparse controller achieves improved load disturbance rejection in the flotation cells compared to the LQ controller. These improvements are consistently observed in both linear and nonlinear simulations. In addition, the imposed structure, results in gain matrices that are easier to adjust and interpret. Importantly, the sparse controllers generated for the Aitik mine are directly suitable for industrial deployment and offer an effective alternative to the existing dense LQ design.", "url": "" }, { "id": "Mo-MoB03.6", "code": "MoB03.6", "title": "Study of Advanced Motion Controllers Adapted for a Safety-Critical Drilling Process", "day": "Monday", "date": "August 24, 2026", "time": "13:35-13:40", "sessionCode": "MoB03", "sessionTitle": "Shotgun: Process and Power Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Diepeveen, Jullian", "affiliation": "Eindhoven University of Technology" }, { "name": "Pavlov, Alexey", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Steur, Erik", "affiliation": "Eindhoven University of Technology" }, { "name": "Ruderman, Michael", "affiliation": "University of Agder" } ], "keywords": [ "Advanced process control", "Nonlinear signal processing in MMM systems", "Reliability and safety in processes" ], "abstract": "The so-called gas kick scenario is a complex time-varying nonlinear and, most importantly, safety-critical dynamic process during drilling operations. It requires advanced pressure regulation on the top of the drilling system without whole sensing of the well-process variables. Adapted from the available advanced motion controllers, i.e. HIGS and nonlinear integral gain control, the nonlinear control architectures are proposed for standpipe pressure control in a well killing procedure. The proposed controllers use a nested structure with a feedback linearized inner PID-loop and extends the usual outer PI-loop for the standpipe pressure. The control performance is analyzed through the use of a high fidelity simulator (OpenLab), showing improvements of the overall control behavior for well killing procedures.", "url": "" }, { "id": "Mo-MoB03.7", "code": "MoB03.7", "title": "Response Matrix Identification & Slow Feedback Controller Design for EuXFEL to Mitigate the Tidal Effects", "day": "Monday", "date": "August 24, 2026", "time": "13:40-13:45", "sessionCode": "MoB03", "sessionTitle": "Shotgun: Process and Power Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Sharan, Bindu", "affiliation": "Deutsches Elektronen-Synchrotron DESY" }, { "name": "Bradarić, Danis", "affiliation": "University of Sarajevo" }, { "name": "Hespe, Christian", "affiliation": "Deutsches Elektronen-Synchrotron DESY" }, { "name": "Holmberg, Johan", "affiliation": "Lund University" }, { "name": "Kammering, Raimund", "affiliation": "Deutsches Elektronen-Synchrotron DESY" }, { "name": "Czwalinna, Marie Kristin", "affiliation": "DESY" }, { "name": "Eichler, Annika", "affiliation": "DESY" } ], "keywords": [ "Advanced process control", "Process modeling, identification, and estimation techniques", "Industrial applications of process control" ], "abstract": "This paper presents a structured methodology for identifying response matrices and designing slow feedback controllers at the European XFEL. We determine the response matrix using an iterative least-squares algorithm inspired by Sparse Identification of Nonlinear Dynamical Systems (SINDy), incorporating prior knowledge of zero elements to improve accuracy. To better reflect real-world behaviour, we extend the system from a static to a dynamic model by introducing an inherent time delay. For reference tracking, PID gain matrices are obtained by reformulating the problem as a state-feedback problem using a Linear Quadratic Regulator (LQR). The controller is applied to a model identified from open-loop data, ensuring consistency with experimental beam dynamics. Finally, we introduce two additional PI controllers to compensate for tidal effects influencing bunch arrival time and energy. Simulation results show that this framework effectively stabilises the beam and mitigates slow drifts, providing a reliable foundation for accelerator operation.", "url": "" }, { "id": "Mo-MoB03.8", "code": "MoB03.8", "title": "Distributed Nonlinear Model Predictive Control Frame for Microgrids with Constant Power Loads", "day": "Monday", "date": "August 24, 2026", "time": "13:45-13:50", "sessionCode": "MoB03", "sessionTitle": "Shotgun: Process and Power Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Toro, Vladimir", "affiliation": "Universidad Santo Tomás" }, { "name": "Tellez-Castro, Duvan", "affiliation": "Universidad Distrital Francisco José De Caldas" }, { "name": "Rakoto, Naly", "affiliation": "IMT Atlantique and LS2N, Nantes, France" } ], "keywords": [ "Control of multi-scale, distributed, and particulate systems", "Control and optimization for sustainability and energy systems", "Power systems stability" ], "abstract": "This paper presents the analysis and design of a control law for a set of continuous current converters that supply a constant-power load. The controller implements a distributed consensus-enhanced nonlinear MPC scheme based on the nonlinear model of the source–load dynamics, incorporating a consensus term as a constraint. The MPC problem is solved at each iteration using a dedicated optimization solver. The proposed controller enhances voltage regulation throughout the entire system while relying solely on local information. The effectiveness of the controller is demonstrated through a simulation model evaluated under several constant-power-load scenarios.", "url": "" }, { "id": "Mo-MoB03.9", "code": "MoB03.9", "title": "Integrated Framework and Application of Planning and Scheduling under Uncertain Condition: Large-Scale Crude Oil Scheduling Scenario", "day": "Monday", "date": "August 24, 2026", "time": "13:50-13:55", "sessionCode": "MoB03", "sessionTitle": "Shotgun: Process and Power Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Xie, Yunhao", "affiliation": "China University of Petroleum, Beijing" }, { "name": "He, Renchu", "affiliation": "China University of Petroleum, Beijing" }, { "name": "Sun, Lin", "affiliation": "China University of Petroleum, Beijing" }, { "name": "Feng, Enbo", "affiliation": "East China University of Science and Technology" } ], "keywords": [ "Control of multi-scale, distributed, and particulate systems", "Machine learning and artificial intelligence in chemical process control", "Control and optimization of supply chains in chemical processes" ], "abstract": "To address the complexity and uncertainty in large-scale refinery crude oil scheduling, this study presents a Wasserstein Distance-based Distributionally Robust Chance-Constrained Long–Short Term Integrated Optimization (WDRCCLSO) model. Crude demand uncertainty is modeled via a data-driven WDRCC formulation, mitigating risks of supply imbalance. A hybrid MP/PSO optimization strategy solves the model, using mathematical programming (MP) for long-term allocation and particle swarm optimization (PSO) for short-term scheduling. Results show that the proposed approach efficiently produces robust and feasible short-term operational schedules.", "url": "" }, { "id": "Mo-MoB03.10", "code": "MoB03.10", "title": "A MATLAB-Based Simulation Tool for Fast and Efficient Control System Investigation for Laser-Powder Bed Fusion Process", "day": "Monday", "date": "August 24, 2026", "time": "13:55-14:00", "sessionCode": "MoB03", "sessionTitle": "Shotgun: Process and Power Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Al-Saadi, Taha", "affiliation": "Sultan Qaboos University" }, { "name": "Rossiter, J. Anthony", "affiliation": "Univ of Sheffield" }, { "name": "Panoutsos, George", "affiliation": "University of Sheffield" } ], "keywords": [ "Industrial applications of process control", "Process modeling, identification, and estimation techniques", "Advanced process control" ], "abstract": "Additive manufacturing, particularly the Laser Powder Bed Fusion (L-PBF) process, requires precise control of melt-pool dynamics to ensure consistent part quality and repeatability. However, the lack of fast and accessible control-oriented simulation tools limits the ability to design, test, and validate advanced control strategies. This paper presents a modular and computationally efficient MATLAB/Simulink-based simulation framework developed specifically for L-PBF process control studies. The proposed tool estimates melt-pool temperature and cross-sectional area while accounting for track-to-track and layer-to-layer heat accumulation effects. It enables rapid integration of various control algorithms, including proportional–integral–derivative (PID), feedforward, fuzzy logic, and many other, within closed-loop configurations. Validation against Rosenthal’s analytical solution and the heat balanced model demonstrates a good prediction errors with more than 500× improvement in computation speed compared to finite-element simulations. The results confirm that the proposed simulator provides an accurate, flexible, and user-friendly platform for rapid prototyping, control system education, and research in metal additive manufacturing.", "url": "" }, { "id": "Mo-MoB03.11", "code": "MoB03.11", "title": "Performance Assessment of Robust PID Controllers with Machine Learning", "day": "Monday", "date": "August 24, 2026", "time": "14:00-14:05", "sessionCode": "MoB03", "sessionTitle": "Shotgun: Process and Power Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Ruggeri, Diego", "affiliation": "University of Brescia" }, { "name": "Beschi, Manuel", "affiliation": "University of Brescia" }, { "name": "Visioli, Antonio", "affiliation": "University of Brescia" } ], "keywords": [ "Machine learning and artificial intelligence in chemical process control", "Monitoring, performance assessment, and fault detection in chemical process control", "Process performance monitoring/statistical process control" ], "abstract": "In this paper we present a performance assessment methodology, based on machine learning, for Proportional-Integral-Derivative (PID) controllers. A set-point step response is evaluated and a control loop that exhibits a high integrated absolute error and a maximum sensitivity far from the optimal one is detected. Further, a performance index that gives the distance of the current maximum sensitivity to the optimal one is determined. In this way, the robustness of the loop is explicitly taken into account. Results obtained from simulated routine data demonstrate that models based on a convolutional autoencoder can achieve high performance on the proposed tasks.", "url": "" }, { "id": "Mo-MoB03.12", "code": "MoB03.12", "title": "Privacy-Preserving Nonlinear DMPC for Multi-Agent Consensus with CKKS Encryption", "day": "Monday", "date": "August 24, 2026", "time": "14:05-14:10", "sessionCode": "MoB03", "sessionTitle": "Shotgun: Process and Power Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Gao, Ruiyang", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Wu, Jing", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Long, Chengnian", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes" ], "abstract": "In this paper, a distributed model predictive control strategy for nonlinear multi-agent systems under encrypted communication is investigated. To address the challenges caused by encrypted couplings in conventional distributed model predictive control, a distributed optimization strategy based on the alternating direction method of multipliers is developed. This approach decomposes the global non-convex optimization problem into local subproblems, while all exchanged information is protected via the Cheon-Kim-Kim-Song homomorphic encryption scheme combined with randomized masking. Furthermore, a theoretical relationship between encryption depth and control error, enabling a systematic balance between privacy strength and control performance is derived. Simulation results demonstrate that the proposed strategy effectively preserves privacy while maintaining closed-loop performance and robustness.", "url": "" }, { "id": "Mo-MoB03.13", "code": "MoB03.13", "title": "Constraints Reduction in a Multi-Model Predictive Controller Applied to a Propylene Polymerization Reactor", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:15", "sessionCode": "MoB03", "sessionTitle": "Shotgun: Process and Power Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Vargan, Jozef", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Kurucz, Gyula", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Klauco, Martin", "affiliation": "Czech Technical University" }, { "name": "Latifi, M.A.", "affiliation": "Cnrs - Ensic, B.p. 20451" }, { "name": "Fikar, Miroslav", "affiliation": "Slovak University of Technology in Bratislava" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes", "Advanced process control", "Industrial applications of chemical process control" ], "abstract": "Industrial processes are often governed by complex nonlinear dynamics, posing significant challenges for control design. While nonlinear predictive control can effectively manage such behavior, its high computational demand limits practical implementation. An alternative approach is to approximate the nonlinear system using a set of linear models within a multi-model predictive control (mMPC) framework, thereby reducing computational complexity. However, the inclusion of constraints into all models remains computationally demanding. To address this issue, two reduced-constraint mMPC formulations are proposed: one based on the static gain matrix of individual models (mMPCsg) and another on their unforced responses (mMPCur). Application to a MIMO propylene polymerization reactor - heat exchanger system demonstrates a considerable reduction in computation time while preserving control performance and maintaining constraint violations at levels comparable to the full-constraint mMPC.", "url": "" }, { "id": "Mo-MoB03.14", "code": "MoB03.14", "title": "Data-Driven Model Predictive Anti-Slug Control for Offshore Gas-Lifted Oil Wells", "day": "Monday", "date": "August 24, 2026", "time": "14:15-14:20", "sessionCode": "MoB03", "sessionTitle": "Shotgun: Process and Power Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Gude, Tore", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Imsland, Lars", "affiliation": "Norwegian University of Science and Technology" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes", "Process modeling, identification, and estimation techniques", "Industrial applications of process control" ], "abstract": "This paper models the dynamics of a slugging oil well using the Sparse Identification of Nonlinear Dynamics (SINDy) method based on simulated data from the high-fidelity OLGA simulator. The identified model closely predicts the unstable dynamics (slugging) of an oil well, even though the model is not parsimonious and lacks interpretability. The model is used in a Model Predictive Control (MPC) framework to stabilize slugging flow, and is validated in closed-loop simulations in OLGA. The controller stabilizes slugging flow for a wider range of operating points and at higher choke valve openings than a PI controller, allowing increased production from the oil well.", "url": "" }, { "id": "Mo-MoB03.15", "code": "MoB03.15", "title": "A Practical Framework for Process Anomaly Detection Analysis in Multivariate Time Series", "day": "Monday", "date": "August 24, 2026", "time": "14:20-14:25", "sessionCode": "MoB03", "sessionTitle": "Shotgun: Process and Power Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Arbetová, Patrícia", "affiliation": "Slovak University of Technology in Bratislava, Faculty of Chemical and Food Technology" }, { "name": "Fáber, Rastislav", "affiliation": "Slovak University of Technology in Bratislava, Faculty of Chemical and Food Technology" }, { "name": "Ľubušký, Karol", "affiliation": "Slovnaft, A.s" }, { "name": "Paulen, Radoslav", "affiliation": "Slovak University of Technology in Bratislava" } ], "keywords": [ "Monitoring, performance assessment, and fault detection in chemical process control", "Data-driven methods for FDI/FTC", "Machine learning and artificial intelligence in chemical process control" ], "abstract": "Online analyzers provide frequent product-quality measurements, yet may drift, become miscalibrated, or fail. Laboratory measurements are more reliable but sparse and delayed, which makes direct anomaly detection difficult. This paper uses a multi-fidelity (MF) soft sensor as a laboratory-quality reference for anomaly detection in multivariate industrial time series. Deviations between the online analyzer and the MF reference define pseudo ground-truth labels over the dense online timeline. Under these labels, we compare three detector strategies: univariate output rules, input-space detectors with feature selection and dimensionality reduction, and model-based residual detectors. The industrial case study shows that output-only rules produce few false alarms but miss most pseudo-labeled anomalies, while input-space detectors using physically meaningful variables give the best sensitivity-specificity trade-off. Since independent industrial fault labels are not available, the reported metrics measure agreement with the MF reference, not a confirmed detection of real faults.", "url": "" }, { "id": "Mo-MoB03.16", "code": "MoB03.16", "title": "Modeling and Numerical Simulation of Gas–Liquid Flow in an Elastic Foam-Bed Reactor with a Perforated Moving Plate", "day": "Monday", "date": "August 24, 2026", "time": "14:25-14:30", "sessionCode": "MoB03", "sessionTitle": "Shotgun: Process and Power Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Cheng, Xiaoyu", "affiliation": "Université Claude Bernard Lyon 1" }, { "name": "Jallut, Christian", "affiliation": "Université Claude Bernard Lyon 1" }, { "name": "Maschke, Bernhard", "affiliation": "Univ Claude Bernard of Lyon" }, { "name": "Tricas, Laura", "affiliation": "CP2M" }, { "name": "Edouard, David", "affiliation": "University Lyon1" } ], "keywords": [ "Process modeling, identification, and estimation techniques" ], "abstract": "The elastic foam-bed reactor (EFR) uses a moving plate that periodically compresses a deformable open cell polyurethane foam, which changes the local porosity and flow resistance in a controlled way. We present a one-dimensional dynamic model that represents the plate motion and its effect on the fluid flow dynamics inside the reactor filled with two blocks of deformable foam driven by the plate motion. The model consists in the mass and momentum balances for the gas and liquid phases coupled to the controlled deformation of the foam bed. The resulting set of equations is solved using an arbitrary Lagrangian-Eulerian discontinuous Galerkin (ALE–DG) method. The simulations show that the plate movement induces clear oscillation in phase fractions, velocities, and pressure drop, providing useful insight into the flow patterns and phase-distribution dynamics of reactors with structured packing driven by a moving plate.", "url": "" }, { "id": "Mo-MoB03.17", "code": "MoB03.17", "title": "A Quantum-Enhanced Hybrid Approach for Parameter Estimation in Gas-Phase Fixed-Bed Adsorption Experiments", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:35", "sessionCode": "MoB03", "sessionTitle": "Shotgun: Process and Power Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "G. Matias, Rui D.", "affiliation": "LSRE-LCM, ALiCE, Faculty of Engineering, University of Porto" }, { "name": "Ferreira, Alexandre", "affiliation": "Laboratory of Separation and Reaction Engineering Associate Laboratory LSRE-LCM, Department of Chemical Engineering, Faculty Of" }, { "name": "Nogueira, Idelfonso", "affiliation": "NTNU" }, { "name": "Ribeiro, Ana Mafalda", "affiliation": "Laboratory of Separation and Reaction Engineering Associate Laboratory LSRE-LCM, Department of Chemical Engineering, Faculty Of" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Machine learning and artificial intelligence in chemical process control" ], "abstract": "Quantum computing is emerging as one of the most promising paradigms for computational science. This work presents a hybrid quantum-classical optimization framework that combines a Variational Quantum Circuit with a classical feedforward neural network, optimized via Bayesian methods, to estimate parameters in a mathematical model of CO2/CH4 fixed-bed adsorption based separations. The hybrid algorithm is compared with conventional correlation-based methods and direct Bayesian optimization of physical parameters. Results demonstrate that the quantum-classical approach consistently identifies parameter sets that improve the fit to experimental data despite higher dimensionality.", "url": "" }, { "id": "Mo-MoB03.18", "code": "MoB03.18", "title": "A Neural Network-Based Grey-Box Model of Solvent-Based Carbon Capture", "day": "Monday", "date": "August 24, 2026", "time": "14:35-14:40", "sessionCode": "MoB03", "sessionTitle": "Shotgun: Process and Power Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Martinsen, Emil Skov", "affiliation": "Technical University of Denmark" }, { "name": "Kloppenborg Møller, Jan", "affiliation": "Technical University of Denmark" }, { "name": "Madsen, Henrik", "affiliation": "Tech. Univ. of Denmark" }, { "name": "Einbu, Aslak", "affiliation": "SINTEF Industry" }, { "name": "Mejdell, Thor", "affiliation": "SINTEF" }, { "name": "Kvamsdal, Hanne M.", "affiliation": "SINTEF Industry" }, { "name": "Tobiesen, Andrew", "affiliation": "SINTEF Industry" }, { "name": "Goranovic, Goran", "affiliation": "Technical University of Denmark (DTU)" }, { "name": "Ritschel, Tobias K. S.", "affiliation": "Technical University of Denmark" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Machine learning and artificial intelligence in chemical process control" ], "abstract": "To lower the operational costs of solvent-based carbon capture, model-based control plays a key role. Such control strategies require accurate, computationally efficient, and adaptive dynamic models. In this work, we propose a neural network-embedded grey-box model for solvent-based carbon capture systems, which combines physical knowledge of the system with a neural network to capture complex and unknown dynamics. We train and test the model on real-world experimental data from the Tiller pilot plant in Trondheim, Norway. We implement a disturbance-adaptive extended Kalman filter for adaptive state estimation and prediction and demonstrate that the proposed model provides accurate predictions on unseen test data and adaptively mitigates steady state offsets.", "url": "" }, { "id": "Mo-MoB03.19", "code": "MoB03.19", "title": "Dynamic Model Identification of Power Systems for Electromechanical Oscillation Damping Control", "day": "Monday", "date": "August 24, 2026", "time": "14:40-14:45", "sessionCode": "MoB03", "sessionTitle": "Shotgun: Process and Power Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Frascarelli, Matteo", "affiliation": "University of Pisa" }, { "name": "Bacci di Capaci, Riccardo", "affiliation": "University of Pisa" }, { "name": "Vaccari, Marco", "affiliation": "University of Pisa" }, { "name": "Deihimi Kordkandi, Reza", "affiliation": "CITCEA-UPC, Departament d’Enginyeria El`ectrica, Universitat Polit`ecnica De Catalunya" }, { "name": "Cheah Mañé, Marc", "affiliation": "CITCEA-UPC, Departament d’Enginyeria Eléctrica, Universitat Politécnica De Catalunya" }, { "name": "Pannocchia, Gabriele", "affiliation": "University of Pisa" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Power systems stability", "Electrical transmission systems" ], "abstract": "This paper develops reduced-order linear models for power system dynamic analysis using data-driven identification approaches. Nonlinear Root Mean Square (RMS) simulations from a commercial software platform provide the reference trajectories, while different subspace and polynomial methods are applied to recover the dominant modes relevant for low-frequency oscillation damping control. The models identified are validated in simulation and prediction against rigorous nonlinear time-domain simulations to assess their ability to reproduce key dynamic behaviors. Results show that the models that were obtained capture the essential oscillatory dynamics with high reliability, offering an effective basis for tuning controllers when analytic linearization of the original system is impractical.", "url": "" }, { "id": "Mo-MoB03.20", "code": "MoB03.20", "title": "Load Allocation Optimization for Common-Header Boiler Systems", "day": "Monday", "date": "August 24, 2026", "time": "14:45-14:50", "sessionCode": "MoB03", "sessionTitle": "Shotgun: Process and Power Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Zhu, Yun", "affiliation": "Zhejiang University" }, { "name": "Zhu, Yucai", "affiliation": "Zhejiang University" } ], "keywords": [ "Real-time optimization and control in chemical processes", "Advanced process control", "Process modeling, identification, and estimation techniques" ], "abstract": "This paper presents an optimization method to improve the thermal efficiency of a common header boiler system. The optimization method uses the load of each boiler as the optimization variable and total coal consumption as the loss function. The proposed optimization method is gradient-based, with the gradient for each iteration obtained through system identification using test data, eliminating the need for an accurate model of the process. For the boiler header system, a cascade control structure has been proposed. Performing identification tests while ensuring the stability of the header load can avoid triggering nonlinearity. A two-layer model predictive control approach is employed, with the static layer continuously updating load allocation based on iterative optimization results, while the dynamic layer achieves fast tracking of load setpoints. The effectiveness of the proposed method is validated through a simulation case involving three boilers in a common header system.", "url": "" }, { "id": "Mo-MoB04.1", "code": "MoB04.1", "title": "Safety-Oriented Control Parameter Optimization for Nonlinear Systems Via ESO-Based Reachability Analysis", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:15", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Zhou, Yu", "affiliation": "National University of Defense Technology" }, { "name": "Li, Jie", "affiliation": "National University of Defense Technology" }, { "name": "Xiong, Zehao", "affiliation": "National University of Defense Technology" }, { "name": "Wang, Xiangke", "affiliation": "National University of Defense Technology" } ], "keywords": [ "Human machine safety", "Mechatronic system modeling, design, optimization", "Mechatronic system estimation, identification, control" ], "abstract": "For the safe control of nonlinear systems with model uncertainties, this paper proposes a reachability analysis and parameter optimization method based on an extended state observer (ESO) and zonotopes. The ESO and feedback control reshape the system dynamics, simplifying reachable set computation by treating the estimation error as a bounded uncertainty. The method reveals how ESO and controller bandwidths affect the safety boundary, enabling a safety-oriented parameter optimization strategy that systematically selects parameters to keep the reachable set away from unsafe regions. Thereby, safety assurance is shifted from post-hoc verification to proactive design. Simulation results validate the effectiveness of the proposed framework.", "url": "" }, { "id": "Mo-MoB04.2", "code": "MoB04.2", "title": "Online Trust Profiling and Adaptation for Human-Autonomy Interaction", "day": "Monday", "date": "August 24, 2026", "time": "13:15-13:20", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Williams, Daniel A.", "affiliation": "The University of Melbourne" }, { "name": "van Bockel, Joshua", "affiliation": "The University of Melbourne" }, { "name": "Chapman, Airlie Jane", "affiliation": "University of Melbourne" }, { "name": "Little, Daniel R.", "affiliation": "The University of Melbourne" }, { "name": "Manzie, Chris", "affiliation": "The University of Melbourne" } ], "keywords": [ "Human machine teaming", "Human machine cooperation & integration", "Cognitive processes and human machine systems" ], "abstract": "In human-autonomy interactions, the human supervisor's trust level is a critical factor in determining the quality of interaction. An observer subsystem can allow the autonomous system to estimate supervisor trust and react accordingly. Previously, a switched linear model was shown to capture key trust dynamics. A challenge for model identification is that continually polling a human's trust levels is impractical. To address this, an observer structure that uses intermittent human feedback is proposed. The observer is validated in a real-world scenario through a series of human trials; these trials show consequent benefits for task performance.", "url": "" }, { "id": "Mo-MoB04.3", "code": "MoB04.3", "title": "SEMG-Based Low-Latency Finger Classification and Voltage-Domain Flexion-Trajectory Estimation for Finger Motion Reproduction", "day": "Monday", "date": "August 24, 2026", "time": "13:20-13:25", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Won, Jiwoong", "affiliation": "Tokyo Denki University" }, { "name": "Iwata, Takaaki", "affiliation": "Tokyo Denki University" }, { "name": "Iwase, Masami", "affiliation": "Tokyo Denki University" } ], "keywords": [ "Human mechatronics and human-machine interaction", "Teleoperation", "Human-robot interaction" ], "abstract": "This study validates an sEMG-based computational pipeline for finger classification and voltage-domain finger-flexion trajectory estimation toward prosthetic-hand control. To enable low-latency software-side processing, the framework integrates lightweight TD feature extraction, a two-stage SVM classifier, and finger-specific MISO-NARX models. Experiments showed that the top twenty configurations all exceeded 90% E2E classification accuracy, with the best configuration reaching 91.28%. The optimized NARX models showed strong agreement with the measured voltage-domain finger-flexion trajectories (R 2 = 0.907-0.975). The measured software-side E2E processing delay from sEMG input to estimated trajectory output was approximately 40 ms; however, motor control, motor actuation, mechanical response, and physical prosthetic-hand motion were not included in this measurement. These results show that the proposed pipeline can perform finger classification and voltage-domain flexion-trajectory estimation accurately and rapidly under controlled experimental conditions, suggesting its potential as a signal-processing basis for future real-time prosthetic-hand control.", "url": "" }, { "id": "Mo-MoB04.4", "code": "MoB04.4", "title": "Wrist Angle Estimation Based on sEMG and Skin Deformation", "day": "Monday", "date": "August 24, 2026", "time": "13:25-13:30", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Tojo, Shun", "affiliation": "Tokyo Denki University" }, { "name": "Won, Jiwoong", "affiliation": "Tokyo Denki University" }, { "name": "Iwata, Takaaki", "affiliation": "Tokyo Denki University" }, { "name": "Iwase, Masami", "affiliation": "Tokyo Denki University" } ], "keywords": [ "Human-robot interaction", "Mechatronic system estimation, identification, control", "Biomedical and biomimetic mechatronic systems" ], "abstract": "The purpose of this study is to improve the accuracy of joint-angle estimation during wrist-angle holding motions in robotic hands using a nonlinear autoregressive model with exogenous inputs (NARX). Although sEMG provides informative signals during the initiation of wrist flexion, its amplitude typically attenuates during sustained holds, causing NARX-based angle estimates to drift toward the neutral position. To address this limitation, forearm skin deformation measured by pressure sensors is incorporated as force myography (FMG) and fused with sEMG as inputs to the NARX model. The proposed sEMG-FMG integration reduces fluctuations in the estimated angle during holding motions and enables accurate representation of wrist posture throughout both flexion and hold phases of motion. The effectiveness of the proposed model is experimentally evaluated by comparing wrist-angle estimates obtained using sEMG-only, FMG-only, and sEMG+FMG inputs. In future work, this approach aims to support a two-degree-of-freedom servo system incorporating Electro-Mechanical Delay (EMD) and Zero- Phase Error Tracking Control (ZPETC), followed by evaluation on a robotic hand.", "url": "" }, { "id": "Mo-MoB04.5", "code": "MoB04.5", "title": "Experimental Validation of an Approximate Analytical Predictor for the Torque-Actuated Spring-Mass Hopper", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:35", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Ozturk, Ahmet Safa", "affiliation": "Bilkent University" }, { "name": "Uyanik, Ismail", "affiliation": "Hacettepe University" }, { "name": "Morgul, Omer", "affiliation": "Bilkent Univ" } ], "keywords": [ "Humanoid and legged robots", "Mechatronic system modeling, design, optimization", "Biomedical and biomimetic mechatronic systems" ], "abstract": "This paper presents the experimental validation of an approximate analytical predictor for a torque-actuated, dissipative spring-mass hopper. While the spring-mass template effectively models running dynamics, its non-integrable stance phase necessitates approximations for real-time control. We investigate the predictive accuracy of an Approximate Analytical Solution (AAS) that accounts for leg damping, air drag, and active hip torque, using a comprehensive multi-stride dataset collected from a custom monopedal robot. Our comparative analysis demonstrates that the AAS accurately predicts the system's coupled dynamics with high fidelity, closely matching numerical integration results while offering significantly greater computational efficiency. These findings validate the utility of torque-actuated analytical models for developing robust, model-based controllers for physical legged platforms.", "url": "" }, { "id": "Mo-MoB04.6", "code": "MoB04.6", "title": "Investigating Sensitivity of Initial Conditions in Robotic Systems Using a Multibody Dynamics Framework", "day": "Monday", "date": "August 24, 2026", "time": "13:35-13:40", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Abokhalil, Heba", "affiliation": "E-JUST" }, { "name": "Nada, Ayman Ali", "affiliation": "Egypt-Japan University of Science and Technology" } ], "keywords": [ "Humanoid and legged robots", "Medical and rehabilitation robotics", "Mechatronic system modeling, design, optimization" ], "abstract": "This paper presents a computational framework for analyzing the sensitivity of multibody system dynamics with respect to initial conditions, with direct applications to rehabilitation robotics and biomechanical systems. The methodology is based on a variational approach that augments the state-space formulation with sensitivity equations, enabling the evaluation of how small perturbations in initial positions and velocities influence system trajectories. A pendulum-like planar subsystem, extracted from a lower-limb exoskeleton model, is used as a case study to demonstrate the framework's effectiveness. The system is reduced via coordinate partitioning, and the dynamics are integrated alongside sensitivity matrices using a modular set of MATLAB routines. Numerical simulations under different initial configurations reveal distinct sensitivity behaviors, highlighting regions of dynamic stability versus heightened reactivity. The results provide valuable insight into the role of initialization in multibody system design and control strategies. This framework can be extended using adjoint sensitivity formulations, quantitative metrics, and uncertainty quantification for high-dimensional, real-time applications.", "url": "" }, { "id": "Mo-MoB04.7", "code": "MoB04.7", "title": "Induction Machines for Precision Positioning: Part I - Parameter Estimation for Torque Bound Construction", "day": "Monday", "date": "August 24, 2026", "time": "13:40-13:45", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Zhao, Qianhong", "affiliation": "University of Virginia" }, { "name": "Wang, Yebin", "affiliation": "Mitsubishi Electric Research Laboratories" }, { "name": "Fujita, Tomoya", "affiliation": "Mitsubishi Electric Corp" }, { "name": "Sato, Go", "affiliation": "Mitsubishi Electric Corporation" } ], "keywords": [ "Mechatronic system estimation, identification, control" ], "abstract": "This paper investigates parameter estimation of an induction machine (IM) for torque bound construction when the IM serves as the actuator in a precision positioning system. The problem is significant because accurate knowledge of torque bound is essential for trajectory planning and control in precision positioning systems. The parameter estimation problem differs from the well-studied speed-sensorless estimation problem along two dimensions: speed measurement is available and all parameters in the IM model are treated as unknown. To this end, we first determine the subset of parameters required to construct torque bound, thereby avoid estimating all parameters. Then a flux-free representation of the IM model is derived to facilitate parameter estimation based on voltages, currents, and speed measurements. With the flux-free model established, a dynamic regressor extension and mixing based adaptive law is employed to ensure convergent estimation of the subset of parameters, under a less restrictive persistent excitation condition. Simulation validates the effectiveness of the proposed scheme.", "url": "" }, { "id": "Mo-MoB04.8", "code": "MoB04.8", "title": "Adaptive RLUDE Disturbance-Rejection Control for Quadrotors", "day": "Monday", "date": "August 24, 2026", "time": "13:45-13:50", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Chen, Xin", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Wei, Wei", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Wang, Chen", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Huang, Hehong", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Song, Yanhe", "affiliation": "Yanshan University" }, { "name": "Guo, Qing", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Peng, Chen", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Zhang, Xinyu", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Xie, Siyu", "affiliation": "University of Electronic Science and Technology of China" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Adaptive and adaptable automation", "High-performance motion control systems" ], "abstract": "Quadrotor control is inherently challenged by strong nonlinearities, attitude–position coupling, parameter variations, external disturbances, and sensing limitations, which collectively degrade tracking performance. To address these challenges, this paper presents an adaptive disturbance-rejection framework based on reinforcement learning and uncertainty disturbance estimation (RLUDE). In this framework, a finite-time-convergent (FTC) estimator is employed to obtain the reference derivatives and unmeasurable states. In parallel, reinforcement learning adaptively adjusts the UDE parameters to improve the estimation and compensation of lumped uncertainties. Building upon the FTC estimator and the RLUDE scheme, the controller is developed with an error-coupled policy update mechanism, which can enhance transient performance and ensure steady-state accuracy. Furthermore, Lyapunov analysis establishes conditions for zero steady-state error and guarantees ultimately bounded tracking performance. Consequently, simulation and experimental results show that the proposed method effectively reduces transient overshoot and steady-state error under disturbances and parameter uncertainties, thereby improving the trajectory-tracking accuracy and robustness of quadrotor unmanned aerial vehicles (UAVs).", "url": "" }, { "id": "Mo-MoB04.9", "code": "MoB04.9", "title": "Finite-Time Control Based on Differential Flatness for Wheeled Mobile Robots with Experimental Validation", "day": "Monday", "date": "August 24, 2026", "time": "13:50-13:55", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Imtiaz Ur, Rehman", "affiliation": "Laboratoire ImViA EA 7535, équipe VIBOT" }, { "name": "Labbadi, Moussa", "affiliation": "Bretagne INP" }, { "name": "Abadi, Amine", "affiliation": "Laboratoire ImViA EA 7535, équipe VIBOT" }, { "name": "Lew Yan Voon, Lew Fock Chong", "affiliation": "Laboratoire ImViA EA 7535, équipe VIBOT" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Aerial, field, and marine robotics" ], "abstract": "A robust tracking control strategy is designed to empower wheeled mobile robots (WMRs) to track predetermined routes while operating in diverse fields and encountering disturbances like strong winds or uneven path conditions, which affect tracking performance. Ensuring the applicability of this tracking method in real-world scenarios is essential. To accomplish this, the WMR model is initially transformed into a linear canonical form by leveraging the differential flatness of its kinematic model, facilitating controller design. Subsequently, a novel integral nonlinear hyperplane-based sliding mode control (INH-SMC) technique is proposed for WMR under disturbances. The stability of the technique is analyzed and verified. Finally, its practical viability is demonstrated through a comparative real-world indoor experiment on a TurtleBot3 WMR subjected to disturbances, confirming the feasibility and efficacy of the proposed approach.", "url": "" }, { "id": "Mo-MoB04.10", "code": "MoB04.10", "title": "Extended State Observer–Based Control for a Ball-Balancing Platform with Base Variations", "day": "Monday", "date": "August 24, 2026", "time": "13:55-14:00", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Chen, Chih-Chia", "affiliation": "National Cheng Kung University" }, { "name": "Sung, Hsin-Yu", "affiliation": "National Cheng Kung University" }, { "name": "Peng, Chao-Chung", "affiliation": "Department of Aeronautics and Astronautics, National Chen Kung University, Tainan 701, Taiwan" } ], "keywords": [ "Mechatronic system estimation, identification, control", "High-performance motion control systems", "Aerial, field, and marine robotics" ], "abstract": "This paper investigates the modeling, disturbance estimation, and control of a ball–balancing mechanism platform operating on a moving base. Such systems arise in maritime, mobile-robotic, and field-deployment scenarios where continuous base oscillations degrade positioning accuracy and destabilize conventional controllers, making robust state estimation and compensation essential. To address the relevant issues, the nonlinear dynamics of the ball–plate system are first derived using the Lagrange formulation, explicitly accounting for inertial effects induced by the base motion. To enable real-time implementation, an inverse-kinematics mapping is developed to convert the desired platform pose into actuator commands while incorporating base pose variations. Based on a linearized model, a proportional–derivative (PD) controller augmented with an extended state observer (ESO) is designed to estimate both system states and lumped disturbances. Simulation studies on the full nonlinear model demonstrate that under quantization noise and identical PD control gains, the proposed ESO achieves more accurate disturbance reconstruction and improves trajectory-tracking performance compared with a differentiation-based estimator. These results highlight the effectiveness of ESO-enhanced control for precision balancing tasks conducted in oscillatory environments.", "url": "" }, { "id": "Mo-MoB04.11", "code": "MoB04.11", "title": "Feedforward Control with Dual Neural Networks under Partial Load-Side Measurement", "day": "Monday", "date": "August 24, 2026", "time": "14:00-14:05", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Okumura, Shinji", "affiliation": "Mitsubishi Electric" }, { "name": "Li, Na (Lina)", "affiliation": "SEAS Harvard" }, { "name": "Ikeda, Hidetoshi", "affiliation": "Mitsubishi Electric" }, { "name": "Sekiguchi, Hiroyuki", "affiliation": "Mitsubishi Electric" }, { "name": "Wang, Yebin", "affiliation": "Mitsubishi Electric Research Laboratories" } ], "keywords": [ "Mechatronic system estimation, identification, control", "High-performance motion control systems", "Mechatronic system modeling, design, optimization" ], "abstract": "Modern motion control systems generally employ both feedforward and feedback controllers to perform high-speed, high-precision positioning tasks. Recently, neural networks (NNs) have been paired with a physics-based feedforward controller to regulate the motor-side position. This paper advances NN-based feedforward controller design in two aspects. We first extend the architecture to facilitate simultaneous regulation of both the motor-side position and load-side position by introducing two NNs, each trained offline to reproduce signals obtained from multivariable iterative learning control. We then show that this straightforward extension alone cannot guarantee satisfactory tracking performance when the load-side position is partially measurable. To address this limitation, a sample-efficient direct learning approach is proposed to fine-tune the NNs online by minimizing the tracking errors. Extensive simulations validate the effectiveness of the proposed method.", "url": "" }, { "id": "Mo-MoB04.12", "code": "MoB04.12", "title": "Adaptive Observer for Superconducting Cavity Bandwidth and Detuning Estimation", "day": "Monday", "date": "August 24, 2026", "time": "14:05-14:10", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Richter, Bozo", "affiliation": "Deutsches Elektronen Synchrotron DESY" }, { "name": "Speidel, Leon Hendrik", "affiliation": "TU Hambug" }, { "name": "Eichler, Annika", "affiliation": "DESY" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Mechatronic system modeling, design, optimization" ], "abstract": "This contribution presents an observer design for real-time estimation of time-varying parameters in superconducting RF cavities, targeting low-complexity FPGA implementation in high-bandwidth low-level RF control systems. Based on a linear time-varying state-space description with augmented states for detuning and excess half bandwidth, an adaptive observer is synthesized via a time-varying Lyapunov transformation to achieve time-invariant error dynamics using idealized model assumptions. The resulting time-varying observer is evaluated in a simulation of pulsed operation including measurement noise, and is compared to an existing observer implementation to assess estimation accuracy, robustness, and implementation effort.", "url": "" }, { "id": "Mo-MoB04.13", "code": "MoB04.13", "title": "Identification of a Robot Joint with Gear and Link Flexibility Using Dual Encoders", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:15", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Zimmermann, Stefanie Antonia", "affiliation": "Linköping University" }, { "name": "Moberg, Stig", "affiliation": "ABB AB - Robotics" }, { "name": "Gunnarsson, Svante", "affiliation": "Linkoping University" }, { "name": "Norrlöf, Mikael", "affiliation": "ABB AB" }, { "name": "Enqvist, Martin", "affiliation": "Linköping University" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Mechatronics for robotic systems", "Mechatronic system modeling, design, optimization" ], "abstract": "Conventional models for robot manipulators assume rigid bodies and flexible joints. In this paper, a new joint model is presented which augments the conventional flexible joint model by lumped parameters on the arm side of the gearbox, accounting for flexibility and damping of bearings and links. A two-step method is used for identification of this model: First, the system’s frequency response function is estimated from measurements of the motor and gear angular position, as well as the motor torque. Second, the model parameters are found by optimization. The focus of this work is to separately identify gear and arm side stiffness. It is experimentally demonstrated that this is possible, using dual encoder measurements. Results of a simulation study as well as experimental results from a collaborative robot are presented.", "url": "" }, { "id": "Mo-MoB04.14", "code": "MoB04.14", "title": "A Control Allocation Strategy for Tendon-Driven Arms Modeled Via the Augmented Rigid Body Approach", "day": "Monday", "date": "August 24, 2026", "time": "14:15-14:20", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Pineda Rivera, Rogelio", "affiliation": "CIMAT" }, { "name": "Espinosa Loera, Isaac Yael", "affiliation": "Centro De Investigación En Matemáticas CIMAT" }, { "name": "Flores, Gerardo", "affiliation": "Texas A&M International University" }, { "name": "Becerra, Hector M.", "affiliation": "Centro De Investigación En Matemáticas (CIMAT)" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Mechatronics for robotic systems", "Soft robotics" ], "abstract": "This paper presents an integrated control framework for motor-driven, tendon-actuated continuum arms, building upon established modeling approaches based on the piecewise constant curvature (PCC) assumption and the augmented rigid body model (ARBM). The main contribution of the paper is a control allocation strategy that consistently maps curvature-level control efforts into physically realizable tendon tensions and motor torques, ensuring non-negativity and energetic consistency. The proposed allocation scheme enables the direct use of curvature-based controllers while explicitly accounting for the structure of tendon-driven actuation. By integrating curvature-space control, tendon force allocation, and motor–tendon dynamics within a unified framework, this work extends existing PCC–ARBM formulations to electrically actuated tendon-driven continuum arms.", "url": "" }, { "id": "Mo-MoB04.15", "code": "MoB04.15", "title": "Prior Knowledge Matching for Aircraft Equipment Fastener Assembly Defect Detection", "day": "Monday", "date": "August 24, 2026", "time": "14:20-14:25", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Zhang, Yuanhao", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Yin, Chun", "affiliation": "University of ElectronicScience and Technology of China, Chengdu611731, P.R. China" }, { "name": "Liu, Junyang", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Yan, Zhongbao", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Cao, Jiuwen", "affiliation": "Hangzhou Dianzi University" } ], "keywords": [ "Mechatronic system fault detection, diagnostics, hardware-in-the-loop simulation", "Adaptive and adaptable automation", "Decision support systems" ], "abstract": "Fastener assembly errors critically impact aviation manufacturing quality and safety, yet existing deep learning methods face challenges in compliance verification under variable assembly standards. We propose a collaborative detection framework integrating deep learning with deformable template matching. An improved YOLO11-AEDSF performs feature perception, followed by a deformable matching algorithm that encodes standards as a priori constraints to align with the perceptual results. The model is lightweighted via sparse pruning and knowledge distillation, reducing GFLOPs from 6.3 to 2.8 to meet real-time demands. On a custom dataset, the framework achieves 97.6% mAP@0.5, a 6.42-point improvement over the 91.18% baseline, enabling fastener defect detection under diverse assembly standards.", "url": "" }, { "id": "Mo-MoB04.16", "code": "MoB04.16", "title": "Perspectives on Reliability-Aware Force Control for Contact-Rich Robotics", "day": "Monday", "date": "August 24, 2026", "time": "14:25-14:30", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Kato, Takahiro", "affiliation": "The University of Tokyo" }, { "name": "Khan, Samir", "affiliation": "The University of Tokyo" }, { "name": "Takeishi, Naoya", "affiliation": "Haute école Specialisée De Suisse Occidentale" }, { "name": "Yairi, Takehisa", "affiliation": "Department of Aeronautics and Astronautics, the University of Tokyo" } ], "keywords": [ "Mechatronic system fault detection, diagnostics, hardware-in-the-loop simulation", "Human machine safety", "Human-robot interaction" ], "abstract": "This survey develops Reliability-Aware Force Control as an integrative framework for contact-rich robotics, addressing the gap between methodological maturity and operational trustworthiness. Three interrelated challenges are treated jointly: sensorless force estimation in friction-dominated regimes, fault-tolerant control that disambiguates contact from component failures, and formal safety guarantees via control barrier functions. Central to the analysis is the zero-velocity observability barrier, where static friction renders external forces structurally unobservable; emerging responses (dynamic friction models, active excitation, learning-augmented observers) are reviewed against this limit. Fault-detection methods are examined for their ability to discriminate intentional contact from sensor and actuator faults, and passivity-based stability and robust control barrier functions are assessed as mechanisms for formal safety certificates under estimation uncertainty. Case studies from human-robot collaboration, surgical robotics, and autonomous space servicing ground the developments in operational requirements. Identified research gaps include thermally-adaptive friction compensation, co-design of learned observers with verifiable safety, and resolution of the static observability barrier, together forming a roadmap for transitioning force control from laboratory demonstrations to safety-critical autonomy.", "url": "" }, { "id": "Mo-MoB04.17", "code": "MoB04.17", "title": "Model-Based Estimation of Battery SOC and Capacity in Robotic Systems with Experimental Validation", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:35", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Hellani, Hassanein", "affiliation": "Aix-Marseille Univ, CNRS, LIS" }, { "name": "Ribeiro, Warley F. R.", "affiliation": "Aix-Marseille Universite" }, { "name": "Azari, Hamidreza", "affiliation": "Aix-Marseille Univ" }, { "name": "Chauchat, Paul", "affiliation": "Aix-Marseille Université" }, { "name": "Graton, Guillaume", "affiliation": "Ecole Centrale De Marseille" } ], "keywords": [ "Mechatronic system fault detection, diagnostics, hardware-in-the-loop simulation", "Mechatronic system modeling, design, optimization", "Mechatronics for robotic systems" ], "abstract": "This paper presents a model-based approach for the joint estimation of the state of charge (SOC) and capacity of a lithium-ion battery integrated within a robotic power system. Unlike most SOC estimation approaches that rely on directly measured battery current, the proposed method reconstructs the battery current from the motor model and robot dynamics, enabling SOC and capacity estimation. The proposed method is implemented within a complete robotic framework simulation and validated using real robot data. The results demonstrate high accuracy and stability of the estimation under dynamic load conditions, confirming the effectiveness of the proposed method for embedded battery management in robotic applications.", "url": "" }, { "id": "Mo-MoB04.18", "code": "MoB04.18", "title": "Modeling and Optimization of a Contactless Air-Based Wafer Actuator for Enhanced Flatness and Precision", "day": "Monday", "date": "August 24, 2026", "time": "14:35-14:40", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Kakolyris, Giorgos", "affiliation": "Delft University of Technology" }, { "name": "van Ostayen, Ron", "affiliation": "Delft Universtiy of Technology" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "High-performance motion control systems", "Mechatronics for mobility systems" ], "abstract": "Thin wafers are essential elements in the high-tech industry. Currently, wafer handling is performed using contact pads, which can generate particles that may contaminate the chips, leading to a considerable yield loss. In addition, the increasing demand for energy efficiency drives the development of larger and thinner wafers. This increases wafer deformation and ultimately leads to breakage. To address both limitations, this work presents a systems-oriented approach to the design, modeling, and optimization of an air-based, contactless wafer actuator intended to improve handling precision while minimizing wafer deformation. Several design concepts are evaluated in terms of force generation and airflow consumption. The selected concept is then further refined using a coupled fluid–structure interaction and topology-optimization framework aimed at minimizing wafer deformation by tuning the airflow inlet configuration. The resulting actuator can accelerate a 100 mm silicon wafer at 2.3 g, requires 15.2 g/s of airflow, and limits wafer deformation to 15.2 μm.", "url": "" }, { "id": "Mo-MoB04.19", "code": "MoB04.19", "title": "Swing Amplitude Adjustment Method of an Extensible Single-Rod Brachiation Robot", "day": "Monday", "date": "August 24, 2026", "time": "14:40-14:45", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Osawa, Aoto", "affiliation": "Tokyo University of Agriculture and Technology" }, { "name": "Lieskovský, Juraj", "affiliation": "Czech Technical University in Prague" }, { "name": "Busek, Jaroslav", "affiliation": "Department of Instrumentation and Control Enginnering, Faculty of Mechanical Engineering, Czech Technical University in Prague" }, { "name": "Vyhlidal, Tomas", "affiliation": "Czech Technical Univ in Prague, Faculty of Mechanical Engineering" }, { "name": "Mizuuchi, Ikuo", "affiliation": "Tokyo University of Agriculture and Technology" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "Mechatronic system estimation, identification, control", "Aerial, field, and marine robotics" ], "abstract": "In this paper, we propose and parameterize a method for adjusting the swing amplitude during the excitation phase of an extensible single-rod brachiation robot for brachiation motion based on the next bar position. Using the proposed method, we achieved a brachiation behavior in which the 0.74 m long extensible robot brachiates from one bar to another which are at: i) the same height, ii) the other is 0.14 m higher than the former. This was achieved without an aerial phase in both cases as the bars were in a smaller distance than the robot length. This is followed by a brachiation experiment with an aerial phase, where the bar distance is 0.79 m.", "url": "" }, { "id": "Mo-MoB04.20", "code": "MoB04.20", "title": "From Object-Oriented Simulation to Model Based MPC Design - an Automated Procedure", "day": "Monday", "date": "August 24, 2026", "time": "14:45-14:50", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Chevathamanon, Patarachai", "affiliation": "RPTU University of Kaiserslautern-Landau" }, { "name": "Liu, Steven", "affiliation": "University of Kaiserslautern Landau" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "Mechatronic system estimation, identification, control", "Mechatronic system fault detection, diagnostics, hardware-in-the-loop simulation" ], "abstract": "This paper presents an automated procedure for obtaining a linearized, state-space representation for MPC design directly from an object-oriented simulation model. The method integrates structural analysis, successive linearization, and causalization. A lightweight user interface is provided to configure MPC settings, enabling closed-loop online optimization in conjunction with the object-oriented simulation while requiring minimal user intervention. A water-boosting station case study demonstrates that the automatically obtained state-space model captures the dominant system dynamics and enables efficient, energy-aware flow control.", "url": "" }, { "id": "Mo-MoB04.21", "code": "MoB04.21", "title": "Performance Evaluation of Embedded MPC-QP Solvers on STM32-Based Real-Time Platforms", "day": "Monday", "date": "August 24, 2026", "time": "14:50-14:55", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Jin, Duyong", "affiliation": "Inha University" }, { "name": "Gwon, Minwoo", "affiliation": "Inha University" }, { "name": "Kim, Kwangki", "affiliation": "Inha University" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "Mechatronics for robotic systems", "Task and motion planning" ], "abstract": "Model Predictive Control (MPC) has traditionally been restricted to desktop-based control systems due to its computational complexity. Recent advances in semiconductor integration have made it feasible to implement MPC on single-chip microcontrollers. Despite this progress, systematic research and practical demonstrations of MPC on embedded hardware remain relatively scarce. This paper implements linear MPC using open-source Quadratic Programming (QP) and Second-Order Cone Programming (SOCP) solvers on an STM32 NUCLEO-F767ZI (Cortex-M7) microcontroller and assess their performance through Processor-in-the-Loop Simulations (PiLS). The results highlight the distinct characteristics of each solver and demonstrate their practical applicability to embedded MPC implementations.", "url": "" }, { "id": "Mo-MoB04.22", "code": "MoB04.22", "title": "Motor Cost Re-Optimization in Indirect Human Movement Pattern Adaptation", "day": "Monday", "date": "August 24, 2026", "time": "14:55-15:00", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Xu, Yangmengfei", "affiliation": "The University of Melbourne" }, { "name": "Crocher, Vincent", "affiliation": "The University of Melbourne" }, { "name": "Fong, Justin", "affiliation": "University of Melbourne" }, { "name": "Tan, Ying", "affiliation": "The Univ of Melbourne" }, { "name": "Oetomo, Denny Nurjanto", "affiliation": "The University of Melbourne" } ], "keywords": [ "Medical and rehabilitation robotics", "Human-robot interaction" ], "abstract": "Human movement resolves kinematic redundancy by organizing high-dimensional joint activity into low-dimensional coordination patterns, or synergies, which are plastic and can be reshaped for rehabilitation and skill training. While explicit error correction can reduce task errors, it may also induce slacking, limiting genuine learning. Indirect shaping control (ISC) was proposed to induce movement pattern change implicitly, without explicit reference trajectories. In a previous experiment, 20 participants performed reaching tasks while a robotic system applied a hand force that varied with the arm’s swivel angle, creating an energetic bias that altered their movement patterns. Although this setup induced adaptation under ISC, the underlying motor-cost mechanisms remained unquantified. In this work, we retrospectively analyzed the same dataset using a rigid-body inverse-dynamics model to estimate motor cost associated with swivel-angle change. Motor cost was quantified using the torque-time integral (TTI) and decomposed into natural and robot-induced components, linking cost variation to swivel angle and hand velocity. This study provides a quantitative description of implicit adaptation and insights for designing effective implicit interventions.", "url": "" }, { "id": "Mo-MoB04.23", "code": "MoB04.23", "title": "Adaptive Bias Adjustment of Event Cameras for Pose Estimation", "day": "Monday", "date": "August 24, 2026", "time": "15:00-15:05", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Tao, Xingyu", "affiliation": "University of Glasgow" }, { "name": "Zhao, Dezong", "affiliation": "University of Glasgow" } ], "keywords": [ "Robot perception and sensing", "Adaptive and adaptable automation", "Robotic learning and adaptation" ], "abstract": "Object pose estimation is a key task in computer vision, whose goal is to accurately obtain a representation of the object pose in the real world. Unlike traditional frame-based cameras, event cameras offer high temporal resolution, low latency, and a high dynamic range, making them well-suited for capturing fast-moving objects and handling challenging lighting conditions. The accurate estimation of pose of objects using event cameras is highly influenced by the system's ability to adapt to changing environmental conditions, particularly variations in lighting. The Bias of event camera refers to a set of configuration parameters that control the sensitivity and behavior of the individual pixels in the sensor. Traditional methods with fixed bias settings often struggle to maintain precision in dynamic environments. To address this, an adaptive bias adjustment mechanism is proposed which dynamically responds to light intensity fluctuations, enhancing the reliability of pose estimation. This real-time adjustment ensures that the event camera can capture relevant data without being affected by external changes, leading to more stable and accurate tracking. The real-world experiment shows that the system achieves precise pose estimation in various lighting conditions, with errors under 5%.", "url": "" }, { "id": "Mo-MoB04.24", "code": "MoB04.24", "title": "HRNet Pose Estimation of Target AUVs", "day": "Monday", "date": "August 24, 2026", "time": "15:05-15:10", "sessionCode": "MoB04", "sessionTitle": "Shotgun: Mechatronics, Robotics and Components I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Uth, Esben Thomsen", "affiliation": "Aalborg University" }, { "name": "Mai, Christian", "affiliation": "Aalborg University" }, { "name": "Liniger, Jesper", "affiliation": "Aalborg University" }, { "name": "Pedersen, Simon", "affiliation": "Aalborg University" } ], "keywords": [ "Robot perception and sensing", "Autonomous navigation", "Aerial, field, and marine robotics" ], "abstract": "This study presents a proof-of-concept framework for keypoint-based pose estimation of Autonomous Underwater Vehicles (AUVs) using deep learning, addressing the growing demand for reliable perception in underwater missions. A high-resolution architecture, HRNet-W32, originally developed for human pose estimation, is adapted to the underwater domain through a custom semantic keypoint model representing nine structural features of a survey-type AUV. Due to the absence of publicly available underwater keypoint datasets, a synthetic dataset of 1,400 images is generated using physically-based rendering in seven Jerlov water types, spanning clear oceanic to turbid coastal conditions. The dataset provides controlled variability in visibility, viewpoint, and illumination, enabling systematic evaluation of domain-transfer performance. The adapted HRNet model is fine-tuned on this dataset and evaluated using Object Keypoint Similarity (OKS), mean Average Precision (mAP), and pose-estimation accuracy derived from front–rear geometric cues. Results show strong keypoint detection performance with reliable pose estimation achievable in 64% of test images, despite substantial visibility degradation in high-turbidity water. The proposed synthetic-to-real pipeline and keypoint formulation provide a foundation for future onboard AUV perception and embedded real-time implementation.", "url": "" }, { "id": "Mo-MoB05.1", "code": "MoB05.1", "title": "Regression-Based Geometric Compensation of Finger Abduction and Adduction Strength Measurements Using a Hand Interossei Muscle Dynamometer (HIMDNA)", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:25", "sessionCode": "MoB05", "sessionTitle": "LB: Mechatronics for Biomedical and Robotic Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Cho, Seung Yeon", "affiliation": "Seoul National University" }, { "name": "Gimm, Geunwu", "affiliation": "Seoul National University College of Medicine" }, { "name": "Park, Sungwoo", "affiliation": "Institute of Convergence Medicine and Innovative Technology Seoul National University Hospital" }, { "name": "Cho, Minwoo", "affiliation": "Seoul National University Hospital" }, { "name": "Kim, Sungwan", "affiliation": "Seoul National University, Seoul" } ], "keywords": [ "Biomedical and biomimetic mechatronic systems", "Mechatronic system modeling, design, optimization", "Application of mechatronic principles" ], "abstract": "Quantitative measurement of finger abduction and adduction forces is important for evaluation of hand interossei muscle function since it serves as clinical condition indicator of ulnar nerve. However, direct comparison across individuals is complicated by subject-specific finger geometry. This study presents a sensor-based hand interossei muscle dynamometer (HIMDNA) and investigates the influence of finger lengths on measured force. Using multi-finger experimental data from 39 healthy adults, a regression-based length compensation method was implemented to account for geometric variability while preserving the overall force scale. The results demonstrated a positive association between finger length and measured force in several finger–direction conditions. Application of the proposed compensation reduced the coefficient of variation in multiple conditions, indicating improved inter-subject variation. These findings suggest that simple anthropometric adjustment can mitigate geometry-related variability in finger force measurements and may enhance the utility of HIMDNA in biomechanical and clinical applications.", "url": "" }, { "id": "Mo-MoB05.2", "code": "MoB05.2", "title": "Integration of Head-Mounted Display into Robotic Surgical System: Vision Interface Based on VIVE XR Elite", "day": "Monday", "date": "August 24, 2026", "time": "13:25-13:40", "sessionCode": "MoB05", "sessionTitle": "LB: Mechatronics for Biomedical and Robotic Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Kim, Young Gyun", "affiliation": "Seoul National University" }, { "name": "Shim, Jae Woo", "affiliation": "Seoul National University" }, { "name": "Kang, Seongjoon", "affiliation": "Seoul National University" }, { "name": "Cho, Seung Yeon", "affiliation": "Seoul National University" }, { "name": "Kim, Byeong Soo", "affiliation": "Seoul National University Hospital" }, { "name": "Kim, Yoon Jae", "affiliation": "Seoul National University Hospital" }, { "name": "Kim, Sungwan", "affiliation": "Seoul National University, Seoul" } ], "keywords": [ "Human-robot interaction", "Mechatronic system integration", "Human machine cooperation & integration" ], "abstract": "This paper presents the integration of a head-mounted display (HMD) into the da Vinci research kit (dVRK) as a next generation vision interface, replacing the conventional stereo viewer (SV). Among the HMD candidates, the VIVE XR Elite was selected as the target device, and a stereo video streaming pipeline was constructed using GStreamer and Unity on Ubuntu 20.04 and Windows 11 workstation. Two independent control features were implemented: streaming toggle and passthrough toggle, each operable via keyboard input or speech recognition. Performance evaluation measured display latency and view-mode switching latency. The HMD exhibited a display latency of 100 ± 20 ms, compared to 70 ± 10 ms for the SV. View-mode switching latency was on the order of microseconds, well within clinically acceptable thresholds. The results demonstrate that the VIVE XR Elite is a viable replacement for the SV in robotic surgical systems.", "url": "" }, { "id": "Mo-MoB05.3", "code": "MoB05.3", "title": "An Extended Kinematic Model for Mecanum-Wheeled Mobile Robots: The Pose Deviation Problem", "day": "Monday", "date": "August 24, 2026", "time": "13:40-13:55", "sessionCode": "MoB05", "sessionTitle": "LB: Mechatronics for Biomedical and Robotic Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Ortiz Hernández, José Carlos", "affiliation": "Autonomous University of Baja California, Faculty of Engineering, Mexicali, Mexico" }, { "name": "Rosas, David Isaias", "affiliation": "Universidad Autonoma De Baja California" }, { "name": "Pena Ramirez, Jonatan", "affiliation": "CICESE" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Task and motion planning", "Autonomous navigation" ], "abstract": "This manuscript proposes an Extended Kinematic Model for a class of Mecanum-wheeled mobile robots. Starting from the ideal kinematic model, a systematic pose deviation term is introduced to account for experimentally observed orientation drift, with particular emphasis on lateral motion. The proposed extension is derived through curve-fitting analysis and rotation matrix theory to enable coordinate transformation within the extended framework, revealing a repeatable and bounded deviation behavior. Numerical simulations and preliminary experimental results validate the proposed model and suggest that the modeling strategy enhances pose deviation prediction in omnidirectional robots. Furthermore, the extended model provides a foundation for the design of compensation-based control strategies.", "url": "" }, { "id": "Mo-MoB05.4", "code": "MoB05.4", "title": "Continuous Obstacle Negotiation with a Small-Scale Wheeled Robot Via Quasi-Direct-Drive Jumping", "day": "Monday", "date": "August 24, 2026", "time": "13:55-14:10", "sessionCode": "MoB05", "sessionTitle": "LB: Mechatronics for Biomedical and Robotic Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "An, Seunghyun", "affiliation": "Seoul National University" }, { "name": "Jung, Gwang-Pil", "affiliation": "SeoulTech" }, { "name": "Jung, Hyeonho", "affiliation": "Seoul National University" }, { "name": "Cho, KyuJin", "affiliation": "Seoul National University" } ], "keywords": [ "Mechatronics for mobility systems", "High-performance motion control systems" ], "abstract": "Small wheeled robots can traverse efficiently on flat terrain, but practical deployment is often limited by consecutive obstacles such as steps, stairs, and debris. Most small jumping robots achieve high takeoff power using elastic energy storage and rapid release, but the required preparation/recharge time and added mechanisms reduce agility when repeated jumps are needed. This paper presents a small-scale wheeled robot that performs continuous quasi-direct-drive (QDD) jumping by directly converting motor torque into takeoff force through a rack-and-pinion leg and short motor-overdrive current pulses. To enable continuous obstacle negotiation, the actively driven leg supports jump-angle setting, jump-height modulation, and mid-air leg retraction, enabling rapid successive jumps over obstacle sequences; it also supports recovery behaviors such as self-righting after overturning. Experiments demonstrate up to 2 m/s driving with 180 deg/s steering, vertical jumps up to 80 cm, and stair climbing via repeated jumps. Although the jump command supplies currents beyond the rated limit, the pulse duration is short (e.g., tens of milliseconds), and thermal analysis provides safe operating limits for repeated use.", "url": "" }, { "id": "Mo-MoB05.5", "code": "MoB05.5", "title": "Impact-Driven Wall Attachment Suction Cup Module for Sensor Anchoring", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:25", "sessionCode": "MoB05", "sessionTitle": "LB: Mechatronics for Biomedical and Robotic Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Lee, Pilwoo", "affiliation": "Seoul National University" }, { "name": "Oh, Minseo", "affiliation": "Seoul National University" }, { "name": "Cho, KyuJin", "affiliation": "Seoul National University" } ], "keywords": [ "Mechatronics for robotic systems", "Soft robotics", "Aerial, field, and marine robotics" ], "abstract": "Deploying sensor nodes via small-scale UAVs is essential for micro-robot localization, yet achieving reliable wall attachment remains challenging due to the inherent control instabilities of small drones near vertical surfaces. Conventional attachment methods often require high-precision hovering and specific approach maneuvers, which are difficult to maintain under aerodynamic disturbances. To address these limitations, this paper presents an impact-driven passive suction cup module that utilizes the collision itself to trigger anchoring, bypassing the need for sustained high-precision control. We propose a functionally decoupled architecture that separates volume expansion from the sealing interface, employing a triple-layer lip structure for high-roughness adaptation. The mechanism uses a preloaded spring that rapidly generates a vacuum upon impact. Additionally, an adaptive universal joint compensates for non-orthogonal approach angles. Experimental results demonstrate successful in-flight attachment and reliable adhesion on surfaces with grain sizes up to 68 µm. This module enables resource-constrained robots to deploy sensors on demand, facilitating accurate localization and swarm operations.", "url": "" }, { "id": "Mo-MoB05.6", "code": "MoB05.6", "title": "Ingestible Microbiome Sampling Capsule (IMSC) for Non-Invasive Gut Microbiota Analysis", "day": "Monday", "date": "August 24, 2026", "time": "14:25-14:40", "sessionCode": "MoB05", "sessionTitle": "LB: Mechatronics for Biomedical and Robotic Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Park, Sanghyeon", "affiliation": "DGIST" }, { "name": "Park, Sukho", "affiliation": "DGIST" } ], "keywords": [ "Medical and rehabilitation robotics", "Biomedical and biomimetic mechatronic systems", "Micro and nano mechatronic systems" ], "abstract": "The gut microbiome is strongly associated with various systemic diseases, and its composition varies significantly depending on the specific location along the gastrointestinal (GI) tract. However, conventional fecal analysis provides limited spatial information, while endoscopic sampling is invasive and susceptible to environmental disturbance and contamination. In this study, we propose an ingestible microbiome sampling capsule (IMSC) capable of self-alignment and vacuum-assisted sampling. The capsule collects intestinal fluid when the protective coating dissolves at the target site and automatically seals to prevent contamination. Both in vitro and ex vivo experiments demonstrated the feasibility and effectiveness of the proposed system.", "url": "" }, { "id": "Mo-MoB05.7", "code": "MoB05.7", "title": "A Backlash-Free Precision Ophthalmic Robot Manipulator with Compliant Strips", "day": "Monday", "date": "August 24, 2026", "time": "14:40-14:55", "sessionCode": "MoB05", "sessionTitle": "LB: Mechatronics for Biomedical and Robotic Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Chen, Wen-Han", "affiliation": "National Taiwan University" }, { "name": "Lee, Yu-Hsiu", "affiliation": "National Taiwan University" } ], "keywords": [ "Medical and rehabilitation robotics", "Mechatronics for robotic systems", "Mechatronic system integration" ], "abstract": "This work presents a miniaturized dual-stage robotic prototype (110 times 80 times 90text{ mm}^3) for ophthalmic surgery. To eliminate backlash in micro-transmissions, we developed a Compliant Bevel Gear based on Rolling-contact Architected Materials (CRAMS), utilizing elastic deformation for gap-free power transmission. The system employs a 2R1P kinematic chain. Workspace evaluation shows that with motor limits of pm15^circ, the system provides a stable surgical range (> 4^circ for both Y and Z-axis rotations) and high resolution (< 0.052^circ/step). Experimental results confirm the Remote Center of Motion (RCM) offset remains within 0.05 mm (needle width) with high repeatability. The consistency indicates that deviations stem from manufacturing tolerances rather than random backlash. This study realizes a clinical-potential prototype in a compact scale, with future work focusing on enhancing stiffness via precision metal machining.", "url": "" }, { "id": "Mo-MoB05.8", "code": "MoB05.8", "title": "A Preliminary Study of Hand-Pose-Aligned Handover for Robotic Scrub Nurse", "day": "Monday", "date": "August 24, 2026", "time": "14:55-15:10", "sessionCode": "MoB05", "sessionTitle": "LB: Mechatronics for Biomedical and Robotic Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Kang, Seongjoon", "affiliation": "Seoul National University" }, { "name": "Shim, Jae Woo", "affiliation": "Seoul National University" }, { "name": "Gimm, Geunwu", "affiliation": "Seoul National University College of Medicine" }, { "name": "Lee, Jong Hyeon", "affiliation": "Seoul National University" }, { "name": "Baek, Changhoon", "affiliation": "Seoul National University Hospital" }, { "name": "Cho, Minwoo", "affiliation": "Seoul National University Hospital" }, { "name": "Kim, Sungwan", "affiliation": "Seoul National University, Seoul" } ], "keywords": [ "Medical and rehabilitation robotics", "Robot perception and sensing", "Human-robot interaction" ], "abstract": "This study presents a vision-based robotic scrub nurse (VRSN) designed to deliver surgical instruments directly to the surgeon’s hand with appropriate orientation. The system integrates speech recognition, 6D instrument pose estimation, hand pose estimation, and grip state classification to enable hand-pose-aligned handover. In feasibility tests using eight surgical instruments, the system achieved a 95% handover success rate and completed instrument delivery in a mean time of 8.91 seconds. The results demonstrate the system’s capability to reliably perform repetitive instrument handovers while maintaining orientation requirements aligned with surgical workflow.", "url": "" }, { "id": "Mo-MoB06.1", "code": "MoB06.1", "title": "Integrated Date-Driven Robust Adaptive Control for Nonlinear Systems with Saturated Inputs", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB06", "sessionTitle": "Data-Driven Control Theory II", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Fazeli, Seyed", "affiliation": "University of Alberta" }, { "name": "Wang, Haihan", "affiliation": "University of Alberta" }, { "name": "Zhao, Qing", "affiliation": "Univ. of Alberta" } ], "keywords": [ "Data-driven control theory", "Adaptive observer design", "Nonlinear adaptive control" ], "abstract": "In this paper, we propose an integrated data-driven robust adaptive control (IDRAC) scheme that combines sliding mode control with an adaptive tracking observer for MIMO nonlinear systems with input saturation and external disturbances. Using only input–output data, the method avoids explicit modeling and closes the gap between separately designed controllers and observers in data-driven control. Simulations on two case studies show improved tracking performance, reduced RMS errors, and guaranteed closed-loop H∞ stability, making IDRAC suitable for modern industrial applications.", "url": "" }, { "id": "Mo-MoB06.2", "code": "MoB06.2", "title": "Iterative Model Free Safe Exploration Using Event-Triggered Impulsive Perturbations", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB06", "sessionTitle": "Data-Driven Control Theory II", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Lala, Timotei", "affiliation": "Politehnica University of Timisoara" }, { "name": "Ioan, Silea", "affiliation": "Politehnica University of Timisoara" } ], "keywords": [ "Data-driven control theory", "Consensus and reinforcement learning control", "Neural and fuzzy adaptive control" ], "abstract": "Abstract: This paper presents Iterative Model-Free Safe Exploration (IMFSE), a novel algorithm that ensures both safety and stability during state-action exploration in model-free Q-learning through a two-layered protection mechanism. The first layer implements an event-based stability preserving mechanism, leveraging the Value Function of an initial stabilizable controller as a Lyapunov function to monitor the system energy levels, enabling controlled random exploration commands that maintain asymptotic stability. The second layer guarantees forward invariance of the safe set through an iterative exploration process with gradually increasing perturbation variance, employing nearest neighbors search in a set containing states with high risk of transitioning to the unsafe region. IMFSE is validated on an Electronic Brake System (EBS), ensuring zero-safety violations and showing 2.9x improved performance over the initial controller.", "url": "" }, { "id": "Mo-MoB06.3", "code": "MoB06.3", "title": "Data-Driven Adaptive Event-Triggered Control for Discrete-Time Systems", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB06", "sessionTitle": "Data-Driven Control Theory II", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Digge, Vijayanand", "affiliation": "Université Catholique De Louvain" }, { "name": "Saradagi, Akshit", "affiliation": "Luleå University of Technology" }, { "name": "Bianchin, Gianluca", "affiliation": "Université Catholique De Louvain" } ], "keywords": [ "Data-driven control theory", "Event-based control" ], "abstract": "This paper presents a data-driven framework for synthesizing adaptive event-triggered control (ETC) for discrete-time linear systems with unknown dynamics. We propose a state-dependent triggering mechanism that adapts both relative and absolute thresholds online at each event instant. The linear controller gains and adaptive event-triggering rules are synthesized in a data-driven manner from open-loop system data, via data-dependent linear matrix inequalities (LMIs), and guarantee exponential stability of the event-triggered closed-loop system. Simulations validate that the proposed adaptive strategy yields substantially longer inter-event times and significantly reduced communication loads compared to classical static triggering rules.", "url": "" }, { "id": "Mo-MoB06.4", "code": "MoB06.4", "title": "Data-Driven Subspace Identification and Reduction for Switched Affine System: Application to Power Converter Control", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB06", "sessionTitle": "Data-Driven Control Theory II", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Soliman, Marwan Ahmed Sakr AbdelAzim", "affiliation": "ENSEEIHT-Laplace" }, { "name": "Kergus, Pauline", "affiliation": "CNRS" }, { "name": "Kader, Zohra", "affiliation": "ENSEEIHT-LAPLACE" } ], "keywords": [ "Data-driven control theory", "Hybrid and switched systems modeling", "Stability and stabilization of hybrid systems" ], "abstract": "Switching control strategies designed using the hybrid system framework are promising in the field of power electronics, by providing stability guarantees and robustness to parameter variations. However, they rely on solving Linear Matrix Inequalities (LMIs), which resolution does not scale well with the dimension of the considered system. To address this challenge, this work proposes a data-driven methodology that combines system identification and model order reduction for hybrid systems. The objective is to identify reduced-order models that preserve the essential states of the converter, in order to perform control design. The effectiveness of the method is demonstrated through an application to a DC–DC buck mode power converter circuit with two legs from the OwnTech Foundation.", "url": "" }, { "id": "Mo-MoB06.5", "code": "MoB06.5", "title": "The Innovation Null Space of the Kalman Predictor: A Stochastic Perspective for DeePC", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB06", "sessionTitle": "Data-Driven Control Theory II", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Liu, Aihui", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Jansson, Magnus", "affiliation": "KTH (Royal Inst of Technology)" } ], "keywords": [ "Data-driven control theory", "Kalman filtering", "Stochastic control" ], "abstract": "For linear systems with Gaussian noise, the steady-state Kalman predictor is the MMSE-optimal conditional-mean predictor. We show that the Kalman predictor admits a data-enabled representation in which the corresponding DeePC decision vector g lies in the null space of the future innovation Hankel matrix. This motivates viewing this null space as an ideal target subspace for stochastic DeePC formulations. Under this viewpoint, we explain several existing data-driven predictive control methods: regularized DeePC schemes act as soft versions of this condition, instrumental-variable methods enforce it asymptotically, and ARX-based approaches explicitly estimate the innovation subspace.", "url": "" }, { "id": "Mo-MoB06.6", "code": "MoB06.6", "title": "Data-Driven Reachability Verification with Probabilistic Guarantees under Koopman Spectral Uncertainty (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB06", "sessionTitle": "Data-Driven Control Theory II", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Ding, Jianqiang", "affiliation": "Aalto University" }, { "name": "Deka, Shankar", "affiliation": "Aalto University" } ], "keywords": [ "Data-driven control theory", "Reachability analysis, verification and abstraction of hybrid systems" ], "abstract": "Providing rigorous reachability guarantees for unknown complex systems is a crucial and challenging task. In this paper, we present a novel data-driven framework that addresses this challenge by leveraging Koopman operator theory. Instead of operating in the state space, the proposed method encodes model uncertainty from finite data directly into Koopman spectral representation with quantifiable error bounds. Leveraging this spectral information, we systematically determine time intervals within which trajectories from the initial set are guaranteed, with a prescribed probability, to reach the target set. We finally demonstrate the efficacy of our framework in numerical examples.", "url": "" }, { "id": "Mo-MoB07.1", "code": "MoB07.1", "title": "Federated Learning in Open Multi-Agent Systems Via Peaceman-Rachford Splitting (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB07", "sessionTitle": "Open Multi-Agent Systems: Control, Optimization, and Learning I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Deplano, Diego", "affiliation": "University of Cagliari" }, { "name": "Bastianello, Nicola", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Franceschelli, Mauro", "affiliation": "University of Cagliari" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Multi-agent systems", "Distributed optimization", "Distributed control and estimation" ], "abstract": "Modern AI systems are built on networks of agents that acquire data, perform local computations, and communicate with neighbors to cooperatively address optimization and learning tasks. This paper introduces Open FedPRS, a novel federated algorithm to address a broad class of these problems in open networks, where the number of participating agents may vary due to several factors, such as autonomous decisions, heterogeneous resource availability, or failures. Extending the current literature, the convergence analysis of the proposed algorithm is based on the Theory of Open Operators, which allows one to prove (1) linear convergence and (2) bounded asymptotic error of the distance between the trained model and the optimal model, rather than exploiting the commonly employed regret-based metrics that only describe cumulative performance over a finite-time horizon. As an illustrative example, the proposed algorithm is used to solve logistic learning problems.", "url": "" }, { "id": "Mo-MoB07.2", "code": "MoB07.2", "title": "Beyond Scaffold: A Unified Spatio-Temporal Gradient Tracking Method (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB07", "sessionTitle": "Open Multi-Agent Systems: Control, Optimization, and Learning I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Huang, Yan", "affiliation": "KTH - Kungliga Tekniska Högskolan" }, { "name": "Xu, Jinming", "affiliation": "Zhejiang University" }, { "name": "Chen, Jiming", "affiliation": "Zhejiang University" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Distributed optimization", "Multi-agent systems", "Consensus" ], "abstract": "In distributed and federated learning algorithms, communication overhead is often reduced by performing multiple local updates between communication rounds. However, due to data heterogeneity across nodes and the local gradient noise within each node, this strategy can lead to the drift of local models away from the global optimum. To address this issue, we propose a unified spatio-temporal gradient tracking algorithm, termed {myalg}, for distributed stochastic optimization over time-varying graphs. {myalg} tracks the global gradient across neighboring nodes to mitigate data heterogeneity, while maintaining a running average of local gradients that substantially suppresses noise with only slight storage overhead. We further show that an extension of Scaffold, a well-known federated learning algorithm, admits a natural interpretation as an {myalg} scheme. Without assuming bounded data heterogeneity, we prove that {myalg} attains a linear convergence rate for strongly convex and smooth objective functions. Notably, compared with traditional gradient tracking methods, {myalg} reduces the topology-dependent noise term from sigma^2 to sigma^2/tau, where sigma^2 denotes the noise level and tau is the number of local updates per communication round, thereby improving communication efficiency.", "url": "" }, { "id": "Mo-MoB07.3", "code": "MoB07.3", "title": "Distributed Nash Equilibrium Seeking for Open Aggregative Games: A Spanning Tree Based Approach (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB07", "sessionTitle": "Open Multi-Agent Systems: Control, Optimization, and Learning I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Liu, Yuxuan", "affiliation": "Nanjing University of Science and Technology" }, { "name": "Ye, Maojiao", "affiliation": "Nanjing University of Science and Technology" }, { "name": "Yan, Yuyue", "affiliation": "The University of Tokyo" }, { "name": "Xu, Wenqi", "affiliation": "Harbin Institute of Technology" }, { "name": "Ding, Lei", "affiliation": "Nanjing University of Posts and Telecommunications" } ], "keywords": [ "Multi-agent systems", "Control over networks", "Distributed control and estimation" ], "abstract": "This paper studies open aggregative games, where each agent is allowed to join or leave the game during the decision making process and aims to minimize its cost function when it is involved in the game. Under the partial information setting where agents communicate only with their neighbors over a network, a distributed algorithm is developed to address the formulated problem. In the proposed algorithm, agents transmit the decision variables over the constructed spanning tree to obtain the estimation of the aggregate term. Based on the estimated aggregate term, agents calculate the gradients and update their decision variables by gradient play. Analytical results shows that agents' action profile can linearly converge to the neighborhood of the resulting Nash equilibrium when the agent set changes. Finally, a numerical example is provided to verify the effectiveness of the proposed method.", "url": "" }, { "id": "Mo-MoB07.4", "code": "MoB07.4", "title": "Affine-Coupled Distributed Optimization Via Distributed Proximal Jacobian ADMM with Quantized Communication (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB07", "sessionTitle": "Open Multi-Agent Systems: Control, Optimization, and Learning I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Du, Xu", "affiliation": "Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Han, Boyu", "affiliation": "HKUST(gz)" }, { "name": "Notarnicola, Ivano", "affiliation": "University of Bologna" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Rikos, Apostolos I.", "affiliation": "Hong Kong University of Science and Technology (Gz)" } ], "keywords": [ "Distributed optimization", "Control of networks", "Distributed control and estimation" ], "abstract": "This paper investigates distributed resource allocation optimization over directed graphs with limited communication bandwidth. We develop a novel distributed algorithm that integrates the centralized Proximal Jacobian Alternating Direction Method of Multipliers (PJ-ADMM) with a finite-level quantized consensus scheme, enabling nodes to cooperatively solve the optimization in a distributed fashion. Under the assumption of convex objective functions, we establish that the proposed algorithm achieves sublinear convergence to a neighborhood of the optimal solution, with the convergence accuracy explicitly bounded by the quantization level. Numerical experiments validate that the algorithm achieves competitive performance compared to existing approaches while exhibiting communication efficiency.", "url": "" }, { "id": "Mo-MoB07.5", "code": "MoB07.5", "title": "Mix-CALADIN: A Distributed Algorithm for Consensus Mixed-Integer Optimization (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB07", "sessionTitle": "Open Multi-Agent Systems: Control, Optimization, and Learning I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Han, Boyu", "affiliation": "HKUST(gz)" }, { "name": "Du, Xu", "affiliation": "Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Rikos, Apostolos I.", "affiliation": "Hong Kong University of Science and Technology (Gz)" } ], "keywords": [ "Distributed optimization", "Multi-agent systems", "Consensus" ], "abstract": "This paper addresses distributed consensus optimization problems with mixed-integer variables, with a specific focus on Boolean variables. We introduce a novel distributed algorithm that extends the Consensus Augmented Lagrangian Alternating Direction Inexact Newton (C-ALADIN) framework by incorporating specialized techniques for handling Boolean variables without relying on local mixed-integer solvers. Under the mild assumption of Lipschitz continuity of the objective functions, we establish rigorous convergence guarantees for both convex and non-convex mixed-integer programming problems. Numerical experiments demonstrate that the proposed algorithm achieves competitive performance compared to existing approaches while providing rigorous convergence guarantees.", "url": "" }, { "id": "Mo-MoB07.6", "code": "MoB07.6", "title": "Distributed Online Method for Nonconvex Optimization in Open Network Environments (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB07", "sessionTitle": "Open Multi-Agent Systems: Control, Optimization, and Learning I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Ishikawa, Daichi", "affiliation": "The University of Osaka" }, { "name": "Yasuda, Hinano", "affiliation": "Osaka University" }, { "name": "Hayashi, Naoki", "affiliation": "Osaka University" }, { "name": "Inuiguchi, Masahiro", "affiliation": "Osaka University" } ], "keywords": [ "Distributed optimization", "Multi-agent systems", "Distributed control and estimation" ], "abstract": "This paper investigates a distributed algorithm for online nonconvex optimization over an open multiagent system. We consider a gradient-based approach in which a group of active agents connected to the network cooperatively seeks a stationary point of the nonconvex optimization problem. In an open multiagent system, the network configuration is not constant but changes over time according to the arrival and departure of agents. We derive a uniform performance bound for the proposed algorithm in terms of a discounted aggregate-gradient measure. Numerical experiments demonstrate the effectiveness of the proposed algorithm.", "url": "" }, { "id": "Mo-MoB08.1", "code": "MoB08.1", "title": "Multisine Input Signal Design for Constrained, \"Plant-Friendly\" System Identification of Nonlinear Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB08", "sessionTitle": "JO-JSC: Learning and Experiments", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Banerjee, Sarasij", "affiliation": "Arizona State University" }, { "name": "Hekler, Eric", "affiliation": "University of California at San Diego" }, { "name": "Rivera, Daniel E.", "affiliation": "Arizona State University" } ], "keywords": [ "Active learning and experiment design", "Nonlinear system identification", "Data-driven control theory" ], "abstract": "This paper presents a methodology for optimizing \"plant-friendly\" multisine input signals to identify nonlinear dynamic systems under time-domain input and output constraints, without requiring a global parametric model. The goal is to construct an informative dataset for open-loop, data-driven identification while maintaining operational requirements. A weighted optimization framework is proposed to minimize the output crest factor arising from a data-driven model, with penalties for input and output constraint violations. Model-on-Demand (MoD) estimation is employed to simulate outputs using prior data, effectively predicting nonlinear responses without global modeling. This MoD-based formulation enables evaluation of output crest factors and output constraint compliance with minimal modeling effort and expanded impact. The resulting non-smooth, non-convex problem is solved using the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm, which perturbs the multisine phase vector to achieve the desired performance efficiently. This method supports the concept of identification test monitoring, as illustrated in this paper. Within the identification test loops, each optimized excitation is applied to gather new estimation data, iteratively refining MoD predictions and improving constraint satisfaction. The method’s effectiveness is demonstrated through a safety-critical case study on a Susceptible-Infected-Recovered (SIR) epidemiological network, showing that the optimized excitation yields highly informative data for identification while keeping the infection spread within safe limits.", "url": "" }, { "id": "Mo-MoB08.2", "code": "MoB08.2", "title": "Regularized GLISp for Sensor-Guided Human-In-The-Loop Optimization (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB08", "sessionTitle": "JO-JSC: Learning and Experiments", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Cercola, Matteo", "affiliation": "Politecnico Di Milano" }, { "name": "Lomuscio, Michele", "affiliation": "Politecnico Di Milano" }, { "name": "Piga, Dario", "affiliation": "SUPSI-USI" }, { "name": "Formentin, Simone", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Active learning and experiment design", "Physics informed and grey box model identification" ], "abstract": "Human-in-the-loop calibration is often addressed via preference-based optimization, where algorithms learn from pairwise comparisons rather than explicit cost evaluations. While effective, methods such as Preferential Bayesian Optimization or Global optimization based on active preference learning with radial basis functions (GLISp) treat the system as a black box and ignore informative sensor measurements. In this work, we introduce a sensor-guided regularized extension of GLISp that integrates measurable descriptors into the preference-learning loop through a physics-informed hypothesis function and a least-squares regularization term. This injects grey-box structure, combining subjective feedback with quantitative sensor information while preserving the flexibility of preference-based search. Numerical evaluations on an analytical benchmark and on a human-in-the-loop vehicle suspension tuning task show faster convergence and superior final solutions compared to baseline GLISp.", "url": "" }, { "id": "Mo-MoB08.3", "code": "MoB08.3", "title": "Efficient Reinforcement Learning from Human Feedback Via Bayesian Preference Inference (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB08", "sessionTitle": "JO-JSC: Learning and Experiments", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Cercola, Matteo", "affiliation": "Politecnico Di Milano" }, { "name": "Capretti, Valeria", "affiliation": "Politecnico Di Milano" }, { "name": "Formentin, Simone", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Active learning and experiment design", "Probabilistic and Bayesian methods for system identification" ], "abstract": "Learning from human preferences is a cornerstone of aligning machine learning models with subjective human judgments. Yet, collecting such preference data is often costly and time-consuming, motivating the need for more efficient learning paradigms. Two established approaches offer complementary advantages: RLHF scales effectively to high-dimensional tasks such as LLM fine-tuning, while PBO achieves greater sample efficiency through active querying. We propose a hybrid framework that unifies RLHF’s scalability with PBO’s query efficiency by integrating an acquisition-driven module into the RLHF pipeline, thereby enabling active and sample-efficient preference gathering. We validate the proposed approach on two representative domains: (i) high-dimensional preference optimization and (ii) LLM fine-tuning. Experimental results demonstrate consistent improvements in both sample efficiency and overall performance across these tasks.", "url": "" }, { "id": "Mo-MoB08.4", "code": "MoB08.4", "title": "A Novel Hybrid Cascaded Forecaster Network for Day-Ahead Normal and Spike Price Prediction in Electricity Markets (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB08", "sessionTitle": "JO-JSC: Learning and Experiments", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Memarzadeh, Gholamreza", "affiliation": "Vali-E-Asr University of Rafsanjan" }, { "name": "Keynia, Farshid", "affiliation": "Graduate University of Advanced Technology, Kerman" }, { "name": "Amirteimoury, Faezeh", "affiliation": "Islamic Azad University Kerman Branch" }, { "name": "Heydari, Azim", "affiliation": "Graduate University of Advanced Technology, Kerman" }, { "name": "Fekih, Afef", "affiliation": "Univ of Louisiana at Lafayette" } ], "keywords": [ "AI and ML for environmental systems", "Natural resources management", "Optimal control and operation of environment systems" ], "abstract": "Electricity price forecasting plays a vital role in daily trading operations, enabling market participants to make informed bidding and operational decisions. However, this task remains highly challenging due to the volatility of electricity markets, the influence of renewable energy integration, and the significant economic impact of pricing decisions on producers and consumers. This study proposes a Hybrid Cascaded Forecaster Network (HCaFN) for day-ahead electricity price forecasting. The proposed framework first applies a Wavelet Transform (WT)–based decomposition to enhance forecasting performance under renewable generation uncertainty. It then employs the Mutual Information–Interaction Gain (MI-IG) technique for feature selection, ensuring that the most relevant and least redundant input variables, particularly those associated with price spikes, are retained. The preprocessed data are used to train multiple forecasting models, including the Multi-Layer Perceptron (MLP), Cascade Neural Network (CaNN), and Extreme Learning Machine (ELM). Finally, a stacking ensemble learning strategy is implemented to further reduce price uncertainty and improve overall prediction accuracy in the day-ahead electricity market. Experimental results demonstrate that the proposed HCaFN model consistently outperforms several state-of-the-art electricity price forecasting approaches.", "url": "" }, { "id": "Mo-MoB08.5", "code": "MoB08.5", "title": "Stability-Constrained Policy Optimization under Unknown Rewards (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB08", "sessionTitle": "JO-JSC: Learning and Experiments", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Banker, Thomas", "affiliation": "University of California Berkeley" }, { "name": "Lawrence, Nathan P.", "affiliation": "University of British Columbia" }, { "name": "Mesbah, Ali", "affiliation": "University of California, Berkeley" } ], "keywords": [ "Learning methods for control" ], "abstract": "A major challenge in reinforcement learning (RL) is guaranteeing an agent’s closed- loop stability under unknown, possibly sparse, reward functions. While model-free RL is flexible to a variety of systems and rewards, model-based control strategies such as optimization- based control naturally accommodate prior system models to provide guarantees on safety and stability. However, these models may not be representative of the true global performance objective, resulting in suboptimal policies. In this paper, we present a policy search RL approach that decouples the stability requirement from the global performance objective. The key idea is to use an optimization-based policy structure as an effective stabilizing parameterization with which the agent can learn to maximize an unknown reward in a model-free fashion. Specifically, the agent employs a predictive control architecture and implicitly learns a stabilizing terminal cost, which is constructed through fixed-point iterations of the discrete algebraic Riccati equation. By implicitly differentiating this fixed-point, derivatives of the stability condition inform policy gradients. The proposed approach is shown to design high-performance, stabilizing policies for various sparse, global performance objectives. Furthermore, the proposed approach can account for uncertainty in the dynamics using the stochastic discrete algebraic Riccati equation to promote robust stability. This work demonstrates a principled policy search RL approach, integrating prior models and system observations in an agent’s design, towards safe and reliable decision-making under uncertainty.", "url": "" }, { "id": "Mo-MoB09.1", "code": "MoB09.1", "title": "Identification of Delayed MISO Fractional-Order Continuous-Time Systems", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB09", "sessionTitle": "Linear System Identification II", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Doctolero, Samuel", "affiliation": "University of Calgary" }, { "name": "Westwick, David", "affiliation": "University of Calgary" } ], "keywords": [ "Linear system identification", "Nonlinear system identification", "Filtering and smoothing" ], "abstract": "Separated and Joint identification methods are developed to solve the parameters of multi-input-single-output (MISO) continuous-time systems with fractional-orders and time-delays. Moreover, the identification methods assume that the denominator polynomials are not common and the input transfer functions each have different non-integer orders. Parameter Jacobian matrices are computed analytically instead of relying on numerical approximations. The two proposed methods are compared against each other and similar methods using a Monte-Carlo simulation with a time-delayed two-input continuous-time fractional-order system with arbitrary parameters. Finally, a simple suggestion is given with regards to choosing between the two proposed methods.", "url": "" }, { "id": "Mo-MoB09.2", "code": "MoB09.2", "title": "On the Fundamental Limit of the Stochastic Gradient Identification Algorithm under Non-Persistent Excitation", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB09", "sessionTitle": "Linear System Identification II", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Yao, Senhan", "affiliation": "Academy of Mathematics and Systems Science, Chinese Academy of Sciences" }, { "name": "Zhang, Longxu", "affiliation": "Academy of Mathematics and Systems Science, Chinese Academy of Sciences" } ], "keywords": [ "Linear system identification", "Probabilistic and Bayesian methods for system identification" ], "abstract": "Stochastic gradient (SG) methods are fundamental to system identification and machine learning, enabling online parameter estimation in large-scale and streaming-data settings. As a classical identification method, the SG algorithm has been extensively studied for decades. Under non-persistent excitation, the strongest currently available convergence result assumes that the condition number of the Fisher information matrix is O ((log r n ) α ), where r n = 1 + Σ i=1 n || φ i || 2 . Existing theory establishes strong consistency when α ≤ 1/3, whereas the same condition with α > 1 is insufficient to guarantee strong consistency. We prove that strong consistency holds throughout the range 0 ≤ α < 1. The proof is based on a new algebraic framework that yields substantially sharper matrix norm bounds. This result nearly resolves the four-decade-old Chen--Guo conjecture by establishing strong consistency throughout the previously open range 1/3 < α < 1.", "url": "" }, { "id": "Mo-MoB09.3", "code": "MoB09.3", "title": "Empirical Bayes Estimation for a Class of Dynamic Stochastic Systems with Non-Gaussian Noise Distribution", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB09", "sessionTitle": "Linear System Identification II", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Jasimino, Bastián", "affiliation": "Universidad De Santiago De Chile" }, { "name": "Orellana, Rafael", "affiliation": "Universidad De Santiago De Chile" }, { "name": "Cedeño, Angel L.", "affiliation": "Universidad Técnica Federico Santa María" }, { "name": "Coronel Mendez, María de los Angeles", "affiliation": "Universidad Tecnologica Metropolitana" } ], "keywords": [ "Linear system identification", "Probabilistic and Bayesian methods for system identification" ], "abstract": "We develop a system identification methodology for a class of stochastic linear regression dynamic systems under a non-Gaussian noise distribution using the Empirical Bayes approach. The non-Gaussian noise and the probability density function of the system model parameters are approximated utilizing Gaussian Mixture Models. An Expectation-Maximization based algorithm is formulated to estimate the Gaussian mixture parameters, obtaining closed-form expressions for the estimators. Our proposal exhibits an accurate approximation of both system parameters and non-Gaussian noise distributions using Gaussian Mixture Models.", "url": "" }, { "id": "Mo-MoB09.4", "code": "MoB09.4", "title": "Construction of Finite Sample Confidence Sets for Frequency Response Function Using Sign-Perturbed Sums", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB09", "sessionTitle": "Linear System Identification II", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Balasubramanian, Maheswaran", "affiliation": "The University of Melbourne," }, { "name": "Weyer, Erik", "affiliation": "University of Melbourne" } ], "keywords": [ "Linear system identification", "Probabilistic and Bayesian methods for system identification", "Statistical inference" ], "abstract": "Identification of the Frequency Response Function (FRF) is of great interest across many disciplines, particularly for control purposes. Confidence sets for FRF parameters quantify the uncertainty around the estimates. Traditionally, these sets are obtained either by assuming a noise distribution or using asymptotic theory. In this work, we employ a finite sample method, the Sign-Perturbed Sums (SPS) method, to construct confidence sets for FRF parameters with mild assumptions on the noise and without relying on asymptotic theory. The true FRF values belong to the confidence sets, which are constructed using a finite number of data points, with a guaranteed probability. We compare SPS with the Leave-out Sign-dominant Correlation Regions (LSCR) (cite{ko_non-asymptotic_2015}) using Monte Carlo simulations. The results show that, for single-frequency sinusoidal inputs, the SPS confidence set is smaller than the LSCR counterpart at the same confidence level. For multi-sinusoidal inputs, a computationally efficient algorithm which constructs approximate SPS confidence sets individually for each frequency is proposed, and the constructed sets are smaller than the LSCR sets in the simulation examples.", "url": "" }, { "id": "Mo-MoB09.5", "code": "MoB09.5", "title": "A Sampled-Data Model for a Class of Dynamic Systems with Applications to AC Power Electronics", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB09", "sessionTitle": "Linear System Identification II", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Coronel Mendez, María de los Angeles", "affiliation": "Universidad Tecnologica Metropolitana" }, { "name": "Orellana, Rafael", "affiliation": "Universidad De Santiago De Chile" }, { "name": "Silva, Cesar", "affiliation": "Universidad Tecnica Federico Santa Maria" }, { "name": "Aguero, Juan C", "affiliation": "Universidad Santa Maria" } ], "keywords": [ "Linear system identification", "Time/parameter varying system identification" ], "abstract": "We derive sampled-data models for rotational linear time-varying stochastic systems motivated by AC power electronics, with inputs in stationary and rotating reference frames. Two sampling architectures are treated: instantaneous sampling and an averaging anti-aliasing filter. In both cases, discrete-time matrices and noise covariances follow from a single augmented matrix exponential, which, in the classical non-rotating case, has a lower dimension than in existing stochastic formulations. The framework generalizes earlier deterministic results for PMSM and LCL filters. An RL example illustrates the model's accuracy and potential for state estimation, identification, and predictive control.", "url": "" }, { "id": "Mo-MoB09.6", "code": "MoB09.6", "title": "Stability and Performance Bounds of Sliding-Window Normalized Least Mean Square Algorithm", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB09", "sessionTitle": "Linear System Identification II", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Li, Rongjiang", "affiliation": "Academy of Mathematics and Systems Science, Chinese Academy of Sciences" }, { "name": "Guo, Lei", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Linear system identification", "Time/parameter varying system identification", "Estimation and filtering" ], "abstract": "This paper investigates a class of sliding-window normalized least mean square (SWNLMS) algorithm for online identification of time-varying stochastic regression models. Instead of using only the current data point as in the classical NLMS algorithm, the proposed algorithm computes a smoother and more reliable update direction by re-evaluating and averaging the normalized gradient contributions from the most recent p data points at each iteration. The SWNLMS has been noted in the literature to exhibit better convergence behaviour than the classical NLMS algorithm. Our main contribution is to establish the L_q-exponential stability of the proposed SWNLMS algorithm and derive explicit upper bounds for the estimation errors. Importantly, these results are obtained without relying on independence or stationarity assumptions that are widely imposed in the existing literature. A sentencing prediction study based on real-world datasets further demonstrates the effectiveness of the proposed algorithm.", "url": "" }, { "id": "Mo-MoB10.1", "code": "MoB10.1", "title": "A Hybrid Systems Formulation to Phenotype Switching for Ratiometric Control in a Bacterial Community (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB10", "sessionTitle": "JO-NAHS: Hybrid and Switched Systems Modeling", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Petrelli, Sara", "affiliation": "University of Padova" }, { "name": "Cimolato, Chiara", "affiliation": "University of Padova" }, { "name": "Delvenne, Jean-Charles", "affiliation": "UCLouvain" }, { "name": "Schenato, Luca", "affiliation": "Univ of Padova" }, { "name": "Bellato, Massimo", "affiliation": "Università Di Padova" } ], "keywords": [ "Hybrid and switched systems modeling", "Discrete event modeling and simulation" ], "abstract": "Synthetic biology combines engineering and biology to create novel systems by inserting artificial genetic circuits into living cells. A key challenge is to ensure the co-existence of multiple cells communities. To address this issue an innovative approach employing genetic toggle switches has been recently proposed to regulate transitions between the communities. However, to study such systems, it is essential to develop accurate, yet informative, models. We propose a hybrid system formulation where the continuous dynamics are determined by two continuous-time Markov chains reproducing the qualitative behavior of the original system. We first derive necessary conditions, expressed in terms of the system's parameters, to guarantee the desired partitioning of the two communities. We then establish sufficient conditions and show that they do not coincide with the necessary ones, thereby revealing a gap between necessity and sufficiency. Moreover, in a symmetric scenario, we provide valuable insights into how the Markov chain parameters influence and shape the system's steady-state behavior.", "url": "" }, { "id": "Mo-MoB10.2", "code": "MoB10.2", "title": "Learning Local Control Barrier Functions for Hybrid Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB10", "sessionTitle": "JO-NAHS: Hybrid and Switched Systems Modeling", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Yang, Shuo", "affiliation": "University of Pennsylvania" }, { "name": "Chen, Yu", "affiliation": "Shanghai Jiao Tong Univ" }, { "name": "Yin, Xiang", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Pappas, George J.", "affiliation": "Univ of Pennsylvania" }, { "name": "Mangharam, Rahul", "affiliation": "University of Pennsylvania" } ], "keywords": [ "Hybrid and switched systems modeling", "Reachability analysis, verification and abstraction of hybrid systems", "Stochastic hybrid systems" ], "abstract": "Hybrid dynamical systems are ubiquitous as practical robotic applications often involve both continuous states and discrete switchings. Safety is a primary concern for hybrid robotic systems. Existing safety-critical control approaches for hybrid systems are either computationally inefficient, detrimental to system performance, or limited to smallscale systems. To amend these drawbacks, in this paper, we propose a learning-enabled approach to construct local Control Barrier Functions (CBFs) to guarantee the safety of a wide class of nonlinear hybrid dynamical systems. The end result is a safe neural CBFbased switching controller. Our approach is computationally efficient, minimally invasive to any reference controller, and applicable to large-scale systems. We empirically evaluate our framework and demonstrate its efficacy and flexibility through two robotic examples including a high-dimensional autonomous racing case, against other CBF-based approaches and model predictive control", "url": "" }, { "id": "Mo-MoB10.3", "code": "MoB10.3", "title": "On the Computation of ReLU-Based RNNs Equivalent to CPWA Models of~Dynamical~Systems and Vice Versa (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB10", "sessionTitle": "JO-NAHS: Hybrid and Switched Systems Modeling", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Ledda, Marco", "affiliation": "University of Cagliari" }, { "name": "Deplano, Diego", "affiliation": "University of Cagliari" }, { "name": "Giua, Alessandro", "affiliation": "University of Cagliari, Italy" }, { "name": "Franceschelli, Mauro", "affiliation": "University of Cagliari" } ], "keywords": [ "Hybrid and switched systems modeling", "Learning methods for control", "Machine and deep learning for system identification" ], "abstract": "This paper develops computational procedures to translate between ReLU recurrent neural networks (RNNs) and continuous piecewise-affine (CPWA) dynamical systems. We show that every ReLU RNN induces a finite polyhedral partition of the state–input space with affine dynamics on each region and provide an explicit constructive algorithm to compute its corresponding CPWA representation. Conversely, we show that every CPWA dynamical system admits an exact realization as a ReLU RNN and provide a constructive procedure to compute the associated network parameters. The computational procedures are illustrated by numerical examples for both directions, showcasing exactly matching trajectories and validating the implementation of the proposed transformations.", "url": "" }, { "id": "Mo-MoB10.4", "code": "MoB10.4", "title": "Hybrid Zero Dynamics Control and Performance Analysis of Sideways Walking with a Compass Gait Model (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB10", "sessionTitle": "JO-NAHS: Hybrid and Switched Systems Modeling", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Chen, Tan", "affiliation": "Michigan Technological University" } ], "keywords": [ "Hybrid and switched systems modeling", "Stability and stabilization of hybrid systems" ], "abstract": "Sideways walking is valuable not only for expanding the locomotion capabilities of biped robots but also for providing insight into human gait patterns, including those observed in rehabilitation. This paper presents the Hybrid Zero Dynamics (HZD) control and performance analysis for biped sideways walking. As sideways walking involves minimal knee flexion and small ankle pronation-supination, a two-link compass gait model can effectively capture their dynamics and is therefore adopted. Unlike forward walking in the sagittal plane, which typically exhibits a one-periodic gait due to leg symmetry, sideways walking demonstrates a two-periodic gait consisting of two steps within one stride: the extending step and the contracting step. To achieve two-periodic gait control, an invariance condition is required, and this paper derives the relationships among the gait parameters that satisfy this condition. When searching for feasible gaits, the boundary conditions and stability condition are analytically derived. The stability of two randomly selected gaits is further validated through linearization and a Poincare map. Finally, using the same compass gait model, a comparison between sideways and forward walking is conducted in terms of speed and cost of transport (CoT). The results show that sideways walking is generally less energy-efficient and has a lower and a smaller range of optimal speed than forward walking, which is consistent with observations in human locomotion.", "url": "" }, { "id": "Mo-MoB10.5", "code": "MoB10.5", "title": "Certifying Set Attractivity for Discrete-Time Uncertain Nonlinear Switched Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB10", "sessionTitle": "JO-NAHS: Hybrid and Switched Systems Modeling", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Anderson, Alejandro", "affiliation": "University of Trento" }, { "name": "Hernandez Vargas, Esteban A.", "affiliation": "UNAM" }, { "name": "Giordano, Giulia", "affiliation": "Università Degli Studi Di Trento" } ], "keywords": [ "Hybrid and switched systems modeling", "Stability and stabilization of hybrid systems" ], "abstract": "We introduce a new class of functions, called Attractivity Guarantee (AG)-functions, to certify the attractivity of sets for uncertain nonlinear switched systems in discrete time. The existence of an AG-function associated with a set guarantees the robust local attractivity of that set under the system dynamics. We propose a constructive method for obtaining piecewise-continuous AG-functions based on contractive sets for the system: the existence of a robust control contractive set for the dynamics implies the existence of an appropriate AG-function, and hence the robust local attractivity of the set itself. We illustrate the proposed framework through the case study of a nonlinear switched system modelling antimicrobial resistance.", "url": "" }, { "id": "Mo-MoB10.6", "code": "MoB10.6", "title": "Robust Predictive Control Design for Uncertain Discrete Switched Affine Systems Subject to an Input Delay (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB10", "sessionTitle": "JO-NAHS: Hybrid and Switched Systems Modeling", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Portilla, Gerson", "affiliation": "Universidad De Sevilla" }, { "name": "Albea, Carolina", "affiliation": "Universidad De Sevilla" }, { "name": "Seuret, Alexandre", "affiliation": "University of Sevilla" } ], "keywords": [ "Hybrid and switched systems modeling", "Stability and stabilization of hybrid systems", "Event-based control" ], "abstract": "Robust stabilization conditions for uncertain switched affine systems subject to a unitary input delay are presented. They are obtained through the Lyapunov framework and a min-switching state-feedback predictive control law. The result relies on a prediction scheme considering nominal system parameters. By constructing a Lyapunov function that considers the prediction error, we demonstrate the exponential convergence of the system trajectories and system prediction to a robust limit cycle. An example is provided to validate the obtained result.", "url": "" }, { "id": "Mo-MoB11.1", "code": "MoB11.1", "title": "Obstacle Avoidance Path Planning for Drill-Pipe Handling Manipulator Based on Improved RRT-Connect Algorithm (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB11", "sessionTitle": "Advanced Control and Intelligent Automation Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 201", "authors": [ { "name": "Wang, Yujie", "affiliation": "China University of Geosciences" }, { "name": "Wu, Jundong", "affiliation": "China University of Geosciences" }, { "name": "Peng, Guangyu", "affiliation": "CCTEG Xi’an Research Institute (Group) Co., Ltd" }, { "name": "Wang, Yawu", "affiliation": "China University of Geosciences" }, { "name": "Yang, Aoxue", "affiliation": "China University of Geosciences" }, { "name": "Dong, Hongbo", "affiliation": "CCTEG Xi’an Research Institute (Group) Co., Ltd" } ], "keywords": [ "Cloud control and robotics", "Soft computing and robust intelligent control" ], "abstract": "In coal mine drilling, the drill-pipe handling manipulator operates in a confined workspace, which makes it difficult to plan collision-free paths. This paper develops an improved rapidly-exploring random tree connect (RRT-Connect) algorithm to achieve obstacle avoidance path planning for the drill-pipe handling manipulator at any tilt angle of the main unit. The kinematic model of the drilling rig system is established, and collision models are simplified. To cope with the changing workspace caused by varying tilt angle of the main unit, a tilt-angle-based step-size strategy is introduced into the RRT-Connect algorithm to adaptively determine step-size, improving planning speed and success rate. To improve the smoothness of the planned path, the initial path is further optimized in terms of path-length and waypoint-count, where a bisection search and a caching mechanism are employed to accelerate the optimization process. Simulation and experimental results demonstrate that the proposed method efficiently generates high-quality, collision-free paths for the manipulator at any tilt angle of the main unit.", "url": "" }, { "id": "Mo-MoB11.2", "code": "MoB11.2", "title": "A Runoff Forecasting Method Considering Multi-Source Direction Verification and Multi-Scale Decoupling for Areas with Sparse Monitoring Stations (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB11", "sessionTitle": "Advanced Control and Intelligent Automation Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 201", "authors": [ { "name": "Zhou, Yue", "affiliation": "School of Artificial Intelligence and Automation, China University of Geosciences (Wuhan)" }, { "name": "Cao, Weihua", "affiliation": "China University of Geosciences" }, { "name": "Li, Yupeng", "affiliation": "China University of Geosciences" }, { "name": "Yuan, Yan", "affiliation": "China University of Geosciences" } ], "keywords": [ "Machine learning for modeling and prediction", "Data fusion and mining in control" ], "abstract": "Runoff forecasting is critical for water resource management and disaster prevention. This study proposes a method for regions with sparse monitoring stations. The approach integrates multi-source data validation and stratification to determine flow directions accurately. It models relationships among runoff-generating mechanisms within river strata. The method employs a coupled high- and low-frequency modeling approach and a spatially hierarchical coupled model to improve prediction accuracy. The framework also addresses multi-source data conflicts, enhancing reliability in areas with limited observations. A case study demonstrates the effectiveness of the proposed method, showing improved runoff prediction compared with conventional approaches.", "url": "" }, { "id": "Mo-MoB11.3", "code": "MoB11.3", "title": "Modeling of Photo Thermally Driven Liquid Crystal Elastomer-Based Artificial Iris (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB11", "sessionTitle": "Advanced Control and Intelligent Automation Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 201", "authors": [ { "name": "Cheng, Chao", "affiliation": "China University of Geosciences" }, { "name": "Wu, Jundong", "affiliation": "China University of Geosciences" }, { "name": "Wang, Yawu", "affiliation": "China University of Geosciences" }, { "name": "Yan, Ze", "affiliation": "China University of Geosciences" }, { "name": "Ye, Wenjun", "affiliation": "University of Liverpool" }, { "name": "Su, Chun-Yi", "affiliation": "Concordia Univ" } ], "keywords": [ "AI-driven modeling and control" ], "abstract": "Liquid crystal elastomers (LCEs) exhibit strong potential in soft robotics and bio-inspired systems due to their photothermal actuation and excellent shape recovery performance. In particular, ring shaped LCE are well suited for applications in artificial iris. Since the iris function relies on the dynamic contraction of its annular structure, the radial deformation accuracy of ring shaped LCE actuators is critical for effective light regulation. To enable precise control, this work develops a model for the designed ring shaped LCE actuator. The model comprises two sub-models: a temperature model that describes the relationship between input voltage and temperature, and a deformation model that characterizes the relationship between temperature and output deformation. The temperature model is established based on the thermodynamic characteristics of the actuator. As for the deformation model, a radial deformation coefficient and a distribution function are introduced to describe the geometric characteristics of the ring actuator, thereby formulating the relationship between radial deformation and temperature. Based on collected experimental data, the model parameters are identified using a nonlinear least-squares method. The model identification results demonstrate that the overall model accurately captures the deformation behavior of the ring shaped LCE and reflects its underlying physical properties, providing a solid foundation for precise control in future applications.", "url": "" }, { "id": "Mo-MoB11.4", "code": "MoB11.4", "title": "Optimal Control of Imaging Quality for Bio-Inspired Crystalline Lens Based on Dielectric Elastomer (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB11", "sessionTitle": "Advanced Control and Intelligent Automation Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 201", "authors": [ { "name": "Jiang, Chenyang", "affiliation": "China University of Geosciences" }, { "name": "Wu, Jundong", "affiliation": "China University of Geosciences" }, { "name": "Meng, Qingxin", "affiliation": "China University of Geosciences" }, { "name": "Wang, Yawu", "affiliation": "China University of Geosciences" }, { "name": "Lai, Xuzhi", "affiliation": "China University of Geosciences" }, { "name": "She, Jinhua", "affiliation": "Tokyo Univ. of Tech" } ], "keywords": [ "Bio-inspired algorithms and optimization-based control", "Adaptive dynamic programming for control", "Knowledge-based and data-driven control" ], "abstract": "Conventional zoom systems rely on numerous lens groups and complex mechanical transmission structures, and their applications face significant limitations due to rigidity and large size. To address this challenge, this paper designs a bio-inspired crystalline lens structure based on dielectric elastomer (DE). This structure utilizes the electrically-driven compliant deformation of the DE film to dynamically and continuously adjust its focal length, thereby achieving clear imaging. Inspired by the physiological mechanism of the human visual system that adaptively optimizes imaging quality, a visual feedback control strategy centered on a sharpness metric is established to achieve high-quality imaging. To quantify imaging quality in real time, a sharpness evaluation function based on gradient operators is employed. Using imaging sharpness as the feedback signal, an adaptive extremum seeking control method is adopted to automatically search for the optimal driving voltage, enabling the focal length of the bio-inspired lens to dynamically converge to the extremum of the sharpness function and thereby achieving continuous image quality optimization. Adaptive focusing experiments under different object distances validate the effectiveness of the proposed method.", "url": "" }, { "id": "Mo-MoB11.5", "code": "MoB11.5", "title": "Mixed-Sensitivity Robust Control and Equivalent Input Disturbance Compensation for Drilling Feed Speed (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB11", "sessionTitle": "Advanced Control and Intelligent Automation Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 201", "authors": [ { "name": "Chen, Shipeng", "affiliation": "China University of Geosciences" }, { "name": "Lu, Chengda", "affiliation": "China University of Geosciences" }, { "name": "Wang, Yibing", "affiliation": "China University of Geosciences" }, { "name": "Zhang, Youzhen", "affiliation": "CCTEG Xi'an Research Institute (Group) Co., Ltd" }, { "name": "Li, Quanxin", "affiliation": "CCTEG Xi'an Research Institute (Group) Co., Ltd" }, { "name": "Dong, Hongbo", "affiliation": "CCTEG Xi’an Research Institute (Group) Co., Ltd" }, { "name": "Wu, Min", "affiliation": "China University of Geosciences" } ], "keywords": [ "Soft computing and robust intelligent control", "Cloud control and robotics" ], "abstract": "Variations in formation hardness induce fluctuations in feed speed during coal mine drilling, affecting drilling efficiency and stability. This paper presents a robust feed speed control method to address formation changes. A dynamic feed system model is first established, where formation variations are represented as parameter variations and external disturbances. A mixed-sensitivity-based robust controller is then designed to handle parameter variations, and an equivalent-input-disturbance approach is implemented to suppress external disturbances. Simulation results using field drilling data demonstrate that the developed control system effectively stabilizes the feed speed and outperforms existing control methods.", "url": "" }, { "id": "Mo-MoB11.6", "code": "MoB11.6", "title": "Adaptive Parameter Mapping Framework for Cross-Line Plate Shape Prediction in Quenching Process (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB11", "sessionTitle": "Advanced Control and Intelligent Automation Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 201", "authors": [ { "name": "Liu, Xianzhe", "affiliation": "China University of Geosciences, Wuhan" }, { "name": "Chen, Luefeng", "affiliation": "China University of Geosciences" }, { "name": "Wu, Min", "affiliation": "China University of Geosciences" }, { "name": "Hu, Jie", "affiliation": "China University of Geosciences" }, { "name": "Ding, Min", "affiliation": "China University of Geosciences" }, { "name": "Pedrycz, Witold", "affiliation": "Department of Electrical and Computer Engineering, University of Alberta" } ], "keywords": [ "Model driven engineering of control systems", "AI-driven modeling and control", "Machine learning for modeling and prediction" ], "abstract": "Accurate flatness prediction is essential for quality control in steel plate quenching processes, yet many production lines lack online flatness gauges, resulting in severe label scarcity for data-driven modeling. This work is motivated by an industrial observation that operators on different quenching lines tend to adopt stable but machine-specific parameter settings for steel plates with identical specifications. Based on this observation, an adaptive parameter mapping framework is proposed for cross-line flatness prediction. Unlike conventional domain adaptation methods that align latent feature distributions, the proposed method directly models the correspondence between process-parameter spaces across production lines. A specification-conditioned gradient boosting decision tree is used to map the target-line parameters into the equivalent parameter domain of a reference line equipped with online gauges. The mapped parameters are then fed into a pretrained multilayer perceptron flatness predictor, enabling model reuse without retraining on target-line online labels. Experiments on two industrial quenching lines demonstrate that the proposed method achieves superior prediction accuracy under label-scarce conditions, reducing RMSE to 0.716 I-Unit and achieving an R² of 0.817. The proposed framework provides a practical solution for cross-line predictive quality control in steel manufacturing.", "url": "" }, { "id": "Mo-MoB13.1", "code": "MoB13.1", "title": "MPC for Tracking for Anesthesia Dynamics", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB13", "sessionTitle": "Model Predictive Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Raymond, Maxim", "affiliation": "CNRS, LAMIH" }, { "name": "Moussa, Kaouther", "affiliation": "INSA Hauts-De-France, LAMIH" }, { "name": "Fiacchini, Mirko", "affiliation": "GIPSA-Lab, CNRS" }, { "name": "Lauber, Jimmy", "affiliation": "INSA - Polytechnic University Hauts-De-France" } ], "keywords": [ "Model predictive control", "Control in system biology" ], "abstract": "In this paper, an MPC for tracking formulation is proposed for the control of anesthesia dynamics. It seamlessly enables the optimization of the steady-states pair that is not unique due to the MISO nature of the model. Anesthesia dynamics is a multi-time scale system with two types of states characterized, respectively, by fast and slow dynamics. In anesthesia control, the output equation depends only on the fast dynamics. Therefore, the slow states can be treated as disturbances, and compensation terms can be introduced. Subsequently, the system can be reformulated as a nominal one allowing the design of an MPC for tracking strategy. The presented framework ensures recursive feasibility and asymptotic stability, through the design of appropriate terminal ingredients in the MPC for tracking framework. The controller performance is then assessed on a patient in a simulation environment.", "url": "" }, { "id": "Mo-MoB13.2", "code": "MoB13.2", "title": "Successive Convex Optimization for Transformer Encoder Model Predictive Control", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB13", "sessionTitle": "Model Predictive Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Chen, Xingxiao", "affiliation": "University of Oxford" }, { "name": "Cannon, Mark", "affiliation": "University of Oxford" } ], "keywords": [ "Model predictive control", "Learning methods for optimal control", "Design methods for data-based control" ], "abstract": "We propose a data-driven Model Predictive Control (MPC) framework that employs a transformer encoder to generate multi-step predictions. To handle the nonconvex attention mechanism, we derive difference of convex (DC) representations of the transformer encoder components and embed them in a successive convex programming (SCP) iteration. Recursive feasibility and convergence of the SCP iterates are guaranteed, and each iterate yields a solution estimate satisfying the problem constraints. Under mild assumptions, the SCP iteration converges to a locally optimal solution of the MPC problem. The approach is illustrated on a benchmark nonlinear control problem.", "url": "" }, { "id": "Mo-MoB13.3", "code": "MoB13.3", "title": "Smooth Sampling-Based Model Predictive Control Using Deterministic Samples", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB13", "sessionTitle": "Model Predictive Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Walker, Markus", "affiliation": "Karlsruhe Institute of Technology (KIT)" }, { "name": "Reith-Braun, Marcel", "affiliation": "Karlsruhe Institute of Technology (KIT)" }, { "name": "Hoang, Tai", "affiliation": "Karlsruhe Institute of Technology" }, { "name": "Neumann, Gerhard", "affiliation": "Karlsruhe Institute of Technology" }, { "name": "Hanebeck, Uwe", "affiliation": "Karlsruhe Institute of Technology (KIT)" } ], "keywords": [ "Model predictive control", "Numerical methods for optimal control" ], "abstract": "Sampling-based model predictive control (MPC) is effective for nonlinear systems but often produces non-smooth control inputs due to random sampling. To address this issue, we extend the model predictive path integral (MPPI) framework with deterministic sampling and improvements from cross-entropy method (CEM)–MPC, such as iterative optimization, proposing deterministic sampling MPPI (dsMPPI). This combination leverages the exponential weighting of MPPI alongside the efficiency of deterministic samples. Experiments demonstrate that dsMPPI achieves smoother trajectories compared to state-of-the-art methods.", "url": "" }, { "id": "Mo-MoB13.4", "code": "MoB13.4", "title": "Gauss-Newton Accelerated MPPI Control", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB13", "sessionTitle": "Model Predictive Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Homburger, Hannes", "affiliation": "HTWG Konstanz University of Applied Sciences" }, { "name": "Baumgärtner, Katrin", "affiliation": "University of Freiburg" }, { "name": "Diehl, Moritz", "affiliation": "University of Freiburg" }, { "name": "Reuter, Johannes", "affiliation": "University of Applied Sciences Konstanz" } ], "keywords": [ "Model predictive control", "Optimal control theory", "Real-time optimal control" ], "abstract": "Model Predictive Path Integral (MPPI) control is a sampling-based optimization method that has recently attracted attention, particularly in the robotics and reinforcement learning communities. MPPI has been widely applied as a GPU-accelerated random search method to deterministic direct single-shooting optimal control problems arising in model predictive control (MPC) formulations. MPPI offers several key advantages, including flexibility, robustness, ease of implementation, and inherent parallelizability. However, its performance can deteriorate in high-dimensional settings since the optimal control problem is solved via Monte Carlo sampling. To address this limitation, this paper proposes an enhanced MPPI method that incorporates a Jacobian reconstruction technique and the second-order Generalized Gauss-Newton method. This novel approach is called Gauss–Newton accelerated MPPI. The numerical results show that the Gauss-Newton accelerated MPPI approach substantially improves MPPI scalability and computational efficiency while preserving the key benefits of the classical MPPI framework, making it a promising approach even for high-dimensional problems.", "url": "" }, { "id": "Mo-MoB13.5", "code": "MoB13.5", "title": "Robust Constraint Removal for Model Predictive Control on Embedded Hardware", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB13", "sessionTitle": "Model Predictive Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Dyrska, Raphael", "affiliation": "Ruhr-Universität Bochum" }, { "name": "Lammersmann, Benedikt", "affiliation": "Ruhr University Bochum" }, { "name": "Monnigmann, Martin", "affiliation": "Ruhr-Universität Bochum" } ], "keywords": [ "Model predictive control", "Optimal control theory", "Uncertain systems" ], "abstract": "We extend an approach to detecting inactive constraints in model predictive control (MPC) to the case with additive disturbances. We employ a helper function to detect inactive constraints after the disturbance on the system state occurred. Implementations on an ARM Cortex M7-based microcontroller show savings on the average computation time of up to 76% for examples of various complexity and horizon lengths.", "url": "" }, { "id": "Mo-MoB13.6", "code": "MoB13.6", "title": "End-To-End Elastic Tube MPC: Design, Analysis, and Embedded Implementation", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB13", "sessionTitle": "Model Predictive Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Holaza, Juraj", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Plsicik Pavlovicova, Erika", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Serhiienko, Sofiia", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Oravec, Juraj", "affiliation": "Slovak University of Technology in Bratislava" } ], "keywords": [ "Model predictive control", "Robust control applications", "Uncertain systems" ], "abstract": "This paper presents an extension of the MPT+ toolbox that provides an end-to-end workflow for Elastic Tube Model Predictive Control (MPC), from controller synthesis to embedded implementation. The module automatically computes non-trivial tube-propagation matrices, constraint tightening, terminal ingredients, and the stabilising feedback law, without the necessity for external interventions. The constructed Elastic Tube MPC controllers are designed and analysed with only a few lines of code. To illustrate the benefits of the proposed approach, the evaluated MPC controller is analysed and validated using numerical simulations of closed-loop control and laboratory implementation using a pocket-sized embedded heat-exchanger system, demonstrating successful disturbance rejection and constraint satisfaction.", "url": "" }, { "id": "Mo-MoB14.1", "code": "MoB14.1", "title": "A Gauss-Newton-Induced Structure-Exploiting Algorithm for Differentiable Optimal Control", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB14", "sessionTitle": "Learning Methods for Optimal Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Chen, Yuankun", "affiliation": "Jilin University" }, { "name": "Nie, Zifei", "affiliation": "Jilin University" }, { "name": "Gong, Xun", "affiliation": "Jilin University" }, { "name": "Hu, Yunfeng", "affiliation": "Jilin University" }, { "name": "Chen, Hong", "affiliation": "Tongji University" } ], "keywords": [ "Learning methods for optimal control", "Numerical methods for optimal control", "Model predictive control" ], "abstract": "Differentiable optimal control, particularly differentiable nonlinear model predictive control (NMPC), provides a powerful framework that enjoys the complementary benefits of machine learning and control theory. A key enabler of differentiable optimal control is the computation of derivatives of the optimal trajectory with respect to problem parameters., i.e., trajectory derivatives.Previous works compute trajectory derivatives by solving a differential Karush–Kuhn–Tucker (KKT) system, and achieve this efficiently by constructing an equivalent auxiliary system. However, we find that directly exploiting the matrix structures in the differential KKT system yields significant computation speed improvements.Motivated by this insight, we propose FastDOC, which applies a Gauss–Newton approximation of Hessian and takes advantage of the resulting block-sparsity and positive semidefinite properties of the matrices involved. These structural properties enable us to accelerate the computationally expensive matrix factorization steps, resulting in a factor-of-two speedup in theoretical computational complexity, and in a synthetic benchmark FastDOC achieves up to a 180% time reduction compared to the baseline method.Finally, we validate the method on an imitation learning task for human-like autonomous driving, where the results demonstrate the effectiveness of the proposed FastDOC in practical applications.", "url": "" }, { "id": "Mo-MoB14.2", "code": "MoB14.2", "title": "Distributed Control of Network Systems in the Space of Stabilizing Graph Neural Network Policies", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB14", "sessionTitle": "Learning Methods for Optimal Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Cao, John", "affiliation": "University of Oxford" }, { "name": "Furieri, Luca", "affiliation": "University of Oxford" } ], "keywords": [ "Learning methods for optimal control", "Distributed robust controller synthesis", "Stability of nonlinear systems" ], "abstract": "We study distributed control of networked systems through reinforcement learning, where neural policies must be simultaneously scalable, expressive and stabilizing. We introduce a policy parameterization that embeds Graph Neural Networks (GNNs) into a Youla-like magnitude-direction parameterization, yielding distributed stochastic controllers that guarantee network-level closed-loop stability by design. The magnitude is implemented as a stable operator consisting of a GNN acting on disturbance feedback, while the direction is a GNN acting on local observations. We prove robustness of the policy to perturbations in both the graph topology and model parameters. Numerical experiments validate the effectiveness of the proposed approach.", "url": "" }, { "id": "Mo-MoB14.3", "code": "MoB14.3", "title": "Robust Risk-Aware MPPI Control Using Online Learning (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB14", "sessionTitle": "Learning Methods for Optimal Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Kim, Jung-Su", "affiliation": "Seoul National University of Science & Technology" }, { "name": "Fauz, Hanif Edma", "affiliation": "Seoul National University of Science and Technology" } ], "keywords": [ "Learning methods for optimal control", "Model predictive control", "Applications of optimal control" ], "abstract": "This paper proposes a robust Risk-aware MPPI (Model Predictive Path Integral) control by quantifying the uncertainty using SGP (Sparse Gaussian Process). During operation, the system collects state and input data to identify uncertainty affecting state transitions by comparing them with the data from the nominal model. Subsequently, the control inputs and estimated uncertainty are used to train the SGP. The trained SGP generates a mean and variance of the uncertainty, effectively compensating for the discrepancy between nominal and real dynamics. Specifically, the nominal model is refined using the estimated mean of the uncertainty, while the estimated variance of the uncertainty is incorporated into the Risk-aware MPPI framework. Validation is conducted through simulation experiments in Gazebo using an F1TENTH car with bicycle dynamics, navigating a track both with and without obstacles. In simulation, the proposed method exhibits improved safety and trajectory tracking performance compared to baseline MPPI techniques.", "url": "" }, { "id": "Mo-MoB14.4", "code": "MoB14.4", "title": "Proper Orthogonal Decomposition for Learning Value Functions of Fluid Flows from Data of Model Predictive Control", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB14", "sessionTitle": "Learning Methods for Optimal Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Sasaki, Yasuo", "affiliation": "Nagoya University" } ], "keywords": [ "Learning methods for optimal control", "Model predictive control", "Optimal control of PDE systems" ], "abstract": "We propose a method to learn a value function using approximation with a neural network and dimensionality reduction of state vectors. The proposed method is based on a learning problem whose loss function is a sum of approximation errors between a value function and a neural network and between their gradients. By analyzing this baseline loss function, we introduce a loss function for the dimensionality reduction and a loss function for the value function for the reduced-order states. To demonstrate the proposed method, a value function of a two-dimensional flow around a circular cylinder governed by the discretized Navier-Stokes equations is learned from data of model predictive control.", "url": "" }, { "id": "Mo-MoB14.5", "code": "MoB14.5", "title": "Neural Network Controller with Mixture-Of-Experts Architecture for Autonomous Guidance and Control under Signal Temporal Logic Specifications", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB14", "sessionTitle": "Learning Methods for Optimal Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Serizawa, Kazunobu", "affiliation": "The University of Osaka" }, { "name": "Hashimoto, Kazumune", "affiliation": "Osaka University" }, { "name": "Ikemoto, Junya", "affiliation": "The University of Osaka" }, { "name": "Kishida, Masako", "affiliation": "University of Tsukuba" }, { "name": "Takai, Shigemasa", "affiliation": "The University of Osaka" } ], "keywords": [ "Learning methods for optimal control" ], "abstract": "This paper proposes a neural network controller with Mixture-of-Experts (MoE) architecture for autonomous guidance and control under Signal Temporal Logic (STL) specifications. The overall STL specifications, including waypoint visits, periodic charging, and obstacle avoidance, is decomposed into sub-STL specifications, each handled by an expert controller. A gating network assigns weights to the expert controllers based on the state and time, and the control input is the weighted sum of the expert outputs. Numerical experiments on a path-planning problem for a drone demonstrate that the proposed controller satisfies the complex and long-horizon STL specification and adapts to different maximum flight times by retraining only the gating network while reusing expert controllers.", "url": "" }, { "id": "Mo-MoB14.6", "code": "MoB14.6", "title": "Characterizing All Locally Exponentially Stabilizing Controllers As a Linear Feedback Plus Learnable Nonlinear Youla Dynamics", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB14", "sessionTitle": "Learning Methods for Optimal Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Furieri, Luca", "affiliation": "University of Oxford" } ], "keywords": [ "Learning methods for optimal control", "Stability of nonlinear systems" ], "abstract": "We derive a state-space characterization of all dynamic state-feedback controllers that make an equilibrium of a nonlinear input-affine continuous-time system locally exponentially stable. Specifically, any controller obtained as the sum of a linear state-feedback u=Kx, with K stabilizing the linearized system, and the output of internal locally exponentially stable controller dynamics is itself locally exponentially stabilizing. Conversely, every dynamic state-feedback controller that locally exponentially stabilizes the equilibrium admits such a decomposition. The result can be viewed as a state-space nonlinear Youla-type parametrization specialized to local, rather than global, and exponential, rather than asymptotic, closed-loop stability. The residual locally exponentially stable controller dynamics can be implemented with stable recurrent neural networks and trained as neural ODEs to achieve high closed-loop performance in nonlinear control tasks.", "url": "" }, { "id": "Mo-MoB15.1", "code": "MoB15.1", "title": "Three Dimensional Impact Angle Constrained Cooperative Guidance against Moving Targets", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB15", "sessionTitle": "Cooperative and Output Feedback Nonlinear Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Huang, Ang", "affiliation": "Beihang University" }, { "name": "Li, Xiaoduo", "affiliation": "Beihang University" }, { "name": "Yu, Jianglong", "affiliation": "Beihang University" }, { "name": "Dong, Xiwang", "affiliation": "Beihang University" }, { "name": "Chen, Jintao", "affiliation": "Tsinghua University" } ], "keywords": [ "Cooperative nonlinear control", "Decentralized control", "Lyapunov methods" ], "abstract": "Three dimensional impact angle constrained cooperative guidance problems are investigated in this paper. Departing from existing approaches, this paper dynamically reorders the predefined desired impact angle formation based on the initial guidance states to enhance multi-directional cooperative guidance performance. Firstly, an line-of-sight angle constrained guidance law is derived by leveraging the relationship between the desired and current angular rate vectors. Secondly, an efficient iterative algorithm is employed to determine the optimal impact angles through closed-loop performance analysis. Thirdly, a fixed-time distributed optimization strategy is designed to dynamically reconfigure the desired impact angles, and a cooperative guidance algorithm is developed. Finally, the effectiveness of the analytical results is validated through numerical simulation.", "url": "" }, { "id": "Mo-MoB15.2", "code": "MoB15.2", "title": "Distributed Cooperative Control of Quadrotor Formations Using Lyapunov Transformations", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB15", "sessionTitle": "Cooperative and Output Feedback Nonlinear Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Barreiro de Araújo, Miguel", "affiliation": "Instituto Superior Técnico" }, { "name": "Oliveira, Paulo Jorge", "affiliation": "Instituto Superior Técnico" }, { "name": "Silvestre, Carlos", "affiliation": "University of Macau" } ], "keywords": [ "Cooperative nonlinear control", "Distributed nonlinear control", "Lyapunov methods" ], "abstract": "This paper develops a non-linear distributed feedback control strategy for formation trajectory tracking of multi-quadrotor systems. Sufficient conditions for closed-loop stability are formulated in terms of the stability margin of the individual vehicles and the information flow among agents. The design exploits Lyapunov transformations that recast the non-linear dynamics into equivalent Linear Time Invariant (LTI) representations, without local linearization, thereby enabling the use of linear analysis and synthesis tools. The resulting controller ensures stable formation tracking of constant-velocity references with zero steady-state error and rejection of constant wind disturbances.", "url": "" }, { "id": "Mo-MoB15.3", "code": "MoB15.3", "title": "LQG-Based Stabilizing Control of Underactuated Cart-Pendulum System Using Position-Only Information", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB15", "sessionTitle": "Cooperative and Output Feedback Nonlinear Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Yu, Junyao", "affiliation": "Linyi University" }, { "name": "Zhang, Ancai", "affiliation": "Linyi University" }, { "name": "Liang, Xiao", "affiliation": "Linyi University" }, { "name": "Yuan, Quan", "affiliation": "Linyi University" } ], "keywords": [ "Stability of nonlinear systems", "Lagrangian and Hamiltonian systems", "Robust control applications" ], "abstract": "Underactuated cart-pendulum system is a canonical nonlinear system. It has been widely used to verify the effectiveness of stabilizing control methods in the fields of robotics and automation. However, almost all control methods require both the position and velocity information of system. In addition, all presented control methods have not taken into account the presence of both process noise and measurement noise in the system. In order to solve this problem, this paper develops a new stabilizing control method by using position-only information of cart-pendulum system. This method firstly gives the system's continuous-time model by Euler-Lagrange modeling method. And the model is discretized based on Zero-Order Hold (ZOH) discretization. And then, a Linear Quadratic Gaussian (LQG) stabilizing controller is designed for the discrete-time cart-pendulum system when the process noise and measurement noise exist. This controller can not only effectively reject the influence of external disturbances, but also achieve stabilizing control of the cart-pendulum system with less energy. Simulation results show the validity and strong robustness of our presented control method.", "url": "" }, { "id": "Mo-MoB15.4", "code": "MoB15.4", "title": "Global Asymptotic Stabilization of a Chain of Integrators in the Presence of Output Saturation", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB15", "sessionTitle": "Cooperative and Output Feedback Nonlinear Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Liu, Songjin", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Li, Yuanlong", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Lin, Zongli", "affiliation": "University of Virginia" } ], "keywords": [ "Lyapunov methods", "Saturation and discontinuity", "Output feedback nonlinear control" ], "abstract": "In this paper, we address the problem of global stabilization of a chain of integrators in the presence of output saturation. A family of continuous output feedback laws incorporating an adaptive gain is proposed to achieve global asymptotic stabilization of the integrator chain. This result improves the existing results in two aspects: 1) the output feedback laws are continuous; and 2) the introduced adaptive gain ensures enhanced transient performance and lower control consumption. Simulation results illustrate the effectiveness of the proposed method.", "url": "" }, { "id": "Mo-MoB15.5", "code": "MoB15.5", "title": "Fuzzy Boundary Control Design for a Class of Semilinear Fractional-Order Distributed Parameter Systems", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB15", "sessionTitle": "Cooperative and Output Feedback Nonlinear Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Shan, Tingfang", "affiliation": "Hunan University of Science and Technology" }, { "name": "Huang, Jianping", "affiliation": "Hunan University of Science and Technology" } ], "keywords": [ "Output feedback nonlinear control", "Lyapunov methods", "Boundary control of distributed parameter systems" ], "abstract": "This paper considers the fuzzy boundary control strategy for a class of semilinear fractional-order distributed parameter systems. We have discussed such kinds of problems for both distributed measurement form and collocated boundary measurement form. Firstly,a Takagi-Sugeno (T-S) fuzzy PDE model is employed to represent the semilinear fractional-order PDE system. Based on the T-S fuzzy PDE model,two kinds of fuzzy boundary control schemes are developed to guarantee the stability of the resulting closed-loop system. These controllers utilize only boundary actuators. Then, utilizing the Lyapunov functional method and the Wirtinger's inequality, we derive sufficient conditions in terms of standard linear matrix inequalities ensuring Mittag-Leffler stability. Finally, two numerical examples are given to verify the validity of the theoretical findings.", "url": "" }, { "id": "Mo-MoB15.6", "code": "MoB15.6", "title": "Robust Cooperative Guidance without Range Measurement Information", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB15", "sessionTitle": "Cooperative and Output Feedback Nonlinear Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Li, Heng", "affiliation": "Beihang University" }, { "name": "Zhang, Zheng", "affiliation": "Beihang University" }, { "name": "Yu, Jianglong", "affiliation": "Beihang University" }, { "name": "Wang, Qing", "affiliation": "Buaa University" }, { "name": "Feng, Zhi", "affiliation": "Beihang University" }, { "name": "Dong, Xiwang", "affiliation": "Beihang University" } ], "keywords": [ "Cooperative nonlinear control", "Stability of nonlinear systems", "Lyapunov methods" ], "abstract": "This paper investigates a 3-D cooperative guidance problem for passive homing missiles, which cannot measure target range but only line-of-sight angles. To overcome this limitation, a robust cooperative guidance method without range measurements is proposed. First, an improved weighted-average consensus-based unscented Kalman filter is designed to fuse local estimates, enhancing estimate accuracy for strong nonlinear systems. Second, leveraging the reliably estimated range, a robust cooperative guidance law is developed. Then, a consensus weighting mechanism is designed to address the observability loss caused by small lead angles, and the stability of the time-to-go consensus error is proven using Lyapunov theory. Finally, numerical simulations demonstrate the effectiveness and superiority of the proposed method.", "url": "" }, { "id": "Mo-MoB16.1", "code": "MoB16.1", "title": "Safe Exploration for Nonlinear Processes Using Online Gaussian Process Learning", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB16", "sessionTitle": "Robust Control Applications", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Tonini, Stefano", "affiliation": "ABB Corporate Research" }, { "name": "Rastegarpour, Soroush", "affiliation": "ABB Research Corporate" }, { "name": "Feyzmahdavian, Hamid Reza", "affiliation": "ABB Corporate Research" }, { "name": "Bastianello, Nicola", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Robust control applications", "Learning methods for optimal control", "Nonlinearity learning from data" ], "abstract": "This paper proposes a safe data-driven control framework for nonlinear systems with partially known dynamics. The method ensures stability and constraint satisfaction during online learning, assuming only a stabilizable linear approximation of the process is available. Unmodeled nonlinear dynamics are captured by a Gaussian process residual learned in real time. Safety is enforced through a probabilistic control-invariant set derived from Lyapunov theory, guaranteeing high-probability stability. A convex quadratic program computes control inputs that maximize information gain while respecting probabilistic safety constraints. The framework provides finite-sample safety guarantees and allows adaptive expansion of the invariant set as uncertainty decreases. Numerical results validate the approach, demonstrating safe and informative exploration under model uncertainty.", "url": "" }, { "id": "Mo-MoB16.2", "code": "MoB16.2", "title": "Equivalent Input-Disturbance Enhanced Control Barrier Function for Safety-Critical Control", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB16", "sessionTitle": "Robust Control Applications", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Tian, Shengnan", "affiliation": "Wuhan University of Science and Technology" }, { "name": "Chen, Yang", "affiliation": "Wuhan University of Science and Technology" }, { "name": "Hu, Mian", "affiliation": "Wuhan University of Science and Technology" }, { "name": "Sun, Ye", "affiliation": "Wuhan University of Science and Technology" }, { "name": "Lu, Chengda", "affiliation": "China University of Geosciences" }, { "name": "Wu, Min", "affiliation": "China University of Geosciences" } ], "keywords": [ "Robust control applications", "Robust estimation", "Disturbance rejection and input-to-state stability" ], "abstract": "Safety-critical control in the presence of disturbances has garnered significant attention, with the design of control barrier functions (CBFs) being central to addressing such challenges. However, conventional and robust CBF formulations often exhibit limitations when confronted with unknown and time-varying disturbances. To overcome these issues, this study incorporates the equivalent input disturbance (EID) approach into the CBF framework, enabling real-time disturbance estimation and compensation and thereby enhancing the robustness of CBF-based safety-critical control. The core idea of the developed safety-critical controller is to construct a composite control input consisting of a nominal safety-critical controller and an EID-based compensation term. The EID estimator counteracts the adverse effects of disturbances on the system output, while the nominal safety-critical controller enforces the safety requirement. Notably, the EID estimator is decoupled from the quadratic programming optimization, making the optimization independent of the disturbance-rejection performance. Distinct from existing methods, the presented method estimates the disturbance-induced effect on the system output rather than the disturbance itself, thus removing the need for prior knowledge of the disturbance structure. The EID-enhanced control strategy further provides two tunable degrees of freedom, effectively mitigating disturbance impacts on both control performance and safety assurance. A case study on an adaptive cruise control system demonstrates the effectiveness and superiority of the EID-enhanced safety-critical control method in the context of disturbance mitigation.", "url": "" }, { "id": "Mo-MoB16.3", "code": "MoB16.3", "title": "Gradient Based Algorithms for Minimax Optimization with Optimal Convergence Rate", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB16", "sessionTitle": "Robust Control Applications", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Wu, Alex Xinting", "affiliation": "Australian National University" }, { "name": "Petersen, Ian R", "affiliation": "The Australian National University" }, { "name": "Shames, Iman", "affiliation": "The University of Melbourne" } ], "keywords": [ "Robust control applications", "Uncertain systems", "Robust controller synthesis" ], "abstract": "This paper studies a class of minimax optimization problems in which the gradient of the cost function with respect to each player is sector bounded. To solve such problems, we consider gradient based algorithms which can be represented as a discrete-time Lur'{e} system. We show that constructing an algorithm with a given convergence rate is equivalent to solving an associated state-feedback H^infty control problem. This reveals that the optimal worst-case convergence rate within the class of algorithms under consideration is achieved by a standard gradient descent/ascent method. These results provide a control-theoretic characterization of convergence guarantees for minimax optimization and clarify the fundamental limitations of gradient based algorithms for the class of minimax cost functions under consideration.", "url": "" }, { "id": "Mo-MoB16.4", "code": "MoB16.4", "title": "RS-LQR Based Yaw Control for DEP Aircraft: Hybrid and Fault-Tolerant Approaches", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB16", "sessionTitle": "Robust Control Applications", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Yu, Junho", "affiliation": "Gyeongsang National University" }, { "name": "Kim, Yoonsoo", "affiliation": "Gyeongsang National University" } ], "keywords": [ "Robust control applications", "Linear systems", "Applications of optimal control" ], "abstract": "This study introduces two robust yaw control techniques for distributed electric propulsion (DEP) aircraft, built upon an Robust servomechanism linear quadratic regulator (RSLQR) framework. The first is a hybrid yaw control method that integrates aerodynamic surfaces with electric propulsion via input-weight tuning, enabling improved robustness and trajectory tracking. The second is a fault-tolerant control (FTC) scheme designed to manage electric propulsor failures. Faults are detected in real time by monitoring yawing moment anomalies, after which control effort is adaptively reallocated. A yawing moment equalization strategy is also employed to mitigate the dynamic effects of asymmetric thrust. Proposed methods are implemented and verified through computer simulations, confirming enhanced performance and robustness.", "url": "" }, { "id": "Mo-MoB16.5", "code": "MoB16.5", "title": "Optimal Distributed Control of Electric Cycloidal Propulsion for Thrust Tracking", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB16", "sessionTitle": "Robust Control Applications", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Nandy, Subhashis", "affiliation": "Senior Researcher, Gyeongsang National University" }, { "name": "Kim, Yoonsoo", "affiliation": "Gyeongsang National University" } ], "keywords": [ "Robust control applications", "Distributed nonlinear control", "Lyapunov methods" ], "abstract": "Electric cycloidal propulsion with distributed blade-level actuation offers enhanced maneuverability and sustainability for aerial and marine platforms. However, its over-actuated and complex geometric characteristics introduce significant challenges in achieving accurate thrust control and power-optimal operation. Moreover, precise blade pitch angle tracking under uncertainties and disturbances remains challenging. Consequently, blade pitch angle tracking errors directly affect thrust generation and overall propulsion performance. To address these challenges, this article presents a unified power-optimal control framework that simultaneously achieves thrust tracking and energy minimization. For low-level blade actuation, a robust finite-time blade pitch controller is developed using a command-filtered adaptive backstepping approach. Numerical simulations demonstrate improved thrust convergence and robust thrust tracking performance under uncertainties and disturbances, consistent with experimental observations.", "url": "" }, { "id": "Mo-MoB16.6", "code": "MoB16.6", "title": "Distributed Positivity-Based Sliding Mode Control of Network Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB16", "sessionTitle": "Robust Control Applications", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Vacchini, Edoardo", "affiliation": "University of Pavia" }, { "name": "Cucuzzella, Michele", "affiliation": "University of Groningen" }, { "name": "Kawano, Yu", "affiliation": "Hiroshima University" }, { "name": "Ferrara, Antonella", "affiliation": "University of Pavia" } ], "keywords": [ "Sliding mode control", "Positive linear systems", "Distributed robust controller synthesis" ], "abstract": "In this paper, we develop a novel decentralized procedure for the design of sliding subspaces for linear network systems leveraging the concept of positive systems. In a nutshell, each subsystem is in closed-loop with a sliding mode control law that steers the system towards a suitably designed sliding subspace, on which the controlled system in sliding mode behaves as a dissipative positive system. This opens the possibility of using positivity arguments to establish stability even in the case in which the system cannot be rendered positive by means of classical approaches. The proposal is validated via numerical examples.", "url": "" }, { "id": "Mo-MoB17.1", "code": "MoB17.1", "title": "Flatness of Nonlinear SISO Hamiltonian Systems", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB17", "sessionTitle": "Lagrangian and Hamiltonian Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Sira-Ramirez, Hebertt J.", "affiliation": "CINVESTAV-IPN" }, { "name": "Medina Covarrubias, Adan", "affiliation": "Centro De Investigación Y De Estudios Avanzados Del Instituto Politécnico Nacional" } ], "keywords": [ "Lagrangian and Hamiltonian systems", "Output feedback nonlinear control" ], "abstract": "A method is presented for establishing the flatness property of Single-Input Single-Output (SISO) nonlinear Hamiltonian Systems of the affine-in-the-control type. The emphasis is placed on an intrinsic approach which utilizes operations natural in Hamiltonian systems (Poisson brackets, iterated brackets, functional linear independence, partial differential equations etc). An alternative method is thus proposed to the differential geometric approach based on vector fields, directional derivatives, adjoint operators and distributions.", "url": "" }, { "id": "Mo-MoB17.2", "code": "MoB17.2", "title": "A Geometric Task-Space Port-Hamiltonian Formulation for Redundant Manipulators", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB17", "sessionTitle": "Lagrangian and Hamiltonian Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Califano, Federico", "affiliation": "University of Twente" }, { "name": "Rota, Camilla", "affiliation": "Sapienza University Rome" }, { "name": "Zanella, Riccardo", "affiliation": "University of Twente" }, { "name": "Franchi, Antonio", "affiliation": "University of Twente and Sapienza University of Rome" } ], "keywords": [ "Lagrangian and Hamiltonian systems", "Passivity-based control" ], "abstract": "We present a novel geometric port-Hamiltonian formulation of redundant manipulators performing a differential kinematic task η = J(q)dot{q}, where q is a point on the configuration manifold, η is a velocity-like task space variable, and J(q) is a linear map representing the task. The proposed model emerges from a change of coordinates from canonical Hamiltonian dynamics, and decomposes the standard Hamiltonian momentum variable into a task-space and a null-space component. Properties of this model and relation to Lagrangian formulations present in the literature are highlighted. Finally, we apply the proposed model in an Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) design to stabilize and shape the impedance of a 7-DOF Emika Panda robot in simulation.", "url": "" }, { "id": "Mo-MoB17.3", "code": "MoB17.3", "title": "Tracking Control for a Dynamic Model of an Underwater Submersible", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB17", "sessionTitle": "Lagrangian and Hamiltonian Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Hampsey, Matthew", "affiliation": "The Australian National University" }, { "name": "van Goor, Pieter", "affiliation": "University of Sydney" }, { "name": "Banavar, Ravi", "affiliation": "Indian Institute of Technology" }, { "name": "Mahony, Robert", "affiliation": "Australian National University" } ], "keywords": [ "Lagrangian and Hamiltonian systems", "Passivity-based control" ], "abstract": "Underwater vehicles are naturally modelled as rigid bodies on SE(3) subjected to added mass effects. The passivity of the Hamiltonian structure of the system can be exploited to design energy-based stabilising controllers, however, the extension of these control designs to tracking control is not trivial since the error system for the classical error formulations is not itself Hamiltonian. In this paper, we show that a novel choice of error function leads to error dynamics that are Hamiltonian. We go on to derive an energy-based tracking control for a fully coupled model of a submersible vehicle. Asymptotic convergence of the control scheme is proved and the control is demonstrated in a simulation study of the Blue Robotics BlueROV2 Heavy submersible.", "url": "" }, { "id": "Mo-MoB17.4", "code": "MoB17.4", "title": "A Hamiltonian Approach for Modeling and Control of a Current-Fed Resonant Converter", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB17", "sessionTitle": "Lagrangian and Hamiltonian Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Sanchez-Contreras, Agustin", "affiliation": "Universidad Nacional Autonoma De Mexico" }, { "name": "Cardenas, Victor", "affiliation": "Universidad Autonoma De San Luis Potosi" }, { "name": "Espinosa-Perez, Gerardo", "affiliation": "Universidad Nacional Autonoma De Mexico" } ], "keywords": [ "Lagrangian and Hamiltonian systems", "Passivity-based control", "Application of nonlinear analysis and design" ], "abstract": "Current-Fed Resonant Converters define a particular kind of power converters that has gained a great importance in the context of high-frequency-link power conversion systems. In spite of its practical importance, its transcendence for the systems theoretic control community is at some extent diminished due to the lack of a mathematical model suitable to develop high performance model-based control schemes. In this paper the modeling problem of this class of converters is approached from a Port-Controlled Hamiltonian systems perspective proposing a novel representation for the system obtained without neither simplifying nor reducing the order of the system.The proposed model enables control schemes with proven mathematical properties. This is demonstrated by a Passive PI controller with formal stability proof. Both the model and controller are numerically validated.", "url": "" }, { "id": "Mo-MoB17.5", "code": "MoB17.5", "title": "Passivity Based Control for Generalized Port-Hamiltonian DC-DC Converters with Conduction Losses", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB17", "sessionTitle": "Lagrangian and Hamiltonian Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Perez-Galicia, Victor", "affiliation": "Universidad Nacional Autonoma De Mexico" }, { "name": "Ramos-García, Fernanda", "affiliation": "Universidad Nacional Autónoma De México, UNAM" }, { "name": "Cardenas, Victor", "affiliation": "Universidad Autonoma De San Luis Potosi" }, { "name": "Espinosa-Perez, Gerardo", "affiliation": "Universidad Nacional Autonoma De Mexico" } ], "keywords": [ "Passivity-based control", "Lagrangian and Hamiltonian systems", "Application of nonlinear analysis and design" ], "abstract": "This paper addresses the modeling and control of DC-DC converters with conduction losses within the port-controlled Hamiltonian (pcH) framework. The main objective is to develop a generalized energy-based representation that explicitly incorporates losses in passive elements and switching devices while preserving the structural properties required for Passivity-Based Control (PBC) design. The proposed formulation provides a more realistic model than the ideal lossless representation commonly used in control design schemes and allows different converter topologies to be described under a unified framework. Based on this PCH model with conduction losses, a passivity-based control law is designed to regulate the desired operating point and guarantee closed-loop stability. As a case study, the methodology is applied to the Boost converter. Numerical validation is carried out in MATLAB/Simulink using a Simscape implementation, where the proposed control scheme is compared with the controller obtained from the ideal generalized model.", "url": "" }, { "id": "Mo-MoB17.6", "code": "MoB17.6", "title": "On Hybrid Control of PD Control and Kinetic Potential Energy Shaping with Applications to Trajectory Tracking", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB17", "sessionTitle": "Lagrangian and Hamiltonian Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Iwase, Masaoki", "affiliation": "Kyoto University, Mitsubishi Electric" }, { "name": "Fujimoto, Kenji", "affiliation": "Kyoto University" }, { "name": "Maruta, Ichiro", "affiliation": "Kyoto University" } ], "keywords": [ "Passivity-based control", "Lagrangian and Hamiltonian systems", "Stability of nonlinear systems" ], "abstract": "This paper proposes a hybrid control method for a mechanical system which makes some of the configuration variables controlled by PD control and the others by the Kinetic Potential Energy Shaping (KPES) method, and its application to a trajectory tracking problem. The conventional energy shaping method allows one to design a stabilizing controller with a Lyapunov function candidate consisting of an artificial potential function which plays a role of a design parameter. The potential function depends only on position, and this framework is a natural generalization of PD control. KPES generalizes the conventional method such that it allows one to select an artificial potential function depending on both position and momentum. Previous studies report that KPES is effective in applications to trajectory tracking control. While one of the advantages of the conventional method is that it preserves passivity of the original plant system, thereby improving the safety of the control system, KPES does not preserve passivity in general. The closed-loop system constructed by the proposed method enables position control of subsystems by KPES including trajectory tracking, and preserves passivity of the entire system when an external force acts on the subsystems controlled by PD control. Such a controlled system will be useful from a safety perspective for the position and force hybrid control task of a robot manipulator. This paper presents a method to design a feedback system that guarantees asymptotic stability of the entire system and preserves passivity, and discusses its application to trajectory tracking problem. Furthermore, a numerical example for a robot manipulator is provided.", "url": "" }, { "id": "Mo-MoB18.1", "code": "MoB18.1", "title": "Physics and Knowledge-Guided Machine Learning for Fault Diagnostic in Nuclear Rotating Machinery (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB18", "sessionTitle": "Artificial Intelligence and Digital Twins for Next-Generation Prognostics and Health Management in Smart Manufacturing II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Mahamadou Saley, Amaratou", "affiliation": "INSA Lyon" }, { "name": "Cheutet, Vincent", "affiliation": "Université De Lyon, INSA Lyon, Laboratoire DISP (EA4570)" }, { "name": "Moyaux, Thierry", "affiliation": "Université De Lyon (INSA)" }, { "name": "Sekhari, Aicha", "affiliation": "University Lyon 2" }, { "name": "Danielou, Jean-Baptiste", "affiliation": "EQUANS Ineo Nucléaire" } ], "keywords": [ "Manufacturing prognostics and health management", "Industrial artificial intelligence", "Intelligent manufacturing systems" ], "abstract": "Analysing vibration data for fault detection and diagnostic in nuclear rotating machinery has gained increasing importance with the rise of Industry 4.0 and smart maintenance strategies. However, the scarcity of fault data and the heterogeneity of available knowledge—combining sensor signals, expert insights, maintenance documentation, and physical understanding—limit the performance of purely data-driven methods. Although physics-informed approaches help address this challenge, most rely on a single type of prior knowledge and do not combine knowledge, physics, and data within a unified diagnostic process. This work proposes a hybrid, physics- and knowledge-guided machine learning methodology that integrates tacit and explicit knowledge, physical modelling, and vibration-based data analysis. Domain knowledge guides data preprocessing, supports physical modelling to generate fault scenarios for physics-based data augmentation, and enhances explainability during diagnostic. The approach is validated on an industrial nuclear fan, achieving 0.976 mean average precision, 0.974 F1-score, and 0.97 recall for fault detection, and 0.86 accuracy for fault diagnostic. The results demonstrate the benefit of jointly combining domain knowledge, physics-based reasoning, and machine learning to achieve reliable and explainable fault diagnostic in nuclear rotating machinery.", "url": "" }, { "id": "Mo-MoB18.2", "code": "MoB18.2", "title": "Learning Physics-Informed Surrogate Model of Linear Elastic Displacement Fields from Geometry (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB18", "sessionTitle": "Artificial Intelligence and Digital Twins for Next-Generation Prognostics and Health Management in Smart Manufacturing II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Barlogis, Rodolphe", "affiliation": "PROMES-CNRS" }, { "name": "Tamssaouet, Ferhat", "affiliation": "Université De Toulouse" }, { "name": "Falcoz, Quentin", "affiliation": "PROMES-CNRS" }, { "name": "Grieu, Stéphane", "affiliation": "PROMES-CNRS" } ], "keywords": [ "Maintenance engineering, management and services", "Manufacturing prognostics and health management" ], "abstract": "This work aims to develop a fast and physically consistent surrogate model for real-time structural health monitoring of fractured elastic domains. We propose a physics-informed DeepONet framework that predicts displacement fields from both boundary conditions and fracture geometry, using a dedicated encoding strategy for the latter and without relying on finite-element-generated training data. The traction-free condition on the fracture boundary is imposed weakly through a localized penalty term. The presented numerical example focuses on one representative fracture geometry, demonstrating the feasibility of the formulation and laying the groundwork for extensions to surrogate modeling across diverse fracture geometries.", "url": "" }, { "id": "Mo-MoB18.3", "code": "MoB18.3", "title": "A Standardized Methodology to Develop LIVE Digital Twins for Predictive Maintenance (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB18", "sessionTitle": "Artificial Intelligence and Digital Twins for Next-Generation Prognostics and Health Management in Smart Manufacturing II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Bondoc, Andrew", "affiliation": "University of Ontario Institute of Technology" }, { "name": "Barari, Ahmad", "affiliation": "University of Ontario Institute of Technology" } ], "keywords": [ "Maintenance engineering, management and services", "Cyber-physical production systems", "Manufacturing prognostics and health management" ], "abstract": "Digital Twin (DT) technologies have been at the forefront of Industry 4.0 and Smart Maintenance. However, there are many challenges associated with developing a DT such as managing large data sets, complex simulations, sensor fusion, and standardization in the industry. LIVE Digital Twin is a novel methodology for developing a DT for Predictive Maintenance (PdM) which addresses the limitations. The four phases, Learn, Identify, Verify, and Extend, are presented in the form of two case studies to highlights the developmental process of a LIVE DT. A Pipeline System and a Rotary Machine are presented to highlight four phases of LIVE.", "url": "" }, { "id": "Mo-MoB18.4", "code": "MoB18.4", "title": "AI-Based Maintenance Scheduling Framework Considering Disassembly Impact (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB18", "sessionTitle": "Artificial Intelligence and Digital Twins for Next-Generation Prognostics and Health Management in Smart Manufacturing II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Le, Huu-Truong", "affiliation": "Université De Lorraine, CRAN, CNRS" }, { "name": "Do, Phuc", "affiliation": "IMT Mines Alès" }, { "name": "Voisin, Alexandre", "affiliation": "Université De Lorraine, CNRS, CRAN" }, { "name": "Franciosi, Chiara", "affiliation": "Université De Lorraine, CNRS, CRAN, F-54000, Nancy, France" } ], "keywords": [ "Maintenance engineering, management and services", "Manufacturing prognostics and health management", "Industrial artificial intelligence" ], "abstract": "This paper introduces a two-phase AI-based framework for maintenance scheduling in multi-component systems, explicitly accounting for economic and structural dependence, particularly structural disassembly effects that occur when components must be removed to access a given component for maintenance operation. Phase 1 integrates a discrete-state degradation model with a disassembly matrix and adjusts transition probabilities to capture disassembly-induced deterioration. The system is represented as a graph structure, where component states and disassembly relationships are encoded through Graph Convolutional Networks (GCNs) to learn structure-aware representations of system degradation. Phase 2 uses these updated dynamics within a Deep Reinforcement Learning-based decision-making approach to optimize maintenance actions. Numerical experiments on a 10-component system demonstrate that the framework effectively captures structure-driven degradation shifts and yields cost-efficient, proactive maintenance policies.", "url": "" }, { "id": "Mo-MoB18.5", "code": "MoB18.5", "title": "Quantifying the Impact of Prognostics Uncertainty on Maintenance Cost", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB18", "sessionTitle": "Artificial Intelligence and Digital Twins for Next-Generation Prognostics and Health Management in Smart Manufacturing II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Gay, Antonin", "affiliation": "CRAN CNRS/Université De Lorraine" }, { "name": "Voisin, Alexandre", "affiliation": "Université De Lorraine, CNRS, CRAN" }, { "name": "Do, Phuc", "affiliation": "IMT Mines Alès" }, { "name": "Jimenez, Hanser", "affiliation": "Université De Lorraine, CNRS, CRAN" }, { "name": "Khelassi, Ahmed", "affiliation": "ArcelorMittal" }, { "name": "Iung, Benoît", "affiliation": "Lorraine University" } ], "keywords": [ "Manufacturing prognostics and health management", "Maintenance engineering, management and services" ], "abstract": "Prognostics is a key enabler of predictive maintenance, yet its economic impact is rarely quantified through explicit analytical relations between prognostic uncertainty and maintenance cost. This paper proposes a probabilistic maintenance cost model that integrates remaining useful life (RUL) prediction uncertainty through a Gaussian error model characterized by the RMSE. Failures are modeled by a Weibull distribution, while recall and specificity are analytically derived as functions of the RMSE and embedded into a decision-tree-based formulation of the average maintenance cost. The resulting closed-form expression links prognostics accuracy to maintenance efficiency and enables re-optimization of the preventive maintenance interval under prognostic uncertainty. Monte Carlo simulations are used for validation, and an industrial-inspired case study based on a descaling valve in a hot strip mill demonstrates that realistic prognostics performance can yield substantial cost reductions compared with purely preventive maintenance.", "url": "" }, { "id": "Mo-MoB19.1", "code": "MoB19.1", "title": "Funnel Control with Input Filter for Nonlinear Systems of Relative Degree Two", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB19", "sessionTitle": "Output Regulation and Tracking", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Dennstädt, Dario", "affiliation": "Universität Paderborn" }, { "name": "Schaa, Janina", "affiliation": "Martin Luther University Halle-Wittenberg" }, { "name": "Berger, Thomas", "affiliation": "Martin-Luther-Universität Halle-Wittenberg" } ], "keywords": [ "Adaptive control design", "Output regulation and tracking", "Output feedback nonlinear control" ], "abstract": "We address the problem of output reference tracking for unknown nonlinear multiinput, multi-output systems with relative degree two and bounded-input bounded-state (BIBS) stable internal dynamics. We propose a novel model-free adaptive controller that ensures the evolution of the tracking error within prescribed performance funnel boundaries. By applying an output filter, the control objective is achieved without utilizing derivative information of system’s output. The controller is illustrated by a numerical example.", "url": "" }, { "id": "Mo-MoB19.2", "code": "MoB19.2", "title": "Design of Event-Triggered High-Gain Adaptive Output Feedback Controller Using a Parallel Feedforward Compensator", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB19", "sessionTitle": "Output Regulation and Tracking", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Michino, Ryuji", "affiliation": "Kumamoto Industrial Research Institute" }, { "name": "Mizumoto, Ikuro", "affiliation": "Kumamoto Univ" } ], "keywords": [ "Adaptive control design", "Output regulation and tracking", "Passivity-based control" ], "abstract": "This paper proposes a design methodology for an event-triggered high-gain adaptive output feedback control scheme applicable to nonlinear plants with higher-order relative degrees. By employing high-gain feedback, the proposed approach eliminates the need for input error compensation when extending to an event-triggered control framework. Furthermore, by incorporating a parallel feedforward compensator (PFC), the method enables the construction of relatively simple output feedback controllers even for plants with complex high-order dynamics. The validity of the proposed scheme is demonstrated by numerical simulations.", "url": "" }, { "id": "Mo-MoB19.3", "code": "MoB19.3", "title": "Online Learning-Based Control with Guaranteed Error Bounds for a Class of Nonlinear Systems", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB19", "sessionTitle": "Output Regulation and Tracking", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Husmann, Ricus", "affiliation": "University of Rostock" }, { "name": "Weishaupt, Sven", "affiliation": "University of Rostock" }, { "name": "Husmann, Malin Lotta", "affiliation": "Dresden University of Technology" }, { "name": "Aschemann, Harald", "affiliation": "University of Rostock" } ], "keywords": [ "Adaptive control design", "Output regulation and tracking", "Robust learning systems" ], "abstract": "In this paper, we present a learning-based control for a class of nonlinear systems that guarantees exponential stability as well as bounded output errors. The control is based on the Gaussian Process Submodel Online Learning (GPSOL) algorithm and the Disturbance Error Rate Limiting (DERL) algorithm, both of which were developed in previous work. The GPSOL algorithm provides a method to learn Gaussian Process (GP) models for subsystems online, whereas the DERL algorithm allows to limit the rate of the prediction error of these GP models. The focus of this paper is the utilization of the GP model within an adaptive controller and the derivation of corresponding stability conditions and system peak-to-peak gains by means of linear matrix inequalities (LMIs). These peak-to-peak gains are then used to prescribe a desired prediction error rate for the DERL algorithm to achieve user-defined output error bounds. The gains and the related bounds were successfully verified using a simulation model. Furthermore, results form a successful experimental validation of the bounds and the overall control structure on a pneumatic test rig are presented. While the control scheme and error bounds proposed in this paper are limited to first-order single-input-single-output systems, an extension to certain classes of higher-order and multiple-input-multiple-output systems is expected to be forthcoming.", "url": "" }, { "id": "Mo-MoB19.4", "code": "MoB19.4", "title": "An Improved Input-Constrained Funnel Controller for Nonlinear Systems", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB19", "sessionTitle": "Output Regulation and Tracking", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Berger, Thomas", "affiliation": "Martin-Luther-Universität Halle-Wittenberg" } ], "keywords": [ "Adaptive control design", "Output regulation and tracking", "Saturation and discontinuity" ], "abstract": "We present an improvement of a recent funnel controller design for uncertain nonlinear multi-input, multi-output systems modeled by higher order functional differential equations in the presence of input constraints. The objective is to guarantee the evolution of the tracking error within a performance funnel with prescribed desired shape for the case of inactive saturation. Compared to its precursor, controller complexity is significantly reduced, much fewer design parameters are involved and simulations exhibit a superior performance.", "url": "" }, { "id": "Mo-MoB19.5", "code": "MoB19.5", "title": "Mitigating Dynamic Tip-Over During Mobile Crane Slewing Using Input Shaping", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB19", "sessionTitle": "Output Regulation and Tracking", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Kaur, Navneet", "affiliation": "University of Washington" }, { "name": "Adams, Christopher", "affiliation": "Georgia Institute of Technology" }, { "name": "Singhose, William E.", "affiliation": "Georgia Institute of Technology" }, { "name": "Devasia, Santosh", "affiliation": "Univ of Washington" } ], "keywords": [ "Analytic design", "Linear systems", "Output regulation and tracking" ], "abstract": "Payload swing during rapid slewing of mobile cranes poses a safety risk, as it generates overturning moments that can lead to tip-over accidents of mobile cranes. Currently, to limit the risk of tip-over, mobile crane operators are forced to either reduce the slewing speed (which lowers productivity) or reduce the load being carried to reduce the induced moments. Both of these approaches reduce productivity. This paper seeks to enable rapid slewing without compromising safety by applying input shaping to the crane-slewing commands generated by the operator. A key advantage of this approach is that the input shaper requires only the information about the rope length, and does not require detailed mobile crane dynamics. Simulations and experiments show that the proposed method reduces residual payload swing and enables significantly higher slewing speeds without tip over, reducing slewing completion time by at least 38% compared to unshaped control. Human control with input shaping improves task completion time by 13%, reduces the peak swing by 18%, and reduces the potential of collisions by 82% when compared to unshaped control. Moreover, shaped control with a human had no tip-over, whereas large swing led to tip-over without input shaping. Thereby, the proposed method substantially recovers the operational-safety envelope of mobile cranes (designed to avoid tip-over using static analysis) that would otherwise be lost in dynamic conditions. Videos and demonstrations are available at https://youtu.be/dVy3bbIhrBU.", "url": "" }, { "id": "Mo-MoB19.6", "code": "MoB19.6", "title": "Relay Tracking Control for a Class of LPV Systems", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB19", "sessionTitle": "Output Regulation and Tracking", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Maaloul, Bassim", "affiliation": "University of Lille CRIStAL UMR 9189" }, { "name": "Tang, Ying", "affiliation": "Université De Lille, CNRS-CRIStAL UMR 9189" }, { "name": "Efimov, Denis", "affiliation": "Inria" }, { "name": "Hetel, Laurentiu", "affiliation": "CNRS" } ], "keywords": [ "Switching stability and control", "Output regulation and tracking" ], "abstract": "This article presents a relay tracking control design for a class of Linear Parameter- Varying (LPV) systems, that guarantees the local practical stability of the tracking error. The relay feedback synthesis method is based on the existence of a linear parameter-dependent control law that ensures a desired tracking. The efficiency of the proposed method is illustrated through simulations.", "url": "" }, { "id": "Mo-MoB20.1", "code": "MoB20.1", "title": "Attention-Based Dynamic Latent Variable Models for Batch Process Monitoring", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB20", "sessionTitle": "Fault Diagnosis and Tolerant-Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Liu, Jingxiang", "affiliation": "Dalian Maritime University" }, { "name": "FeiHong, Gan", "affiliation": "Dalian Maritime University" }, { "name": "Chen, Junghui", "affiliation": "Chung-Yuan Christian Univ" } ], "keywords": [ "Data-driven methods for FDI/FTC", "Fault detection and isolation methods", "Batch and semi-batch process control" ], "abstract": "Batch processes commonly encounter significant dynamic challenges arising from feedback control mechanisms and pervasive device inertia—factors that are frequently neglected in existing monitoring methodologies. Furthermore, current approaches predominantly emphasize dynamics within the sampling sequence while overlooking critical dynamic variations occurring across the batching sequence. To address these limitations and enhance the practical applicability of batch process modeling and monitoring, this study introduces attention-based dynamic latent variable models for batch process monitoring, encompassing both unsupervised and supervised variants. The proposed methodology employs attention mechanisms to capture time-varying relationships among latent variables through three complementary strategies: variable attention, sample attention, and integrated variable-sample attention. This framework enables more effective extraction of dynamic features for individual samples, thereby facilitating real-time, within-batch monitoring suitable for online implementation. The effectiveness of the proposed approach is demonstrated through a numerical case study and an industrial penicillin fermentation process.", "url": "" }, { "id": "Mo-MoB20.2", "code": "MoB20.2", "title": "KT-HFCA: A KAN-Transformer with Heterogeneous-Feature Cross-Attention for Incipient Fault Detection in Industrial Processes", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB20", "sessionTitle": "Fault Diagnosis and Tolerant-Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Yu, Xiaomin", "affiliation": "China University of Petroleum-Beijing" }, { "name": "Chen, Maoyin", "affiliation": "China University of Petroleum (Beijing)" } ], "keywords": [ "Fault detection and isolation methods", "Process performance monitoring/statistical process control", "Health/condition monitoring in processes" ], "abstract": "Detecting incipient faults in complex industrial processes is critical for safety and reliability but remains challenging due to their subtle signatures and the mixed characteristics of process data. This paper proposes KAN-Transformer with heterogeneous-feature cross-attention (KT-HFCA), a novel deep learning framework that integrates a Kolmogorov-Arnold Network (KAN) with a Transformer and a HFCA mechanism. The framework begins with dual-channel feature extraction to capture heterogeneous process characteristics. A novel cross-attention mechanism is then designed, where queries, keys, and values are derived from heterogeneous features to enable comprehensive information interaction. Subsequently, the KAN is integrated into the Transformer architecture to capture deep nonlinear temporal dependencies. Simulations on incipient faults 3, 9 and 15 in the Tennessee Eastman Process (TEP) demonstrate that the superior performance of the proposed KT-HFCA, compared to conventional methods, including PCA and ICA, as well as other deep learning approaches.", "url": "" }, { "id": "Mo-MoB20.3", "code": "MoB20.3", "title": "On the Fragility of PWA Control Despite Robustness Design Objectives", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB20", "sessionTitle": "Fault Diagnosis and Tolerant-Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Yang, Songlin", "affiliation": "CentraleSupele, Paris Saclay University" }, { "name": "Olaru, Sorin", "affiliation": "CentraleSupelec" }, { "name": "Rodriguez-Ayerbe, Pedro", "affiliation": "Supelec" }, { "name": "Grancharova, Alexandra", "affiliation": "University of Chemical Technology and Metallurgy" } ], "keywords": [ "Computational methods for FDI", "Fault-tolerant control methods" ], "abstract": "This paper revisits the two often confused concepts of fragility and robustness, clarifies their distinction, and systematises existing results within the framework of linear discrete-time systems equipped with linear or piecewise affine (PWA) controllers. Robust control, as an a priori procedure, addresses model uncertainties during controller synthesis. In contrast, fragility analysis is an a posteriori procedure that examines the sensitivity of a designed controller to parameter perturbations, an aspect often overlooked in both research and practical implementations, leading to faulty closed-loop functioning. The objective is to clarify the fundamental distinctions between these two concepts and to identify their potential interconnections. Linear systems in closed loop with PWA controllers serve as a generic framework to expose the gap between the two notions, with particular attention to partition induced fragility.", "url": "" }, { "id": "Mo-MoB20.4", "code": "MoB20.4", "title": "Incipient Fault Detection with Cointegration-Based Dissimilarity Analysis for Geological Drilling Process", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB20", "sessionTitle": "Fault Diagnosis and Tolerant-Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Yang, Aoxue", "affiliation": "China University of Geosciences" }, { "name": "Lai, Xuzhi", "affiliation": "China University of Geosciences" }, { "name": "Lu, Chengda", "affiliation": "China University of Geosciences" }, { "name": "Wu, Min", "affiliation": "China University of Geosciences" } ], "keywords": [ "Health/condition monitoring in processes", "Computational methods for FDI", "Data-driven methods for FDI/FTC" ], "abstract": "During geological drilling, the timely fault detection is essential to prevent serious accidents and ensure process safety. Due to the small magnitude of early faults, the distribution of drilling data is generally more sensitive than time domain signals. Meanwhile, considering the characteristic of distribution shift in drilling data, a cointegration-based dissimilarity analysis method is proposed for incipient fault detection of geological drilling process. Aiming at the nonstationary caused by distribution drift, the equilibrium errors, denoted as stationary features, are obtained by discovering the long-term equilibrium relationship among nonstationary drilling variables. Then, the dissimilarity of distributions between different feature sets is analyzed, and a monitoring statistic is constructed. On this basis, the monitoring strategy involving offline modeling and online monitoring is designed. Industrial case studies based on real drilling data are conducted, and the ability of the proposed method for improving the performance of early detection of drilling faults is illustrated.", "url": "" }, { "id": "Mo-MoB20.5", "code": "MoB20.5", "title": "Cross-Group Interaction-Based Autoencoder with MIC for Industrial Process Monitoring", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB20", "sessionTitle": "Fault Diagnosis and Tolerant-Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Meng, Jiao", "affiliation": "Harbin Institute of Technology" }, { "name": "Chu, Minghui", "affiliation": "Harbin Institute of Technology" }, { "name": "Liu, Qingquan", "affiliation": "Harbin Institute of Technology" }, { "name": "Huo, Xin", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Health/condition monitoring in processes", "Distributed/networked FDI/FTC", "Data-driven methods for FDI/FTC" ], "abstract": "Modern industrial processes generate multivariate time-series data with strong coupling and temporal dynamics, which pose significant challenges for accurate and interpretable process monitoring. To this end, this paper proposes a cross-group interaction-based autoencoder with maximal information coefficient (MIC-CGIAE) for industrial process monitoring. A physically meaningful variable grouping strategy is achieved by quantifying pairwise dependencies. Grouped-autoencoders are developed to extract intra-group temporal features, while a cross-group interaction mechanism is introduced to explicitly model and regulate intergroup dependencies. A multi-objective loss function enhances generalization, and a composite monitoring score enables robust abnormal identification. Experiments on a three-phase flow facility demonstrate that MIC-CGIAE adapts to varying operating conditions, supports intuitive fault localization, and exhibits strong engineering practicality", "url": "" }, { "id": "Mo-MoB20.6", "code": "MoB20.6", "title": "Fault-Tolerant Control of a Three-Wheeled Omnidirectional Mobile Robot under Single Actuator Failure", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB20", "sessionTitle": "Fault Diagnosis and Tolerant-Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Villalba-Aguilera, Elena", "affiliation": "Universitat Politècnica De Catalunya" }, { "name": "Blesa, Joaquim", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "Ponsa, Pere", "affiliation": "Technical Univ of Catalonia" } ], "keywords": [ "Applications of FDI/FTC", "Fault-tolerant control methods", "Structural analysis/quantitative methods for FDI/FTC" ], "abstract": "This paper presents a Fault-Tolerant Control (FTC) strategy for a Three-Wheeled Omnidirectional Mobile Robot (TWOMR) subject to partial or total wheel actuator faults. The proposed approach adapts the control structure through geometric reconfiguration, actuator-authority scaling and consistent updates of the state-estimation and path planning blocks, while keeping the Linear Parameter-Varying Model Predictive Control (MPC-LPV) controller unchanged. These mechanisms reshape the degraded kinematics so that the robot behaves as close as possible to the nominal model. Simulation results show that the robot achieves accurate trajectory and orientation tracking in all fault scenarios.", "url": "" }, { "id": "Mo-MoB21.1", "code": "MoB21.1", "title": "Capacity Management Strategies for Energy Storage Charging Stations in Vehicle-To-Grid Integration Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB21", "sessionTitle": "Stabilization Control of Energy-Storage-Powered Charging Stations and Voltage Regulation for Distribution Network under Vehicle Grid Interaction", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Zhang, Jing", "affiliation": "Hunan University" }, { "name": "Lin, Gang", "affiliation": "Hunan University" }, { "name": "Li, Yong", "affiliation": "Hunan University" }, { "name": "Huang, Yang", "affiliation": "Hunan University" } ], "keywords": [ "Electric vehicles and charging stations", "Distributed optimization for smart grids", "Energy management systems" ], "abstract": "To address the challenge of insufficient power supply for electric vehicle charging in remote areas, this study integrates photovoltaic generation, wind power and a hybrid energy storage system to ensure continuous and stable electricity delivery. A power allocation strategy for a lithium-battery-supercapacitor hybrid storage system is proposed. The variational mode decomposition (VMD) algorithm is first applied to decompose the power command, and sample entropy is used for power reconstruction based on the selected number of modes 𝐾. Considering the SOC conditions of the storage units, a fuzzy controller is introduced to define fuzzy rules for secondary power allocation. In addition, a multi-objective model incorporating lithium-battery lifetime and annual comprehensive cost is formulated. An improved multi-objective particle swarm optimization algorithm is then employed to obtain the optimal capacity configuration of the hybrid energy storage system.", "url": "" }, { "id": "Mo-MoB21.2", "code": "MoB21.2", "title": "Coordinated Active Power Control for Multiple Wind Farms to Enhance Transient Synchronization Stability During Low Voltage Ride-Through (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB21", "sessionTitle": "Stabilization Control of Energy-Storage-Powered Charging Stations and Voltage Regulation for Distribution Network under Vehicle Grid Interaction", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Lin, Leyan", "affiliation": "HUNAN UNIVERSITY" }, { "name": "Peng, Yanjian", "affiliation": "Changsha University of Science and Technology" }, { "name": "Li, Yong", "affiliation": "Hunan University" }, { "name": "Zhu, Hongyu", "affiliation": "Hunan University" }, { "name": "Bao, Wenyan", "affiliation": "Hunan University" }, { "name": "Cao, Yijia", "affiliation": "Hunan University" }, { "name": "Zhan, Yuxuan", "affiliation": "Hunan University" }, { "name": "Xiao, Shuai", "affiliation": "Hunan University" } ], "keywords": [ "Electric vehicles and charging stations", "Power systems stability", "Electrical transmission systems" ], "abstract": "This paper focuses on a system composed of multiple wind farms (WFs) and investigates in depth their transient synchronization stability and coordinated active power control mechanism during low voltage ride-through (LVRT). First, a dynamic grid-connected model of WFs considering the coupling effect of public transmission line impedance is established, revealing the intrinsic coupling mechanism among different WFs. Then, from the perspectives of equilibrium point existence and transient synchronization behavior, the impacts of line parameters and fault severity on system stability are analyzed. Furthermore, a coordinated active power control strategy for WFs is proposed. By regulating each wind farm’s active power injection during faults, the proposed strategy dynamically compensates network power losses, thereby maintaining system synchronization stability. Finally, simulations on the Matlab/Simulink platform verify the correctness and feasibility of the proposed control strategy.", "url": "" }, { "id": "Mo-MoB21.3", "code": "MoB21.3", "title": "Data-Driven Stability Assessment and Critical Short-Circuit Ratio Prediction for Multi-Station Renewable Power Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB21", "sessionTitle": "Stabilization Control of Energy-Storage-Powered Charging Stations and Voltage Regulation for Distribution Network under Vehicle Grid Interaction", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Zhu, Hongyu", "affiliation": "Hunan University" }, { "name": "Peng, Yanjian", "affiliation": "Changsha University of Science and Technology" }, { "name": "Li, Yong", "affiliation": "Hunan University" }, { "name": "Lin, Leyan", "affiliation": "HUNAN UNIVERSITY" }, { "name": "Cao, Yijia", "affiliation": "Hunan University" }, { "name": "Xiao, Shuai", "affiliation": "Hunan University" } ], "keywords": [ "Electric vehicles and charging stations", "Power systems stability", "Forecasting of power supply and demand" ], "abstract": "可再生能源（RES）的整合导致系统强度下降，稳定性评估的不确定性增加。传统的确定性短路比（SCR）方法无法捕捉多站可再生能源系统的复杂耦合和随机特性。本文提出了一个基于数据的数据的临界短路比（CSCR）评估与预测框架，全面考虑电压和角度稳定性约束以及可再生能源输出的不确定性。构建了一个嵌入不确定性的数据集，并开发了类型感知消息传递神经网络（MPNN），以整合异构节点特征和拓扑关系，实现可再生能源系统高效的CSCR预测。此外，引入了不确定性感知损失函数，以增强结果的物理一致性和可解释性。基于城市级电网的案例研究表明，该方法在预测准确性和鲁棒性方面优于传统数据驱动模型，为可再生能源系统稳定性", "url": "" }, { "id": "Mo-MoB21.4", "code": "MoB21.4", "title": "High-ImpedanceGroundingFaultDetectioninResonant-Grounded Distribution Networks (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB21", "sessionTitle": "Stabilization Control of Energy-Storage-Powered Charging Stations and Voltage Regulation for Distribution Network under Vehicle Grid Interaction", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Chen, Jiefa", "affiliation": "Hunan University" }, { "name": "Li, Yong", "affiliation": "Hunan University" } ], "keywords": [ "Electrical distribution systems" ], "abstract": "With the continuous expansion of distribution networks, the concealment and detection difficulty of high-impedance grounding faults have become increasingly prominent. Existing fault detection methods in distribution networks often fail to effectively identify high-impedance faults due to weak fault currents and complex signal characteristics. To achieve effective fault line detection, this paper proposes a high-impedance single-phase grounding fault detection method based on multi-resolution wavelet transform. First, the transient process of a single-phase grounding fault in a resonant-grounded system is analyzed. Based on the frequency-domain distribution characteristics of the zero-sequence voltage under fault conditions, the zero-sequence voltage is processed using wavelet transform, and the detail coefficients in different frequency bands are reconstructed. Finally, the decision characteristic value within the first fundamental frequency cycle after the fault is calculated to achieve effective fault detection. The results demonstrate that the method offers good reliability and accuracy. The research provides a theoretical foundation and technical support for high-resistance grounding fault detection in distribution network lines and holds practical significance for improving power supply reliability.", "url": "" }, { "id": "Mo-MoB21.5", "code": "MoB21.5", "title": "An Identification Method of Device Inertia Considering Different Frequency Response Stages (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB21", "sessionTitle": "Stabilization Control of Energy-Storage-Powered Charging Stations and Voltage Regulation for Distribution Network under Vehicle Grid Interaction", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Lin, XiaoYuan", "affiliation": "Hunan University" }, { "name": "Lin, Gang", "affiliation": "Hunan University" }, { "name": "Li, Yong", "affiliation": "Hunan University" } ], "keywords": [ "Electrical transmission systems", "Power systems stability" ], "abstract": "As renewable energy sources progressively replace synchronous machines in the grid, power system inertia declines, increasing the risk of frequency instability following active power disturbances. Therefore, accurately identifying inertia is essential for power system security. However, existing identification methods rarely account for the overlap between the response times of primary frequency regulation and inertial response, and rely on large amounts of data. To address this issue, this paper proposes a simple yet effective method for device-level inertia identification that considers the multi-stage frequency response. The method first decomposes the device's port power to determine an inertia correction factor and then employs adaptive variable-order polynomial fitting on frequency and power measurement data for accurate inertia estimation. We further validate the proposed method on an improved IEEE 39-bus New England system using DIgSILENT PowerFactory, under both single- and multi-device scenarios.", "url": "" }, { "id": "Mo-MoB21.6", "code": "MoB21.6", "title": "Decoupled Voltage Support Strategy for High-Power Grid-Forming EV Charging in Low X/R Grids", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB21", "sessionTitle": "Stabilization Control of Energy-Storage-Powered Charging Stations and Voltage Regulation for Distribution Network under Vehicle Grid Interaction", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Allimuthu, Sivadharshini", "affiliation": "SeoulTech" }, { "name": "Lee, Young Il", "affiliation": "Seoul National Univ of Science and Technology" } ], "keywords": [ "Electric vehicles and charging stations", "Electrical distribution systems" ], "abstract": "The transition towards resilient microgrids treats electric vehicles (EVs) as critical distributed energy resources (DERs) with potential for both vehicle-to-grid (V2G) and grid-to vehicle (G2V) services. Unlike conventional grid-following (GFL) inverters, grid-forming (GFM) inverters enable islanded operation during grid blackouts, offering superior resilience. However, GFM inverters face distinct stability limitations in weak distribution networks. This paper investigates the performance of GFM-based EV chargers during high-power G2V operation, revealing that resistive grid characteristics (low X/R ratios) cause self-induced voltage coupling that can drive the inverter into current saturation. To address this, this paper proposes an event triggered hysteresis based control strategy that coordinates the GFM with D-STATCOM, to support the voltage at point of common coupling (PCC). The proposed method decouples the active power loading of the EV from the PCC voltage drop, utilizing the coordinated STATCOM control to inject the necessary reactive current. PLECS simulation results demonstrate that the proposed hybrid GFM-GFL (D STATCOM) architecture extends the charger’s safe operating area in weak grids and prevents voltage collapse without inducing control instabilities.", "url": "" }, { "id": "Mo-MoB22.1", "code": "MoB22.1", "title": "Personalized Building Climate Control with Contextual Preferential Bayesian Optimization", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB22", "sessionTitle": "Smart Buildings and Building Automation", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Wang, Wenbin", "affiliation": "EPFL" }, { "name": "Shi, Jicheng", "affiliation": "EPFL" }, { "name": "Jones, Colin, N", "affiliation": "EPFL" } ], "keywords": [ "Smart buildings and building automation", "Big data and machine learning applied to smart cities" ], "abstract": "Efficient tuning of building climate controllers to optimize occupant utility is essential for ensuring overall comfort and satisfaction. However, this is a challenging task since the latent utility are difficult to measure directly. Time-varying contextual factors, such as outdoor temperature, further complicate the problem. To address these challenges, we propose a contextual preferential Bayesian optimization algorithm that leverages binary preference feedback together with contextual information to enable efficient real-time controller tuning. We validate the approach by tuning an economic MPC controller on BOPTEST, a high-fidelity building simulation platform. Over a two-month simulation period, our method outperforms the baseline controller and achieves an improvement of up to 23% in utility. Moreover, for different occupant types, we demonstrate that the algorithm automatically adapts to individual preferences, enabling personalized controller tuning.", "url": "" }, { "id": "Mo-MoB22.2", "code": "MoB22.2", "title": "Generalizability of Learning-Based Occupancy Detection in Residential Buildings", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB22", "sessionTitle": "Smart Buildings and Building Automation", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Farjadnia, Mahsa", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Eshkofti, Katayoun", "affiliation": "KTH" }, { "name": "Apell, Albin", "affiliation": "Royal Institute of Technology" }, { "name": "Hjalmarsson, Tilde", "affiliation": "KTH" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Fontan, Angela", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Molinari, Marco", "affiliation": "KTH" } ], "keywords": [ "Smart buildings and building automation", "Big data and machine learning applied to smart cities" ], "abstract": "This paper investigates non-intrusive occupancy detection methods for residential buildings using environmental sensor data from the KTH Live-In Lab in Stockholm, Sweden. Three machine learning approaches, namely, logistic regression (LR), support vector machines (SVM), and long short-term memory (LSTM) network enhanced with an attention mechanism, are evaluated in terms of predictive performance and computational complexity. The analysis considers the trade-off between sensor availability (investment cost) and prediction accuracy in real applications, as well as the models’ cross-apartment generalizability. Hyperparameters for both the SVM and LSTM models are optimized using Bayesian optimization. All three models are evaluated on data collected from apartments not used during training, and on data generated from a calibrated digital model of the testbed. Results show that all models achieve comparable performance on the same-apartment test data (accuracy approximately 0.83, F1 score approximately 0.86). When assessed on cross-apartment data, the LSTM model demonstrates the strongest generalization capability (accuracy of 0.84, F1 score of 0.85), while LR provides a competitive, low-complexity alternative for applications that do not require cross-apartment generalization.", "url": "" }, { "id": "Mo-MoB22.3", "code": "MoB22.3", "title": "Predictive Adaptive Control of a Heating System for a University Building", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB22", "sessionTitle": "Smart Buildings and Building Automation", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Putra, Lingga Aksara", "affiliation": "Technical University of Munich" }, { "name": "Atagün, Can", "affiliation": "Technical University of Munich" }, { "name": "Gaderer, Matthias", "affiliation": "Technical University of Munich" } ], "keywords": [ "Smart buildings and building automation", "Control and management of energy systems", "Energy market" ], "abstract": "Smart automation is essential for reducing heating costs in buildings. However, cost optimization must be achieved without compromising the consistent fulfillment of heat demand. Various reinforcement learning methods have been proposed to address this challenge. This study introduces a combined approach utilizing economic MPC and neuroadaptive MRAC as an alternative solution. The economic MPC determines the optimal trajectory for cost minimization, while the neuroadaptive MRAC maintains this trajectory despite variations in building dynamics or unexpected disturbances. Results indicate that the proposed method reduces heating costs by approximately 12% more than PPO-based reinforcement learning, while consistently meeting heat demand.", "url": "" }, { "id": "Mo-MoB22.4", "code": "MoB22.4", "title": "Meta-Reinforcement Learning for Control of Data Center Cooling", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB22", "sessionTitle": "Smart Buildings and Building Automation", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Robson, Lauren", "affiliation": "Imperial College London" }, { "name": "Ergetu, Endrias", "affiliation": "OctaiPipe" }, { "name": "Tsay, Calvin", "affiliation": "Imperial College London" } ], "keywords": [ "Smart buildings and building automation", "Control and optimization for sustainability and energy systems" ], "abstract": "Despite the success stories of reinforcement learning (RL) in HVAC control, training and deploying bespoke RL models for cooling control in individual data centers remains costly and inefficient. This work investigates meta-reinforcement learning, specifically the PEARL algorithm, as a more scalable solution. Using simulated data center environments, we demonstrate that a single, meta-trained agent can rapidly adapt to unseen conditions, including varied weather and IT loads. Moreover, the meta-trained agent can match specialized models trained from scratch for specific environments in terms of performance. This approach promises a significant reduction in engineering effort, enabling one pre-trained model to be deployed for related cooling control challenges across a diverse fleet of facilities.", "url": "" }, { "id": "Mo-MoB22.5", "code": "MoB22.5", "title": "Multi-Objective MIQP Economic and Thermal Optimization for a Smart Building with PV Generation, BESS, HVAC and Thermal Storage", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB22", "sessionTitle": "Smart Buildings and Building Automation", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Shahrouei, Zohreh", "affiliation": "University of Cagliari, Polytechnic of Bari" }, { "name": "Ennassiri, Yassine", "affiliation": "University of Genoa" }, { "name": "Usai, Elio", "affiliation": "Univ. Degli Studi Di Cagliari" }, { "name": "Pisano, Alessandro", "affiliation": "Univ. Di Cagliari" } ], "keywords": [ "Smart buildings and building automation", "Energy storage systems", "Energy communities" ], "abstract": "This work presents a multi-objective optimization framework for a two-zone building with Heating Ventilation and Air Conditioning system supported by a thermal storage, photovoltaic generation, and a battery energy storage system. The chosen objective functions minimize total energy cost and thermal discomfort through the weighted sum scalarization approach, resulting in a Mixed Integer Quadratic Programming (MIQP) formulation. To tackle non-convexity, a convex relaxed QP formulation is proposed. Simulations show that MIQP and QP achieve identical values of the optimization performance indices, and the QP optimizer naturally avoids simultaneous grid energy buy/sell and BESS charge/discharge. Future work will explore distributed multi-agent extensions of the proposed QP problem with theoretical guarantees.", "url": "" }, { "id": "Mo-MoB23.1", "code": "MoB23.1", "title": "Verifiable Computations for Dynamic Encrypted Control (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB23", "sessionTitle": "Encrypted Control and Optimization II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Schlor, Sebastian", "affiliation": "University of Stuttgart" }, { "name": "Allgower, Frank", "affiliation": "University of Stuttgart" } ], "keywords": [ "Safety and security in networked control", "IT/OT-security in automation systems" ], "abstract": "Encrypted control can preserve the privacy of data and parameters while the necessary computations can be outsourced to a cloud server. To ensure the integrity of the received values from the cloud, i.e., that they have not been changed, however, strong assumptions or verification algorithms are needed. Previous methods require computationally expensive cryptographic protocols or are only applicable to static computations. In this paper, we present a novel type of verification algorithm for linear dynamic encrypted control. We utilize system-theoretic input-output properties of the controller for artificial challenge signals, which are processed in the cloud in parallel with the requested control input, to check the correctness of the results at the plant. This results in almost no additional computational load, wrong computations are revealed with high probability, and no replay attacks are possible.", "url": "" }, { "id": "Mo-MoB23.2", "code": "MoB23.2", "title": "Quantization and Security Parameter Design for Overflow-Free Confidential FRIT (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB23", "sessionTitle": "Encrypted Control and Optimization II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Park, Jungjin", "affiliation": "The University of Electro-Communications" }, { "name": "Kaneko, Osamu", "affiliation": "The University of Electro-Communications" }, { "name": "Kogiso, Kiminao", "affiliation": "University of Electro-Communications" } ], "keywords": [ "Safety and security in networked control", "Knowledge-based and data-driven control" ], "abstract": "This study proposes a systematic design procedure for determining the quantization gain and the security parameter in the Confidential Fictitious Reference Iterative Tuning (CFRIT), enabling overflow-free and accuracy-guaranteed encrypted controller tuning. Within an encrypted data-driven gain tuning, the range of quantization errors induced during the encoding (encryption) process can be estimated from operational data. Based on this insight, explicit analytical conditions on the quantization gain and the security parameter are derived to prevent overflow in computing over encrypted data. Furthermore, the analysis reveals a quantitative relationship between quantization-induced errors and the deviation between the gains obtained by CFRIT and non-confidential Fictitious Reference Iterative Tuning (FRIT), clarifying how parameter choice affects tuning accuracy. A numerical example verifies the proposed procedure by demonstrating that the designed parameters achieve accurate encrypted tuning within a prescribed tolerance while preventing overflow.", "url": "" }, { "id": "Mo-MoB23.3", "code": "MoB23.3", "title": "Operator-Aware Encrypted Bilateral Teleoperation under Obstacle Contacts with Detection and Cancellation of Cyber Attacks (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB23", "sessionTitle": "Encrypted Control and Optimization II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Kosha, Katsumasa", "affiliation": "The University of Tokyo" }, { "name": "Miyazaki, Tetsuro", "affiliation": "The University of Tokyo" }, { "name": "Teranishi, Kaoru", "affiliation": "The University of Osaka" }, { "name": "Kogiso, Kiminao", "affiliation": "University of Electro-Communications" }, { "name": "Kawashima, Kenji", "affiliation": "The University of Tokyo" } ], "keywords": [ "Safety and security in networked control", "Remote control", "Networking for teleoperation" ], "abstract": "We propose an operator-aware framework for secure bilateral teleoperation under encrypted control, considering obstacle contacts. Although bilateral control enables remote manipulation with force sensing, detecting and cancelling false data injection (FDI) attacks on dynamics with obstacles remain unclear. To address this challenge, we present a Security Threat Index (STI) that provides operator-facing feedback of detection and cancellation, considering the contact force of the obstacle. STI enhances the resilience of the detection and cancellation method under contacts. Experiments on a pneumatic teleoperation testbed interacting with an obstacle demonstrate reliable detection and cancellation across diverse scenarios, while STI effectively visualizes the risk of detection failure.", "url": "" }, { "id": "Mo-MoB23.4", "code": "MoB23.4", "title": "Replay-Attack-Detectable Encrypted Bilateral Control System under Communication Delays (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB23", "sessionTitle": "Encrypted Control and Optimization II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Miyagawa, Shota", "affiliation": "The University of Tokyo" }, { "name": "Kosha, Katsumasa", "affiliation": "The University of Tokyo" }, { "name": "Miyazaki, Tetsuro", "affiliation": "The University of Tokyo" }, { "name": "Teranishi, Kaoru", "affiliation": "The University of Osaka" }, { "name": "Kogiso, Kiminao", "affiliation": "University of Electro-Communications" }, { "name": "Kawashima, Kenji", "affiliation": "The University of Tokyo" } ], "keywords": [ "Safety and security in networked control", "Remote control", "Networking for teleoperation" ], "abstract": "This study presents a stable control method that mitigates false-positive detections caused by communication delays in dynamic encrypted control for encrypted bilateral control systems. While keyed-homomorphic public-key encryption enhances security, significant delays in remote environments may trigger false alarms and destabilize control. We propose a delay-tolerant scheme that prevents false positives by retaining information about previously used keys during key exchange. Experiments using a pneumatic bilateral control system demonstrated both the instability caused by delays and the improved stability achieved with the proposed method.", "url": "" }, { "id": "Mo-MoB23.5", "code": "MoB23.5", "title": "Asymptotic Tracking Control of Dynamic Reference Over Homomorphically Encrypted Data with Finite Modulus", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB23", "sessionTitle": "Encrypted Control and Optimization II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Feng, Shuai", "affiliation": "Nanjing University of Science and Technology" }, { "name": "Kim, Junsoo", "affiliation": "Seoul National University of Science and Technology" } ], "keywords": [ "Cyber security networked control", "Control over networks", "Quantized systems" ], "abstract": "This paper considers a tracking control problem, in which the dynamic controller is encrypted with an additively homomorphic encryption scheme and the output of a process tracks a dynamic reference asymptotically. Our paper is motivated by the following problem: When dealing with both asymptotic tracking and dynamic reference, we find that the control input is generally subject to overflow issues under a finite modulus, though the dynamic controller consists of only integer coefficients. First, we provide a new controller design method such that the coefficients of the tracking controller can be transformed into integers leveraging the zooming-in factor of dynamic quantization. By the internal model principle on the actuator side, we present the control input as a linear combination of the previous control inputs, in which the information of the reference model is utilized. Leveraging the property above, we design an algorithm on the actuator side such that it can restore the control input from the lower bits under a finite modulus. A lower bound of the modulus is also provided. In the second part of the paper, we utilize a finite-range quantizer to design the encrypted controller. A lower bound of quantization range is provided, which can ensure that the quantizer is free of saturation. At last, we propose an innovation based encrypted control architecture, which requires the same modulus.", "url": "" }, { "id": "Mo-MoB24.1", "code": "MoB24.1", "title": "Host-Aware Digital Twin and Combinatorial Library Design for Identifiable Characterization of Genetic Bioparts (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB24", "sessionTitle": "Challenges in Synthetic Biology", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Picó, Jesús", "affiliation": "Universitat Politecnica De Valencia" }, { "name": "Arboleda-Garcia, Mario Andres", "affiliation": "Universitat Politècnica De Valencia" }, { "name": "Rodríguez-Penas, David", "affiliation": "MBG, CSIC" }, { "name": "Banga, Julio R.", "affiliation": "MBG-CSIC (Spanish Council for Scientific Research)" }, { "name": "Vignoni, Alejandro", "affiliation": "Universitat Politècnica De Valencia" }, { "name": "Boada, Yadira", "affiliation": "Universitat Politècnica De València" } ], "keywords": [ "Synthetic biology", "Modelling, parameter identification and state estimation in biosystems", "Dynamics and control of gene expression and metabolic pathways" ], "abstract": "Quantitative design of synthetic gene circuits requires models that capture how genetic bioparts behave under the dynamic constraints imposed by the host cell. However, transcriptional and translational parameters are difficult to identify due to structural parameter coupling and nonlinear dependence on growth-dependent resource allocation. We introduce a host-aware digital twin combined with a combinatorial library design that together enable reliable estimation of promoter, RBS, and plasmid-origin parameters in E. coli. The digital twin integrates a mechanistic model of cellular physiology with real-time measurements of specific growth rate, providing a dynamic link between intracellular resource availability and effective gene-expression kinetics. Conditioning the host model on measured growth rates constrains admissible host–circuit states and improves robustness of the identification process. The combinatorial library acts as a structured perturbation experiment, generating a sparse and structurally full-rank sensitivity matrix that resolves parameter coupling and ensures practical identifiability. Applied to a 35-member combinatorial gene-expression library, the framework yields transferable parameter estimates, including identifiable intrinsic translation-initiation parameters for RBSs, that accurately predict protein synthesis rates across growth regimes and genetic contexts. Overall, this work establishes a scalable identification methodology that connects DNA-level part composition to predictable circuit behaviour through a host-aware, model-based characterization pipeline for synthetic biological systems.", "url": "" }, { "id": "Mo-MoB24.2", "code": "MoB24.2", "title": "Host-Aware Control of Gene Expression Using Data-Enabled Predictive Control (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB24", "sessionTitle": "Challenges in Synthetic Biology", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Perreault, Liam", "affiliation": "University of Oxford" }, { "name": "Kempf, Idris", "affiliation": "University of Oxford" }, { "name": "Sechkar, Kirill", "affiliation": "University of Oxford" }, { "name": "Lugagne, Jean-Baptiste", "affiliation": "Boston University" }, { "name": "Papachristodoulou, Antonis", "affiliation": "Univ of Oxford" } ], "keywords": [ "Dynamics and control of gene expression and metabolic pathways", "Synthetic biology" ], "abstract": "Cybergenetic gene expression control in bacteria enables applications in engineering biology, drug development, and biomanufacturing. AI-based controllers offer new possibilities for real-time, single-cell-level regulation but typically require large datasets and re-training for new systems. Data-enabled Predictive Control (DeePC) offers better sample efficiency without prior modelling. We apply DeePC to a system with two inputs (optogenetic control and media concentration) and two outputs (expression of gene of interest and host growth rate). Using basis functions to address nonlinearities, we demonstrate that DeePC remains robust to parameter variations and performs among the best control strategies while using the least data.", "url": "" }, { "id": "Mo-MoB24.3", "code": "MoB24.3", "title": "Activity-Cloud Framework for Translation Engineering in E. Coli: A Web-Based Tool for Coarse-Grained Shine–Dalgarno Sequence Design (Late-breaking/Discussion Paper) (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB24", "sessionTitle": "Challenges in Synthetic Biology", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Zach, Pavel", "affiliation": "Universitat Politècnica De Valencia" }, { "name": "Boada, Yadira", "affiliation": "Universitat Politècnica De València" }, { "name": "Picó, Jesús", "affiliation": "Universitat Politecnica De Valencia" }, { "name": "Vignoni, Alejandro", "affiliation": "Universitat Politècnica De Valencia" } ], "keywords": [ "Synthetic biology", "Dynamics and control of gene expression and metabolic pathways", "Modelling, parameter identification and state estimation in biosystems" ], "abstract": "We present the Activity-Cloud Framework, a data-driven methodology to organize Shine– Dalgarno (SD) core sequences into functional clusters enabling coarse-grained translation engineering in E. coli. Using the comprehensive 4096-variant SD library of Bonde et al. (2016), we construct a hierarchical representation of the SD-core sequence space and identify “activity clouds” via density- based clustering. These clouds capture consistent translation-rate bands and enable both forward (sequence → ETR) and inverse (ETR → sequence) design. A publicly available web interface provides interactive exploration, prediction, and design tools for translation initiation engineering.", "url": "" }, { "id": "Mo-MoB24.4", "code": "MoB24.4", "title": "Online Data-Driven Upstream Bioprocess Exploration and Optimization (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB24", "sessionTitle": "Challenges in Synthetic Biology", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Briat, Corentin", "affiliation": "FHNW" }, { "name": "Planchestainer, Matteo", "affiliation": "FHNW" }, { "name": "Villiger, Thomas", "affiliation": "FHNW" }, { "name": "Jaques, Colin", "affiliation": "Lonza" } ], "keywords": [ "Dynamics and control of gene expression and metabolic pathways", "Kinetic modelling, analysis and optimization of metabolism", "Pharmaceutical processes, food engineering and industrial biotechnology" ], "abstract": "Efficient and cost-effective production of biologically active ingredients, such as monoclonal antibodies, requires advanced bioprocess development strategies that ensure both productivity and robustness. Recent advances in high-throughput experimentation, computational power, and machine learning have enabled the extraction of actionable insights from the large data streams generated by complex biological systems. This presentation describes a data-driven framework that integrates perfusion bioprocesses with online modeling and optimization to support continuous process exploration. By combining modern artificial intelligence techniques with real-time monitoring and automatic feedback control, the approach systematically identifies and refines key process parameters to enhance productivity and consistency. The proposed workflow bridges human expertise and automation, demonstrating a path toward reliable and autonomous bioprocess operation.", "url": "" }, { "id": "Mo-MoB24.5", "code": "MoB24.5", "title": "CO2-Based Kalman Filtering for Apple Ripening Status", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB24", "sessionTitle": "Challenges in Synthetic Biology", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Boillereaux, Lionel", "affiliation": "Oniris VetAgroBio" }, { "name": "Keraudren, Alan", "affiliation": "DPKL" }, { "name": "Vidot, Kevin", "affiliation": "DPKL" }, { "name": "Toublanc, Cyril", "affiliation": "Oniris, Nantes Université, CNRS, GEPEA, UMR 6144, F-44000 Nantes, France" }, { "name": "Havet, Michel", "affiliation": "Oniris VetAgroBio - UMR GEPEA" } ], "keywords": [ "Modelling, parameter identification and state estimation in biosystems", "Monitoring, observers and software sensors for biosystems", "Automation for post harvest technology" ], "abstract": "Accurate, non-destructive monitoring of fruit ripening during cold storage is essential to reduce losses and optimize supply. A model capturing the dynamics of starch hydrolysis, associated ethylene release, and the resulting respiration rate is designed. An Extended Kalman Filter is developed to enable real-time estimation of fruit ripening from the measurement of the respiration rate, thereby enabling the identification of the ethylene peak characteristic of the climacteric phase. Results demonstrate the potential of this approach as a non-invasive tool for monitoring and predicting apple maturity during storage.", "url": "" }, { "id": "Mo-MoB24.6", "code": "MoB24.6", "title": "Estimation in Bioprocessing with Delayed Substrate Measurements", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB24", "sessionTitle": "Challenges in Synthetic Biology", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Sartori, Giacomo", "affiliation": "University of Padova, NTNU Trondheim" }, { "name": "Carmel, Lipe", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Neves Reis Pedreira, Vitor", "affiliation": "NTNU" }, { "name": "Bar, Nadav S.", "affiliation": "Norwegian Univ of Science and Technology" } ], "keywords": [ "Monitoring, observers and software sensors for biosystems", "Modelling, parameter identification and state estimation in biosystems" ], "abstract": "Microbial fermentations are complex bioprocesses that rely on multiple sensors and accurate state estimation to enable effective process control. Standard estimators typically assume synchronous measurements, yet substrate concentrations are often measured using technologies that provide accurate but delayed and infrequent data. We present a state–parameter estimation framework that efficiently integrates such out-of-sequence substrate measurements. The method combines an Extended Kalman Filter (EKF) for real-time estimation with a Rauch–Tung–Striebel (RTS) smoother that retrospectively updates past states whenever delayed substrate samples become available, producing significantly smoother substrate trajectories. To increase robustness to model mismatch and multiphase operation, a subset of kinetic parameters (e.g., yield coefficients and maximum specific growth rate) are optimized online during the process. The proposed approach is validated in real fermentation experiments, where it consistently reduces RMSE relative to a standard EKF and maintains accuracy under variable delay distributions and sparse sampling. The framework is lightweight to implement, relies solely on established EKF/RTS components, and supports both real-time monitoring and offline reconstruction of fermentation dynamics.", "url": "" }, { "id": "Mo-MoB25.1", "code": "MoB25.1", "title": "Bronchoconstriction Tracking Methodology in Asthma, Using Non-Invasively Monitored Spontaneous Breathing (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB25", "sessionTitle": "Digital Twins: From Sensors (Zero) to Systems to Clinical Outcomes (Hero)", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Guy, Ella F. S.", "affiliation": "University of Canterbury" }, { "name": "Chan, Amy", "affiliation": "University of Auckland" }, { "name": "Holder-Pearson, Lui", "affiliation": "University of Canterbury" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" } ], "keywords": [ "Biomedical system modeling, identification, and simulation", "Decision support and control in medicine", "Healthcare management, disease control, critical care" ], "abstract": "Asthma is a common but treatable condition. However, poorly managed Asthma has many associated risks and Asthma mortality numbers are still high despite treatment availability, suggesting that current Asthma monitoring and management techniques are not effective for all people with Asthma. In this study, model-based metrics were assessed in comparison to simulated Asthma severities. Thus, establishing the potential for non-invasive measurements to be used to reliably track patient-specific Asthma severity and response to treatment. This work establishes a method of obtaining metrics to monitor the degree of Asthma airway restriction. Thus, enabling earlier intervention during Asthma exacerbations which could decrease the severity of events and treatment expense. In addition, providing a foundation for predictive technologies and automated monitoring.", "url": "" }, { "id": "Mo-MoB25.2", "code": "MoB25.2", "title": "Estimating Hormone Concentrations in the Pituitary-Thyroid Feedback Loop from Irregularly Sampled Measurements (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB25", "sessionTitle": "Digital Twins: From Sensors (Zero) to Systems to Clinical Outcomes (Hero)", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Siriya, Seth", "affiliation": "Leibniz University Hannover" }, { "name": "Wolff, Tobias M.", "affiliation": "Leibniz University Hannover" }, { "name": "Krauss, Isabelle", "affiliation": "Leibniz University Hannover" }, { "name": "Lopez, Victor G.", "affiliation": "Leibniz University Hannover, Institute for Automatic Control" }, { "name": "Müller, Matthias A.", "affiliation": "Leibniz University Hannover" } ], "keywords": [ "Decision support and control in medicine", "Modelling, parameter identification and state estimation in biosystems", "Digital twins in healthcare, model-based therapeutics" ], "abstract": "Model-based control techniques have recently been investigated for the recommendation of medication dosages to address thyroid diseases. These techniques often rely on knowledge of internal hormone concentrations that cannot be measured from blood samples. Moreover, the measurable concentrations are typically only obtainable at irregular sampling times. In this work, we empirically verify a notion of sample-based detectability that accounts for irregular sampling of the measurable concentrations on two pituitary-thyroid loop models representing patients with hypo- and hyperthyroidism, respectively, and include the internal concentrations as states. We then implement sample-based moving horizon estimation for the models, and test its performance on virtual patients across a range of sampling schemes. Our study shows robust stability of the estimator across all scenarios, and that more frequent sampling leads to less estimation error in the presence of model uncertainty and misreported dosages.", "url": "" }, { "id": "Mo-MoB25.3", "code": "MoB25.3", "title": "Towards Quantitative Modelling and Control of Agitation and Sedation in the ICU: Are Current Subjective Scores Enough? (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB25", "sessionTitle": "Digital Twins: From Sensors (Zero) to Systems to Clinical Outcomes (Hero)", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "O'Sullivan, Ryan", "affiliation": "University of Canterbury" }, { "name": "Pretty, Christopher", "affiliation": "University of Canterbury" }, { "name": "Desaive, Thomas", "affiliation": "University of Liege" }, { "name": "Lambermont, Bernard", "affiliation": "University of Liege" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" } ], "keywords": [ "Digital twins in healthcare, model-based therapeutics", "Clinical trial, clinical validation", "Decision support and control in medicine" ], "abstract": "Agitation and sedation are critical aspects of patient management in the intensive care unit (ICU), yet current clinical practices rely primarily on subjective scoring systems, such as the Richmond Agitation-Sedation Scale (RASS). This study investigates the suitability of these scores for quantitative modelling of sedation dynamics. Retrospective electronic chart data from 1,057 mechanically ventilated ICU patients were analyzed, including sedative and analgesic dosing, vital signs, and RASS scores. Dose–response sensitivity analysis revealed a weak correlation between changes in sedative dose and subsequent RASS scores (R² = 0.071 overall; R² = 0.103 during out of target sedation events). A substantial proportion of observations exhibited unexpected or inconsistent relationships between sedation dose and RASS change, highlighting the limitations of subjective scores for capturing underlying pharmacodynamic effects. These findings suggest that current charted sedation scores are insufficient for reliable predictive modelling, potentially limiting the development of model-based sedation control. High-frequency, objective, and quantitative measures of agitation and sedation may be required to enable robust modelling and support optimized, individualized ICU sedation strategies.", "url": "" }, { "id": "Mo-MoB25.4", "code": "MoB25.4", "title": "Prediction of Oliguria in Sepsis-Associated Acute Kidney Injury (SA-AKI) Based on the First 12 Hours of Intensive Care (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB25", "sessionTitle": "Digital Twins: From Sensors (Zero) to Systems to Clinical Outcomes (Hero)", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Muhammad, Farhah", "affiliation": "Advanced Medical and Dental Institute" }, { "name": "Suhaimi, Fatanah", "affiliation": "Universiti Sains Malaysia" }, { "name": "Mazlan, Mohd Zulfakar", "affiliation": "Department of Anaesthesiology and Intensive Care, School of Medical Sciences, Universiti Sains Malaysia" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" } ], "keywords": [ "Healthcare management, disease control, critical care" ], "abstract": "Sepsis-associated acute kidney injury (SA-AKI) contributes to high morbidity and mortality rates, with oliguria further complicating. This study investigated temporal physiological trajectories based on mean arterial pressure (MAP) and urine output during the initial 12 hours of ICU admission for early oliguria prediction. This study has been done to 137 prospective SA-AKI patients from Hospital Universiti Sains Malaysia (HUSM). Distinct trajectories of urine output have been observed between oliguria and non-oliguria groups, with MAP remaining largely above the clinically recommended threshold of 65 mmHg. In comparison of oliguria risk stratification, machine learning such as logistic regression, decision tree, support vector machine and boosted ensemble classifiers were used and the boosted ensemble yielded the best predictive performance (sensitivity = 0.942, specificity = 0.948). Apart from that, serum creatinine, lactate, and sodium as the most influential predictive features. These findings illustrate the capability of trajectory-based and interpretable machine learning frameworks for the early diagnosis of oliguria risk in critically ill patients with SA-AKI.", "url": "" }, { "id": "Mo-MoB25.5", "code": "MoB25.5", "title": "Model Predictive Control for Primary-Secondary Adaptive Therapy in Metastatic Castrate-Resistant Prostate Cancer", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB25", "sessionTitle": "Digital Twins: From Sensors (Zero) to Systems to Clinical Outcomes (Hero)", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Pena-Campos, Johan Sebastian", "affiliation": "Pontificia Universidad Javeriana" }, { "name": "Ocampo-Martinez, Carlos", "affiliation": "Universitat Politecnica De Catalunya (UPC)" }, { "name": "Caicedo, Alexander", "affiliation": "Leuven University" }, { "name": "Patino, Diego", "affiliation": "Pontificia Universidad Javeriana" } ], "keywords": [ "Healthcare management, disease control, critical care", "Intensive and chronic care or treatment", "Dynamics and control of biologically motivated nonlinear systems" ], "abstract": "Metastatic castrate-resistant prostate cancer (mCRPC) poses significant therapeutic challenges due to the rapid emergence of drug-resistant cell populations. This paper presents a Model Predictive Control (MPC) framework for primary-secondary (P-S) adaptive therapy, utilizing Abiraterone as the primary agent and Docetaxel as the secondary agent to manage resistant phenotypes. Building on established four-population mathematical models of prostate cancer cell dynamics (androgen-dependent x_{T^+}, testosterone-producing x_{T^P}, androgen-independent x_{T^{-/+}}, and Docetaxel-resistant x_{T^{-/-}}), two MPC formulations are proposed: one incorporating intra-variability metrics and another integrating active-set constraints with inter-variability and dosage smoothness objectives. Simulation results demonstrate that the dual-agent MPC approach substantially extends time to progression compared to single-agent strategies and static treatment schedules, while maintaining controlled drug exposure. The proposed framework enables systematic management of treatment-sensitive and resistant phenotypes, supporting the paradigm of treating mCRPC as a chronic, manageable condition.", "url": "" }, { "id": "Mo-MoB25.6", "code": "MoB25.6", "title": "Characterizing Resistive Components of an Airway in a Manikin Model (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB25", "sessionTitle": "Digital Twins: From Sensors (Zero) to Systems to Clinical Outcomes (Hero)", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Hawke, Kirsty Alexandra", "affiliation": "University of Canterbury" }, { "name": "Guy, Ella F. S.", "affiliation": "University of Canterbury" }, { "name": "Holder-Pearson, Lui", "affiliation": "University of Canterbury" }, { "name": "Russell, Isabelle J.A.", "affiliation": "University of Canterbury" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" } ], "keywords": [ "Modeling and control in mechanical ventilation", "Biomedical system modeling, identification, and simulation", "Digital twins in healthcare, model-based therapeutics" ], "abstract": "Airway resistance is a significant parameter in respiratory diagnosis and care. However, in a range of non-invasive or mask-based care modes mask leaks and other factors can significantly impact measures of airway resistance. This study presents a bench-testing approach to identify resistive components using a manikin head simulation model and venturi-based sensors. Airflow and pressure were measured across five configurations simulating airway pathways and resistance was calculated using pressure-flow relationships. Results demonstrated configuration-dependent variability, with nasal-only pathways exhibiting the highest resistance, approximately ten times greater. Mask resistance values generally aligned with manufacturer specifications, validating the setup.", "url": "" }, { "id": "Mo-MoB26.1", "code": "MoB26.1", "title": "Real-Time Thermal Management for Connected Battery Electric Vehicles under Multiple Coupling Constraints (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB26", "sessionTitle": "Thermal Management of Electrified Vehicles II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Wang, Zhaolei", "affiliation": "Yanshan University" }, { "name": "Tang, Wenbin", "affiliation": "YanShan University" }, { "name": "Fang, Jiayi", "affiliation": "Beijing Institute of Technology" }, { "name": "Hua, Kedan", "affiliation": "Yanshan University" }, { "name": "Zhang, Yahui", "affiliation": "Yanshan University" } ], "keywords": [ "Nonlinear and optimal automotive control", "Adaptive and robust control of automotive systems", "Hybrid, electric and alternative drive vehicles" ], "abstract": "Batteries, motors, and power components in electric vehicles generate significant heat during operation, increasing energy consumption and safety risks. To this end, the IFAC WC 2026 benchmark addresses thermal management for connected battery-electric vehicles (BEVs). To address this benchmark issue, this paper proposes an integrated thermal management strategy that uses data-driven predictive modeling to capture system nonlinearities. An enhanced convolutional neural network-long short-term memory with a multilayer perceptron predicts vehicle speed and system dynamics. The strategy leverages nonlinear model predictive control (NMPC) for dynamic control under coupled constraints, improving cabin-temperature regulation and component thermal safety while reducing energy use.Its effectiveness and efficiency were validated using traffic data in the benchmark's simulator.", "url": "" }, { "id": "Mo-MoB26.2", "code": "MoB26.2", "title": "Physics-Constrained Speed Prediction for Intelligent Connected Vehicles Using Differentiable Optimization (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB26", "sessionTitle": "Thermal Management of Electrified Vehicles II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Tang, Xi", "affiliation": "Yanshan University" }, { "name": "Zhang, Xiao", "affiliation": "Yanshan University" }, { "name": "Jiao, Xiaohong", "affiliation": "Yanshan University" }, { "name": "Wang, Zhong", "affiliation": "Yanshan University" }, { "name": "Fang, Yiming", "affiliation": "Yanshan University" }, { "name": "Wen, Shuhuan", "affiliation": "Yanshan University" } ], "keywords": [ "AI and learning-based control for automotive systems", "Autonomous vehicles", "Intelligent transportation systems" ], "abstract": "Accurate speed prediction for intelligent connected vehicles (ICVs) is crucial for optimizing thermal management, energy efficiency, and related applications. Precise forecasting remains difficult due to complex urban traffic and diverse vehicle-to-everything (V2X) data sources. To address this, this paper introduces a CNN–LSTM (Convolutional Neural Network–Long Short-Term Memory) framework with a differentiable optimization layer. This framework uniquely fuses vehicle-to-infrastructure (V2I) data, onboard sensor data (OSD), and historical speed records to capture spatiotemporal driving dynamics. By analyzing car-following and free-driving data, the framework identifies key vehicle attributes and driving styles. It extracts metrics like the following distance and pedal use to set constraints. The differentiable optimization layer integrates physical laws and driver preferences, ensuring predictions are physically valid and behaviorally realistic. Finally, simulation validation using the IFAC2026 benchmark challenge dataset shows that the method achieves higher accuracy and better physical consistency than existing techniques across various traffic conditions.", "url": "" }, { "id": "Mo-MoB26.3", "code": "MoB26.3", "title": "A Batch Reinforcement Learning Approach for Air Conditioning Systems of EVs Based on Q-Learning (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB26", "sessionTitle": "Thermal Management of Electrified Vehicles II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Li, Haipeng", "affiliation": "Dalian University of Technology" }, { "name": "Shen, Tielong", "affiliation": "Dalian University of Technology" } ], "keywords": [ "AI and learning-based control for automotive systems" ], "abstract": "Aiming at the challenge of temperature control for electric vehicle (EV) air conditioning (AC) systems with the goal of reducing energy consumption, this paper proposes a control strategy based on Batch Reinforcement Learning (BRL). The proposed method leverages a Q-learning framework with experience replay to extract the optimal policy directly from a static historical dataset. This approach effectively avoids the low sample efficiency and safety risks associated with online trial and error exploration, while realizing model-free control without the high hardware overhead of deep neural networks. Simulation results based on the IFAC 2026 Benchmark platform demonstrate that, compared with traditional on/off and PID control, the proposed strategy reduces energy consumption by 17.19% and 8.61% respectively. Furthermore, it significantly reduces actuator oscillation while ensuring cabin thermal comfort, showing great potential for engineering applications.", "url": "" }, { "id": "Mo-MoB26.4", "code": "MoB26.4", "title": "A Dual NMPC Scheme for Thermal Management System of Battery Electric Vehicle (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB26", "sessionTitle": "Thermal Management of Electrified Vehicles II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Wang, Jiawei", "affiliation": "Dalian University of Technology" }, { "name": "Shen, Tielong", "affiliation": "Dalian University of Technology" } ], "keywords": [ "Nonlinear and optimal automotive control", "Electric and solar vehicles", "Automotive system identification and modelling" ], "abstract": "In this paper, a dual model predictive control (MPC) approach is proposed for thermal management system of pure electric vehicles (EVs), which involves multi-coupled cooling circuits actuated by multi-actuators. To sake of simplicity, the thermal state of the electric motor and the battery are targeted and dual loop MPC is constructed corresponding to the divided two groups of control actuators. Furthermore, a Bayesian-optimization algorithm-based parameter tuning approach is proposed for deciding the design parameter of MPCs. In order to demonstrate effectiveness of the proposed control scheme, an industrial-scaled EV simulator is used to simulate the control performance under previously unknown driving scenarios.", "url": "" }, { "id": "Mo-MoB26.5", "code": "MoB26.5", "title": "A Hierarchical and Scenario-Based MPC Framework for Battery Thermal Management of Electric Vehicle under Real-World Driving Cycles", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB26", "sessionTitle": "Thermal Management of Electrified Vehicles II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Ma, Qian", "affiliation": "Jilin University" }, { "name": "Ma, Yan", "affiliation": "Jilin University" }, { "name": "Gao, Jinwu", "affiliation": "Jilin University" }, { "name": "Chen, Hong", "affiliation": "Tongji University" } ], "keywords": [ "Hybrid, electric and alternative drive vehicles", "Vehicle dynamic systems", "Intelligent transportation systems" ], "abstract": "性能对温度极为敏感。因此，需要设计高效的电池热管理系统（BTMS）以维持电池温度，但这一过程也消耗大量能量。同时，BTMS的优化需要对未来速度做出长期且可靠的预测以提升性能，但这是一项具有挑战性的任务。为解决这些不确定性并缩短计算时间，本文提出了一种基于层次的基于场景的模型预测控制（H-SC-MPC）策略，用于BTMS以维持电池温度并降低冷却系统的能耗。基于历史数据的上层场景式MPC规划了长期预测范围内的最优温度轨迹。低层控制则在短时间域内采用高采样频率，实现温度轨迹跟踪，进一步降低能耗。通过使用交通数据进行模拟验证了该方法的有效性。结果显示，所提议的H-SC-MPC方法相比单一MPC降低了4.6%的BTMS能耗，且温度约束违规", "url": "" }, { "id": "Mo-MoB27.1", "code": "MoB27.1", "title": "Generalist AI Control: Towards Multi-Purpose Adaptive Algorithms (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB27", "sessionTitle": "JO-CEP: Embodied-AI in Marine Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Klinsmann, Agyei", "affiliation": "University of Hertfordshire" }, { "name": "Sarhadi, Pouria", "affiliation": "University of Hertfordshire" } ], "keywords": [ "AI and embodied-AI in marine systems", "Autonomous marine systems and vehicles", "Guidance, navigation and control of aircraft and spacecraft" ], "abstract": "Traditional controllers are designed for specific systems and do not transfer across different system orders and dynamics. We present a Generalist Controller, a learning-based controller capable of controlling systems of varying orders and dynamics. The approach introduces a novel dynamic state-space representation using masking, enabling a single neural network, trained in one shot, to handle systems with different dimensions without architectural modifications by assigning a system tag to each system. We generated 314,630 demonstrations from 25 diverse systems, including stable, unstable, minimum-phase, and non-minimum-phase dynamics, spanning linear and nonlinear systems from autonomous underwater and aerospace vehicles to mechanical systems and chemical processes. The model learns cross-system control strategies through multi-scale temporal processing and a mixture-of-experts architecture. Simulation results demonstrate that the proposed generalist controller achieves comparable performance to system-specific LQI controllers across all tested systems, including challenging cases such as non-minimum-phase and unstable dynamics, whilst generalising to unseen operating conditions including actuator saturation, noise, disturbance, and reference trajectories not encountered during training. This work represents a significant step towards generalist control policies within a defined family of dynamical systems, demonstrating effective control across a range of single-input single-output (SISO) systems of varying order and dynamics using a single learned policy without system-specific tuning.", "url": "" }, { "id": "Mo-MoB27.2", "code": "MoB27.2", "title": "DMIAN: Deep Learning-Based Multi-IMU Fusion for Enhanced Marine Aided Navigation (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB27", "sessionTitle": "JO-CEP: Embodied-AI in Marine Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Batos, Matko", "affiliation": "Faculty of Electrical Engineering and Computing" }, { "name": "Nad, Dula", "affiliation": "University of Zagreb Faculty of Electrical Engineering and Computing" } ], "keywords": [ "AI and embodied-AI in marine systems", "Autonomous marine systems and vehicles", "Marine robotics" ], "abstract": "Learned inertial odometry has advanced rapidly across domains, especially in the GNSS-denied environments. This paper introduces a learning-based approach that combines multiple IMUs with a DVL to improve velocity estimation for marine vehicles. The proposed method employs a multi-head attention Long Short-Term Memory network to fuse tempo- rally and spatially distributed inertial signals with aiding velocity measurements. The model outputs both velocity estimates and their corresponding covariances, which are integrated as measurement updates within an EKF. This hybrid design allows learned features to complement traditional state estimation while maintaining filter consistency. The system is implemented and validated on the H2OmniX platform through a diverse set of trajectories. The method takes less than 5 ms for inference both on the GPU and the CPU, demonstrating less than 0.11 m/s RMSE and more than 0.88 of R2 in unseen trajectories through all ablation studies. The multi- IMU and DVL fusion provides the most accurate results, whereas the models with other IMU configurations continue to deliver reliable estimations when additional data are unavailable. Project website: https://labust.github.io/dmian/.", "url": "" }, { "id": "Mo-MoB27.3", "code": "MoB27.3", "title": "Predicting Oil Spill Diffusion through Generative Adversarial Models (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB27", "sessionTitle": "JO-CEP: Embodied-AI in Marine Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Patané, Luca", "affiliation": "University of Messina" }, { "name": "Maio, Antonino", "affiliation": "University of Messina" }, { "name": "Faraci, Carla", "affiliation": "University of Messina" }, { "name": "Iuppa, Claudio", "affiliation": "University of Messina" }, { "name": "Cavallaro, Luca", "affiliation": "University of Catania" }, { "name": "Roman, Federico", "affiliation": "University of Trieste" }, { "name": "Xibilia, M. Gabriella", "affiliation": "Universita' Degli Studi Di Messina" } ], "keywords": [ "AI and embodied-AI in marine systems", "Decision and support in marine systems", "Modelling, identification and control in marine systems" ], "abstract": "Oil spills remain a critical threat to marine ecosystems, especially in high-risk and densely trafficked areas such as ports. Traditional physics-based models for predicting oil dispersion, though grounded in fluid dynamics, are often constrained by high computational cost and limited suitability for real-time applications. To overcome these challenges, this work introduces a deep learning framework based on a Conditional Deep Convolutional Generative Adversarial Network (cDC-GAN) for fast and accurate prediction of oil spill diffusion in port environments. Key environmental variables (wind direction and intensity, coastline geometry, and time after release) are used as conditioning inputs, each represented as a separate image channel. The method has been validated with an oil spill dataset from the Augusta port in Italy, achieving an intersection-over-union (IoU) exceeding 0.9 and inference times below 30 milliseconds per diffusion sequence. Comparison with DiffusionLSTM models has been performed, showing the superiority of the proposed approach. The proposed model effectively captures complex spatial interactions between the oil slick and coastal boundaries, demonstrating strong potential as a real-time decision-support tool for environmental monitoring and emergency response operations. The proposed cDC-GAN framework provides a data-driven predictive model that can be integrated into autonomous marine vehicle control and navigation systems, enabling adaptive planning, real-time situational awareness, and decision-making during emergency interventions.", "url": "" }, { "id": "Mo-MoB27.4", "code": "MoB27.4", "title": "Integrating Rule Awareness and Semantic Reasoning in Collision-Free Vessel Path Planning (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB27", "sessionTitle": "JO-CEP: Embodied-AI in Marine Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Kougiatsos, Nikos", "affiliation": "Delft University of Technology" }, { "name": "Dhyani, Abhishek", "affiliation": "Delft University of Technology" }, { "name": "Reppa, Vasso", "affiliation": "Delft University of Technology" } ], "keywords": [ "AI and embodied-AI in marine systems", "Marine system guidance, navigation and control", "Maritime transport operation and automation" ], "abstract": "This paper presents the design of an intelligent guidance framework for collision-free navigation of Autonomous Surface Vessels (ASVs), integrating traffic rule awareness and reasoning characteristics. The proposed framework leverages the available qualitative information related to traffic rules and the operational environment(s), in the form of semantic information, as well as sensor information to make online path planning decisions. A modular finite-state machine assigns traffic roles, while the path planner computes a collision-free envelope and reasons over a path, considering both vessels and infrastructure. A Line-of-Sight algorithm and controller enforce the selected path. The method’s effectiveness is demonstrated in a multi-environment case study involving two head-on encounter scenarios, showcasing its adaptability and efficiency across short-sea and inland waterway operations.", "url": "" }, { "id": "Mo-MoB27.5", "code": "MoB27.5", "title": "Fusion of LiDAR, and AIS Data for Improved Maritime Object Detection (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB27", "sessionTitle": "JO-CEP: Embodied-AI in Marine Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Obradovic, Juraj", "affiliation": "FER, University of Zagreb" }, { "name": "Možnik, Dorian", "affiliation": "University of Zagreb, FER" }, { "name": "Ferreira, Fausto", "affiliation": "University of Zagreb" }, { "name": "Soerensen, Asgeir", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Miskovic, Nikola", "affiliation": "University of Zagreb Faculty of Electrical Engineering and Computing" } ], "keywords": [ "AI and embodied-AI in marine systems", "Perception and filtering in marine systems", "Autonomous marine systems and vehicles" ], "abstract": "Maritime autonomous systems require robust perception to address the high rate of human-error-caused accidents in the maritime domain. We present a hybrid detection framework combining YOLO11-based neural network detection on bird’s-eye-view LiDAR projections with deterministic algorithms for identifying isolated floating objects and coast-anchored vessels. Our approach achieves significantly improved recall on real marina data compared to neural network detection alone. We further integrate AIS data using Kalman filtering and path matching, demonstrating high matching accuracy under realistic noise conditions. The system operates at real-time rates on modest GPU hardware, making it suitable for autonomous navigation applications.", "url": "" }, { "id": "Mo-MoB27.6", "code": "MoB27.6", "title": "Interval State Estimation for Unmanned Underwater Vehicles: A Nonlinear Switching System Approach (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB27", "sessionTitle": "JO-CEP: Embodied-AI in Marine Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Ma, Youdao", "affiliation": "Harbin Institute of Technology" }, { "name": "Wang, Zhenhua", "affiliation": "Harbin Institute of Technology" }, { "name": "Li, Jitao", "affiliation": "Harbin Engineering University" }, { "name": "Meslem, Nacim", "affiliation": "INP De Grenoble / CNRS" } ], "keywords": [ "Autonomous marine systems and vehicles" ], "abstract": "This paper addresses the interval state estimation problem for unmanned underwater vehicles subject to unknown but bounded system uncertainties. A nonlinear switching model is developed to describe the dynamics of an unmanned underwater vehicle. Building upon this model, an iterative zonotope-based interval estimation algorithm is presented, which integrates polytope intersection, prediction, union, and correction steps. In addition, advanced zonotopic computation techniques are employed to handle nonlinear mappings and set unions. To enhance estimation accuracy, the interval hull width of a zonotope is used as an optimization criterion. Simulation results illustrate the effectiveness and high performance of the proposed method.", "url": "" }, { "id": "Mo-MoB28.1", "code": "MoB28.1", "title": "Dual-Stage Risk-Aware Predictive Control System for Terrain Following Using Unmanned Aircraft with Rangefinders (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB28", "sessionTitle": "JO-CEP: Control of Aerospace and Autonomous Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Padilla Moure, Pol", "affiliation": "Cranfield University" }, { "name": "Cho, Namhoon", "affiliation": "Seoul National University" }, { "name": "Tsourdos, Antonios", "affiliation": "Cranfield University" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Aerial and space robotics", "Avionics and on-board equipments" ], "abstract": "This study is focused on terrain following capability for fixed-wing tier 1 UAS (<25kg), designing a system to perform very low-level altitude flights adapting to the terrain contour and avoiding obstacles. To the best of our knowledge, this study addresses combination of uncertain digital elevation models (DEMs) and real-time observations with risk-awareness for the first time in the context of terrain following. An innovative dual-stage system is proposed, using cubic B-spline curves to generate an upper envelope combining DEM datasets and direct ground mapping measurements through onboard sensors, and nonlinear model predictive control (NMPC) to track the reference envelope with altitude range constraints. The system is designed for real-time implementation, employing moving window predictions, and an aggressiveness modulation to improve solver times while safely overcoming obstacles. The cubic B-spline DEM-Obstacle envelope is a geometric object that is found through solving a quadratic program with guaranteed convergence. The NMPC uses the full nonlinear longitudinal dynamic model of the UAS to provide optimal vertical guidance and control to the nonlinear underactuated platform, tracking the envelope. The performance is critically sensitive to the rangefinder angular uncertainty, forcing higher flight paths while maintaining minimal collision risk. Chance constraint formulation in the envelope allows improvements through moderate risk allowance, balancing a trade-off between risk and performance. Although the obstacle avoidance sensors are essential, the best performance is achieved using both a quality elevation dataset and sensor suite, employing high resolution DEMs and LiDAR rangefinders.", "url": "" }, { "id": "Mo-MoB28.3", "code": "MoB28.3", "title": "Recurrent Convolutional Neural Networks for LiDAR-Based Pose Initialization of Rotating Spacecraft (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB28", "sessionTitle": "JO-CEP: Control of Aerospace and Autonomous Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Bechis, Luca", "affiliation": "Politecnico Di Torino" }, { "name": "Sarvadon, Jean-Luc", "affiliation": "Politecnico Di Torino" }, { "name": "Ricioppo, Petre", "affiliation": "Politecnico Di Torino" }, { "name": "Mancini, Mauro", "affiliation": "Politecnico Di Torino" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "AI for aircraft and spacecraft navigation, guidance and control", "Aerial and space robotics" ], "abstract": "Accurate pose estimation is essential for autonomous in-orbit servicing and proximity operations. This work proposes a Recurrent Convolutional Neural Network (RCNN) used in coarse pose estimation of known, possibly tumbling spacecraft using LiDAR-derived depth images. By processing temporal sequences of 2D point-cloud projections, the RCNN effectively handles symmetries, occlusions, and degraded sensing. Simulations across various spacecraft geometries, angular velocities, and ranges show that the RCNN yields lower initialization errors and higher convergence rates than conventional CNN, suggesting that the proposed RCNN is suitable for real-time LiDAR-based relative navigation from a computational standpoint.", "url": "" }, { "id": "Mo-MoB28.4", "code": "MoB28.4", "title": "A Structural Resilient Compensator Via Feedback Linearization for Non-Morphing Multirotors (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB28", "sessionTitle": "JO-CEP: Control of Aerospace and Autonomous Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Baldini, Alessandro", "affiliation": "Università Politecnica Delle Marche" }, { "name": "Felicetti, Riccardo", "affiliation": "Università Politecnica Delle Marche" }, { "name": "Freddi, Alessandro", "affiliation": "Universita' Politecnica Delle Marche" }, { "name": "Monteriù, Andrea", "affiliation": "Università Politecnica Delle Marche" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Trajectory tracking and path following for AVs", "Aerial and space robotics" ], "abstract": "This paper proposes a structural approach to design a resilient dynamic state feedback (compensator) for a large class of non-morphing multirotors, which are commonly approximated as rigid bodies. The control algorithm is based on a dynamic extension, which is given in closed form. Starting from a baseline full state dynamic feedback linearization, a structural augmentation is designed to cope with mismatching disturbances. Moreover, it is shown how internal observers are inherently embedded into the compensator, which can account for disturbances generated by nonlinear exogenous systems. The proposed solution is validated in a Hardware-in-the-Loop scenario on a commercial microcontroller widely adopted in unmanned aerial vehicles. The results demonstrate effective compensation performance, while preserving real-time execution on standard autopilot platforms with low CPU utilization.", "url": "" }, { "id": "Mo-MoB28.5", "code": "MoB28.5", "title": "Zonotopic Tube-Based LPV MPC for Autonomous Driving Using Physics-Informed Neural Networks (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB28", "sessionTitle": "JO-CEP: Control of Aerospace and Autonomous Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Ziyad, Houssaini", "affiliation": "Centrale Lille" }, { "name": "Ifqir, Sara", "affiliation": "CRIStAL Lab, Centrale Lille Institute" }, { "name": "Rahmani, Ahmed", "affiliation": "Ecole Centrale De Lille" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" } ], "keywords": [ "AI and learning-based control for automotive systems" ], "abstract": "This paper proposes a novel tube-based Linear Parameter-Varying Model Predictive Control (LPV-MPC) framework for robust and adaptive autonomous driving under bounded disturbances. The vehicle dynamics are described by a discrete-time LPV model with adaptive coefficients, where both scheduling parameters and external disturbances are estimated online using a Physics-Informed Neural Network (PINN). By enforcing physical consistency within the learning process, the PINN provides reliable real-time estimates beyond the training domain, ensuring robustness to unseen conditions. Unlike conventional tube MPC schemes that propagate a single nominal trajectory, the proposed controller propagates zonotopic tubes that explicitly capture uncertainty and enable less conservative constraint enforcement. A Riccati- based feedback law is embedded within the tube to guarantee constraint satisfaction and bounded error dynamics. Experimental data from a real Renault Zo´e and closed-loop simulations with its nonlinear bicycle model confirm the effectiveness of the proposed LPV-PINN MPC in achieving adaptive, real-time, and safe control performance.", "url": "" }, { "id": "Mo-MoB28.6", "code": "MoB28.6", "title": "Tube-Based Safe Reinforcement Learning Using Control Barrier Functions for Autonomous Vehicles (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB28", "sessionTitle": "JO-CEP: Control of Aerospace and Autonomous Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Jeddi, Seyed Hossein", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "Nejjari, Fatiha", "affiliation": "Universitat Politecnica De Catalunya" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" } ], "keywords": [ "AI and learning-based control for automotive systems", "Nonlinear and optimal automotive control" ], "abstract": "This paper presents a Safe Reinforcement Learning (Safe RL) framework for general nonlinear systems that systematically integrates Control Barrier Functions (CBFs) with a tube-based robust invariant set tightening mechanism. The CBF layer guarantees constraints satisfaction by enforcing discrete-time safety conditions during both policy learning and online execution, while the tube-based formulation enhances robustness against model uncertainties, parameter variations, and bounded disturbances. The proposed architecture provides a unified framework that jointly ensures safety and robustness while allowing adaptive, data-driven policy improvement. To demonstrate the effectiveness of the method, simulation studies are performed on a nonlinear five-state vehicle model, confirming that the proposed approach achieves stable, safe, and constraint-admissible tracking performance under a wide range of operating conditions.", "url": "" }, { "id": "Mo-MoB29.1", "code": "MoB29.1", "title": "Optimization for Resilient Multimodal Cargo Evacuation: A Case of Terminal Des Flandres", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB29", "sessionTitle": "Security, Privacy, and Optimization in Control Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Jiang, Jun", "affiliation": "Université De Lille" }, { "name": "Ifqir, Sara", "affiliation": "CRIStAL Lab, Centrale Lille Institute" }, { "name": "Ali, Ame Saleh", "affiliation": "University of Lille, CRIStAL CNRS 9189" }, { "name": "Merzouki, Rochdi", "affiliation": "University of Lille/CRIStAL CNRS 9189" } ], "keywords": [ "Multi-modal transportation systems", "Automatic control, optimization, real-time operations in transportation", "Modeling and simulation of transportation systems" ], "abstract": "This paper focus on the resilient multimodal cargo evacuation in anticipation of system-of-systems (SoS) facing disruptive scenarios, such as, natural disasters, infrastructure issues, technical/operational issues, etc. To address these challenges, we develop an optimization framework based on hypergraph modeling, which captures the interactions among different physical component systems (PCSs) operating across multiple sites. The proposed approach integrates resilient planning to proactively allocate operating time among PCSs under predictable scenarios, aiming to mitigate performance degradation while minimizing overall operational costs. The inland transportation network considered in this study includes barge, road, and rail modes, while maritime transportation is carried out by ship. The results demonstrate the effectiveness of the hypergraph-based optimization in enhancing SoS resilience and ensuring efficient cargo evacuation under various disruptive conditions. Finally, the effectiveness of the proposed method is validated by simulation with data collected from the Terminal des Flandres, a major cargo and transportation hub located in the port area of Dunkirk, France. The simulation result demonstrates the capability of the proposed framework to enhance SoS resilience and ensure efficient cargo evacuation under various disruptive conditions.", "url": "" }, { "id": "Mo-MoB29.2", "code": "MoB29.2", "title": "SDN-Enabled Routing and Distributed Control Co-Design for Microgrids with Multi-Hop Communication", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB29", "sessionTitle": "Security, Privacy, and Optimization in Control Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Hu, Jingyu", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Chen, Yong", "affiliation": "Uestc" }, { "name": "Zhang, Hongye", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Ali, Ikram", "affiliation": "Shenzhen University" } ], "keywords": [ "Cyber physical systems", "Safety and security in networked control", "Information models for control engineering" ], "abstract": "This paper proposes a co-design framework that integrates Software-Defined Networking (SDN)-enabled routing with distributed secondary control (DSC) to enhance the resilience of microgrids operating over multi-hop networks. The framework adopts a three layer structure, comprising the application layer control logic links, the network layer forwarding links, and the physical power grid. A routing abstraction maps application layer links to SDN-managed paths, whose delay and loss metrics are converted into time-varying weights for the distributed controller, enabling dynamic adaptation to network conditions. In response to communication deterioration, the SDN control plane is able to preserve the application layer topology through rerouting. If the resulting path quality cannot meet the stability requirements of the control system, an application layer topology switching is triggered. Simulation results demonstrate that the proposed framework leverages SDN architecture that enables flexible management of network resources, achieving accurate frequency/voltage regulation and robustness against communication failures.", "url": "" }, { "id": "Mo-MoB29.3", "code": "MoB29.3", "title": "Client Selection in Federated Learning-Based Remote State Estimation (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB29", "sessionTitle": "Security, Privacy, and Optimization in Control Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Huang, Lingying", "affiliation": "Southeast University" }, { "name": "Yang, Chao", "affiliation": "East China University of Science and Technology" }, { "name": "Li, Yuzhe", "affiliation": "Northeastern University" }, { "name": "Li, Shihua", "affiliation": "Southeast University" } ], "keywords": [ "Cyber physical systems", "Safety and security in networked control", "Remote data acquisition and fusion" ], "abstract": "This paper addresses the client selection problem in federated learning (FL)-driven remote state estimation for cyber-physical systems (CPS). While FL offers a privacy-preserving framework for collaborative model training across multi sensors, existing FL frameworks often fail to account for the spatiotemporal dependencies inherent to state estimation tasks. We propose a FL-based protocol that enables secure state estimation by transmitting lightweight model updates instead of raw sensor data, thereby mitigating eavesdropping risks in wireless environments. A key challenge arises from the incommensurability of heterogeneous sensors measuring distinct state dimensions, which complicates optimal client selection under resource constraints. To address this, we develop a heuristic sensor selection approach that dynamically prioritizes sensors based on innovation norms, effectively balancing estimation accuracy and communication efficiency. Theoretical analysis demonstrates that the proposed FL-based protocol achieves minimum mean-squared error (MMSE) estimation while preserving temporal dependencies. Simulations further demonstrate the effectiveness of our proposed approaches. This work integrates FL principles with CPS-specific constraints, offering a scalable solution for secure state estimation in many resource-constrained applications.", "url": "" }, { "id": "Mo-MoB29.4", "code": "MoB29.4", "title": "Active Defense against False Data Injection Attacks in Robotic Manipulators", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB29", "sessionTitle": "Security, Privacy, and Optimization in Control Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Gualandi, Gabriele", "affiliation": "Mälardalen University" }, { "name": "Larsson, Carl Mikael", "affiliation": "Mälardalen University" }, { "name": "Papadopoulos, Alessandro Vittorio", "affiliation": "Mälardalen University" } ], "keywords": [ "Safety and security in networked control", "Cloud control and robotics", "Cyber physical systems" ], "abstract": "Robotic systems are vulnerable to False Data Injection Attacks (FDIAs), where adversaries corrupt sensor signals to gain malicious control. Feedback linearization exposes robotic systems to integrator vulnerability, exposing to stealthy attacks that can cause significant deviations in end-effector behavior without raising alarms. This paper addresses the resilience of manipulators against finite-horizon FDIAs by formalizing two defense methods, namely anomaly-aware virtual damping and manipulability reduction, with probabilistic guarantees on nominal task execution. Simulations on a 7-DOF redundant manipulator show that the proposed defense substantially reduces the impact of FDIA compared to threshold-based ADS like the Chi-squared, while preserving nominal task performance in the absence of attack.", "url": "" }, { "id": "Mo-MoB29.5", "code": "MoB29.5", "title": "A System-Theoretic Zero-Knowledge Proof Protocol for Secure Sensor Verification (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB29", "sessionTitle": "Security, Privacy, and Optimization in Control Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Li, Longyu", "affiliation": "East China University of Science and Technology" }, { "name": "Yang, Wen", "affiliation": "East China University of Science and Techonology" }, { "name": "Ding, Wenjie", "affiliation": "East China University of Science and Technology" } ], "keywords": [ "Safety and security in networked control", "Cyber physical systems", "Remote data acquisition and fusion" ], "abstract": "In wireless sensor networks, verifying the reliability of newly joined nodes is crucial to maintaining system security. Traditional methods of cryptographic authentication often introduce significant computational overload or rely on pre-existing key exchanges, making them unsuitable for physical layers with limited resources. This paper proposes a zero-knowledge proof protocol based on system theory, which allows verifiers to assess whether sensor nodes conforms to a known dynamic model without accessing private measurements or internal parameters. The protocol is lightweight, model-based, and achieves three key properties: completeness, soundness, and zero-knowledge. In addition, we validate the effectiveness of the protocol through numerical simulations.", "url": "" }, { "id": "Mo-MoB29.6", "code": "MoB29.6", "title": "Majorization-Based Evolutionary Algorithm for Balanced System Optimization (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB29", "sessionTitle": "Security, Privacy, and Optimization in Control Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Liu, Zhaobo", "affiliation": "Shenzhen University" }, { "name": "Zeng, Tao", "affiliation": "Shenzhen University" }, { "name": "Mo, Yanfang", "affiliation": "Lingnan University, Hong Kong" }, { "name": "Wang, Miaomiao", "affiliation": "City University of Hong Kong" }, { "name": "Hong, Wenjing", "affiliation": "Shenzhen University" }, { "name": "Zhu, Zexuan", "affiliation": "Shenzhen University" } ], "keywords": [ "Bio-inspired algorithms and optimization-based control", "Control architecture for multi agent systems", "Soft computing and robust intelligent control" ], "abstract": "Complex system optimization problems often require solutions that are both efficient and balanced, a challenge naturally arising in homogeneous vector-based performance evaluation. Traditional multi-objective evolutionary algorithms typically address this issue by introducing problem-specific balance metrics, which limits their general applicability. This paper proposes Majorization-NSGA-II (M-NSGA-II), an algorithm that redefines solution ranking by replacing Pareto dominance with the weak majorization preorder. This selection mechanism provides a principled preference for balanced solutions and is theoretically justified for aggregate performance criteria that are Schur-convex and monotonically increasing. Through a multi-UAV path-planning case study, we show that M-NSGA-II discovers solutions that are simultaneously more efficient, more balanced, and more robust in terms of worst-agent cost. These results indicate that majorization-based ranking is an effective framework for balanced optimization in homogeneous multi-component systems.", "url": "" }, { "id": "Mo-MoB30.1", "code": "MoB30.1", "title": "Using Robots in the Rehabilitation of Older Adults – Literature Review (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB30", "sessionTitle": "Technology‑Supported Mobility, Care, and Well‑Being across the Lifespan", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Muršec, Dominika", "affiliation": "Alma Mater Europaea" }, { "name": "Bogataj, David", "affiliation": "Alma Mater Europaea University" } ], "keywords": [ "Control and automation to improve social and political stability", "Digital culture", "Diversity and inclusion in digital culture" ], "abstract": "The global trend of aging increases the need for effective rehabilitation approaches tailored to older adults, who often face chronic diseases, multimorbidity, and functional decline. Robotic technologies are emerging as promising tools for improving the health of older adults. A literature review was conducted using Web of Science and PubMed databases and 16 studies were included in the final review. Most studies have used robots for physical rehabilitation, including gait training, balance support, and muscle strengthening with wearable robots, exoskeletons, sensor systems, etc. A smaller number addressed cognitive or psychosocial aspects using socially supportive or companion robots. Reported outcomes showed improvements in walking speed, muscle strength, and balance across multiple studies. Acceptance of robotic technology among older adults was generally positive. Evidence suggests that robots can meaningfully support rehabilitation in older adults, particularly in mobility and functional performance. However, more user-centered research is needed to fully understand the benefits, challenges, and long-term implications of integrating robots into rehabilitation of older adults.", "url": "" }, { "id": "Mo-MoB30.2", "code": "MoB30.2", "title": "From Technophobia to Technology Readiness: Interventions Supporting Older Adults in AI-Enabled Smart Communities (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB30", "sessionTitle": "Technology‑Supported Mobility, Care, and Well‑Being across the Lifespan", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Rotovnik Omerzu, Ana", "affiliation": "Alma Mater Europea" }, { "name": "Bogataj, David", "affiliation": "Alma Mater Europaea University" } ], "keywords": [ "Digital culture", "Diversity and inclusion in digital culture", "Smart city design and planning" ], "abstract": "The growing trend of an ageing population can be accompanied by the emergence of technophobia among the elderly. Technophobia can pose a significant challenge in smart communities that rely on modern AI-based solutions to improve the quality of life of older adults. The aim of this systematic review is to identify the sociodemographic and psychosocial factors linked to technophobia in older adults, evaluate interventions that reduce technophobia and enhance technology readiness, and examine how AI-enabled Smart Communities support their digital inclusion. We used the integrative review method of scientific literature from two databases (WoS and PubMed) and analysed 15 articles in detail across the three domains outlined above. The most effective way to reduce technophobia among the elderly is through an intergenerational learning model involving a group of young (digital natives) who transfer digital skills to the elderly. Although AI-enabled technologies used in smart communities could promise reduce technophobia, several gaps exsist. Older adults are willing to adopt technology when it is adapted to their needs and when they receive support during the learning process. Studies typically assess single technologies rather than integrated AI-enabled community ecosystems. Gaps highlight the need for long-term, equity-focused research that evaluates how AI-enabled Smart Communities can sustainably reduce technophobia and promote meaningful digital participation among older adults.", "url": "" }, { "id": "Mo-MoB30.3", "code": "MoB30.3", "title": "Digital Transformation of Social Care Services: Readiness, Challenges, and Opportunities in Slovenia (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB30", "sessionTitle": "Technology‑Supported Mobility, Care, and Well‑Being across the Lifespan", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Mežnarec-Novosel, Suzanna", "affiliation": "University Alma Mater Europaea" }, { "name": "Bogataj, David", "affiliation": "Alma Mater Europaea University" } ], "keywords": [ "Digital culture", "Diversity and inclusion in digital culture", "Social networks and opinion dynamics" ], "abstract": "Introduction: Slovenia’s social care system faces demographic pressures and workforce constraints, prompting interest in digitally enabled innovation. Methods: We conducted a national online survey of social care providers (simple size, n=220) using the 1KA platform. Most evaluative items used Likert-type rating scales with an additional “I cannot assess” response option to assess current service delivery, openness to digital technologies, and familiarity with artificial intelligence (AI). Results: Providers rated their service effectiveness positively (mean [M] = 3.9), while state support and funding scored lower (mean (M) =2.6). Openness was greatest for simple, user-friendly tools, such as video calls and tablets, with M ≈ 3.1–3.3 and more cautious for advanced solutions, including virtual reality and augmented reality (VR/AR), social robots, and care robots. Familiarity with AI remains limited with 29% of respondents reporting familiarity, while responses across items showed moderate variability (standard deviations [SD] ≈ 1.5–1.7). Discussion: Findings suggest readiness for pragmatic, low-barrier digital adoption alongside clear capacity gaps. Conclusion: Targeted training, pilot implementations, and system-level support are needed to translate national digital strategies into everyday social care practice.", "url": "" }, { "id": "Mo-MoB30.4", "code": "MoB30.4", "title": "The Role of ICT in Empowering Rural Communities: Reducing Social Isolation and Enhancing Local Resilience – Literature Review (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB30", "sessionTitle": "Technology‑Supported Mobility, Care, and Well‑Being across the Lifespan", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Imamovic Leric Lejla, Lejla", "affiliation": "Faculty of Organisation Studies in Novo Mesto" }, { "name": "Cumhur Demiralp, Demiralp", "affiliation": "Hakkari University" }, { "name": "Ljevo Nerman, Nerman", "affiliation": "Faculty of Management and Business Economy" }, { "name": "Nedeljko, Mihael", "affiliation": "Institute INRISK, Trebnje, Slovenia" } ], "keywords": [ "Social networks for smart cities", "Digital culture", "Advanced technology, conflict and post-conflict" ], "abstract": "The purpose of this article is to review the literature and to examine the existing roles of Information and Communication Technologies (ICT) in strengthening the social, economic, and institutional capacities of rural communities. Exposure to social isolation and vulnerability to environmental pressures is most evident in rural areas due to challenges in key sectors such as infrastructure and limited access to information. By synthesizing various scientific studies, the analysis explains the importance of ICT in connecting communities, enabling knowledge exchange, and supporting the development of adaptive capacities, without which local communities cannot build local resilience. The literature review highlights the obstacles associated with implementing ICT, including the mentioned challenges, and discusses strategies presented in the literature for overcoming these barriers through context-sensitive, community-oriented, and policy-supported approaches. The findings confirm that ICT contributes to improved and easier access to sectors facing challenges—such as public services, agricultural productivity, and governance transparency—thereby reducing vulnerability and promoting more inclusive rural development. Overall, the article emphasizes the importance of ICT and its role as a key mechanism for reducing social isolation, fostering sustainable rural transformation, and strengthening the empowerment of rural populations.", "url": "" }, { "id": "Mo-MoB30.5", "code": "MoB30.5", "title": "Psychosocial Risks and Well-Being of Older Workers in Digitally Transforming Organisations: Literature Review (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB30", "sessionTitle": "Technology‑Supported Mobility, Care, and Well‑Being across the Lifespan", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Nedeljko, Mihael", "affiliation": "Institute INRISK, Trebnje, Slovenia" }, { "name": "Vidnar, Nataša", "affiliation": "Community Healthcare Centre Dr. Adolf Drolc, Maribor, Slovenia" }, { "name": "Vlaisavljević, Željko", "affiliation": "University Clinical Centre of Serbia, Belgrade, Serbia" }, { "name": "Lokajner, Gordana", "affiliation": "The Nurse and Midwifery Organisation of Ljubljana, Slovenia" }, { "name": "Kaučič, Boris Miha", "affiliation": "Institute for Training, Work and Care Dr. Marijan Borštnar Dornava, Slovenia" } ], "keywords": [ "Social networks for smart cities", "Smart city design and planning", "Digital culture" ], "abstract": "The rapid pace of digitalization and organizational transformation has introduced new psychosocial challenges for older workers. This paper explores the relationship between technological change, psychosocial risks, and the well-being of older employees, emphasizing how digital transformation and work organization influence their mental health and work ability. A structured literature review was conducted using the Scopus database, focusing on publications related to older workers, technological change, and psychosocial well-being. The review followed the PRISMA 2020 framework to ensure transparency in study selection and inclusion. Inclusion criteria were limited to peer-reviewed, full-text articles in English, resulting in a final sample of studies examining technostress, work design, and organizational support for older workers. The reviewed studies (11) consistently show that older workers are more prone to technostress, perceived skill obsolescence, and work-related anxiety when organizational support and digital training are lacking. Poor work design, high demands, and limited autonomy increase psychosocial strain, while inclusive practices, ergonomic design, and continuous learning significantly enhance well-being and work ability. The findings highlight that technological and organizational changes create both risks and opportunities for older workers. Supportive leadership, lifelong learning, and human-centered digital practices are essential to mitigate psychosocial risks and support sustainable employment and well-being among older workers. The review emphasizes the need for age-inclusive strategies within digitally transforming organizations to ensure sustainable well-being and employability.", "url": "" }, { "id": "Mo-MoB32.1", "code": "MoB32.1", "title": "An Adaptive Nonlinear Dynamic Inversion Control Framework for Capturing and Detumbling Uncooperative Satellites with a Space Manipulator System", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB32", "sessionTitle": "Robotic Grasping and Manipulation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Mercadante, Pier Luigi", "affiliation": "ONERA" }, { "name": "Kraïem, Sofiane", "affiliation": "ONERA" }, { "name": "Rognant, Mathieu", "affiliation": "ONERA" }, { "name": "Cassaro, Mario", "affiliation": "ONERA, the French Aerospace Lab" } ], "keywords": [ "Autonomous navigation", "Robotic grasping and manipulation", "Task and motion planning" ], "abstract": "The challenge of mitigating space debris and enhancing satellite longevity has led to increased interest in Active Debris Removal (ADR) and On-Orbit Servicing (OOS) operations. This paper presents an adaptive control framework for a rotation-free floating Space Manipulator System (SMS) to capture and detumble a tumbling satellite under model uncertainties. The proposed approach combines Nonlinear Dynamic Inversion (NDI) with a Model Reference Adaptive Control (MRAC) law to handle strong nonlinear coupling and unknown target dynamics. Robust control gains are synthesized through a Linear Matrix Inequality (LMI)-based procedure to ensure stability across both pre- and post-capture phases within the manipulator workspace. To mitigate high-frequency oscillations induced by abrupt momentum transfer during capture, a low-pass filtering mechanism is integrated into the adaptive loop. The effectiveness of the proposed method is validated through high-fidelity simulations involving targets with varying inertial properties and uncertainties. Comparative results against an NDI-Nonlinear Disturbance Observer (NDO) based controller demonstrate enhanced robustness and stability during post-capture detumbling.", "url": "" }, { "id": "Mo-MoB32.2", "code": "MoB32.2", "title": "Dynamic Grabbing and Stabilization of a Heavy Oscillating Payload", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB32", "sessionTitle": "Robotic Grasping and Manipulation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Asani, Zemerart", "affiliation": "Vrije University Brussels" }, { "name": "Vanderborght, Bram", "affiliation": "Vrije Universiteit Brussel" }, { "name": "Garone, Emanuele", "affiliation": "Université Libre De Bruxelles" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "Mechatronics for robotic systems", "Robotic grasping and manipulation" ], "abstract": "Control of dynamically grabbing moving objects is a major challenge in control of robotic systems, particularly when the mass of the object significantly exceeds the robot payload capacity. Traditional approaches primarily focus on lightweight objects or static scenarios, often neglecting the complexities of high-momentum interactions and post-contact stabilization. This paper addresses the problem of dynamically catching and stabilizing a heavy, oscillating load suspended from a pendulum. A parametric shrinking nonlinear model predictive control (P-SHNMPC) strategy is proposed for pre-impact synchronization, and a state-dependent compliance controller for oscillation damping. Numerical simulations confirm safe grabbing and effective oscillation damping of a payload significantly exceeding the robot’s load capacity.", "url": "" }, { "id": "Mo-MoB32.3", "code": "MoB32.3", "title": "UAM-Planner: A Highly Dynamic Planner for Underactuated Aerial Manipulation", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB32", "sessionTitle": "Robotic Grasping and Manipulation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Liu, Yifan", "affiliation": "Tongji University" }, { "name": "Sun, Chenyang", "affiliation": "Tongji University" }, { "name": "Shen, Runjie", "affiliation": "Tongji University" } ], "keywords": [ "Robotic grasping and manipulation", "Autonomous navigation", "Aerial, field, and marine robotics" ], "abstract": "In this work, we present the Underactuated Aerial Manipulator Planner (UAM-Planner), a trajectory planning system designed for single-joint underactuated aerial manipulators. The UAM-Planner addresses the quasi-static limitation of existing methods by generating high-dynamic, safe flight trajectories that fully tap into the system's inherent potential. The trajectory is composed of three distinct components: warm-up, joint, and task trajectories. The task trajectory incorporates differential-algebraic equation (DAE) for precise end-effector execution. In contrast, the warm-up and joint trajectories are based on piecewise polynomial optimization using spatio-temporal optimization. Additionally, the Euclidean Signed Distance Field (ESDF) is employed to ensure collision-free operation throughout all trajectory components. The proposed method is validated in a variety of complex simulation and real-world environments, demonstrating its precision and robustness.", "url": "" }, { "id": "Mo-MoB32.4", "code": "MoB32.4", "title": "Geometric Formulation of Unified Force-Impedance Control on SE(3) for Robotic Manipulators", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB32", "sessionTitle": "Robotic Grasping and Manipulation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Seo, Joohwan", "affiliation": "University of California, Berkeley" }, { "name": "Potu Surya Prakash, Nikhil", "affiliation": "University of California Berkeley" }, { "name": "Lee, Soomi", "affiliation": "University of California, Berkeley" }, { "name": "Kruthiventy, Arvind", "affiliation": "University of California Berkeley" }, { "name": "Teng, Megan", "affiliation": "University of California, Berkeley" }, { "name": "Choi, Jongeun", "affiliation": "Yonsei University" }, { "name": "Horowitz, Roberto", "affiliation": "Univ. of California at Berkeley" } ], "keywords": [ "Robotic grasping and manipulation", "Robotic learning and adaptation" ], "abstract": "In this paper, we present a geometric unified force–impedance control (GUFIC) framework on the SE manifold that enables force tracking while guaranteeing passivity. Building upon unified force–impedance control (UFIC) and geometric impedance control (GIC), GUFIC incorporates the SE(3) manifold structure through a differential–geometric formulation and augments energy tanks for both force-tracking and impedance control to ensure closed-loop passivity. The proposed framework resolves the implementation difficulty of UFIC by introducing velocity and force fields, which enable causal updates of desired motion and force. Defined entirely on SE(3), GUFIC inherits the SE(3) invariance and equivariance properties of GIC, improving generalization and sample efficiency when integrated with learning-based policies. The proposed control law is validated in a simulation environment under scenarios requiring tracking an SE(3) trajectory, incorporating both position and orientation, while exerting a force on a surface. The implementation is available at url{https://github.com/Joohwan-Seo/GUFIC_mujoco}.", "url": "" }, { "id": "Mo-MoB32.5", "code": "MoB32.5", "title": "Adaptive Radial Basis Function Neural Network and Extended State Observer-Based Control for Robust Trajectory Tracking of Robotic Manipulators", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB32", "sessionTitle": "Robotic Grasping and Manipulation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Huang, Cheng-Tze", "affiliation": "National Central University" }, { "name": "Wu, Jim-Wei", "affiliation": "National Central University" }, { "name": "Yu, Jen-te", "affiliation": "Chung Yuan Christian University" } ], "keywords": [ "Robotic grasping and manipulation", "Robotic learning and adaptation", "Task and motion planning" ], "abstract": "Uncertainties in the robotic manipulator model can lead to inaccurate model information, such as manufacturing errors in mechanical components or the presence of external disturbances. These factors can affect the trajectory tracking accuracy of systems during most motion processes. To address this issue, an adaptive radial basis function neural network (ARBFNN) is designed to approximate unknown nonlinear dynamic functions. The control architecture integrates a conventional proportional–derivative (PD) controller, a feedforward compensator, and an extended state observer (ESO) to enhance system robustness. Since the ARBFNN cannot fully approximate nonlinear external disturbances and internal uncertainties, the ESO is used to compensate for the residual estimation errors further. The stability of the closed-loop system is further formalized using Lyapunov theory, ensuring that all error signals remain bounded. Finally, simulations are conducted to verify the effectiveness and robustness of the proposed trajectory tracking method.", "url": "" }, { "id": "Mo-MoB33.1", "code": "MoB33.1", "title": "LARS: Multi-Target Task Scheduling for UAV with Lambda-Balanced and Risk-Aware (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB33", "sessionTitle": "Control and Optimization for Low-Altitude Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Yu, Bin", "affiliation": "School of Automation, Hangzhou Dianzi University" }, { "name": "Lu, Qiang", "affiliation": "Hangzhou Dianzi University" }, { "name": "Yu, Fengmin", "affiliation": "Hangzhou Dianzi University" }, { "name": "Liu, Xiongding", "affiliation": "Hangzhou Dianzi University" }, { "name": "Huang, Na", "affiliation": "Hangzhou Dianzi University" }, { "name": "Zhang, Botao", "affiliation": "Hangzhou Dianzi University" }, { "name": "Choi, Youngjin", "affiliation": "Hanyang Univ" }, { "name": "Yao, Ruoyan", "affiliation": "School of Automation, Hangzhou Dianzi University" }, { "name": "Shi, Yifang", "affiliation": "Hangzhou Dianzi University" } ], "keywords": [ "Decision making under uncertainty", "Smart city security and resilience", "Smart city control and optimization" ], "abstract": "To enhance the safety and efficiency of an unmanned aerial vehicle (UAV) in executing multi-target task scheduling in complex environments, a lambda-balanced and risk-aware scheduling method (LARS) is proposed. In this method, the real-time scores of candidate trajectories output by the planning network are treated as confidence signals of environmental uncertainty. Then, these signals are processed through exponential mapping and temporal aggregation to obtain target-level confidence values. Combined with the 2D flight distance, they form a joint cost with a weighted parameterλ, which is used to determine the next target of the UAV to visit online. In the single-UAV multi-goal experiments within the Flightmare forest scenario, we compare LARS with Greedy (λ=0), Score-RS (λ=1), and the YOPO-Fixed baseline without task-level scheduling. The results show thatλ≈0.3 achieves a stable Pareto trade-off among total time, total path length, and average confidence, outperforming all three baselines. Moreover, the risk-sensitive (RS) aggregation accurately captures the increased risk exposure in narrow passages and obstacle-dense areas, validating the effectiveness of elevating the internal score of an end-to-end planner to a task-level scheduling signal.", "url": "" }, { "id": "Mo-MoB33.2", "code": "MoB33.2", "title": "Event-Triggered Attitude Synchronization of Unknown Networked Quadrotors Via Reinforcement Learning (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB33", "sessionTitle": "Control and Optimization for Low-Altitude Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Zhang, YunLin", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Zhao, Wanbing", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Shao, Jinliang", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Li, Tieshan", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Zheng, Wei Xing", "affiliation": "Western Sydney University" } ], "keywords": [ "Smart city control and optimization", "Low-altitude economy", "AI for smart cities" ], "abstract": "This paper focuses on the attitude synchronization control problem of networked quadrotors under the effects of unknown system parameters, communication link faults, and input constraints. An event-triggered synchronization control scheme is proposed, which consists of an event-triggered distributed observer and a reinforcement learning (RL)-based optimal controller. First, the event-triggered distributed observer is utilized to estimate the global attitude reference, which is resilient to communication link faults by utilizing only limited locally exchanged information. Then, an RL-based optimal controller is employed to achieve synchronization with input constraints, with only sampled system data used for parameter updating. Simulation results verify the effectiveness of the proposed controller.", "url": "" }, { "id": "Mo-MoB33.3", "code": "MoB33.3", "title": "Distributed Formation Control for Multi-UAV Systems with Disturbances and Actuation Bandwidth Limitations (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB33", "sessionTitle": "Control and Optimization for Low-Altitude Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Zheng, Zhiyuan", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Wang, Erquan", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Zhu, Yang", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Zheng, Wei Xing", "affiliation": "Western Sydney University" }, { "name": "Shao, Jinliang", "affiliation": "University of Electronic Science and Technology of China" } ], "keywords": [ "Smart city control and optimization", "Low-altitude economy" ], "abstract": "Most existing quadrotor-swarm control methods neglect actuator dynamics, yet non-ideal actuator characteristics such as limited bandwidth and passband gain can degrade control performance or even cause instability. This paper addresses this issue by inserting a novel actuator compensator into the classic distributed control framework to counteract the impact of non-ideal actuator responses. Specifically, the proposed hierarchical control framework integrates a distributed observer, which estimates the leader’s states and formation deviations under switching topologies, with a backstepping-based local controller. This local controller is constructed by a cascade connection of a tracking controller that is designed under ideal actuator responses and an actuator compensator that dynamically improves the actuation bandwidth and passband gain. The Lyapunov method is utilized to prove the overall system stability, and the performance is analyzed via the singular perturbation theorem. Finally, real-world experiments are conducted to verify the effectiveness and advantages of the proposed framework.", "url": "" }, { "id": "Mo-MoB33.4", "code": "MoB33.4", "title": "Sampling Trajectory Control for Unmanned Aerial Vehicles Based on Radio Map Estimation (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB33", "sessionTitle": "Control and Optimization for Low-Altitude Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Li, Tong", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Ma, Zhuangzhuang", "affiliation": "Henan University" }, { "name": "Li, Song", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Shao, Jinliang", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Zheng, Wei Xing", "affiliation": "Western Sydney University" } ], "keywords": [ "Smart city control and optimization", "Low-altitude economy", "System dynamics and control in CPHS" ], "abstract": "Radio map (RM) is extensively used in wireless communication systems and unmanned aerial vehicle (UAV) networks. Conventional RM estimation methods mainly rely on sampled data while rarely considering the influence of sampling trajectories. This paper investigates the sampling trajectory control problem with the aim of improving RM estimation performance through UAV position optimization. Based on the analysis of the RM estimation error model, two sampling principles are proposed: the full-coverage sampling principle and the signal-source-neighborhood priority principle. These principles provide quantitative criteria for autonomous sampling trajectory control. Then a collaborative optimization framework based on multi-agent hierarchical reinforcement learning (HRL) and a generative adversarial network with weak supervision learning is developed. The proposed sampling principles are formalized as skills in multi-agent HRL, enabling a decision-making paradigm that spans collaborative strategy generation and individual motion control. Numerical simulations verify the effectiveness of the proposed algorithm.", "url": "" }, { "id": "Mo-MoB33.5", "code": "MoB33.5", "title": "Robust Adaptive State Estimation for Urban UAVs Based on Multivariate Power Exponential Distribution (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB33", "sessionTitle": "Control and Optimization for Low-Altitude Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "He, Jiacheng", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Bai, Mingming", "affiliation": "Zhejiang University" }, { "name": "Peng, Bei", "affiliation": "University of Electronic Science and Technology of China" } ], "keywords": [ "Social transportation and social energy" ], "abstract": "To address complex time-varying noise in urban UAV navigation, this paper proposes a robust adaptive Kalman filter based on the multivariate power exponential distribution (MPED). Unlike existing methods constrained by rigid distribution shapes, the MPED establishes a unified framework that flexibly adapts to diverse noise profiles by adjusting shape parameters. Variational Bayesian inference is employed to jointly estimate navigation states and distribution parameters online. Simulation results demonstrate the method’s superior adaptability and accuracy in dynamic environments compared to existing algorithms.", "url": "" }, { "id": "Mo-MoB34.1", "code": "MoB34.1", "title": "Polyhedral Obstacle Avoidance Control of a Mobile Robot Via Bilateral Teleoperation with Haptic Feedback (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB34", "sessionTitle": "Cyber-Physical-Human Systems: From Individual Empowerment to Societal Impact II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Yasui, Kazuki", "affiliation": "Meiji University" }, { "name": "Liu, Yen-Chen", "affiliation": "National Cheng Kung University" }, { "name": "Ibuki, Tatsuya", "affiliation": "Meiji University" } ], "keywords": [ "System dynamics and control in CPHS", "Safety-critical and resilient systems", "Cyber-physical and human systems (CPHS)" ], "abstract": "This paper presents a control method of teleoperated obstacle avoidance for a mobile robot using a haptic device. By utilizing a haptic device as a controlling device, the operator can perceive obstacles virtually. We consider a polyhedral obstacle and propose a cooperative control method using a non-smooth control barrier function between the robot and the operator to ensure that the robot safely avoids the obstacle during teleoperation. The safety control input of the robot is obtained by solving the constrained optimization problem. To ensure the stable interaction in a human-in-the-loop system, we consider a passivity-based approach combined with a strategy of an energy tank. The effectiveness of the proposed method is demonstrated through physical experiments with an unmanned aerial vehicle as a mobile robot.", "url": "" }, { "id": "Mo-MoB34.2", "code": "MoB34.2", "title": "A Sensor-Scheduling Approach to Predict Human Reliance During Automated Driving (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB34", "sessionTitle": "Cyber-Physical-Human Systems: From Individual Empowerment to Societal Impact II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Bossi, Emanuele", "affiliation": "Embry-Riddle Aeronautical University" }, { "name": "Jeevanandam, Sibibalan", "affiliation": "Purdue University" }, { "name": "Jain, Neera", "affiliation": "Purdue University" } ], "keywords": [ "Human-centric automation/AI Systems, and human agency", "Cyber-physical and human systems (CPHS)" ], "abstract": "This paper introduces a hybrid dynamic modeling framework that predicts driver reliance on automation using intermittent self-reports of cognitive states. Leveraging a hybrid dynamic model of trust, risk perception, and workload, we replace restrictive threshold rules with a decision-tree classifier and enable online parameter adaptation. We further introduce a reliance-accuracy-based sensor-scheduling scheme that selectively triggers self-reports. Human-subject experiments (with 16 participants) show that the sensor-scheduling approach preserves mean prediction accuracy while using only one-third of available self-reports, demonstrating the value of adaptive cognitive-modeling for automated driving.", "url": "" }, { "id": "Mo-MoB34.3", "code": "MoB34.3", "title": "On Supplementing Private Recommendation with Incentive to Steer Regret Matching Agents in Nonatomic Routing Games (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB34", "sessionTitle": "Cyber-Physical-Human Systems: From Individual Empowerment to Societal Impact II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Savla, Ketan", "affiliation": "University of Southern California" } ], "keywords": [ "Game theories", "Cyber-physical urban systems", "Cyber-physical and human systems (CPHS)" ], "abstract": "We study the following repeated non-atomic routing game. In every round, nature chooses a state in an i.i.d. manner according to a publicly known distribution, which influences link latency functions. The system planner makes private route recommendations to the agents according to a signaling strategy. We study asymptotic behavior under two models for agent decision. First, for the classical regret matching model, we adapt prior results from incentive design for convergence to equilibrium in extensive form games, to provide sufficient conditions on incentives to steer the agent population towards the flow induced by the recommendation strategy. Second, we consider a nested decision model, where the agents choose between obeying and not obeying the recommendation in the first level, and conditional on not obeying, they choose a route in the second level according to a fixed strategy. For such a model, we show that, under an obedient recommendation strategy, the flows almost surely converge to the induced equilibrium without incentives. These results illustrate relationship between incentives and non-equilibrium decision models.", "url": "" }, { "id": "Mo-MoB34.4", "code": "MoB34.4", "title": "Early Prediction of Dissatisfaction Tipping in Essential Cyber-Physical-Human Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB34", "sessionTitle": "Cyber-Physical-Human Systems: From Individual Empowerment to Societal Impact II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Kibangou, Alain", "affiliation": "GIPSA-Lab, Univ. Grenoble Alpes, CNRS" } ], "keywords": [ "Cyber physical social systems (CPSS)", "Cyber-physical and human systems (CPHS)", "Decision making under uncertainty" ], "abstract": "Using the SDS (satisfaction-Dissatisfaction-Satisfaction) model, this paper studies how dissatisfaction propagates in essential service systems when service quality decays. We characterize conditions under which dissatisfaction trajectories remain monotone or become non-monotone due to interactions between initial perceptions, word-of-mouth effects, and declining quality. Tipping behavior occurs when the long-term equilibrium exceeds a critical dissatisfaction threshold, making escalation difficult to reverse. While tipping occurrence can be derived analytically, predicting its timing is challenging and lacks a closed-form solution. To address this, we propose a model-based surrogate learning framework. It integrates a neural classifier trained on SDS-generated trajectories that reliably detects impending tipping events with a safety-oriented regressor that predicts tipping time while penalizing late estimates. The approach supports timely intervention in critical service contexts and contributes to Cyber-Physical-Human Systems by linking mechanistic behavioral dynamics with learning-based surrogate prediction of analytically intractable quantities.", "url": "" }, { "id": "Mo-MoB34.5", "code": "MoB34.5", "title": "Impact of Attitude and Bounded Rationality on Collective Behavioral Transitions (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB34", "sessionTitle": "Cyber-Physical-Human Systems: From Individual Empowerment to Societal Impact II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Song, Chen", "affiliation": "Nanyang Technological University" }, { "name": "Cvetkovic, Vladimir", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Fontan, Angela", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Su, Rong", "affiliation": "Nanyang Technological University" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Decision making under uncertainty", "Cyber-physical and human systems (CPHS)" ], "abstract": "The theory of planned behavior (TPB) is one of the most influential frameworks in social psychology, stating that a person's behavior is driven by intention, which is primarily shaped by attitude, subjective norms, and perceived behavioral control. Despite its strong empirical support, TPB remains a static conceptual framework without explicit mathematical formulations that capture the temporal evolution of its components. To address this gap, we develop a dynamic agent-based modeling framework that integrates the core principles of TPB with a behavior-to-attitude feedback mechanism. Specifically, we define behaviors based on their feedback effects on attitude and examine when the population undergoes collective transitions by either adopting a beneficial behavior or rejecting a harmful one. Results from our model demonstrate that collective transitions can be effectively controlled by adjusting two key behavioral parameters that reflect agents' attitude influence and decision rationality. These findings provide quantitative insights on TPB, highlighting the key factors that drive collective behavioral transitions and the need for further socio-psychological case studies.", "url": "" }, { "id": "Mo-MoB34.6", "code": "MoB34.6", "title": "On the Covariance Matrix of the Stationary Distribution of Stochastic Noncooperative Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB34", "sessionTitle": "Cyber-Physical-Human Systems: From Individual Empowerment to Societal Impact II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Yang, Zehan", "affiliation": "Institute of Science Tokyo" }, { "name": "Hayakawa, Tomohisa", "affiliation": "Tokyo Institute of Technology" } ], "keywords": [ "Game theories" ], "abstract": "In this paper, we investigate the steady-state covariance structure of stochastic noncooperative systems. We first establish necessary and sufficient conditions for the existence of a steady-state covariance matrix. We then derive a characterization of when a positive-definite steady-state covariance matrix exists, formulated as the solvability of a Lyapunov-like equation. Furthermore, we propose a zero-sum tax/subsidy mechanism to ensure that the stochastic noncooperative system has a prescribed positive-definite steady-state covariance matrix, and we characterize the set of admissible steady-state covariance matrices that can be achieved in the two-agent case.", "url": "" }, { "id": "Mo-MoB35.1", "code": "MoB35.1", "title": "Opening up Control Theory through Dance and Music: Insights from the \"Art & Control Show\" (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB35", "sessionTitle": "Beyond Art & Control & Engineering", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Stoica, Cristina", "affiliation": "CentraleSupélec, Université Paris-Saclay" }, { "name": "Pfeiffer, Laurent", "affiliation": "Inria" }, { "name": "Belin, Noémie", "affiliation": "France" }, { "name": "Aline, Ryss", "affiliation": "Univ. Paris-Saclay" }, { "name": "Braganti-Coral, Juliette", "affiliation": "Univ. Paris-Saclay" }, { "name": "Ascar, Cxii", "affiliation": "Univ. Paris-Saclay" }, { "name": "Daviddi, Nais", "affiliation": "Univ. Paris-Saclay" }, { "name": "Dubos, Coline", "affiliation": "France" }, { "name": "Jacobs, Alexandra", "affiliation": "France" }, { "name": "Lavenus, Pierre", "affiliation": "France" }, { "name": "Reuzé, Sylvain", "affiliation": "France" } ], "keywords": [ "Cognitive and emotional control/AI systems, arts and control" ], "abstract": "In the context of the Open Invided Track ''Beyond Art & Control & Engineering'', this paper offers detailed insights of the ''Art & Control Show'', an innovative event aiming at popularizing control theory through art. The event was held in June 2025 at the occasion of the joint IFAC SSSC TDS COSY 2025 conference and a video is available on the IFAC YouTube channel www.youtube.com/watch?v=awx-LOdbNH8. The show took the form of a live show combining classical music, contemporary improvisation dance, and control theory. The dancers interpreted several notions from control theory which they chose from the conference keywords. Their approach to appropriate and embody these notions, with which they were originally not familiar, is extensively described. Feedback from the project participants and the audience is also included.", "url": "" }, { "id": "Mo-MoB35.2", "code": "MoB35.2", "title": "Art and Control Engineering: Developing a 2D Animated Cartoon on System Modeling for Students by Students (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB35", "sessionTitle": "Beyond Art & Control & Engineering", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Ferrer, Thomas", "affiliation": "CentraleSupélec" }, { "name": "Lallemand, Leila", "affiliation": "CentraleSupélec" }, { "name": "Etcheverry, Lina", "affiliation": "CentraleSupélec" }, { "name": "Elbaz, Eden", "affiliation": "CentraleSupélec" }, { "name": "Ducournau, Antoine", "affiliation": "CentraleSupélec" }, { "name": "Stoica, Cristina", "affiliation": "CentraleSupélec, Université Paris-Saclay" }, { "name": "Rossiter, J. Anthony", "affiliation": "Univ of Sheffield" }, { "name": "Visioli, Antonio", "affiliation": "University of Brescia" }, { "name": "Guzman, Jose Luis", "affiliation": "University of Almeria" }, { "name": "Douglas, Brian", "affiliation": "Resourcium" }, { "name": "McDonald, Julie", "affiliation": "CentraleSupélec" }, { "name": "Ung, Miléna", "affiliation": "2D Animator Freelance" }, { "name": "Bayle de Jessé, Louis", "affiliation": "Freelancer" } ], "keywords": [ "Cognitive and emotional control/AI systems, arts and control" ], "abstract": "In the context of the Open Invited Track ''Beyond Art & Control & Engineering'', this paper presents some insights from the development of an animated cartoon by undergraduate students of CentraleSupélec on system modeling, as part of the oosCaR – 2D Animated Cartoons for Control Education Rise'' project. Combining Art and Control Education, the aim of this short animated cartoon is to support students' learning by adding an artistic dimension to basic system modeling concepts. The creative process design of this animated cartoon is detailed in this paper, from the brainstorming phase to the artistic and scientific considerations leading to the script and the storyboard. Feedback from the project participants and lessons learned are also provided. The video is available on the IFAC YouTube channel https://tinyurl.com/ooscarsystemmodeling.", "url": "" }, { "id": "Mo-MoB35.3", "code": "MoB35.3", "title": "Control Theory for All: Educational Outreach Via Kuberknots | Voyage into Cybernetics (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB35", "sessionTitle": "Beyond Art & Control & Engineering", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Thien, Rebbecca TY", "affiliation": "Universite Paris Saclay" }, { "name": "Amini, Nina Hadis", "affiliation": "L2S, CentraleSupelec, CNRS" } ], "keywords": [ "Cognitive and emotional control/AI systems, arts and control", "Mentoring in control engineering" ], "abstract": "This paper explores how control theory can be communicated through creative discussions with experts to popularise the topic's concepts beyond traditional academic boundaries via modern outreach platforms, using the podcast Kuberknots | Voyage into Cybernetics~Kuberknots. Through concise, expert-led chat-like interviews, the initiative aims to make fundamental concepts such as feedback, estimation, game theory, and quantum control accessible beyond traditional academic settings. The approach emphasises inclusivity by amplifying diverse voices, engaging under-represented groups, and showcasing researchers from around the world, while connecting control principles to everyday applications. Listener feedback and early analytics indicate that the initiative effectively supports public engagement. Building on these findings, the paper outlines future developments to further enhance the societal reach of Control Engineering communication and outreach.", "url": "" }, { "id": "Mo-MoB35.4", "code": "MoB35.4", "title": "Educational Mentoring Via Kuberknots | Voyage into Cybernetics (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB35", "sessionTitle": "Beyond Art & Control & Engineering", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Thien, Rebbecca TY", "affiliation": "Universite Paris Saclay" }, { "name": "Mazenc, William", "affiliation": "Collège Lycee Saint Michel" }, { "name": "Damm, Gabriela", "affiliation": "Collège La Guyonnerie" }, { "name": "Baudel, Boris", "affiliation": "Universite Paris Saclay" } ], "keywords": [ "Cognitive and emotional control/AI systems, arts and control", "Cyber-physical and human systems (CPHS)", "Open-source tools for increased impact of control" ], "abstract": "This paper presents an educational mentoring initiative in mentoring two middle school students during their one-week internship in the Laboratoire des Signaux et Systèmes(L2S) by demonstrating control systems through the podcast Kuberknots | Voyage into Cybernetics (in short Kuberknots). This activity involved students observing a research discussion in the field of control theory between a postdoctoral researcher and a master's student. The format also provided space for students to ask questions and actively participate in the discussion. In addition, a recording session was conducted to give students firsthand experience of how an episode is produced. Reflections on student engagement and learning outcomes by the students are presented, together with practical insights for replicating similar initiatives.", "url": "" }, { "id": "Mo-MoB35.5", "code": "MoB35.5", "title": "Exploring the Role of AI Tools in Control Education: A Preliminary Survey-Based Study (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB35", "sessionTitle": "Beyond Art & Control & Engineering", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Moreno, Ubirajara F.", "affiliation": "Federal Univ of Santa Catarina" }, { "name": "Zakova, Katarina", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Grover, Martha", "affiliation": "Georgia Institute of Technology" }, { "name": "Visioli, Antonio", "affiliation": "University of Brescia" }, { "name": "Guzman, Jose Luis", "affiliation": "University of Almeria" }, { "name": "Moura Oliveira, Paulo", "affiliation": "Univ. De Tras Os Montes E Alto Douro" }, { "name": "Varagnolo, Damiano", "affiliation": "NTNU - Norwegian University of Science and Technology" } ], "keywords": [ "Generative AI in control education" ], "abstract": "Artificial Intelligence (AI) is reshaping engineering practice, prompting new questions about its role in control engineering education. This paper presents preliminary findings from a survey aimed at understanding how AI tools are currently perceived and used in the control-education community, and conducted by IFAC TC 9.4 \"Control Education\" and the Subcommittee “The Future of Undergraduate Education in Control” during 2025. The survey examines three dimensions: AI as a tool for solving control problems, for teaching, and for learning. Early results from around 80 control teachers distributed worldwide show broad but uneven adoption: most respondents report using AI for modelling, coding assistance, or data-driven control design, yet significant concerns persist regarding the reliability of AI-generated solutions. While many educators allow students to use AI tools, they simultaneously emphasize the need for supervision and critical verification. The findings highlight both enthusiasm and caution, pointing to a rapidly evolving landscape in which AI offers meaningful opportunities but also challenges to traditional pedagogical practices.", "url": "" }, { "id": "Mo-MoB36.1", "code": "MoB36.1", "title": "A Novel Market-Clearing Dynamic System for Scalable Bipartite Local Energy Markets (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB36", "sessionTitle": "JO-CEP: Energy Management Systems and Control II", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Erazo-Caicedo, David", "affiliation": "Universidad De Los Andes" }, { "name": "Olaru, Sorin", "affiliation": "CentraleSupelec" }, { "name": "Panciatici, Patrick", "affiliation": "N/A" }, { "name": "Revelo Fuelagán, Javier", "affiliation": "Universidad De Nariño" }, { "name": "Quijano, Nicanor", "affiliation": "Universidad De Los Andes" }, { "name": "Jiménez-Estévez, Guillermo", "affiliation": "Universidad De Los Andes" } ], "keywords": [ "Energy market", "Energy management systems", "Distributed optimization for smart grids" ], "abstract": "This paper introduces a novel dynamic model for local energy markets (LEMs) based on a market-clearing price (MCP) mechanism. Unlike existing optimization-based approaches, this model explicitly represents agent dynamics under competitive assumptions, providing a fundamentally different way to analyze LEMs. By reducing state variables by up to 75%, it overcomes scalability limitations and enables real-time applicability in large systems. The model preserves participant autonomy and privacy while ensuring social welfare maximization. Theoretical analysis proves convergence to a unique MCP, and numerical simulations confirm its efficiency, highlighting its potential for practical deployment.", "url": "" }, { "id": "Mo-MoB36.2", "code": "MoB36.2", "title": "A Linear Framework for Low-Complexity SoC Estimation in Lithium-Ion Batteries Validated with Real Data (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB36", "sessionTitle": "JO-CEP: Energy Management Systems and Control II", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Valente de Bessa, Isaias", "affiliation": "Federal University of Santa Catarina" }, { "name": "Besancon, Gildas", "affiliation": "Grenoble INP - UGA" }, { "name": "Bratcu, Antoneta Iuliana", "affiliation": "Grenoble Institute of Technology and Management" }, { "name": "Coutinho, Daniel", "affiliation": "Universidade Federal De Santa Catarina" }, { "name": "Munteanu, Iulian", "affiliation": "Grenoble Alpes University, GIPSA-Lab" } ], "keywords": [ "Energy storage systems" ], "abstract": "Lithium-ion battery monitoring requires reliable state-of-charge (SoC) estimation, due to be impossible its measure. Model-based approaches using equivalent circuit models (ECMs) are popular, however often involve nonlinear output equations. This work proposes a SoC estimator based on a second-order ECM with a linearized output. An immersion-based transformation increases the system order, yielding state-affine dynamics with a current-dependent parameter and a linear output. Observability analysis guarantees convergence, and a Kalman filter is designed for SoC estimation. Experimental results with real data from an electromobility use case show a root mean square error (RMSE) below 1.2% under varying initial conditions.", "url": "" }, { "id": "Mo-MoB36.3", "code": "MoB36.3", "title": "Capacity Estimation of Lithium-Ion Batteries Using Invariance Property in Open Circuit Voltage Relationship (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB36", "sessionTitle": "JO-CEP: Energy Management Systems and Control II", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Wang, Yang", "affiliation": "Delft University of Technology" }, { "name": "Zagorowska, Marta", "affiliation": "TU Delft" }, { "name": "Ferrari, Riccardo M.G.", "affiliation": "Delft University of Technology" } ], "keywords": [ "Energy storage systems", "Life cycle assessment for energy systems", "Process modeling, identification, and estimation techniques" ], "abstract": "Lithium-ion (Li-ion) batteries are ubiquitous in electric vehicles (EVs) as efficient energy storage devices. The reliable operation of Li-ion batteries depends critically on the accurate estimation of battery capacity. However, conventional estimation methods require extensive training datasets from costly battery tests for modeling, and a full cycle of charge and discharge is often needed to estimate the capacity. To overcome these limitations, we propose a novel capacity estimation method that leverages only one cycle of the open-circuit voltage (OCV) test in modeling and allows for estimating the capacity from partial charge or discharge data. Moreover, by applying it with OCV identification algorithms, we can estimate the capacity from dynamic discharge data without requiring dedicated data collection tests. We observed an invariance property in the OCV versus state of charge relationship across aging cycles. Leveraging this invariance, the proposed method estimates the capacity by solving an OCV alignment problem using only the OCV and the discharge capacity data from the battery. Simulation results demonstrate the method's efficacy, achieving a mean absolute relative error of 0.82% in capacity estimation across 14 samples from 344 aging cycles.", "url": "" }, { "id": "Mo-MoB36.4", "code": "MoB36.4", "title": "Truncated Levenberg-Marquardt for Solid Oxide Electrolyzer Parameter Estimation (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB36", "sessionTitle": "JO-CEP: Energy Management Systems and Control II", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Yazbeck, Zaman", "affiliation": "GENVIA SAS" }, { "name": "Bribiesca Argomedo, Federico", "affiliation": "INSA Lyon, Laboratoire Ampère" }, { "name": "Pham, Minh Tu", "affiliation": "INSA De Lyon" }, { "name": "Morel, Bertrand", "affiliation": "CEA Liten" }, { "name": "Dimitriou, Vincent", "affiliation": "GENVIA SAS" } ], "keywords": [ "Hydrogen systems for energy generation and storage", "Energy storage systems", "Process modeling, identification, and estimation techniques" ], "abstract": "Identifiability remains a key issue in parameter estimation, especially for highly parameterized electrochemical systems with limited measurements. This paper proposes a framework for Solid Oxide Electrolyzer Stacks (SOES) parameter estimation. Practical identifiability is assessed through the sensitivity of measured outputs with respect to parameters. This sensitivity is computed via sensitivity differential equations, which reveal the collinear sensitivity directions of parameters and motivate a truncated Levenberg-Marquardt optimization with singular-value decomposition (SVD) to prioritize high-sensitivity and non-collinear directions. This unconstrained optimization yields physically meaningful parameters, outperforms other methods through improved step selection, limits the condition number of the linear system of equations solved at each iteration, and mitigates measurement noise effects. The methodology is validated on synthetic data where parameters are known, confirming the accurate estimation of informative parameters under both noise-free and noisy conditions.", "url": "" }, { "id": "Mo-MoB36.5", "code": "MoB36.5", "title": "Reduced Order Modeling and Unscented Kalman Observer for Hydro Generators (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB36", "sessionTitle": "JO-CEP: Energy Management Systems and Control II", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Auzeloux, Guillaume", "affiliation": "GipsaLab Grenoble INP - UGA" }, { "name": "Besancon, Gildas", "affiliation": "Grenoble INP - UGA" }, { "name": "Robert, Gerard", "affiliation": "EDF - Hydro Engineering Centre" } ], "keywords": [ "Hydropower", "Control and management of energy systems" ], "abstract": "This paper addresses the problem of internal information reconstruction in a synchronous generator, in the context of a hydroelectric power plant. First a simplified dynamical model is proposed, with the purpose of estimating the fluxes and rotor angle, from the only measurements of stator currents and voltages. The problem also includes the specificity of unknown gain on the excitation current. As a solution, an Unscented Kalman approach is proposed, for which a convergence analysis is provided taking advantage of a recent observer version for it. Its success is illustrated by applications to real data, and a comparison with EKF.", "url": "" }, { "id": "Mo-MoB36.6", "code": "MoB36.6", "title": "Optimal Control of H-Mode Tokamak Plasma Temperature Based on Pontryagin's Principle (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB36", "sessionTitle": "JO-CEP: Energy Management Systems and Control II", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Jmal, Slim", "affiliation": "GIPSA Lab - Université Grenoble Alpes" }, { "name": "Tacchi, Matteo", "affiliation": "Univ. Grenoble Alpes, CNRS, Grenoble INP (Institute of Engineering Univ. Grenoble Alpes), GIPSA-Lab" }, { "name": "Witrant, Emmanuel", "affiliation": "Université Grenoble Alpes" } ], "keywords": [ "Nuclear power", "Control and management of energy systems" ], "abstract": "This paper studies the decay of an objective functional using a new control technique within Pontryagin's framework. Convergence analysis is carried out on the infinite-dimensional space of Tokamak plasma dynamical state as described by weakly decoupled nonlinear partial differential equations. An adjoint-based optimal control is derived to minimize the deviation from a predefined dynamical trajectory leading to the desired target state at stationary regime, by turning Pontryagin's transversality conditions into a continuum of horizons. A feedback controller is proposed to steer the system efficiently in real time, as opposed to an open-loop controller resulting from the classical Pontryagin's setting. An algorithm synthesizing the constraint-free optimal controller is used for profile tracking based on experimental data.", "url": "" }, { "id": "Mo-MoB37.1", "code": "MoB37.1", "title": "No-Wait Scheduling Algorithm with Joint Routing Planning for Time Sensitive Networks (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:10-13:30", "sessionCode": "MoB37", "sessionTitle": "Sensing, Communication, and Decision-Making for Urban Cyber-Physical Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Ma, Kai", "affiliation": "Yanshan University" }, { "name": "Li, Yifu", "affiliation": "YanShan University" }, { "name": "Li, Hui", "affiliation": "Yanshan University" }, { "name": "Yang, Jie", "affiliation": "Yanshan University" } ], "keywords": [ "Smart city control and optimization", "Cyber-physical urban systems", "Smart city security and resilience" ], "abstract": "This paper develops a no-wait scheduling algorithm with joint route planning to address the stringent real-time requirements of Time-Triggered (TT) streams in Time-Sensitive Network (TSN). The proposed approach incorporates reliability-aware multipath routing to optimize path selection and reduce traffic conflicts in complex network environments. In addition, a no-wait scheduling mechanism is adopted to ensure that TT streams are transmitted strictly within their allocated time windows, thereby eliminating queueing delays at switch output ports. Simulation results verify that the proposed method significantly alleviates traffic conflicts in multipath TSN transmissions while achieving deterministic low-latency performance for TT streams.", "url": "" }, { "id": "Mo-MoB37.2", "code": "MoB37.2", "title": "Information-Driven Trajectory Planning for Bearing-Only Target Tracking with Unknown Model (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:30-13:50", "sessionCode": "MoB37", "sessionTitle": "Sensing, Communication, and Decision-Making for Urban Cyber-Physical Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Fu, Yingbo", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Su, Haifan", "affiliation": "City University of Hong Kong" }, { "name": "Kang, Haodong", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Yang, Ziwen", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Zhu, Shanying", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Decision making under uncertainty", "Cyber-physical and human systems (CPHS)", "Human-centric automation/AI Systems, and human agency" ], "abstract": "This paper addresses target tracking by an autonomous underwater vehicle (AUV) with only a bearing sensor under the unknown target model and limited onboard resources. We propose a solution that integrates recursive Gaussian Process learning for probabilistic motion modeling from streaming data, which alleviates the computational and memory burden. An information-driven metric guides the planning, formulated as a differential-flatness-based optimization problem, to collect informative bearings. Unlike most heuristic methods, this paper derives a probabilistic ultimate bound that characterizes the dynamic performance evolution under data collection, paving the way for embodied intelligence in AUVs. Gazebo simulations demonstrate the effectiveness of the proposed scheme under severely maneuvering target motion.", "url": "" }, { "id": "Mo-MoB37.3", "code": "MoB37.3", "title": "MPC Based on Neural Network and Model Predictive Path Integral for Multi-Zone HVAC (I)", "day": "Monday", "date": "August 24, 2026", "time": "13:50-14:10", "sessionCode": "MoB37", "sessionTitle": "Sensing, Communication, and Decision-Making for Urban Cyber-Physical Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Xiao, Jiayi", "affiliation": "Shandong University" }, { "name": "Zhang, Chenghao", "affiliation": "Shandong University" }, { "name": "Lei, Wang", "affiliation": "Shandong University" }, { "name": "Wang, Xinli", "affiliation": "Shandong University" }, { "name": "Yin, Xiaohong", "affiliation": "Shandong University" }, { "name": "Li, Shaoyuan", "affiliation": "Shanghai Jiao Tong Univ" }, { "name": "Liu, Wentao", "affiliation": "Qingdao University of Science and Technology" } ], "keywords": [ "Building automation" ], "abstract": "Model Predictive Control (MPC) has shown strong potential for improving energy efficiency while maintaining thermal comfort in Heating, Ventilation, and Air Conditioning (HVAC) systems. However, in multi-zone HVAC systems, accurate dynamic models are difficult to obtain, despite the fact that MPC requires precise models to ensure reliable performance. Furthermore, complex dynamic models impose a significant computational burden on online MPC optimization. To address these challenges, a neural network based model predictive path integral algorithm (NN-MPPI) is proposed. First, an LSTM-Attention network is developed to build a dynamic model for the thermal dynamics and energy consumption of the multi-zone HVAC system. This model is embedded within an MPC framework, where the control inputs are optimized over a receding horizon and solved using a gradient-free MPPI method that leverages parallel sampling for efficient and stable optimization. Experiments on an EnergyPlus-based multi-zone HVAC simulation platform show that the proposed algorithm can reduce energy consumption by 10.4% compared to the rule-based baseline while maintaining acceptable thermal comfort. It also achieves superior computational efficiency compared with gradient-based MPC.", "url": "" }, { "id": "Mo-MoB37.4", "code": "MoB37.4", "title": "Resilient Distributed NE Seeking for Games of Heterogeneous Linear Networks under FDI Attacks (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:10-14:30", "sessionCode": "MoB37", "sessionTitle": "Sensing, Communication, and Decision-Making for Urban Cyber-Physical Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Yang, Yikun", "affiliation": "Beihang University" }, { "name": "Bai, Jialuo", "affiliation": "Beihang University" }, { "name": "Feng, Zhi", "affiliation": "Beihang University" }, { "name": "Dong, Xiwang", "affiliation": "Beihang University" } ], "keywords": [ "Cyber-physical urban systems", "Decision making under uncertainty", "Smart city control and optimization" ], "abstract": "This paper addresses resilient adaptively distributed Nash Equilibrium (NE) seeking problems for noncooperative games under false data injection (FDI) attacks. Unlike existing distributed NE seeking works, it is challenging to achieve distributed NE seeking of networked players subject to heterogeneous linear dynamics and unknown FDI attacks. By incorporating a consensus-based gradient-play technique with a distributed identifier design to compensate for the adverse impacts of attacks, a resilient distributed NE seeking is achieved asymptotically in a partial-information setting. Leveraging Lyapunov stability theory and nonsmooth analysis, the global asymptotic convergence to the NE is proven. Finally, the effectiveness of the proposed design is verified through simulation results.", "url": "" }, { "id": "Mo-MoB37.5", "code": "MoB37.5", "title": "Decentralized State Estimation for Interconnected Systems: A Data-Driven Approach (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:30-14:50", "sessionCode": "MoB37", "sessionTitle": "Sensing, Communication, and Decision-Making for Urban Cyber-Physical Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Gui, Yalin", "affiliation": "Zhejiang University of Technology" }, { "name": "Hu, Zhongyao", "affiliation": "Zhejiang University of Technology" }, { "name": "Chen, Bo", "affiliation": "Zhejiang University of Technology" }, { "name": "Wang, Zheming", "affiliation": "Zhejiang University of Technology" }, { "name": "Yu, Li", "affiliation": "Zhejiang Univ of Technology" } ], "keywords": [ "Cyber-physical urban systems", "Urban energy distribution systems" ], "abstract": "This paper proposes a decentralized data-driven observer for discrete-time interconnected systems. Unlike traditional model-based methods, such as Luenberger observers and Kalman filters, a novel strategy based on Willems’ fundamental lemma is introduced. Specifically, by using offline input-state-output data, a full-order observer is designed directly without requiring subsystem matrices or coupling terms, thereby bypassing the complex modeling process. Moreover, under a generalized persistency of excitation condition on the offline data, necessary and sufficient conditions for the existence and asymptotic stability of the datadriven observer are derived. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed method.", "url": "" }, { "id": "Mo-MoB37.6", "code": "MoB37.6", "title": "Detection and Diagnosis of Minor Faults in Nonlinear Dynamic Processes Using Sparse Auto-Encoder and Maximum Mean Discrepancy (I)", "day": "Monday", "date": "August 24, 2026", "time": "14:50-15:10", "sessionCode": "MoB37", "sessionTitle": "Sensing, Communication, and Decision-Making for Urban Cyber-Physical Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Ma, Kai", "affiliation": "Yanshan University" }, { "name": "Deng, Chenglong", "affiliation": "Yanshan University" }, { "name": "Li, Hui", "affiliation": "Yanshan University" }, { "name": "Zhang, Cheng", "affiliation": "Shenyang University of Chemical Technology" }, { "name": "Yang, Jie", "affiliation": "Yanshan University" } ], "keywords": [ "AI for smart cities", "Smart city security and resilience", "Cyber-physical urban systems" ], "abstract": "This paper introduces a fault detection and diagnosis approach that combines a Sparse Auto-encoder with Maximum Mean Discrepancy (SAE-MMD), aimed at tackling the difficulty of detecting and diagnosing minor faults in nonlinear dynamic systems. Firstly, the SAE is used to obtain the residuals of the original data. Secondly, the sliding window method and MMD are used to construct a new statistic in the residual space for fault detection. After identifying the data as faulty, the new statistic is fed into the MMD-SVM classifier for fault diagnosis. By incorporating MMD statistics as additional information into the original data, MMD-SVM enhances the performance of fault diagnosis. Through the experimental data of the Tennessee Eastman (TE) process, the simulation experiment is carried out, and the Principal Component Analysis (PCA), SAE and other methods are compared. Experimental simulations demonstrate that the proposed method is effective in detecting and diagnosing faults.", "url": "" }, { "id": "Mo-MoC01.1", "code": "MoC01.1", "title": "Joint Identification of System Parameters and Packet Loss Rate for FIR Systems with Event-Triggered Communication under Communication Constraints (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC01", "sessionTitle": "JO-NAHS: Control under Communication Constraints", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Han, Tianning", "affiliation": "Chinese Academy of Sciences" }, { "name": "Wang, Ying", "affiliation": "KTH Royal Institute of Technology," }, { "name": "Zhao, Yanlong", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Control under communication constraints", "Nonlinear system identification", "Event-based control" ], "abstract": "This paper investigates joint estimation of the system parameters and packet loss rate for finite impulse response systems with binary-valued observations under event-triggered communication and packet loss. To address the trade-off between identifying system parameters, unknown packet loss rate and minimizing communication cost, switching difference-driven communication mechanism is proposed, where the data transmission switches between two strategies. One mode maintains continuous communication to estimate the unknown packet loss rate, while the other follows the difference-driven communication rule to reduce communication cost. Based on this, a joint compensation difference-driven algorithm is developed to jointly estimate the system parameters and the packet loss rate, which is proved to achieve almost sure convergence and asymptotic normality. Besides, the communication rate of the proposed algorithm is characterized. An optimization strategy for data transmission is further formulated to minimize the communication rate while ensuring convergence performance, yielding an optimal rule for selecting transmitted data for packet loss rate estimation. This provides a practical guideline for balancing estimation performance and communication cost in networked systems. Numerical simulations are illustrated to show the theoretical results.", "url": "" }, { "id": "Mo-MoC01.2", "code": "MoC01.2", "title": "Periodic Feedback Control Design for Cyberdefense Method Based on Software Rejuvenation (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC01", "sessionTitle": "JO-NAHS: Control under Communication Constraints", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Luque, Irene", "affiliation": "University of Seville" }, { "name": "Chanfreut, Paula", "affiliation": "Eindhoven University of Technology" }, { "name": "Maestre, Jose M.", "affiliation": "University of Seville" } ], "keywords": [ "Cyber security networked control", "Hybrid and switched systems modeling" ], "abstract": "TThis paper presents a feedback design framework for periodic cyber-defense via software rejuvenation (SWR). We recast the intra-cycle switched dynamics into a single resampled model and build a cycle cost equivalent to the accumulated stage cost. From this, we derive a cycle-wise state-feedback gain that enforces constraints despite disturbances and possible input hijacking during mission control segments. Finally, we propose a maximal robust positively invariant (RPI) set for the cyclic dynamics, yielding a certified safe operating region. Simulations on a benchmark show improved performance and larger safe sets compared to mode-by-mode LQR and periodic Riccati designs.", "url": "" }, { "id": "Mo-MoC01.5", "code": "MoC01.5", "title": "μ-Stealthy Deception Attack against Distributed State Estimation (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC01", "sessionTitle": "JO-NAHS: Control under Communication Constraints", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Mao, Dancheng", "affiliation": "East China University of Science and Technology" }, { "name": "Niu, Yugang", "affiliation": "East China Univ of Science & Technology" }, { "name": "Chen, Bei", "affiliation": "Shanghai University of Engineering Science" } ], "keywords": [ "Cyber security networked control", "Kalman filtering", "Distributed control and estimation" ], "abstract": "This research focuses on the design of a mu-stealthy attack strategy against distributed state estimation, achieving the balance between attack effectiveness and stealthiness. By fusing innovations from local and neighboring sensors, the steady-state filter gain and estimation error covariance (EEC) are derived, and the innovation properties are analyzed for targeted attacks. An attack model based on neighboring innovations is proposed, and the worst-case attack parameters are determined step-wise through equivalent transformation of the optimization problem, addressing the nonlinear optimization challenges posed by distributed estimation coupling. Simulations validate the optimality and stealthiness of our attack strategy, providing valuable insights for secure estimation design.", "url": "" }, { "id": "Mo-MoC01.5", "code": "MoC01.5", "title": "Improved Lyapunov-Krasovskii Functional and Its Application for Stability Analysis for Discrete-Time Neural Networks with Time-Varying Delay (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC01", "sessionTitle": "JO-NAHS: Control under Communication Constraints", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Park, PooGyeon", "affiliation": "Pohang Univ. of Sci. & Tech" }, { "name": "Park, Yongbeom", "affiliation": "Pohang Univ. of Sci. & Tech" } ], "keywords": [ "Control under communication constraints", "Control of networks", "Stability and stabilization of hybrid systems" ], "abstract": "Stability analysis is an essential task in the application and implementation of neural networks. However, time delays in neural networks can lead system inefficiency or undesirable system behaviors. This paper proposes an improved stability criterion for discrete-time neural networks with time-varying delay. A novel Lyapunov-Krasovskii functional is introduced for a less conservative stability analysis. The proposed Lyapunov-Krasovskii functional is intentionally designed to capture the relationships between states at different time steps and the forward difference. To derive a more precise upper bound estimation for the summation of quadratic terms, the extended affine Bessel summation inequality is utilized. Furthermore, we employed the modified free-matrix-based sufficient condition for negative-definiteness of a cubic polynomial to alleviate excessive computational burden. Two numerical examples demonstrate that the proposed stability criterion is less conservatism compared to existing methods.", "url": "" }, { "id": "Mo-MoC01.6", "code": "MoC01.6", "title": "Learning a Network Digital Twin As a Hybrid System (I)", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC01", "sessionTitle": "JO-NAHS: Control under Communication Constraints", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Mavridis, Christos", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Barbosa, Fernando S.", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Farhadi, Hamed", "affiliation": "Chalmers University of Technology" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Control under communication constraints", "Hybrid and switched systems modeling", "Machine and deep learning for system identification" ], "abstract": "Network digital twin (NDT) models are virtual models that replicate the behavior of physical communication networks and are considered a key technology component to enable novel features and capabilities in future 6G networks. In this work, we focus on NDTs that model the communication quality properties of a multi-cell, dynamically changing wireless network over a workspace populated with multiple moving users. We propose an NDT modeled as a hybrid system, where each mode corresponds to a different base station and comprises sub-modes that correspond to areas of the workspace with similar network characteristics. The proposed hybrid NDT is identified and continuously improved through an annealing optimization-based learning algorithm, driven by online data measurements collected by the users. The advantages of the proposed hybrid NDT are studied with respect to memory and computational efficiency, data consumption, and the ability to timely adapt to network changes. Finally, we validate the proposed methodology on real experimental data collected from a two-cell 5G testbed.", "url": "" }, { "id": "Mo-MoC01.6", "code": "MoC01.6", "title": "Output-Feedback Control with Wireless Channel State Detection and Actuation Message Dropout Compensation (I)", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC01", "sessionTitle": "JO-NAHS: Control under Communication Constraints", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Zacchia Lun, Yuriy", "affiliation": "Università Degli Studi Dell’Aquila" }, { "name": "Santucci, Fortunato", "affiliation": "Univ of L'Aquila" }, { "name": "D'Innocenzo, Alessandro", "affiliation": "Università Degli Studi Di L'Aquila" } ], "keywords": [ "Control under communication constraints" ], "abstract": "This paper presents a framework for designing optimal output-feedback controllers that use wireless sensing and actuation links with imperfect channel-state information. Remote system state estimation is performed using a prediction–correction filter that resembles the traditional Kalman filter and incorporates current measurement inputs. The controller computes the current and tentative future control inputs based on the estimated remote system state and the detected wireless channel state. These control inputs are transmitted to actuators as messages. The message dropout compensation strategy for actuation involves scaling the most recent control input when no previously received tentative control inputs are available. We analytically solve finite- and infinite-horizon output-feedback control problems and prove the validity of the separation principle, assuming a reliable mechanism for acknowledging actuation message transmission. We validate the results using an illustrative numerical example that demonstrates the practicality and effectiveness of our framework.", "url": "" }, { "id": "Mo-MoC02.1", "code": "MoC02.1", "title": "Reinforcement Learning vs. Model-Based Control in Electric Vehicle Charging Microgrids (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC02", "sessionTitle": "JO-CEP: Control and Management of Energy Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Burgaud, Valentin", "affiliation": "LAPLACE, University of Toulouse, CNRS, INPT, UPS" }, { "name": "Le Goff, Gregoire", "affiliation": "LAPLACE, University of Toulouse, CNRS, INPT, UPS" }, { "name": "Kergus, Pauline", "affiliation": "CNRS" }, { "name": "Fadel, Maurice", "affiliation": "LAPLACE/University of Toulouse/CNRS/INPT/UPS" } ], "keywords": [ "Control and management of energy systems", "Energy management systems", "Electric vehicles and charging stations" ], "abstract": "This paper addresses the energy management system in electric vehicle charging microgrids by comparing Reinforcement Learning (RL) methods with model-based optimization strategies, namely an Optimal Control strategy (serves as a reference to evaluate other methods), Model predictive Control, and a standard Rule-based approach is also considered. They are compared with model-free RL algorithms: Deep Deterministic Policy Gradient, Twin Delayed DDPG, and Soft Actor-Critic. The evaluation focuses on cost efficiency, constraint handling, and the influence of system perturbations. Results highlight complementary strengths and trade-offs between artificial intelligence control and model-based optimization for real-time microgrid management. To conclude, an evaluation will be carried out during operation in an unforeseen scenario to assess resilience.", "url": "" }, { "id": "Mo-MoC02.2", "code": "MoC02.2", "title": "Polytopic Approximation of Parameterized Feasible Sets (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC02", "sessionTitle": "JO-CEP: Control and Management of Energy Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Wen, Yilin", "affiliation": "North China Electric Power University" }, { "name": "Zhu, Xiaoming", "affiliation": "North China Electric Power University" }, { "name": "Zhao, Bo", "affiliation": "North China Electric Power University" }, { "name": "Guo, Yi", "affiliation": "Beijing Institute of Technology" } ], "keywords": [ "Control and management of energy systems", "Energy management systems", "Smart buildings and building automation" ], "abstract": "Feasible set computation is crucial in the analysis and implementation of constrained control systems, yet efficient methods for parameterized systems with nonlinear constraints and discontinuous inputs remain limited. This paper proposes a neural network-based approach that approximates parameterized feasible sets with polytopes, capable of effectively handling such complex scenarios. The key contribution is an explicit loss function for the polytopic approximation problem, which enables the gradient backpropagation for neural network training. We validate the proposed method through an illustrative example and an application to a microgrid control system, demonstrating its effectiveness in representing and computing diverse feasible sets.", "url": "" }, { "id": "Mo-MoC02.3", "code": "MoC02.3", "title": "What Price to Pay? Auto-Tuning a Building MPC Controller for Optimal Economic Cost (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC02", "sessionTitle": "JO-CEP: Control and Management of Energy Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Yu, Jiarui", "affiliation": "EPFL" }, { "name": "Shi, Jicheng", "affiliation": "EPFL" }, { "name": "Xu, Wenjie", "affiliation": "Swiss Federal Institute of Technology Lausanne" }, { "name": "Jones, Colin, N", "affiliation": "EPFL" } ], "keywords": [ "Smart buildings and building automation" ], "abstract": "Demand-side management (DSM) programs introduce complex pricing, requiring advanced control for cost minimization. Model Predictive Control (MPC) offers a solution but its performance hinges on appropriate hyperparameter tuning. We propose using Constrained Bayesian Optimization (CONFIG) to automate this process. In a case study, our optimized MPC reduced electricity costs by 26.90% compared to a rule-based controller and by 17.46% versus an manually tuned MPC. Analysis of real contracts further showed that optimal DSM program selection can lower monthly bills by up to 20.18%, demonstrating a data-driven path to significant consumer savings.", "url": "" }, { "id": "Mo-MoC02.4", "code": "MoC02.4", "title": "Uncertainty-Aware Degradation Trajectory Forecasting for Fuel Cell Prognostics and Health Management (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC02", "sessionTitle": "JO-CEP: Control and Management of Energy Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Salehi, Zeynab", "affiliation": "University of Alberta" }, { "name": "Fakouri Hasanabadi, Masood", "affiliation": "University of Alberta" }, { "name": "Smith, Daniel J.", "affiliation": "Cummins" }, { "name": "Amir Reza, Hanifi", "affiliation": "University of Alberta" }, { "name": "Shahbakhti, Mahdi", "affiliation": "University of Alberta" } ], "keywords": [ "Control and management of energy systems", "Hydrogen systems for energy generation and storage", "Thermal systems modelling" ], "abstract": "Accurate forecasting of remaining useful life (RUL) for solid oxide fuel cells (SOFCs) is essential to improve operational reliability and support health-aware control and predictive maintenance. In this paper, a novel prognostics framework is proposed for forecasting the state-of-health (SOH) trajectory using two Bayesian sequence models: Informer and long short-term memory (LSTM). Aleatoric uncertainty is modeled with a variance output head trained using a Gaussian negative log-likelihood, and epistemic uncertainty is estimated via Monte Carlo (MC) dropout. The two uncertainty sources are combined to form calibrated confidence bands for SOH and derived RUL. Variable forecast horizons are handled using an exponentially weighted zero-padding technique, ensuring uniform sequence length while enforcing SOH degradation toward end-of-life (EOL). Degradation experiments under redox cycling are used to evaluate the proposed method. The Bayesian Informer achieves high forecasting accuracy with a mean absolute error (MAE) of 0.0087 and coverage of 93%, while producing credible RUL distributions from first-hitting times (FHT) of MC trajectories.", "url": "" }, { "id": "Mo-MoC02.5", "code": "MoC02.5", "title": "Accelerating MINLP-Based District Cooling Operational Planning Using Neural Network Controller (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC02", "sessionTitle": "JO-CEP: Control and Management of Energy Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Okamoto, Morimasa", "affiliation": "Waseda University" }, { "name": "Wasa, Yasuaki", "affiliation": "Waseda University" } ], "keywords": [ "Control and management of energy systems", "Thermal systems modelling", "Multi-energy networks" ], "abstract": "This paper proposes a data-driven controller design method to accelerate the generation of highly accurate optimal operational plans for district cooling systems (DCSs). A key industrial challenge in DCSs is accurately optimizing both continuous and binary control variables. To address this challenge, we propose a specialized multi-head neural network that incorporates a Straight-Through Estimator and a Gumbel-Sigmoid function. The proposed controller approximates the optimal control law derived from mixed-integer nonlinear programming (MINLP) and is trained using a two-stage strategy to balance estimation accuracy and feasibility. Consequently, the MINLP problem can be reformulated as a differentiable optimization problem, enabling efficient gradient-based training. Practical case studies demonstrate that the proposed controller generates near-optimal 24-hour operational plans in less than one second.", "url": "" }, { "id": "Mo-MoC02.6", "code": "MoC02.6", "title": "Data Center Chiller Plant Optimization Via Mixed-Integer Nonlinear Differentiable Predictive Control (I)", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC02", "sessionTitle": "JO-CEP: Control and Management of Energy Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Boldocky, Jan", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Faulkner, Cary", "affiliation": "Pacific Northwest National Laboratory" }, { "name": "Michael, Elad", "affiliation": "University of Melbourne" }, { "name": "Gulan, Martin", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Tuor, Aaron", "affiliation": "Pacific Northwest National Laboratory" }, { "name": "Drgona, Jan", "affiliation": "Pacific Northwest National Laboratory" } ], "keywords": [ "Control and management of energy systems", "Control and optimization for sustainability and energy systems", "Advanced process control" ], "abstract": "This paper presents a computationally tractable framework for real-time predictive control of multi-chiller plants whose operation involves discrete and continuous control decisions coupled through nonlinear dynamics, resulting in a mixed-integer optimal control problem. To address this challenge, the Differentiable Predictive Control (DPC)---a self-supervised, model-based learning methodology for approximately solving parametric optimal control problems---is extended to accommodate mixed-integer control policies. The proposed framework is benchmarked against a state-of-the-art mixed-integer Model Predictive Control (MPC) solver and a fast heuristic Rule-Based Controller (RBC). Simulation results demonstrate that the proposed approach achieves significant energy savings over the RBC while maintaining orders-of-magnitude faster computation times than MPC, offering a scalable and practical alternative to conventional combinatorial mixed-integer control formulations.", "url": "" }, { "id": "Mo-MoC03.1", "code": "MoC03.1", "title": "Full-State Performance-Guaranteed Asymptotic Tracking Control for Nonlinear Systems with Time-Varying Parameters: A Fully Actuated System Approach", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC03", "sessionTitle": "Fundamental Theory and Control Design of FAS", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Ding, Yi", "affiliation": "Harbin Institute of Technology" }, { "name": "Duan, Guang-Ren", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Global fully actuated systems", "Control using FAS approach" ], "abstract": "This paper investigates the full-state performance-guaranteed asymptotic tracking problem of fully actuated systems (FASs) with the nonlinear uncertainties, unknown time-varying parameters, multiplicative input matrices perturbation and input disturbances. Different from the existing stabilization results for perturbed FASs, by integrating the FAS approach, speed transformation and congelation of variables method, a novel robust adaptive control scheme is developed, which guarantees the prescribed-time prescribed performance and asymptotic convergence of full-state tracking errors. Furthermore, the boundedness of all closed-loop signals is rigorously proven via Lyapunov analysis. A simulation study on the single-link flexible-joint robotic manipulators demonstrates the effectiveness and feasibility of our proposed scheme.", "url": "" }, { "id": "Mo-MoC03.2", "code": "MoC03.2", "title": "Saturated-Observer-Based Fault Tolerance for Unknown Fully Actuated Systems", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC03", "sessionTitle": "Fundamental Theory and Control Design of FAS", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Cai, Miao", "affiliation": "Southeast University" }, { "name": "Zhou, Donghua", "affiliation": "Shandong Univ. of Science and Technology" } ], "keywords": [ "Global fully actuated systems", "Control using FAS approach" ], "abstract": "This paper proposes a saturated-observer-based fault-tolerant tracking controller for fully actuated systems (FASs) with unknown dynamics and measurement noise. The early FAS theory requires complete system information, but unknown dynamics, actuator faults and measurement noise all have negative impacts on the acquisition of information. Although fault-tolerant controllers based on traditional observers can ensure the stability of error systems, their stability is sensitive to measurement noise. To a certain extent, in order to suppress the damage caused by measurement noise to system observation and trajectory tracking, a saturation observer technique has been applied for the fault-tolerant control design of unknown FASs. The ultimate error system stability has been verified through mathematical proof and simulation results.", "url": "" }, { "id": "Mo-MoC03.3", "code": "MoC03.3", "title": "Learning-Based Fault-Avoidant Control for Stochastic Fully Actuated Systems", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC03", "sessionTitle": "Fundamental Theory and Control Design of FAS", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Liu, Xueqing", "affiliation": "Southeast University" }, { "name": "Sheng, Li", "affiliation": "China University of Petroleum (East China)" }, { "name": "Gao, Ming", "affiliation": "China University of Petroleum (East China)" }, { "name": "Zhou, Donghua", "affiliation": "Shandong Univ. of Science and Technology" } ], "keywords": [ "Global fully actuated systems", "Fully-actuated systems in industry" ], "abstract": "As a novel fault-tolerant strategy, fault-avoidant control has shown great potential, especially in fully actuated systems. However, existing fault-avoidant control methods require manual design of control barrier functions based on historical diagnosis data when the fault region is unknown, limiting their application in complex systems. This paper investigates the fault-avoidant tracking control problem for stochastic fully actuated systems with unknown local faults. A novel learning-based fault-avoidance control approach is proposed, which designs an end-to-end stochastic control barrier function (SCBF) via a customized loss function. This design ensures that the fault-sensitive state accurately captures the boundary of the fault region while effectively avoiding it. A fault-avoidant controller is then designed by solving an optimization problem that incorporates SCBF constraints and tracking objectives. The effectiveness of the proposed method is demonstrated through a simulation study on a rotary steerable drilling tool system.", "url": "" }, { "id": "Mo-MoC03.4", "code": "MoC03.4", "title": "Fully Actuated System Approach for Nonlinear Systems with Uncontrollable Unstable Linearization", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC03", "sessionTitle": "Fundamental Theory and Control Design of FAS", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Gu, Dake", "affiliation": "Northeast Electric Power University" }, { "name": "Liu, Yindong", "affiliation": "Northeast Electric Power University" } ], "keywords": [ "Sub-fully actuated systems", "Control using FAS approach" ], "abstract": "This paper investigates a class of nonlinear chained systems with odd-power input channels and uncontrollable unstable linearization. Instead of pursuing global smooth stabilization, which is impossible for this class, a sub-fully actuated system formulation is developed by successively differentiating the output. This transformation makes the input gain and its singularity set explicit. Recursive reconstruction maps are then introduced to express the state variables in the output-jet space, allowing the singularity boundaries to be characterized in terms of the closed-loop linear dynamics. A real odd-root feedback law is designed to impose assignable linear input-output dynamics on the nonsingular feasible domain. To address singularity avoidance, a constructive zero-crossing test is provided to determine whether a given initial condition belongs to the region of exponential attraction before simulation. The behavior of the controller near singular boundaries is also discussed. Numerical examples illustrate both admissible trajectories and boundary-crossing cases, thereby clarifying the scope and limitation of the proposed substabilizing controller.", "url": "" }, { "id": "Mo-MoC03.5", "code": "MoC03.5", "title": "Sub-Stabilization of Nonlinear Sub-Fully Actuated Systems Over Finite Fields and Its Applications on Local Synchronization", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC03", "sessionTitle": "Fundamental Theory and Control Design of FAS", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Yu, Miao", "affiliation": "Shandong University" }, { "name": "Li, Yiliang", "affiliation": "Shandong University" }, { "name": "Xia, Jianwei", "affiliation": "Liaocheng University" }, { "name": "Feng, Jun-e", "affiliation": "Shandong University" } ], "keywords": [ "Sub-fully actuated systems", "Control using FAS approach" ], "abstract": "This paper investigates the sub-stabilization problem of nonlinear sub-fully actuated systems (sub-FASs) over finite fields. First, feasible sets and singular sets are introduced based on the algebraic properties of finite fields. Next, the sub-FASs, sub-stability and sub-stabilization are formally defined for single-order system over finite fields. Moreover, sub-stabilization controllers are designed within the feasible set framework, and necessary and sufficient conditions of sub-stabilization are established. Furthermore, the proposed results are extended to address the local synchronization problem of nonlinear finite field networks (FFNs), and a distributed control protocol is derived. Finally, an illustrative example is presented to demonstrate the validity of these results.", "url": "" }, { "id": "Mo-MoC03.6", "code": "MoC03.6", "title": "Controller Design for a Class of Uncertain Time-Delay Non-Affine FASs", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC03", "sessionTitle": "Fundamental Theory and Control Design of FAS", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Zhang, Xue", "affiliation": "Harbin Institute of Technology" }, { "name": "Duan, Guang-Ren", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Unidirectionally connected FASs", "Global fully actuated systems", "Control using FAS approach" ], "abstract": "Based on the fully actuated system (FAS) approach, this paper focuses on the robust control problem of a class of non-affine FASs with time-delay and unknown nonlinear coupled uncertainties. For FASs satisfying the linear growth condition, a sequential state feedback controller is proposed by introducing gain scaling matrices and constructing the appropriate Lyapunov-Krasovskii functionals. The constructed controller enables the closed-loop system to achieve global asymptotical stability. The effectiveness of the proposed control method is validated through the simulation on a flexible-joint robot system.", "url": "" }, { "id": "Mo-MoC04.1", "code": "MoC04.1", "title": "Large-Time Behaviour of Continuously Measured Qubits Subject to Energy Relaxation (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC04", "sessionTitle": "Quantum Control I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Liang, Weichao", "affiliation": "Xi'an Jiaotong University" }, { "name": "Song, Pengtao", "affiliation": "Xi'an Jiaotong University" }, { "name": "Zhang, Jing", "affiliation": "Tsinghua University" }, { "name": "Zhang, Guofeng", "affiliation": "The Hong Kong Polytechnic University" } ], "keywords": [ "Quantum control", "Quantum filtering", "Robust quantum control" ], "abstract": "We study the large-time behaviour of a continuously measured qubit subject to T_1 noise, with and without Hamiltonian state feedback. Without feedback, we analyse three regimes: (i) without T_1 and pumping, we recover exponential quantum state reduction towards {rho_e,rho_g}; (ii) with T_1 only, we show that the ground state is globally exponentially attractive in mean and almost surely; (iii) with relaxation and pumping, we characterize the invariant distribution of the Bloch coordinate z and the associated ergodic properties. With Hamiltonian state feedback, the excited state is no longer an equilibrium, the trajectories becomes strongly mixing with a unique invariant measure. Using exit-time estimates and occupation-time bounds, we quantify how feedback, measurement strength, and pumping jointly determine the long-run fraction of time spent near rho_e and provide a practical stability interpretation.", "url": "" }, { "id": "Mo-MoC04.2", "code": "MoC04.2", "title": "Measurement-Based Feedback Control of a Cavity Coupled to a Waveguide at Two Distant Points (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC04", "sessionTitle": "Quantum Control I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Tang, Tian", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Wu, Guangpu", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Dong, Zhiyuan", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Liu, Hao", "affiliation": "Beihang University" }, { "name": "Xue, Shibei", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Quantum control", "Quantum optimal control", "Quantum filtering" ], "abstract": "With the development of technology, a cavity can be coupled to a waveguide at two distant coupling points which results in non-Markovian dynamics of the cavity. In this paper, to control the state of the cavity to a target state, we design a measurement-based feedback controller. We first establish a state-space model incorporating time-delay effects. Based on the model, we design an H_infty filter for estimation of the state of the cavity with which a feedback controller is designed. Numerical simulations demonstrate the effectiveness of our method.", "url": "" }, { "id": "Mo-MoC04.3", "code": "MoC04.3", "title": "Robust Parametric Quantum Gates against Stochastic Time-Varying Noise (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC04", "sessionTitle": "Quantum Control I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Zhang, Zigui", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "He, Yang", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Miao, Zibo", "affiliation": "Harbin Institute of Technology, Shenzhen" } ], "keywords": [ "Quantum control", "Quantum systems", "Quantum optimal control" ], "abstract": "The performance of quantum processors in the noisy intermediate-scale quantum (NISQ) era is severely constrained by environmental noise and other uncertainties. While the recently proposed quantum control robustness landscape (QCRL) offers a powerful framework for generating robust control pulses for parametric gate families, its application has been practically restricted to quasi-static noise. To address the spectrally complex, time-varying noise prevalent in reality, we propose filter function-enhanced QCRL (FF-QCRL), which integrates the filter function formalism into the QCRL framework. The resulting FF-QCRL algorithm minimizes a generalized robustness metric that faithfully encodes the impact of stochastic processes, enabling the efficient generation of control pulses that implement parametric gates while preserving robustness against realistic, time-varying noise. Numerical validation on single-qubit gates confirms the effectiveness of our proposed method.", "url": "" }, { "id": "Mo-MoC04.4", "code": "MoC04.4", "title": "Measurement-Based Initial Point Smoothing and Control Approach to Quantum Memory Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC04", "sessionTitle": "Quantum Control I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Vladimirov, Igor", "affiliation": "Australian National University" }, { "name": "Petersen, Ian R", "affiliation": "The Australian National University" }, { "name": "Shi, Guodong", "affiliation": "The University of Sydney" } ], "keywords": [ "Quantum linear systems", "Quantum filtering", "Quantum optimal control" ], "abstract": "This paper is concerned with a quantum memory system for storing quantum information in the form of its initial dynamic variables in the presence of environmental noise. In order to compensate for the deviation from the initial conditions, the classical parameters of the system Hamiltonian are affected by the actuator output of a measurement-based classical controller. The latter uses an observation process produced by a measuring apparatus from the quantum output field of the memory system. The underlying system is modelled as an open quantum harmonic oscillator whose Heisenberg evolution is governed by linear Hudson-Parthasarathy quantum stochastic differential equations. The controller is organised as a classical linear time-varying system, so that the resulting closed-loop system has quantum and classical dynamic variables. We apply linear-quadratic-Gaussian control and fixed-point smoothing at the level of the first two moments and consider controllers with a separation structure which involve a continuously updated estimate for the initial quantum variables. The initial-point smoother is used for actuator signal formation so as to minimise the sum of a mean-square deviation of the quantum memory system variables at a given time horizon from their initial values and an integral quadratic penalty on the control signal.", "url": "" }, { "id": "Mo-MoC04.5", "code": "MoC04.5", "title": "Stochastic Noise Identification and Decomposition for Atomic Spin Inertial Sensors (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC04", "sessionTitle": "Quantum Control I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Li, Jiahang", "affiliation": "Beihang University" }, { "name": "Wang, Zhuo", "affiliation": "Beihang University" }, { "name": "Haoying, Pang", "affiliation": "Beihang University" }, { "name": "Ruigang, Wang", "affiliation": "Hangzhou Normal University" }, { "name": "Li, Feng", "affiliation": "Beihang University" }, { "name": "Fang, Xiujie", "affiliation": "Beihang University" }, { "name": "Xu, Xin", "affiliation": "Beihang University" }, { "name": "Lei, Xusheng", "affiliation": "Beihang University" }, { "name": "Chen, Li", "affiliation": "Wenzhou TCM Hospital of Zhejiang Chinese Medical University" } ], "keywords": [ "Quantum systems" ], "abstract": "Atomic spin inertial sensors require high long-term bias stability, which is strongly constrained by stochastic noise. A stochastic noise decomposition method is developed based on Allan variance analysis. The static output is modeled as the superposition of several typical inertial noise types, whose power spectral densities and Allan variances are expressed in a unified form and identified in practical units. Using the identified parameters, shaping-filter algorithms generate synthetic noise that reproduces the measured Allan deviation and bias stability. Decomposition into three noise classes shows that long-term components dominate the bias stability and thus set the main performance limit.", "url": "" }, { "id": "Mo-MoC05.1", "code": "MoC05.1", "title": "An Embedded Coupled-Motor Platform for Reproducible Experiments on Linear Time-Periodic Systems", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:45", "sessionCode": "MoC05", "sessionTitle": "LB: Modeling, Identification, and Estimation Techniques", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Karaçam, Sudenur", "affiliation": "Hacettepe University" }, { "name": "Yilmaz, Onurcan", "affiliation": "Hacettepe University" }, { "name": "Uyanik, Ismail", "affiliation": "Hacettepe University" } ], "keywords": [ "Time/parameter varying system identification", "Linear system identification", "Physics informed and grey box model identification" ], "abstract": "Linear Time-Periodic (LTP) dynamics arise in many electromechanical and robotic systems but remain difficult to study experimentally due to limited reproducible hardware platforms. This paper presents an embedded coupled-motor test bench designed for deterministic realization and analysis of LTP behavior. Two rigidly coupled brushless actuators are controlled at 500 Hz, enabling direct torque actuation and multi-signal measurement. Periodic feedback modulation on the load side induces controlled LTP dynamics, producing characteristic harmonic coupling verified through frequency-domain analysis. The platform provides a repeatable experimental environment for validation of LTP modeling, identification, and control methods.", "url": "" }, { "id": "Mo-MoC05.2", "code": "MoC05.2", "title": "Adaptive Suboptimal Control of a Bilinear System Subject to Unknown-But-Bounded Disturbances", "day": "Monday", "date": "August 24, 2026", "time": "15:45-16:00", "sessionCode": "MoC05", "sessionTitle": "LB: Modeling, Identification, and Estimation Techniques", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Solovchuk, Klavdiia", "affiliation": "Scientific Research Forensic Center of the MIA of Ukraine" }, { "name": "Volkov, Oleksandr", "affiliation": "International Research and Training Center for Information Technologies and Systems of NAS of Ukraine and MES of Ukraine" }, { "name": "Zhiteckii, Leonid", "affiliation": "Institute of Cybernetics" } ], "keywords": [ "Nonlinear system identification", "Model reference adaptive control", "Nonlinear adaptive control" ], "abstract": "The adaptive suboptimal control of the first-order discrete-time, time-invariant scalar bilinear system with unknown parameters in the presence of arbitrary bounded unmeasurable disturbances is addressed in this paper. It is made the assumption that there are some estimates on their admissible values determining the nonstochastic parametric uncertainty sets of this system to be controlled. As the performance criterion defining its ultimate behavior, the upper limit on the absolute values of the output error is introduced. Assuming that all parameters are known, the conditions guaranteeing the optimality of the nonadaptive controller are established. The case dealing with the adaptive suboptimal control is studied. In this case, one supposes that the bounds on the disturbances are known a priori. Asymptotic properties of the closed-loop system containing the adaptive controller are established. A numerical example and simulation result are given to illustrate the theoretical studies.", "url": "" }, { "id": "Mo-MoC05.3", "code": "MoC05.3", "title": "Modeling Milling Via Physics-Informed Neural Networks", "day": "Monday", "date": "August 24, 2026", "time": "16:00-16:15", "sessionCode": "MoC05", "sessionTitle": "LB: Modeling, Identification, and Estimation Techniques", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Yoon, Minhyuk", "affiliation": "Seoul National University" }, { "name": "Kim, H. Jin", "affiliation": "Seoul National Univ" } ], "keywords": [ "Physics informed and grey box model identification", "Machine and deep learning for system identification" ], "abstract": "To address the challenges of system identification in milling, this study introduces a physics-guided deep learning architecture. We utilize a PINN (Physics-informed neural network) to learn inherent parameters in the milling system dynamics. The governing dynamics equation is feed into the loss function. A key component of our work is the application of NTK (Neural tangent kernel) theory to systematically assign weights to the loss terms, enhancing convergence. The proposed method was validated through simulations, showing it can reliably reconstruct physical parameters from the time-series dataset with an error margin below 15%.", "url": "" }, { "id": "Mo-MoC05.4", "code": "MoC05.4", "title": "A Serial Hybrid Modeling Framework for Bioelectrochemical Systems: Application to CO2-To-Acetate Conversion in Microbial Electrosynthesis", "day": "Monday", "date": "August 24, 2026", "time": "16:15-16:30", "sessionCode": "MoC05", "sessionTitle": "LB: Modeling, Identification, and Estimation Techniques", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Kim, Ji Hun", "affiliation": "Pusan National University" }, { "name": "Son, Sang Hwan", "affiliation": "Pusan National University" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Biological and pharmaceutical systems" ], "abstract": "Microbial electrosynthesis (MES) is a promising technology for converting CO2 into value-added chemicals, but its performance prediction is challenging due to complex interactions among electrochemical reactions and microbial activity. This study proposes a serial hybrid modeling framework integrating a physics-based fundamental model with an artificial neural network (ANN). The fundamental model describes main reaction-transport behaviors, while the ANN predicts additional current from nonlinear side reactions. The model accurately reproduced experimental trends (R2=0.91) and revealed that side reactions are strongly influenced by ammonium concentration and pH. This framework provides practical insights for optimizing MES energy efficiency.", "url": "" }, { "id": "Mo-MoC05.5", "code": "MoC05.5", "title": "Dynamic Modeling of a Fluidized Bed Reactor for Carbon Nanotube Synthesis", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:45", "sessionCode": "MoC05", "sessionTitle": "LB: Modeling, Identification, and Estimation Techniques", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Choi, Jaehun", "affiliation": "Pusan National University" }, { "name": "Son, Sang Hwan", "affiliation": "Pusan National University" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Control of multi-scale, distributed, and particulate systems" ], "abstract": "Carbon nanotubes (CNTs), prized for their exceptional properties, are crucial for lithium-ion batteries, requiring large-scale production, with fluidized bed reactors (FBRs) using chemical vapor deposition as a key method. However, the complexity of FBRs necessitates a mathematical model that accurately captures their characteristics to enable the design of large-scale CNT production systems. This study presents a model of an FBR for CNT synthesis using ethylene as feedstock, aiming to predict CNT growth on catalyst particles over time based on reaction kinetics derived from experimental data. The model utilizes a two-region model, distinguishing the behavior within the FBR into the emulsion phase, where gas and solids are well-mixed, and the bubble phase. The developed model captures the complex internal behavior of the reactor by integrating the time-dependent growth characteristics of solid particles on the catalyst and classifying this behavior into two distinct modes. This model can be extended to optimize operating conditions for large-scale CNT production and reactor design, enabling more efficient commercial synthesis of CNTs.", "url": "" }, { "id": "Mo-MoC05.8", "code": "MoC05.8", "title": "Iterative Input-Output Data-Driven Parameter Estimation Method (IOD-PEM)", "day": "Monday", "date": "August 24, 2026", "time": "17:15-17:30", "sessionCode": "MoC05", "sessionTitle": "LB: Modeling, Identification, and Estimation Techniques", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Shimizu, Keiko", "affiliation": "Central Research Institute of Electric Power Industry" } ], "keywords": [ "Nonlinear system identification", "Data-driven control theory" ], "abstract": "Motivated by the idea of Iterative Feedback Tuning (IFT, proposed by Hjalmarsson, 1998), this paper studies how a similar concept can be used for parameter estimation of a plant model. Instead of analytically deriving the sensitivity of the plant model output with respect to its parameters, we design a simulation-based \"pseudo-sensitivity\" and use it directly in a Gauss-Newton-type update. To emphasize the input-output data-driven nature of this approach, we call the proposed method the Iterative Input-Output Data-driven Parameter Estimation Method (IOD-PEM). As the first step, we consider a static turbine efficiency model whose output is a simple nonlinear function of the valve position. For this model, we first formulate a conventional method based on the analytical sensitivity of the plant model. In this paper, this method is referred to as the Direct Differentiation Method (DDM), following the terminology used in sensitivity analysis (Wang, 2013; Haukaas, 2024). After that, we develop IOD-PEM, in which the analytical sensitivity is replaced by a simulation-based signal computed from appropriately separated input signals. In particular, IOD-PEM utilizes the assumption that the plant input can be split into two factors so that the two unknown parameters appear in a separable form. The plant structure is unchanged; only the input excitation is modified, and a simple additional simulation is introduced to generate the pseudo-sensitivity. A numerical example shows that IOD-PEM yields essentially the same parameter estimates as DDM for the considered turbine model. Following the simulation study, we analyze why the proposed method works even though the pseudo-sensitivity is not equal to the true sensitivity and actually contains a constant bias term. It is shown that, for the considered example, the gradient obtained with the pseudo-sensitivity is equal to the true gradient multiplied by a positive scalar. Therefore, the Gauss-Newton iteration converges to the same optimum, while the step length is merely rescaled.", "url": "" }, { "id": "Mo-MoC06.1", "code": "MoC06.1", "title": "Learning Storage Functions for Nonlinear Systems from Data", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC06", "sessionTitle": "Data-Driven Control Theory III", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Bazanella, Alexandre S.", "affiliation": "Univ. Federal Do Rio Grande Do Sul" } ], "keywords": [ "Data-driven control theory", "Learning methods for control", "Nonlinear system identification" ], "abstract": "In this paper a data-driven methodology to estimate the storage function of a dissipative system is presented. The methodology consists in parametrizing the storage function with a dictionary then running a linear program. Implementation issues are discussed, including the handling of noise in the data. Smoothness assumptions on the unknown vector fields describing the system are required, which is a standard requirement for data-driven analysis of nonlinear systems. Results on a benchmark are presented to illustrate the method's properties. Successful estimates are obtained with two kinds of dictionaries, suggesting that good results can be obtained even with fully general (in this case polynomial) dictionaries.", "url": "" }, { "id": "Mo-MoC06.2", "code": "MoC06.2", "title": "Data-Driven Design of TITO Controllers with Inverted Decouplers", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC06", "sessionTitle": "Data-Driven Control Theory III", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Campestrini, Luciola", "affiliation": "Univ of Rio Grande Do Sul" }, { "name": "Bazanella, Alexandre S.", "affiliation": "Univ. Federal Do Rio Grande Do Sul" }, { "name": "Varriale da Silva, Eduardo", "affiliation": "Altus Sistemas De Automação S.A" } ], "keywords": [ "Data-driven control theory", "Linear system identification" ], "abstract": "A data-driven method is proposed for designing controllers with inverted decouplers. The method is based on the Optimal Controller Identification (OCI) design, which is extended to deal with this specific control structure - i.e. a decoupler plus a single-loop controller for each decoupled loop. The method is evaluated on a classical benchmark, the Wood and Berry distillation column. A comparison with the standard multivariable control structure shows that the final controller has better statistical properties.", "url": "" }, { "id": "Mo-MoC06.3", "code": "MoC06.3", "title": "Closed-Loop Consistent, Causal Data-Driven Predictive Control Via SSARX", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC06", "sessionTitle": "Data-Driven Control Theory III", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Liu, Aihui", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Jansson, Magnus", "affiliation": "KTH (Royal Inst of Technology)" } ], "keywords": [ "Data-driven control theory", "Linear system identification" ], "abstract": "We propose a fundamental-lemma-free predictor-based data-driven predictive control (DDPC) method for synthesizing model predictive control (MPC)-like policies directly from input–output data. Unlike the well-known DeePC approach and other DDPC methods that rely on Willems’ fundamental lemma, our method avoids stacked Hankel representations and the DeePC decision variable g. Instead, we develop a closed-loop consistent, causal DDPC scheme based on the multistep predictor Subspace-ARX (SSARX). The method first (i) estimates predictor/observer Markov parameters using a high-order ARX model to decouple the noise, then (ii) learns a multi-step past-to-future map by regression, optionally with a reduced-rank constraint. The SSARX predictor is strictly causal, which allows it to be integrated naturally into an MPC formulation. Our experimental results show that SSARX performs competitively with other methods when applied to closed-loop data affected by measurement and process noise.", "url": "" }, { "id": "Mo-MoC06.4", "code": "MoC06.4", "title": "A Physics-Informed Scenario Approach with Data Mitigation for Safety Verification of Nonlinear Systems", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC06", "sessionTitle": "Data-Driven Control Theory III", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Aminzadeh, Ali", "affiliation": "Tampere University" }, { "name": "Ashoori, MohammadHossein", "affiliation": "Newcastle University" }, { "name": "Nejati, Amy", "affiliation": "Newcastle University" }, { "name": "Lavaei, Abolfazl", "affiliation": "Newcastle University" } ], "keywords": [ "Data-driven control theory", "Learning methods for control" ], "abstract": "This paper develops a physics-informed scenario approach for safety verification of nonlinear systems using barrier certificates (BCs) to ensure that system trajectories remain within safe regions over an infinite time horizon. Designing BCs often relies on an accurate dynamics model; however, such models are often imprecise due to the model complexity involved, particularly when dealing with highly nonlinear systems. In such cases, while scenario approaches effectively address the safety problem using collected data to construct a guaranteed BC for the unknown dynamical system, they often require solving an optimization problem with substantial amounts of data. To address this, we propose a physics-informed scenario approach that selects data samples such that the outputs of the physics-based model and the observed data are sufficiently close. This approach guides the scenario optimization process to eliminate redundant samples and potentially reduce the required dataset size. We validate our approach through three case studies, showcasing its practical application in reducing the required data.", "url": "" }, { "id": "Mo-MoC06.5", "code": "MoC06.5", "title": "Koopman Based Data-Enabled Predictive Control for Control-Affine Systems", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC06", "sessionTitle": "Data-Driven Control Theory III", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Fu, Xingyun", "affiliation": "Tsinghua University" }, { "name": "You, Keyou", "affiliation": "Tsinghua University" } ], "keywords": [ "Data-driven control theory", "Nonlinear system identification" ], "abstract": "The identification and control of nonlinear systems often require complex mathematical tools. This paper introduces a data-driven predictive control method with theoretical guarantees, using the Koopman operator to approximate nonlinear dynamics. Based on behavioral system theory, we analyze the approximation error to ensure robust control design. Building upon this representation, we develop a data-driven predictive control algorithm with performance guarantees. Numerical simulations confirm the method's effectiveness.", "url": "" }, { "id": "Mo-MoC06.6", "code": "MoC06.6", "title": "Performance Limits of Discriminating Stochastic Linear Systems", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC06", "sessionTitle": "Data-Driven Control Theory III", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Liu, Kunpeng", "affiliation": "Tsinghua University" }, { "name": "You, Keyou", "affiliation": "Tsinghua University" } ], "keywords": [ "Data-driven control theory", "Statistical inference", "Statistical analysis" ], "abstract": "System discrimination in this work focuses on identifying the true system model among a set of candidates using a sequence of noisy input-output data, which is a fundamental problem in control theory and signal processing. In particular, we quantify the performance limits of discriminating two linear time-invariant (LTI) stochastic systems using the I/O data generated by a given control input, and explicitly derive the best exponential decay rate of the discrimination error in terms of the weighted H∞-distance of the two systems and the tensity of noise and input signal. Then, our theoretical findings are validated through numerical simulations, illustrating the consistency of the empirical error rate with the theoretical one.", "url": "" }, { "id": "Mo-MoC07.1", "code": "MoC07.1", "title": "Distributed Adaptive Estimation with ISS Guarantees for Sensor Networks with Partially Unknown Source Dynamics (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC07", "sessionTitle": "Open Multi-Agent Systems: Control, Optimization, and Learning II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Wafi, Moh. Kamalul", "affiliation": "Northeastern University" }, { "name": "Montazeri Hedesh, Hamidreza", "affiliation": "Northeastern University" }, { "name": "Siami, Milad", "affiliation": "Northeastern University" } ], "keywords": [ "Adaptive observer design", "Distributed control and estimation", "Multi-agent systems" ], "abstract": "This paper studies distributed adaptive estimation over sensor networks with partially unknown source dynamics. We present parallel continuous-time and discrete-time designs in which each node runs a local adaptive observer and exchanges information over a directed graph. For both time scales, we establish stability of the network coupling operators, prove boundedness of all internal signals, and show convergence of each node’s estimate to the source despite model uncertainty and disturbances. We further derive input-to-state stability (ISS) bounds that quantify robustness to bounded process noise. A key distinction is that the discrete-time design uses constant adaptive gains and per-step regressor normalization to handle sampling effects, whereas the continuous-time design does not. A unified Lyapunov framework links local observer dynamics with graph topology. Simulations on star, cyclic, and path networks corroborate the analysis, demonstrating accurate tracking, robustness, and scalability with the number of sensing nodes.", "url": "" }, { "id": "Mo-MoC07.2", "code": "MoC07.2", "title": "Fully Distributed Adaptive Tracking Consensus of Open Multi-Agent Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC07", "sessionTitle": "Open Multi-Agent Systems: Control, Optimization, and Learning II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Li, Xiaodong", "affiliation": "Southeast University" }, { "name": "Lv, Yuezu", "affiliation": "Beijing Institute of Technology" }, { "name": "Yang, Tao", "affiliation": "Northeastern University" }, { "name": "Wen, Guanghui", "affiliation": "Southeast University" } ], "keywords": [ "Adaptive control of multi-agent systems", "Distributed control and estimation", "Multi-agent systems" ], "abstract": "This paper investigates the tracking consensus problem of open multi-agent systems, where both switching communication topologies and dynamic membership variations pose significant challenges for distributed control and stability analysis. Additional difficulties arise from the absence of full-state information and the requirement for fully distributed implementation. To overcome these challenges, a reduced-order input-free observer is designed to estimate the required local states using only output information. Building on this observer, a fully distributed adaptive protocol is proposed, relying solely on interaction information exchanged among neighboring agents. To characterize the open property of the network, a new analytical framework is introduced to describe the evolution of the tracking error. Based on this framework, an average dwell time condition is derived to ensure piecewise uniform ultimate boundedness tracking consensus for the oMAS. A numerical simulation is provided to demonstrate the effectiveness of the proposed approach.", "url": "" }, { "id": "Mo-MoC07.3", "code": "MoC07.3", "title": "Distributed Non-Uniform Scaling Control of Multi-Agent Formation with Dynamic Agent Joining (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC07", "sessionTitle": "Open Multi-Agent Systems: Control, Optimization, and Learning II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "He, Tao", "affiliation": "Chongqing University" }, { "name": "Jing, Gangshan", "affiliation": "Chongqing University" } ], "keywords": [ "Multi-agent systems", "Control of networks", "Consensus" ], "abstract": "Non-uniform scaling formation control, which enables multi-agent systems to adjust their collective shape by scaling with different ratios along different coordinate axes, offers enhanced flexibility for maneuvering in complex environments. However, like most existing formation maneuver strategies, it typically assumes a fixed set of agents, limiting its applicability in scenarios requiring dynamic team expansion. This paper introduces a distributed control framework that enables a formation to incorporate new agents during non-uniform scaling maneuvers in arbitrary dimensions. The main contributions are two-fold: (i) designing a distributed strategy for agent joining that preserves the spectral properties of the graph Laplacian and the convergent space for the original formation; and (ii) achieving distributed maneuver control with global asymptotic convergence using only relative position measurements, without velocity information or global parameters. Numerical simulations validate the effectiveness of the proposed framework.", "url": "" }, { "id": "Mo-MoC07.4", "code": "MoC07.4", "title": "Event-Triggered Consensus in Open Multi-Agent Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC07", "sessionTitle": "Open Multi-Agent Systems: Control, Optimization, and Learning II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Huang, Yuliang", "affiliation": "Beijing Institute of Technology" }, { "name": "Lv, Yuezu", "affiliation": "Beijing Institute of Technology" }, { "name": "Duan, Peihu", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Zhou, Jialing", "affiliation": "Beijing Institute of Technology" }, { "name": "Fu, Junjie", "affiliation": "Southeast University" } ], "keywords": [ "Multi-agent systems", "Consensus", "Event-based control" ], "abstract": "This paper investigates consensus of open multi-agent systems (OMASs) subject to dynamic population changes, where agents may join or leave the network at arbitrary times. Population switching introduces state-dimension variations and induces abrupt changes in the global consensus error. To explicitly characterize these effects, we derive computable upper bounds on the consensus-error jumps caused by agent arrivals and departures. For the continuous-time evolution between switching events, a dynamic event-triggered control protocol is proposed to regulate information updates without requiring continuous communication and to guarantee the exclusion of Zeno behavior. By constructing a piecewise Lyapunov function and imposing a lower bound on the average dwell time, we establish that the closed\u0002loop OMASs achieves uniformly ultimately bounded consensus despite arbitrary population fluctuations. Numerical simulations illustrate the effectiveness of the proposed approach in managing dynamic agent interactions and validating the theoretical results.", "url": "" }, { "id": "Mo-MoC07.5", "code": "MoC07.5", "title": "Topology Estimation for Open Multi-Agent Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC07", "sessionTitle": "Open Multi-Agent Systems: Control, Optimization, and Learning II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Wang, Nana", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Sekercioglu, Pelin", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Dimarogonas, Dimos V.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Time/parameter varying system identification", "Hybrid and switched systems modeling", "Control of networks" ], "abstract": "We address the problem of interaction topology identification in open multi-agent systems (OMAS) with dynamic node sets and fast switching interactions. In such systems, new agents join and interactions change rapidly, resulting in intervals with short dwell time and rendering conventional segment-wise estimation and clustering methods unreliable. To overcome this, we propose a projection-based dissimilarity measure derived from a consistency property of local least-squares operators, enabling robust mode clustering. Aggregating intervals within each cluster yields accurate topology estimates. The proposed framework offers a systematic solution for reconstructing the interaction topology of OMAS subject to fast switching. Finally, we illustrate our theoretical results via numerical simulations.", "url": "" }, { "id": "Mo-MoC07.6", "code": "MoC07.6", "title": "Consensus Tracking of Perturbed Open Multi-Agent Systems with Repelling Antagonistic Interactions (I)", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC07", "sessionTitle": "Open Multi-Agent Systems: Control, Optimization, and Learning II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Xue, Mengqi", "affiliation": "Tongji University" }, { "name": "Xiong, Yuchao", "affiliation": "Tongji University" }, { "name": "Song, Yue", "affiliation": "Tongji University" } ], "keywords": [ "Multi-agent systems", "Stability and stabilization of hybrid systems", "Consensus" ], "abstract": "An open multi-agent system (OMAS) features migrating agents which produce a flexible network that is naturally switching and size-varying. Meanwhile, agent migrations also make an OMAS prone to environmental adversities. In this work, we investigate the consensus tracking problem of OMASs suffering migration-induced adversities, including non-vanishing agent dynamics/state perturbations and repelling antagonistic interactions among agents, over an intermittently disconnected signed digraph. The OMAS is interpreted into a perturbed multi-mode multi-dimensional (M^3D) system in which unstable subsystems are created when repelling interactions dominate the cooperative ones in the network regardless of its connectivity. To handle the destabilizing effect brought by repelling interactions and non-vanishing perturbations, we extend the stability theory for M^3D systems and apply it to the OMAS to show that ultimately bounded consensus tracking can be achieved if the network switching satisfies the piecewise average dwell time and activation time ratio conditions. Particularly, for vanishing perturbations, asymptotic tracking can be ensured under weaker switching conditions.", "url": "" }, { "id": "Mo-MoC08.1", "code": "MoC08.1", "title": "Active Learning MPC Objective Functions from Preferences", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC08", "sessionTitle": "Learning Methods for Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "El Hasnaouy, Hasna", "affiliation": "IMT School for Advanced Studies Lucca" }, { "name": "Krupa, Pablo", "affiliation": "IMT School for Advanced Studies" }, { "name": "Zanon, Mario", "affiliation": "IMT Institute for Advanced Studies Lucca" }, { "name": "Bemporad, Alberto", "affiliation": "IMT Institute for Advanced Studies Lucca" } ], "keywords": [ "Learning methods for control", "Active learning and experiment design" ], "abstract": "Designing the objective function in Model Predictive Control (MPC) is challenging when performance assessment criteria are available only from human judgment. We adopt a preference-based learning (PbL) approach to learn the MPC objective function from preferences over trajectory pairs. However, the real-world application of PbL is often restricted by the significant cost or limited availability of human preference queries. To address this, Active Learning (AL) strategies seek to improve sampling efficiency, reducing the labeling effort required to obtain a well-performing classifier. We present two AL strategies for learning the MPC objective function from human preferences over pairwise system trajectories: a pool-based strategy that selects trajectory pairs that are both uncertain under the current surrogate and diverse relative to previously labeled comparisons, and a query-synthesis strategy that incorporates new trajectories using the current surrogate-driven MPC. Numerical results show that the proposed strategies yield closed-loop behaviors that align more with the expressed preference using fewer number of queries compared to a random sampling approach.", "url": "" }, { "id": "Mo-MoC08.2", "code": "MoC08.2", "title": "Learning-Based Predictive Control with Bayesian Neural Networks under Safety Guarantees", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC08", "sessionTitle": "Learning Methods for Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Boca de Giuli, Laura", "affiliation": "Politecnico Di Milano" }, { "name": "La Bella, Alessio", "affiliation": "Politecnico Di Milano" }, { "name": "Prajapat, Manish", "affiliation": "ETH Zurich" }, { "name": "Kohler, Johannes", "affiliation": "Imperial College London" }, { "name": "Scampicchio, Anna", "affiliation": "Chalmers University of Technology" }, { "name": "Zeilinger, Melanie N.", "affiliation": "ETH Zurich" } ], "keywords": [ "Learning methods for control", "Active learning and experiment design", "Probabilistic and Bayesian methods for system identification" ], "abstract": "This paper proposes a safe active learning algorithm in which a model predictive controller optimises system operation and simultaneously explores informative dynamics to learn model parameters, all while ensuring that safety constraints are satisfied. The recursively updated model consists of a recurrent neural network with a Bayesian last layer. The algorithm is complemented with guarantees of recursive feasibility, safety, and finite termination of exploration. The proposed framework is validated in simulation on a benchmark energy system, demonstrating that the algorithm ensures a finite exploration of the system dynamics while optimising the operation and satisfying physical constraints.", "url": "" }, { "id": "Mo-MoC08.3", "code": "MoC08.3", "title": "Safe Bayesian Optimization for Uncertain Correlation Matrices in Linear Models of Co-Regionalization", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC08", "sessionTitle": "Learning Methods for Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Lübsen, Jannis Olaf", "affiliation": "Hamburg University of Technology" }, { "name": "Eichler, Annika", "affiliation": "DESY" } ], "keywords": [ "Learning methods for control", "Gaussian process" ], "abstract": "This paper extends safety guarantees for multi-task Bayesian optimization with uncertain co-regionalization matrices from intrinsic co-regionalization models to linear models of co-regionalization. The latter allows for more flexible modeling of the inter-task correlations by composing multiple features. We derive uniform error bounds for vector-valued functions sampled from a Gaussian process with a linear model of co-regionalization kernel. Furthermore, we show the potential performance gains of linear models of co-regionalization in a numerical comparison on a safe multi-task Bayesian optimization benchmark.", "url": "" }, { "id": "Mo-MoC08.4", "code": "MoC08.4", "title": "Model-Free Q-Learning Control of Shape Memory Alloy Actuators: Experimental Comparison of LS and RLS Estimators", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC08", "sessionTitle": "Learning Methods for Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Badrnoebashar, Helaleh", "affiliation": "TUD Dresden University of Technology, Institute of Control Theory (RST), Dresden, Germany" }, { "name": "Acevedo Velazquez, Aline Iobana", "affiliation": "TUD Dresden University of Technology" }, { "name": "Wang, Zhenbi", "affiliation": "TUD Dresden University of Technology" }, { "name": "Röbenack, Klaus", "affiliation": "TU Dresden" } ], "keywords": [ "Learning methods for control", "Data-driven control theory", "Consensus and reinforcement learning control" ], "abstract": "Controlling actuators driven by smart materials such as shape-memory alloys (SMAs) remains challenging due to their strong nonlinearities, hysteresis, and multiphysics coupling, which limit the effectiveness of classical model-based control strategies. This paper presents a model-free Q-learning framework for real-time trajectory tracking of an SMA-based compliant actuator, enabling the joint learning of state-feedback and feedforward control policies without requiring an explicit plant model. The Q-function parameters were identified using both Least Squares (LS) and Recursive Least Squares (RLS) methods with a forgetting factor. Experimental results show that both estimators achieved nearly identical convergence and tracking performance. The minor deviations observed were attributed to the sequential numerical update of RLS rather than to conceptual differences. These results demonstrate that RLS can serve as an efficient online alternative to LS, suitable for deployment on embedded control hardware such as the Arduino Portenta H7, and confirm the viability of reinforcement learning for SMA actuator control where accurate models are difficult to obtain.", "url": "" }, { "id": "Mo-MoC08.5", "code": "MoC08.5", "title": "Conditional Invertible Neural Networks for Data-Driven UAV Control: A 2-D Proof of Concept", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC08", "sessionTitle": "Learning Methods for Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Wittke, Christian", "affiliation": "Helmut Schmidt University" }, { "name": "Myschik, Stephan", "affiliation": "University of the Bundeswehr Munich" }, { "name": "Niggemann, Oliver", "affiliation": "Helmut-Schmidt-Universität / Universität Der Bundeswehr Hamburg" } ], "keywords": [ "Learning methods for control", "Data-driven control theory", "Machine and deep learning for system identification" ], "abstract": "We investigate conditional invertible neural networks (cINNs) as probabilistic inverse-dynamics models for multirotor control. For a planar X8 coaxial multicopter, we learn p(u | st, ct) from an incremental nonlinear dynamic inversion (INDI) teacher using rationalquadratic spline coupling and invertible linear mixing. Open-loop reproduction reaches R2 = 0.944 , mean CRPS 0.0915, and log-probability–error correlation ρ = −0.60 . Over 15 closed-loop scenarios, position RMSE matches INDI (9.7 vs. 9.5 m) with 47% tracking acceptably; failures separate into attitude divergence under aggressive steps and phase lag under high-frequency references, isolating command bandwidth and data coverage as dominant failure mechanisms.", "url": "" }, { "id": "Mo-MoC08.6", "code": "MoC08.6", "title": "Convergence Analysis of Natural Power Method and Its Applications to Control", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC08", "sessionTitle": "Learning Methods for Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Tsuzuki, Daiki", "affiliation": "Kyoto University" }, { "name": "Ohki, Kentaro", "affiliation": "Tokai University" } ], "keywords": [ "Learning methods for control" ], "abstract": "This paper analyzes the discrete-time natural power method, demonstrating its convergence to the dominant r-dimensional subspace corresponding to the r eigenvalues with the largest absolute values. This contrasts with the Oja flow, which targets eigenvalues with the largest real parts. We leverage this property to develop methods for model order reduction and low-rank controller synthesis for discrete-time LTI systems, proving preservation of key system properties. We also extend the low-rank control framework to slowly-varying LTV systems, showing its utility for tracking time-varying dominant subspaces.", "url": "" }, { "id": "Mo-MoC09.1", "code": "MoC09.1", "title": "Loss Handling Strategies for Multi-Sensor Static Observers", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC09", "sessionTitle": "Estimation and Filtering", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Sinnema, Yde", "affiliation": "Lund University" } ], "keywords": [ "Estimation and filtering", "Adaptive observer design", "Control over networks" ], "abstract": "There seems to be no clear-cut answer to the question of how to handle lost measurements in control systems with static observers. Nevertheless, this choice can have a major effect on the estimation and control performance. This paper focuses on systems with multiple sensor channels and presents four strategies to cope with partial or full measurement losses that do not assume any knowledge of the loss probability distribution. Our analysis enables the choice of a suitable strategy for a given system.", "url": "" }, { "id": "Mo-MoC09.2", "code": "MoC09.2", "title": "Event-Triggered Parameter Estimator for Sensor Fusion", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC09", "sessionTitle": "Estimation and Filtering", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Méndez Castillo, Ariana Ruth", "affiliation": "Cinvestav Gdl-Mx" }, { "name": "Perez-Salesa, Irene", "affiliation": "University of Zaragoza" }, { "name": "Aldana-López, Rodrigo", "affiliation": "Universidad De Zaragoza" }, { "name": "Ramirez-Trevino, Antonio", "affiliation": "CINVESTAV-IPN" }, { "name": "Aragues, Rosario", "affiliation": "Universidad De Zaragoza" } ], "keywords": [ "Estimation and filtering", "Event-based control", "Control over networks" ], "abstract": "This paper studies event triggered parameter estimation in sensor fusion systems where sensors transmit measurements to a gradient based estimator. We introduce a regressor driven local triggering rule that requires no knowledge of the current parameter estimate and depends solely on the regressor signals. Under a persistent excitation condition on the aggregate regressor, we derive explicit design inequalities on the estimator gain and event thresholds that guarantee global exponential convergence. The analysis is based on a time varying Lyapunov function. We further provide a sufficient condition on the regressor dynamics that enforces a uniform lower bound on inter event times, excluding Zeno behavior. Simulations show substantial communication savings while preserving exponential convergence.", "url": "" }, { "id": "Mo-MoC09.3", "code": "MoC09.3", "title": "Nonparametric Procedure for Estimating Multiple Dispersion Functions", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC09", "sessionTitle": "Estimation and Filtering", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Chernyshov, Kirill", "affiliation": "V.A. Trapeznikov Institute of Control Sciences" } ], "keywords": [ "Estimation and filtering" ], "abstract": "This paper introduces a measure of dependence between a pair of random processes, each of which represents a conditional mathematical expectation with respect to m random processes, where m is finite but not bounded a priori. The proposed measure, which is built upon estimates of the conditional expectations associated with the processes under consideration, may be regarded as a further generalization of dispersion functions. Almost sure convergence of the nonparametric estimators for this measure is demonstrated using observed sample data. These estimators are subsequently employed to construct sample analogs of certain nonlinear measures of stochastic dependence between random processes; in particular, a dependence measure that satisfies Kolmogorov’s consistency criterion is derived. As a direct corollary, consistency of the dependence measure in the sense of Rényi is also established.", "url": "" }, { "id": "Mo-MoC09.4", "code": "MoC09.4", "title": "Smoothers for Lagrangian and Eulerian Grid-Based State Estimators", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC09", "sessionTitle": "Estimation and Filtering", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Matousek, Jakub", "affiliation": "University of West Bohemia" }, { "name": "Dunik, Jindrich", "affiliation": "University of West Bohemia" } ], "keywords": [ "Estimation and filtering", "Diffusion process", "Kalman filtering" ], "abstract": "This paper addresses state-estimation problems in non-linear stochastic dynamical systems, with particular focus on the smoothing stage. The main contribution is the derivation of the Lagrangian grid-based smoother. In addition, the existing Eulerian smoothing formulations are collected and organized into a coherent framework that clarifies their relationships and computational structure. A unified overview of the analytical computational and memory complexities of both Eulerian and Lagrangian smoothers is also provided. The proposed Lagrangian smoother has been implemented in textsc{Matlab}textsuperscript{textregistered}, and the code is publicly availablefootnote{url{https://github.com/pesslovany/Matla b-LagrangianPMF-simulated-smoothing}}.", "url": "" }, { "id": "Mo-MoC09.5", "code": "MoC09.5", "title": "UKF/UIFIR Fusion Filter for Nonlinear Systems with Unpredictable Disturbance", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC09", "sessionTitle": "Estimation and Filtering", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Zhao, Shunyi", "affiliation": "Jiangnan University" }, { "name": "Zhu, Yuhang", "affiliation": "Jiangnan University" }, { "name": "Liu, Fei", "affiliation": "Jiangnan University" } ], "keywords": [ "Estimation and filtering", "Kalman filtering", "Nonlinear adaptive control" ], "abstract": "This paper proposes a novel fusion filter for nonlinear systems that combines the robustness of the unscented iterative finite impulse response (UIFIR) filter with the high estimation accuracy of the unscented Kalman filter (UKF). An interacting multiple model framework is employed to adaptively configure the inputs of the two subfilters under different operating conditions. The corresponding model likelihoods are normalized and used as time-varying weights to fuse the two state estimates. The resulting fusion filter inherits the strengths of both subfilters: it achieves higher accuracy in disturbance-free conditions and improved robustness in the presence of disturbances. The mobile robot simulation examples demonstrate the effectiveness of the proposed method.", "url": "" }, { "id": "Mo-MoC09.6", "code": "MoC09.6", "title": "Optimal Joint State and Unknown Input Estimation for Linear Systems", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC09", "sessionTitle": "Estimation and Filtering", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Breukelman, Enno", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Sandberg, Henrik", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Estimation and filtering", "Cyber security networked control" ], "abstract": "In this paper, we address the problem of estimating internal states and unknown inputs to a stochastic discrete-time linear time-invariant (LTI) system. We consider an LTI system with potentially correlated, but zero-mean and white, process and measurement noise. By allowing for a delayed estimation of states and inputs from a stacked vector of multiple measured outputs, we cover a large class of LTI systems. First, we establish a necessary and sufficient condition under which a delayed estimation is unbiased. Then, we propose an algorithm that jointly and with minimum variance estimates the internal states and the unknown input.", "url": "" }, { "id": "Mo-MoC10.2", "code": "MoC10.2", "title": "Temporal Logic Resilience for Continuous-Time Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC10", "sessionTitle": "JO-NAHS: Discrete Event and Hybrid Systems I", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Das, Ratnangshu", "affiliation": "Indian Institute of Science, Bangalore" }, { "name": "Monir, Negar", "affiliation": "Newcastle University" }, { "name": "Ait Si, Youssef", "affiliation": "University Mohammed VI Polytechnic" }, { "name": "Saoud, Adnane", "affiliation": "University Mohammed VI Polytechnic (UM6P)" }, { "name": "Soudjani, Sadegh", "affiliation": "Max Planck Institute for Software Systems" }, { "name": "Jagtap, Pushpak", "affiliation": "Indian Institute of Science" } ], "keywords": [ "Diagnosis of discrete event and hybrid systems", "Fault detection and diagnosis" ], "abstract": "In this paper, we present a novel framework for quantifying a lower bound on resilience in continuous-time (non)linear systems subject to external disturbances while ensuring satisfaction of signal temporal logic specifications. Unlike robustness, which evaluates how well a system satisfies a specification under a given disturbance, resilience measures the maximum disturbance a system can tolerate from a given initial state while maintaining specification satisfaction. We first derive bounds on the perturbed trajectories and then use them to formulate a computational method based on scenario optimization to efficiently compute the maximum admissible disturbance. We validate our approach through case studies, including dc motor, temperature regulation, a nonlinear numerical example, and a vehicle collision avoidance case.", "url": "" }, { "id": "Mo-MoC10.3", "code": "MoC10.3", "title": "Asymptotic Analysis of a Competitive Epidemic Model with Virality Growth (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC10", "sessionTitle": "JO-NAHS: Discrete Event and Hybrid Systems I", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Kravitzch, Emmanuel", "affiliation": "Université De Lorraine-CRAN" }, { "name": "Satheeskumar Varma, Vineeth", "affiliation": "CRAN - Université De Lauraine" }, { "name": "Morarescu, Irinel Constantin", "affiliation": "Universite De Lorraine" } ], "keywords": [ "Discrete event modeling and simulation", "Event-based control", "Multi-agent systems" ], "abstract": "This paper analyses a mathematical model of two competing agents seeking to attract a larger share of the population. The model is formulated as a bi-virus susceptible-infected-susceptible (SI2S) model with controlled virality. Each agent increases its virality whenever its infected population falls below a threshold. This event-based strategy triggers the action of boosting virality to ensure survival. Within this framework, we prove that this strategic behavior induces sustained oscillations, driven by the alternating dominance of each agent in terms of virality. The main contribution of this work is the analytical characterization of this dynamics. We derive a one-dimensional discrete-time map that governs the evolution of the virality gap between the two competing agents. This result demonstrates that a reactive ’innovation race’ leads to a stable coexistence, preventing the winner take-all outcome often observed in competitive dynamics.", "url": "" }, { "id": "Mo-MoC10.4", "code": "MoC10.4", "title": "Enforcing Opacity with Publicly Known Edit Functions under Incomparable Observations (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC10", "sessionTitle": "JO-NAHS: Discrete Event and Hybrid Systems I", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Duan, Wei", "affiliation": "Polytechnic University of Bari" }, { "name": "Hu, Shaopeng", "affiliation": "Xidian University" }, { "name": "He, Zhou", "affiliation": "Shaanxi University of Science and Technology" }, { "name": "Liu, Ruotian", "affiliation": "Polytechnic University of Bari" }, { "name": "Fanti, Maria Pia", "affiliation": "Polytechnic of Bari" } ], "keywords": [ "Discrete event modeling and simulation", "Event-based control", "Reachability analysis, verification and abstraction of hybrid systems" ], "abstract": "This paper investigates opacity enforcement via publicly known and constrained edit functions under incomparable observations. We first formalize the notion of ik-enforceability, which combines admissibility, consistency, confidentiality, and integrity requirements. A game-theoretic synthesis framework is then developed, consisting of three pruning stages and one merging stage, including: (i) construct an edit game structure to capture all feasible constrained edit actions; (ii) prune problematic states that violate admissibility, confidentiality, and consistency; (iii) employ an identifying observer to model the reverse-engineering capability of the intruder; and (iv) merge states to consistent the edit actions under the defender observation. The resulting edit mechanism provides necessary and sufficient conditions for synthesizing ik-enforcing edit functions.", "url": "" }, { "id": "Mo-MoC10.5", "code": "MoC10.5", "title": "Dense-Time Discrete Event Observer for Temporal Detection of Stealthy Cyber-Attacks (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC10", "sessionTitle": "JO-NAHS: Discrete Event and Hybrid Systems I", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Gaouar, Mouna", "affiliation": "Aix Marseille University" }, { "name": "Ammour, Rabah", "affiliation": "Aix-Marseille University" }, { "name": "Demongodin, Isabel", "affiliation": "Aix-Marseille University" }, { "name": "Lefebvre, Dimitri", "affiliation": "Univ Le Havre" } ], "keywords": [ "Discrete event modeling and simulation", "Petri nets" ], "abstract": "This paper presents a dense-time discrete event observer for bounded Time Synchronized Petri Nets with Outputs under partial observability. After computing a Synchronized State Class Graph, the synchronized state classes are extended with time markers. An observer is then determined and used to detect logically stealthy active cyber-attacks in cyber-physical systems. Temporal detection is achieved by verifying whether the observed timed sequence corresponds to an admissible path in the observer. Even when the sequence of observed events remains consistent with the logical behavior, any temporal deviation from the modeled dynamics indicates an inconsistency with the system specification, thereby revealing an attack.", "url": "" }, { "id": "Mo-MoC10.6", "code": "MoC10.6", "title": "Model Predictive Online Monitoring of Dynamical Systems for Nested Signal Temporal Logic Specifications (I)", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC10", "sessionTitle": "JO-NAHS: Discrete Event and Hybrid Systems I", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Han, Tao", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Li, Shaoyuan", "affiliation": "Shanghai Jiao Tong Univ" }, { "name": "Yin, Xiang", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Diagnosis of discrete event and hybrid systems", "Reachability analysis, verification and abstraction of hybrid systems", "Discrete event modeling and simulation" ], "abstract": "This paper investigates the online monitoring problem for cyber-physical systems under signal temporal logic (STL) specifications. The objective is to design an online monitor that evaluates system correctness at runtime based on partial signal observations up to the current time so that alarms can be issued whenever the specification is violated or will inevitably be violated in the future. We consider a model-predictive setting where the system’s dynamic model is available and can be leveraged to enhance monitoring accuracy. However, existing approaches are limited to a restricted class of STL formulae, permitting only a single application of temporal operators. This work addresses the challenge of nested temporal operators in the design of model-predictive monitors. Our method utilizes syntax tree structures to resolve dependencies between temporal operators and introduces the concept of basic satisfaction vectors. A new model-predictive monitoring algorithm is proposed by recursively updating these vectors online while incorporating pre-computed satisfaction regions derived from offline model analysis. We prove that the proposed approach is both sound and complete, ensuring no false or missed alarms. Case studies are provided to demonstrate the effectiveness of our method", "url": "" }, { "id": "Mo-MoC13.1", "code": "MoC13.1", "title": "A New Duality-Free Framework for Convex Optimisation with Superlinear Convergence and Effective Warm-Starting", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC13", "sessionTitle": "Model Predictive Control III", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Cummins, Michael", "affiliation": "Imperial College London" }, { "name": "Kerrigan, Eric C.", "affiliation": "Imperial College London" } ], "keywords": [ "Real-time optimal control", "Convex optimization", "Model predictive control" ], "abstract": "Modern second-order solvers for convex optimisation, such as interior point methods, rely on primal-dual information and are difficult to warm-start, limiting their applicability in real-time control. We propose the PVM, a duality‑free framework that reformulates the constrained problem as the unconstrained minimisation of a value function. The resulting problem always has a solution, yields a certificate of infeasibility and is amenable to warm‑starting. Using this new framework, we develop a second‑order algorithm for Quadratic Programming and establish sufficient conditions for superlinear convergence to an arbitrarily small neighbourhood of the solution. Numerical experiments on a strictly constrained LQR problem demonstrate competitive performance with state‑of‑the‑art solvers from a cold start and up to 70% reduction in Newton iterations when warm starting.", "url": "" }, { "id": "Mo-MoC13.2", "code": "MoC13.2", "title": "MP-MPPI: A Motion Primitive Guided Sampling-Based Optimizer for Model Predictive Control", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC13", "sessionTitle": "Model Predictive Control III", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Mathisen, Marlon", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Vaaler, Aksel", "affiliation": "NTNU" }, { "name": "Egeland, Olav", "affiliation": "Norwegian Univ. of Sci. & Tech" }, { "name": "Kelasidi, Eleni", "affiliation": "Norwegian University of Science and Technology, NTNU" } ], "keywords": [ "Model predictive control", "Real-time optimal control", "Non-smooth and discontinuous optimal control" ], "abstract": "This paper proposes a novel method that extends the Model Predictive Path Integral (MPPI) method with motion primitives for additional structured sampling, which enhances the convergence towards a globally optimal solution. By evaluating motion primitives and perturbed control sequences in a real-time sampling-based optimization loop, this work addresses the limitations of the path planning capabilities of sampling-based controllers. The algorithm is implemented on a quadcopter simulator and tested on an obstacle field navigation task. It is demonstrated that the proposed approach enhances exploration of the control space while maintaining the fast, reactive behavior required for real-time control.", "url": "" }, { "id": "Mo-MoC13.3", "code": "MoC13.3", "title": "Learning Myopic Mixed-Integer Nonlinear Model Predictive Control from Expert Demonstrations", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC13", "sessionTitle": "Model Predictive Control III", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Orrico, Christopher Anthony", "affiliation": "TU Eindhoven" }, { "name": "Heemels, Maurice", "affiliation": "Eindhoven University of Technology" }, { "name": "Krishnamoorthy, Dinesh", "affiliation": "Norwegian University of Science and Technology (NTNU)" } ], "keywords": [ "Model predictive control", "Learning methods for optimal control", "Optimal control of hybrid systems" ], "abstract": "Applying nonlinear model predictive control (NMPC) to systems with hybrid dynamics or discrete actions typically yields mixed-integer nonlinear programs (MINLPs), whose real-time solution remains a major challenge and limits the applicability of mixed-integer NMPC (MINMPC). This paper proposes a myopic MINMPC framework that incorporates value-function approximation to substantially reduce the online computational burden. Using Bellman’s principle of optimality, we shorten the prediction horizon and append a value function learned offline from expert state–action demonstrations via inverse optimization with optimality residual minimization. A central feature is the dual treatment of discrete decisions, whereby integer constraints are relaxed during offline learning to enable KKT-residual-based value function synthesis, while the online controller enforces the true integer constraints to ensure feasibility. The learned value function induces a policy that is approximately policy-consistent with the expert demonstrations. The resulting controller achieves high closed-loop performance with a significantly shorter horizon, enabling real-time MINMPC. The effectiveness of the approach is demonstrated on the Lotka–Volterra fishing problem and a satellite attitude control system with discrete actuators.", "url": "" }, { "id": "Mo-MoC13.4", "code": "MoC13.4", "title": "Nonlinear Model Predictive Control for Hybrid Heavy-Duty Transport Application Using Neural State Space Models and Lifetime Prognostics", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC13", "sessionTitle": "Model Predictive Control III", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Hernandez-Torres, David", "affiliation": "CEA" }, { "name": "Roatta, Misael", "affiliation": "Univ. Grenoble Alpes, CEA, LETI" }, { "name": "Kravos, Andraž", "affiliation": "University of Ljubljana, Faculty of Mechanical Engineering, LICeM" }, { "name": "Morvillier, Raphaël", "affiliation": "Univ. Grenoble Alpes, CEA, LETI" }, { "name": "Schott, Pascal", "affiliation": "Univ. Grenoble Alpes, CEA, LITEN" } ], "keywords": [ "Model predictive control", "Real-time optimal control", "Optimal control of hybrid systems" ], "abstract": "Decarbonizing heavy-duty transport is a critical challenge, with Hybrid Fuel Cell Electric Vehicles (FCEVs) emerging as a leading solution due to their high energy density and fast refueling capabilities. However, the efficiency and durability of the Proton Exchange Membrane Fuel Cell (PEMFC) stack are highly sensitive to dynamic load cycling. In response, this paper presents an advanced Energy Management System (EMS) based on a nonlinear Model Predictive Controller (MPC) designed to optimize the power split between the fuel cell and the battery pack in real-time. To overcome the computational burden of physical models while retaining high fidelity, we utilize Neural State Space (NSS) models for both the fuel cell system and the battery. These neural networks capture nonlinear dynamics, including complex aging physics with voltage degradation, thermal behavior and non-linear efficiencies with low computational cost, enabling the implementation of MPC for on-board implementation. Furthermore, we propose a novel health-aware framework where the MPC cost function adapts dynamically based on a Remaining Useful Life (RUL) estimation algorithm. We validate the proposed strategy against a standard rule-based Low-Pass Filter (LPF) baseline and compare MPC performance under both ”Persistence” (conservative) and ”Oracle” (perfect future knowledge) prediction horizons. Results demonstrate that the NSS-MPC with RULintegration successfully shifts load dynamics to the battery as the fuel cell ages, trading a marginal increase in consumption for an extension of component lifetime.", "url": "" }, { "id": "Mo-MoC13.5", "code": "MoC13.5", "title": "Bayesian Model Predictive Control for Quantum State Regulation under Decoherence", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC13", "sessionTitle": "Model Predictive Control III", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Nuchkrua, Thanana", "affiliation": "National Chung Cheng University" }, { "name": "Boonto, Sudchai", "affiliation": "King Mongkut’s University of Technology Thonburi" }, { "name": "Liu, Xiaoqi", "affiliation": "University of Illinois Chicago" }, { "name": "Kornmaneesang, Woraphrut", "affiliation": "National Taiwan Normal University" } ], "keywords": [ "Model predictive control", "Stochastic optimal control problems", "Adaptive control design" ], "abstract": "We develop a Bayesian Model Predictive Control (BMPC) framework for adaptive quantum state regulation under model uncertainty. The method embeds Bayesian parameter inference directly into the receding-horizon optimization, enabling the controller to update uncertain Hamiltonian parameters online while computing constrained control inputs in real time. We formulate the BMPC architecture for Lindblad open-system dynamics and establish theoretical guarantees showing that posterior contraction drives the BMPC law toward the nominal MPC law, recovering its stability properties. Numerical experiments on single-qubit state-transfer tasks demonstrate that BMPC preserves high fidelity under parameter drift, decoherence, and measurement noise, and that short prediction horizons are sufficient for real-time feasibility --- making BMPC a principled and practical strategy for quantum feedback control under uncertainty.", "url": "" }, { "id": "Mo-MoC13.6", "code": "MoC13.6", "title": "IMMPC: An Internal Model Based MPC for Rejecting Unknown Disturbances", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC13", "sessionTitle": "Model Predictive Control III", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Brändle, Felix", "affiliation": "University of Stuttgart" }, { "name": "Allgower, Frank", "affiliation": "University of Stuttgart" } ], "keywords": [ "Model predictive control" ], "abstract": "Model predictive control (MPC) is a powerful control method that allows for the direct inclusion of state and input constraints into the controller design. However, errors in the model, e.g., caused by unknown disturbances, can lead to constraint violation, loss of feasibility, and deteriorate closed-loop performance. In this paper, we propose a new MPC scheme based on the internal model principle. This enables the MPC to reject unknown disturbances if the dynamics of the linear signal generator are known. We formulate the disturbance rejection problem as a stability problem to ensure feasibility, constraint satisfaction, and convergence to the optimal reachable output trajectory. The controller is validated on a fourtank system.", "url": "" }, { "id": "Mo-MoC14.1", "code": "MoC14.1", "title": "Graph-Structure-Based Reinforcement Learning Approach for Multi-Agent Obstacle Avoidance Navigation", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC14", "sessionTitle": "Learning Methods for Optimal Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Zhao, He-Ting", "affiliation": "Beihang University" }, { "name": "Wu, Huai-Ning", "affiliation": "Beihang University (Beijing University of Aeronautics and Astronautics)" } ], "keywords": [ "Learning methods for optimal control", "Large-scale and networked optimization problems", "Robust learning systems" ], "abstract": "In unknown environments, autonomous navigation of multi-agent systems remains a core challenge for large-scale deployment of unmanned systems. This paper proposes a graph-structure-based reinforcement learning approach (Graph-RL) for multi-agent obstacle avoidance navigation. Scene interactions are modeled as a dynamic graph, where nodes represent agents and obstacles, and edges encode interaction information. To enhance safety, we incorporate a Conditional Value at Risk (CVaR) mechanism into the loss function and train the policy network end-to-end. Simulation results show that the proposed Graph-RL approach achieves safe and efficient cooperative navigation in multi-agent scenarios of various scales.", "url": "" }, { "id": "Mo-MoC14.2", "code": "MoC14.2", "title": "An Offline Functional Learning Approach for Nonlinear Receding-Horizon Feedback Control", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC14", "sessionTitle": "Learning Methods for Optimal Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Khalyavin, Leon", "affiliation": "Imperial College London" }, { "name": "Moreschini, Alessio", "affiliation": "Imperial College London" }, { "name": "Scandella, Matteo", "affiliation": "University of Bergamo" }, { "name": "Parisini, Thomas", "affiliation": "Imperial C., Aalborg U. & Univ. of Trieste" } ], "keywords": [ "Learning methods for optimal control", "Model predictive control" ], "abstract": "In this preliminary work, we introduce a policy-iteration functional learning framework for the offline synthesis of approximate receding-horizon (RH) control policies in feedback form for discrete-time nonlinear dynamic systems. The proposed iterative learning scheme relies on a recently developed discrete Frechet derivative operator, which guarantees that all generated policy sequences remain within the intersection of the cost function’s sublevel sets, independent of the selected learning rate. Simulation results demonstrate the effectiveness of the proposed approach.", "url": "" }, { "id": "Mo-MoC14.3", "code": "MoC14.3", "title": "High-Dimensional Surrogate Modeling for Closed-Loop Learning of Neural-Network-Parameterized Model Predictive Control", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC14", "sessionTitle": "Learning Methods for Optimal Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Hirt, Sebastian", "affiliation": "TU Darmstadt" }, { "name": "Suwanto, Valentinus Lucky", "affiliation": "TU Darmstadt" }, { "name": "Alsmeier, Hendrik", "affiliation": "TU Darmstadt" }, { "name": "Pfefferkorn, Maik", "affiliation": "Technical University of Darmstadt" }, { "name": "Findeisen, Rolf", "affiliation": "TU Darmstadt" } ], "keywords": [ "Learning methods for optimal control", "Model predictive control", "Parametric optimization" ], "abstract": "Learning controller parameters from closed-loop data can improve closed-loop performance. Bayesian optimization is a sample-efficient black-box method that builds a probabilistic surrogate of closed-loop performance from few experiments and uses it to select informative controller parameters. However, it often struggles with dense high-dimensional controller parameterizations, as encountered, for example, in tuning model predictive controllers, because standard surrogate models fail to capture the structure of such spaces. This work investigates Bayesian neural networks as surrogate models to mitigate this limitation. Comparing Gaussian processes with Matérn kernels, finite-width Bayesian neural networks, and infinite-width Bayesian neural networks on a cart--pole task, we find that Bayesian-neural-network-based surrogates achieve faster and more reliable closed-loop cost convergence and enable successful optimization in parameter spaces with hundreds of dimensions. Infinite-width Bayesian neural networks maintain performance beyond one thousand parameters, whereas Matérn-kernel Gaussian processes rapidly lose effectiveness. These results indicate that Bayesian neural network surrogate models are promising for learning dense high-dimensional controller parameterizations and provide practical guidance for surrogate selection in learning-based controller design.", "url": "" }, { "id": "Mo-MoC14.4", "code": "MoC14.4", "title": "Learning Model Predictive Control for Non-Stationary Iterative Tasks", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC14", "sessionTitle": "Learning Methods for Optimal Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Hashimoto, Wataru", "affiliation": "The University of Osaka" }, { "name": "Hashimoto, Kazumune", "affiliation": "Osaka University" } ], "keywords": [ "Model predictive control", "Learning methods for optimal control", "Optimal control theory" ], "abstract": "Learning Model Predictive Control (LMPC) extends Model Predictive Control to iterative tasks, where data from previous executions improve future performance. Classical LMPC constructs its terminal set and cost from stored trajectories and typically assumes stationary dynamics and cost functions, which limits applicability when operating conditions vary. We propose Non-Stationary LMPC (NS-LMPC), which adapts its terminal ingredients across iterations. NS-LMPC enlarges an invariant terminal set via tube-based MPC arguments and extends the terminal cost via a distance-regularized construction, guaranteeing recursive feasibility, safety, and practical stability. Furthermore, under bounded inter-iteration drift, we establish theoretical guarantees of near-monotonic closed-loop performance improvement.", "url": "" }, { "id": "Mo-MoC14.5", "code": "MoC14.5", "title": "Distributed Switching Model Predictive Control Meets Koopman Operator for Dynamic Obstacle Avoidance", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC14", "sessionTitle": "Learning Methods for Optimal Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Azarbahram, Ali", "affiliation": "Chalmers University of Technology," }, { "name": "Yuca Huanca, Chrystian Pool Edmundo", "affiliation": "Politecnico Di Milano" }, { "name": "Incremona, Gian Paolo", "affiliation": "Politecnico Di Milano" }, { "name": "Colaneri, Patrizio", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Model predictive control", "Nonlinearity learning from data", "Cooperative nonlinear control" ], "abstract": "This paper introduces a Koopman-enhanced distributed switched model predictive control (SMPC) framework for safe and scalable navigation of quadrotor unmanned aerial vehicles (UAVs) in dynamic environments with moving obstacles. The proposed method integrates switched motion modes and data-driven prediction to enable real-time collision-free coordination. Koopman operator approximates nonlinear obstacle dynamics as linear models based on online measurements, enabling localization and accurate trajectory forecasting. These predictions are embedded into a distributed SMPC structure, where each UAV makes autonomous decisions using local and cluster-based information. This computationally efficient architecture is particularly promising for applications in surface transportation, including coordinated vehicle flows, shared infrastructure with pedestrians or cyclists, and urban UAV traffic. Simulation results demonstrate reliable formation control and real-time obstacle avoidance, highlighting the framework’s broad relevance for intelligent and cooperative mobility systems.", "url": "" }, { "id": "Mo-MoC14.6", "code": "MoC14.6", "title": "A Slack-Based Stochastic MPC View of ReLU Network", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC14", "sessionTitle": "Learning Methods for Optimal Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Maestre, Jose M.", "affiliation": "University of Seville" } ], "keywords": [ "Model predictive control", "Applications of optimal control", "Learning methods for optimal control" ], "abstract": "A slack-based framework that reinterprets ReLU neural network training as stochastic model predictive control is introduced, enabling explicit probabilistic constraints on neuron activation rates without binary variables. Analytical expressions for slack variable distributions and first-order optimality conditions are derived, establishing a formal equivalence between backpropagation and the shadow price recursion of constrained optimization. Experiments on a regression benchmark and a real-world inventory problem validate the framework, showing that the proposed activation bound provides a tunable mechanism for balancing cost and risk in hybrid neural-MPC architectures.", "url": "" }, { "id": "Mo-MoC15.1", "code": "MoC15.1", "title": "A Stability Condition for Switching Structured Networks of Linear Systems with First-Order Dynamics", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC15", "sessionTitle": "System Structure and Control: Structured and Interconnected Dynamical Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Zattoni, Elena", "affiliation": "Alma Mater Studiorum Universita' Di Bologna" }, { "name": "Perdon, Anna Maria", "affiliation": "Accademia Marchigiana Di Scienze, Lettere Ed Arti" }, { "name": "Conte, Giuseppe", "affiliation": "Accademia Marchigiana Di Scienze, Lettere Ed Arti" } ], "keywords": [ "System structure and control", "Structured linear systems", "Structural and geometric control" ], "abstract": "This work presents a stability condition for a class of dynamic networks. The network topology is subject to switching and is structured, namely, in each configuration, the link from one node to another either exists, with its weight taking an unknown real value, or is absent, thus being represented by a fixed zero. The nodes are switching linear systems with mode dynamics of the first order. The switching is ruled by a time-dependent signal. Under the assumption that the dynamics of each node, with no inputs from the others, is globally uniformly exponentially stable, a necessary condition for the global uniform exponential stability of the dynamic network for all values of the unknown real parameters is derived. This condition is also sufficient, being a direct consequence of results on cascaded systems. This work therefore provides a complete characterization of structural global uniform exponential stability for the class of dynamic networks considered and the condition derived is purely topological. Since the dynamic network can be modelled as a switching linear system with a partially structured pattern, the condition is expressed through the notion of essential graph, a directed graph that captures the information strictly relevant to stability analysis from the family of digraphs associated with the modes of the considered switching system.", "url": "" }, { "id": "Mo-MoC15.2", "code": "MoC15.2", "title": "On Capturing Linear Controllability through a Conley Index", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC15", "sessionTitle": "System Structure and Control: Structured and Interconnected Dynamical Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Jongeneel, Wouter", "affiliation": "KTH Royal Institute of Technology, Digital Futures" }, { "name": "Scolamiero, Martina", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "System structure and control", "Linear systems" ], "abstract": "The Conley index of an isolated invariant set is a topological invariant that captures qualitative dynamical behaviour in a neighbourhood of this set. In this note we show that Conley indices can capture controllability of linear control systems, both in the continuous-time and discrete-time case. In particular, by means of an appropriately designed smooth feedback, a single Conlex index can capture linear controllability.", "url": "" }, { "id": "Mo-MoC15.4", "code": "MoC15.4", "title": "Structural Sign Herdability in Temporally Switching Networks with Fixed Topology", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC15", "sessionTitle": "System Structure and Control: Structured and Interconnected Dynamical Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "M, Pradeep", "affiliation": "Indian Institute of Technology Kanpur" }, { "name": "Tripathy, Twinkle", "affiliation": "Indian Institute of Technology Kanpur" } ], "keywords": [ "Structured linear systems", "Positive linear systems", "Linear parameter-varying systems" ], "abstract": "This paper investigates structural sign (SS) herdability in a special class of temporally switching networks with fixed topology. We show that when the topology of the underlying digraph remains unchanged across all snapshots, the network attains SS herdability even in the presence of signed or layer dilations, a condition not applicable to static networks. This reveals a fundamental structural advantage of temporal dynamics and highlights a novel mechanism through which switching can overcome the classical obstructions to herdability. To validate these conclusions, we utilize a more relaxed form of sign matching within each snapshot of the temporal network. Furthermore, we show that when all snapshots share the same underlying topology, the temporally switching network achieves SS herdability within just two snapshots, which is fewer than the number required for structural controllability. Several examples are included to demonstrate these results.", "url": "" }, { "id": "Mo-MoC15.5", "code": "MoC15.5", "title": "Minimal Input Cardinality Disturbance Decoupling of Coupled Oscillators Via Output Feedback with Application to Power Networks", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC15", "sessionTitle": "System Structure and Control: Structured and Interconnected Dynamical Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Lebon, Luca Claude Gino", "affiliation": "Linköping University" }, { "name": "Lindberg, Johan", "affiliation": "Lund University" }, { "name": "Altafini, Claudio", "affiliation": "Linkoping University" } ], "keywords": [ "Structural and geometric control", "Disturbance rejection and input-to-state stability", "Control of complex systems" ], "abstract": "In this paper, we identify the smallest set of control input nodes and an associated output feedback law that achieves complete disturbance decoupling for a class of coupled oscillator networks. The focus is specifically on systems linearized around a stable phase-locked synchronized state. The proposed theoretical framework is applied to the linearized swing dynamics of power grids operating near synchronization. In this context, the disturbance decoupling problem corresponds to isolating subsets of nodes from exogenous disturbances by means of batteries that can both add or withdraw active power. Numerical simulations carried out on the IEEE New England 39-bus system show that the proposed methodology not only yields a minimal actuator placement ensuring effective disturbance rejection, but also preserves the internal stability of the closed-loop system.", "url": "" }, { "id": "Mo-MoC15.5", "code": "MoC15.5", "title": "Dynamic State Feedback Q-Sparse Control for Linear Systems", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC15", "sessionTitle": "System Structure and Control: Structured and Interconnected Dynamical Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Safarika, Eleftheria", "affiliation": "Imperial College London" }, { "name": "Astolfi, Alessandro", "affiliation": "King Abdullah University of Science and Technology (KAUST)" } ], "keywords": [ "Linear systems", "Switching linear systems", "System structure and control" ], "abstract": "This paper solves the sparse control problem for a class of multi-input linear systems using a dynamic state feedback controller comprising stable filters. It is shown that writing the system in an adapted set of coordinates reveals an interconnected cascade structure and allows a systematic solution. The results are illustrated through a simple case study, wherein the system is sparsely controlled by switching through families of controllers in lexicographical order. Indicative energy metrics are presented, revealing the tradeoff of sparsity.", "url": "" }, { "id": "Mo-MoC15.6", "code": "MoC15.6", "title": "A Surrogate-Node Approach to Strong Structural Controllability and Minimal Input Selection in Large-Scale Networks", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC15", "sessionTitle": "System Structure and Control: Structured and Interconnected Dynamical Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Schmidtke, Vincent", "affiliation": "University of Kassel" }, { "name": "Stursberg, Olaf", "affiliation": "University of Kassel" } ], "keywords": [ "Structured linear systems", "System structure and control" ], "abstract": "Ensuring strong structural controllability in large-scale networks with higher-order node dynamics is challenging when full actuation is not feasible. This work introduces surrogate nodes, which replace higher-order canonical forms in the controllability analysis of linear structured single-input-single-output node systems. The surrogate-node network allows structured networks of arbitrary node dimension to be analyzed by considering only a single node per system. Building on this formulation, a minimal input selection heuristics is proposed. Both the surrogate-node network and the heuristics are illustrated through a numerical example, demonstrating their scalability and practical applicability.", "url": "" }, { "id": "Mo-MoC16.1", "code": "MoC16.1", "title": "Regional Stability of Systems Controlled by ReLU Neural Networks Emulating MPC", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC16", "sessionTitle": "Stability and Disturbance in Nonlinear Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Zanette, Daniel", "affiliation": "Universidade Federal Do Rio Grande Do Sul" }, { "name": "Cabral, Leonardo", "affiliation": "Universidade De Caxias Do Sul (UCS)" }, { "name": "Gomes Da Silva Jr, Joao Manoel", "affiliation": "Universidade Federal Do Rio Grande Do Sul (UFRGS)" }, { "name": "Valmorbida, Giorgio", "affiliation": "L2S, CentraleSupelec" } ], "keywords": [ "Stability of nonlinear systems", "Lyapunov methods", "Model predictive control" ], "abstract": "This work studies the problem of regional exponential stability analysis of a discrete-time linear system controlled by a ReLU Neural Network (NN) that emulates a model predictive control (MPC). To ensure that the input constraints of the MPC are satisfied, a saturation is applied to the output of the NN emulating the MPC. It is shown that the closed-loop system is equivalent to a piecewise affine system that can be described by an implicit representation based on ramp (i.e. ReLU) functions. Using this representation, piecewise quadratic Lyapunov function candidates and properties verified uniquely by the ReLU function, Linear Matrix Inequalities (LMI) based conditions for the regional stability certification of the origin of the closed-loop system are derived. From these conditions an optimization problem to maximize the region of attraction of the origin is proposed. A numerical example demonstrates the application of the proposed results.", "url": "" }, { "id": "Mo-MoC16.2", "code": "MoC16.2", "title": "Global Asymptotic Stabilization of Non-Homogeneous Bilinear Single-Input Discrete Time-Invariant Complex Systems", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC16", "sessionTitle": "Stability and Disturbance in Nonlinear Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Zaitsev, Vasilii", "affiliation": "Udmurt State University" } ], "keywords": [ "Lyapunov methods", "Stability of nonlinear systems", "Output feedback nonlinear control" ], "abstract": "The problem of global asymptotic stabilization by state feedback is considered for time-invariant bilinear non-homogeneous control systems in the complex space with discrete-time with single input. We use the Jurdjevic--Quinn stabilization technique, the Barbashin--Krasovskii theorem and the technique of realification. Sufficient conditions for global asymptotic stabilization of the zero solution by real state feedback are obtained. Illustrative examples are given.", "url": "" }, { "id": "Mo-MoC16.3", "code": "MoC16.3", "title": "Scaled Graph Bounding Techniques for Reset Systems", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC16", "sessionTitle": "Stability and Disturbance in Nonlinear Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "de Groot, Timo", "affiliation": "Technische Universiteit Eindhoven" }, { "name": "Heemels, Maurice", "affiliation": "Eindhoven University of Technology" }, { "name": "Oomen, Tom", "affiliation": "Eindhoven University of Technology" }, { "name": "van den Eijnden, Sebastiaan", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Stability of nonlinear systems" ], "abstract": "Reset systems can overcome fundamental limitations of linear time-invariant control. The recently introduced notion of scaled (relative) graphs provides a promising framework for developing graphical analysis and design tools for reset systems, in line with widely adopted loopshaping methods for linear systems. The aim of this paper is to derive techniques for over-bounding the scaled graph of reset systems, and obtain insights in their accuracy. We exploit connections between quadratic dissipativity and scaled graphs to recast the over-bounding problem as the search for piecewise quadratic storage functions. Using specific sampling techniques, we reveal a fundamental limitation of general scaled graph approximation methods that are based on quadratic dissipativity.", "url": "" }, { "id": "Mo-MoC16.4", "code": "MoC16.4", "title": "Stability Verification of Dynamic Simulator with Runge-Kutta 4th-Order Integrator", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC16", "sessionTitle": "Stability and Disturbance in Nonlinear Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Kim, Jongrae", "affiliation": "University of Leeds" } ], "keywords": [ "Stability of nonlinear systems" ], "abstract": "Stability verification via computer-based simulation is an important step in finalising the design of control systems before deploying a controller to hardware. While Monte Carlo simulations provide a means to verify stability with full model complexity, they only yield probabilistic results. Some safety-critical systems may require a strict guarantee of stability. The heart of a dynamic system simulator is the numerical integrator, and the 4th-order Runge–Kutta method is one of the most commonly used ones. This paper focuses on simulators that use the 4th-order Runge–Kutta integrator. By including full model complexity in the simulator, firstly, an upper bound on the propagated states over a given time interval is established,accommodating any type of nonlinear component in the simulator. Secondly, using the bound, an algorithm to verify stability with a finite number of simulations over the given range of state space is established. The algorithm provides a deterministic stability guarantee over the continuous state space. Finally, we demonstrate the effectiveness and limitations of the proposed algorithm using an inverted pendulum system with a reinforcement learning controller combined with a Linear Quadratic Regulator, where the system includes a detailed electric motor model.", "url": "" }, { "id": "Mo-MoC16.5", "code": "MoC16.5", "title": "Feedback Linearization Framework for Disturbance Affected Nonlinear Systems", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC16", "sessionTitle": "Stability and Disturbance in Nonlinear Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Rahmatullah, Amna", "affiliation": "Tallinn University of Technology" }, { "name": "Kaldmäe, Arvo", "affiliation": "Tallinn University of Technology" } ], "keywords": [ "Application of nonlinear analysis and design" ], "abstract": "This paper studies the feedback linearization problem for nonlinear continuous-time control systems affected by a disturbance variable. Although feedback linearization is a well established method in nonlinear control, the problem has received very little attention in the case when the system state equations depend on some unknown inputs (or disturbances). Previous result on this topic used, like in the classical case, a state and input transformations to achieve the linearized form. In this paper additionally a disturbance transformation is used to relax the otherwise restrictive solvability conditions. Necessary and sufficient conditions are found for the existence of a state and an input transformations and for the existence of a state, an input and a disturbance transformations that linearize the system state equations. An algorithm is given to find the necessary transformations and the results are illustrated by several examples. Finally, various issues related to the solvability of the problem and applicability of such design method are discussed in the conclusion section.", "url": "" }, { "id": "Mo-MoC16.6", "code": "MoC16.6", "title": "Resilience of Distributed Gradient Algorithm under DoS Attack with Enhanced Lyapunov Function", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC16", "sessionTitle": "Stability and Disturbance in Nonlinear Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Lee, Ti-Chung", "affiliation": "National Sun Yat-Sen University" }, { "name": "Tan, Chung-Ting", "affiliation": "National Sun Yat-Sen University" }, { "name": "Wu, Wen-Kai", "affiliation": "National Sun Yat-Sen University" }, { "name": "Chung, Shang-Hsuan", "affiliation": "National Sun Yat-Sen University" }, { "name": "Shih, Cheng-Sin", "affiliation": "Department of Electrical Engineering, National Sun Yat-Sen University, Taiwan" } ], "keywords": [ "Lyapunov methods", "Switching linear systems", "Convex optimization" ], "abstract": "Cyber networked systems are susceptible to denial-of-service (DoS) attacks, which disrupt communication links and alter network topology, thereby degrading the performance of underlying control mechanisms. This paper investigates the problem of decentralized optimization and consensus in the presence of DoS attacks by employing a well-established distributed gradient algorithm. To overcome the restrictive dwell-time assumptions commonly adopted in existing literature, a more relaxed condition termed general uniform joint connectivity (GUJC) is introduced. By constructing an enhanced Lyapunov function, we develop a simplified yet rigorous stability analysis that guarantees resilient convergence despite adversarial disruptions. Numerical simulations validate the effectiveness of the theoretical results.", "url": "" }, { "id": "Mo-MoC17.1", "code": "MoC17.1", "title": "Observer Design for Networked Linear Systems with Fast and Slow Dynamics under Measurement Noise", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC17", "sessionTitle": "Sampled-Data Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Wang, Weixuan", "affiliation": "The University of Melbourne" }, { "name": "Maass, Alejandro I.", "affiliation": "Pontificia Universidad Católica De Chile" }, { "name": "Nesic, Dragan", "affiliation": "Univ of Melbourne" }, { "name": "Tan, Ying", "affiliation": "The Univ of Melbourne" }, { "name": "Postoyan, Romain", "affiliation": "CRAN, CNRS, Université De Lorraine" }, { "name": "Heemels, Maurice", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Sampled-data/digital control", "Observer design", "Control of hybrid systems" ], "abstract": "This paper addresses the emulation-based observer design for networked control systems (NCS) with linear plants that operate at two time scales in the presence of measurement noise. The system is formulated as a hybrid singularly perturbed dynamical system, enabling the systematic use of singular perturbation techniques to derive explicit bounds on the maximum allowable transmission intervals (MATI) for both fast and slow signals transmitted over a single communication channel. Under the resulting conditions, the proposed observer guarantees that the estimation error satisfies a global exponential derivative-input-to-state stability (DISS)-like property, where the ultimate bound scales proportionally with the magnitudes of the measurement noise and the time derivative of the control input.", "url": "" }, { "id": "Mo-MoC17.2", "code": "MoC17.2", "title": "Distributed Observers for LTI Systems with Delayed Sampled Outputs", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC17", "sessionTitle": "Sampled-Data Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Hosono, Miki", "affiliation": "Tokyo Metropolitan University" }, { "name": "Oguchi, Toshiki", "affiliation": "Tokyo Metropolitan University" } ], "keywords": [ "Observer design", "Decentralized control", "Sampled-data/digital control" ], "abstract": "This paper addresses the distributed state estimation problem for linear time-invariant (LTI) systems using asynchronous, aperiodic, and delayed sampled-data measurements. In particular, we consider the combined effects of measurement delays, measurement sampling, communication delays, and communication sampling among observers, all of which are assumed to be bounded. We propose a distributed observer framework in which multiple observers perform local state estimation using their own sensor measurements and exchange information over a communication network to reconstruct the global system state. By employing a Lyapunov–Krasovskii functional approach, we derive sufficient linear matrix inequality (LMI) conditions that guarantee the stability of the estimation error dynamics. Based on these conditions, a systematic design procedure is developed for both the observer gains and the coupling gains. The effectiveness of the proposed method is demonstrated through a numerical example.", "url": "" }, { "id": "Mo-MoC17.3", "code": "MoC17.3", "title": "Heuristic Feedforward Control Design with Extended Bandwidth", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC17", "sessionTitle": "Sampled-Data Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Lai, Po-Yang", "affiliation": "National Taiwan University" }, { "name": "Lee, Yu-Hsiu", "affiliation": "National Taiwan University" } ], "keywords": [ "Sampled-data/digital control", "Analytic design", "Controller constraints and structure" ], "abstract": "Conventional feedforward controller design largely hinges on handling the unstable zeros of the plant. The zero-phase error tracking controller (ZPETC) compensates for plant phase distortion at the cost of magnitude error, while the zero-magnitude error tracking controller (ZMETC) cancels magnitude error but introduces phase distortion. ZPETC is better suited for time-critical tasks, whereas ZMETC is advantageous in contouring control. To extend the effective bandwidth of these controllers, a systematic analysis is conducted. The study shows that the extended-bandwidth ZPETC problem can be formulated as a linear-phase finite-impulse-response filter design, whereas the extended-bandwidth ZMETC corresponds to an all-pass phase compensation filter design. Established filter design methods are then applied, with initial experimental results demonstrated on a galvanometer system.", "url": "" }, { "id": "Mo-MoC17.4", "code": "MoC17.4", "title": "Joint Periodic Sampling and Control Design of LTI Systems", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC17", "sessionTitle": "Sampled-Data Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Deaecto, Grace S.", "affiliation": "FEM/UNICAMP" }, { "name": "S. Oliveira, Lucas Ruan", "affiliation": "University of Campinas" }, { "name": "Geromel, Jose C.", "affiliation": "UNICAMP" } ], "keywords": [ "Sampled-data/digital control", "Linear systems", "Robust linear matrix inequalities" ], "abstract": "This paper tackles the joint design problem of periodic sampling schedule and control of linear time invariant systems. For a given periodic sampling schedule policy, the corresponding state feedback controller is determined from the solution of a convex problem expressed through LMIs, making possible the determination of the optimal periodic sampling schedule by dynamic programming, being numerically solved by some appropriate enumeration technique. The closed-loop system performance is assessed through the usual H2 norm. The results reported in this paper indicate that the proposed periodic control structure can yield good performance to the closed-loop system whenever the optimal periodic scheduling is implemented. Two examples are solved, presented and discussed in order to illustrate the theoretical contributions when compared to similar results available in the literature.", "url": "" }, { "id": "Mo-MoC17.5", "code": "MoC17.5", "title": "Optimal Planning and Control under Signal Temporal Logic Specifications", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC17", "sessionTitle": "Sampled-Data Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Pan, Zuodong", "affiliation": "Dalian University of Technology" }, { "name": "Fang, Xu", "affiliation": "No.2 Linggong Road, Ganjingzi District," }, { "name": "Ren, Wei", "affiliation": "Dalian University of Technology" } ], "keywords": [ "Sampled-data/digital control", "Non-smooth and discontinuous optimal control", "Optimization-based estimation and control" ], "abstract": "This paper addresses the planning and control problem for nonlinear systems under Signal Temporal Logic (STL) specifications. We first decompose an STL task into finite local tasks. A sampling-based method generates sequences of local waypoints to satisfy all local tasks, from which the corresponding satisfaction pair sets are derived. Following a local-to-global strategy, all sequences of local waypoints are synthesized into a global one, based on which a safe corridor is then constructed. Leveraging the safe corridor and the satisfaction pair sets, an optimization problem is formulated and solved to derive a position trajectory that satisfies the STL task. Finally, numerical examples and comparative results are presented to demonstrate the efficacy of the proposed approach.", "url": "" }, { "id": "Mo-MoC17.6", "code": "MoC17.6", "title": "Model-Based Phase-Tuned Active Vibration Feedback Control of a Voice-Coil-Actuated SDOF Mass–Spring System", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC17", "sessionTitle": "Sampled-Data Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Shah, Syed Shazaib", "affiliation": "Beihang University" }, { "name": "Zhang, Qicheng", "affiliation": "Beihang University" }, { "name": "Tan, Daoliang", "affiliation": "Beihang University" }, { "name": "Zhang, Dayi", "affiliation": "Beihang University" } ], "keywords": [ "Adaptive control design", "Model validation", "Sampled-data/digital control" ], "abstract": "This paper reports an experimental study on Active Vibration Control (AVC) of a Single Degree Of Freedom (SDOF) mass–spring system actuated by a piezoelectric Voice Coil Motor (VCM). A coupled electromechanical Equation of Motion (EQM) is derived that maps the commanded voltage to platform acceleration under harmonic excitation, explicitly accounting for spring, inertial, and back-electromotive-force (back-EMF) effects. Using manufacturer data, the model is validated in terms of phase (voltage–acceleration lag) and magnitude (displacement response) through dedicated Non-Real-Time (non-RT) experiments. The validated conversion law is then used to generate equal-magnitude counter-vibrations, first with manually tuned phase and subsequently via an automated phase-sweep feedback loop. The manual phase-tuning trial demonstrates near-complete cancellation of a 10Hz, 1.25mm disturbance, highlighting the central role of phase in vibration attenuation. An automated phase-sweep feedback controller is finally implemented that, in Real Time (RT), sweeps and locks to the phase of least residual vibration across test frequencies up to 200Hz, providing a pragmatic route to embedding phase alignment ahead of more sophisticated compute-intensive adaptive algorithms.", "url": "" }, { "id": "Mo-MoC18.1", "code": "MoC18.1", "title": "Maturity Model for Technical Documentation for Small and Medium Sized Enterprises (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC18", "sessionTitle": "The Role of Interoperability and Standards in Realizing Digital Twins for Sustainable and Digital Manufacturing Transformation", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Koch, Christian", "affiliation": "TU Dortmund University" }, { "name": "Deuse, Jochen", "affiliation": "University of Technology Sydney" } ], "keywords": [ "Enterprise architecture", "Enterprise interoperability", "Digital transformation" ], "abstract": "This article presents the initial four steps of developing a maturity model for technical documentation tailored to small and medium-sized enterprises in machinery and plant engineering. Following a structured development methodology, the study combines literature review, expert interviews, and iterative development to define maturity levels, characteristics, and assessment criteria. The model captures variations in documentation capabilities, providing a structured framework to evaluate current practices, identify development potentials, and guide progress through successive stages. The scientific contribution lies in consolidating and structuring diverse maturity perspectives into a unified framework-oriented approach that operationalizes technical documentation requirements.", "url": "" }, { "id": "Mo-MoC18.2", "code": "MoC18.2", "title": "Knowledge-Driven Digital Twin Architecture for Semantic Rule Integration in the Process Industry (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC18", "sessionTitle": "The Role of Interoperability and Standards in Realizing Digital Twins for Sustainable and Digital Manufacturing Transformation", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Wagstyl, David", "affiliation": "RIF Institute for Research and Transfer E.V" }, { "name": "Weitkamp, Kevin", "affiliation": "RIF Institute for Research and Transfer E.V" }, { "name": "Wöstmann, René", "affiliation": "RIF e.V. - Institut Für Forschung Und Transfer" }, { "name": "Deuse, Jochen", "affiliation": "University of Technology Sydney" } ], "keywords": [ "Cyber-physical production systems", "Intelligent manufacturing systems", "Manufacturing plant simulation, control and optimization" ], "abstract": "Digital Twin implementations in the process industry are increasingly confronted with heterogeneous, weakly integrated data and lack explicit mechanisms for semantic, rule-based decision-making. Existing standards and frameworks focus on syntactic interoperability and structural asset representation, but do not provide an architecture that unifies data management, knowledge representation, and executable rule semantics. This paper introduces a knowledge-driven Digital Twin architecture that embeds semantic rules as first-class components within a layered reference model for brownfield environments. The architecture comprises Physical, Network, Data, Knowledge & Semantic, Service, and Administration Layers and integrates a structured rule corpus including trend, threshold, dependency, temporal, composite, and prediction rules. A OWL-based ontology model formalizes the rule taxonomy together with their inputs, parameters, scopes, and outputs, and explicitly separates generic rule definitions on the process side from their parameter-bound instances on the procedure side, enabling reusable, context-aware reasoning patterns. The approach is instantiated in a laboratory-scale fermentation process, where a knowledge graph and continuous rule evaluation enable phase-oriented interpretation of sensor data, semantic annotation of procedural phases, and closed-loop adjustment of temperature setpoints. The results demonstrate that the proposed architecture facilitates interpretable, modular decision support and forms a transferable basis for semantic rule integration in Digital Twins.", "url": "" }, { "id": "Mo-MoC18.3", "code": "MoC18.3", "title": "Ontology-Driven Semantic Integration of Industrial Data into Knowledge Graphs for Digital Twin Applications (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC18", "sessionTitle": "The Role of Interoperability and Standards in Realizing Digital Twins for Sustainable and Digital Manufacturing Transformation", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Weitkamp, Kevin", "affiliation": "RIF Institute for Research and Transfer E.V" }, { "name": "Wagstyl, David", "affiliation": "RIF Institute for Research and Transfer E.V" }, { "name": "Schlunder, Philipp", "affiliation": "Daibe UG" }, { "name": "Wolf, Nicolas", "affiliation": "Bitburger Braugruppe GmbH" }, { "name": "Deuse, Jochen", "affiliation": "University of Technology Sydney" } ], "keywords": [ "Cyber-physical production systems", "Digital transformation", "Intelligent manufacturing systems" ], "abstract": "Ontology-driven semantic integration is a key prerequisite for deploying interpretable Digital Twins in industrial environments, yet practical workflows for connecting legacy systems to semantic models are still rare. This paper presents an ontology-based workflow that reorganizes heterogeneous industrial information (recipe and process models, procedural structures, equipment hierarchies, measured variables, and data-source references) around an ISA-88-aligned domain ontology. An Ontology-Guided Mapping Component transforms plant-specific metadata from spreadsheets and engineering exports into RDF individuals, which are then projected via a Graph Transformation Component into an operational knowledge graph. The approach is implemented in a pilot-scale brewing laboratory, where 351 nodes and 647 relationships capture the semantic structure of a complete example recipe, its execution environment, and linked sensor data sources. The resulting graph supports domain-aware queries that bridge procedures, equipment, and data access, demonstrating the feasibility of the workflow as a semantic foundation for Digital Twin applications in batch-oriented process industries.", "url": "" }, { "id": "Mo-MoC18.4", "code": "MoC18.4", "title": "Six Sigma, Quo Vadis? a Retrospective and the Path to Sustainable Intelligent Optimization in the Age of Artificial Intelligence (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC18", "sessionTitle": "The Role of Interoperability and Standards in Realizing Digital Twins for Sustainable and Digital Manufacturing Transformation", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "West, Nikolai", "affiliation": "Technical University Dortmund" }, { "name": "Terschluse, Felix", "affiliation": "RIF Institute for Research and Transfer E.V" }, { "name": "Stemann, Dietmar", "affiliation": "MTS Consulting Partner" }, { "name": "Deuse, Jochen", "affiliation": "University of Technology Sydney" } ], "keywords": [ "Digital transformation", "Simulation and optimization in production, operations and services", "Sustainable and circular supply chain and production" ], "abstract": "Six Sigma has evolved from a manufacturing quality methodology into a comprehensive framework for process control and optimization. This survey traces its evolution through five distinct eras: Statistical Foundations (1920s-1980s), Methodological Formation (1986-1995), Strategic Dissemination (1995-2003), Methodological Synthesis (2003-2015), and Digital Transformation (2015-present). We analyze the key drivers of phase transitions, from product complexity and competitive pressure to data explosion and AI autonomy, and examine how Six Sigma’s core principles of variation reduction and systematic improvement have adapted to technological change. The methodology shifted from reactive inspection to proactive prevention, then to strategic integration, adaptive synthesis, and finally to predictive optimization. Critically, we identify an emerging sixth era, Human-AI Orchestration, where autonomous systems promise unprecedented optimization capability while raising urgent questions about sustainability integration. Parallel to digital transformation, Green Six Sigma emerged as environmental objectives moved from peripheral to strategic. However, AI-driven optimization systems currently optimize what we measure: if sustainability metrics remain absent from algorithmic objective functions, autonomous systems risk achieving perfect efficiency toward environmental catastrophe. We argue that the window for proactive sustainability integration is narrow and closing, as infrastructure being deployed now will shape industrial environmental impact for decades through technological lock-in and path dependency. This historical analysis reveals Six Sigma’s resilience as an adaptive control framework while demonstrating that the current transition differs from predecessors in urgency, complexity, and stakes. The control engineering community possesses technical capability to integrate sustainability into intelligent systems; what remains is collective will to design systems optimizing for sustainable prosperity rather than efficient degradation.", "url": "" }, { "id": "Mo-MoC18.5", "code": "MoC18.5", "title": "A Cartography of Digital Twin Maturity Models and Research Challenges (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC18", "sessionTitle": "The Role of Interoperability and Standards in Realizing Digital Twins for Sustainable and Digital Manufacturing Transformation", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Pires, Flavia", "affiliation": "Instituto Politecnico De Braganca" }, { "name": "Karnouskos, Stamatis", "affiliation": "SAP" }, { "name": "Ahmad, Bilal", "affiliation": "Universityof Warwick, UK" }, { "name": "Leitão, Paulo", "affiliation": "Polytechnic Institute of Bragança" } ], "keywords": [ "Intelligent manufacturing systems", "Digital transformation", "Systems-of-systems" ], "abstract": "Digital Twin (DT) technology has experienced rapid growth in recent years, leading to an increasing need for robust models to assess the maturity of DT implementations. A maturity assessment provides a structured approach for evaluating DT capabilities, identifying gaps, supporting strategic planning, monitoring technological evolution, fostering capability development, and reducing deployment risks. This paper performs a systematic literature review of existing DT maturity models and analyses their capabilities using a unified assessment framework derived from ISO 23247 and the Digital Twin Consortium reference architecture. This review's key findings highlight several key areas, including the absence of generic and standardised maturity models, the prevalence of qualitative assessment approaches, limited validation through real-world case studies, an insufficient assessment of security capabilities and the DT ecosystem's maturity, and a lack of automation and tool support for conducting maturity evaluations. These findings provide insights into critical research gaps and future directions for advancing DT maturity assessment.", "url": "" }, { "id": "Mo-MoC18.6", "code": "MoC18.6", "title": "A Data-Driven SCOR-Based Framework for Mapping Supply Chain Resilience KPIs (I)", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC18", "sessionTitle": "The Role of Interoperability and Standards in Realizing Digital Twins for Sustainable and Digital Manufacturing Transformation", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Himmiche, Sara", "affiliation": "Université Savoie Mont Blanc, SYMME Laboratory" }, { "name": "Baghdadi-Bait, Oumaima", "affiliation": "SYMME, Université Savoie Mont Blanc" }, { "name": "Maire, Jean-Luc", "affiliation": "University Savoie Mont Blanc, SYMME" }, { "name": "Montoya-Torres, Jairo R.", "affiliation": "École De Technologie Supérieure" }, { "name": "Jimenez, Jose Fernando", "affiliation": "Universite Savoie Mont Blanc" } ], "keywords": [ "Viable and resilient supply chain and production", "Supply chain management in manufacturing", "Digital supply chain and production" ], "abstract": "The resilience of Supply Chains increasingly depends on the ability to assess, structure, and interpret resilience-oriented performance indicators. However, existing Key Performance Indicators repositories remain heterogeneous, weakly standardized, and only partially aligned with process reference models such as SCOR. This study proposes a hybrid text analytics pipeline combining keyword based rules and Sentence-BERT embeddings to classify resilience KPIs across SCOR processes and managerial intent categories. The results reveal consistent clusters of strategic, operational, functional, and systemic indicators, and highlight a substantial set of transversal KPIs that conventional taxonomies do not capture. These results are formalized into an OWL ontology populated with all KPIs, providing a machine-interpretable semantic model that unifies processes, resilience dimensions, and KPI types. The ontology enhances semantic interoperability and forms a foundation for digital resilience assessment and decision-support systems.", "url": "" }, { "id": "Mo-MoC19.1", "code": "MoC19.1", "title": "Mutual Information of Tsallis in the Evaluation of Professional Skills of Human-Operator Teams (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC19", "sessionTitle": "System Identification for Manufacturing Control Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Chernyshov, Kirill", "affiliation": "V.A. Trapeznikov Institute of Control Sciences" } ], "keywords": [ "Cyber-physical production systems", "Complex dynamic systems" ], "abstract": "This article describes a methodology for assessing the professional skills of a process plant operator team using system identification methods. This methodology aims to construct an input/output data model reflecting the actual level of professional competencies and skills of the team. This input/output data model is based on the use of a “proxy,” or indirect variable, such as time. Specifically, the time required for an operator team to make a decision on the behavior of a process based on information provided by sources distributed throughout the control panel, such as group viewing displays (GVDs), is examined. It is proposed to record this time using eye trackers. The model characteristics obtained in this way and calculated based on observations of the actual workflow of an experienced operator team constitute a tool for assessing the operator team’s experience.", "url": "" }, { "id": "Mo-MoC19.2", "code": "MoC19.2", "title": "Decision Support System for Power Generation Plant Based on Predictive Just-In-Time Learning (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC19", "sessionTitle": "System Identification for Manufacturing Control Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Shlyakhov, Mikhail", "affiliation": "V.A. Trapeznikov Institute of Control Sciences" }, { "name": "Bakhtadze, Natalia", "affiliation": "V.A. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences" }, { "name": "Zaikin, Oleg", "affiliation": "Warsaw School of Computer Science" }, { "name": "Mukhtarov, Kirill", "affiliation": "V.A. Trapeznikov Institute of Control Sciences" } ], "keywords": [ "Manufacturing plant simulation, control and optimization", "Model-driven enterprise-system engineering", "Manufacturing prognostics and health management" ], "abstract": "A decision support system for process operators of thermal power plants is presented. Based on pre-trained digital predictive identification models, algorithms for generating control actions have been developed allowing for obtaining specified values of equipment operating parameters in a given range within a finite acceptable time period. The effectiveness of the model has been confirmed by the results of case studies using historical process data of a thermal power plant boiler operation. The prospects for various directions of further research are analyzed.", "url": "" }, { "id": "Mo-MoC19.3", "code": "MoC19.3", "title": "Cluster-Local Regularization for Associative Search (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC19", "sessionTitle": "System Identification for Manufacturing Control Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Bakhtadze, Natalia", "affiliation": "V.A. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences" }, { "name": "Chereshko, Alexey", "affiliation": "V.A. Trapeznikov Institute of Control Sciences" }, { "name": "Kushnarev, Vladislav", "affiliation": "V.A. Trapeznikov Institute of Control Sciences" }, { "name": "Elpashev, Denis", "affiliation": "V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences" }, { "name": "Smirnova, Gulnara", "affiliation": "Kazan National Research Technical University Named after A.N.Tupolev" }, { "name": "Sabitov, Rustem", "affiliation": "Kazan National Research Technical University Named after A.N.Tupolev" } ], "keywords": [ "Data-driven and AI-based modelling of production and logistics", "Model-driven enterprise-system engineering", "Manufacturing prognostics and health management" ], "abstract": "The paper considers two approaches to determining the regularization parameter in the associative search algorithm: cluster and cluster-local regularization. In the first case, each cluster is assigned a single regularization parameter equal to the maximum value among all cluster points, which greatly simplifies and speeds up calculations. In the second case, an individual regularization parameter is calculated for the current state of the system based on the nearest elements of the same cluster using nuclear weighting. This mechanism provides a more accurate adaptation to the local properties of the data and reduces the redundancy of regularization.", "url": "" }, { "id": "Mo-MoC19.4", "code": "MoC19.4", "title": "A Gradient-Type Method for Parameter Identification Based on a Decentralized Square-Root Information Filter (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC19", "sessionTitle": "System Identification for Manufacturing Control Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Tsyganov, Andrey", "affiliation": "Ulyanovsk State Pedagogical University" }, { "name": "Tsyganova, Julia", "affiliation": "Innopolis University" }, { "name": "Golubkov, Aleksey", "affiliation": "Ulyanovsk State Pedagogical University" } ], "keywords": [ "Decentralized and distributed control for large-scale systems", "Large-scale complex systems", "Complex dynamic systems" ], "abstract": "The paper proposes a new gradient-type method for identifying parameters of discrete-time linear stochastic systems using decentralized square-root information filter (DSRIF). The main result of the paper is a new method for computation of the gradient of identification criterion formulated in terms of DSRIF outputs (matrix square roots of information matrices and corresponding estimates of information vectors) as well as their matrix derivatives on the parameter of uncertainty. The method proposed uses the original approach of algorithmic differentiation of the matrix orthogonal transformations. Results of numerical experiments of circular motion tracking with various configurations of measurement models validate the efficiency of our method. In general, this work suggests a unified framework for decentralized square-root information filtering and gradient-type parameter identification suitable for different real-life applications, such as environmental monitoring and adaptive signal processing.", "url": "" }, { "id": "Mo-MoC20.1", "code": "MoC20.1", "title": "Multi-Attention Convolutional Network for Particle Size Distribution Analysis", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC20", "sessionTitle": "Control in Mining, Mineral and Metal Processing", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Olivier, Laurentz", "affiliation": "Analyte / University of Pretoria" }, { "name": "Craig, Ian Keith", "affiliation": "University of Pretoria" } ], "keywords": [ "Image analysis and computer vision in MMM systems", "Machine learning and artificial intelligence in MMM process control", "Soft sensors in MMM systems" ], "abstract": "A multi-attention based convolutional neural network was used to estimate the ore particle size distribution from images of the ore on a conveyor belt. Images of pre-sized iron ore was used to train the neural network. The multi-attention mechanism and a modern efficient network layout produce superior performance compared to previous implementations. Ore size distribution information is useful feedforward information for intervention by an automatic controller or operations personnel.", "url": "" }, { "id": "Mo-MoC20.2", "code": "MoC20.2", "title": "A Decision-Making Scheme Considering Coal-Rock Strength and Drilling Conditions for Drilling Operating Parameters in Underground Coal Mines", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC20", "sessionTitle": "Control in Mining, Mineral and Metal Processing", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Zeng, Kanghui", "affiliation": "China University of Geosciences, Wuhan" }, { "name": "Lu, Chengda", "affiliation": "China University of Geosciences" }, { "name": "Yang, Xiao", "affiliation": "China University of Geosciences, Wuhan" }, { "name": "Wang, Yibing", "affiliation": "China University of Geosciences" }, { "name": "Zhang, Youzhen", "affiliation": "CCTEG Xi'an Research Institute (Group) Co., Ltd" }, { "name": "Li, Quanxin", "affiliation": "CCTEG Xi'an Research Institute (Group) Co., Ltd" }, { "name": "Wu, Min", "affiliation": "China University of Geosciences" } ], "keywords": [ "Measurement while drilling", "Soft sensors in MMM systems", "Predictive maintenance and equipment condition monitoring" ], "abstract": "Drilling operating parameters are often mismatched with coal-rock strength and drilling conditions in underground coal mines, which leads to low efficiency, equipment wear, and safety risks. To address this mismatch, a decision-making scheme is developed for feed speed and rotation speed considering coal-rock strength and drilling conditions. Coal-rock strength is quantified by specific energy derived from a torsional–axial dynamics model of the drill string, while drilling conditions are recognized by clustering fluctuation features of specific energy, torque, and main pump pressure with a Gaussian mixture model. Subsequently, the perceived coal-rock strength and drilling conditions are used as inputs to a Mamdani fuzzy inference system to determine the optimal operating parameters. To enhance objectivity, kernel density estimation is utilized to generate data-driven membership functions from field measurements to enhance objectivity. A conditional triggering mechanism updates the decided parameters only when changes in the drilling conditions occur, so that unnecessary adjustments are avoided. The effectiveness of the proposed scheme is demonstrated through an industrial case study based on actual drilling data, showing improved efficiency and operational safety.", "url": "" }, { "id": "Mo-MoC20.3", "code": "MoC20.3", "title": "Optimization of the Cooling Section in Continuous Steel Strip Processing Lines", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC20", "sessionTitle": "Control in Mining, Mineral and Metal Processing", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Sannes, Solveig", "affiliation": "Technische Universität Wien" }, { "name": "Jadachowski, Lukasz", "affiliation": "TU Wien" }, { "name": "Niederer, Matrin", "affiliation": "AIT Austrian Institute of Technology GmbH" }, { "name": "Steinboeck, Andreas", "affiliation": "TU Wien" } ], "keywords": [ "MMM process modeling, identification, and estimation techniques", "Process modeling, identification, and estimation techniques" ], "abstract": "The design of continuous steel strip processing lines is a complex, multifaceted, and iterative process. In particular, the cooling section must be designed to realize temperature trajectories during cooling which produce desired material properties that rely on specific phase transformations. By using a dynamic phase transformation and cooling model, the process can be optimized. This optimization routine may assist the design of annealing lines, yielding the required cooling zone lengths, heat transfer coefficients, and reference temperature trajectories that can serve as inputs for control strategies to produce desired material properties.", "url": "" }, { "id": "Mo-MoC20.4", "code": "MoC20.4", "title": "Control-Oriented Model of Fluid Velocity in a Confined Vortex", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC20", "sessionTitle": "Control in Mining, Mineral and Metal Processing", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Gasparini, Luca", "affiliation": "TU Wien" }, { "name": "Schimkowitsch, Bernhard", "affiliation": "TU Wien" }, { "name": "Cseh, Daniel Zoltan", "affiliation": "TU Wien" }, { "name": "Kugi, Andreas", "affiliation": "TU Wien" }, { "name": "Steinboeck, Andreas", "affiliation": "TU Wien" } ], "keywords": [ "MMM process modeling, identification, and estimation techniques", "Soft sensors in MMM systems", "Digital twins for power and process systems" ], "abstract": "Vortices are important fluid flow phenomena that, for instance, occur in the mold of a continuous casting machine used to produce steel slabs. For high product quality, the flow pattern in the mold should consist of two symmetric, stable double rolls, i.e., vortices. Real-time models are required to monitor the rolls in the mold because flow measurements in liquid steel are highly complex. Laboratory water models of a continuous casting machine represent a common approach for studying control-oriented modeling solutions. This paper considers a laboratory setup consisting of a cylinder filled with water in which a vortex can be generated by a pump. Starting from a distributed-parameter system, a low-dimensional state-space model is proposed and validated using laboratory measurements. These results will serve as an important basis for extending the proposed approach to more realistic industrial scenarios.", "url": "" }, { "id": "Mo-MoC20.5", "code": "MoC20.5", "title": "Operating Condition-Temporal Difference Aware Deep Attention Network for Industrial Virtual Metrology", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC20", "sessionTitle": "Control in Mining, Mineral and Metal Processing", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Xu, Jiawei", "affiliation": "Hangzhou Normal University" }, { "name": "Xu, Zhaowen", "affiliation": "Hangzhou Normal University" }, { "name": "Wei, Chihang", "affiliation": "Hangzhou Normal University" }, { "name": "Shao, Weiming", "affiliation": "China University of Petroleum (East China)" } ], "keywords": [ "Soft sensors in MMM systems", "Machine learning and artificial intelligence in chemical process control", "Data-driven methods for FDI/FTC" ], "abstract": "Existing methods face challenges in high-dimensional dynamic environments due to limited feature extraction and inadequate temporal dependency modeling. They fail to account for operating condition heterogeneity and temporal continuity in industrial processes, leading to poor generalization. To this end, this paper proposes a novel framework termed as supervised stacked local preserving operating condition-temporal difference aware deep attention network (S2P-DAAN). A transformer architecture integrating condition-aware and time-aware attention mechanisms is constructed while a novel multi-head attention mechanism is then designed to capture complex process dynamics by simultaneously evaluating operating condition similarity and temporal proximity. Experimental results demonstrate that the proposed S2P-DAAN framework significantly enhances the accuracy and robustness of quality variable prediction.", "url": "" }, { "id": "Mo-MoC21.1", "code": "MoC21.1", "title": "Coordinated Optimal Scheduling of PV–Storage–Charging–Discharging (V2G) in Industrial Parks (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC21", "sessionTitle": "Vehicle-To-Grid Enabled Synergy of Transportation and Energy Systems: Modelling, Control and Optimization", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Shi, Ruifeng", "affiliation": "North China Electric Power University" }, { "name": "Kang, Xi", "affiliation": "North China Electric Power University" }, { "name": "Lee, Kwang Y.", "affiliation": "Baylor University" } ], "keywords": [ "Electric vehicles integration in energy networks" ], "abstract": "With the rapid growth of electric vehicle (EV) ownership, large-scale stochastic plug-in behavior intensifies load fluctuations in park-level microgrids, which poses serious challenges to energy scheduling. Focusing on the operating characteristics of industrial parks with multiple areas, high energy demand, varying load conditions, and high EV penetration, this paper constructs a “source–grid–load–storage–vehicle” integrated energy system architecture for industrial parks. By exploiting the coordinated operation of distributed photovoltaics (PV), energy storage systems (ESS), and EVs, a coordinated optimal scheduling strategy for PV, storage, and bidirectional charging and discharging (V2G) with multiple types of EVs is proposed, aiming to minimize electricity purchase costs, mitigate load fluctuations, and increase the renewable energy utilization rate. Case studies show that, compared with a PV–storage–charging scheme, the proposed PV–storage–charging–discharging coordinated scheduling scheme reduces the park's electricity purchase cost by about 31.2%, decreases load fluctuations by 36.3%, and enables efficient local consumption of renewable energy, which verifies the effectiveness of the proposed model.", "url": "" }, { "id": "Mo-MoC21.2", "code": "MoC21.2", "title": "Online Aging-Aware Energy Optimization for Vehicle-Home-Grid Integration (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC21", "sessionTitle": "Vehicle-To-Grid Enabled Synergy of Transportation and Energy Systems: Modelling, Control and Optimization", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Popolizio, Francesco", "affiliation": "Chalmers University of Technology" }, { "name": "Wik, Torsten", "affiliation": "Chalmers Univ of Technology" }, { "name": "Lee, Chih Feng", "affiliation": "Polestar Performance AB" }, { "name": "Zou, Changfu", "affiliation": "Chalmers University of Technology" } ], "keywords": [ "Real time simulators for energy systems", "Energy market", "Electric vehicles and charging stations" ], "abstract": "This paper investigates the economic impact of vehicle-home-grid integration through an online optimization algorithm that manages energy flows between an electric vehicle, a household, and the electrical grid. The algorithm exploits vehicle-to-home (V2H) for self-consumption and vehicle-to-grid (V2G) for energy trading, adapting in real-time via a hybrid long short-term memory (LSTM) network for household load prediction and a nonlinear battery degradation model including cycle and calendar aging. Simulations show annual economic benefits up to €3046.81 compared to smart unidirectional charging, despite a modest 1.96% increase in battery aging. Even under unfavorable market conditions, with no V2G revenue, V2H alone provides yearly savings of €425.48. Sensitivity analyses on battery capacity, household load, and price ratios confirm the consistent benefits of bidirectional energy exchange, highlighting the role of EVs as active energy nodes for sustainable management.", "url": "" }, { "id": "Mo-MoC21.3", "code": "MoC21.3", "title": "Optimal Sizing of Charging Energy Hubs for Heavy-Duty Electric Transport through Co-Optimization", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC21", "sessionTitle": "Vehicle-To-Grid Enabled Synergy of Transportation and Energy Systems: Modelling, Control and Optimization", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Izadi, Maedeh", "affiliation": "Eindhoven University of Technology" }, { "name": "Fernandez-Zapico, Diego", "affiliation": "Eindhoven University of Technology" }, { "name": "Salazar, Mauro", "affiliation": "Eindhoven University of Technology" }, { "name": "Hofman, Theo", "affiliation": "Technische Universiteit Eindhoven" } ], "keywords": [ "Electric vehicles and charging stations", "Energy management systems", "Distributed optimization for smart grids" ], "abstract": "Electrification of heavy-duty vehicles places substantial stress on distribution grids, and Charging Energy Hubs (CEHs) mitigate these impacts by integrating charging infrastructure with renewable energy sources and battery storage. Optimal sizing of CEH components is therefore a critical investment decision, yet challenging because design choices depend strongly on operational dynamics. This work presents a mixed-integer linear programming model for the optimal sizing of CEH components, using a co-design approach that jointly optimizes component sizing and operational decisions. A case study for a heavy-duty fleet demonstrates the effectiveness of the method for cost-efficient, scalable, and grid-compliant CEH planning.", "url": "" }, { "id": "Mo-MoC21.4", "code": "MoC21.4", "title": "Optimal Predictive Energy Management of Battery-Supercapacitor Electric Vehicles", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC21", "sessionTitle": "Vehicle-To-Grid Enabled Synergy of Transportation and Energy Systems: Modelling, Control and Optimization", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Bentaleb, Ahmed", "affiliation": "University Cadi Ayyad" }, { "name": "Tnourji, Abdellah", "affiliation": "Engineering School of Aerospace Science" }, { "name": "El hajjaji, Ahmed", "affiliation": "Univ. De Picardie Jules Verne" }, { "name": "Mpanda Mabwe, Augustin", "affiliation": "UniLaSalle" }, { "name": "Benzaouia, Mohammed", "affiliation": "National School of Applied Sciences, Mohamed First University, Oujda, Morocco" } ], "keywords": [ "Energy management systems" ], "abstract": "Hybrid energy storage systems (HESSs) combining lithium-ion batteries and supercapacitors (SCs) can simultaneously provide high energy density and high power density, making them attractive for electric vehicle applications. The key challenge is to design an energy management strategy (EMS) that allocates power between the two sources to reduce system losses, satisfy transient power demand, and mitigate battery stress. This paper proposes an iterative dynamic programming and model predictive control (IDP--MPC) predictive energy management strategy based on receding-horizon optimal control. A sequential optimization framework is developed to reduce HESSs losses and smooth the battery current profile. Simulation results on the UDDS cycle show that the proposed controller effectively splits the load demand and achieves near-optimal performance with substantially reduced computation time relative to full-horizon dynamic programming (DP).", "url": "" }, { "id": "Mo-MoC21.5", "code": "MoC21.5", "title": "An Efficient Method for the Optimal Control of Microgrids under Uncertainties Using Local Reduction", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC21", "sessionTitle": "Vehicle-To-Grid Enabled Synergy of Transportation and Energy Systems: Modelling, Control and Optimization", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Scaccia, Edoardo", "affiliation": "Imperial College London" }, { "name": "Kerrigan, Eric C.", "affiliation": "Imperial College London" }, { "name": "Sadowska, Anna", "affiliation": "SLB Cambridge Research" } ], "keywords": [ "Energy management systems", "Energy storage systems", "Energy communities" ], "abstract": "The problem of optimal sizing and power scheduling in microgrids subject to uncertainties is well known to the control community. Commonly, the optimal control problem is cast as a mixed-integer program to model the logical constraints arising in energy storage systems, and is then solved approximately using numerical methods such as the scenario approach. In this paper, we propose and compare two formulations of a robust microgrid sizing and power scheduling optimal control problem with logical constraints and uncertainties in the user's power demand, solar power generation, grid electricity prices and battery efficiencies. The first formulation uses binary variables and big-M constraints, leading to a mixed-integer linear program. The second formulation casts the problem as a continuous nonlinear program through an exact smooth reformulation of the logical constraints, consisting of additional modelling variables and non-convex constraints. We then propose a novel local reduction algorithm, extending an existing method, to solve both problems. The two formulations are compared by evaluating the solutions returned by local reduction using 100,000-sample Monte Carlo simulations and achieve promising results, with both averaging feasibility rates above 90%.", "url": "" }, { "id": "Mo-MoC21.6", "code": "MoC21.6", "title": "A Network‑Coupled ADMM Framework for Distributed Energy Management in Multi‑Bus Shipboard Microgrids", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC21", "sessionTitle": "Vehicle-To-Grid Enabled Synergy of Transportation and Energy Systems: Modelling, Control and Optimization", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Kopka, Timon", "affiliation": "Delft University of Technology" }, { "name": "Coraddu, Andrea", "affiliation": "Delft University of Technology" }, { "name": "Polinder, Henk", "affiliation": "Delft Univ. of Technology" } ], "keywords": [ "Distributed optimization for smart grids", "Energy management systems", "Control and management of energy systems" ], "abstract": "Shipboard power systems are evolving toward complex, multi-bus architectures integrating an increasingly broad variety of power generation and energy storage modules. A key challenge lies in reliability demands and adaptability to topology and parameter alterations. Centralized energy management strategies struggle with scalability and computational burden, motivating distributed approaches. This work proposes a network‑coupled ADMM framework for real‑time energy management in DC shipboard microgrids. The method extends single‑bus ADMM optimization to multi‑bus systems by introducing bus tie switch agents that couple two bus-level optimization processes, optimizing inter-bus power transfers. The framework enables modular integration of diverse components, adapts to changing network topologies, and ensures consensus across buses. Case studies on single‑ and dual‑bus configurations demonstrate convergence, resilience, and improved cost‑optimal dispatch. The approach provides a scalable, plug‑and‑play solution for distributed energy management in complex shipboard microgrids.", "url": "" }, { "id": "Mo-MoC22.1", "code": "MoC22.1", "title": "On Distributed Secondary Control of Infrastructure Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC22", "sessionTitle": "New Trends in Control and Optimization in Smart City Networks", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Qu, Zhihua", "affiliation": "University of Central Florida" }, { "name": "Simaan, Marwan A.", "affiliation": "Univ of Central Florida" } ], "keywords": [ "Distributed optimization and control for smart cities", "Control and optimization for sustainability and energy systems", "Power systems stability" ], "abstract": "Infrastructure systems are complex dynamical systems involving many subsystems with multi-level hierarchical controls. The so-called secondary controls which coordinate subsystems’ actions, are often distributed, and may involve varying communication topologies. As engineered systems, infrastructure systems contain controllable and observable subsystems, and the dynamic interactions among the subsystems are dependent only upon their outputs. On the other hand, it is well known that controls with constrained information such as decentralized control and distributed control may suffer from the problem of fixed modes. Standard tests on fixed modes are combinatorial, and their direct applications to infrastructure systems are too cumbersome due to the size and nature of their secondary controls. In this paper, we use the two fundamental properties of infrastructure systems to analytically show that distributed secondary controls do not induce fixed modes under any communication topology. Furthermore, structural properties on individual subsystems such as the matching condition are explored to conclude that classes of infrastructure systems with arbitrary interconnection topology have no fixed mode. Wide-area distributed control of power systems is one of such infrastructure systems. These results provide theoretical guarantee that distributed secondary controls can successfully be designed and implemented for those classes of infrastructure systems.", "url": "" }, { "id": "Mo-MoC22.2", "code": "MoC22.2", "title": "Entropy-Like Estimator for the Nowcasting of PV Power Production in Sustainable Microgrids (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC22", "sessionTitle": "New Trends in Control and Optimization in Smart City Networks", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Ferro, Giulio", "affiliation": "Università Degli Studi Di Genova" }, { "name": "Indiveri, Giovanni", "affiliation": "University of Genova" }, { "name": "Robba, Michela", "affiliation": "University of Genova" } ], "keywords": [ "Solar energy", "Big data and machine learning applied to smart cities", "Electrical distribution systems" ], "abstract": "This paper presents a novel method for short-term photovoltaic (PV) power forecasting designed for real-time Model Predictive Control (MPC) applications. Traditional forecasting approaches often rely on extensive preprocessing to remove measurement outliers caused by sensor faults, communication errors, or general disturbances. While effective, these procedures add computational overhead and may eliminate valuable information. The proposed method applies the Least Entropy-Like (LEL) estimator. This robust linear regression technique identifies and implicitly discards outliers using an entropy based loss function, thereby eliminating the need for data cleaning. The approach is evaluated using high-resolution measurements collected from the Smart Polygeneration Microgrid at the Savona Campus of the University of Genoa, including solar irradiance and module temperature data sampled at one-minute intervals. Results demonstrate that the LEL-based forecasting model achieves high prediction accuracy and low variance even in the presence of corrupted measurements, outperforming widely used state-of-the-art M-estimators.", "url": "" }, { "id": "Mo-MoC22.3", "code": "MoC22.3", "title": "Distributionally Robust Model Predictive Control for Virtual Power Plants (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC22", "sessionTitle": "New Trends in Control and Optimization in Smart City Networks", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Recke, Nikolas Leander", "affiliation": "University of Oslo" }, { "name": "Hudoba de Badyn, Mathias", "affiliation": "University of Oslo" } ], "keywords": [ "Control and management of energy systems", "Energy management systems", "Power plant control" ], "abstract": "This paper presents a distributionally robust model predictive control (DRMPC) framework for the optimal Virtual Power Plant (VPP) operation under electricity price uncertainty. A unified VPP model is formulated that captures the interaction between buildings, battery storage, and renewable generation, all influenced by exogenous weather and market signals. The proposed approach integrates data-driven forecasting with quantile-based uncertainty quantification to construct time-varying Wasserstein ambiguity sets that adapt to forecast dispersion and distributional shifts. This yields a tractable DR-MPC formulation that incorporates predictive distribution information directly into real-time decision making. The method is evaluated using real weather and market data from a Nordic case study across two seasonal scenarios. The results show that DR-MPC improves economic performance relative to standard forecast-based MPC when the ambiguity radius is chosen appropriately, with consistent gains of up to 0.8 % for small radii across both seasonal scenarios. Larger radii become overly conservative and reduce revenue, underscoring the importance of proper radius selection. These findings demonstrate the practical value of distributionally robust optimization for uncertainty-aware VPP operation.", "url": "" }, { "id": "Mo-MoC22.4", "code": "MoC22.4", "title": "An Optimization Model for the Pickup and Delivery Problem with Electric and Hydrogen-Based Trucks (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC22", "sessionTitle": "New Trends in Control and Optimization in Smart City Networks", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Ferro, Giulio", "affiliation": "Università Degli Studi Di Genova" }, { "name": "Parodi, Luca", "affiliation": "University of Genoa" }, { "name": "Robba, Michela", "affiliation": "University of Genova" }, { "name": "Roggero, Ginevra", "affiliation": "University of Genoa" } ], "keywords": [ "Electric vehicles integration in energy networks", "Transportation networks", "Electric vehicles and charging stations" ], "abstract": "The transportation sector is a major contributor to greenhouse gas emissions, and a progressive decarbonization of vehicle fleets is expected in the near future. Several technological alternatives—such as electric, hybrid, and hydrogen-based systems—are available, each requiring a detailed assessment in terms of energy consumption, costs, and associated benefits. This paper presents the formulation and development of a mathematical model designed to support the management of pick-up and delivery operations for a real company. The model incorporates multiple truck technologies (electric, diesel, and hydrogen) and computes, for each, the corresponding energy consumption and primary energy requirements. The optimization problem is addressed through various solution approaches, including mathematical programming and metaheuristics such as simulated annealing and particle swarm optimization, and is evaluated across multiple case studies. Particular attention is devoted to assessing the efficiency and scalability of the different solution techniques when applied to large-scale logistics scenarios.", "url": "" }, { "id": "Mo-MoC22.6", "code": "MoC22.6", "title": "Deployment of an Internet-Of-Things Testbed for Home Automation (I)", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC22", "sessionTitle": "New Trends in Control and Optimization in Smart City Networks", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Baldi, Simone", "affiliation": "Southeast University" }, { "name": "Luo, Miao", "affiliation": "Southeast University" }, { "name": "Chen, Xiaoting", "affiliation": "Southeast University" }, { "name": "Liu, Di", "affiliation": "Imperial College London" } ], "keywords": [ "Smart buildings and building automation", "Energy management systems", "Control and management of energy systems" ], "abstract": "Home automation offers exciting opportunities for deploying Internet-of-Things (IoT) ecosystems with sensing and actuation capabilities. However, factors like closed software, limited device support, sensing-only functionalities, restrict research and development scopes in many IoT platforms. We present an IoT testbed for home automation located at Southeast University and designed based on the open-source Home Assistant platform. The testbed incorporates a wide set of sensing and actuation devices for light and temperature control: the models developed for the devices are presented, as well as their experimental validation.", "url": "" }, { "id": "Mo-MoC23.1", "code": "MoC23.1", "title": "Optimization and Control of Hybrid Systems Modeled by Guarded Event Nets (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC23", "sessionTitle": "JO-NAHS: Supervisory Control and Cyber Attack", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Julvez, Jorge", "affiliation": "Univ of Zaragoza" } ], "keywords": [ "Petri nets", "Optimal control of discrete event and hybrid systems", "Event-based control" ], "abstract": "This work introduces Guarded Event Nets (GENs), a modeling formalism inspired by Petri Nets that can model hybrid systems with piecewise-constant dynamics. The dynamics of a GEN depend on convex regions defined on the state space. To enhance the modeling power, GENs allow overlapping regions, uncertainty in the dynamics, nondeterministic marking changes triggered by transitions, and the inclusion of untimed events. In order to avoid mode-mismatch errors, we derive an event-driven formulation that ensures that no region boundaries are crossed during a given time interval. The proposed framework is demonstrated through three case studies, one of which employs a model predictive control approach.", "url": "" }, { "id": "Mo-MoC23.2", "code": "MoC23.2", "title": "Enforcing OR-GMECs in Petri Nets by Transition Splitting (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC23", "sessionTitle": "JO-NAHS: Supervisory Control and Cyber Attack", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Ma, Ziyue", "affiliation": "Xidian University" }, { "name": "Giua, Alessandro", "affiliation": "University of Cagliari, Italy" } ], "keywords": [ "Petri nets", "Supervisory control and automata" ], "abstract": "Generalized Mutual Exclusion Constraints (GMECs) are a well-established mechanism for enforcing state specifications in Petri nets. This paper focuses on the enforcement of OR-GMECs in Petri nets through a novel method based on transition splitting. The enforcement of disjunctive GMECs (OR-GMECs) remains challenging, as existing methods often incur high structural complexity or require external automaton-based controllers. In this paper, we propose a novel method for enforcing OR-GMECs by implicit places and transition splitting. The proposed method yields a compact closed-loop Petri net whose size grows only quadratically with the number of increasing transitions. The resulting closed-loop system preserves the place/transition net structure, so that it can be further analyzed using existing Petri net structural techniques and tools.", "url": "" }, { "id": "Mo-MoC23.3", "code": "MoC23.3", "title": "Resilient Non-Fragile H_infinity Control for Parabolic Stochastic Systems under Deception Attacks: Finite-Time Stability (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC23", "sessionTitle": "JO-NAHS: Supervisory Control and Cyber Attack", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Shukla, Nidhi", "affiliation": "Indian Institute of Technology Roorkee" }, { "name": "Dabas, Jaydev", "affiliation": "Indian Institute of Technology Roorkee" } ], "keywords": [ "Stochastic control", "Cyber security networked control", "Resilient networked control systems" ], "abstract": "This paper presents a comprehensive framework for resilient non-fragile H_infinity control design for a second-order stochastic PDE system subject to parametric uncertainties, external disturbances, and deception attacks, in the mean-square finite-time stability case. By employing Lyapunov stability theory and transforming the resulting conditions into nonlinear matrix inequalities, we establish computational procedures for controller synthesis that guarantee finite-time boundedness while achieving prescribed H_infinity performance attenuation levels. The proposed approach simultaneously addresses three practical constraints: parametric uncertainties, controller gain perturbations, and stochastic deception attacks, within a spatially distributed stochastic PDE framework, shifting the focus from conventional asymptotic stability to finite-time stability for greater practical relevance. Numerical examples with comprehensive Monte Carlo simulations are provided to demonstrate the effectiveness of the proposed approach, including systematic validation of non-fragile robustness and attack resilience.", "url": "" }, { "id": "Mo-MoC23.4", "code": "MoC23.4", "title": "Current-State Anonymity and Opacity Subject to State Attacks in Discrete Event Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC23", "sessionTitle": "JO-NAHS: Supervisory Control and Cyber Attack", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Li, Xiaoyan", "affiliation": "North University of China" }, { "name": "Hadjicostis, Christoforos", "affiliation": "University of Cyprus" } ], "keywords": [ "Supervisory control and automata" ], "abstract": "This paper introduces and analyzes novel notions of current-state anonymity and opacity, subject to state attacks, within the context of discrete event systems modeled with nondeterministic finite automata. When a state attack is performed, the intruder learns whether or not the current state of the system falls into a specific subset of states. Thus, state attacks provide additional state information to the intruder during the operation of the system. The system is considered to be current-state anonymous (resp. opaque) under a state attack if the intruder can never be certain that the current state of the system is unique (resp. the current state of the system belongs to a subset of secret states), based on its observations and the additional knowledge provided by any state attacks. A necessary and sufficient condition is presented to check the underlying current-state anonymity (resp. opacity) of a given system subject to a large class of state attacks. We also provide pertinent complexity analysis of the corresponding verification method and illustrative examples that elucidate the proposed concepts of state-attack anonymity (resp. opacity) subject to the specified class of state attacks.", "url": "" }, { "id": "Mo-MoC23.5", "code": "MoC23.5", "title": "Markov Clustering Based Fully Automated Nonblocking Hierarchical Supervisory Control of Large-Scale Discrete-Event Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC23", "sessionTitle": "JO-NAHS: Supervisory Control and Cyber Attack", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Liu, Yingying", "affiliation": "Osaka Metropolitan University" }, { "name": "Cai, Zhaojian", "affiliation": "Osaka Metropolitan University" }, { "name": "Cai, Kai", "affiliation": "Osaka Metropolitan University" } ], "keywords": [ "Supervisory control and automata", "Discrete event modeling and simulation" ], "abstract": "In this paper we revisit the abstraction-based approach to synthesize a hierarchy of decentralized supervisors and coordinators for nonblocking control of large-scale discrete-event systems (DES), and augment it with a new clustering method for automatic and flexible grouping of relevant components during the hierarchical synthesis process. This method is known as Markov clustering, which not only automatically performs grouping but also allows flexible tuning of the sizes of the resulting clusters using a single parameter. Compared to the existing abstraction-based approach that lacks an effective grouping method for general cases, our proposed approach based on Markov clustering provides a fully automated and effective hierarchical synthesis procedure applicable to general large-scale DES. Moreover, it is proved that the resulting hierarchy of supervisors and coordinators collectively achieves global nonblocking (and maximally permissive) controlled behavior under the same conditions as those in the existing abstraction-based approach. Finally, a benchmark case study is conducted to empirically demonstrate the effectiveness of our approach.", "url": "" }, { "id": "Mo-MoC23.6", "code": "MoC23.6", "title": "Maximally Permissive Data-Driven Supervisory Control of Discrete-Event Systems with Forcible Events (I)", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC23", "sessionTitle": "JO-NAHS: Supervisory Control and Cyber Attack", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Gu, Chao", "affiliation": "Queen’s University Belfast" }, { "name": "Gao, Chao", "affiliation": "Kyoto University" }, { "name": "Cai, Kai", "affiliation": "Osaka Metropolitan University" } ], "keywords": [ "Supervisory control and automata", "Optimal control of discrete event and hybrid systems", "Data-driven control theory" ], "abstract": "This paper studies maximally permissive data-driven supervisory control for structure-unknown discrete-event systems with forcible events. Two data sets are assumed: a subset of event sequences generated by the structure-unknown plant and a subset of impossible behaviors of the system derived from prior knowledge. In the model-based case, forcible-controllability ensures the existence of a supervisor enforcing the specification using forcible events. Forcible-informativity and forcible-informatizability are its data-driven counterparts: the former assesses forcible-controllability for a given specification using only data, while the latter evaluates whether the data can identify a smaller, non-empty forcibly-controllable specification. We show that whenever forcible-informatizability holds, there exists a unique non-empty supremal forcibly-informative sublanguage of the specification. Based on the notion of a non-forcibly informative state, we propose an algorithm that computes this sublanguage, enabling the synthesis of the corresponding maximally permissive data-driven forcing supervisor.", "url": "" }, { "id": "Mo-MoC24.1", "code": "MoC24.1", "title": "Implementation of Biomolecular LQR with Partial State Observation (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC24", "sessionTitle": "Biological Control and Estimation", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Zhang, Xiaoyu", "affiliation": "Southeast University" }, { "name": "Fang, Zhou", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Dynamics and control of gene expression and metabolic pathways", "Systems biology for biotechnology", "Dynamics and control of biologically motivated nonlinear systems" ], "abstract": "Biological systems adeptly balance the cost and precision of regulation, though the mechanisms enabling such balance remain poorly understood. To address this, we develop a biomolecular Linear Quadratic Regulator (LQR) framework for one- and two-gene expression systems, supported by theoretical analysis and numerical validation. To accommodate the limited measurability of biological contexts, we further design a reduced-order biomolecular observer that estimates unmeasured states using accessible molecular species. Interestingly, some resulting closed-loop biochemical networks structurally recapitulate common gene regulatory motifs—such as autoregulation and the incoherent feedforward loop. This correspondence provides a rationale for the prevalence of these specific motifs in biology.", "url": "" }, { "id": "Mo-MoC24.2", "code": "MoC24.2", "title": "Stochastic Gene Expression under Sequestration: Noise Reduction and Emergent Distributions (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC24", "sessionTitle": "Biological Control and Estimation", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Morozova, Olha", "affiliation": "Comenius University" }, { "name": "Oravcová, Ivana", "affiliation": "Univerzita Komenského V Bratislave" }, { "name": "Zabaikina, Iryna", "affiliation": "Comenius University Bratislava" }, { "name": "Bokes, Pavol", "affiliation": "Comenius University" }, { "name": "Singh, Abhyudai", "affiliation": "University of Delaware" } ], "keywords": [ "Dynamics and control of gene expression and metabolic pathways", "Biological networks inference and modelling", "Kinetic modelling, analysis and optimization of metabolism" ], "abstract": "Gene expression noise can be modulated by protein sequestration, a mechanism we investigate through a stochastic modeling framework. We examine how the distribution of free (non-sequestered) protein depends on sequestration cooperativity (monomers, dimers, multimers) and on the timescale separation between sequestration and protein turnover. For non-cooperative sequestration, faster kinetics drive the distribution from a high-noise to a lower-noise gamma form, while the right-tail remains governed by the high-noise limit --- providing numerical evidence for a non-commutativity between tail asymptotics and fast sequestration. For cooperative sequestration, the distribution departs from gamma, exhibiting left skewness or multimodality. These results highlight how sequestration mechanisms shape protein variability in nontrivial ways.", "url": "" }, { "id": "Mo-MoC24.3", "code": "MoC24.3", "title": "DNA Turing Patterns within a Polymerase Chain Reaction Model: Reaction-Diffusion Mechanism and Control (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC24", "sessionTitle": "Biological Control and Estimation", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Cheng, Haokuan", "affiliation": "Nanjing University of Posts and Telecommunications" }, { "name": "Cao, Yang", "affiliation": "Southeast University" }, { "name": "Zhang, Xiaoyu", "affiliation": "Southeast University" }, { "name": "Xu, Ziqi", "affiliation": "Nanjing Normal University" }, { "name": "Gao, Shangce", "affiliation": "University of Toyama" } ], "keywords": [ "Dynamics and control of biologically motivated nonlinear systems", "Modelling, parameter identification and state estimation in biosystems", "Biological networks inference and modelling" ], "abstract": "Polymerase chain reaction (PCR) is a pivotal tool in modern molecular biology, yet most existing models focus on temporal kinetics and largely neglect spatial effects. Here we develop a two-dimensional reaction–diffusion model of PCR that incorporates both self-diffusion and cross-diffusion to capture microscopic DNA amplification dynamics. Linear stability analysis yields explicit Turing bifurcation criteria and amplitude equations that predict the threshold for pattern emergence and the morphological transition from hexagonal to striped structures. A state-feedback controller is embedded to modulate the extension reaction kinetics. Numerical simulations corroborate the theoretical analysis, confirming that the interplay of reaction-diffusion effects and control inputs effectively governs pattern formation. This work establishes a novel framework for understanding, predicting, and controlling DNA synthesis patterns in PCR, with potential applications in high-fidelity diagnostics and microfluidic device design.", "url": "" }, { "id": "Mo-MoC24.4", "code": "MoC24.4", "title": "Accelerating Reaction Network Identification Via Frequency-Domain Analysis (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC24", "sessionTitle": "Biological Control and Estimation", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Fang, Zhou", "affiliation": "Chinese Academy of Sciences" }, { "name": "Sun, Wenying", "affiliation": "Southeast University" }, { "name": "Zhang, Xiaoyu", "affiliation": "Southeast University" }, { "name": "Khammash, Mustafa H.", "affiliation": "Swiss Federal Institute of Technology (ETH)" } ], "keywords": [ "Modelling, parameter identification and state estimation in biosystems", "Biomedical system modeling, identification, and simulation", "Systems biology for biotechnology" ], "abstract": "The identification of intracellular reaction networks from single-cell data lies at the heart of many biological studies, as it can unravel key mechanisms in living organisms and provide insights for their rational engineering. However, the stochastic and nonlinear nature of these reacting systems poses significant challenges for this identification problem, making the state-of-the-art methods (e.g., particle-filtering-based approaches) computationally demanding. This paper reports a noteworthy numerical observation that for certain systems, the Fourier transform can render the likelihood of the measurement data approximately Gaussian, therefore resulting in a substantially accelerated Bayesian inference algorithm. A gene transcription example is presented to illustrate this finding and demonstrate the efficiency of the proposed Fourier-based method.", "url": "" }, { "id": "Mo-MoC24.5", "code": "MoC24.5", "title": "Evaluating Valid Parameter Regimes for Biocircuits (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC24", "sessionTitle": "Biological Control and Estimation", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Liu, Qinguo", "affiliation": "Westlake University" }, { "name": "Ren, Xinying", "affiliation": "Eastern Institute of Technology, Ningbo" }, { "name": "Xiao, Fangzhou", "affiliation": "Westlake University" } ], "keywords": [ "Synthetic biology", "Dynamics and control of gene expression and metabolic pathways", "Biological networks inference and modelling" ], "abstract": "Biocircuit functions are often valid only in specific parameter regimes, yet these regimes are rarely made explicit. We use a holistic analysis method based on regimes to derive validity conditions and introduce the Realizability Index (R-index), quantifying the size of the valid regions in log-parameter space. The framework is applied to Michaelis-Menten kinetics, Hill functions, and enzymatic negative-feedback adaptation, showing how circuit structure and experimental control variables shape functional realizability. Our analysis shows the Hill function's R-index goes to zero in sequential binding with increasing Hill coefficient. We also resolve an active debate about whether negative-feedback adaptation is realizable when competitive binding is taken into account, and demonstrates the superiority of the holistic R-index method over numerical parameter scans that lead to incorrect conclusions. R-index defines a validity-aware language for studying and designing functional biocircuits.", "url": "" }, { "id": "Mo-MoC24.6", "code": "MoC24.6", "title": "Closing the Loop on Phage-Bacteria Coevolution (I)", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC24", "sessionTitle": "Biological Control and Estimation", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Pearson, Joshua", "affiliation": "University of Oxford" }, { "name": "Sechkar, Kirill", "affiliation": "University of Oxford" }, { "name": "Steel, Harrison Callum Bertram", "affiliation": "University of Oxford" } ], "keywords": [ "Modelling, parameter identification and state estimation in biosystems", "Modelling and control of microbial communities", "Systems biology for biotechnology" ], "abstract": "Bacteria and their viruses, bacteriophages (phages), continually coevolve in nature. In contrast, laboratory-based coevolution experiments usually last less than a month before becoming dormant or extinct as one species is outcompeted by the other. Consequently, there is a poor understanding of phage-bacteria coevolution and hence the long-term efficacy of bacteriophage therapies (an approach to tackling antimicrobial resistance). We propose a novel approach to coevolution experiments that would address this challenge: instead of open-loop resource-constrained cultures, we develop a closed-loop control approach to stabilise the typically unstable or oscillatory phage-bacteria population dynamics. Achieving this requires the control system to compensate for delays in phage incubation and respond to an evolving system, while only measuring bacterial density. To this end, we develop a model of phage-bacteria dynamics, prototype delay-compensating predictive control strategies, and demonstrate a measurement-aware state observer. Overall, this approach shows the ability to stabilise coevolution, avoiding the common outcomes of unstable dynamics or winner-takes-all competition.", "url": "" }, { "id": "Mo-MoC25.1", "code": "MoC25.1", "title": "Rigorous Quantitative Analysis of Nonlinear Uncertain Biomolecular Systems Using Validated Methods (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC25", "sessionTitle": "Challenges in Computational Systems Biology", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Prakash, Rudra", "affiliation": "Indian Institute of Technology Delhi" }, { "name": "Sivaramakrishnan, Janardhanan", "affiliation": "Indian Institute of Technology Delhi" }, { "name": "Sen, Shaunak", "affiliation": "Indian Institute of Technology Delhi" } ], "keywords": [ "Modelling, parameter identification and state estimation in biosystems", "Dynamics and control of biologically motivated nonlinear systems", "Synthetic biology" ], "abstract": "This paper studies the rigorous computation of steady states in nonlinear, potentially multistable biomolecular systems subject to parametric uncertainty. Standard numerical methods may fail to provide complete or guaranteed solutions in these settings. To address this limitation, we evaluate validated interval-analysis methodologies. We present algorithms based on the interval Newton and interval Krawczyk methods to compute certified enclosures of all steady states (stable and unstable) in multidimensional nonlinear systems. We further compare these methods with interval bisection and interval constraint propagation. Numerical examples are provided for biologically plausible models, including feedback and feedforward gene networks. Based on these results, guidance is provided on method selection for different classes of biomolecular systems, supporting rigorous analysis and design of synthetic biological circuits.", "url": "" }, { "id": "Mo-MoC25.2", "code": "MoC25.2", "title": "Can Optimal Control Explain the Microbial Heat-Shock Response? a Bilevel Optimization Approach (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC25", "sessionTitle": "Challenges in Computational Systems Biology", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Yabo, Agustín G.", "affiliation": "INRAE" }, { "name": "Innerarity Imizcoz, Javier", "affiliation": "Université Côte D'Azur" }, { "name": "Djema, Walid", "affiliation": "INRIA" }, { "name": "Mairet, Francis", "affiliation": "Ifremer" }, { "name": "Gouze, Jean-Luc", "affiliation": "INRIA" } ], "keywords": [ "Dynamics and control of gene expression and metabolic pathways", "Modelling, parameter identification and state estimation in biosystems", "Kinetic modelling, analysis and optimization of metabolism" ], "abstract": "This paper presents preliminary results seeking to explain the microbial heat-shock response from a dynamical resource allocation perspective, under the hypothesis that microorganisms have been shaped by natural selection to maximize growth. Within this framework, natural regulatory mechanisms can potentially be predicted as solutions of an optimal control problem. While the optimal trajectories of such problems are inherently able to reproduce the main qualitative features of the desired transient response, we also address the problem of matching experimental measurements of E. coli exposed to a heat-shock. To this end, we seek to estimate the parameters of a bacterial growth model, so that the corresponding optimal trajectories match the experimental data. This nested formulation defines a bilevel optimization problem, that we solve with a two-level numerical approach: a global evolutionary algorithm for the upper-level calibration problem and a nonlinear optimal control solver for the lower-level problem. Our results show good agreement between data and predictions, and provide a promising perspective for better understanding stress-response mechanisms in microorganisms.", "url": "" }, { "id": "Mo-MoC25.3", "code": "MoC25.3", "title": "Calibrating Multiscale Microbial Models Using Direct and Indirect Measurements (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC25", "sessionTitle": "Challenges in Computational Systems Biology", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Hope, William Benjamin Brinton", "affiliation": "University of Warwick" }, { "name": "Carlos Xose, Sequeiros-Ferreiro", "affiliation": "University of Vigo" }, { "name": "Darlington, Alexander", "affiliation": "University of Warwick" } ], "keywords": [ "Modelling, parameter identification and state estimation in biosystems", "Dynamics and control of biologically motivated nonlinear systems", "Synthetic biology" ], "abstract": "Advances in experimental techniques enable high-resolution measurements of specific molecular species in living cells but practical constraints mean data capture is often limited to sampling at mid-growth. Whilst such measurements directly capture cellular state, the lack of temporal resolution makes building dynamic models challenging due to weak practical identifiability. However, measuring molecules over time remains technically challenging and relies on indirect measurements, such as fluorescent proteins, which introduces further uncertain parameters. Here, we evaluate how measurement type influences calibration of a multi-scale metabolic and gene expression E. coli model commonly used in synthetic biology. We constructed two synthetic datasets: one composed of direct, but static, measurements of growth rate and ribosomal mass fraction, and a second composed of indirect measurements over time mimicking the use of GFP-tagged ribosomal species and optical density for population. Across all simulated experiments, model calibration using indirect temporal data consistently outperformed use of direct static measurements despite addition of unknown conversion factor. With dynamic data parameter estimates were more tightly constrained, distributed closer to their ground-truth values, and had lower Fisher information-derived error. Our results suggest better experimental design choices for accurately calibrating future microbial growth.", "url": "" }, { "id": "Mo-MoC25.4", "code": "MoC25.4", "title": "Modelling Acetyl-CoA Regulation Accounts for Isoamyl Acetate Synthesis During Wine Alcoholic Fermentation (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC25", "sessionTitle": "Challenges in Computational Systems Biology", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Dangelser, William", "affiliation": "INRAE" }, { "name": "Arness, Kevin", "affiliation": "INRAE" }, { "name": "Yabo, Agustín G.", "affiliation": "INRAE" }, { "name": "Casenave, Céline", "affiliation": "INRA" } ], "keywords": [ "Modelling, parameter identification and state estimation in biosystems", "Kinetic modelling, analysis and optimization of metabolism" ], "abstract": "The wine's aromatic profile is one of the main quality guarantees for consumers. Most of the aromas are produced during the alcoholic fermentation performed by yeasts. Therefore, there is a huge interest in understanding and controlling their synthesis. The smooth running of the fermentation relies on the assimilable nitrogen available in the must, which is often limiting; therefore, nitrogen additions can be performed affecting the final aroma concentrations. Moreover, temperature plays a major role in the dynamics of volatile compounds. In this context, modelling aroma synthesis during wine fermentation is essential. So far, the models developed have been been mostly empirical; here, we introduce a mechanistic approach focusing first on isoamyl acetate, an acetate ester. By modelling enzymes levels and the regulation of the precursor acetyl coenzyme A of the synthesis reaction we are able to predict the synthesis dynamics under various environmental conditions. In the future, the model will be extended to a broader family of aromas and used for real-time process control.", "url": "" }, { "id": "Mo-MoC25.5", "code": "MoC25.5", "title": "Genome-Scale Metabolic Modeling for Systems-Level Understanding of the Impact of Copper Deficiency across Dietary Conditions", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC25", "sessionTitle": "Challenges in Computational Systems Biology", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Lee, Naeun", "affiliation": "University of Nebraska-Lincoln" }, { "name": "Lee, Jaekwon", "affiliation": "University of Nebraska-Lincoln" }, { "name": "Song, Hyun-Seob", "affiliation": "University of Nebraska-Lincoln" } ], "keywords": [ "Kinetic modelling, analysis and optimization of metabolism", "Systems biology for biotechnology", "Dynamics and control of gene expression and metabolic pathways" ], "abstract": "Copper (Cu) is an essential trace element that supports fundamental cellular processes. Although numerous studies aimed to experimentally characterize its biological roles, the systemic metabolic consequences of Cu deficiency remain underexplored. Here, we applied a genome-scale model of human metabolism to investigate how Cu limitation, in combination with dietary conditions (i.e., balanced and high-fat diets), reprograms cellular metabolism. We formulated a flux minimization problem to estimate condition-specific flux distributions within the metabolic network. Our model predicted that, under both dietary conditions, Cu deficiency suppresses glycolysis, tricarboxylic acid cycle, and ATP turnover and perturbs amino acid metabolic pathways. Cu deficiency under the high-fat diet not only exacerbated these metabolic disruptions, but also induced additional alterations (not observed under the balanced diet) in the pathways related to terpenoid backbone biosynthesis, the carnitine shuttle, and ascorbate–aldarate metabolism. This result highlights complex interactions between Cu deficiency and dietary macronutrient composition. The modeling framework developed through this work provides a practical guideline widely useful for studying micronutrient-diet interactions in human and animal health.", "url": "" }, { "id": "Mo-MoC25.6", "code": "MoC25.6", "title": "Hybrid Modeling and Control of Syngas Fermentation Bridging Batch and Continuous Operation", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC25", "sessionTitle": "Challenges in Computational Systems Biology", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Richter, Lukas", "affiliation": "Karlsruhe Institute of Technology," }, { "name": "Jerono, Pascal", "affiliation": "Karlsruhe Institute of Technology" }, { "name": "Ebel, Christian", "affiliation": "IKFT, KIT" }, { "name": "Sauer, Jörg", "affiliation": "Karlsruhe Institute of Technology," }, { "name": "Meurer, Thomas", "affiliation": "Karlsruhe Institute of Technology (KIT)" } ], "keywords": [ "Modelling, parameter identification and state estimation in biosystems", "Dynamics and control of biologically motivated nonlinear systems" ], "abstract": "A semi--parametric hybrid syngas fermentation model for cultivation of Clostridium ljungdahlii is derived, where the microbial growth and conversion rates are represented by Gaussian processes. By separating the reactor--specific effects from the cellular kinetics, the model allows training based only on state information from a batch process. The resulting model is evaluated in an open--loop continuous fermentation scenario and a closed--loop continuous ethanol selectivity control using nonlinear model predictive control. It is demonstrated that the mechanistic parts effectively bridge the knowledge gap between different operation modes, while the experimental effort for model parametrization is reduced.", "url": "" }, { "id": "Mo-MoC26.1", "code": "MoC26.1", "title": "How to Build a Port-GENERIC Model from the Bond Graphs of a Thermo-Visco-Elastic System (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC26", "sessionTitle": "Thermodynamics Foundations of Mathematical Systems Theory", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Kotyczka, Paul", "affiliation": "Technical University of Munich" }, { "name": "Betsch, Peter", "affiliation": "Karlsruhe Institute of Technology" } ], "keywords": [ "Process modeling, identification, and estimation techniques" ], "abstract": "We show how to systematically obtain the matrices of a special GENERIC form with control ports from the bond graphs of a thermo-visco-elastic model problem. Besides the classical bond graph, displaying all reversible and irreversible energy conversions, we consider a second graph whose bonds carry the entropy flows. Entropy creation due to energy conversion into heat or heat transfer is represented in this second graph through modulated (negative) resistive elements. The advantage of this approach is that the canonical Poisson and Onsager matrices of the special GENERIC formulation according to Mielke (2011) can be immediately read off the two bond graphs. The 1D thermo-visco-elastic pendulum is an illustrative example to display the successive composition of the model from interconnection through the ports and thermal interfaces.", "url": "" }, { "id": "Mo-MoC26.2", "code": "MoC26.2", "title": "Structure-Adaptive Entropy-Based Port–Hamiltonian Formulation of a Tubular Reactor with Diffusion, Convection, and Irreversible Reaction (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC26", "sessionTitle": "Thermodynamics Foundations of Mathematical Systems Theory", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Zhou, Weijun", "affiliation": "Zhejiang University City College" }, { "name": "Hamroun, Boussad", "affiliation": "Univ Lyon, Université Claude Bernard Lyon 1, CNRS, LAGEP UMR 5007, VILLEURBANNE" } ], "keywords": [ "Advanced process control", "Process modeling, identification, and estimation techniques" ], "abstract": "This paper addresses the entropy-based port-Hamiltonian formulation of a tubular reactor with convection, diffusion-heat coupling, and irreversible reaction. Although the direct entropy-variable description ensures thermodynamic consistency, it is constrained by structural difficulties related to the reaction representation and the boundary power pairing, both influenced by the state-dependent entropy metric. To overcome these issues, a set of Structure- Adapted Effort Coordinates is introduced through a Cholesky metric factorisation of the entropy metric. In these coordinates, the reversible operator is obtained with a constant principal part and a boundary pairing cast in a canonical form in the sense of boundary control, facilitating the use of power-preserving boundary kernels and providing a structural basis for well-posedness analysis. The formulation yields nonnegative entropy generation and preserves mass balance.", "url": "" }, { "id": "Mo-MoC26.3", "code": "MoC26.3", "title": "Inverse Optimal Control for Parabolic Systems with Stability-Guaranteed Graph Neural Networks (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC26", "sessionTitle": "Thermodynamics Foundations of Mathematical Systems Theory", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Guan, YaCun", "affiliation": "Hangzhou Dianzi University" }, { "name": "Wang, Siwei", "affiliation": "Hangzhou Dianzi University" }, { "name": "Yang, Hao", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Jiang, Bin", "affiliation": "Nanjing University of Aeronautics and Astronautics" } ], "keywords": [ "Reliability and safety in processes", "Advanced process control" ], "abstract": "Inverse optimal control for parabolic systems seeks to recover spatially distributed cost operators from observed control behavior while ensuring that the resulting feedback stabilizes the system. Most methods rely on diagonal cost assumptions or black-box learning models lacking interpretability and stability guarantees, making them unsuitable for spatiotemporal integral cost functional that encodes interactions across distinct spatial locations and manifests as off-diagonal terms in the discretized cost operator. This paper develops a two-stage framework that identifies cost operators with spatial interactions while guaranteeing exponential stability. The first stage employs graph neural networks with a Riccati-residual loss to efficiently capture spatial interactions, and the second refines these estimates via semidefinite programming that enforces algebraic Riccati constraints through Schur-complement relaxation. The resulting cost operator provably stabilizes the closed-loop system with a computable decay rate. Numerical simulations demonstrate the effectiveness of the proposed method. The approach enables data-driven control design that flexibly captures complex cost structures while providing rigorous stability certificates for safety-critical systems.", "url": "" }, { "id": "Mo-MoC26.4", "code": "MoC26.4", "title": "Port-Metriplectic Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC26", "sessionTitle": "Thermodynamics Foundations of Mathematical Systems Theory", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Kirchhoff, Jonas", "affiliation": "Martin-Luther Universität Halle-Wittenberg" }, { "name": "Maschke, Bernhard", "affiliation": "Univ Claude Bernard of Lyon" } ], "keywords": [ "Process modeling, identification, and estimation techniques" ], "abstract": "In this paper, we suggest a novel definition of port-metriplectic systems obtained by using the relation between metriplectic 4-brackets and conservative-irreversible 4-brackets associated with irreversible Hamiltonian systems. Therefore, we define a class of 4-brackets associated with the definition of dissipative interfaces of the system with its environment and derive conjugated port-input and output maps. We show that the port-metriplectic systems satisfy an energy and an entropy balance equation where the entropy creation at the dissipative interface is taken into account. We illustrate this construction on the elementary example of two compartments exchanging heat between themselves and with a thermostat.", "url": "" }, { "id": "Mo-MoC26.5", "code": "MoC26.5", "title": "MoE-SINDy: A Stable Method for Ecological System Identification (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC26", "sessionTitle": "Thermodynamics Foundations of Mathematical Systems Theory", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Yang, Zhen", "affiliation": "Nanyang Technological University" }, { "name": "Jin, Zhenghong", "affiliation": "Nanyang Technological University" }, { "name": "Chen, Hongjian", "affiliation": "Nanyang Technological University" } ], "keywords": [ "Process modeling, identification, and estimation techniques" ], "abstract": "This paper introduces a Mixture of Experts Sparse Identification of Nonlinear Dynamics (MoE-SINDy) method. MoE-SINDy employs multiple specialized experts together with a state-dependent gating mechanism, allowing complex dynamical regimes to be captured by different sparse coefficient structures. This design maintains competitive local derivative accuracy while enhancing long-horizon rollout stability, providing strong robustness against measurement noise, and accelerating convergence across training epochs. It enables precise identification of complex dynamical systems arising in biology and ecology.", "url": "" }, { "id": "Mo-MoC26.6", "code": "MoC26.6", "title": "On the Convergence Rate Lower Bound of Biochemical Computational Modules (I)", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC26", "sessionTitle": "Thermodynamics Foundations of Mathematical Systems Theory", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Fan, Yuzhen", "affiliation": "Zhejiang University" }, { "name": "Gao, Chuanhou", "affiliation": "Zhejiang University" }, { "name": "He, Shibo", "affiliation": "Zhejiang University" }, { "name": "Chen, Jiming", "affiliation": "Zhejiang University" } ], "keywords": [ "Machine learning and artificial intelligence in chemical process control" ], "abstract": "Biochemical reaction networks have become a central theoretical framework for implementing molecular computation. A key challenge is finite time computational accuracy, as computation outputs are encoded in limiting steady states (LSSs) of species concentrations while practical implementations operate for only finite time. This work proposes a concise characterization of convergence rate for biochemical computational modules with multiple output species, and rigorously establishes it as being bounded by the eigenvalue with largest (least negative) real part of the Jacobian matrix. Two numerical examples illustrate how the theoretical lower bound shapes the convergence rate range and reveals its dependence on reaction rate constants. This formulation enables systematic evaluation of biochemical computation speed and provides a practical design measure for constructing high-accuracy and error-controlled biochemical computational modules.", "url": "" }, { "id": "Mo-MoC27.1", "code": "MoC27.1", "title": "Advancing Model Predictive Control for Autonomous Ships: From Theory to Practice (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC27", "sessionTitle": "Autonomous Ship Navigation, Safety and Mission Planning", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Marx, Johannes Richard", "affiliation": "University of Rostock" }, { "name": "Kurowski, Martin", "affiliation": "University of Rostock" }, { "name": "Jeinsch, Torsten", "affiliation": "University of Rostock" } ], "keywords": [ "Autonomous marine systems and vehicles", "Marine system guidance, navigation and control" ], "abstract": "Model predictive control is widely used and is increasingly being employed in the control of ships. One challenge is the practical implementation to consider the non-linear system behavior and the restrictive limitations in the form of state and actuator constraints, dead times, model uncertainties, and changing environmental conditions. In this context, the paper describes the progress from the theoretical concepts to the successful implementation of model predictive trajectory control for ships and its application to the research vessel DENEB (52 m long, 11 m wide). It presents new approaches for taking into account the coupling of control variables within the actuator allocation, especially for underactuated vehicles, for disturbance and dead times rejection, as well as the methods which are necessary in practice for reducing the control effort in magnitude and time. The paper concludes with a comparison of simulations and practical tests with the DENEB in the port of Rostock.", "url": "" }, { "id": "Mo-MoC27.2", "code": "MoC27.2", "title": "Hybrid Systems Software Tools for Mission Planning of Marine Vehicles (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC27", "sessionTitle": "Autonomous Ship Navigation, Safety and Mission Planning", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "McKee, Ryan", "affiliation": "Queens University Belfast/University of Liverpool" }, { "name": "Naeem, Wasif", "affiliation": "Queen's University of Belfast" }, { "name": "Athanasopoulos, Nikolaos", "affiliation": "Queen's University Belfast" }, { "name": "Lecallard, Benoit", "affiliation": "Artemis Technologies Ltd" } ], "keywords": [ "Simulation and digital-twin in marine systems", "Marine system guidance, navigation and control", "Decision and support in marine systems" ], "abstract": "Modern maritime autonomous navigation should adapt to dynamic environments and offer interpretable and certifiable safety guarantees while maintaining compliance with the International Regulations for Preventing Collisions at Sea (COLREGs). We present a hybrid automaton based framework that combines decision making capabilities while being transparent and verifiable. Our approach structures vessel behavior into interpretable modes and enables the design of motion planning algorithms based on rule-driven switching between modes. We introduce HybrautNav, a modular ROS~2-based navigation stack, packaged as a Docker-deployable module suitable for embedded platforms. We present the software architecture that integrates HybrautNav into a mission-planning system with real-time risk assessment and scenario management. We use a configurable USV simulation suite on representative encounter scenarios, measuring collision rates, COLREGs compliance metrics, and operator interpretability.", "url": "" }, { "id": "Mo-MoC27.3", "code": "MoC27.3", "title": "Risk-Aware Adaptive Path Planning for Autonomous Ships Using Safe Corridor Graphs (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC27", "sessionTitle": "Autonomous Ship Navigation, Safety and Mission Planning", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Monnet, Stephen", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Adetunji, Aduragbemi Samuel", "affiliation": "Norwegian University of Science and Technology (NTNU)" }, { "name": "Bakkeheim, Jostein", "affiliation": "Kongsberg Maritime" }, { "name": "Rokseth, Børge", "affiliation": "NTNU" }, { "name": "Johansen, Tor Arne", "affiliation": "Norwegian University of Science and Technology" } ], "keywords": [ "Decision and support in marine systems", "Autonomous marine systems and vehicles", "Marine system guidance, navigation and control" ], "abstract": "Following a planned path for autonomous ships is often challenged by environmental disturbances and dynamic hazards, making strict path adherence impractical. We propose a corridor-based framework in which navigation corridors are precomputed to be free of static obstacles. The system builds a graph of such corridors connecting mission start and target points and evaluates alternative routes using a risk model that accounts for traffic, metocean, and other operational factors. By navigating within the corridors, the ship can adjust its trajectory to optimize energy consumption, avoid obstacles, and respond to environmental conditions while remaining within safe boundaries. The proposed corridors are designed to be compatible with ECDIS, allowing all possible alternative corridors to be verified beforehand, ensuring compliance with navigation rules and facilitating human oversight.", "url": "" }, { "id": "Mo-MoC27.4", "code": "MoC27.4", "title": "A Unified Vessel Dynamics and Environmental Modeling Framework for Realistic Vessel Trajectory Prediction", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC27", "sessionTitle": "Autonomous Ship Navigation, Safety and Mission Planning", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Tiwari, Taruna", "affiliation": "Otto-Von-Guericke University Magdeburg" }, { "name": "Noack, Benjamin", "affiliation": "Otto Von Guericke University (OVGU)" } ], "keywords": [ "Maritime transport operation and automation", "Marine system guidance, navigation and control", "Decision and support in marine systems" ], "abstract": "Understanding and predicting maritime vessel movement is essential for navigational safety, efficient port operations, and environmental oversight. This study presents a physics-based modeling approach for predicting cargo ship trajectories, employing the Maneuvering Model Group (MMG) model in conjunction with environmental forces. The model explicitly incorporates wind, currents effects to simulate cargo ship motion with high fidelity. The contribution of this work lies in validating this unified MMG–environmental model against real vessel tracks and testing it across two distinct scenarios, open water and constrained inland waterway. Validation against historical vessel tracks demonstrates that the model can replicate observed trajectories with strong agreement, achieving path similarity scores up to 0.96 in constrained inland waterways and above 0.92 in open-water conditions when environmental forces are considered. These results highlight the utility of physics-based modeling as a robust tool for maritime transportation planning, tracking, and decision-making in complex navigational environments, when extensive data are limited.", "url": "" }, { "id": "Mo-MoC27.5", "code": "MoC27.5", "title": "Energy-Optimal Trajectory Planning for Unmanned Surface Vehicles Via Multi-Strategy Improved Quantum-Behaved Particle Swarm Optimization", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC27", "sessionTitle": "Autonomous Ship Navigation, Safety and Mission Planning", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Shi, Wenlong", "affiliation": "Harbin Engineering University" }, { "name": "Zhang, Lanyong", "affiliation": "Harbin Engineering University" }, { "name": "Feng, Zhiguang", "affiliation": "Harbin Engineering University" } ], "keywords": [ "Autonomous marine systems and vehicles", "Decision and support in marine systems" ], "abstract": "Realizing energy-optimal trajectory planning is pivotal for extending the endurance of Unmanned Surface Vehicles (USVs) in complex marine environments. However, conventional approaches often compromise fidelity by simplifying ocean currents as static, uniform fields—neglecting spatiotemporal variability—and suffer from premature convergence when employing traditional heuristic algorithms. To bridge these gaps, this study proposes a robust integrated planning framework. First, a realistic time-varying ocean current model utilizing superimposed Lamb vortices is established to effectively characterize the nonlinear impact of fluid dynamics on propulsion energy. Second, a Multi-Strategy Improved Quantum-behaved Particle Swarm Optimization (IQPSO) algorithm is developed to tackle the optimization complexity. This algorithm incorporates dynamic opposition-based learning for robust initialization, integrates a Golden Sine mechanism to enhance local exploitation, and employs Lévy flight strategies to effectively circumvent local optima stagnation. Furthermore, a comprehensive objective function is constructed to balance energy efficiency, navigational safety, and path smoothness. Simulation results demonstrate that the IQPSO significantly outperforms state-of-the-art algorithms in convergence rate and stability. Crucially, semi-physical validation experiments involving a real USV confirm the framework's practical feasibility and superior energy efficiency in realistic, dynamic navigational scenarios.", "url": "" }, { "id": "Mo-MoC28.1", "code": "MoC28.1", "title": "Hybrid Heuristic Algorithm for Mission Planning of Agile Earth Observation Satellites", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC28", "sessionTitle": "Satellite Mission Planning, Orbital Operations and Space Guidance", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Herland, Øydis", "affiliation": "Norwegian University of Science and Technology (NTNU)" }, { "name": "van den Broek, Jochem", "affiliation": "Eindhoven University of Technology" }, { "name": "Kristiansen, Bjørn Andreas", "affiliation": "Norwegian University of Science and Technology (NTNU)" }, { "name": "Gravdahl, Jan Tommy", "affiliation": "Norwegian University of Science and Technology (NTNU)" }, { "name": "Berg, Simen", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Langer, Dennis David", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Johansen, Tor Arne", "affiliation": "Norwegian University of Science and Technology" } ], "keywords": [ "Aerospace mission control and operations", "Automatic control, optimization, real-time operations in transportation", "Mission planning and decision making for AVs" ], "abstract": "Agile Earth-observation satellites offer flexible imaging through fast three-axis maneuverability,thereby expanding the solution space for observation scheduling. Additionally, each observation task must be paired with a onboard processing task and feasible downlink opportunity. A multi-objective optimization problem is formulated, balancing target priority and image quality objectives. As a solver, this article presents a hybrid heuristic algorithm that combines the Non-dominated Sorting Genetic Algorithm II with Adaptive Large Neighbourhood Search. The method is tailored to the Hyperspectral Satellite for Ocean Observation 2 (HYPSO-2) mission, by jointly scheduling observation, buffering, and downlinking tasks. The algorithm is validated through in-orbit testing on HYPSO-2 and through simulations, where it outperforms two greedy baseline algorithms. The results demonstrate both the robustness of the proposed algorithm and its applicability in operational contexts.", "url": "" }, { "id": "Mo-MoC28.2", "code": "MoC28.2", "title": "Long-Horizon Autonomous Mission Planning for Agile Satellites Via Constrained Deep Reinforcement Learning", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC28", "sessionTitle": "Satellite Mission Planning, Orbital Operations and Space Guidance", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Wang, Yuchen", "affiliation": "Harbin Institute of Technology" }, { "name": "Yang, Baoqing", "affiliation": "Harbin Institute of Technology" }, { "name": "Ma, Jie", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Aerospace mission control and operations", "Guidance, navigation and control of aircraft and spacecraft" ], "abstract": "Agile Earth Observation Satellite (AEOS) mission planning faces intrinsic conflicts between rigid observation windows and cumulative on-board resource constraints. Standard Deep Reinforcement Learning (DRL) approaches often suffer from decision myopia and inability to effectively adhere to operational safety constraints. To address these challenges, this paper proposes an integrated framework combining a Temporal Resource Estimation Network (TREN) with Lagrangian Constrained Proximal Policy Optimization (LC-PPO). TREN leverages a Gated Transformer-XL architecture to extract long-horizon temporal dependencies, effectively alleviating myopia. Simultaneously, LC-PPO employs adaptive Lagrangian multipliers acting as integral controllers to enforce dynamically regulate energy and momentum boundaries under stochastic conditions. Simulation results demonstrate that the proposed method significantly outperforms standard baselines in cumulative yield and constraint satisfaction, exhibiting emergent foresight behaviors such as proactive maintenance.", "url": "" }, { "id": "Mo-MoC28.3", "code": "MoC28.3", "title": "Nonlinear Model Predictive Control for High-Thrust Geostationary Station Keeping Using Averaged Dynamics", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC28", "sessionTitle": "Satellite Mission Planning, Orbital Operations and Space Guidance", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Pavlasek, Natalia", "affiliation": "University of Washington" }, { "name": "Acikmese, Behcet", "affiliation": "University of Washington" }, { "name": "Di Cairano, Stefano", "affiliation": "Mitsubishi Electric Research Laboratory" }, { "name": "Weiss, Avishai", "affiliation": "Mitsubishi Electric Research Laboratories" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Aerospace mission control and operations" ], "abstract": "Sequential convex programming (SCP) shows promise for fuel-optimal sparse control of high-thrust satellites in geostationary earth orbit (GEO), but is highly vulnerable to converge to local minima in the neighborhood of an initial guess. In particular, when optimizing for the time at which to perform a maneuver, these algorithms tend to find solutions within a few hours of the times at which they are initialized. In this work, we propose an algorithm that relies on averaged dynamics to form a proxy system with fewer nonconvexities than the true system. We use a consensus-based optimization framework to reach a consensus between the average and the true system, enabling the SCP to explore more of the solution space and enabling larger deviations of the converged solution from the initial guess. We demonstrate the performance of the proposed method against that of standard SCP on a problem in which the goal is to extend the time between east-west station-keeping maneuvers for a GEO satellite. Simulations are performed using NASA’s General Mission Analysis Tool, a high-fidelity space mission simulator.", "url": "" }, { "id": "Mo-MoC28.4", "code": "MoC28.4", "title": "Tracking the Effective Surface Area of Non-Convex Satellites", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC28", "sessionTitle": "Satellite Mission Planning, Orbital Operations and Space Guidance", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Fosso, Lauritz Rismark", "affiliation": "SINTEF Ocean" }, { "name": "Kristiansen, Raymond", "affiliation": "UiT the Arctic University of Norway" }, { "name": "Gravdahl, Jan Tommy", "affiliation": "Norwegian University of Science and Technology (NTNU)" }, { "name": "Ohrem, Sveinung Johan", "affiliation": "SINTEF Ocean" }, { "name": "Bocci, Alessio", "affiliation": "UiT the Arctic University of Norway" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Aerospace mission control and operations", "Aerial and space robotics" ], "abstract": "This paper presents a novel framework to track the effective surface area of non-convex satellites, enabling the use of aerodynamic drag in low Earth orbit for orbital control. The proposed framework enables the satellite to track the effective surface area while simultaneously performing other maneuvers. We introduce this framework through a backstepping control algorithm, and exemplify its advantages with an extension, to simultaneously maximize solar panel exposure. The equilibria of the closed-loop systems are shown to be asymptotically stable, and simulation results confirm the effectiveness of the proposed framework.", "url": "" }, { "id": "Mo-MoC28.5", "code": "MoC28.5", "title": "Scenario-Based Model Predictive Control for Station Keeping on Near-Rectilinear Halo Orbit", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC28", "sessionTitle": "Satellite Mission Planning, Orbital Operations and Space Guidance", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Shimane, Yuri", "affiliation": "University of California, Irvine" }, { "name": "Isaji, Masafumi", "affiliation": "Georgia Institute of Technology" }, { "name": "Weiss, Avishai", "affiliation": "Mitsubishi Electric Research Laboratories" }, { "name": "Di Cairano, Stefano", "affiliation": "Mitsubishi Electric Research Laboratory" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Space exploration and transportation", "Condition monitoring and maintenance of aerospace systems" ], "abstract": "This paper considers a scenario-based model predictive control (ScnMPC) for the stochastic station-keeping problem of spacecraft on the Near-Rectilinear Halo Orbit. The station-keeping problem is characterized by (i) the need for explicit propellant minimization, which directly translates to mission duration, (ii) its sparse control opportunity with long time intervals, typically extending to a few days, and (iii) nonconvex dynamics and uncertainties that are well-characterized. Taking advantage of the low control cadence, the ScnMPC solves an extensive nonconvex scenario-based optimal control problem via sample average approximation, taking into account the known distributions of uncertainties. We conduct numerical experiments highlighting the benefit of the presented approach over deterministic station-keeping MPC.", "url": "" }, { "id": "Mo-MoC29.1", "code": "MoC29.1", "title": "AdArduRover+: An Autopilot for Ground Vehicles with Hybrid Adaptation (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC29", "sessionTitle": "Autonomous Vehicle Systems in Conditions of Uncertainty", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Sun, Danping", "affiliation": "Southeast University" }, { "name": "Liu, Di", "affiliation": "Imperial College London" }, { "name": "Baldi, Simone", "affiliation": "Southeast University" }, { "name": "Yu, Wenwu", "affiliation": "Southeast University" }, { "name": "Duan, Guang-Ren", "affiliation": "Harbin Institute of Technology" }, { "name": "Astolfi, Alessandro", "affiliation": "King Abdullah University of Science and Technology (KAUST)" } ], "keywords": [ "Autonomous vehicles", "Learning and adaptation in autonomous vehicles", "Guidance, navigation and control for AVs" ], "abstract": "Autopilots, representative open-source examples being ArduPilot and PX4, are a key component of any autonomous vehicle: unfortunately, the autonomy of the vehicle is limited by the capability of the autopilot to handle uncertainty. This work presents AdArduRover+, an advancement of ArduPilot’s ArduRover module for ground vehicles. AdArduRover+ embeds an adaptation mechanism that handles a combination of linear-in-parameters (LIP) and nonlinear-in-parameters (NLIP) uncertainties: we refer to such mechanism as hybrid LIP-NLIP adaptation. The effectiveness of the proposed solution is confirmed by analysis and by hardware-in-the-loop experiments against several autopilot variants.", "url": "" }, { "id": "Mo-MoC29.2", "code": "MoC29.2", "title": "Guaranteed Autonomous Vehicles Localization under Uncertain Observation Times Using Constrained Zonotopes Set-Membership Filtering (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC29", "sessionTitle": "Autonomous Vehicle Systems in Conditions of Uncertainty", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Nogueira, Rafael Accácio", "affiliation": "Univ Angers - Polytech Angers - LARIS" }, { "name": "Fergani, Soheib", "affiliation": "LAAS-CNRS" } ], "keywords": [ "Autonomous vehicles", "Guidance, navigation and control for AVs", "Kalman filtering techniques in automotive control" ], "abstract": "This paper presents a set-based state estimator using constrained zonotopes for nonlinear systems with uncertain and asynchronous observation times. This estimator also accounts for parametric uncertainty in the observation equation and allows multiple state propagation models to be combined. The method provides guaranteed state enclosures despite observation time uncertainty, which is critical in practical autonomous vehicle applications. Its effectiveness is illustrated on two academic case studies representative of real-world scenarios: a one-dimensional vehicle platooning problem and a two-dimensional vehicle localization problem. The filter is compared against other methods highlighting its performance.", "url": "" }, { "id": "Mo-MoC29.3", "code": "MoC29.3", "title": "Robust Observer-Based Control for Roundabout Trajectory Tracking of AVs under Measurement Uncertainties (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC29", "sessionTitle": "Autonomous Vehicle Systems in Conditions of Uncertainty", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Bougherara, Selsabil", "affiliation": "Université Polytechnique Hauts-De-France" }, { "name": "Arezki, Hasni", "affiliation": "UPHF" }, { "name": "Sentouh, Chouki", "affiliation": "LAMIH UMR CNRS 8201, Université Polytechnique Hauts-De-France, Valenciennes, France" }, { "name": "Popieul, Jean-Christophe", "affiliation": "University of Valenciennes/LAMIH" } ], "keywords": [ "Autonomous vehicles", "Trajectory tracking and path following for AVs", "Nonlinear and optimal automotive control" ], "abstract": "Autonomous navigation in roundabouts requires accurate trajectory tracking under coupled longitudinal-lateral dynamics. This paper proposes an observer-based control law that jointly designs the state estimator and feedback controller in the presence of nonlinear measurement uncertainty. The method handles vehicle nonlinearities through polytopic Jacobian matrices and measurement uncertainties using Young's inequality, enabling tractable Linear Matrix Inequality (LMI) synthesis. A Lyapunov analysis ensures exponential stability of the augmented error dynamics, guaranteeing boundedness of the combined tracking and estimation errors in the presence of uncertainties. Validation using real data from the high-fidelity SHERPA-LAMIH driving simulator shows rapid observer convergence from large initial errors and consistent trajectory tracking during the approach, insertion, and circulation phases.", "url": "" }, { "id": "Mo-MoC29.4", "code": "MoC29.4", "title": "Distributed Traffic State Estimation in V2X-Enabled Connected Vehicle Networks (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC29", "sessionTitle": "Autonomous Vehicle Systems in Conditions of Uncertainty", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "de Heij, Vincent", "affiliation": "University of Groningen" }, { "name": "Niazi, M. Umar B.", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Ahmed, Saeed", "affiliation": "Faculty of Science and Engineering, University of Groningen" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Multi-vehicle systems", "Intelligent transportation systems", "Kalman filtering techniques in automotive control" ], "abstract": "This paper presents a distributed traffic state estimation framework in which infrastructure sensors and connected vehicles act as cooperative sensing nodes, sharing local estimates via Vehicle-to-Everything (V2X) communication. The proposed algorithm applies a distributed Kalman filter to a second-order macroscopic traffic flow model, using a consensus protocol to fuse heterogeneous spatiotemporal estimates from V2X neighbors and explicit projection steps to preserve physical consistency in density and flow. Microscopic simulations of a highway segment with transient congestion show that the estimator accurately reconstructs nonlinear shockwave dynamics under sparse infrastructure sensing and intermittent connectivity. Statistical analysis across connected vehicle penetration rates reveals notable phase transitions in network observability.", "url": "" }, { "id": "Mo-MoC29.5", "code": "MoC29.5", "title": "Distributed Unknown Input Observer for Vehicle Platoons under Sensor Faults (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC29", "sessionTitle": "Autonomous Vehicle Systems in Conditions of Uncertainty", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Meng, Shengya", "affiliation": "Universite De Lorraine" }, { "name": "Nguyen, Quang Huy", "affiliation": "University Lorraine" }, { "name": "Alma, Marouane", "affiliation": "Université De Lorraine, France" }, { "name": "Zemouche, Ali", "affiliation": "CRAN UMR CNRS 7039, University of Lorraine" }, { "name": "Haddad, Madjid", "affiliation": "SEGULA Technologies" } ], "keywords": [ "Multi-vehicle systems", "Intelligent transportation systems", "Autonomous vehicles" ], "abstract": "This paper presents a novel integrated observer framework for vehicle platoons that combines distributed observers (DOs) and unknown input observers (UIOs) to enhance state estimation in vehicle platoons under sensor faults. The DO estimates the states of all vehicles using local measurements and intervehicle communication. To counter the degradation in estimation accuracy that could be caused by sensor faults, a UIO is designed to simultaneously estimate these faults and reconstruct the correct local measurements. To construct the UIO, the position-velocity-acceleration model of the platoon is reformulated as a descriptor system, with sensor faults treated as an extended state. Unlike existing methods, the proposed DO utilizes the corrected measurements provided by the UIO instead of the faulty sensor data. This integration ensures robust and accurate state estimation even in the presence of sensor faults. The effectiveness of the proposed integrated observer structure is demonstrated using real-world QCar2 data.", "url": "" }, { "id": "Mo-MoC29.6", "code": "MoC29.6", "title": "Basis Function Point-To-Point Iterative Learning Control Applied to UAVs (I)", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC29", "sessionTitle": "Autonomous Vehicle Systems in Conditions of Uncertainty", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Hamidalddin, Ahmed", "affiliation": "University of Southampton" }, { "name": "Freeman, Christopher Thomas", "affiliation": "University of Southampton" }, { "name": "Belkhatir, Zehor", "affiliation": "University of Southampton" } ], "keywords": [ "AI for aircraft and spacecraft navigation, guidance and control", "Aerial and space robotics", "Autonomous vehicles" ], "abstract": "Point-to-point iterative learning control (ILC) has become a popular methodology for systems that repeatedly need to track a finite set of output locations at predefined time instants. However, existing formulations often generate oscillatory, high-frequency feedforward inputs due to model inversion. This paper proposes a novel framework which embeds a low-dimensional basis-function subspace into the point-to-point norm-optimal ILC cost, restricting the learned reference to smooth, task-aligned families while preserving transparent convergence, robustness and optimality properties. The approach is applied to a cascaded PID-controlled quadrotor, where experiments on a Crazyflie 2.1 nano–UAV show that basis-function point-to-point ILC eliminates high-frequency oscillations of unrestricted point-to-point ILC and yields rapid and repeatable error reduction.", "url": "" }, { "id": "Mo-MoC30.1", "code": "MoC30.1", "title": "Functional Abilities and Transitions between Care Environments: Developing a Research Framework from a Social Gerontology and Occupational Therapy Perspective (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC30", "sessionTitle": "Supporting Ageing Populations: Care Transitions, Urban Design, and Digital Infrastructure", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Sicherl, Zorana", "affiliation": "Alma Mater Europea, Maribor Slovenia, University of Ljubljana, Faculty of Health Sciences, Slovenia" }, { "name": "Šabeder, Renata", "affiliation": "University Alma Mater Europaea Slovenia" }, { "name": "Bogataj, David", "affiliation": "Alma Mater Europaea University" } ], "keywords": [ "Cyber-physical and human systems (CPHS)" ], "abstract": "Transitions of care are critical periods for older adults, influencing functional ability, participation, and independence. Although research has examined specific settings such as hospital discharge or community rehabilitation, less attention has been given to how multiple transitions accumulate and shape everyday functioning. This article proposes an integrated micro–meso–macro framework that combines insights from social gerontology and occupational therapy to provide a more comprehensive understanding of transitions. The micro level focuses on intrinsic capacity, daily routines, and the person-environment fit; the meso level examines interprofessional communication, coordination, and the organisation of services; and the macro level highlights policies, funding structures, and system-wide standards that influence continuity of care. By linking these levels, the framework explains why similar transitions can unfold differently in organisational contexts and services. It also emphasises the importance of recognising functional ability and meaningful activity as essential outcomes of transitional care. This perspective offers a foundation for future research and supports the development of more coherent, person-centred approaches to transitions for older adults.", "url": "" }, { "id": "Mo-MoC30.2", "code": "MoC30.2", "title": "Nature-Based Solutions for Older Adults in Age-Friendly Oriented Cities: Impacts on Climate Resilience, Social Inclusion, and Quality of Life (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC30", "sessionTitle": "Supporting Ageing Populations: Care Transitions, Urban Design, and Digital Infrastructure", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Hribar Podkrajšek, Ana", "affiliation": "Alma Mater Europea University" }, { "name": "Bogataj, David", "affiliation": "Alma Mater Europaea University" } ], "keywords": [ "Decision making under uncertainty", "Urban energy distribution systems" ], "abstract": "As global populations age and climate change intensifies, older urban residents face increasing risks from extreme heat and poor air quality. While research in automation and control within IFAC TC 9.5 has traditionally focused on digital social infrastructures like ambient assisted living and smart city systems, the natural environment as a component of this infrastructure remains under-examined. This structured narrative review synthesizes evidence from ten recent empirical and model-based studies to evaluate how nature-based solutions, including urban tree canopies, green roofs, and blue-green systems, impact the resilience and quality of life of older adults. The findings identify three primary areas of contribution: thermal mitigation, which reduces heat loads and modelled mortality risks in high-risk districts; psychosocial benefits through psychological restoration and enhanced social interaction; and the role of spatial equity, where benefits depend on barrier-free accessibility Most of the reviewed studies are limited to short observation periods, leaving open questions about longer term impacts and about how disadvantaged older adults are represented in the evidence.We conclude that nature-based solutions should be integrated into the TECIS research agenda as relational infrastructures that complement digital assistive technologies for climate-resilient and age-friendly smart cities.", "url": "" }, { "id": "Mo-MoC30.3", "code": "MoC30.3", "title": "Integration of Telecare into Rural Care Infrastructure: Literature Review and Research Agenda (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC30", "sessionTitle": "Supporting Ageing Populations: Care Transitions, Urban Design, and Digital Infrastructure", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Emerllahu, Visar", "affiliation": "New University European Faculty of Law" }, { "name": "Bogataj, David", "affiliation": "Alma Mater Europaea University" } ], "keywords": [ "Digital culture", "Advanced technology, conflict and post-conflict", "Control and automation to improve social and political stability" ], "abstract": "An ageing global population, which is further compounded by enduring discrepancies in access to health care between urban and rural settings, has created a sense of urgency regarding the development of new health care delivery models. Telecare has emerged as the most promising solution to these geographic challenges, but several neurovascular challenges hinder its adoption. The purpose of this review article is to provide a comprehensive synthesis of the literature related to the adoption of telecare in rural communities. We identify essential infrastructure needs, discuss complex adoption barriers, and outline a research agenda to design future policies and practices that improve healthcare equity. A systematic review of the literature was conducted, searching the Web of Science and other relevant sources for publications focusing on issues, barriers, and best practices in the deployment of telecare in underserved rural areas. The review highlights that telecare has the potential to enhance health outcomes and service efficiency, as well as provide access to specialist support, but only if considerable barriers can be overcome. Key bottlenecks presented included insufficient technology resources, low digital literacy among patients and providers, financial issues, the lack of standard regulatory protocols, and social-cultural resistance. A successful rollout also depends on technical preparedness, seamless integration into healthcare systems, and strong community involvement.", "url": "" }, { "id": "Mo-MoC30.4", "code": "MoC30.4", "title": "Community-Based Housing and Quality of Life in Ageing Societies (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC30", "sessionTitle": "Supporting Ageing Populations: Care Transitions, Urban Design, and Digital Infrastructure", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Lesjak, Matic", "affiliation": "Alma Mater Europea" }, { "name": "Bogataj, David", "affiliation": "Alma Mater Europaea University" } ], "keywords": [ "Control approaches for reaching the United Nations SDGs", "Social networks for smart cities" ], "abstract": "Population ageing is increasing pressure on long-term care systems and creates a need for new forms of housing and support for older adults. This paper reviews innovative community-based housing models, including co-housing, assisted living facilities, senior villages, Serviced Housing for Older People (SHOP) and continuing care retirement communities (CCRCs). The review focuses on how these models relate to quality of life, well-being, social inclusion, perceived safety and reduced loneliness. The findings suggest that community-based housing can combine privacy and autonomy with everyday social contact, mutual support and a stronger sense of belonging. From an IFAC/TECIS perspective, the paper understands community-based housing as a human-centred socio-technical arrangement in ageing societies. It concludes that future housing and care systems should consider not only technological innovation, but also social infrastructure, community relations and living environments that support ageing well.", "url": "" }, { "id": "Mo-MoC30.5", "code": "MoC30.5", "title": "Digitalization and Social Inequalities in Later Life: Scoping Review on Technology Anxiety and Urban Ageing", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC30", "sessionTitle": "Supporting Ageing Populations: Care Transitions, Urban Design, and Digital Infrastructure", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Kotherja, Ortenca", "affiliation": "Lectur Faculty of Social Science , Tirane, Albania" }, { "name": "Bogataj, David", "affiliation": "Alma Mater Europaea University" } ], "keywords": [ "Control and automation to improve social and political stability", "Diversity and inclusion in digital culture", "Regulation, policy, and legal issues in control/AI" ], "abstract": "This study examines the impact of digitalization and urbanization on the psycho-emotional well-being of older adults, based on the analysis of 18 articles published between 2020 and 2025. The results indicate that rapid urbanization has created challenges for older adults, including social isolation, urban stress, and physical limitations, while the use of digital technologies, including the internet and mental health applications, helps reduce loneliness and anxiety, improving quality of life. However, barriers such as limited technological literacy, privacy concerns, and lack of guidance affect the adoption of these tools. The studies also highlight the importance of community infrastructure and green spaces in promoting physical health, subjective well-being, and social participation, while interpersonal interactions, such as nurse–resident relationships in care homes, are crucial for mental health promotion. This review emphasizes that combining sustainable urbanization, technology, and social support can significantly enhance the lives of older adults and mitigate the negative effects of anxiety and social isolation. Recommendations include improving access to and training in digital technologies, developing accessible applications, increasing social interactions, creating community green infrastructure, and implementing institutional policies that support the well-being of older adults.", "url": "" }, { "id": "Mo-MoC32.1", "code": "MoC32.1", "title": "Efficient COLREGs-Compliant Collision Avoidance Using Turning Circle-Based Control Barrier Function (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC32", "sessionTitle": "JO-MECH: High-Performance Motion Control Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Lee, Changyu", "affiliation": "Kongju National University" }, { "name": "Park, Jinwook", "affiliation": "KAIST" }, { "name": "Kim, Jinwhan", "affiliation": "KAIST" } ], "keywords": [ "High-performance motion control systems", "Autonomous navigation", "Aerial, field, and marine robotics" ], "abstract": "This paper proposes a computationally efficient collision avoidance algorithm using turning circle-based control barrier functions (CBFs) that comply with international regulations for preventing collisions at sea (COLREGs). Conventional CBFs often lack explicit consideration of turning capabilities and avoidance direction, which are key elements in developing a COLREGs-compliant collision avoidance algorithm. To overcome these limitations, we introduce two CBFs derived from left and right turning circles. These functions establish safety conditions based on the proximity between the traffic ships and the centers of the turning circles, effectively determining both avoidance directions and turning capabilities. The proposed method formulates a quadratic programming problem with the CBFs as constraints, ensuring safe navigation without relying on computationally intensive trajectory optimization. This approach significantly reduces computational effort while maintaining performance comparable to model predictive control-based methods. Simulation results validate the effectiveness of the proposed algorithm in enabling COLREGs-compliant, safe navigation, demonstrating its potential for reliable and efficient operation in complex maritime environments.", "url": "" }, { "id": "Mo-MoC32.2", "code": "MoC32.2", "title": "Numerical Describing Function Analysis of Closed-Loop Discrete-Time Reset Control Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC32", "sessionTitle": "JO-MECH: High-Performance Motion Control Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "van Eijk, Luke Franciscus", "affiliation": "Delft University of Technology" }, { "name": "Kostic, Dragan", "affiliation": "Eindhoven University of Technology" }, { "name": "HosseinNia, S Hassan", "affiliation": "Delft University of Technology" } ], "keywords": [ "High-performance motion control systems", "Mechatronic system estimation, identification, control" ], "abstract": "In this paper we study the digital implementation of reset controllers on mechatronic systems, particularly focusing on their frequency-domain behaviour. We demonstrate that the frequency-domain behaviour of a closed-loop discrete-time (DT) reset control system (RCS) can be significantly different compared to a continuous-time (CT) counterpart. Furthermore, we propose a novel frequency-domain performance prediction method -- based on the describing function approach -- which can take these discretization effects into account. In an example we show that predictions obtained with the proposed method, are more accurate compared to predictions obtained using existing methods aimed for CT RCSs.", "url": "" }, { "id": "Mo-MoC32.3", "code": "MoC32.3", "title": "Robust Reset Control Design by Loop Shaping for Piezoelectric-Actuated Positioner in Presence of Nonlinearity (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC32", "sessionTitle": "JO-MECH: High-Performance Motion Control Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Sebghati, Ashkan", "affiliation": "The Faculty of Mechanical Engineering, Delft University of Technology" }, { "name": "HosseinNia, S Hassan", "affiliation": "Delft University of Technology" } ], "keywords": [ "High-performance motion control systems", "Mechatronic system estimation, identification, control" ], "abstract": "Loop shaping is widely used in precision motion control, but conventional approaches, focused on phase margin and open-loop gain, are inadequate for piezo positioning systems where open-loop phase critically affects performance. This paper proposes generalized loop-shaping guideline tailored for nonlinear piezo-actuated stages. A constant-in-gain lead-in-phase reset controller is developed to implement the guideline by overcoming waterbed effect in linear control. An intuitive methodology for shaping filter design is presented to ensure reliable reset control implementation. Using (higher-order) sinusoidal input describing functions, nonlinear motion control is designed. Experiments demonstrate closed-loop bandwidth flatness (±1 dB) and enhanced sensitivity function.", "url": "" }, { "id": "Mo-MoC32.4", "code": "MoC32.4", "title": "Robust Mixed-Sensitivity H∞ Control Synthesis Integrating Active Damping for Piezoelectric Nanopositioning System under Payload-Induced Uncertainties (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC32", "sessionTitle": "JO-MECH: High-Performance Motion Control Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Natu, Aditya", "affiliation": "Delft University of Technology" }, { "name": "Araga, Manavi", "affiliation": "TU Delft" }, { "name": "HosseinNia, S Hassan", "affiliation": "Delft University of Technology" } ], "keywords": [ "High-performance motion control systems", "Mechatronic system estimation, identification, control", "Mechatronic system modeling, design, optimization" ], "abstract": "Piezoelectric nanopositioning systems exhibit low damping and resonance modes that are highly sensitive to loading conditions, resulting in performance degradation under payload variations. Conventional damping and robust control methods typically address these challenges separately, overlooking the coupling between damping and tracking dynamics as well as the influence of higher-order resonant modes. This paper proposes a dual-loop control framework that integrates active damping with mixed-sensitivity H∞ synthesis to achieve robust reference tracking and disturbance rejection under large resonance frequency variations. A Non-Minimum-Phase Resonant Controller (NRC) is implemented in the inner loop to suppress the dominant resonance and reduce system uncertainty. Generalized plant formulation and systematic weighting design guidelines of arbitrary order are developed to explicitly incorporate higher-order modes in the outer-loop H∞ synthesis. The proposed approach is validated through simulations and experiments on an industrial piezoelectric nanopositioning system, demonstrating improved robustness and precision across the full payload range.", "url": "" }, { "id": "Mo-MoC32.5", "code": "MoC32.5", "title": "How to Parameterize Feedforward Filters? a Data-Driven Sparse Optimization Approach (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC32", "sessionTitle": "JO-MECH: High-Performance Motion Control Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Ickenroth, Tjeerd", "affiliation": "Eindhoven University of Technology" }, { "name": "Cerullo, Armando", "affiliation": "Eindhoven University of Technology" }, { "name": "Oomen, Tom", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "High-performance motion control systems", "Mechatronic system estimation, identification, control", "Mechatronic system modeling, design, optimization" ], "abstract": "Feedforward control is essential for achieving high performance in broad applications such as motion control. The aim of this paper is to automate the parameterization of feedforward controllers, which is always done explicitly or implicitly in applications. A data-driven method is presented that automatically selects feedforward components via sparse optimization that are essential for performance, while at the same time this allows for an interpretable and low-order parameterization by selecting from a pre-specified library. Experimental validation on an industrial flatbed printer demonstrates that the presented method achieves a threefold reduction in tracking error and reaches exceptional performance levels, comparable to a benchmark iterative learning control result, while maintaining task generalization. These results show that sparse learning enables automated feedforward structure selection, providing a systematic route toward next-generation data-driven feedforward design in precision mechatronics.", "url": "" }, { "id": "Mo-MoC33.1", "code": "MoC33.1", "title": "Sensorless Tension Control for Tethered UAVs Via an Equivalent Thrust Constraint (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC33", "sessionTitle": "Resilient Control, Motion Control, and Navigation of eVTOL Aircrafts in Smart City", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Xu, Minghui", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Li, Mao", "affiliation": "Hubei Provincial Key Laboratory for Low-Frequency Electromagnetic Communication Technology" }, { "name": "Zhong, Miao", "affiliation": "Hubei Provincial Key Laboratory for Low-Frequency Electromagnetic Communication Technology, Hubei, China" }, { "name": "Yu, Gan", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Yu, Yao", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Zhang, Weidong", "affiliation": "Shanghai Jiaotong Univ" }, { "name": "Xie, Wei", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Industrial and service applications of AI and intelligent automation", "Control and automation to improve social and political stability" ], "abstract": "Excessive tether tension poses a critical safety risk for tethered UAVs. This paper proposes a tension-constrained trajectory tracking framework without direct force sensing under time-varying disturbances. The unmeasured tether tension is estimated by a disturbance observer using measurable UAV states. An equivalent thrust constraint is derived from the tension limit and embedded into a saturated backstepping control law, which robustly steers the UAV toward and within a neighborhood of the reference trajectory while respecting safety bounds. Rigorous Lyapunov analysis establishes uniform ultimate boundedness, and simulation results demonstrate the effectiveness and robustness of the proposed strategy.", "url": "" }, { "id": "Mo-MoC33.2", "code": "MoC33.2", "title": "Resilient Safety-Critical Optimal Control for Multi-UAV Formations (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC33", "sessionTitle": "Resilient Control, Motion Control, and Navigation of eVTOL Aircrafts in Smart City", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Mei, Tong", "affiliation": "Shandong University of Aeronautics" }, { "name": "Ma, Wenlai", "affiliation": "Shandong University of Aeronautics" }, { "name": "Wang, Ruian", "affiliation": "Shandong University of Aeronautics" }, { "name": "Lei, Yunjie", "affiliation": "Shandong University of Aeronautics" }, { "name": "Hao, Wei", "affiliation": "Shandong University of Aeronautics" } ], "keywords": [ "Safety-critical and resilient systems", "Low-altitude economy", "Cyber-physical and human systems (CPHS)" ], "abstract": "This paper investigates optimization-based safety formation control for multi-UAV systems subject to obstacle constraints and time-varying unknown disturbances. An interval observer is first designed to estimate disturbance bounds, and feedforward compensation is introduced to improve disturbance rejection. Based on the compensated nominal system, an optimal formation tracking controller is developed to enhance trajectory tracking and resource utilization. Furthermore, HOCBF-based quadratic programming is incorporated to guarantee forward invariance of the safe set, thereby preventing both inter-UAV and UAV-obstacle collisions. Simulation results verify the effectiveness and robustness of the proposed framework.", "url": "" }, { "id": "Mo-MoC33.3", "code": "MoC33.3", "title": "Geometric Cascade Control of UAV Slung Load System with Offset (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC33", "sessionTitle": "Resilient Control, Motion Control, and Navigation of eVTOL Aircrafts in Smart City", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Liu, Yongqing", "affiliation": "University of Alberta" }, { "name": "Lv, Zongyang", "affiliation": "University of Victoria" }, { "name": "Lynch, Alan Francis", "affiliation": "Univ of Alberta" }, { "name": "Zhao, Qing", "affiliation": "Univ. of Alberta" } ], "keywords": [ "Low-altitude economy", "Smart city control and optimization" ], "abstract": "This paper considers the motion control of a multirotor slung load system (SLS) where the point of suspension is offset from the vehicle’s center of mass (CoM). We derive a model of the SLS using the Newton-Euler Equations, where the suspension point defines a reference frame which simplifies the coupling effects of the offset. Based on this model, we design a cascade geometric controller with an outer loop to track the payload’s position, a middle loop to track the payload’s attitude, and an inner loop to track the vehicle’s attitude. We prove exponential stability of the tracking error dynamics for each loop and the entire closed-loop error dynamics. Simulations validate the performance of the proposed control.", "url": "" }, { "id": "Mo-MoC33.4", "code": "MoC33.4", "title": "UAV with Off-Centered Cable-Suspended Payload: Modeling and Nonlinear Control (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC33", "sessionTitle": "Resilient Control, Motion Control, and Navigation of eVTOL Aircrafts in Smart City", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Jia, Yanmei", "affiliation": "Dalian Minzu University" }, { "name": "Lv, Zongyang", "affiliation": "University of Victoria" }, { "name": "Wu, Yuhu", "affiliation": "Dalian University of Technology" } ], "keywords": [ "Low-altitude economy", "Mentoring in control engineering", "Social transportation and social energy" ], "abstract": "This work investigates the modeling and control of a UAV with an off-center cable-suspended payload system. In this work, a dynamic model is developed by introducing a new state of the position of the suspension point of the payload. Based on this perspective, a cascaded control strategy is designed, which consists of an inner-loop UAV attitude controller, a middle-loop swing angle controller, and an outer-loop payload velocity control. According to the specific structure of the constructed dynamic model, a virtual acceleration-based middle-loop control law for the payload's tether point is designed to regulate the dynamics of the slung load, without the need to consider the any coupled dynamics between the UAV and the suspended payload. An inner-loop UAV attitude controller is developed without any simplification on the internal coupling dynamics. Furthermore, the proposed control strategy enables real-time estimation of the cable's tensile force without requiring any additional ergometer, thereby facilitating continuous force monitoring and preventing overload during operation. Through a Lyapunov-based approach, the local exponential stability of the closed-loop system is rigorously verified. Finally, the proposed control strategy is validated through real-flight experiments to demonstrating the effectiveness and performance of the proposed control system.", "url": "" }, { "id": "Mo-MoC33.5", "code": "MoC33.5", "title": "Neural Network-Based Adaptive Event-Triggered Control for Dual-Arm Unmanned Aerial Manipulator Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC33", "sessionTitle": "Resilient Control, Motion Control, and Navigation of eVTOL Aircrafts in Smart City", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Wang, Yang", "affiliation": "Nankai University" }, { "name": "Yu, Hai", "affiliation": "Nankai University" }, { "name": "He, Wei", "affiliation": "Nankai University" }, { "name": "Han, Jianda", "affiliation": "Nankai University" }, { "name": "Fang, Yongchun", "affiliation": "Nankai Univ" }, { "name": "Liang, Xiao", "affiliation": "Nankai University" } ], "keywords": [ "Low-altitude economy", "Smart city control and optimization" ], "abstract": "This paper investigates the control problem of dual-arm unmanned aerial manipulator systems (DAUAMs). Strong coupling between the dual-arm and the multirotor platform, together with unmodeled dynamics and external disturbances, poses significant challenges to stable and accurate operation. An adaptive event-triggered control scheme with neural network-based approximation is proposed to address these issues while explicitly considering communication constraints. First, a dynamic model of the DAUAM system is derived, and a command-filter-based backstepping framework with error compensation is constructed. Then, a neural network is employed to approximate external frictions, and an event-triggered mechanism is designed to reduce the transmission frequency of control updates, thereby alleviating communication and energy burdens. Lyapunov-based analysis shows that all closed-loop signals remain bounded and that the tracking error converges to a neighborhood of the desired trajectory within a fixed time. Finally, experiments on a self-built DAUAM platform demonstrate that the proposed approach achieves accurate trajectory tracking.", "url": "" }, { "id": "Mo-MoC33.6", "code": "MoC33.6", "title": "Fault-Tolerant Attitude Control of a Coaxial Tilt-Rotor eVTOL under a Servo Stuck Fault (I)", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC33", "sessionTitle": "Resilient Control, Motion Control, and Navigation of eVTOL Aircrafts in Smart City", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Hou, Zheng", "affiliation": "Dalian University of Technology" }, { "name": "Tang, Jiaxin", "affiliation": "Dalian University of Technology" }, { "name": "Lv, Zongyang", "affiliation": "University of Victoria" }, { "name": "Wu, Yuhu", "affiliation": "Dalian University of Technology" } ], "keywords": [ "Low-altitude economy" ], "abstract": "This paper proposes a fault-tolerant control (FTC) strategy based on a control reallocation scheme to address the servo stuck fault of a coaxial tilt-rotor (CTR) eVTOL aircraft. A stuck fault observer is designed to estimate the actual stuck tilt angle of the faulty servo. Subsequently, a nominal control allocation scheme and a control reallocation scheme are developed for the CTR eVTOL, corresponding to the fault-free scenario and the servo stuck fault scenario, respectively. The control reallocation scheme is activated when the stuck fault occurs, eliminating the need for control law reconfiguration. An adaptive attitude controller is proposed to guarantee the stability of the CTR eVTOL under servo stuck fault. The effectiveness is demonstrated by the real-time ground bench experiments.", "url": "" }, { "id": "Mo-MoC34.1", "code": "MoC34.1", "title": "Stealthy Coverage Control for Human-Enabled Real-Time 3D Reconstruction (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC34", "sessionTitle": "Cyber-Physical-Human Systems: From Individual Empowerment to Societal Impact III", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Terunuma, Reiji", "affiliation": "Institute of Science Tokyo" }, { "name": "Abe, Takuma", "affiliation": "Institute of Science Tokyo" }, { "name": "Nakamura, Yuta", "affiliation": "Institute of Science Tokyo" }, { "name": "Hatanaka, Takeshi", "affiliation": "Institute of Science Tokyo" } ], "keywords": [ "Cyber-physical and human systems (CPHS)", "Human-centric automation/AI Systems, and human agency", "System dynamics and control in CPHS" ], "abstract": "In this paper, we propose a novel semi-autonomous image sampling strategy, called stealthy coverage control, for human-enabled 3D structure reconstruction. The present mission involves a fundamental problem: while the number of images required to accurately reconstruct a 3D model depends on the structural complexity of the target scene to be reconstructed, it is not realistic to assume prior knowledge of the spatially non-uniform structural complexity. We approach this issue by leveraging human flexible reasoning and situational awareness. Specifically, we design a semi-autonomous system that leaves identification of regions that need more images and navigation of the drones to such regions to a human operator. To this end, we first present a way to reflect the human intention in autonomous coverage control. Subsequently, in order to avoid operational conflicts between manual control and autonomous coverage control, we develop the stealthy coverage control that decouples the drone motion for efficient image sampling from navigation by the human. Simulation studies on a Unity/ROS2-based simulator demonstrate the effectiveness of the present semi-autonomous system.", "url": "" }, { "id": "Mo-MoC34.1", "code": "MoC34.1", "title": "Robot Navigation Control Incorporating Control Barrier Function with Data-Driven Human Behavior Estimation (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC34", "sessionTitle": "Cyber-Physical-Human Systems: From Individual Empowerment to Societal Impact III", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Miyamoto, Mana", "affiliation": "Waseda University" }, { "name": "Wasa, Yasuaki", "affiliation": "Waseda University" }, { "name": "Kishida, Masako", "affiliation": "University of Tsukuba" } ], "keywords": [ "Cyber-physical and human systems (CPHS)", "System dynamics and control in CPHS", "Human-centric automation/AI Systems, and human agency" ], "abstract": "This paper proposes a navigation control method for autonomous mobile robots that considers human behavioral changes in pedestrian areas. The proposed method extends conventional data-driven approaches by incorporating the Social Force Model to simulate pedestrian reactions to robot presence. We introduce individual cooperativeness parameters for pedestrians to represent diverse avoidance behaviors, and implement constraint-based control for the robot to ensure collision-free navigation even in crowded environments. Simulation results using real-world pedestrian trajectory datasets demonstrate the effectiveness of the proposed method for safe, adaptive navigation among heterogeneous pedestrian populations.", "url": "" }, { "id": "Mo-MoC34.1", "code": "MoC34.1", "title": "Data-Driven Fairness Adjustment for Handbike vs Bicycle Power (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC34", "sessionTitle": "Cyber-Physical-Human Systems: From Individual Empowerment to Societal Impact III", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Berruti, Maddalena", "affiliation": "Politecnico Di Torino" }, { "name": "Doshmanziari, Roya", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Pappalardo, Riccardo", "affiliation": "Politecnico Di Torino" }, { "name": "Varagnolo, Damiano", "affiliation": "NTNU - Norwegian University of Science and Technology" } ], "keywords": [ "Cyber-physical and human systems (CPHS)", "System dynamics and control in CPHS" ], "abstract": "This study investigates how to promote fairness in indoor cycling exercise gaming by addressing the performance gap between handbike and bicycle users, i.e., the fact that relying on upper-body propulsion, handbike users generate lower power outputs, resulting in in-game disadvantages. We test data-driven models that convert handbike power into equivalent bicycle power at matched perceived effort, and validate it using an embedded hardware setup in live conditions. Data from 27 participants performing workouts on both devices were analyzed. Results showed adjusted handbike power closely matches bicycle power. Future work will expand datasets and explore personalized real-time adjustments.", "url": "" }, { "id": "Mo-MoC34.3", "code": "MoC34.3", "title": "Workforce Competency Framework for the Agentic AI Era (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC34", "sessionTitle": "Cyber-Physical-Human Systems: From Individual Empowerment to Societal Impact III", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Chalutz-Ben Gal, Hila", "affiliation": "Bar-Ilan University" }, { "name": "Cohen, Yuval", "affiliation": "Afeka Tel Aviv College of Engineering" } ], "keywords": [ "Human-centric automation/AI Systems, and human agency", "Cognitive and emotional control/AI systems, arts and control", "Cyber-physical and human systems (CPHS)" ], "abstract": "Abstract: Agentic artificial intelligence (AI) is revolutionizing organizational work by shifting from simple tool use to collaborative sense–plan–act systems, reshaping both human roles and required competencies. This paper presents a research-based framework for workforce development in the agentic AI era, mapping four key human roles—Builder, Operator, Orchestrator/Manager, and Assurer/Steward—to specific skill sets and observable proficiency levels. Drawing on recent literature and real-world practices, the framework integrates technical, ethical, and operational dimensions and encourage targeted upskilling through work-based learning, micro-credentials, and competency measurement. Detailed tables guide organizations in clarifying decision rights, oversight structures, and accountability pathways, ensuring safety, equity, and transparency as AI autonomy expands. The framework also addresses the challenges of inclusive skilling, governance maturity, and empirical validation, offering actionable strategies for organizations navigating the transformation to agentic workplaces. By aligning roles, skills, and governance, this work provides a practical roadmap for safe, adaptive, and equitable human–AI collaboration as agentic systems become central to organizational success.", "url": "" }, { "id": "Mo-MoC34.6", "code": "MoC34.6", "title": "Evaluating Machine Learning Approaches for Industrial Movement Classification Using Wearable Sensors (I)", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC34", "sessionTitle": "Cyber-Physical-Human Systems: From Individual Empowerment to Societal Impact III", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Løtveit, Johanne", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Doshmanziari, Roya", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Sylte, Maria Ulseth", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Haugland, Lars Einar", "affiliation": "Aker Solutions and University of Bergen, Faculty of Medicine" }, { "name": "Andersen, Åsmund", "affiliation": "Aker Solutions AS" }, { "name": "Varagnolo, Damiano", "affiliation": "NTNU - Norwegian University of Science and Technology" } ], "keywords": [ "Cyber-physical and human systems (CPHS)", "Human-centric automation/AI Systems, and human agency", "Digital culture" ], "abstract": "This study investigates the use of wearable sensors, including arm and back Inertial measurement Units (IMUs) and pressure-sensing insoles, to classify activities associated with musculoskeletal disorder risk in industrial environments. Data from 20 participants performing representative activities were analyzed using multiple machine learning models and sensor combinations. Results indicate that simpler models, such as Support Vector Machines, achieve performance comparable to more complex methods. Plantar-pressure data alone provides limited discriminatory power, while IMU-based and combined sensor setups perform better. Overall, the findings demonstrate the feasibility of real-time wearable systems for detecting and preventing high-risk activities in industrial settings.", "url": "" }, { "id": "Mo-MoC35.1", "code": "MoC35.1", "title": "A Comparative Analysis of Clustering Algorithms for Optimizing Delivery Logistics in Pharmaceutical Distribution Networks (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC35", "sessionTitle": "Toward Human-Centric Intelligent Manufacturing: Advances and Challenges II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Senanayake, Upeksha", "affiliation": "University of Moratuwa" }, { "name": "Thibbotuwawa, Amila", "affiliation": "University of Moratuwa" }, { "name": "Dahanayake, Mahekha", "affiliation": "University of Twente" }, { "name": "Nielsen, Izabela", "affiliation": "Aalborg University" }, { "name": "Patalas-Maliszewska, Justyna", "affiliation": "University of Zielona Góra" } ], "keywords": [ "Data-driven and AI-based modelling of production and logistics", "Supply chain management in manufacturing", "AI-based enterprise systems" ], "abstract": "The surge in pharmaceutical deliveries post-Covid-19 has underscored the need for enhanced logistical efficiency in pharmaceutical companies (PCs). This study investigates the efficacy of clustering algorithms for segmenting delivery customers in a pharmaceutical delivery region to optimize operations and improve service quality. A synthetic dataset of 50 customers in New York City, including location coordinates, urgency, and delivery volume, was generated and standardized for analysis. Six clustering algorithms K-Means, Agglomerative, BIRCH, Mini-Batch K-Means, Spectral, and Gaussian Mixture were evaluated using internal (Silhouette Score, Davies-Bouldin Index, Calinski-Harabasz Index) and external (Rand Index, Adjusted Rand Index, etc.) metrics. Results indicate Mini-Batch K-Means excels for initial customer segmentation, while Gaussian Mixture is optimal for validating existing groupings. BIRCH offers the fastest computation for large datasets. These findings guide PCs in selecting appropriate clustering meth-ods for efficient delivery strategies, with potential applications in other industries.", "url": "" }, { "id": "Mo-MoC35.2", "code": "MoC35.2", "title": "The Relationship between Node Strength and Lost Sales: A Simulation Study of Material Disruptions (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC35", "sessionTitle": "Toward Human-Centric Intelligent Manufacturing: Advances and Challenges II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Nguyen, Phu", "affiliation": "Berlin School of Economics and Law" }, { "name": "Ivanov, Dmitry", "affiliation": "Berlin School of Economics and Law" } ], "keywords": [ "Viable and resilient supply chain and production", "Supply chain and logistics engineering, simulation and optimization", "Production and operations management" ], "abstract": "One objective of supply chain stress testing is to understand which nodes, when disrupted, cause the most significant operational impact. While conventional wisdom assumes that high-volume suppliers and materials are most vulnerable, recent findings on hidden critical suppliers and materials challenge the underlying assumption of a monotonic relationship. Our paper investigates the functional form of the relationship between node strength and resilience performance, particularly lost sales, through discrete-event simulation of a three-echelon supply chain. We systematically disrupt individual nodes among 292 materials across six disruption durations ranging from 3 to 8 weeks. Using real-world network structure with simulated operational dynamics, we compare linear, quadratic, and logarithmic specifications through model comparison. Results reveal that the relationship between node in-strength and lost sales follows a quadratic pattern rather than a linear or logarithmic one. The quadratic relationship persists across five inventory control policies, demonstrating the robustness of the finding. Our results imply that mid-range weighted in-degree materials exhibit heightened vulnerability compared to both low- and high-weighted in-degree extremes. The counterintuitive finding challenges conventional value-based prioritization methods and suggests that firms should conduct supply chain stress testing rather than focusing solely on high-volume nodes.", "url": "" }, { "id": "Mo-MoC35.3", "code": "MoC35.3", "title": "A Time-Based Multimodal Framework for Efficient Inter-Terminal Transport in Transshipment Ports (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC35", "sessionTitle": "Toward Human-Centric Intelligent Manufacturing: Advances and Challenges II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Priyashanka, Nipun", "affiliation": "University of Moratuwa" }, { "name": "Weerasinghe, Buddhi Chathumal Alwis", "affiliation": "Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam" }, { "name": "Thibbotuwawa, Amila", "affiliation": "University of Moratuwa" }, { "name": "Nielsen, Izabela", "affiliation": "Aalborg University" }, { "name": "Waszkowski, Robert", "affiliation": "Cybernetics Faculty, Military University of Technology" } ], "keywords": [ "Simulation and optimization in production, operations and services", "Supply network dynamics and control", "Industry X.0 for production and logistics" ], "abstract": "Inter-terminal transport (ITT) is a critical component of transshipment hubs where containers must move efficiently between terminals within the same port. At the Port of Colombo, where transshipment accounts for over 80% of volume, reliance on unimodal trucking creates congestion and operational delays. This research proposes a Time-Dynamic Multimodal Framework that integrates truck, rail, and barge transport into a unified decision-support system. A Mixed-Integer Linear Programming (MILP) model is developed to optimize mode selection based on container-specific dwell times-defined as the window between discharge and outbound vessel cut-off-alongside volume and terminal constraints. By analyzing the distribution matrix of terminal pairs and modal characteristics, the model prioritizes mass transport for non-urgent volumes while reserving trucks for time-critical moves. Results indicate that this multimodal approach effectively breaks the unimodal bottleneck, significantly reducing internal truck trips by 82.6% and associated emissions while maintaining 100% service reliability. This study provides a scalable blueprint for Colombo and other Asian transshipment hubs to enhance ITT efficiency and sustainability through integrated, time-sensitive multimodal optimization.", "url": "" }, { "id": "Mo-MoC35.4", "code": "MoC35.4", "title": "Engineering Design of an Adhesive Application Device for High-Quality Bonded Joints (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC35", "sessionTitle": "Toward Human-Centric Intelligent Manufacturing: Advances and Challenges II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Rudawska, Anna", "affiliation": "Lublin University of Technology" }, { "name": "Gola, Arkadiusz", "affiliation": "Faculty of Mechanical Engineering, Lublin University of Technology" }, { "name": "Piotrowska, Katarzyna", "affiliation": "Lublin University of Technology" }, { "name": "Banaszak, Zbigniew", "affiliation": "Koszalin University of Technology" } ], "keywords": [ "Production and operations management" ], "abstract": "This article discusses issues related to improving the bonding process, focusing on one of the bonding stages: adhesive application. The design of a special adhesive coater is presented, taking into account design, technological, and operational assumptions, to streamline the production of adhesive structures. One of the advantages of this device design is the ability to achieve a uniform and repeatable thickness of adhesive applied to the bonded surfaces. This is crucial in the process of creating adhesive joints, as it influences the quality and strength of the joint. Although the device can be used as a standalone station at this stage, it is dedicated to single-piece and small-batch production, with certain modifications it can also be adapted to work in an automated production line.", "url": "" }, { "id": "Mo-MoC35.5", "code": "MoC35.5", "title": "Industry 5.0 for the Creative Sector: Practical Challenges of Virtualised Music Production and Distribution (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC35", "sessionTitle": "Toward Human-Centric Intelligent Manufacturing: Advances and Challenges II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Paulina, Golinska-Dawson", "affiliation": "Poznan University of Technology" }, { "name": "Antosz, Katarzyna", "affiliation": "Rzeszow University of Technology" }, { "name": "Gola, Arkadiusz", "affiliation": "Faculty of Mechanical Engineering, Lublin University of Technology" } ], "keywords": [ "Sustainable and circular supply chain and production", "Industry X.0 for production and logistics" ], "abstract": "The principles of Industry 5.0 are reshaping creative sectors, with the music industry serving as an example of large-scale dematerialisation. This exploratory study investigates the challenges associated with the virtualisation of music production and distribution, understood as the transition from physical media to digitally driven, human-centric ecosystems. The research examines operational, social, and economic dimensions of transformations within Music 5.0. Particular attention is paid to the evolution of music distribution models, the impact of dematerialisation on logistics, and the emergence of decentralised, metadata-intensive production environments. Empirical data was collected through structured interviews with music industry professionals, focusing on changes in digital logistics, distributed production workflows, metadata management requirements, digital skill sets, and the environmental implications of streaming. The challenges identified by industry experts were subsequently classified and evaluated using the Fuzzy DEMATEL method. The findings provide a deeper understanding of the factors influencing virtualisation in music production and distribution and highlight their implications for the development of human-centric, resilient, and intelligent manufacturing frameworks within the creative sector.", "url": "" }, { "id": "Mo-MoC36.1", "code": "MoC36.1", "title": "A Data and Model-Driven Approach to Carbon Emission Flow Tracking and Response for Industrial Parks (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC36", "sessionTitle": "Complex Energy System Operation Optimization and Fast Algorithm Design", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "He, Jiaye", "affiliation": "Xian Jiaotong University" }, { "name": "Zhai, Qiaozhu", "affiliation": "Xi'an Jiaotong Univ" }, { "name": "Zhao, Jiexing", "affiliation": "Xi'an Jiaotong University" }, { "name": "Zhou, Yuzhou", "affiliation": "Xi'an Jiaotong University" }, { "name": "Cao, Xiaoyu", "affiliation": "Xi'an Jiaotong University" }, { "name": "Ma, Hao", "affiliation": "Xi'an Jiaotong University" } ], "keywords": [ "Urban energy distribution systems", "Smart city control and optimization", "Cyber-physical urban systems" ], "abstract": "Industrial parks play an important role in regional low-carbon transitions. Tracking the carbon emission flow is crucial for carbon emission reduction. However, existing tracking approaches face challenges in simultaneously achieving high accuracy, interpretability, and computational efficiency. Moreover, the resulting flow information is only weakly coupled to park-level scheduling, limiting the optimization under carbon constraints. To address these limitations, this paper proposes a data- and model-driven framework for carbon emission flow tracking and response in industrial parks. The tracking model embeds physical constraints into the training model, maintaining high prediction accuracy and low computational burden while preserving physical interpretability. The dispatch model optimizes carbon emission costs and enforces carbon emission constraints, thereby coordinating generator outputs and jointly reducing overall operating cost and total park emissions. Numerical tests implemented on a realistic industrial park verify the effectiveness of the proposed approach.", "url": "" }, { "id": "Mo-MoC36.1", "code": "MoC36.1", "title": "Multi-Stage Robust Optimization of Microgrid with 5G Base Stations Based on Affine Decision Rules (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC36", "sessionTitle": "Complex Energy System Operation Optimization and Fast Algorithm Design", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Li, Fanfan", "affiliation": "Xi'an Jiaotong University" }, { "name": "Zhou, Yuzhou", "affiliation": "Xi'an Jiaotong University" }, { "name": "Zhai, Qiaozhu", "affiliation": "Xi'an Jiaotong Univ" }, { "name": "Zhao, Jiexing", "affiliation": "Xi'an Jiaotong University" }, { "name": "Cao, Xiaoyu", "affiliation": "Xi'an Jiaotong University" }, { "name": "Guan, Xiaohong", "affiliation": "Xi'an Jiaotong University" } ], "keywords": [ "Urban energy distribution systems", "Cyber-physical urban systems" ], "abstract": "Integrating 5G base stations into microgrids unlocks demand-response potential to reduce system costs and enhance renewable absorption. However, this operation is complicated by multi-source uncertainties across renewable generation, electrical load, and communication. This paper proposes a robust optimization model that explicitly distinguishes between delay\u0002tolerant and real-time traffic. We employ affine decision rules to explicitly construct the relationship between decision variables and uncertain parameters, thereby guaranteeing system robustness. Numerical results show that the cost is reduced by 5.81% compared to operating the base stations independently. Furthermore, unlike scenario-based methods, which incur a 9.6% load shedding rate, the proposed method ensures zero shedding with superior computational efficiency, effectively balancing economy and safety.", "url": "" }, { "id": "Mo-MoC36.4", "code": "MoC36.4", "title": "A Multi-Stage Generation, Transmission and Storage Expansion Planning Method for Large-Scale Power Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC36", "sessionTitle": "Complex Energy System Operation Optimization and Fast Algorithm Design", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Han, Zhihan", "affiliation": "Xi'an Jiaotong University" }, { "name": "Zhai, Qiaozhu", "affiliation": "Xi'an Jiaotong Univ" }, { "name": "Zhao, Jiexing", "affiliation": "Xi'an Jiaotong University" }, { "name": "Zhou, Yuzhou", "affiliation": "Xi'an Jiaotong University" }, { "name": "Cao, Xiaoyu", "affiliation": "Xi'an Jiaotong University" } ], "keywords": [ "Urban energy distribution systems", "Smart city control and optimization", "Cyber-physical urban systems" ], "abstract": "The extensive integration of renewable energy has accelerated the low-carbon transition of power systems, while simultaneously introducing new challenges for system planning, particularly in large-scale systems. This paper establishes a comprehensive planning model for generation, transmission, and energy storage. An acceleration method is then proposed for long-term planning in large-scale systems. The overall approach implements long-term planning through a multi-stage rolling-horizon framework, where the core concept of each single-stage planning relies on a greedy strategy and a renewable energy consumption bottleneck identification method. The efficacy of the proposed method is validated on a 3266-bus system.", "url": "" }, { "id": "Mo-MoC36.5", "code": "MoC36.5", "title": "Multistage Distributionally Robust Maintenance Optimization of Multiple Electrolyzer Systems with Nonstationary Lifetime (I)", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC36", "sessionTitle": "Complex Energy System Operation Optimization and Fast Algorithm Design", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Li, Longyan", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Ning, Chao", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Decision making under uncertainty", "Smart city control and optimization" ], "abstract": "Effective maintenance and operation scheduling is crucial for utility-scale multiple electrolyzer systems. This scheduling heavily depends on accurate remaining useful lifetime (RUL) predictions for electrolyzer stacks, which are challenged by sensor noise, limited data, and decision-dependent uncertainties from maintenance actions. This paper proposes a multistage scheduling framework to overcome these issues. Within this framework, the non-stationary RUL is characterized by a time series process, and the associated uncertainty is rigorously quantified using a novel decision-dependent ambiguity set. For computational tractability, the problem is reformulated into a mixed-integer linear program via a lifted decision rule approach. This ensures reliable scheduling that balances safety and profitability.", "url": "" }, { "id": "Mo-MoC36.6", "code": "MoC36.6", "title": "An Efficient Feasible Solution Construction Method for Economic Dispatch with Integral Constraints for Energy-Intensive Enterprises (I)", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC36", "sessionTitle": "Complex Energy System Operation Optimization and Fast Algorithm Design", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Ying, Yuqian", "affiliation": "Xi'an Jiaotong University" }, { "name": "Zhai, Qiaozhu", "affiliation": "Xi'an Jiaotong Univ" }, { "name": "Zhou, Yuzhou", "affiliation": "Xi'an Jiaotong University" }, { "name": "Zhao, Jiexing", "affiliation": "Xi'an Jiaotong University" }, { "name": "Han, Zhihan", "affiliation": "Xi'an Jiaotong University" }, { "name": "Cao, Xiaoyu", "affiliation": "Xi'an Jiaotong University" } ], "keywords": [ "Urban energy distribution systems", "Cyber-physical urban systems", "Smart city control and optimization" ], "abstract": "Energy-intensive enterprises (EIEs) exhibit highly volatile electricity demand and strong energy sensitivity, making reliable economic dispatch (ED) essential for microgrid operations. However, traditional discrete-time models often fail to generate feasible schedules. Recent studies have introduced integral constraints to enhance modeling accuracy, but existing solution methods remain computationally expensive or depend on feasible initial points. To address these challenges, this paper proposes an efficient method to construct feasible solutions. First, an ED model with integral constraints is formulated and reformulated into a nonlinear programming (NLP) model via convex energy boundary constraints. Then, a cost-priority strategy of power and energy allocation is designed by analyzing the constraint structure to generate high-quality feasible solutions rapidly. Case studies on 8-unit and 54-unit systems show the proposed method matches Gurobi’s cost-effectiveness while achieving a three-order-of-magnitude speedup, validating its potential for real-time dispatch.", "url": "" }, { "id": "Mo-MoC37.1", "code": "MoC37.1", "title": "Globally Exponentially Stable Adaptive Control of Switched Linear Systems: A Memory Augmented Approach", "day": "Monday", "date": "August 24, 2026", "time": "15:30-15:50", "sessionCode": "MoC37", "sessionTitle": "Dissemination: Stochastic, Nonlinear and Adaptive Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Patel, Pritesh", "affiliation": "University of Southampton, UK" }, { "name": "Roy, Sayan Basu", "affiliation": "Indraprastha Institute of Information Technology Delhi" }, { "name": "Bhasin, Shubhendu", "affiliation": "Indian Institute of Technology Delhi" } ], "keywords": [ "Model reference adaptive control", "Hybrid and switched systems modeling", "Nonlinear adaptive control" ], "abstract": "This paper introduces a switched model reference adaptive control (S-MRAC) architecture for uncertain switched multi-input multi-output (MIMO) linear time-invariant (LTI) systems with a switched reference model. One distinctive aspect of the suggested method is the use of memory to augment the parameter estimator, leading to parameter learning even during inactive periods of the subsystems. Together with an intermittently initial excitation (IIE) condition, the memory augmentation-based approach guarantees exponential stability of the tracking and parameter estimation error systems. An online parameter estimator with a dual-layer low-pass filter and a bank of memory filters is at the heart of the proposed architecture. The addition of the sigma- modification term in adaptive law facilitates the computation of a unified expression of dwell time that is valid for both excitation and non-excitation scenarios. Further, the dwell time expression is tunable and thus, allows for fast switching. Simulation results are showcased to confirm the efficacy of the suggested outcome.", "url": "" }, { "id": "Mo-MoC37.2", "code": "MoC37.2", "title": "Orchestrating On-Board Sensors for Global Hybrid Robust Stabilization of Unicycles", "day": "Monday", "date": "August 24, 2026", "time": "15:50-16:10", "sessionCode": "MoC37", "sessionTitle": "Dissemination: Stochastic, Nonlinear and Adaptive Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Ballaben, Riccardo", "affiliation": "University of Trento" }, { "name": "Astolfi, Alessandro", "affiliation": "King Abdullah University of Science and Technology (KAUST)" }, { "name": "Braun, Philipp", "affiliation": "The Australian National University" }, { "name": "Zaccarian, Luca", "affiliation": "LAAS-CNRS and University of Trento" } ], "keywords": [ "Nonlinear control of switched & hybrid systems", "Lyapunov methods", "Stability of nonlinear systems" ], "abstract": "We consider mobile robots described through unicycle dynamics equipped with on-board range sensors and cameras, one facing forward and one facing backward, providing measurements of the distance and misalignment to a target. We propose a hybrid control law combining the two on-board measurements and discuss stability results for the closed-loop expressed in the on-board camera-based coordinates, using Lyapunov-based arguments. We prove robustness of the stability properties to uncertainties affecting the sensors and external perturbations acting on the robot. The results are illustrated via simulations.", "url": "" }, { "id": "Mo-MoC37.3", "code": "MoC37.3", "title": "A Stochastic Shared Control Approach for Real-Time Driving Assistance Via Behavior Online Learning", "day": "Monday", "date": "August 24, 2026", "time": "16:10-16:30", "sessionCode": "MoC37", "sessionTitle": "Dissemination: Stochastic, Nonlinear and Adaptive Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Lang, Yilin", "affiliation": "Zhejiang University" }, { "name": "Li, Zhaoyang", "affiliation": "Zhejiang University" }, { "name": "Yao, Jinke", "affiliation": "Zhejiang University" }, { "name": "Li, Yanan", "affiliation": "University of Sussex" }, { "name": "Ren, Qinyuan", "affiliation": "Zhejiang University" } ], "keywords": [ "Shared control", "Human machine cooperation & integration", "Human centered automation" ], "abstract": "Efficient driving assistance systems improve safety and reduce driver workload, but uncertain driver behaviors can trigger driver–vehicle conflicts. This paper proposes a real-time stochastic shared control framework with online driver behavior learning. A Gaussian process predicts driver steering behavior and is continuously updated with new driving data. Building on this inference, a stochastic optimal shared steering controller is designed to handle uncertainty and enhance comfort. Computational efficiency is increased using ADMM-based distributed optimization within a model predictive control implementation. Multi-subject lane-keeping experiments show improved lane-keeping accuracy, smoother steering, and faster computation.", "url": "" }, { "id": "Mo-MoC37.4", "code": "MoC37.4", "title": "Higher-Order Lie Bracket Approximation and Averaging of Control-Affine Systems with Application to Extremum Seeking", "day": "Monday", "date": "August 24, 2026", "time": "16:30-16:50", "sessionCode": "MoC37", "sessionTitle": "Dissemination: Stochastic, Nonlinear and Adaptive Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Pokhrel, Sameer", "affiliation": "University of Cincinnati" }, { "name": "Eisa, Sameh", "affiliation": "University of Cincinnati" } ], "keywords": [ "Stability of nonlinear systems", "Application of nonlinear analysis and design", "Adaptive control design" ], "abstract": "This paper provides a rigorous derivation for what is known in the literature as the Lie bracket approximation of control-affine systems in a more general and sequential framework for higher-orders. In fact, by using chronological calculus, we show that said Lie bracket approximations can be derived, and considered, as higher-order averaging terms. Hence, the theory provided in this paper unifies both averaging and approximation theories of control-affine systems. In particular, the Lie bracket approximation of order (n) turns out to be a higher-order averaging of order (n+1). The derivation and formulation provided in this paper can be directly reduced to the first and second-order Lie bracket approximations available in the literature. However, we do not need to make many of the assumptions provided/needed in the literature and show that they are in fact natural corollaries from our work. Moreover, we use our results to show that important and useful information about control-affine extremum seeking systems can be obtained and used for significant performance improvement, including a faster convergence rate influenced by higher-order derivatives. We provide multiple numerical simulations to demonstrate both the conceptual elements of this work as well as the significance of our results on extremum seeking with comparison against the literature.", "url": "" }, { "id": "Mo-MoC37.5", "code": "MoC37.5", "title": "The Singular Angle of Nonlinear Systems", "day": "Monday", "date": "August 24, 2026", "time": "16:50-17:10", "sessionCode": "MoC37", "sessionTitle": "Dissemination: Stochastic, Nonlinear and Adaptive Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Chen, Chao", "affiliation": "The University of Manchester" }, { "name": "Zhao, Di", "affiliation": "Nanjing University" }, { "name": "Khong, Sei Zhen", "affiliation": "National Sun Yat-Sen University" } ], "keywords": [ "Uncertain systems", "Stability of nonlinear systems", "Passivity-based control" ], "abstract": "In this paper, we introduce an angle notion called the singular angle for nonlinear systems from an input-output perspective. The proposed system singular angle, based on the angle between L2-signals, describes an upper bound for the ''rotating effect'' from system input to output signals. It quantifies passivity and serves as a counterpart to system L2-gain. It also provides an alternative to a recently defined notion of system phase which adopts complexification of real-valued signals via the Hilbert transform. A nonlinear small angle theorem is established for feedback stability analysis, which involves a comparison of the loop system angle with pi. The theorem generalizes the classical passivity theorem via a tradeoff between the singular angles of open-loop systems.", "url": "" }, { "id": "Mo-MoC37.6", "code": "MoC37.6", "title": "Optimal Quantum Gate Design for Bloch-Band Interferometry", "day": "Monday", "date": "August 24, 2026", "time": "17:10-17:30", "sessionCode": "MoC37", "sessionTitle": "Dissemination: Stochastic, Nonlinear and Adaptive Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Sulehria, Ali", "affiliation": "University of Colorado Boulder" }, { "name": "Shao, Jieqiu", "affiliation": "University of New Mexico" }, { "name": "Nicotra, Marco M.", "affiliation": "University of Colorado Boulder" } ], "keywords": [ "Quantum optimal control", "Quantum control", "Quantum systems" ], "abstract": "Recent advancements in quantum sensing have led to a new generation of trapped-atom interferometers that can be \"programmed\" by performing a sequence of elementary operations, or gates. The objective of each gate is to promote a specific transition between the Bloch states of the free Hamiltonian. This paper details how quantum optimal control was used to generate a library of high-fidelity gates for Bloch-band interferometry. For ease of generalization, the gates featured in this paper are grouped into three broad categories: state-to-state transfer, relative phase unitary, and global phase unitary, each of which is associated with a different quantum optimal control problem formulation. Specific examples of Bloch-band interferometry gates are presented throughout the paper. Sample modifications to account for actuator bandwidth are also provided.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "A Scenario Approach to the Robustness of Nonconvex–Nonconcave Minimax Problems", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Peng, Huan", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Chen, Guanpu", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Cyber security networked control", "Resilient networked control systems" ], "abstract": "This paper investigates probabilistic robustness of nonconvex–nonconcave minimax problems via the scenario approach. Specifically, under convex strategy sets for all players, inspired by recent advances in scenario optimization, we first establish a probabilistic robustness guarantee for an ε-stationary point, overcoming the dependence on the non-degeneracy assumption by proving the monotonicity of the stationary residual in the number of scenarios. Furthermore, in the presence of nonconvex strategy sets, we reveal the fundamental difficulty of obtaining a tight theoretical bound based on this recent framework. Consequently, we establish a relaxed, yet rigorously valid, probabilistic bound for a global minimax point. A numerical experiment corroborates our theoretical findings.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Model-Free Optimal Capturing Strategy for Multi-Agent Pursuit-Evasion Differential Games Via Reinforcement Learning", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Shi, Ran", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Zhang, Hai-Tao", "affiliation": "Huazhong (Central China) Univeristy of ScienceandTechnology" }, { "name": "Li, Jialuo", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Ding, Jianing", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Liu, Xiaohua", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Yuan, Bowen", "affiliation": "Huazhong University of Science and Technology" } ], "keywords": [ "Multi-agent systems" ], "abstract": "This paper investigates a multi-agent pursuit-evasion (MPE) differential game problem subject to unknown dynamics and external disturbances, where the pursuers seek to intercept the escaping evaders. The core theoretical challenge lies in determining the optimal capturing strategy for this complex game scenario. To address this, a target-selection algorithm is first introduced for pursuers, decomposing the collective MPE differential game into multiple single-pursuer-single-evader (SPSE) sub-games. Subsequently, a zero-sum differential game framework is established to derive the associated optimal game strategies. Sufficient conditions are derived to guarantee the capturability of the associated closed-loop game system. Furthermore, a data-driven reinforcement learning (RL) algorithm is developed for the online learning of the optimal game protocol. Finally, numerical simulations are conducted to validate the effectiveness of the proposed game strategy.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "From String to Mesh Stability of Nonlinear Multi-Agent Systems in Discrete-Time (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Duarte Vargas, Leonardo", "affiliation": "L2S - Université Paris-Saclay" }, { "name": "Iovine, Alessio", "affiliation": "CNRS, CentraleSupélec" }, { "name": "Mattioni, Mattia", "affiliation": "Università Degli Studi Di Roma La Sapienza" }, { "name": "Stoica, Cristina", "affiliation": "CentraleSupélec, Université Paris-Saclay" } ], "keywords": [ "Multi-agent systems" ], "abstract": "This paper provides a new scalable verification test to ensure that disturbances do not amplify along the interconnection of a multi-agent system composed of heterogeneous agents in discrete-time. The proposed Mesh Stability extends the concept of String Stability to networks with general topology. The developed theoretical approaches are illustrated with a simulation example of a vehicle platoon in a ring road.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Spatio-Temporal Reconnection for Multi-Robot Networks Using Adaptive Prescribed-Time CBFs", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Liu, Hao", "affiliation": "University of Illinois Chicago" }, { "name": "Yang, Yupeng", "affiliation": "University of North Carolina at Charlotte" }, { "name": "Zhang, Yanze", "affiliation": "University of Illinois at Chicago" }, { "name": "Luo, Wenhao", "affiliation": "University of Illinois Chicago" } ], "keywords": [ "Multi-agent systems", "Adaptive control of multi-agent systems", "Control of networks" ], "abstract": "In multi-robot systems, maintaining persistent communication graph connectivity is often overly restrictive, especially when robots have limited communication ranges but operate in large environments. Instead, allowing robots to temporarily disconnect and later reconnect is often more desirable for efficient task execution while still ensuring timely information sharing across the team. In this paper, we propose an adaptive prescribed-time control barrier function (adaptive PT-CBF) framework that enables robots to temporarily disconnect and re-enter the communication range within an adjustable and feasible prescribed time. Moreover, we introduce a reconnection triggering mechanism that jointly considers task execution and reconnection urgency, thereby providing a principled way to decide when reconnection should occur. Theoretical analysis justifies convergence to the satisfying reconnection within a prescribed finite time. Experimental results validate the performance of our proposed adaptive PT-CBF with improved task efficiency and satisfying reconnections.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Conformism–Individualism Trade-Offs in LQG Graphon MFG with Control Mean Field Costs", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Huang, Ziqi", "affiliation": "McGill University" }, { "name": "Caines, Peter E.", "affiliation": "McGill Univ" } ], "keywords": [ "Multi-agent systems", "Control of networks" ], "abstract": "Limitations on the power or resources available to individual agents frequently arise in real-world games. To model such situations, this work studies a class of Linear Quadratic Gaussian Graphon Mean Field Games (LQG–GMFG) whose cost functional incorporates quadratic penalties on deviations from both an agent’s privately desired control and its local control mean field. These penalties represent two distinct motivations: individualism (acting on private preferences) and conformism (avoiding the higher resource costs incurred when acting differently from others). Separate state and control mean-field consistency conditions are imposed, and conditions for the existence of solutions are given. Using spectral decomposition, an explicit value function is obtained for the infinite-horizon, exponentially discounted stationary case, and numerical simulations reveal a trade-off between conformism and individualism.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Compliant Topology Design in Affine Formation Control Via Stress-Energy Minimization", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Wang, Yumeng", "affiliation": "Beijing Institute of Technology" }, { "name": "Yang, Qingkai", "affiliation": "Beijing Institute of Technology" }, { "name": "Chen, Wei", "affiliation": "Beijing Institute of Technology" }, { "name": "Fang, Hao", "affiliation": "Beijing Institute of Technology" } ], "keywords": [ "Multi-agent systems", "Control over networks", "Distributed control and estimation" ], "abstract": "Affine formation control provides an efficient framework for global maneuvers, but it is challenged by local, non-affine deformations. Such deformations induce high internal stress within conventionally rigid interaction topologies, leading to increased control effort. Inspired by structural mechanics, this paper proposes a compliant topology design method by introducing the concept of stress-energy. Specifically, we formulate two l1-regularized semidefinite programs to obtain optimal stress matrices that exhibit omnidirectional and task-specific compliance, respectively. Comparative simulations validate the superiority of our proposed topology construction schemes in reducing control cost and enhancing deformability.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "An Individual-Delay-Reflected Generalized Consensus Analysis for Multi-Agent Systems with Heterogeneous Time-Varying Delays", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Lee, Hye Jin", "affiliation": "POSTECH" }, { "name": "Lee, Ho Sub", "affiliation": "POSTECH" }, { "name": "Lee, Hae Seong", "affiliation": "POSTECH" }, { "name": "Park, PooGyeon", "affiliation": "Pohang Univ. of Sci. & Tech" } ], "keywords": [ "Multi-agent systems", "Control under communication constraints", "Consensus" ], "abstract": "In multi-agent systems, heterogeneous time delays exist for all agents because of the difference in communication environments. Therefore, the consensus analysis of a system considering a homogeneous time-varying delay among all agents results in conservatism. In this study, an individual-delay-reflected generalized consensus is proposed for multi-agent systems with heterogeneous time-varying delays with various bounds. To reflect heterogeneous time-varying delays, the proposed Lyapunov–Krasovskii functional is constructed by dividing the integral term into intervals containing heterogeneous delays and considering augmented vectors with delay states and integral states. Furthermore, by adding zero equality conditions, conservatism is reduced. N-dependent generalized integral inequality is used to allow the user to adjust the computational complexity. Numerical examples demonstrate a reduction in conservatism with the proposed consensus criterion.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "A Scalable L2-Gain Using a Matrix-Weighed Adjacency Matrix", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Axelson-Fisk, Magnus", "affiliation": "Technische Universität Berlin" }, { "name": "Knorn, Steffi", "affiliation": "TU Berlin" } ], "keywords": [ "Multi-agent systems", "Distributed control and estimation" ], "abstract": "We study multi-agent systems composed of linear agents interconnected through state coupling and subject to external disturbances. Considering a broad class of network topologies without imposing structural restrictions, we describe the overall system dynamics using a matrix-weighted adjacency matrix. Building on conditions that guarantee a bounded L2 gain for a given network, we derive sufficient conditions under which an entire family of networks achieves a scalable L2 gain, i.e., a performance bound that remains independent of network size. These results provide a systematic framework for assessing robustness and scalability in dynamically varying multi-agent networks with MIMO agents. The results are illustrated by a numerical example.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Distributed Safety-Aware Affine Formation Generation and Control for Multi-Agent Systems", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhao, Xinyue", "affiliation": "Beijing Insititute of Technology" }, { "name": "Yang, Qingkai", "affiliation": "Beijing Institute of Technology" }, { "name": "Huang, Hailong", "affiliation": "The Hong Kong Polytechnic University" }, { "name": "Feng, Shuai", "affiliation": "Nanjing University of Science and Technology" }, { "name": "Fang, Hao", "affiliation": "Beijing Institute of Technology" } ], "keywords": [ "Multi-agent systems", "Distributed control and estimation", "Consensus" ], "abstract": "Most formation control methods emphasize controller design while overlooking reference formation generation, which is crucial for collaborative performance and safety. This paper proposes a safety-aware formation generation and control framework that enables flexible multi-agent maneuvering in complex environments with dual-layer safety guarantees. First, we introduce parameter-level control barrier function (CBF) that imposes safety directly in the affine-parameter space, ensuring the generated reference formation is inherently collision-free. Then, a distributed consensus algorithm is proposed to drive all agents to consensus on common affine parameters, yielding coherent formation deformations. Finally, a standard agent-level CBF-based quadratic program is employed as a backend controller to track the safe reference trajectories. Simulations in cluttered environments validate the effectiveness of the approach.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Dynamic Consensus of Multi-Agent Systems with Distributed Collision Avoidance and Adaptive Performance Constraints", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Rüger, Marcel", "affiliation": "Universität Kassel" }, { "name": "Stursberg, Olaf", "affiliation": "University of Kassel" } ], "keywords": [ "Multi-agent systems", "Distributed control and estimation", "Consensus" ], "abstract": "This paper proposes a decentralized control framework for collision-free trajectory tracking in homogeneous multi-agent systems with actuation constraints. Building on the concept of adaptive performance functions known for single agents, the method enables each agent to autonomously regulate its transient tracking performance in response to local interactions and control saturation. The core contributions are a dynamic consensus-based reference generation mechanism and a relevance-based selection of potential collision partners using a prediction of the closest approach. A modified flexible performance law ensures that tracking performance is preserved even when avoidance or saturation temporarily dominate the control action. A Lyapunov-based analysis guarantees invariance of the performance envelope and boundedness of all closed-loop signals. Simulation results with interacting agents in a three dimensional space demonstrate collision-free motion and convergence.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Distributed Stabilization of Heterogeneous Multi-Agent Systems: A Lyapunov Approach", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ma, Yuxin", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Li, Xianwei", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Li, Shaoyuan", "affiliation": "Shanghai Jiao Tong Univ" } ], "keywords": [ "Multi-agent systems", "Distributed control and estimation", "Control of networks" ], "abstract": "This paper addresses the problem of distributed stabilization for heterogeneous linear multi-agent systems (MASs). It is assumed that all agents use relative state/output information, while only a subset can utilize absolute measurements. We present a Lyapunov-based approach, proposing both state- and output-feedback protocols. Under the standard stabilizability and detectability assumptions, it is shown that the proposed protocols ensure distributed asymptotic stabilization if the directed augmented communication graph contains a spanning tree. The effectiveness of the proposed approach is demonstrated through a simulation example, which verifies the ability of the proposed control strategy to stabilize heterogeneous linear MASs under the specified conditions.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Distributed Multi-Target Enclosing Control Framework for a Split and Merge Task", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "García-Lechuz Sierra, Juan", "affiliation": "University of Zaragoza" }, { "name": "Aragues, Rosario", "affiliation": "Universidad De Zaragoza" }, { "name": "Lopez-Nicolas, Gonzalo", "affiliation": "Universidad De Zaragoza" } ], "keywords": [ "Multi-agent systems", "Distributed control and estimation", "Control of networks" ], "abstract": "This paper studies the problem of cooperative multi-target enclosing. More specifically, we propose a distributed control framework to address the case where it is necessary to split or merge the team of agents as the distance between target groups increases or decreases, respectively. We first present a multi-target enclosing control law combining an affine formation control law with distance-based control terms to adjust formations around targets. Then, a novel weight matrix design is proposed for affine formation control of regular polygons. The distributed nature of this weight design method allows agents to locally compute the weights so that they can reorganize in subgroups or merge while ensuring convergence. Stability analysis of the proposed weight design method is included, as well as a numerical simulation using the proposed enclosing control to illustrate the splitting and merging task.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "The Distance-Based Formation Controller Design for Multi-Agent Systems in Port-Hamiltonian Form", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhao, Jingyi", "affiliation": "Dalian University of Technology" }, { "name": "Wu, Yongxin", "affiliation": "Université Marie Et Louis Pasteur" }, { "name": "Garcia de Marina, Hector", "affiliation": "Universidad De Granada" }, { "name": "Wu, Yuhu", "affiliation": "Dalian University of Technology" }, { "name": "Le Gorrec, Yann", "affiliation": "FEMTO-ST, SupMicroTech Besançon" } ], "keywords": [ "Multi-agent systems", "Distributed control and estimation", "Control over networks" ], "abstract": "Based on the practical scenario where collisions in formation control may lead to agent damage, this paper investigates the integrated problem of distance-based formation control and collision avoidance for multi-agent systems governed by port-Hamiltonian dynamics. A foundational step involves constructing a signed incidence matrix, which, by design, corresponds to a directed acyclic graph and possesses the full column rank property. To overcome the prevalent issue of local minima in traditional artificial potential fields, a novel design utilizing attraction-only potentials is introduced, with collision avoidance rigorously enforced by safety barriers. This framework leads to a unified controller that concurrently manages velocity tracking, target formation acquisition, and inter-agent safety. The stability of the resulting closed-loop system is guaranteed through LaSalle's invariance principle. Numerical simulations demonstrate the validity and effectiveness of the proposed control strategy.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Hierarchical Cooperative Perception for Large-Scale Swarm Herding under Sensing Constraints", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhu, Haonan", "affiliation": "Beihang University" }, { "name": "Chen, Zilu", "affiliation": "Beihang University" }, { "name": "Han, Liang", "affiliation": "Beihang University" } ], "keywords": [ "Multi-agent systems", "Distributed control and estimation", "Control under communication constraints" ], "abstract": "The cooperative herding of high-entropy, non-cooperative swarms is a critical yet challenging problem in multi-agent control. However, existing macroscopic theories often rely on idealized global state availability, leading to perceptual fragmentation when applied under physical sensing constraints. To bridge this gap, we propose a Hierarchical Cooperative Perception (HCP) architecture. By coupling sparse informed observers with dense local actuators, HCP reconstructs non-local potential fields to overcome sensing blind spots without global communication. We derive a macroscopic flux balance analysis grounded in non-reciprocal field theory to establish rigorous stability conditions. Validated through large-scale simulations and high-fidelity PyBullet experiments with hundreds of quadrotors, the approach achieves an 80% higher containment rate than baseline methods. Crucially, the macroscopic formulation renders control complexity invariant to population size, ensuring scalability to massive swarms beyond hardware limits.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Multi-Agent Object Transportation Via Distributed-Optimization-Based Reference Force Design", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Sugawara, Taiga", "affiliation": "The University of Osaka" }, { "name": "Sakurama, Kazunori", "affiliation": "The University of Osaka" } ], "keywords": [ "Multi-agent systems", "Distributed optimization", "Consensus" ], "abstract": "This paper proposes a distributed control framework for cooperative object transportation by multi-agent systems. Reference forces are computed through a constrained optimization that incorporates grasping and avoiding undesired rotation. To ensure scalability, the optimization is solved using a distributed algorithm in which each agent updates its reference force through local computation and limited neighbor-to-neighbor communication. Numerical simulations demonstrate that the proposed method maintains grasping and achieves a desired reference of the object's velocity, enabling flexible and scalable cooperative transport.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Barrier-Certified Multi-Agent Ergodic Coverage Over Complex Surfaces", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Aminzadeh, Ali", "affiliation": "Tampere University" }, { "name": "Gusrialdi, Azwirman", "affiliation": "Tampere University" } ], "keywords": [ "Multi-agent systems", "Distributed optimization", "Control under communication constraints" ], "abstract": "This paper presents a barrier-certified multi-agent ergodic coverage framework for safe and efficient exploration over complex non-Euclidean surfaces. We address the challenge of extending surface ergodic exploration to distributed multi-agent systems (MASs), where globally coupled ergodic statistics must be estimated cooperatively while satisfying safety and communication constraints. Building on the Laplace–Beltrami (LB) eigenbasis, we formulate a distributed ergodic coverage problem on meshable surfaces that enables cooperative exploration with respect to a desired inspection density. Safety is enforced through a unified set of control barrier functions (CBFs) guaranteeing inter-agent collision avoidance, distance-based connectivity, line-of-sight (LOS) preservation, and minimum surface clearance, leading to geometry-dependent couplings. A distributed consensus mechanism enables cooperative estimation of global ergodic statistics without centralized coordination, while maintaining performance and improving scalability. The framework is validated in a simulated 3D wind turbine inspection scenario.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Distributed Algorithms for Coopetition in Multi-Agent Systems", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Du, Hongbo", "affiliation": "Beijing Institute of Technology" }, { "name": "Yu, Hao", "affiliation": "Beijing Institute of Technology" }, { "name": "Liu, Shenyu", "affiliation": "Beijing Institute of Technology" }, { "name": "Shi, Dawei", "affiliation": "Beijing Institute of Technology" }, { "name": "Gao, Bo", "affiliation": "Beijing Institute of Graphic Communication" } ], "keywords": [ "Multi-agent systems", "Distributed optimization", "Distributed control and estimation" ], "abstract": "This paper studied a distributed coopetition problem for multi-agent systems (MASs), where the state of each agent reflects the extent of its contributions in a task. There are two key components in the considered coopetition problem: collaborative tasks and competitive constraints. The former necessitates a cumulative (weighted) contribution from all agents to achieve a desired outcome, while the latter comes from the competition among agents: no single agent exerts significantly more effort than the others (considering the respective weights). First, the proposed coopetition problem is transformed into an equivalent constrained optimization problem. then, a distributed algorithm for solving the coopetition problem is provided from the Karush-Kuhn-Tucker (KKT) conditions of the optimization problem. Subsequently, it is proved that the algorithm can ensure the states of agents to converge to one of its equilibria, which are the necessary and sufficient condition to the coopetition problem. Finally, an example is simulated to illustrate the effectiveness of the theoretical results.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Multi-Robot Adaptive Pursuit Via Dynamic Clustering and Assignment Optimization", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Wang, Ziteng", "affiliation": "Zhejiang University" }, { "name": "Gu, Dingning", "affiliation": "Zhejiang University" }, { "name": "You, Feng", "affiliation": "Zhejiang University" }, { "name": "Li, Xinyue", "affiliation": "Zhejiang University" }, { "name": "Sheng, Kaiyuan", "affiliation": "Zhejiang University" }, { "name": "Liu, Hanchuan", "affiliation": "Zhejiang University" }, { "name": "Hong, Chenhui", "affiliation": "Zhejiang University" }, { "name": "Lin, Yinglian", "affiliation": "Deepwater Engineering Construction Center, CNOOC Shenzhen Branch, Shenzhen" }, { "name": "Xiong, Rong", "affiliation": "Zhejiang University" }, { "name": "Zheng, Xingwen", "affiliation": "Zhejiang University" } ], "keywords": [ "Multi-agent systems", "Distributed optimization", "Distributed control and estimation" ], "abstract": "This paper addresses multi-robot pursuit failures caused by evader clustering, which typically leads to formation overlap and trajectory conflicts. We propose a Dynamic Adaptive Hunting (DAH) framework that replaces static assignments with a real-time dynamic clustering mechanism based on evader spatial distribution. To enhance efficiency, an intra-cluster optimization strategy refines target assignments to suppress trajectory crossings and mitigate the long-tail effect, thereby accelerating overall convergence. At the execution layer, an Artificial Potential Field (APF) controller provides goal-directed guidance with effective collision avoidance. Simulations across varying swarm scales confirm that DAH significantly reduces capture time and travel distance compared to non-optimized baselines, validating its efficacy and scalability in complex, dynamic scenarios.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Safe TSY Null-Space Deep Reinforcement Learning for Bearing-Rigid Quadrotor Formations", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Aliyari, Morteza", "affiliation": "Department of Electrical Engineering, National Taiwan University" }, { "name": "Tsai, Cheng-Huan", "affiliation": "National Taiwan University" }, { "name": "Lin, Tsung-Kai", "affiliation": "National Taiwan University" }, { "name": "Wang, En-Rong", "affiliation": "National Taiwan University" }, { "name": "Chiang, Ming-Li", "affiliation": "National Taiwan University" }, { "name": "Fu, Li-Chen", "affiliation": "National Taiwan Univ" } ], "keywords": [ "Multi-agent systems", "Distributed reinforcement learning", "Consensus and reinforcement learning control" ], "abstract": "This paper presents a safe multi-agent deep reinforcement learning framework for cooperative quadrotor formation flight based on bearing rigidity. A team of UAVs is required to navigate cluttered environments while preserving a desired formation shape and avoiding collisions. A rigidity-based bearing controller guarantees convergence to the desired shape up to global translation, uniform scaling and coordinated yaw (TSY). On top of this analytic layer, we embed a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) architecture whose actors operate only in the TSY null-space, so learning affects the group motion but cannot inject formation distortion. Safety is enforced by a zeroing control barrier function (CBF) quadratic program that filters the nominal control into a safe joint velocity. Unlike conventional safe RL, we differentiate through the CBF–QP and train the centralized critic and decentralized actors on the executed safe actions, eliminating the train–test mismatch between nominal and filtered policies. Simulations in Gazebo with a bearing-rigid three–quadrotor formation show that the proposed method achieves higher success rate, faster and more consistent convergence, and significantly lower formation error than an RL+CBF baseline that acts in the full joint action space.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "A Learning-Based Communication Framework for Multi-Agent Pursuit-Evasion Game", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Chen, Ke", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Peng, Xiangyang", "affiliation": "Tianjin University" }, { "name": "Gong, Youmin", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Yuan, Qiufan", "affiliation": "Shanghai Institute of Aerospace System Engineering" }, { "name": "Ma, Guangfu", "affiliation": "Harbin Institute of Technology" }, { "name": "Mei, Jie", "affiliation": "Harbin Institute of Technology, Shenzhen" } ], "keywords": [ "Multi-agent systems", "Learning methods for control", "Distributed reinforcement learning" ], "abstract": "In multi-agent Pursuit-Evasion (PE) scenarios, effective communication among pursuers is essential for successful coordination and capture efficiency. Traditional PE algorithms often face limitations due to fixed communication structures and inadequate adaptability to dynamic environments. To address these challenges, this study introduces a learning-based communication framework specifically designed for multi-target PE tasks. We enhance the existing Target-oriented Multi-Agent Communication and Cooperation (ToM2C) framework for multi-target PE scenarios by integrating an intensity-based filtering mechanism in place of its original Graph Neural Network (GNN) module. This filtering mechanism enables selective communication among pursuers based on confidence in target assignment predictions. Simulation results demonstrate significant improvements in both capture success rates and communication efficiency. Physical experiments validate sim-to-real transferability, confirming the effectiveness of the proposed approach.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Wasserstein Distributionally Robust Nash Equilibrium Seeking with Heterogeneous Data: A Lagrangian Approach", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Wang, Zifan", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Pantazis, George", "affiliation": "TU Delft" }, { "name": "Grammatico, Sergio", "affiliation": "Delft Univ. of Tech" }, { "name": "Zavlanos, Michael M.", "affiliation": "Duke University" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Multi-agent systems", "Randomized algorithms in stochastic systems" ], "abstract": "We study a class of distributionally robust games where agents are allowed to heterogeneously choose their risk aversion with respect to distributional shifts of the uncertainty. In our formulation, heterogeneous Wasserstein ball constraints on each distribution are enforced through a penalty function leveraging a Lagrangian formulation. We then formulate the distributionally robust game as a variational inequality problem, and show that under certain assumptions the original seemingly infinite-dimensional Nash equilibrium problem is equivalent to a multi-agent but finite-dimensional variational inequality problem with a strongly monotone mapping. Due to the inner maximization problem, it is however still challenging to calculate a distributionally robust Nash equilibrium. To this end, we design an approximate Nash equilibrium seeking algorithm and prove convergence of the average regret to a quantity that diminishes with the number of iterations, thus learning the desired equilibrium up to an a priori specified accuracy. Numerical simulations corroborate our theoretical findings.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "MsCoFFe: A Multi-Stage Composite Feature Enhancement FramEwork for UAV Tiny Object Detection in Road Monitoring of Smart City", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Wang, Ya", "affiliation": "Hangzhou Normal University" }, { "name": "Yao, Le", "affiliation": "Hangzhou Normal University" }, { "name": "Zhu, Zheren", "affiliation": "Hangzhou Normal University" }, { "name": "Yang, Zeyu", "affiliation": "Huzhou Normal University" }, { "name": "Wang, Jiayu", "affiliation": "Beihang University" }, { "name": "Jiang, Xiaoyu", "affiliation": "Beihang University" } ], "keywords": [ "AI for smart cities", "Low-altitude economy", "Cyber-physical urban systems" ], "abstract": "Object detection from Unmanned Aerial Vehicles (UAVs) is pivotal for the road monitoring task of smart city but faces severe challenges due to the prevalence of tiny objects. These targets suffer from spatial information decay, high-frequency feature submergence, and pixel misalignment within Deep Neural Networks (DNNs). To address these systemic bottlenecks, this paper proposes a Multi-stage Composite Feature enhancement FramEwork (MsCoFFe) for the current popular deep learning based UAV vision models. Unlike specific model patches, MsCoFFe is a general and plug-and-play framework designed to reinforce feature fidelity and alignment. It introduces the Feature Complementary Mapping (FCM) and Multi-Kernel Perception (MKP) modules in the backbone to preserve spatial details and enable multi-scale perception. Furthermore, it incorporates High-Frequency Perception (HFP) and Spatial Dependency Perception (SDP) modules in the neck network to amplify weak target signals and dynamically correct pixel shifts via cross-attention. The case study on the VisDrone2019 dataset demonstrate that integrating MsCoFFe into state-of-the-art deep learning object detectors, such as RT-DETR and DEIM, significantly improves detection robustness. Notably, the proposed MsCoFFe increases the AP50 of the DEIM model by 6.8%, validating its effectiveness in complex aerial surveillance scenarios with tiny objects.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "DmmD: Dual mmWave Radar Drone Detection System for Urban Emergency", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Li, Shenglei", "affiliation": "Waseda University" } ], "keywords": [ "AI for smart cities", "Smart city control and optimization", "Cyber-physical urban systems" ], "abstract": "Millimeter-wave radar is attractive for urban emergency response because it remains operative in darkness and visual obscurants, yet existing drone-detection systems trade 3D spatial resolution against temporal continuity. We present DmmD, a dual-mmWave-radar framework that combines a Multi-View Doppler Rectification Layer with an STC-Net based on 3D ConvLSTM. MVDRL aligns Doppler features from orthogonal views using geometric priors before fusion. Experiments on a synchronized dual-IWR6843 platform achieve 97.10 % AP, improve AP1 over CubeDN, and reduce mean localization error to 0.52 m. Barrier tests further show less than 1% point-cloud density reduction through visually opaque materials.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Simultaneous Implementability Problem for Multi-Dimensional Systems in the Behavioral Framework (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ishii, Rei", "affiliation": "The University of Electro-Communications" }, { "name": "Kaneko, Osamu", "affiliation": "The University of Electro-Communications" } ], "keywords": [ "Analytic design", "Linear systems", "Control of complex systems" ], "abstract": "In the behavioral approach to systems and control, a system is characterized by the set of the trajectories, which is referred to as the behavior. Using this approach enables us to obtain solutions that are completely independent of mathematical expressions and to discuss them in a set-theoretical context. As considered in the standard control theory, one fundamental problem is whether a given control specification can be implemented for a particular plant. This issue has also been studied within the behavioral approach. In cases where the dynamics of a plant varies, it becomes important to determine the extent of acceptable changes. We formalized this problem as the simultaneous implementability problem, this means to consider what is a condition under which a single specification can be realized by using a single controller for two different plants. In this paper, we adopt an set-theoretical approach to examine the simultaneous implementability problem in the behavioral approach.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Real-Time Classification of Tyre Models in High-Performance Vehicles: Comparing Model-Based and Learning-Based Approaches (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Milani, Sabrina", "affiliation": "Politecnico Di Milano" }, { "name": "Leoni, Jessica", "affiliation": "Politecnico Di Milano" }, { "name": "Corno, Matteo", "affiliation": "Politecnico Di Milano" }, { "name": "D'Avico, Luca", "affiliation": "Politecnico Di Milano" }, { "name": "Tanelli, Mara", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Automotive system identification and modelling", "Modeling, supervision, control and diagnosis of automotive systems", "AI and learning-based control for automotive systems" ], "abstract": "Automatic real-time tyre identification is crucial for improving vehicle performance, safety, and efficiency. This capability is valuable in racing applications, where it can support consistency checks and strategic decisions, and even more relevant in urban and aftermarket scenarios, where tyre information is often unavailable, and vehicle control systems could benefit from real-time adaptation. Despite its relevance, the literature mainly focuses on tyre usage monitoring. Furthermore, these approaches also reveal a trade-off between practicality and interpretability: model-based methods provide physically meaningful results but often require measurements that are rarely available in real-world vehicles, whereas machine learning methods exploit accessible vehicle signals and achieve high predictive performance, typically at the expense of interpretability. To address this gap, this paper presents and compares two real-time tyre classification strategies: a model-based method designed to rely on accessible vehicle measurements, and an interpretable learning-based approach. Their performance is assessed in both simulation and real-world experiments. While both methods achieve optimal performance in simulation, real-world variability and noise reduce the accuracy of the model-based approach. In contrast, the learning-based classifier maintains an F1-score of 96.5%, proving to be a practical and interpretable solution for real-time tyre recognition.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Structure of Human–Automation Trust in the Japanese Cultural Context: Cross-Cultural Validation of Affect-Based and Cognition-Based Initial Trust", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Cui, Zixin", "affiliation": "University of Tsukuba" }, { "name": "Zhou, Huiping", "affiliation": "University of Tsukuba" }, { "name": "Itoh, Makoto", "affiliation": "University of Tsukuba" } ], "keywords": [ "Cognitive and emotional control/AI systems, arts and control", "Cross-cultural aspects of engineering", "Human-centric automation/AI Systems, and human agency" ], "abstract": "Japanese culture places significant emphasis on emotionality alongside intellectual and logical aspects. This study examined the structure of initial trust in automation within the Japanese cultural context. Through exploratory and confirmatory factor analyses across three AI-enabled automation systems, the two-dimensional structure of initial trust, comprising cognition-based and affect-based initial trust, was supported. This finding is consistent with that observed in the Chinese context, although the specific items retained for each dimension were only partially aligned with those in the original Chinese scale. These results highlight the importance of distinguishing between cognition-based and affect-based trust in assessing initial trust in automation within both Chinese and Japanese cultural settings. Designers and practitioners should explicitly account for these two dimensions in the initial trust management of automation systems, thereby ensuring greater conceptual clarity and more accurate measurement.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "An Interactive Virtual Training System for Twelve-Phase Rectifier Generators in Control Engineering Education (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhou, Xingwei", "affiliation": "Wuhan University" }, { "name": "Hu, Wenshan", "affiliation": "Wuhan University" }, { "name": "Lei, Zhongcheng", "affiliation": "Wuhan University" } ], "keywords": [ "Control education laboratories", "Industry-academia collaboration in control education", "Internet based control education" ], "abstract": "This paper presents an interactive virtual training system for the fault diagnosis and operation of twelve-phase rectifier generators, addressing the high cost and risks of physical training in control engineering education. Developed with Unity3D and Vue.js, the system enables principle learning, operational procedures, and fault injection in a simulated environment. A dedicated assessment module automatically evaluates trainee performance. The platform provides a safe, flexible, and effective tool for enhancing practical understanding and troubleshooting skills of complex marine electrical systems, demonstrating the significant value of virtual simulation technology in modern control education.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Human Skill Evaluation with Multi-Objective Optimization in Context of Unknown Intentions (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Speidel, Piet", "affiliation": "Robert Bosch GmbH" }, { "name": "Hilsch, Michael", "affiliation": "Robert Bosch GmbH" }, { "name": "Alt, Benedikt", "affiliation": "Robert Bosch GmbH" }, { "name": "Schildbach, Georg", "affiliation": "University of Luebeck" } ], "keywords": [ "Cyber-physical and human systems (CPHS)", "Human-centric automation/AI Systems, and human agency", "System dynamics and control in CPHS" ], "abstract": "This paper introduces a novel human skill evaluation framework that leverages multiobjective optimization to address the limitations of assessing human proficiency in dynamic, complex systems with unknown intentions. Previous methods struggle with multi-objective tasks, offer limited interpretability, or require extensive data. Our framework quantifies human skill by measuring the Euclidean distance from a human’s Key Performance Indicator (KPI) vector to the surface of Pareto optimal solutions. We explore various intention assumptions by selecting different points on the Pareto Front and evaluate their impact on skill assessment using manual parking maneuver simulations and demonstrate the framework’s real-time computability. The results highlight the influence of intention assumptions on skill evaluation and demonstrate the potential for a robust, interpretable, and adaptable approach for quantifying human skill.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "A Generalized Nash Equilibrium-Seeking Scheme for Trauma Resuscitation", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ekpo, Promise", "affiliation": "Cornell Tech" }, { "name": "Taylor, Angelique", "affiliation": "Cornell Tech" }, { "name": "Molu, Lekan", "affiliation": "Molux Labs" } ], "keywords": [ "Cyber-physical and human systems (CPHS)", "Social computing", "Game theories" ], "abstract": "Trauma resuscitation is a clinical process for treating life-threatening physiological disorders in safety-critical environments, driven by the experience of healthcare workers (HCWs). Designing and optimizing quantifiable metrics that accurately capture HCW decisions may augment current resuscitation procedures with the potential to improve patient outcomes. This motivates our socio-technical formulation of trauma resuscitation as a distributed generalized Nash equilibrium (GNE)-seeking game with coupled inequality constraints. This method is optimized over a time-varying communication graph. We introduce novel insights from clinical experience to model HCWs behavior. This work facilitates the best possible resuscitation outcome given HCWs’ workloads, schedules, competencies, and limited resources.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Towards Population Models of Human Control with Covariate Effects", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Aguilar-López, José M.", "affiliation": "University of Seville" }, { "name": "Mosquera, Elena", "affiliation": "Universidad De Sevilla" }, { "name": "Hatanaka, Takeshi", "affiliation": "Institute of Science Tokyo" }, { "name": "Maestre, Jose M.", "affiliation": "University of Seville" } ], "keywords": [ "Cyber-physical and human systems (CPHS)", "System dynamics and control in CPHS", "Human-centric automation/AI Systems, and human agency" ], "abstract": "Human operators play critical roles in cyber--physical systems, yet control--theoretic models typically treat inter--subject variability as noise rather than as systematic patterns linked to individual characteristics. This article introduces a population mixed--effects framework for modeling human sensorimotor control that explicitly relates controller parameters to demographic and experiential covariates. Closed--loop identification experiments were conducted with 66 participants performing a single--axis target acquisition task, with the human modeled as a SISO controller and the plant as a kinematic integrator. Comparing PI, PID, and second--order structures, we find that the second--order model with a real zero consistently outperforms PI/PID, and that video game experience emerges as a particularly strong predictor of controller performance, with experienced players exhibiting faster response dynamics and improved tracking accuracy.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Stochastic Energy Management of Hydrogen-Based Geo-Distributed Data Centers", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Chen, Mengxiao", "affiliation": "Xi'an Jiaotong University" }, { "name": "Cao, Xiaoyu", "affiliation": "Xi'an Jiaotong University" }, { "name": "Sun, Xunhang", "affiliation": "Xi'an Jiaotong University" }, { "name": "Tian, Zhaoming", "affiliation": "Xi'an Jiaotong University" }, { "name": "Li, Miaomiao", "affiliation": "Xi’an Jiaotong University" }, { "name": "Dong, Yuchen", "affiliation": "Xi'an Jiaotong University" }, { "name": "Guan, Xiaohong", "affiliation": "Xi'an Jiaotong University" } ], "keywords": [ "Data centers and cloud computing", "Decision making under uncertainty" ], "abstract": "Integrating on-site renewable energy (RE) generation into data centers (DCs) offers a promising pathway toward energy sustainability. However, the inherent intermittency, volatility, and uncertainty of RE may expose DC energy systems to substantial risks of supply–demand imbalance. To address this challenge, this paper develops a stochastic energy management method for hydrogen-based geo-distributed data centers (HBGDCs). A remaining-time bucket mechanism is proposed to explicitly capture the temporal flexibility of DC workloads by dynamically tracking diminishing processing windows. Moreover, to handle forecast errors in renewable generation and workload arrivals, a receding-horizon scheduling framework is designed, in which a scenario-based two-stage stochastic optimization model is integrated. Numerical studies on a typical HBGDC system show that the proposed approach consistently improves operational efficiency under both normal and adversarial conditions, while being highly tolerant to forecast errors.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Dynamic Coalition Game-Based Task Allocation for Multi-Spacecraft Systems with Threat-Adaptive Weights", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Yu, Changping", "affiliation": "Beihang University (BUAA)" }, { "name": "Liu, Yang", "affiliation": "Beihang University, Beijing, P.R.China" }, { "name": "Zheng, Zewei", "affiliation": "Beihang University" }, { "name": "Zhang, Jia'ming", "affiliation": "Beihang University" } ], "keywords": [ "Decision making under uncertainty" ], "abstract": "This paper proposes a dynamic coalition game-theoretic framework for multispacecraft cooperative task allocation in adversarial environments with uncertain target priorities. The key innovation is an augmented time-varying characteristic function that integrates mission beneffts, execution costs, and transition penalties, with threat-adaptive weight mechanisms. We introduce an intelligence conffdence metric that dynamically evolves through observation, enabling adaptive target prioritization. The Shapley value allocation mechanism ensures fairness and stability while a utility maximization formulation with individual rationality constraints prevents coalition deviations. The system dynamically adjusts task assignments in response to changing threat levels, ensuring consistent performance over time by explicitly accounting for the costs of switching tasks and reorganizing teams.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Reinforcement Learning Framework Using Optimal Control and Control Barrier Functions for Reach-Avoid Games with Exclusion Zones (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Santos Franco, Daniel", "affiliation": "Queen's University" }, { "name": "Rabbath, Camille Alain", "affiliation": "Queen's University" }, { "name": "Givigi, Sidney", "affiliation": "Queen's University" } ], "keywords": [ "Differential or dynamic games", "Applications of optimal control", "Control barrier functions and state space constraints" ], "abstract": "We study the reach-avoid problem, where a pursuer aims to capture an evader, targeting a target plane in three-dimensional space (3D) while avoiding exclusion zones. As there is no optimal control for situations involving exclusion zones, we propose using Reinforcement Learning (RL) to generalize the optimal control from scenarios without exclusion zones to those that include them. To guarantee that the pursuer does not enter the exclusion zones, we use Control Barrier Functions (CBF) as both a safety filter and as a measure of reward for the pursuer. We demonstrate the necessity of each proposed component within the framework by conducting an ablation study. Furthermore, the efficacy of the framework is validated through simulation against optimal control with CBF.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Human-Centric Peer-To-Peer Federated Learning with Trusted Data Sharing for Skill Transfer in Industry 5.0 (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Jazi, Mahran", "affiliation": "Tel Aviv University" }, { "name": "Ben-Gal, Irad", "affiliation": "Tel Aviv University" } ], "keywords": [ "Human-centric automation/AI Systems, and human agency", "Decentralized economics/ecosystems (DeEco)" ], "abstract": "Industry~5.0 is reshaping smart manufacturing toward human-centric production, where operators collaborate with AI systems and networked machines. In such environments, workstations, teams, and operators face different tasks and conditions, resulting in non-identically distributed (non-IID) data and heterogeneous expertise. These factors challenge centralized AI deployment and raise privacy, scalability, and robustness concerns. This paper proposes a human-centric peer-to-peer federated learning (P2P-FL) framework for collaborative skill transfer in Industry~5.0. Each worker or production cell is represented by an edge device that trains a local decision-support model and exchanges model parameters with socially or organizationally connected peers over a decentralized graph. To mitigate non-IID effects while preserving privacy and autonomy, we introduce trusted data sharing, where peers share only a small, controlled fraction of local data with selected neighbors. Using MNIST, CIFAR-10, CIFAR-100, and an industrial NEU surface-defect dataset with synthetic non-IID worker profiles, we compare FedAvg, FedProx, and P2P-FL with trusted sharing levels of 20% and 40%. Results show that modest sharing significantly improves final accuracy and macro-level performance while reducing client performance disparities. The findings highlight implications for human--AI collaboration, workforce upskilling, and AI assistants in Industry~5.0 smart manufacturing.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Style-Invariant sEMG Recognition for Human–Robot Interaction (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Cho, Hyeong Rae", "affiliation": "Korea Institute of Robotics & Technology Convergence" }, { "name": "Jang, Sunho", "affiliation": "Korea Institute of Robotics and Technology Convergence" }, { "name": "Hong, Hyung Gil", "affiliation": "Korea Institute of Robotics Technology Convergence" }, { "name": "Yun, Haeyong", "affiliation": "Kiro" }, { "name": "Cho, YongJun", "affiliation": "Korea Institute of Robotics Technology Convergence" } ], "keywords": [ "Human-robot interaction", "Medical and rehabilitation robotics", "AI-powered robotics" ], "abstract": "Surface electromyography (sEMG) is increasingly used in wearable human–robot interaction systems; however, inter-subject variability limits reliable transfer of gesture intent across users. This paper presents a style-invariant learning framework that enhances subject-independent sEMG-based gesture recognition without requiring subject identity labels. The method employs Instance Selective Whitening (ISW) for self-supervised pre-training to suppress subject-specific style from feature covariance, followed by supervised fine-tuning for gesture classification. Experiments on Ninapro DB1, DB2, and DB4 show improved accuracy and reduced cross-subject performance variance. The results suggest the potential of the proposed framework for adaptive sEMG-driven wearable HRI systems, while real-time robotic validation remains an important direction for future work.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Using a Smartphone-Based Brake Testing Application and Real Vehicle Data in Automotive Engineering Education (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Tapak, Peter", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Kocúr, Michal", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Matej, Juraj", "affiliation": "Research and Development Department, TESTEK, A.s., Vajnorská 137, 831 04 Bratislava, Slovakia" } ], "keywords": [ "Industry-academia collaboration in control education", "Control education laboratories", "Control engineering curricula" ], "abstract": "This paper presents the integration of a smartphone-based brake testing application, originally developed for periodic technical inspections (PTI) and expert practice, into an undergraduate course on vehicle motion. The TESTEK mobile application records vehicle acceleration using the internal sensors of Android devices and evaluates braking performance in accordance with UN ECE regulations, providing the mean fully developed deceleration (MFDD) and related indicators. The same application family has been deployed at all PTI stations in the Slovak Republic and has been validated against certified decelerometers, which makes its results suitable both for regulatory use and for education. We describe how real braking tests recorded by this application are reused in the subject Processes of Vehicle Motion as the basis for a kinematics assignment in which students analyse acceleration, velocity, distance and MFDD, and identify individual phases of the braking process. The assignment combines numerical integration, signal preprocessing and interpretation of results in the context of legislation. The proposed approach requires only low-cost hardware (a smartphone and, when needed, a generic OBD interface) yet provides students with authentic, industry-grade data and tools. We outline the course context, the design of the laboratory task, implementation experience and qualitative observations, and discuss planned extensions towards remote laboratories.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Conceptual Questions on Stability, Structure, and Equilibria in State-Space LTI Systems (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Goubej, Martin", "affiliation": "University of West Bohemia" }, { "name": "Varagnolo, Damiano", "affiliation": "NTNU - Norwegian University of Science and Technology" } ], "keywords": [ "Repositories for control education", "Control education learning analytics", "Control engineering curricula" ], "abstract": "We present a small collection of conceptual multiple-choice questions (MCQs) on continuous-time LTI systems, designed for a second-year bachelor course on fundamentals of automatic control or dynamical systems. The questions target four recurrent misconceptions: (i) confusing internal (equilibrium) stability with external (BIBO) stability; (ii) believing that poles on the imaginary axis automatically imply bounded trajectories, irrespective of Jordan structure; (iii) assuming that repeated eigenvalues in state-space realizations necessarily cause loss of controllability or observability; and (iv) overlooking that equilibria and working points are solutions of linear algebraic equations whose existence and uniqueness depend on the column space and null space of the system matrix. The exercises are intended primarily as pen-and-paper MCQs (no calculators or computer algebra required), suitable for in-class formative assessment, written examinations, or as prompts for short oral discussions. The prerequisite learning outcomes (PLOs) include being able to solve linear systems of equations, compute eigenvalues and eigenvectors (and in some questions Jordan blocks), and interpret state-space models and BIBO stability. The assessed intended learning outcomes (ILOs) focus on distinguishing different notions of stability, relating boundedness to Jordan structure, diagnosing controllability/observability from input/output directions, and determining existence and uniqueness of equilibria. Annotated solutions explicitly address the targeted misconceptions and can be used as self-study material by students or as a discussion guide for instructors.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "The Missing Variable: Socio-Technical Alignment in Risk Evaluation", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Flehmig, Niclas", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Lundteigen, Mary Ann", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Yin, Shen", "affiliation": "Norwegian University of Science and Technology" } ], "keywords": [ "Safety-critical and resilient systems", "Human-centric automation/AI Systems, and human agency", "Regulation, policy, and legal issues in control/AI" ], "abstract": "This paper addresses a critical gap in the risk assessment of AI-enabled safety-critical systems. While these systems, where AI systems assist human operators, function as complex socio-technical systems, existing risk evaluation methods fail to account for the associated complex interaction between human, technical, and organizational components. Through a comparative analysis of system attributes from both socio-technical and AI-enabled systems and a review of current risk evaluation methods, we confirm the absence of explicit socio-technical considerations in standard risk expressions. To bridge this gap, we introduce a novel socio-technical alignment ( STA ) variable designed to be integrated into the traditional risk equation. This variable estimates the degree of harmonious interaction between the AI systems, human operators, and organizational processes. A case study on an AI-enabled liquid hydrogen ( LH 2 ) bunkering system demonstrates the variable's relevance. By comparing a naive and a safeguarded system design, we illustrate how the STA -augmented expression captures socio-technical safety implications that traditional risk evaluation overlooks, providing a more system-theoretic basis for risk evaluation.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Output Consensus for Matrix-Weighted Heterogeneous Linear Multi-Agent Systems under Distributed DoS Attacks", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhou, Siwen", "affiliation": "Beihang University" }, { "name": "Liu, Yang", "affiliation": "Beihang University, Beijing, P.R.China" }, { "name": "Li, Wenling", "affiliation": "Beihang University" } ], "keywords": [ "Social networks for smart cities", "Smart city security and resilience", "Cyber-physical urban systems" ], "abstract": "This paper investigates the resilient output consensus control for heterogeneous linear multi-agent systems (MASs) under matrix-weighted networks subject to denial-of-service (DoS) attacks. Matrix-valued interaction weights are employed to characterize the interdependencies among the multidimensional agent states. Differing from prior work on synchronous attacks, a more general scenario is considered where attacks independently and randomly disrupt individual interaction links, modeled by a Markov switching process. First, a fully distributed resilient estimator is proposed, enabling followers to estimate the leader state even under DoS attacks. Based on the estimator, a distributed control protocol is then developed to guarantee asymptotic output tracking in the mean-square sense for all followers. Finally, numerical simulations are conducted to validate the effectiveness of the proposed protocol.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Headland Turning Path Planning towards Coverage Path Planning for a Robotic Vehicle with a Towed Implement in Orchards", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Yamasaki, Yoshitomo", "affiliation": "Hokkaido University" }, { "name": "Noguchi, Noboru", "affiliation": "Hokkaido University" } ], "keywords": [ "Agricultural robotics", "Control in precision agriculture", "Positioning and navigation in agriculture and forestry" ], "abstract": "We proposed turning path planning towards coverage path planning (CPP) for a robotic vehicle towing an agricultural implement. We developed an extended turning path model based on two arcs and a straight segment, considering the turning radius difference. Feasible combinations of turning paths were then verified by simulating the trajectories of the robotic vehicle and the towed implement. The robotic vehicle followed the proposed turning path within 0.10 m on average for both the vehicle and the implement. The proposed method to generate the feasible turning table provided a clue to practical CPP.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "An Adaptive Control Architecture for Slope and Terrain Compensation in Autonomous Navigation in Mediterranean Greenhouses", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Cañadas-Aránega, Fernando", "affiliation": "University of Almeria" }, { "name": "Wollherr, Dirk", "affiliation": "Technical University of Munich" }, { "name": "Guzman, Jose Luis", "affiliation": "University of Almeria" }, { "name": "Moreno, Jose Carlos", "affiliation": "University of Almeria" }, { "name": "Blanco, Jose Luis", "affiliation": "Universidad De Almeria" } ], "keywords": [ "Automatic control in greenhouses", "Agricultural robotics", "Positioning and navigation in agriculture and forestry" ], "abstract": "The ability to move stably over terrain with varying slopes and textures is essential for mobile agricultural robots operating in complex and dynamic environments such as greenhouses, where small terrain irregularities can lead to significant navigation errors. This article presents a novel terrain-adaptation strategy based on the carried payload, ensuring accurate and robust trajectory tracking. The proposed approach is based on: (i) the experimental characterization of the most common types of greenhouse soil, concrete, compacted sand, and gravel, and (ii) the direct measurement of terrain slope using the IMU, in order to estimate the force with which this angle affects the motor input. Based on this information, a cascade trajectory-tracking scheme has been designed, consisting of a model-based predictive controller (MPC) in the outer loop and a PI controller in the inner loop. The system incorporates an adaptive feedforward control through gain scheduling approach, capable of adjusting to disturbances caused by variations in slope and terrain type. Simulation results demonstrate that the differential-drive robot achieves a significant improvement both in error indices and in control signal efficiency, highlighting the effectiveness and robustness of the proposed approach.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Tokenized Coordination Framework with Verifiable State for AAM Manufacturing", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Habbachi, Salwa", "affiliation": "Macau University of Science and Technology" }, { "name": "Rouabah, Younes", "affiliation": "Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Macao 999078," }, { "name": "Goh, Craymon", "affiliation": "Curge Advance Sdn. Bhd., and the Machinery and Engineering Industries Federation (MEIF), Kuala Lumpur 50470, Malaysia" }, { "name": "Zheng, Jademont", "affiliation": "Aterdrip Investment Limited, Hong Kong 999077, China" }, { "name": "Ma, Siji", "affiliation": "Macau University of Science and Technology" }, { "name": "Ding, Wendy", "affiliation": "Obuda University" }, { "name": "Wang, Fei-Yue", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Kovacs, Levente", "affiliation": "Obuda University" } ], "keywords": [ "Blockchain intelligence", "Financial systems", "Decentralized economics/ecosystems (DeEco)" ], "abstract": "The manufacturing process of Advanced Air Mobility (AAM) faces continuous funding challenges which result in longer production times because of concealed system problems, poor coordination, and unmonitored accountability. The paper presents a system framework which combines a Verifiable State Layer with a Token-Driven Coordination Layer to create a single state representation system that supports programmable financial operations, incentive programs, settlement processes, and governance mechanisms. The system uses state assets to represent engineering events which produce immediate feedback for delay detection and parameter adjustment through token dynamics. The research uses thermal-test delay, software rollback, and propulsion failure simulations to demonstrate enhanced liquidity stability, risk exposure, and coordination performance which will serve as a foundation for developing future AAM manufacturing systems.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "A Methodology for Designing Blockchain Architectures in Logistics: An Application to Intra-Hub Physical Internet Operations", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Sassi, Hayder", "affiliation": "Univ. Polytechnique Hauts-De-France, LAMIH" }, { "name": "Perez, Monica-Juliana", "affiliation": "Université Polytechnique Hauts-De-France - LAMIH UMR CNRS 8201" }, { "name": "Trentesaux, Damien", "affiliation": "LAMIH UMR CNRS 8201, SurferLab, University of Valenciennes and Hainaut-Cambresis" }, { "name": "Idel Mahjoub, Yassine", "affiliation": "Université Polytechnique Haut-De-France" } ], "keywords": [ "Blockchain intelligence", "Industrial and service applications of AI and intelligent automation" ], "abstract": "This paper introduces a general methodology for designing and assessing blockchain architectures in logistics systems. The objective is not to promote a specific platform but to provide a structured process that clarifies how architectural decisions—such as asset modelling, event representation, metadata strategies, smart-contract roles and governance configurations—shape the performance, cost, confidentiality and informational value of blockchain-enabled solutions. The methodology is illustrated through an intra-hub Physical Internet (PI or pi) case, where a discrete-event simulation is coupled with a blockchain layer used to certify handling events. In this application, pi-containers are instantiated as digital assets and intra-hub areas as logistical wallets, enabling the analysis of alternative blockchain configurations under controlled operational conditions. The prototype shows the feasibility of integrating blockchain as a non-intrusive certification layer while offering a testbed for scenario-based comparison. The contribution is methodological and exploratory: it formalizes a design workflow, defines relevant evaluation indicators and establishes a foundation for future quantitative assessment of blockchain architectures in logistics and other cyber-physical domains. Future work will execute full simulation campaigns and extend the methodology to additional application areas.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "A Control-Theoretic Framework for Financial Trend Identification Using Multi-Sensor Observations and POMDP Decision Making under Partial Observability", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ghanbarpour, Alireza", "affiliation": "Post Doctoral Researcher" }, { "name": "Ghanbarpour, Alireza", "affiliation": "Post Doctoral Researcher" }, { "name": "Tomizuka, Masayoshi", "affiliation": "Univ of California, Berkeley" } ], "keywords": [ "Business and financial analytics", "Financial systems", "Econometric models and methods" ], "abstract": "Financial markets are dynamic stochastic systems in which essential variables—such as regime direction, liquidity conditions, and volatility structure—are not directly observable. This partial observability creates a decision-making problem analogous to that of autonomous robotic agents operating with limited and noisy sensors. Motivated by this analogy, this paper develops a mathematically rigorous framework that models market trend identification and trading as a Partially Observable Markov Decision Process (POMDP). The proposed approach integrates multi-sensor financial perception through (i) a Support Vector Machine–based regime classifier constructed from multi-scale EMA and stochastic features, and (ii) a structural geometric indicator (EMMAi) that delineates dynamic support, resistance, and trend-confirmation zones. These sensors constitute a heterogeneous observation set analogous to multi-modal robotic perception modules, enabling complementary and noise-resilient information about the latent market state. A full POMDP formulation is derived, specifying the hidden regime space, stochastic transition dynamics, sensor-driven observation model, Bayesian belief-state update, and an action space consisting of directional trading decisions. The belief state provides a probabilistic estimate of the latent market trend and serves as the sufficient statistic for policy computation. Building on tools from optimal control under uncertainty, we compute a risk-aware trading policy via value-based POMDP methods augmented with constraints on drawdown, tail-risk, and action stability—analogous to safety constraints in autonomous robotics. Experimental results on equity index data demonstrate that (i) belief-state estimation substantially improves regime detection relative to direct signal-driven methods, (ii) multi-sensor fusion reduces observational noise and enhances stability, and (iii) the resulting POMDP controller achieves superior risk-adjusted performance and robustness under uncertainty. The proposed formulation introduces a principled control-theoretic foundation for autonomous decision making in financial systems and illustrates the deep methodological parallels between robotics in uncertain environments an", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Heterogeneous Learning Mechanisms in Zero-Sum Games: From Best-Iterate to Last-Iterate Convergence", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Guo, Xinxiang", "affiliation": "Chinese Academy of Sciences" }, { "name": "Zhang, Junyue", "affiliation": "University of Chinese Academy of Sciences" }, { "name": "Mu, Yifen", "affiliation": "Academy of Mathematics and Systems Science, Chinese Academy of Sciences" }, { "name": "Wang, Xiao", "affiliation": "Shanghai University of Finance and Economics" }, { "name": "Panageas, Ioannis", "affiliation": "UC Irvine" } ], "keywords": [ "Computational economics" ], "abstract": "Heterogeneous learning has recently emerged as a promising approach for computing Nash equilibria, yet its last-iterate convergence remains unclear. This paper establishes convergence results in zero-sum games under three dynamics: (1) mirror descent (MD) versus best response; (2) MD versus smoothed best response (SBR); and (3) Tikhonov-regularized MD versus SBR. We prove best-iterate convergence, unilateral last-iterate convergence, and bilateral last-iterate convergence, respectively. These heterogeneous dynamics each offer distinct advantages in computing equilibria and exploiting opponents. Simulations further highlight the significant impact of heterogeneous learning on game dynamics.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Tracking and Counting of Mulch-Occluded Cotton Seedling Based on RT-DETRv2 and CAMEL", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Yang, Yaoyu", "affiliation": "Zhejiang University" }, { "name": "Chang, Fangle", "affiliation": "Zhejiang University" }, { "name": "Yang, Jiahong", "affiliation": "Zhejiang University" }, { "name": "Meng, Ziyang", "affiliation": "Shandong University of Technology" }, { "name": "Xie, Lei", "affiliation": "Zhejiang University" }, { "name": "Su, Hongye", "affiliation": "Zhejiang University" } ], "keywords": [ "Computer vision in agriculture", "Control in precision agriculture" ], "abstract": "Precision agriculture relies heavily on accurate seedling stand counts for yield prediction and crop management. However, automated counting in plastic-mulched cotton fields remains challenging because seedlings are frequently occluded by mulch, affected by specular reflections, and visually similar to one another. To address these limitations, this paper proposes a multi-object tracking (MOT) and counting framework. We first adopt RT-DETRv2 as the core detector to obtain accurate seedling locations in complex field imagery. We then adapt CAMEL, an association module for Context-Aware Multi-Cue ExpLoitation, to replace heuristic matching with a learnable association process. CAMEL uses a Temporal Encoder (TE) to model motion history and a Group-Aware Feature Fusion Encoder (GAFFE) to integrate spatial and appearance cues for improved identity discrimination under occlusion. Finally, a virtual-line counting strategy is used to reduce overcounting caused by trajectory fragmentation. Experimental results show that RT-DETRv2 achieves 67.25 FPS and an mAP@0.5 of 0.987. Compared with DeepSORT and ByteTrack, the CAMEL-based tracker achieves 70.6 HOTA, 85.1 MOTA, 77.8 IDF1, and fewer identity switches. Counting performance is evaluated against manual counts across five video segments, achieving an average counting precision (ACP) of 88.84% and an R2 of 0.95. These results indicate that the proposed framework can support real-time monitoring of cotton seedling emergence under mulch-covered field conditions.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "A Feedforward Compensation Scheme for Multiple Inputs in Propofol Anesthesia", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Jakubowski, Damian", "affiliation": "Wrocław University of Science and Technology" }, { "name": "Pawlowski, Andrzej", "affiliation": "Wroclaw University of Science and Technology" } ], "keywords": [ "Control of physiological and clinical variables", "Pharmacokinetics, tracer kinetic modelling and drug delivery", "Biomedical system modeling, identification, and simulation" ], "abstract": "In this work a control scheme for multiple input signal sources for the anaesthesia process is introduced and analysed. The proposed scenario considers the situation where propofol can be manually adjusted in the presence of the feedback control that is designed to keep the Bispectral Index Scale (BIS) at the desired level. The proposed feedforward compensation scheme is integrated within a Model Predictive Control (MPC) technique that allows one to consider the effect of the manually introduced drug in computation of control signal. In this way, it is possible to handle the external input signal that disturbs the controller actions. When this additional input signal is not considered during computation of control action by feedback controller it could lead to significant performance losses or even unstable behaviour due to improper constraints management. The conceived system is tested through a simulation study that evaluates a possible clinical situation to highlight the performance and advantages of the analysed control approach. The results obtained indicate that the proposed architecture has significant potential in practical clinical applications to improve patient safety as well as to extend the versatility of interventions requiring total intravenous anesthesia where an automatic control system for drug delivery can be used.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "A Knowledge Asset Protocol for Compute-Driven Publishing Ecosystems", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ding, Wendy", "affiliation": "Obuda University" }, { "name": "Wang, Fei-Yue", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Ma, Siji", "affiliation": "Macau University of Science and Technology" }, { "name": "Liang, Xiaolong", "affiliation": "Chinese Academy of Sciences" }, { "name": "Tian, Yong-Lin", "affiliation": "State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijin" }, { "name": "Ge, Jingwei", "affiliation": "University Research and Innovation Center, Obuda University, Budapest H-1034, Hungary" }, { "name": "Kovacs, Levente", "affiliation": "Obuda University" } ], "keywords": [ "Decentralized economics/ecosystems (DeEco)", "Blockchain intelligence", "Econometric models and methods" ], "abstract": "The existing publishing ecosystem fails to support modern AI operations, as these systems require knowledge that is machine-readable, executable, and composable. The combination of blockchain technology with smart-contract systems enables the creation of verifiable assets which can execute automatically and settle transactions through automated processes. This study offers a Knowledge Asset Protocol (KAP) as a method to transform scholarly content into executable on-chain assets that incorporate verification functions, payment systems, and programmatic governance mechanisms. The paper outlines the protocol’s core properties and architecture and demonstrates its applicability. By unifying technical, economic, and governance layers, KAP provides foundational infrastructure for compute-driven publishing ecosystems.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Solvability of the Output Corridor Control Problem by Pulse-Modulated Feedback (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Medvedev, Alexander", "affiliation": "Uppsala University" }, { "name": "Proskurnikov, Anton V.", "affiliation": "Politecnico Di Torino" } ], "keywords": [ "Dynamics and control of biologically motivated nonlinear systems", "Biomedical system modeling, identification, and simulation", "Control of physiological and clinical variables" ], "abstract": "The problem of maintaining the output of a positive time-invariant single-input single-output system within a predefined corridor of values is treated. For third-order plants possessing a certain structure, it is proven that the problem is always solvable under stationary conditions by means of pulse-modulated feedback. The obtained result is utilized to assess the feasibility of patient-specific pharmacokinetic-pharmacodynamic models with respect to patient safety. A population of Wiener models capturing the dynamics of a neuromuscular blockade agent is studied to investigate whether or not they can be driven into the desired output corridor by clinically acceptable sequential drug doses (boluses). It is demonstrated that low values of a parameter in the nonlinear pharmacodynamic part lie behind the detected model infeasibility.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "A Decentralized Financial Model for Knowledge Payment-Based Publishing", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Jiang, Tai", "affiliation": "Macau University of Science and Technology" }, { "name": "Cao, Shuyu", "affiliation": "Institute 706 the Second Academy" }, { "name": "Lin, Fei", "affiliation": "Macau University of Science and Technology" }, { "name": "Wang, Fei-Yue", "affiliation": "Institute of Automation, Chinese Academy of Sciences" } ], "keywords": [ "Financial systems", "Blockchain intelligence", "Business and financial analytics" ], "abstract": "This paper presents JournalDAO, a decentralized knowledge payment-based publishing system integrating blockchain authorization, decentralized finance (DeFi), and tokenized incentives for decentralized science (DeSci). Unlike conventional models where reading is restricted behind paywalls or made free through OA fees, JournalDAO keeps access open while requiring on-chain purchase authorization for citation or other academic and commercial uses. Each purchase distributes revenue to all token holders including authors, reviewers, and publishers according to their token shares, and also adds the purchaser to the holder set. Authors receive incremental tokens as evidence of increasing scholarly recognition, whereas publishers and reviewers retain fixed allocations. The resulting token dilution induces diminishing marginal returns and a transparent break-even structure that rewards early identification of valuable research and makes manipulative self-purchases economically infeasible. Through analytical derivation and case studies, the paper demonstrates how parameter choices shape revenue dynamics, break-even thresholds, and holder distributions. The results indicate that JournalDAO provides a sustainable and tamper-resistant mechanism for compensating intellectual contributions while preserving openness and academic integrity.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "A Gradient-Based Distributed Algorithm for Triopoly Advertising Competition Game Over Interconnected Market Systems", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Jiang, Kaichen", "affiliation": "Dalian University of Technology" }, { "name": "Yue, Mingda", "affiliation": "School of Control Science and Engineering, Dalian University of Technology" }, { "name": "Varga, Balint", "affiliation": "Karlsruhe Institute of Technology (KIT), Campus South" }, { "name": "Wu, Yuhu", "affiliation": "Dalian University of Technology" }, { "name": "Wang, Junsong", "affiliation": "Dalian University of Technology" }, { "name": "Wang, Kaiyu", "affiliation": "Dalian University of Technology" } ], "keywords": [ "Game theories", "Econometric models and methods", "Social networks and opinion dynamics" ], "abstract": "This paper investigates a triopoly advertising competition problem over interconnected market systems using a noncooperative game framework that effectively captures the strategic interactions and conflicting objectives among the three firms. By taking both the targeted advertising efforts of the firms and the continuous co-evolution of consumer opinions across market systems via social network interactions into consideration, we build a noncooperative game model with nonlinear cost functions to analyze the optimal advertising strategy of each firm. To address the challenge of limited information exchange among firms, we design an estimation mechanism for each firm to estimate the current strategy profile and propose a gradient-based distributed algorithm to seek the Nash equilibrium of the game. Finally, numerical simulations are provided for verifying the developed results.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Policy Design for Games on Multiplex Networks Via Graph Limits", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Petrov, Ilya", "affiliation": "Institute of Control Sciences of RAS and HSE University" } ], "keywords": [ "Game theories", "Social networks and opinion dynamics", "Computational economics" ], "abstract": "We study strategic interactions in multiplex networks, where the same agents interact through several types of links. The resulting network games are difficult to analyze directly when the number of agents is large and when actions on different layers interact. We consider a linear--quadratic game with within-layer spillovers and cross-activity interactions, and specialize graphon games framework to constant graph functions on each layer. This yields a representative-agent system in the layer averages, which approximates large sampled network games and keeps the dependence on layer densities and game parameters explicit. The reduction provides a finite-dimensional basis for studying equilibrium responses to incentives and structural changes from control and optimization perspectives.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Sampled Data Closed-Loop Controller of a Pressure-Driven Filtration Device with Dead Volume", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Vincendon, Michael", "affiliation": "Mines Paris - PSL" }, { "name": "Petit, Nicolas", "affiliation": "MINES Paris, PSL University" } ], "keywords": [ "Medical devices, systems and solutions", "Biomedical system modeling, identification, and simulation", "Dynamics and control of biologically motivated nonlinear systems" ], "abstract": "The paper considers a microfluidic device used to filtrate particles in a suspension. The input under consideration is the input pressure, and the output of interest is the particle concentration in one of the two branches. Closed-loop control of this system has been theoretically studied in continuous-time, stressing the complexity induced by a dead volume causing an input varying delay of hydraulic type. To account for instrumentation limitations, we consider a sampled-based control strategy. We recast the control problem as a discrete-time nonlinear two-states dynamics. A closed-loop controller is proposed which is tested experimentally. Exponential convergence in closed-loop to reachable setpoints is obtained.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Reference-Model-Based Control Including Human Torque Estimation for Cable-Driven Rehabilitation System (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ortiz Gutierrez, Nery Uriel", "affiliation": "Université Polytechnique Hauts-De-France" }, { "name": "Guerra, Thierry Marie", "affiliation": "Polytechnic University Hauts-De-France Valenciennes" }, { "name": "Peixoto, Márcia Luciana da Costa", "affiliation": "Université Polytechnique Hauts-De-France" }, { "name": "Pessim, Paulo Sergio Pereira", "affiliation": "Universite Polytechnique Hauts-De-France" }, { "name": "Dequidt, Antoine", "affiliation": "Université De Valenciennes Et Du Hainaut-Cambrésis" }, { "name": "Delprat, Sebastien", "affiliation": "Université Polytechnique Haut De France" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "Paganelli, Sébastien", "affiliation": "University of Valenciennes Et Du Hainaut Cambrésis" } ], "keywords": [ "Rehabilitation engineering and healthcare delivery", "Medical devices, systems and solutions" ], "abstract": "This paper presents a reference-model-based control strategy for human-interactive rehabilitation devices designed to ensure robust assistance during movement. The proposed approach combines feedforward and feedback actions to control the nonlinear system along physiotherapist-defined trajectories. The human torque, which represents the patient’s contribution to movement, is estimated in real-time using a Proportional-Integral Observer. This real-time estimation allows the system to adjust the level of assistance according to the user’s capabilities. Experimental validation in a prototype demonstrates the effectiveness of the proposed approach.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Concept of a Sensor Test Environment for Dusty Agricultural Conditions", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Buckel, Peter", "affiliation": "Technical University of Munich" }, { "name": "Hermann, Johannes", "affiliation": "DHBW Ravensburg" }, { "name": "Wollmann, Jonas", "affiliation": "DHBW Ravensburg" }, { "name": "Dietmüller, Thomas", "affiliation": "DHBW Ravensburg" }, { "name": "Oksanen, Timo", "affiliation": "Technical University of Munich" } ], "keywords": [ "Sensing and perception in agriculture", "Computer vision in agriculture", "Agricultural robotics" ], "abstract": "Dust in agriculture presents a significant challenge for autonomous agricultural machinery. Dust can impair the performance of sensors and algorithms. This work, therefore, presents a concept for a proving ground consisting of an indoor and outdoor area. The indoor area comprises a laboratory test bench where dust circulates in a closed system and a test hall where life-size objects can be placed. The outdoor area features dedicated test setups that enable reproducible data to be recorded with and without dust during real-world agriculture work. The proving ground and the setups are visualized in 3D.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Field-Scale Soil Moisture Mapping from UAV Multispectral-Thermal Data with Augmentation and Reference Correction", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Adamgye, Christian", "affiliation": "University of Alberta" }, { "name": "Agyeman, Bernard", "affiliation": "University of Alberta" }, { "name": "Bo, Song", "affiliation": "University of Alberta" }, { "name": "Liu, Jinfeng", "affiliation": "University of Alberta" } ], "keywords": [ "Sensing and perception in agriculture", "Modeling and estimation in agriculture" ], "abstract": "Efficient irrigation requires accurate field-scale soil moisture estimates. This work develops a UAV sensor fusion approach that combines multispectral and thermal imagery with in-field soil moisture sensors to improve estimation accuracy. This approach has an offline training phase and an online bias-correction phase. In offline training, 296 paired samples (multispectral/thermal features and in-field soil moisture sensor readings) are augmented via quadratic interpolation and denoised with principal component analysis (PCA). A neural network trained on the augmented, PCA-transformed data reduces normalized root mean squared error (NRMSE) from 0.271 to 0.226 compared with training without augmentation and PCA. During online deployment, a reference-sensor bias correction compensates for drift in environmental and field conditions, reducing NRMSE from 0.3267 to 0.1668 while preserving spatial gradients. These results demonstrate that combining augmentation, PCA, and reference-sensor feedback with UAV multispectral-thermal data substantially improves field-scale soil moisture estimation.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Steering Opinion through Dynamic Stackelberg Optimization", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Rastgoftar, Hossein", "affiliation": "University of Arizona" } ], "keywords": [ "Social computing", "System dynamics and control in CPHS", "Social networks and opinion dynamics" ], "abstract": "This paper employs the Friedkin–Johnsen (FJ) model to describe the evolution of opinions in a social network composed of regular and stubborn agents. In the adopted framework, stubborn agents represent influential entities whose opinions are not directly shaped by their neighbors, whereas regular agents update their opinions as a convex combination of their neighbors’ opinions and their own initial beliefs. The goal is to steer the population toward a common reference opinion while respecting the intrinsic preferences of all agents. Without loss of generality, the origin is selected as the desired consensus point by shifting the opinion space, so that any target opinion profile can be mapped to zero. The steering problem is formulated as a finite-horizon Stackelberg game between the stubborn (leader) and regular (follower) subgroups, where stubborn agents strategically adjust their opinions and regular agents adapt their openness to external influence. The decision variables are the stubborn agents’ opinion adjustments and the regular agents’ bounded openness parameters, which jointly determine the nonlinear network dynamics. We propose a bi-level solution scheme that integrates quadratic programming for the followers and dynamic programming for the leaders, and computes the corresponding Stackelberg strategies through forward–backward propagation. Numerical simulations illustrate how the proposed architecture drives the network toward the desired consensus while limiting the magnitude of stubborn opinion change and regular agents’ openness.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "EEG-fNIRS Fusion through Spatial-Temporal Alignment for Cognitive Task (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Feng, Qixuan", "affiliation": "Qingdao University" }, { "name": "Xue, Binqiang", "affiliation": "Qingdao University" }, { "name": "Liu, Yinhua", "affiliation": "Qingdao University" }, { "name": "Kang, Min-Kyoung", "affiliation": "Pusan National University" }, { "name": "Hong, Keum-Shik", "affiliation": "Pusan National University" } ], "keywords": [ "Biomedical signal measurement and processing" ], "abstract": "Cognitive tasks are an important application area in brain-computer interfaces (BCI). Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are commonly used to monitor brain activity. EEG has high temporal resolution and can capture instantaneous brain electrical activities, while fNIRS provides higher spatial resolution and can reflect changes in brain blood flow. Due to the differences in time and space between the two, how to effectively integrate these two signals to improve the accuracy of cognitive tasks has become an important challenge. This paper proposes a fusion method based on spatio-temporal alignment, by optimizing the alignment and fusion process of EEG and fNIRS signals, to overcome the problems of signal asynchrony and noise interference, thereby improving the recognition effect of cognitive tasks. This method can effectively integrate the temporal information of EEG and the spatial information of fNIRS, providing a more comprehensive representation of cognitive states. Experimental results show that compared with traditional methods, the proposed fusion method significantly improves the performance of cognitive tasks. This research provides a new solution for the effective integration of EEG and fNIRS in cognitive tasks and demonstrates the potential of multimodal brain imaging technology in BCI applications.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Safe Reinforcement Learning for Building Thermal Control under Hardware Constraints", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Montazeri`, Mina", "affiliation": "Empa" }, { "name": "Künzli, Stefan", "affiliation": "Empa" }, { "name": "Remlinger, Carl", "affiliation": "SDSC" }, { "name": "Heer, Philipp", "affiliation": "Empa, Urban Energy Systems" } ], "keywords": [ "Demand response", "Big data and machine learning applied to smart cities", "Smart buildings and building automation" ], "abstract": "Reinforcement learning (RL) offers a data-driven alternative to model-based control for building heating systems. However, most existing approaches focus solely on energy efficiency and thermal comfort, overlooking actuator degradation caused by frequent valve switching. This paper presents an RL-based control framework that jointly optimizes energy consumption, occupant comfort, and actuator longevity. Using a physically consistent neural network model trained on real data from the UMAR unit at the NEST building in Dübendorf, Switzerland, two RL algorithms—A2C and PPO—are evaluated under varying switching-penalty strategies and a smooth policy architecture (LipsNet). Results show that a PPO controller with a temperature-dependent switching penalty reduces valve cycles ten-fold while increasing energy use by only 7%. The LipsNet network further achieves comparable energy efficiency with four times fewer switching events. These findings demonstrate that incorporating hardware-aware constraints into RL training can extend actuator lifespan without compromising overall system performance.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Smarter Than Throttling: DVFS and Flow Control for Efficiency-Driven CPU Cooling", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zheng, Jianwen", "affiliation": "Politecnico Di Milano" }, { "name": "Dionigi, Federico", "affiliation": "Politecnico Di Milano" }, { "name": "Terraneo, Federico", "affiliation": "Politecnico Di Milano" }, { "name": "Leva, Alberto", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Energy management systems", "Control and management of energy systems" ], "abstract": "Thermal and performance control in modern CPUs faces a fundamental trade-off: maintaining thermal safety via DVFS (i.e., reducing frequency) limits performance, while overcooling wastes energy. We propose a cascade-like thermal management scheme that acts coordinately on frequency and coolant flow: the former counteracts millisecond-scale load variations to keep the chip safe, while the latter adapts heat removal on a slower time frame to reduce overcooling and associated energy waste. We also present a tuning strategy for the scheme, demonstrate its potential through simulations, and discuss technical viability in realistic settings such as data centres.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "An OPF-Based Analysis of LMP Formation and Congestion Surplus under LCC HVDC Minimum Transfer Requirements", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Kim, Ki-Hyun", "affiliation": "Konkuk University" }, { "name": "Roh, Jae-Hyung", "affiliation": "Konkuk University" }, { "name": "Park, Jong-Bae", "affiliation": "Konkuk University" } ], "keywords": [ "Energy market", "Electrical transmission systems", "Energy management systems" ], "abstract": "This study investigates the effect of the minimum transfer requirement of Line-Commutated Converter (LCC) HVDC systems on nodal price formation and congestion surplus in electricity markets. To systematically examine this characteristic, a two-bus DC Optimal Power Flow (OPF) model is proposed that explicitly incorporates both minimum and maximum power transfer limits. Because thyristor-valve-based LCC HVDC systems require a minimum level of power transfer through the converter, this operational characteristic imposes an asymmetric lower-bound constraint on power flow that does not arise in conventional AC transmission systems. Analytical results derived from the Lagrangian formulation demonstrate that when the minimum transfer requirement becomes binding, this lower-bound constraint directly influences the nodal price difference between regions. Consequently, even when power flows in the forward direction, the price differential may be reversed, giving rise to negative congestion surplus. These findings indicate that the minimum transfer requirement can materially affect nodal prices and market settlement outcomes. Simulation results corroborate the analytical findings, confirming that the minimum transfer requirement can cause congestion surplus to become negative under specific load conditions — an outcome that does not arise in a standard transmission-line model without this constraint. These results suggest that the operational characteristics of LCC HVDC may introduce variability into the settlement revenues of Financial Transmission Rights (FTRs). Accordingly, FTR market participants may benefit from explicitly accounting for the minimum transfer requirement when formulating bidding and hedging strategies, as it can alter both the direction and magnitude of nodal price differences and congestion surplus.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Explainable Artificial Intelligence for Improving Probabilistic Deep Learning in Grid-Scale Load Forecasting", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "van Zyl, C", "affiliation": "University of Pretoria" }, { "name": "Ye, Xianming", "affiliation": "University of Pretoria" }, { "name": "Raj, Naidoo", "affiliation": "University of Pretoria" }, { "name": "Zhu, Bing", "affiliation": "Beihang University" } ], "keywords": [ "Forecasting of power supply and demand", "Energy management systems", "Energy market" ], "abstract": "Probabilistic load forecasting is required when operators must plan for both expected demand and forecast uncertainty. However, feature selection remains difficult for deep probabilistic models because their outputs describe lower and upper quantiles rather than a single point forecast. This study evaluates whether explainable artificial intelligence (XAI) attributions of model-implied predictive spread can support feature selection in probabilistic load forecasting. A Quantile CNN-LSTM is trained on ISO New England load, weather, market, and calendar data to produce 24-hour-ahead 90% prediction intervals. The lower and upper quantile forecasts are transformed into two explanation targets: an interval midpoint, representing demand magnitude, and an interval width, representing predictive spread. SHAP and Permutation Feature Importance (PFI) are used to rank features for each target. The rankings are tested through recursive feature ablation, tracking forecast error, interval width, and prediction-interval coverage. SHAP-based mean and width rankings, and PFI-based mean rankings, improve forecast accuracy by approximately 14–16% and move empirical coverage closer to the nominal 90% level. PFI-based width rankings do not provide the same benefit. Width-based feature selection did not outperform mean-based selection because the same demand and weather variables dominate both targets. The main contribution is therefore diagnostic: width attributions show whether features that drive demand magnitude also drive the model’s predictive spread, enabling feature selection to be evaluated directly from probabilistic model outputs rather than from a separate point-forecasting model.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Monotonicity Analysis of Interval-Optimal Operation Plans for Thermal Power Generation and Inter-Area Power Transmission in Electric Power Networks", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Kojima, Yuga", "affiliation": "Tokyo University of Marine Science and Technology" }, { "name": "Koike, Masakazu", "affiliation": "Tokyo University of Marine Science and Technology" }, { "name": "Ishizaki, Takayuki", "affiliation": "Tokyo Institute of Technology" }, { "name": "Ramdani, Nacim", "affiliation": "Université D'Orléans" } ], "keywords": [ "Forecasting of power supply and demand", "Energy management systems", "Power plant control" ], "abstract": "本研究は広域電力ネットワークの日先運用最適化手法を提案します。太陽光発電(PV)の発電と需要は信頼区間を持つ純電力需要予測プロファイルとして扱われ、需要予測プロファイルに対する運用パラメータの単調性を用いて、区間の内のいかなる実現にも最適性を維持する運用範囲を導出します。オペレーターには、各エリアにおける熱発電、バッテリー貯蔵、エリア間電力転送の上限と下限が提供されており、需要が信頼区間内でどのように振る舞っても最適な運用計画範囲を特定できます。本論文では、面積が少ないにもかかわらず電力網の構造を検討し、運用電力の単調性解析を行います。", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Machine Learning Topology Filtering and Parameter Identification of Power Networks", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ouali, Rabah", "affiliation": "Ecole Centrale De Lille" }, { "name": "Dieulot, Jean-Yves", "affiliation": "Polytech Lille" }, { "name": "Legry, Martin", "affiliation": "Arts Et Métiers ParisTech" }, { "name": "Yim, Pascal", "affiliation": "Ecole Centrale De Lille" }, { "name": "Guillaud, Xavier", "affiliation": "L2EP, Ecole Centrale De Lille, France" }, { "name": "Colas, Frédéric", "affiliation": "ENSAM" } ], "keywords": [ "Power electronics", "Electrical transmission systems" ], "abstract": "This paper presents a methodology for retrieving the impedance parameters of subsystems within a radial power grid from global impedance measurements. The first stage involves filtering the contribution of topological parameters (e.g., connection cables) through a denoising autoencoder. Several network architectures were investigated and compared, including multilayer perceptrons, convolutional neural networks, and recurrent networks for both encoder and decoder structures. In the second stage, the parameters of the subsystems were identified by incorporating the relative proportion of each subsystem within the network into the machine learning algorithm. The proposed method was validated on a case study involving a wind farm equipped with power converters, where the identified parameters achieved an accuracy of up to 5%. The most effective configuration employed a multiplicative operation on the admittance feature map vectors. This study represents an initial step toward the development of aggregated power grid models derived solely from external measurements.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Backstepping Control with Prescribed Error Bounds and Fixed-Time Convergence for DC Microgrids with Constant Power Loads", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Gao, Yiming", "affiliation": "University of Alberta" }, { "name": "Shu, Zhan", "affiliation": "University of Alberta" }, { "name": "Li, Yunwei", "affiliation": "University of Alberta" } ], "keywords": [ "Power electronics", "Power systems stability", "Energy management systems" ], "abstract": "This paper proposes an improved observer-based backstepping control scheme for DC microgrids with constant power loads (CPLs). A prescribed-performance function (PPF) is employed to restrict the tracking error within predefined bounds, while an enhanced fixed-time control achieves a smaller settling-time bound. In addition, a sliding-mode disturbance observer (FT-SMDO) is developed to estimate the time-varying power flow of uncertain CPLs. To ensure optimal estimation performance and eliminate manual gain tuning, the Grey Wolf Optimizer (GWO) is utilized to automatically tune the FT-SMDO parameters. Simulation results demonstrate that the proposed method achieves faster voltage recovery, improved robustness, and superior overall performance compared with existing controllers.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Mechanical Analogy for Power System Dynamics with Park’s Synchronous Machine Models", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Nishino, Taku", "affiliation": "Tokyo Institute of Technology" }, { "name": "Koizumi, Jigen", "affiliation": "Institute of Science Tokyo" }, { "name": "Terao, Kentaro", "affiliation": "Institute of Science Tokyo" }, { "name": "Ishizaki, Takayuki", "affiliation": "Tokyo Institute of Technology" } ], "keywords": [ "Power systems stability" ], "abstract": "This paper proposes a mechanical analogy to provide an intuitive understanding of power system dynamics, especially for novices. Our approach is applicable to multi-machine systems and incorporates the high-fidelity Park's model. We demonstrate a comprehensive mapping where all state variables of the power system, including generators and loads, correspond to states in the analogy. This framework facilitates the understanding of complex nonlinear dynamics and is validated by establishing its rigorous correspondence with the system's energy function.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Homotopic Policy Iteration for Linear Zero-Sum Games: Application to Load Frequency Control", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ning, Yongkai", "affiliation": "Northwestern Polytechnical University" }, { "name": "Hu, Junhao", "affiliation": "AVIC Chengdu Aircraft Design & Research Institute" }, { "name": "Wang, Zhong", "affiliation": "Northwestern Polytechnical University" }, { "name": "Li, Yan", "affiliation": "Northwestern Polytechnical University" } ], "keywords": [ "Power systems stability", "Distributed optimization for smart grids", "Power plant control" ], "abstract": "Load Frequency Control (LFC) is crucial for maintaining power system stability by restoring nominal frequency and balancing inter-area power flows after disturbances.The control‑disturbance interaction can be modeled as a linear zero-sum game within the H_infty control framework. While the Simultaneous Policy Update Algorithm (SPUA) has offered higher computational efficiency than the traditional double-loop method for linear zero-sum games, it relies on the Newton–Kantorovich conditions for convergence, making it highly dependent on specific initial conditions that are difficult to verify, especially in model-free settings.This paper employs a homotopy-based single-loop policy iteration method for solving linear zero-sum games arising in LFC. The method only requires an initial stabilizing controller, obtained through an iterative homotopy procedure, and avoids the need for system dynamics or a predefined initial matrix. As a result, it offers improved computational efficiency and reliable convergence. Simulation studies on a single-area power system demonstrate the method’s robustness and accuracy compared with SPUA approach.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Transient Stability Analysis of Inverter-Based Power Systems Based on Energy Function Convexity", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Terao, Kentaro", "affiliation": "Institute of Science Tokyo" }, { "name": "Nishino, Taku", "affiliation": "Tokyo Institute of Technology" }, { "name": "Ishizaki, Takayuki", "affiliation": "Tokyo Institute of Technology" } ], "keywords": [ "Power systems stability", "Electrical transmission systems" ], "abstract": "This paper performs a numerical analysis of transient stability in power systems using the convexity of the energy function and an analogy with mass-spring-damper systems. The Hessian of the energy function and its eigenvalues are interpreted as the spring constant matrix and spring strength, respectively. Numerical results demonstrate that increasing the spring constant matrix through parameter tuning of the VSG model enhances the system's transient stability. Furthermore, a positive correlation exists between the critical clearing time (CCT) during a ground fault and the stiffness. Using the analogy with the physical system, an intuitive interpretation is provided for the mechanism by which stronger springs increase CCT.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "From the ISS Property to Boundedness of Power Networks with Multiple Synchronous Generators and DERs Using Bounded Integral Control", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Alexandridis, Theodosis", "affiliation": "University of Patras" }, { "name": "Michos, Grigoris", "affiliation": "University of Patras" }, { "name": "Konstantopoulos, George", "affiliation": "University of Patras" } ], "keywords": [ "Power systems stability", "Electrical transmission systems", "Control and management of energy systems" ], "abstract": "We derive the nonlinear dynamical model of an AC power system consisting of multiple Synchronous Generators (SGs) and Distributed Energy Resources (DERs) interfaced with the grid by DC/AC power converters, in a generic meshed network topology that also incorporates the dynamical phenomena of the lines and the loads. In particular, the high-order nonlinear model is used for the SGs, while the converter units of the DERs are considered to operate in grid-forming mode, leading to dynamical modelling in the local rotating frame of each Generating Unit (GU), i.e. each SG and DER; thus facilitating the application of decentralised controllers. Based on the port-Hamiltonian nonlinear dynamical structure obtained for the complete power system, input-to-state stability (ISS) is analytically proven for the first time, as far as the authors know, when taking into account both SGs and grid-forming DC/AC converters in the power system model, considering also the sixth-order nonlinear model for the SGs. Furthermore, bounded integral controllers are designed for each GU that guarantee boundedness of the closed-loop system solution, without requiring any knowledge of system parameters, while additionally satisfying desired input constraints. A 4-bus power network is simulated to validate the ISS and boundedness properties of the developed dynamical model, as well as the input constraint satisfaction provided by the controllers.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Active Power Limiting Control for Angle Stability Enhancement of Grid-Forming Inverters", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Liu, Yiwei", "affiliation": "Chinese University of Hong Kong, Shenzhen" }, { "name": "Yang, Luwei", "affiliation": "Shenzhen Research Institute of Big Data" }, { "name": "Shunbo, Lei", "affiliation": "School of Science and Engineering, Chinese University of Hong Kong, Shenzhen 518172" } ], "keywords": [ "Power systems stability", "Power electronics" ], "abstract": "Maintaining phase-angle stability is crucial for grid-forming inverters in renewable-dominated power systems, particularly under severe disturbances and low short-circuit strength. To enhance stability resilience, the paper proposes a safety filter that shapes the active-power reference to keep the inverter–grid phase difference within a safe margin, thereby mitigating overcurrent and loss-of-synchronism risks. In contrast to traditional current-limiting or mode-switching methods, the proposed safety filter is implemented via a control barrier function and acts as a lightweight modification of the active-power reference while preserving the nominal control architecture during normal operation. Analytical results derived on a reduced-order model establish formal safety guarantees under bounded grid-angle jumps. Extensive reduced-order Monte Carlo simulations across diverse short-circuit scenarios validate reliable angle-margin preservation and the associated safety-intervention trade-off.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Multi-Frequency Stability Assessment of a Grid-Connected Converter Using Takagi-Sugeno Framework", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Rezai, Laila", "affiliation": "HTW Berlin, University of Applied Sciences, Control Systems Group" }, { "name": "Schulte, Horst", "affiliation": "HTW Berlin" } ], "keywords": [ "Power systems stability", "Power electronics", "Power plant control" ], "abstract": "This paper proposes a unified framework for modeling and large-signal stability analysis of grid-connected inverters. It demonstrates how the Takagi-Sugeno (TS) framework provides a rigorous theoretical foundation by representing three-phase inverter systems con- nected to the grid as a state- and input-dependent weighted combination of linear models. This paper details modeling and stability analysis, with particular emphasis on input-to-state stability (ISS), a structural requirement for inverter systems in which grid voltage fluctuations are uncontrollable inputs. To address the practical requirement of fully describing the inverter system’s operating range as defined by grid code specifications, this work presents a modeling method accompanied by LMI-based stability analysis in the large-signal domain—not merely the small-signal range as commonly found in the literature.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Power Management for DC Microgrids with Partially Uncontrollable Storage (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Oliani, Igor", "affiliation": "UFABC" }, { "name": "Lunardi, Angelo", "affiliation": "L2S, CentraleSupélec, CNRS, University Paris-Saclay" }, { "name": "Alfeu, Sguarezi", "affiliation": "Universidade Federal ABC CECS" }, { "name": "Iovine, Alessio", "affiliation": "CNRS, CentraleSupélec" } ], "keywords": [ "Control and management of energy systems", "Energy management systems", "Control and optimization for sustainability and energy systems" ], "abstract": "This paper addresses secondary-layer power management in DC microgrids with hybrid storage configurations, including partially uncontrollable fast devices such as supercapacitors. Unlike conventional approaches, we consider scenarios where fast storage outputs are dictated by primary-layer dynamics, while slower storage units track secondary-layer references. We propose a practical strategy that prevents the state-of-charge of uncontrollable devices from reaching extreme levels by temporarily operating them as energy buffers and introducing a control-mode signal to coordinate DC-bus stabilization and power tracking. The approach is implemented via Model Predictive Control, and simulations demonstrate that it ensures long-term microgrid stability while enhancing robustness and operational flexibility.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Bilevel GA–MILP Optimization of Greenhouse Temperature Setpoints and Multi-Energy Scheduling (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "González Morales, Rubén Avelino", "affiliation": "Universidad De Almería" }, { "name": "García-Mañas, Francisco", "affiliation": "University of Almería" }, { "name": "Rodríguez-Díaz, Francisco", "affiliation": "University of Almería" }, { "name": "Quijano, Nicanor", "affiliation": "Universidad De Los Andes" }, { "name": "Lopez-Jimenez, Jorge", "affiliation": "Universidad De Los Andes" }, { "name": "Becerra-Terón, Antonio", "affiliation": "University of Almería" } ], "keywords": [ "Energy management systems", "Forecasting of power supply and demand" ], "abstract": "Optimizing greenhouse temperature to balance crop productivity and energy efficiency is a major challenge in protected agriculture. This work introduces an optimization framework that integrates climate, crop growth, and Energy Hub modeling. A bilevel GA–MILP (genetic algorithm - mixed integer linear programming) strategy is applied: the GA maximizes profit by calculating heating and cooling setpoints for adequate crop growth, while the MILP focuses on minimizing operational costs by energy scheduling. A simulated case study based on a Mediterranean greenhouse was used to evaluate the approach, achieving up to 43% cost savings compared to manually setting the temperature setpoints. Although this comes with a 8% reduction in revenue, the overall profit increases by 5%, representing a modest economic gain but a significant contribution to the sustainability of food production.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "High-Fidelity Simulation and Control of a Centrifugally-Stiffened Airborne Wind Energy System (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Waibel, Johannes", "affiliation": "EPFL" }, { "name": "Brouillon, Jean-Sébastien", "affiliation": "ETHZ" }, { "name": "Jones, Colin, N", "affiliation": "EPFL" } ], "keywords": [ "Wind power", "Control and management of energy systems" ], "abstract": "Multi-kite Airborne Wind Energy systems harvest wind energy through several kites and tethers. While they are predicted to yield significantly higher power output than single-kite systems, they are also considered more complex, and practical real-world designs have yet to appear. We propose a novel multi-kite system in which the kites are constrained to orbit each other by tethers connecting their inner wingtips. The centrifugal stiffening in this arrangement results in a quasi-rigid rotor that transmits mechanical power to the ground-based generator by pulling out a Y-shaped tether. Such a system is modeled with high fidelity and controlled with simple means. This shows that the proposed architecture is less complex than commonly thought and has important advantages over previously proposed single-kite systems.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Trajectory Control and Trim of Tethered Aircraft Using Motion Primitives (I)", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Vinha, Sérgio", "affiliation": "University of Porto, Faculty of Engineering" }, { "name": "Fernandes, Gabriel M.", "affiliation": "University of Porto, Faculty of Engineering" }, { "name": "Fernandes, Manuel C. R .M.", "affiliation": "Universidade Do Porto" }, { "name": "Fontes, Fernando A. C. C.", "affiliation": "Universidade Do Porto" } ], "keywords": [ "Control and optimization for sustainability and energy systems", "Wind power" ], "abstract": "This paper investigates trajectory control of tethered aircraft flying on circular paths by exploiting motion primitives defined on a spherical surface. Using the motion primitives, we derive a longitudinal model of the aircraft and characterise the trim conditions required to maintain steady flight on a prescribed primitive. These trim conditions are then used as a feedforward law around which simple feedback controllers are designed. The simulation results show that combining trim-based feedforward and low-complexity feedback achieves accurate path-following and speed regulation, illustrating the potential of motion-primitive-based models for the guidance and control of tethered aircraft in airborne wind energy applications.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Improving Hydrogen Purity Production in High-Pressure Alkaline Electrolyzers Using Quadratic Dynamic Matrix Control", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Aguirre, Omar", "affiliation": "Universidad San Francisco De Quito" }, { "name": "Uribe, Jorge", "affiliation": "Universidad San Francisco De Quito" }, { "name": "Camacho, Oscar", "affiliation": "Universidad San Francisco De Quito" }, { "name": "Ocampo-Martinez, Carlos", "affiliation": "Universitat Politecnica De Catalunya (UPC)" } ], "keywords": [ "Hydrogen systems for energy generation and storage", "Control and management of energy systems", "Energy storage systems" ], "abstract": "This work proposes a constrained quadratic dynamic matrix control (QDMC) strategy to reduce hydrogen–oxygen cross-contamination in high-pressure alkaline electrolyzers, thus improving the purity of the supplied gases. To reduce gas contamination, the controller adjusts the opening of the two outlet valves based on the system pressure and the difference in liquid level between the two gas separation chambers. A quadratic dynamic matrix controller (QDMC) with constraints and multiple inputs and outputs (MIMO) is developed. The behavior of the closed-loop system under the proposed controller was assessed through simulation, employing a 25-state high-fidelity non-linear model. The simulation results show a hydrogen purity below 0.35% O2 under all scenarios.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Operational Scheduling of PEM Electrolyzers Using Grid Electricity and Renewables under Carbon-Intensity Constraints", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Hamed, Lina", "affiliation": "McMaster University" }, { "name": "Dalle Ave, Giancarlo", "affiliation": "McMaster University" }, { "name": "Swartz, Christopher L.E.", "affiliation": "McMaster University" } ], "keywords": [ "Hydrogen systems for energy generation and storage", "Control and optimization for sustainability and energy systems", "Demand response" ], "abstract": "Green hydrogen production using Proton Exchange Membrane (PEM) electrolyzers can support the decarbonization of hard-to-electrify sectors. PEM electrolyzer systems can operate either off-grid using only renewable energy or in a grid-connected configuration that supplements renewables with grid electricity. While grid-connected operation improves flexibility and continuity of operation, the carbon intensity (CI) of the hydrogen produced depends on the time-varying emissions associated with the bulk grid. The economic performance of grid-connected systems also depends on how well operation is aligned with low electricity price periods, which requires short-term forecasting. This study develops a rolling horizon optimization (RHO) framework that incorporates updated SARIMA-based electricity price forecasts, renewable availability, and CI limits. A mixed-integer linear programming (MILP) model determines electrolyzer loading, compression, and storage decisions. Several representative operating days with different grid CI levels are examined. Without CI limits, production shifts toward low-price periods, resulting in average CI values between 3.8 and 6.6 kg CO₂e/kg H₂, depending on the CI of the grid electricity used. When CI limits are imposed, grid-only operation cannot satisfy the threshold on high CI days, whereas renewable availability enables low CI production near 1.2–1.6 kg CO₂e/kg H₂. On low CI days, constrained and unconstrained outcomes have negligible differences. These results show that meeting carbon-intensity requirements while maintaining economic performance requires scheduling strategies that account for both price variability and renewable availability.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Comparative Exergy and Techno-Economic Analysis of Hydrogen Storage Systems Integrated with LNG Cold Energy", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ko, Jin", "affiliation": "Yonsei University" }, { "name": "Byun, Juyoung", "affiliation": "Yonsei University" }, { "name": "Song, Kyongmin", "affiliation": "Yonsei University" }, { "name": "Kim, Junghwan", "affiliation": "Yonsei University" } ], "keywords": [ "Hydrogen systems for energy generation and storage", "Process modeling, identification, and estimation techniques", "Energy storage systems" ], "abstract": "Integrating liquefied natural gas (LNG) cold energy into hydrogen systems offers an opportunity to reduce cooling loads and improve process efficiency, yet its system-level benefits across production and storage stages remain underexplored. To address this gap, four hydrogen supply configurations combining two production routes (SMR and ATR) with two storage pathways (LOHC and NH3) were modeled, and exergy and techno-economic analyses were performed with and without LNG cold-energy integration. LNG cold energy reduced cooling and pre-conditioning demands in the storage section, providing moderate improvements in exergy efficiency and operating costs across all cases. LOHC-based systems achieved the highest efficiencies (91–92%) and the lowest levelized hydrogen costs (1.99–2.38 /kg), with the SMR–LOHC configuration exhibiting the most favorable performance. In contrast, NH3-based systems showed lower efficiencies (81–83%) and higher costs (3.26–3.68 /kg) due to additional energy demands associated with high-pressure synthesis and multi-stage compression. This study offers a quantitative assessment of LNG cold-energy use across both production and storage stages and demonstrates its potential to enhance the efficiency and economic viability of LNG-based hydrogen systems, while clarifying system-level trade-offs between LOHC and NH3 storage routes.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Estimators for Hydropower Plant Efficiency Based on Physical Models", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Alonso, Augustin", "affiliation": "Gipsa-Lab" }, { "name": "Robert, Gerard", "affiliation": "EDF - Hydro Engineering Centre" }, { "name": "Besancon, Gildas", "affiliation": "Grenoble INP - UGA" } ], "keywords": [ "Hydropower" ], "abstract": "Monitoring the energy efficiency of hydropower units is critical for production optimisation and predictive maintenance, but direct measurement through thermodynamic tests is costly and seldom performed. Continuous estimation from standard operational data is therefore desirable, yet challenging due to the absence of direct net head instrumentation and to flow-dependent non-stationary noise on industrial sensors. This paper proposes a ``grey-box'' methodology in which three physics-based dynamic models for the net head (Pressure-Based, Surge-Tank-Based, and Upstream-Reservoir-Based) are coupled with Adaptive Cubature Kalman Filters (ACKF) and Smoothers (ACRTSS). Process-noise non-stationarity is handled by a sliding-window variance estimator applied directly on the noisy input signals. Validation on a high-head plant with real industrial data shows that the proposed dynamic smoother reduces the RMSE against thermodynamic references by approximately 30% and improves temporal stability by a factor of five compared with filtered static methods.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Automatic Power Control Method for Start-Up Stage of High-Temperature Gas-Cooled Reactor", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Shen, Pengyu", "affiliation": "Tsinghua University" }, { "name": "Zhu, Yunlong", "affiliation": "Tsinghua University" }, { "name": "Zhang, Jinming", "affiliation": "Tsinghua University" }, { "name": "Zhonghua, Cheng", "affiliation": "INET, Tsinghua University" }, { "name": "Xiong, Huasheng", "affiliation": "Tsinghua University" }, { "name": "Dong, Zhe", "affiliation": "Tsinghua University" }, { "name": "Huang, Xiaojin", "affiliation": "Tsinghua University" } ], "keywords": [ "Nuclear power", "Power plant control" ], "abstract": "To mitigate the high operator workload and operational risks associated with manual control rod operation during the start-up stage of High-Temperature Gas-Cooled Reactors (HTGRs), this paper proposes an automated power control method. The start-up process is divided into two power ranges: 0–30% and 30–50% of Rated Full Power (RFP). The operation of control rod is automated by presetting parameters such as the operation sequence, position limits, step size, and interval time. In the 0–30% RFP stage, the flow rates of the primary circuit coolant and the secondary circuit coolant are fixed. In the 30–50% RFP stage, a linear ramp-up strategy for feedwater flow rate is implemented to effectively suppress the excessively steam temperature and ensure a stable steam temperature increase, while primary helium flow rate remains unchanged. Simulation results demonstrate that the proposed method achieves stable power increase and confirms its control performance and operational safety. Furthermore, this study analyzes the influence of negative temperature feedback on reactor power and examines the stabilizing effect of feedwater regulation on steam temperature. The findings provide the practical insights for the automatic control of start-up stage of HTGRs.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Model Predictive Control of Thermo-Hydraulic Systems Using Primal Decomposition", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Vieth, Jonathan", "affiliation": "Hamburg University of Technology" }, { "name": "Eichler, Annika", "affiliation": "DESY" }, { "name": "Speerforck, Arne", "affiliation": "Hamburg University of Technology" } ], "keywords": [ "Thermal systems modelling", "Control and optimization for sustainability and energy systems", "Energy management systems" ], "abstract": "Decarbonizing the global energy supply requires more efficient heating and cooling systems. Model predictive control enhances the operation of cooling and heating systems but depends on accurate system models, often based on control volumes. We present an automated framework including time discretization to generate model predictive controllers for such models. To ensure scalability, a primal decomposition exploiting the model structure is applied. The approach is validated on an underground heating system with varying numbers of states, demonstrating the primal decomposition’s advantage regarding scalability.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Isodamping Tuning of PIDA Controllers", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Campregher, Francesco", "affiliation": "University of Brescia" }, { "name": "Visioli, Antonio", "affiliation": "University of Brescia" } ], "keywords": [ "Advanced process control" ], "abstract": "In this paper we present a tuning methodology for Proportional-Integral-Derivative-Acceleration (PIDA) controllers, also known as Proportional-Integral-Double-Derivative controllers (PIDD or PIDD2). In particular, the parameters are optimized to achieve the isodamping property at the gain crossover frequency, that is, a flat phase so that the same overshoot is achieved in the set-point response also in case of process gain variations. Simulation results demonstrate that the additional acceleration action allows the user to significantly improve the performance with respect to PID controllers so that PIDA controllers can be a valid alternative to fractional-order PID (FOPID) controllers for which the isodamping tuning is typically used.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Design of a Robust H∞ Mixed Sensitivity Temperature Controller for a Steel Slab Reheating Furnace", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Rivas-Perez, Raul", "affiliation": "Havana Technological University" }, { "name": "Sotomayor Moriano, Javier", "affiliation": "Pontificia Universidad Católica Del Perú" }, { "name": "Pérez Zuñiga, Gustavo", "affiliation": "Pontifical Catholic University of Peru" }, { "name": "Feliu-Batlle, Vicente", "affiliation": "Univ of Castilla-La Mancha. CIF: Q-1368009E" } ], "keywords": [ "Advanced process control", "MMM process modeling, identification, and estimation techniques" ], "abstract": "Robust temperature control in the soaking zone of a steel slab reheating furnace is addressed. A dynamic model of the nominal process is obtained using a system identification technique based on real-time data, resulting in a second-order model. A robust H∞ mixed-sensitivity temperature controller is then designed. Simulations of the control system are carried out using the designed robust controller and a conventional PI controller. A comparative analysis of the simulation results highlights the superior performance of the proposed H∞ controller.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Cascade Model Predictive Control of Air Handling-Unit for Building Temperature Regulation", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Wang, Liuping", "affiliation": "RMIT University" }, { "name": "Guan, Robin", "affiliation": "RMIT University" }, { "name": "Meegahapola, Lasantha", "affiliation": "RMIT University" } ], "keywords": [ "Advanced process control", "Model-predictive and optimization-based control in chemical processes", "Industrial applications of process control" ], "abstract": "Heating Ventilation and Air Conditioning systems have been one of the most energy intensive units in buildings. How to regulate and optimize these systems for reducing energy consumptions while maintaining occupant's comfort level provides a great opportunity in the area of building automation and power grid support. This paper presents an experimental study on the air-handling-unit, which is the fundamental building block of a heating ventilation and air conditioning system. The focus is to address the problems of severe nonlinearity, large time delay and the combination of these two factors. Choosing discrete-time model predictive control as the vehicle for the control system design and implementation, the experimental study shows that a cascade model predictive control system with a dual sampling rate is an effective approach to solve the difficult control problems in a typical heating ventilation and air conditioning system.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "A Rapid-Prototype MPC Tool Based on gPROMS Platform", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Wu, Liang", "affiliation": "Johns Hopkins University" } ], "keywords": [ "Advanced process control", "Model-predictive and optimization-based control in chemical processes", "Industrial applications of process control" ], "abstract": "This paper presents a rapid-prototyping Model Predictive Control (MPC) tool built on the gPROMS platform, supporting the entire MPC design workflow. The gPROMS-MPC tool can not only directly interact with a first-principle-based gPROMS model for closed-loop simulations but also utilizes its mathematical information to derive simplified control-oriented models, basically via linearization techniques. It can inherit the interpretability of the first-principle-based gPROMS model, unlike the PAROC framework, in which the control-oriented models are obtained from black-box system identification based on gPROMS simulation data. The gPROMS-MPC tool allows users to choose when to linearize, such as at each sampling time (successive linearization) or at some specific points to obtain one or multiple good linear models. The gPROMS-MPC tool implements our previous construction-free CDAL and the online parametric active-set qpOASES algorithms to solve sparse or condensed MPC problem formulations, respectively, for possible successive linearization or high state-dimension cases. Our CDAL algorithm is also matrix-free and library-free, thus supporting embedded C-code generation. After many example validations of the tool, here we only show one example to investigate the performance of different MPC schemes.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Sparse State Feedback Control for Industrial Applications", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Gurpegui, Alba", "affiliation": "Lund University" }, { "name": "Norlund, Frida", "affiliation": "Lund University" }, { "name": "Soltesz, Kristian", "affiliation": "Lund University" }, { "name": "Rantzer, Anders", "affiliation": "Lund Univ" } ], "keywords": [ "Advanced process control", "Model-predictive and optimization-based control in chemical processes", "Industrial applications of process control" ], "abstract": "We present an optimization-based methodology for designing sparse state-feedback controllers for industrial applications that are suited for linear control, and demonstrate the framework by designing a level controller for an industrial rougher flotation bank at the Aitik mine. In contrast to the dense linear-quadratic (LQ) controller gains currently operating at the concentrator, our approach enforces a sparsity pattern that is consistent with the interaction structure of the flotation bank and accounts for the worst-case expected inflow disturbances during tuning, while optimizing controller performance through the Integral Absolute Error (IAE) index. The non-zero elements of the sparse gain matrices are optimized using a coordinate search algorithm that handles bound constraints and preserves closed-loop stability. The resulting sparse controller achieves improved load disturbance rejection in the flotation cells compared to the LQ controller. These improvements are consistently observed in both linear and nonlinear simulations. In addition, the imposed structure, results in gain matrices that are easier to adjust and interpret. Importantly, the sparse controllers generated for the Aitik mine are directly suitable for industrial deployment and offer an effective alternative to the existing dense LQ design.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Study of Advanced Motion Controllers Adapted for a Safety-Critical Drilling Process", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Diepeveen, Jullian", "affiliation": "Eindhoven University of Technology" }, { "name": "Pavlov, Alexey", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Steur, Erik", "affiliation": "Eindhoven University of Technology" }, { "name": "Ruderman, Michael", "affiliation": "University of Agder" } ], "keywords": [ "Advanced process control", "Nonlinear signal processing in MMM systems", "Reliability and safety in processes" ], "abstract": "The so-called gas kick scenario is a complex time-varying nonlinear and, most importantly, safety-critical dynamic process during drilling operations. It requires advanced pressure regulation on the top of the drilling system without whole sensing of the well-process variables. Adapted from the available advanced motion controllers, i.e. HIGS and nonlinear integral gain control, the nonlinear control architectures are proposed for standpipe pressure control in a well killing procedure. The proposed controllers use a nested structure with a feedback linearized inner PID-loop and extends the usual outer PI-loop for the standpipe pressure. The control performance is analyzed through the use of a high fidelity simulator (OpenLab), showing improvements of the overall control behavior for well killing procedures.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Response Matrix Identification & Slow Feedback Controller Design for EuXFEL to Mitigate the Tidal Effects", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Sharan, Bindu", "affiliation": "Deutsches Elektronen-Synchrotron DESY" }, { "name": "Bradarić, Danis", "affiliation": "University of Sarajevo" }, { "name": "Hespe, Christian", "affiliation": "Deutsches Elektronen-Synchrotron DESY" }, { "name": "Holmberg, Johan", "affiliation": "Lund University" }, { "name": "Kammering, Raimund", "affiliation": "Deutsches Elektronen-Synchrotron DESY" }, { "name": "Czwalinna, Marie Kristin", "affiliation": "DESY" }, { "name": "Eichler, Annika", "affiliation": "DESY" } ], "keywords": [ "Advanced process control", "Process modeling, identification, and estimation techniques", "Industrial applications of process control" ], "abstract": "This paper presents a structured methodology for identifying response matrices and designing slow feedback controllers at the European XFEL. We determine the response matrix using an iterative least-squares algorithm inspired by Sparse Identification of Nonlinear Dynamical Systems (SINDy), incorporating prior knowledge of zero elements to improve accuracy. To better reflect real-world behaviour, we extend the system from a static to a dynamic model by introducing an inherent time delay. For reference tracking, PID gain matrices are obtained by reformulating the problem as a state-feedback problem using a Linear Quadratic Regulator (LQR). The controller is applied to a model identified from open-loop data, ensuring consistency with experimental beam dynamics. Finally, we introduce two additional PI controllers to compensate for tidal effects influencing bunch arrival time and energy. Simulation results show that this framework effectively stabilises the beam and mitigates slow drifts, providing a reliable foundation for accelerator operation.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Distributed Nonlinear Model Predictive Control Frame for Microgrids with Constant Power Loads", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Toro, Vladimir", "affiliation": "Universidad Santo Tomás" }, { "name": "Tellez-Castro, Duvan", "affiliation": "Universidad Distrital Francisco José De Caldas" }, { "name": "Rakoto, Naly", "affiliation": "IMT Atlantique and LS2N, Nantes, France" } ], "keywords": [ "Control of multi-scale, distributed, and particulate systems", "Control and optimization for sustainability and energy systems", "Power systems stability" ], "abstract": "This paper presents the analysis and design of a control law for a set of continuous current converters that supply a constant-power load. The controller implements a distributed consensus-enhanced nonlinear MPC scheme based on the nonlinear model of the source–load dynamics, incorporating a consensus term as a constraint. The MPC problem is solved at each iteration using a dedicated optimization solver. The proposed controller enhances voltage regulation throughout the entire system while relying solely on local information. The effectiveness of the controller is demonstrated through a simulation model evaluated under several constant-power-load scenarios.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Integrated Framework and Application of Planning and Scheduling under Uncertain Condition: Large-Scale Crude Oil Scheduling Scenario", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Xie, Yunhao", "affiliation": "China University of Petroleum, Beijing" }, { "name": "He, Renchu", "affiliation": "China University of Petroleum, Beijing" }, { "name": "Sun, Lin", "affiliation": "China University of Petroleum, Beijing" }, { "name": "Feng, Enbo", "affiliation": "East China University of Science and Technology" } ], "keywords": [ "Control of multi-scale, distributed, and particulate systems", "Machine learning and artificial intelligence in chemical process control", "Control and optimization of supply chains in chemical processes" ], "abstract": "To address the complexity and uncertainty in large-scale refinery crude oil scheduling, this study presents a Wasserstein Distance-based Distributionally Robust Chance-Constrained Long–Short Term Integrated Optimization (WDRCCLSO) model. Crude demand uncertainty is modeled via a data-driven WDRCC formulation, mitigating risks of supply imbalance. A hybrid MP/PSO optimization strategy solves the model, using mathematical programming (MP) for long-term allocation and particle swarm optimization (PSO) for short-term scheduling. Results show that the proposed approach efficiently produces robust and feasible short-term operational schedules.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "A MATLAB-Based Simulation Tool for Fast and Efficient Control System Investigation for Laser-Powder Bed Fusion Process", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Al-Saadi, Taha", "affiliation": "Sultan Qaboos University" }, { "name": "Rossiter, J. Anthony", "affiliation": "Univ of Sheffield" }, { "name": "Panoutsos, George", "affiliation": "University of Sheffield" } ], "keywords": [ "Industrial applications of process control", "Process modeling, identification, and estimation techniques", "Advanced process control" ], "abstract": "Additive manufacturing, particularly the Laser Powder Bed Fusion (L-PBF) process, requires precise control of melt-pool dynamics to ensure consistent part quality and repeatability. However, the lack of fast and accessible control-oriented simulation tools limits the ability to design, test, and validate advanced control strategies. This paper presents a modular and computationally efficient MATLAB/Simulink-based simulation framework developed specifically for L-PBF process control studies. The proposed tool estimates melt-pool temperature and cross-sectional area while accounting for track-to-track and layer-to-layer heat accumulation effects. It enables rapid integration of various control algorithms, including proportional–integral–derivative (PID), feedforward, fuzzy logic, and many other, within closed-loop configurations. Validation against Rosenthal’s analytical solution and the heat balanced model demonstrates a good prediction errors with more than 500× improvement in computation speed compared to finite-element simulations. The results confirm that the proposed simulator provides an accurate, flexible, and user-friendly platform for rapid prototyping, control system education, and research in metal additive manufacturing.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Performance Assessment of Robust PID Controllers with Machine Learning", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ruggeri, Diego", "affiliation": "University of Brescia" }, { "name": "Beschi, Manuel", "affiliation": "University of Brescia" }, { "name": "Visioli, Antonio", "affiliation": "University of Brescia" } ], "keywords": [ "Machine learning and artificial intelligence in chemical process control", "Monitoring, performance assessment, and fault detection in chemical process control", "Process performance monitoring/statistical process control" ], "abstract": "In this paper we present a performance assessment methodology, based on machine learning, for Proportional-Integral-Derivative (PID) controllers. A set-point step response is evaluated and a control loop that exhibits a high integrated absolute error and a maximum sensitivity far from the optimal one is detected. Further, a performance index that gives the distance of the current maximum sensitivity to the optimal one is determined. In this way, the robustness of the loop is explicitly taken into account. Results obtained from simulated routine data demonstrate that models based on a convolutional autoencoder can achieve high performance on the proposed tasks.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Privacy-Preserving Nonlinear DMPC for Multi-Agent Consensus with CKKS Encryption", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Gao, Ruiyang", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Wu, Jing", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Long, Chengnian", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes" ], "abstract": "In this paper, a distributed model predictive control strategy for nonlinear multi-agent systems under encrypted communication is investigated. To address the challenges caused by encrypted couplings in conventional distributed model predictive control, a distributed optimization strategy based on the alternating direction method of multipliers is developed. This approach decomposes the global non-convex optimization problem into local subproblems, while all exchanged information is protected via the Cheon-Kim-Kim-Song homomorphic encryption scheme combined with randomized masking. Furthermore, a theoretical relationship between encryption depth and control error, enabling a systematic balance between privacy strength and control performance is derived. Simulation results demonstrate that the proposed strategy effectively preserves privacy while maintaining closed-loop performance and robustness.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Constraints Reduction in a Multi-Model Predictive Controller Applied to a Propylene Polymerization Reactor", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Vargan, Jozef", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Kurucz, Gyula", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Klauco, Martin", "affiliation": "Czech Technical University" }, { "name": "Latifi, M.A.", "affiliation": "Cnrs - Ensic, B.p. 20451" }, { "name": "Fikar, Miroslav", "affiliation": "Slovak University of Technology in Bratislava" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes", "Advanced process control", "Industrial applications of chemical process control" ], "abstract": "Industrial processes are often governed by complex nonlinear dynamics, posing significant challenges for control design. While nonlinear predictive control can effectively manage such behavior, its high computational demand limits practical implementation. An alternative approach is to approximate the nonlinear system using a set of linear models within a multi-model predictive control (mMPC) framework, thereby reducing computational complexity. However, the inclusion of constraints into all models remains computationally demanding. To address this issue, two reduced-constraint mMPC formulations are proposed: one based on the static gain matrix of individual models (mMPCsg) and another on their unforced responses (mMPCur). Application to a MIMO propylene polymerization reactor - heat exchanger system demonstrates a considerable reduction in computation time while preserving control performance and maintaining constraint violations at levels comparable to the full-constraint mMPC.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Data-Driven Model Predictive Anti-Slug Control for Offshore Gas-Lifted Oil Wells", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Gude, Tore", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Imsland, Lars", "affiliation": "Norwegian University of Science and Technology" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes", "Process modeling, identification, and estimation techniques", "Industrial applications of process control" ], "abstract": "This paper models the dynamics of a slugging oil well using the Sparse Identification of Nonlinear Dynamics (SINDy) method based on simulated data from the high-fidelity OLGA simulator. The identified model closely predicts the unstable dynamics (slugging) of an oil well, even though the model is not parsimonious and lacks interpretability. The model is used in a Model Predictive Control (MPC) framework to stabilize slugging flow, and is validated in closed-loop simulations in OLGA. The controller stabilizes slugging flow for a wider range of operating points and at higher choke valve openings than a PI controller, allowing increased production from the oil well.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "A Practical Framework for Process Anomaly Detection Analysis in Multivariate Time Series", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Arbetová, Patrícia", "affiliation": "Slovak University of Technology in Bratislava, Faculty of Chemical and Food Technology" }, { "name": "Fáber, Rastislav", "affiliation": "Slovak University of Technology in Bratislava, Faculty of Chemical and Food Technology" }, { "name": "Ľubušký, Karol", "affiliation": "Slovnaft, A.s" }, { "name": "Paulen, Radoslav", "affiliation": "Slovak University of Technology in Bratislava" } ], "keywords": [ "Monitoring, performance assessment, and fault detection in chemical process control", "Data-driven methods for FDI/FTC", "Machine learning and artificial intelligence in chemical process control" ], "abstract": "Online analyzers provide frequent product-quality measurements, yet may drift, become miscalibrated, or fail. Laboratory measurements are more reliable but sparse and delayed, which makes direct anomaly detection difficult. This paper uses a multi-fidelity (MF) soft sensor as a laboratory-quality reference for anomaly detection in multivariate industrial time series. Deviations between the online analyzer and the MF reference define pseudo ground-truth labels over the dense online timeline. Under these labels, we compare three detector strategies: univariate output rules, input-space detectors with feature selection and dimensionality reduction, and model-based residual detectors. The industrial case study shows that output-only rules produce few false alarms but miss most pseudo-labeled anomalies, while input-space detectors using physically meaningful variables give the best sensitivity-specificity trade-off. Since independent industrial fault labels are not available, the reported metrics measure agreement with the MF reference, not a confirmed detection of real faults.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Modeling and Numerical Simulation of Gas–Liquid Flow in an Elastic Foam-Bed Reactor with a Perforated Moving Plate", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Cheng, Xiaoyu", "affiliation": "Université Claude Bernard Lyon 1" }, { "name": "Jallut, Christian", "affiliation": "Université Claude Bernard Lyon 1" }, { "name": "Maschke, Bernhard", "affiliation": "Univ Claude Bernard of Lyon" }, { "name": "Tricas, Laura", "affiliation": "CP2M" }, { "name": "Edouard, David", "affiliation": "University Lyon1" } ], "keywords": [ "Process modeling, identification, and estimation techniques" ], "abstract": "The elastic foam-bed reactor (EFR) uses a moving plate that periodically compresses a deformable open cell polyurethane foam, which changes the local porosity and flow resistance in a controlled way. We present a one-dimensional dynamic model that represents the plate motion and its effect on the fluid flow dynamics inside the reactor filled with two blocks of deformable foam driven by the plate motion. The model consists in the mass and momentum balances for the gas and liquid phases coupled to the controlled deformation of the foam bed. The resulting set of equations is solved using an arbitrary Lagrangian-Eulerian discontinuous Galerkin (ALE–DG) method. The simulations show that the plate movement induces clear oscillation in phase fractions, velocities, and pressure drop, providing useful insight into the flow patterns and phase-distribution dynamics of reactors with structured packing driven by a moving plate.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "A Quantum-Enhanced Hybrid Approach for Parameter Estimation in Gas-Phase Fixed-Bed Adsorption Experiments", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "G. Matias, Rui D.", "affiliation": "LSRE-LCM, ALiCE, Faculty of Engineering, University of Porto" }, { "name": "Ferreira, Alexandre", "affiliation": "Laboratory of Separation and Reaction Engineering Associate Laboratory LSRE-LCM, Department of Chemical Engineering, Faculty Of" }, { "name": "Nogueira, Idelfonso", "affiliation": "NTNU" }, { "name": "Ribeiro, Ana Mafalda", "affiliation": "Laboratory of Separation and Reaction Engineering Associate Laboratory LSRE-LCM, Department of Chemical Engineering, Faculty Of" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Machine learning and artificial intelligence in chemical process control" ], "abstract": "Quantum computing is emerging as one of the most promising paradigms for computational science. This work presents a hybrid quantum-classical optimization framework that combines a Variational Quantum Circuit with a classical feedforward neural network, optimized via Bayesian methods, to estimate parameters in a mathematical model of CO2/CH4 fixed-bed adsorption based separations. The hybrid algorithm is compared with conventional correlation-based methods and direct Bayesian optimization of physical parameters. Results demonstrate that the quantum-classical approach consistently identifies parameter sets that improve the fit to experimental data despite higher dimensionality.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "A Neural Network-Based Grey-Box Model of Solvent-Based Carbon Capture", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Martinsen, Emil Skov", "affiliation": "Technical University of Denmark" }, { "name": "Kloppenborg Møller, Jan", "affiliation": "Technical University of Denmark" }, { "name": "Madsen, Henrik", "affiliation": "Tech. Univ. of Denmark" }, { "name": "Einbu, Aslak", "affiliation": "SINTEF Industry" }, { "name": "Mejdell, Thor", "affiliation": "SINTEF" }, { "name": "Kvamsdal, Hanne M.", "affiliation": "SINTEF Industry" }, { "name": "Tobiesen, Andrew", "affiliation": "SINTEF Industry" }, { "name": "Goranovic, Goran", "affiliation": "Technical University of Denmark (DTU)" }, { "name": "Ritschel, Tobias K. S.", "affiliation": "Technical University of Denmark" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Machine learning and artificial intelligence in chemical process control" ], "abstract": "To lower the operational costs of solvent-based carbon capture, model-based control plays a key role. Such control strategies require accurate, computationally efficient, and adaptive dynamic models. In this work, we propose a neural network-embedded grey-box model for solvent-based carbon capture systems, which combines physical knowledge of the system with a neural network to capture complex and unknown dynamics. We train and test the model on real-world experimental data from the Tiller pilot plant in Trondheim, Norway. We implement a disturbance-adaptive extended Kalman filter for adaptive state estimation and prediction and demonstrate that the proposed model provides accurate predictions on unseen test data and adaptively mitigates steady state offsets.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Dynamic Model Identification of Power Systems for Electromechanical Oscillation Damping Control", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Frascarelli, Matteo", "affiliation": "University of Pisa" }, { "name": "Bacci di Capaci, Riccardo", "affiliation": "University of Pisa" }, { "name": "Vaccari, Marco", "affiliation": "University of Pisa" }, { "name": "Deihimi Kordkandi, Reza", "affiliation": "CITCEA-UPC, Departament d’Enginyeria El`ectrica, Universitat Polit`ecnica De Catalunya" }, { "name": "Cheah Mañé, Marc", "affiliation": "CITCEA-UPC, Departament d’Enginyeria Eléctrica, Universitat Politécnica De Catalunya" }, { "name": "Pannocchia, Gabriele", "affiliation": "University of Pisa" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Power systems stability", "Electrical transmission systems" ], "abstract": "This paper develops reduced-order linear models for power system dynamic analysis using data-driven identification approaches. Nonlinear Root Mean Square (RMS) simulations from a commercial software platform provide the reference trajectories, while different subspace and polynomial methods are applied to recover the dominant modes relevant for low-frequency oscillation damping control. The models identified are validated in simulation and prediction against rigorous nonlinear time-domain simulations to assess their ability to reproduce key dynamic behaviors. Results show that the models that were obtained capture the essential oscillatory dynamics with high reliability, offering an effective basis for tuning controllers when analytic linearization of the original system is impractical.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Load Allocation Optimization for Common-Header Boiler Systems", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhu, Yun", "affiliation": "Zhejiang University" }, { "name": "Zhu, Yucai", "affiliation": "Zhejiang University" } ], "keywords": [ "Real-time optimization and control in chemical processes", "Advanced process control", "Process modeling, identification, and estimation techniques" ], "abstract": "This paper presents an optimization method to improve the thermal efficiency of a common header boiler system. The optimization method uses the load of each boiler as the optimization variable and total coal consumption as the loss function. The proposed optimization method is gradient-based, with the gradient for each iteration obtained through system identification using test data, eliminating the need for an accurate model of the process. For the boiler header system, a cascade control structure has been proposed. Performing identification tests while ensuring the stability of the header load can avoid triggering nonlinearity. A two-layer model predictive control approach is employed, with the static layer continuously updating load allocation based on iterative optimization results, while the dynamic layer achieves fast tracking of load setpoints. The effectiveness of the proposed method is validated through a simulation case involving three boilers in a common header system.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Bearing-Only Solution to the Fermat-Weber Location Problem for Unicycle Agent", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Cheah, Hong Liang", "affiliation": "UNSW" }, { "name": "Deghat, Mohammad", "affiliation": "University of New South Wales" }, { "name": "Guivant, Jose", "affiliation": "UNSW Australia" } ], "keywords": [ "Guidance, navigation and control for AVs", "Automatic control, optimization, real-time operations in transportation", "Control architectures in automotive control" ], "abstract": "This paper addresses bearing-only algorithms for solving the Fermat-Weber Location Problem (FWLP) with a unicycle agent. Unlike existing FWLP solutions for single- or double-integrator agents, our approach accounts for the nonholonomic constraints of wheeled robots. We first develop a bearing-only control law for the case with stationary beacons. Next, we consider saturated control inputs and propose a corresponding bearing-only control law. Finally, we address moving beacons with constant velocities and develop a control law that enables the unicycle agent to track the moving Fermat–Weber point. Both simulations and experiments are provided to demonstrate the effectiveness of the proposed methods.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Vehicle-Following Model Predictive Control for Platooning on Curved Roads Guaranteeing String Stability", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhang, Qihang", "affiliation": "University of Groningen" }, { "name": "Qiu, Meng", "affiliation": "Suzhou University of Technology" }, { "name": "Cao, Ming", "affiliation": "University of Groningen" } ], "keywords": [ "Multi-vehicle systems", "Intelligent transportation systems", "Trajectory tracking and path following for AVs" ], "abstract": "Cutting-corner behavior and loss of string stability are two principal concerns on platoon performance over curved roads. Because vehicle following governs how a platoon responds to curvature, it directly determines the significance of cutting-corner effects. Inspired by Newell’s car-following model, we propose a curved-road following method that uses the predecessor’s time-delayed state as the reference for each follower, enabling accurate tracking while avoiding cutting-corner behavior. Building on this method, we design a model predictive control (MPC) scheme that avoids cutting corners while maintaining the desired inter-vehicle spacing. With appropriately selected controller parameters, the closed-loop platoon preserves string stability. Simulation results validate the proposed following method and show that the MPC controller both prevents cutting-corner behavior and preserves string stability along the platoon.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Fixed-Time Control for the Roll Channel of Dual-Spin Projectiles with Canards", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Tang, Li", "affiliation": "Beijing Information Science and Technology University" }, { "name": "Fan, Junfang", "affiliation": "Beijing Information Science and Technology University" }, { "name": "Ge, Jiahao", "affiliation": "Beijing Information Science and Technology University" }, { "name": "Zhang, Donghao", "affiliation": "Beijing Information Science and Technology University" }, { "name": "Li, Jingtao", "affiliation": "Beijing Institute of Spacecraft System Engineering" } ], "keywords": [ "Nonlinear adaptive control", "Neural and fuzzy adaptive control", "Learning methods for control" ], "abstract": "To address the control challenges posed by the strong nonlinearity and parameter uncertainty in the roll channel of canard-guided dual-spin projectiles, a fixed-time tracking control method based on radial basis function neural networks is proposed. Initially, a seven-degree-of-freedom coupled rigid-body dynamics model for the dual-spin projectile was developed, treating aerodynamic parameter uncertainties as lumped disturbances. The model was then decoupled into roll channel and pitch/yaw channel dynamics subsystems using time-scale separation. Radial basis function neural networks were employed to precisely approximate the model uncertainties. Moreover, filters were introduced to compute the virtual derivatives, effectively preventing the common issue of \"derivative explosion\" in traditional control systems. The designed controller integrates roll angle tracking error feedback with lumped disturbance estimation feedforward, aiming to achieve fixed-time convergence and enhance the system's convergence speed and robustness, thereby ensuring precise roll angle tracking control. Using the Lyapunov method, the uniform ultimate bounded stability of the closed-loop system was demonstrated. Simulation results indicate that under conditions of aerodynamic parameter perturbation with a frequency of 1000 Hz and amplitude deviation of ±30%, the method can achieve an average roll angle tracking error of no more than 0.1 degrees, exhibiting excellent maneuver command tracking precision and robustness.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Adaptive Control with Directional Forgetting for Uncertain Euler-Lagrange Systems", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Manchola, Miguel", "affiliation": "Syracuse University" }, { "name": "Rubino, Nicholas", "affiliation": "Syracuse University" }, { "name": "Duenas, Victor", "affiliation": "Syracuse University" } ], "keywords": [ "Nonlinear adaptive control", "Nonlinear system identification", "Learning methods for control" ], "abstract": "Adaptive control has been extensively used to estimate constant unknown parameters in uncertain nonlinear dynamical systems and to exploit those estimates to improve tracking performance. Memory regressor extension (MRE) methods leverage accumulated input–output data to relax excitation requirements, with full-data MRE integrating the entire history of regressors to drive parameter updates. Alternatively, forgetting-based MRE introduces selective data discounting to retain the benefits of stored information while improving robustness to disturbances. Forgetting-based estimation methods achieve this by constructing an information matrix (IM), i.e., an integral regressor matrix whose stored data is strategically discounted to accommodate changes in the dynamics. Traditional exponential forgetting applies a uniform decay across the entire regressor space, which can cause estimator windup under poor persistence of excitation (PE), where the IM becomes positive semi-definite, and the parameter estimates deteriorate over time. In contrast, directional forgetting (DF) discounts data only along the subspaces spanned by new information in the regressor. Although existing DF approaches, including orthogonal and oblique projection methods, successfully prevent estimator windup, they are often limited to first-order dynamics, assume exact knowledge of the system, and fail to address closed-loop tracking, limiting their applicability. This paper develops a nonlinear adaptive control scheme that incorporates oblique DF into a closed-loop design for uncertain Euler–Lagrange systems, achieving both kinematic tracking and parameter estimation. Integral data-driven regressors and input vectors are used to avoid computing second-order derivatives. A Lyapunov-based analysis establishes global exponential convergence of both tracking and parameter estimation errors under the PE condition. Numerical simulations of a two-degree-of-freedom robotic system validate the developed method, demonstrating satisfactory tracking performance and reliable estimation of constant unknown parameters.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Adaptive Backstepping Fault-Tolerant Control for Large-Scale Time-Delay Systems with Input Saturations", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhang, Jiao-Yang", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Fan, Huijin", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Liu, Lei", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Wang, Bo", "affiliation": "Huazhong University of Science and Techonology" } ], "keywords": [ "Nonlinear adaptive control", "Stochastic adaptive control" ], "abstract": "This article investigates the adaptive backstepping fault-tolerant control (FTC) problem for uncertain large-scale time-delay systems subject to input saturations. By establishing a technical lemma, the growth assumption imposed on the delayed interactions is successfully removed. Then, an adaptive FTC scheme is presented, which is capable of accommodating the stochastic intermittent failures of multiple saturated actuators. With the aid of a Lyapunov-Krasovskii functional, it is proven that all the closed-loop signals remain globally ultimately bounded in probability. Also, it is established that the tracking error can be reduced by tuning design parameters in a explicitly manner.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Multitask Recognition of Types and Operating States of Underwater Engines Based on Mel Spectrogram Decomposition in a GRU-With-Attention Model", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Albuquerque, Luis Paulo", "affiliation": "Universidade Federal Do Rio De Janeiro - UFRJ" }, { "name": "Monteiro Guedes, Pedro Henrique", "affiliation": "Rio De Janeiro State University" } ], "keywords": [ "Perception and filtering in marine systems", "Sensors and actuators in marine systems", "Decision and support in marine systems" ], "abstract": "This work addresses multitask recognition of the active engine (M1–M5) and its operating state from underwater audio. We compare four shared feature-extraction networks, here termed backbones, namely BiLSTM+attention, GRU+attention, a temporal Transformer, and ResNet-50 on spectrograms, all coupled to conditional state heads. Preprocessing uses 0.5 s windows of 64-bin log-mel spectrograms, z-score normalization, and light augmentation (random gain, Gaussian noise, and SpecAugment). Experiments are conducted on the single-engine subset of Wolfset, with evaluation at segment and file levels. Among the reference models, GRU and Transformer reach file-level F1 of 1.00 for engine and up to 0.68 for state. Motivated by these results, we propose a sub-spectrogram GRU variant; with B=8, it yields the best trade-off (mean F1 = 0.800; file-F1: engine = 1.00, state = 0.74). Removing augmentation substantially degrades state recognition (file-F1 0.74→0.47). On a Tesla T4 GPU, end-to-end inference over a complete file under the adopted windowing required 83–113 s with memory usage < 225 MB, supporting batch or near-online monitoring rather than strict real-time deployment.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Non-Linear Model Predictive Control of Vessel Energy Systems", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Löffler, Charlotte", "affiliation": "Delft University of Technology" }, { "name": "Kopka, Timon", "affiliation": "Delft University of Technology" }, { "name": "Geertsma, Rinze", "affiliation": "Delft University of Technology" }, { "name": "Polinder, Henk", "affiliation": "Delft Univ. of Technology" }, { "name": "Coraddu, Andrea", "affiliation": "Delft University of Technology" } ], "keywords": [ "Power and propulsion in marine systems", "Modelling, identification and control in marine systems", "Marine renewable energy systems" ], "abstract": "Ship electrification is a major enabler for zero-emission shipping and the use of alternative fuels and power sources. However, they contribute to higher complexity of energy systems, which leads to suboptimal operation for conventional rule-based control. Alternatively, advanced control can take the available knowledge about the vessel and its operation into account. This paper presents a nonlinear multi-objective Model Predictive Control approach for a hybrid-electric vessel energy system to enhance energy efficiency. In a simulation study, the controller shows the potential to reduce fuel consumption by 2.5 %.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Railway Infrastructure Monitoring: From Diagnosis to Prescriptive Maintenance Bottlenecks", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Bounouh, Aziz", "affiliation": "IMS" }, { "name": "Melchior, Pierre", "affiliation": "Université De Bordeaux - Bordeaux INP/ENSEIRB-MATMECA" }, { "name": "Chevrié, Mathieu", "affiliation": "IMS Laboratory" }, { "name": "Airimitoaie, Tudor-Bogdan", "affiliation": "Univ. Bordeaux" } ], "keywords": [ "Rail transportation modelling and control systems", "Planning, management and security in transportation", "Modeling and simulation of transportation systems" ], "abstract": "This paper provides a control-engineering reading of railway infrastructure monitoring, formally stating the underlying maintenance problem as a partially observed sequential decision problem and reviewing, through this lens, the available observables, the methodological pipelines, and the bottlenecks that prevent closing the loop in practice. While modern sensors achieve sufficient observability, the integration of heterogeneous data into a closed prescriptive loop remains fragmented. We identify three structural challenges: multi-scale temporal fusion, the performance-explainability trade-off, and the lack of longitudinal benchmarks for sequential decision-making. On this basis, we outline a roadmap toward hybrid supervision systems combining physics-based estimators, probabilistic prognosis and constrained decision policies.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Velocity Tracking for Autonomous Railway-Based Urbanloop Pods by Contraction", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Wang, Weihao", "affiliation": "Université De Lorraine" }, { "name": "Kreiss, Jérémie", "affiliation": "Université De Lorraine" }, { "name": "Lorenzetti, Pietro", "affiliation": "CRAN, CNRS, Université De Lorraine" }, { "name": "Licitra, Letizia", "affiliation": "Urbanloop SAS" }, { "name": "Lefebvre, Gaëtan", "affiliation": "Alstom" }, { "name": "Postoyan, Romain", "affiliation": "CRAN, CNRS, Université De Lorraine" } ], "keywords": [ "Rail transportation modelling and control systems", "Trajectory tracking and path following for AVs", "Autonomous vehicles" ], "abstract": "We present a model-based methodology to synthesize velocity controllers for individual Urbanloop pods, which are autonomous railway-based vehicles. They are designed for energy-efficient, low-cost, rapid, and seamless urban transport. First, we derive a physics-based pod dynamical model and rigorously reveal that it exhibits two time scales. We then leverage singular perturbation methods combined with recent contraction theory tools to design the controller, guaranteeing that the pod velocity tracks the given reference velocity profile. This controller combines a contractive output-feedback component with a reference-inducing feedforward term. We prove that the trajectories of the original, full-order model exponentially converge to the reference trajectory up to an error proportional to the time-scale separation parameter. Finally, numerical simulations illustrate the relevance of the approach.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Safety Control of Self-Organized Swarm Coordination under Obstacles and Adversaries", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Li, Jiacheng", "affiliation": "University of Macau" }, { "name": "Zhiyuan, Zhang", "affiliation": "The Department of Electromechanical Engineering, University of Macau" }, { "name": "Liu, Jason J. R.", "affiliation": "University of Macau" }, { "name": "Kishida, Masako", "affiliation": "University of Tsukuba" } ], "keywords": [ "Resilient networked control systems", "Cyber security networked control", "Consensus" ], "abstract": "This paper addresses the safety control problem of a self-organized swarm in environments with obstacles and adversaries. To mitigate adversarial impacts, a reputation mechanism is introduced for both leaderless and virtual-leader scenarios to quantify mutual trust among agents. This mechanism integrates local behavioral assessments with neighbors' reputations, allowing agents with low reputations to be regarded as potentially malicious. Such malicious agents are then isolated through communication weight adjustments at the cyber layer and repulsive potential fields at the physical layer. The distributed safety control laws are designed to ensure self-organizing characteristics and collision-free maneuvers. Simulation results demonstrate that the proposed approach effectively preserves self-organized swarm behavior and guarantees safety despite the coexistence of obstacles and adversaries.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Planetary Terrain Datasets and Benchmarks for Rover Path Planning", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Chancán, Marvin", "affiliation": "Luleå University of Technology" }, { "name": "Banerjee, Avijit", "affiliation": "Luleå University of Technology" }, { "name": "Nikolakopoulos, George", "affiliation": "Luleå University of Technology" } ], "keywords": [ "Space exploration and transportation" ], "abstract": "Planetary rover exploration is attracting renewed interest with several upcoming space missions to the Moon and Mars. However, a substantial amount of data from prior missions remain underutilized for path planning and autonomous navigation research. As a result, there is a lack of space mission-based planetary datasets, standardized benchmarks, and evaluation protocols. In this paper, we take a step towards coordinating these three research directions in the context of planetary rover path planning. We propose two large planetary datasets, MarsPlanBench and MoonPlanBench, derived from high-resolution digital terrain images of Mars and the Moon. In addition, we set up classic and learned path planning algorithms, in a unified framework, and evaluate them on our proposed datasets using a popular path planning benchmark. Through comprehensive experiments, we report new insights on the performance of representative planning algorithms on planetary terrains, for the first time to the best of our knowledge. Our results show that classic methods can achieve up to 100% global path planning success rates on average across challenging terrains such as Moon's north and south poles. Conversely, learning-based models, although showing promising results in less complex environments, still struggle to generalize to planetary domains. Code and datasets available at: https://github.com/mchancan/PlanetaryPathBench.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Leveraging Resonant Orbits with Venus for Low-Energy Multiple Asteroid Flyby Missions", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zubko, Vladislav", "affiliation": "Space Research Institute of the Russian Academy of Sciences" }, { "name": "Chernenko, Olga", "affiliation": "Space Research Institute (IKI) of the Russian Academy of Sciences (RAS)" }, { "name": "Pupkov, Maxim", "affiliation": "Space Research Institute (IKI) of the Russian Academy of Sciences (RAS)" } ], "keywords": [ "Space exploration and transportation", "Aerospace mission control and operations", "Guidance, navigation and control of aircraft and spacecraft" ], "abstract": "This paper presents an optimization-based framework for designing multiple asteroid flyby missions in the inner Solar System. The core of the methodology leverages Venus gravity assists to place the spacecraft on controlled resonant orbits, enabling the construction of complex flyby sequences. We formulate the trajectory design as a two-stage optimization problem: first, a geometric pre-selection identifies candidate asteroids based on resonant orbit manifolds; second, a global-local optimization technique minimizes the total velocity increment (Delta v) while satisfying constraints on gravity-assist turn angles and launch energy. Numerical results demonstrate the method’s efficacy, generating fuel-efficient tours from a 2029 launch that include up to seven asteroid flybys with a launch Delta v under 3.6 km/s. The proposed approach demonstrates that resonant flyby sequences are highly competitive with direct transfers, often reducing propellant require", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Safe and Efficient Optimization-Based Trajectory Planning Using Conformal Prediction", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Dimou, Emmanouil", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Börve, Erik", "affiliation": "Chalmers University of Technology" }, { "name": "Kanellopoulos, Aris", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Murgovski, Nikolce", "affiliation": "Chalmers University of Technology" } ], "keywords": [ "Trajectory and path planning for AVs", "Autonomous vehicles" ], "abstract": "The problem of trajectory planning in stochastic, dynamic environments is inves tigated, with an emphasis on formulating efficient collision avoidance constraints. Black-box predictors provide an estimate of the stochastic obstacles’ state and the uncertainty of this estimate is quantified off-line via the statistical tool of Conformal Prediction. The resulting quantification is combined with elements of convex geometry, leading to the construction of the unsafe sets, regions which the obstacles, admitting polytopic representations, may occupy. The unsafe sets preserve the properties of compactness and convexity. Thus the safety constraints involving them and an agent with polytopic representation, may be efficiently formulated utilizing the Hyperplane Separation Theorem. The proposed optimization-based trajectory planning algorithm provides probabilistic collision avoidance and recursive feasibility guarantees, over finite time horizon, via progressive tightening of the unsafe sets. Its efficacy is demonstrated in the context of autonomous parking scenarios.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Trajectory Planning for Non-Communicating Mobile Robots Using Inverse Optimal Control", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Majer, Nina", "affiliation": "FZI Research Center for Information Technology" }, { "name": "Epple, Yannick", "affiliation": "Karlsruher Institut Für Technologie (KIT), FZI Forschungszentrum Informatik" }, { "name": "Ye, Xin", "affiliation": "FZI Research Center for Information Technology" }, { "name": "Schwab, Stefan", "affiliation": "FZI - Research Center for Information Technology" }, { "name": "Hohmann, Soeren", "affiliation": "KIT" } ], "keywords": [ "Trajectory and path planning for AVs", "Autonomous vehicles", "Cooperative navigation" ], "abstract": "To enable an efficient interaction of non-communicating mobile robots in collision avoidance scenarios, we present a novel combined trajectory planning and prediction algorithm. Inverse optimal control is used to estimate unknown goal states of all robots based on observed past trajectories. Each robot also takes the perspective of other robots in considering self-prediction and solves a joint prediction problem using the estimated goal states. The resulting predictions are then considered for planning. Simulation results of scenarios with 2-8 robots show that the median of the durations until all vehicles reach their goals is 9.8 % faster compared to planning with constant acceleration based estimated goal states. Moreover, the proposed approach never leads to the solver being unable to find a solution to the planning or prediction problem.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "AUG: A Closed-Form Adaptive Understeer Gradient Lateral Controller for Autonomous Racing", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Chang, Seokyung", "affiliation": "Hanyang University" }, { "name": "Jo, Kichun", "affiliation": "Hanyang University" } ], "keywords": [ "Trajectory tracking and path following for AVs", "Guidance, navigation and control for AVs", "Autonomous vehicles" ], "abstract": "Autonomous racing provides a valuable testbed for evaluating controllers in high-speed, traction-limit conditions. On scaled platforms, however, limited sensing and computation restrict the use of Model Predictive Control, motivating lightweight controllers that still capture nonlinear tire effects. This paper proposes the Adaptive Understeer Gradient (AUG) controller, a closed-form steering law that converts L1 guidance-based desired lateral acceleration into steering command while adaptively reflecting tire nonlinearity. It requires only a few parameters, no lookup tables, and can be tuned in real-time. Experiments in simulation and real-world F1TENTH racing show that AUG significantly reduces cross-track error and lap time compared to Pure Pursuit, while requiring far less tuning effort than existing dynamics-aware controllers. The code is available at: https://github.com/skcworld/controller.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "The Path Following Evaluation Metric IAX: A Toolbox for Fair Comparison across Controllers, Craft and Conditions", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Tufte, Andreas Gudahl", "affiliation": "NTNU" }, { "name": "Rambech, Alexander Brevad", "affiliation": "Oslo Metropolitan University" } ], "keywords": [ "Trajectory tracking and path following for AVs", "Guidance, navigation and control for AVs", "Marine system guidance, navigation and control" ], "abstract": "Path following should be evaluated along the path, not in time. We present a metric for comparison of path following using the line integral of the absolute value of the cross-track error along the desired track. The metric, which we term IAX, and its variants, ensure fair comparison regardless of the speed of progression along the path. We demonstrate in two cases that IAX is beneficial over the integral of absolute error (IAE) for such scenarios, and also provides a spatial interpretation in the plot. A toolbox is provided for ease of calculation of the proposed metric.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Multi-Dock Unit-Load Warehouse Design: A Systematic Survey", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Biswas, Sanchita", "affiliation": "S.P. Jain Institute of Management & Research (SPJIMR)" }, { "name": "Rao, Subir", "affiliation": "SPJIMR" } ], "keywords": [ "Transportation logistics" ], "abstract": "This systematic survey reviews the design and operational efficiency of unit-load warehouses utilizing multiple pickup and deposit (P/D) points. We analyze the evolution of facility layouts from traditional parallel aisles to non-traditional configurations, including Fishbone and Flying-V designs, specifically within multi-dock environments. The study categorizes literature based on storage policies, command cycles, and dock arrangements to evaluate their collective impact on travel distance. By synthesizing findings on optimal dock placement, this paper identifies critical research gaps and provides design guidelines for maximizing performance in modern logistics facilities.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Intrusive Uncertainty Quantification for Control Systems with Timing Effects and Parametric Uncertainties", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Vandamme, Antoine", "affiliation": "Robert Bosch GmbH" }, { "name": "Gallant, Melanie", "affiliation": "Robert Bosch GmbH" }, { "name": "Mark, Christoph", "affiliation": "Robert Bosch GmbH" }, { "name": "von Keler, Johannes", "affiliation": "Robert Bosch GmbH" }, { "name": "Beermann, Laura", "affiliation": "Robert Bosch GmbH" }, { "name": "Schmidt, Kevin", "affiliation": "Robert Bosch GmbH" } ], "keywords": [ "Uncertain systems", "Linear parameter-varying systems", "Linear time-delay systems" ], "abstract": "Modern control design for dynamical systems must account for system uncertainties, including both static and dynamic ones. The primary challenge is to develop computationally efficient methods that can reliably capture the resulting stochastic system behavior. This paper proposes a novel and efficient uncertainty quantification method to represent a stochastic dynamical system through its mean and covariance trajectories. The approach models dynamic disturbances as a Gaussian Process, which is then reformulated as a Stochastic Differential Equation (SDE) to avoid the high computational cost of traditional Karhunen-Loève expansions. By combining this SDE representation with a surrogate model based on intrusive polynomial chaos expansion, we can analytically derive the mean and covariance dynamics for the system. This allows for a fast and accurate propagation of both static (parametric and timing) and dynamic uncertainties through the system model, making it suitable for advanced control design and online applications like model predictive control. The approach is illustrated by an application from longitudinal vehicle motion control.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Polynomial Chaos Approximation for Worst-Case Transient Performance of Linear Systems", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Izquierdo Serra, Mario", "affiliation": "Airbus Defence and Space GmbH" }, { "name": "Martin, Maurice", "affiliation": "Airbus Defence and Space GmbH" }, { "name": "Delchambre, Simon", "affiliation": "Airbus Defence and Space GmbH" }, { "name": "Winkler, Stefan", "affiliation": "Airbus DS" }, { "name": "Pfifer, Harald", "affiliation": "Technische Universität Dresden" } ], "keywords": [ "Uncertain systems", "Probabilistic robustness" ], "abstract": "The goal of this paper is to approximate the worst-case transient performance of uncertain linear time-invariant systems, subject to both L2-bounded input signals and known disturbances, e.g., reference tracking commands. System uncertainties are described through real-valued random variables with a known probability distribution. The worst-case performance analysis is formulated as a parametric Riccati differential equation, which is approximately solved using polynomial chaos expansion. The objective is to estimate a bound on the Euclidean norm of the system output at a given time. The effectiveness of the approach is demonstrated on the example of a spacecraft attitude and orbit control system.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Safety-Oriented Control Parameter Optimization for Nonlinear Systems Via ESO-Based Reachability Analysis", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhou, Yu", "affiliation": "National University of Defense Technology" }, { "name": "Li, Jie", "affiliation": "National University of Defense Technology" }, { "name": "Xiong, Zehao", "affiliation": "National University of Defense Technology" }, { "name": "Wang, Xiangke", "affiliation": "National University of Defense Technology" } ], "keywords": [ "Human machine safety", "Mechatronic system modeling, design, optimization", "Mechatronic system estimation, identification, control" ], "abstract": "For the safe control of nonlinear systems with model uncertainties, this paper proposes a reachability analysis and parameter optimization method based on an extended state observer (ESO) and zonotopes. The ESO and feedback control reshape the system dynamics, simplifying reachable set computation by treating the estimation error as a bounded uncertainty. The method reveals how ESO and controller bandwidths affect the safety boundary, enabling a safety-oriented parameter optimization strategy that systematically selects parameters to keep the reachable set away from unsafe regions. Thereby, safety assurance is shifted from post-hoc verification to proactive design. Simulation results validate the effectiveness of the proposed framework.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Online Trust Profiling and Adaptation for Human-Autonomy Interaction", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Williams, Daniel A.", "affiliation": "The University of Melbourne" }, { "name": "van Bockel, Joshua", "affiliation": "The University of Melbourne" }, { "name": "Chapman, Airlie Jane", "affiliation": "University of Melbourne" }, { "name": "Little, Daniel R.", "affiliation": "The University of Melbourne" }, { "name": "Manzie, Chris", "affiliation": "The University of Melbourne" } ], "keywords": [ "Human machine teaming", "Human machine cooperation & integration", "Cognitive processes and human machine systems" ], "abstract": "In human-autonomy interactions, the human supervisor's trust level is a critical factor in determining the quality of interaction. An observer subsystem can allow the autonomous system to estimate supervisor trust and react accordingly. Previously, a switched linear model was shown to capture key trust dynamics. A challenge for model identification is that continually polling a human's trust levels is impractical. To address this, an observer structure that uses intermittent human feedback is proposed. The observer is validated in a real-world scenario through a series of human trials; these trials show consequent benefits for task performance.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "SEMG-Based Low-Latency Finger Classification and Voltage-Domain Flexion-Trajectory Estimation for Finger Motion Reproduction", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Won, Jiwoong", "affiliation": "Tokyo Denki University" }, { "name": "Iwata, Takaaki", "affiliation": "Tokyo Denki University" }, { "name": "Iwase, Masami", "affiliation": "Tokyo Denki University" } ], "keywords": [ "Human mechatronics and human-machine interaction", "Teleoperation", "Human-robot interaction" ], "abstract": "This study validates an sEMG-based computational pipeline for finger classification and voltage-domain finger-flexion trajectory estimation toward prosthetic-hand control. To enable low-latency software-side processing, the framework integrates lightweight TD feature extraction, a two-stage SVM classifier, and finger-specific MISO-NARX models. Experiments showed that the top twenty configurations all exceeded 90% E2E classification accuracy, with the best configuration reaching 91.28%. The optimized NARX models showed strong agreement with the measured voltage-domain finger-flexion trajectories (R 2 = 0.907-0.975). The measured software-side E2E processing delay from sEMG input to estimated trajectory output was approximately 40 ms; however, motor control, motor actuation, mechanical response, and physical prosthetic-hand motion were not included in this measurement. These results show that the proposed pipeline can perform finger classification and voltage-domain flexion-trajectory estimation accurately and rapidly under controlled experimental conditions, suggesting its potential as a signal-processing basis for future real-time prosthetic-hand control.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Wrist Angle Estimation Based on sEMG and Skin Deformation", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Tojo, Shun", "affiliation": "Tokyo Denki University" }, { "name": "Won, Jiwoong", "affiliation": "Tokyo Denki University" }, { "name": "Iwata, Takaaki", "affiliation": "Tokyo Denki University" }, { "name": "Iwase, Masami", "affiliation": "Tokyo Denki University" } ], "keywords": [ "Human-robot interaction", "Mechatronic system estimation, identification, control", "Biomedical and biomimetic mechatronic systems" ], "abstract": "The purpose of this study is to improve the accuracy of joint-angle estimation during wrist-angle holding motions in robotic hands using a nonlinear autoregressive model with exogenous inputs (NARX). Although sEMG provides informative signals during the initiation of wrist flexion, its amplitude typically attenuates during sustained holds, causing NARX-based angle estimates to drift toward the neutral position. To address this limitation, forearm skin deformation measured by pressure sensors is incorporated as force myography (FMG) and fused with sEMG as inputs to the NARX model. The proposed sEMG-FMG integration reduces fluctuations in the estimated angle during holding motions and enables accurate representation of wrist posture throughout both flexion and hold phases of motion. The effectiveness of the proposed model is experimentally evaluated by comparing wrist-angle estimates obtained using sEMG-only, FMG-only, and sEMG+FMG inputs. In future work, this approach aims to support a two-degree-of-freedom servo system incorporating Electro-Mechanical Delay (EMD) and Zero- Phase Error Tracking Control (ZPETC), followed by evaluation on a robotic hand.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Experimental Validation of an Approximate Analytical Predictor for the Torque-Actuated Spring-Mass Hopper", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ozturk, Ahmet Safa", "affiliation": "Bilkent University" }, { "name": "Uyanik, Ismail", "affiliation": "Hacettepe University" }, { "name": "Morgul, Omer", "affiliation": "Bilkent Univ" } ], "keywords": [ "Humanoid and legged robots", "Mechatronic system modeling, design, optimization", "Biomedical and biomimetic mechatronic systems" ], "abstract": "This paper presents the experimental validation of an approximate analytical predictor for a torque-actuated, dissipative spring-mass hopper. While the spring-mass template effectively models running dynamics, its non-integrable stance phase necessitates approximations for real-time control. We investigate the predictive accuracy of an Approximate Analytical Solution (AAS) that accounts for leg damping, air drag, and active hip torque, using a comprehensive multi-stride dataset collected from a custom monopedal robot. Our comparative analysis demonstrates that the AAS accurately predicts the system's coupled dynamics with high fidelity, closely matching numerical integration results while offering significantly greater computational efficiency. These findings validate the utility of torque-actuated analytical models for developing robust, model-based controllers for physical legged platforms.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Investigating Sensitivity of Initial Conditions in Robotic Systems Using a Multibody Dynamics Framework", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Abokhalil, Heba", "affiliation": "E-JUST" }, { "name": "Nada, Ayman Ali", "affiliation": "Egypt-Japan University of Science and Technology" } ], "keywords": [ "Humanoid and legged robots", "Medical and rehabilitation robotics", "Mechatronic system modeling, design, optimization" ], "abstract": "This paper presents a computational framework for analyzing the sensitivity of multibody system dynamics with respect to initial conditions, with direct applications to rehabilitation robotics and biomechanical systems. The methodology is based on a variational approach that augments the state-space formulation with sensitivity equations, enabling the evaluation of how small perturbations in initial positions and velocities influence system trajectories. A pendulum-like planar subsystem, extracted from a lower-limb exoskeleton model, is used as a case study to demonstrate the framework's effectiveness. The system is reduced via coordinate partitioning, and the dynamics are integrated alongside sensitivity matrices using a modular set of MATLAB routines. Numerical simulations under different initial configurations reveal distinct sensitivity behaviors, highlighting regions of dynamic stability versus heightened reactivity. The results provide valuable insight into the role of initialization in multibody system design and control strategies. This framework can be extended using adjoint sensitivity formulations, quantitative metrics, and uncertainty quantification for high-dimensional, real-time applications.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Induction Machines for Precision Positioning: Part I - Parameter Estimation for Torque Bound Construction", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhao, Qianhong", "affiliation": "University of Virginia" }, { "name": "Wang, Yebin", "affiliation": "Mitsubishi Electric Research Laboratories" }, { "name": "Fujita, Tomoya", "affiliation": "Mitsubishi Electric Corp" }, { "name": "Sato, Go", "affiliation": "Mitsubishi Electric Corporation" } ], "keywords": [ "Mechatronic system estimation, identification, control" ], "abstract": "This paper investigates parameter estimation of an induction machine (IM) for torque bound construction when the IM serves as the actuator in a precision positioning system. The problem is significant because accurate knowledge of torque bound is essential for trajectory planning and control in precision positioning systems. The parameter estimation problem differs from the well-studied speed-sensorless estimation problem along two dimensions: speed measurement is available and all parameters in the IM model are treated as unknown. To this end, we first determine the subset of parameters required to construct torque bound, thereby avoid estimating all parameters. Then a flux-free representation of the IM model is derived to facilitate parameter estimation based on voltages, currents, and speed measurements. With the flux-free model established, a dynamic regressor extension and mixing based adaptive law is employed to ensure convergent estimation of the subset of parameters, under a less restrictive persistent excitation condition. Simulation validates the effectiveness of the proposed scheme.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Adaptive RLUDE Disturbance-Rejection Control for Quadrotors", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Chen, Xin", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Wei, Wei", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Wang, Chen", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Huang, Hehong", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Song, Yanhe", "affiliation": "Yanshan University" }, { "name": "Guo, Qing", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Peng, Chen", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Zhang, Xinyu", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Xie, Siyu", "affiliation": "University of Electronic Science and Technology of China" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Adaptive and adaptable automation", "High-performance motion control systems" ], "abstract": "Quadrotor control is inherently challenged by strong nonlinearities, attitude–position coupling, parameter variations, external disturbances, and sensing limitations, which collectively degrade tracking performance. To address these challenges, this paper presents an adaptive disturbance-rejection framework based on reinforcement learning and uncertainty disturbance estimation (RLUDE). In this framework, a finite-time-convergent (FTC) estimator is employed to obtain the reference derivatives and unmeasurable states. In parallel, reinforcement learning adaptively adjusts the UDE parameters to improve the estimation and compensation of lumped uncertainties. Building upon the FTC estimator and the RLUDE scheme, the controller is developed with an error-coupled policy update mechanism, which can enhance transient performance and ensure steady-state accuracy. Furthermore, Lyapunov analysis establishes conditions for zero steady-state error and guarantees ultimately bounded tracking performance. Consequently, simulation and experimental results show that the proposed method effectively reduces transient overshoot and steady-state error under disturbances and parameter uncertainties, thereby improving the trajectory-tracking accuracy and robustness of quadrotor unmanned aerial vehicles (UAVs).", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Finite-Time Control Based on Differential Flatness for Wheeled Mobile Robots with Experimental Validation", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Imtiaz Ur, Rehman", "affiliation": "Laboratoire ImViA EA 7535, équipe VIBOT" }, { "name": "Labbadi, Moussa", "affiliation": "Bretagne INP" }, { "name": "Abadi, Amine", "affiliation": "Laboratoire ImViA EA 7535, équipe VIBOT" }, { "name": "Lew Yan Voon, Lew Fock Chong", "affiliation": "Laboratoire ImViA EA 7535, équipe VIBOT" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Aerial, field, and marine robotics" ], "abstract": "A robust tracking control strategy is designed to empower wheeled mobile robots (WMRs) to track predetermined routes while operating in diverse fields and encountering disturbances like strong winds or uneven path conditions, which affect tracking performance. Ensuring the applicability of this tracking method in real-world scenarios is essential. To accomplish this, the WMR model is initially transformed into a linear canonical form by leveraging the differential flatness of its kinematic model, facilitating controller design. Subsequently, a novel integral nonlinear hyperplane-based sliding mode control (INH-SMC) technique is proposed for WMR under disturbances. The stability of the technique is analyzed and verified. Finally, its practical viability is demonstrated through a comparative real-world indoor experiment on a TurtleBot3 WMR subjected to disturbances, confirming the feasibility and efficacy of the proposed approach.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Extended State Observer–Based Control for a Ball-Balancing Platform with Base Variations", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Chen, Chih-Chia", "affiliation": "National Cheng Kung University" }, { "name": "Sung, Hsin-Yu", "affiliation": "National Cheng Kung University" }, { "name": "Peng, Chao-Chung", "affiliation": "Department of Aeronautics and Astronautics, National Chen Kung University, Tainan 701, Taiwan" } ], "keywords": [ "Mechatronic system estimation, identification, control", "High-performance motion control systems", "Aerial, field, and marine robotics" ], "abstract": "This paper investigates the modeling, disturbance estimation, and control of a ball–balancing mechanism platform operating on a moving base. Such systems arise in maritime, mobile-robotic, and field-deployment scenarios where continuous base oscillations degrade positioning accuracy and destabilize conventional controllers, making robust state estimation and compensation essential. To address the relevant issues, the nonlinear dynamics of the ball–plate system are first derived using the Lagrange formulation, explicitly accounting for inertial effects induced by the base motion. To enable real-time implementation, an inverse-kinematics mapping is developed to convert the desired platform pose into actuator commands while incorporating base pose variations. Based on a linearized model, a proportional–derivative (PD) controller augmented with an extended state observer (ESO) is designed to estimate both system states and lumped disturbances. Simulation studies on the full nonlinear model demonstrate that under quantization noise and identical PD control gains, the proposed ESO achieves more accurate disturbance reconstruction and improves trajectory-tracking performance compared with a differentiation-based estimator. These results highlight the effectiveness of ESO-enhanced control for precision balancing tasks conducted in oscillatory environments.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Feedforward Control with Dual Neural Networks under Partial Load-Side Measurement", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Okumura, Shinji", "affiliation": "Mitsubishi Electric" }, { "name": "Li, Na (Lina)", "affiliation": "SEAS Harvard" }, { "name": "Ikeda, Hidetoshi", "affiliation": "Mitsubishi Electric" }, { "name": "Sekiguchi, Hiroyuki", "affiliation": "Mitsubishi Electric" }, { "name": "Wang, Yebin", "affiliation": "Mitsubishi Electric Research Laboratories" } ], "keywords": [ "Mechatronic system estimation, identification, control", "High-performance motion control systems", "Mechatronic system modeling, design, optimization" ], "abstract": "Modern motion control systems generally employ both feedforward and feedback controllers to perform high-speed, high-precision positioning tasks. Recently, neural networks (NNs) have been paired with a physics-based feedforward controller to regulate the motor-side position. This paper advances NN-based feedforward controller design in two aspects. We first extend the architecture to facilitate simultaneous regulation of both the motor-side position and load-side position by introducing two NNs, each trained offline to reproduce signals obtained from multivariable iterative learning control. We then show that this straightforward extension alone cannot guarantee satisfactory tracking performance when the load-side position is partially measurable. To address this limitation, a sample-efficient direct learning approach is proposed to fine-tune the NNs online by minimizing the tracking errors. Extensive simulations validate the effectiveness of the proposed method.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Adaptive Observer for Superconducting Cavity Bandwidth and Detuning Estimation", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Richter, Bozo", "affiliation": "Deutsches Elektronen Synchrotron DESY" }, { "name": "Speidel, Leon Hendrik", "affiliation": "TU Hambug" }, { "name": "Eichler, Annika", "affiliation": "DESY" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Mechatronic system modeling, design, optimization" ], "abstract": "This contribution presents an observer design for real-time estimation of time-varying parameters in superconducting RF cavities, targeting low-complexity FPGA implementation in high-bandwidth low-level RF control systems. Based on a linear time-varying state-space description with augmented states for detuning and excess half bandwidth, an adaptive observer is synthesized via a time-varying Lyapunov transformation to achieve time-invariant error dynamics using idealized model assumptions. The resulting time-varying observer is evaluated in a simulation of pulsed operation including measurement noise, and is compared to an existing observer implementation to assess estimation accuracy, robustness, and implementation effort.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Identification of a Robot Joint with Gear and Link Flexibility Using Dual Encoders", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zimmermann, Stefanie Antonia", "affiliation": "Linköping University" }, { "name": "Moberg, Stig", "affiliation": "ABB AB - Robotics" }, { "name": "Gunnarsson, Svante", "affiliation": "Linkoping University" }, { "name": "Norrlöf, Mikael", "affiliation": "ABB AB" }, { "name": "Enqvist, Martin", "affiliation": "Linköping University" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Mechatronics for robotic systems", "Mechatronic system modeling, design, optimization" ], "abstract": "Conventional models for robot manipulators assume rigid bodies and flexible joints. In this paper, a new joint model is presented which augments the conventional flexible joint model by lumped parameters on the arm side of the gearbox, accounting for flexibility and damping of bearings and links. A two-step method is used for identification of this model: First, the system’s frequency response function is estimated from measurements of the motor and gear angular position, as well as the motor torque. Second, the model parameters are found by optimization. The focus of this work is to separately identify gear and arm side stiffness. It is experimentally demonstrated that this is possible, using dual encoder measurements. Results of a simulation study as well as experimental results from a collaborative robot are presented.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "A Control Allocation Strategy for Tendon-Driven Arms Modeled Via the Augmented Rigid Body Approach", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Pineda Rivera, Rogelio", "affiliation": "CIMAT" }, { "name": "Espinosa Loera, Isaac Yael", "affiliation": "Centro De Investigación En Matemáticas CIMAT" }, { "name": "Flores, Gerardo", "affiliation": "Texas A&M International University" }, { "name": "Becerra, Hector M.", "affiliation": "Centro De Investigación En Matemáticas (CIMAT)" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Mechatronics for robotic systems", "Soft robotics" ], "abstract": "This paper presents an integrated control framework for motor-driven, tendon-actuated continuum arms, building upon established modeling approaches based on the piecewise constant curvature (PCC) assumption and the augmented rigid body model (ARBM). The main contribution of the paper is a control allocation strategy that consistently maps curvature-level control efforts into physically realizable tendon tensions and motor torques, ensuring non-negativity and energetic consistency. The proposed allocation scheme enables the direct use of curvature-based controllers while explicitly accounting for the structure of tendon-driven actuation. By integrating curvature-space control, tendon force allocation, and motor–tendon dynamics within a unified framework, this work extends existing PCC–ARBM formulations to electrically actuated tendon-driven continuum arms.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Prior Knowledge Matching for Aircraft Equipment Fastener Assembly Defect Detection", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhang, Yuanhao", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Yin, Chun", "affiliation": "University of ElectronicScience and Technology of China, Chengdu611731, P.R. China" }, { "name": "Liu, Junyang", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Yan, Zhongbao", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Cao, Jiuwen", "affiliation": "Hangzhou Dianzi University" } ], "keywords": [ "Mechatronic system fault detection, diagnostics, hardware-in-the-loop simulation", "Adaptive and adaptable automation", "Decision support systems" ], "abstract": "Fastener assembly errors critically impact aviation manufacturing quality and safety, yet existing deep learning methods face challenges in compliance verification under variable assembly standards. We propose a collaborative detection framework integrating deep learning with deformable template matching. An improved YOLO11-AEDSF performs feature perception, followed by a deformable matching algorithm that encodes standards as a priori constraints to align with the perceptual results. The model is lightweighted via sparse pruning and knowledge distillation, reducing GFLOPs from 6.3 to 2.8 to meet real-time demands. On a custom dataset, the framework achieves 97.6% mAP@0.5, a 6.42-point improvement over the 91.18% baseline, enabling fastener defect detection under diverse assembly standards.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Perspectives on Reliability-Aware Force Control for Contact-Rich Robotics", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Kato, Takahiro", "affiliation": "The University of Tokyo" }, { "name": "Khan, Samir", "affiliation": "The University of Tokyo" }, { "name": "Takeishi, Naoya", "affiliation": "Haute école Specialisée De Suisse Occidentale" }, { "name": "Yairi, Takehisa", "affiliation": "Department of Aeronautics and Astronautics, the University of Tokyo" } ], "keywords": [ "Mechatronic system fault detection, diagnostics, hardware-in-the-loop simulation", "Human machine safety", "Human-robot interaction" ], "abstract": "This survey develops Reliability-Aware Force Control as an integrative framework for contact-rich robotics, addressing the gap between methodological maturity and operational trustworthiness. Three interrelated challenges are treated jointly: sensorless force estimation in friction-dominated regimes, fault-tolerant control that disambiguates contact from component failures, and formal safety guarantees via control barrier functions. Central to the analysis is the zero-velocity observability barrier, where static friction renders external forces structurally unobservable; emerging responses (dynamic friction models, active excitation, learning-augmented observers) are reviewed against this limit. Fault-detection methods are examined for their ability to discriminate intentional contact from sensor and actuator faults, and passivity-based stability and robust control barrier functions are assessed as mechanisms for formal safety certificates under estimation uncertainty. Case studies from human-robot collaboration, surgical robotics, and autonomous space servicing ground the developments in operational requirements. Identified research gaps include thermally-adaptive friction compensation, co-design of learned observers with verifiable safety, and resolution of the static observability barrier, together forming a roadmap for transitioning force control from laboratory demonstrations to safety-critical autonomy.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Model-Based Estimation of Battery SOC and Capacity in Robotic Systems with Experimental Validation", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Hellani, Hassanein", "affiliation": "Aix-Marseille Univ, CNRS, LIS" }, { "name": "Ribeiro, Warley F. R.", "affiliation": "Aix-Marseille Universite" }, { "name": "Azari, Hamidreza", "affiliation": "Aix-Marseille Univ" }, { "name": "Chauchat, Paul", "affiliation": "Aix-Marseille Université" }, { "name": "Graton, Guillaume", "affiliation": "Ecole Centrale De Marseille" } ], "keywords": [ "Mechatronic system fault detection, diagnostics, hardware-in-the-loop simulation", "Mechatronic system modeling, design, optimization", "Mechatronics for robotic systems" ], "abstract": "This paper presents a model-based approach for the joint estimation of the state of charge (SOC) and capacity of a lithium-ion battery integrated within a robotic power system. Unlike most SOC estimation approaches that rely on directly measured battery current, the proposed method reconstructs the battery current from the motor model and robot dynamics, enabling SOC and capacity estimation. The proposed method is implemented within a complete robotic framework simulation and validated using real robot data. The results demonstrate high accuracy and stability of the estimation under dynamic load conditions, confirming the effectiveness of the proposed method for embedded battery management in robotic applications.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Modeling and Optimization of a Contactless Air-Based Wafer Actuator for Enhanced Flatness and Precision", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Kakolyris, Giorgos", "affiliation": "Delft University of Technology" }, { "name": "van Ostayen, Ron", "affiliation": "Delft Universtiy of Technology" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "High-performance motion control systems", "Mechatronics for mobility systems" ], "abstract": "Thin wafers are essential elements in the high-tech industry. Currently, wafer handling is performed using contact pads, which can generate particles that may contaminate the chips, leading to a considerable yield loss. In addition, the increasing demand for energy efficiency drives the development of larger and thinner wafers. This increases wafer deformation and ultimately leads to breakage. To address both limitations, this work presents a systems-oriented approach to the design, modeling, and optimization of an air-based, contactless wafer actuator intended to improve handling precision while minimizing wafer deformation. Several design concepts are evaluated in terms of force generation and airflow consumption. The selected concept is then further refined using a coupled fluid–structure interaction and topology-optimization framework aimed at minimizing wafer deformation by tuning the airflow inlet configuration. The resulting actuator can accelerate a 100 mm silicon wafer at 2.3 g, requires 15.2 g/s of airflow, and limits wafer deformation to 15.2 μm.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Swing Amplitude Adjustment Method of an Extensible Single-Rod Brachiation Robot", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Osawa, Aoto", "affiliation": "Tokyo University of Agriculture and Technology" }, { "name": "Lieskovský, Juraj", "affiliation": "Czech Technical University in Prague" }, { "name": "Busek, Jaroslav", "affiliation": "Department of Instrumentation and Control Enginnering, Faculty of Mechanical Engineering, Czech Technical University in Prague" }, { "name": "Vyhlidal, Tomas", "affiliation": "Czech Technical Univ in Prague, Faculty of Mechanical Engineering" }, { "name": "Mizuuchi, Ikuo", "affiliation": "Tokyo University of Agriculture and Technology" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "Mechatronic system estimation, identification, control", "Aerial, field, and marine robotics" ], "abstract": "In this paper, we propose and parameterize a method for adjusting the swing amplitude during the excitation phase of an extensible single-rod brachiation robot for brachiation motion based on the next bar position. Using the proposed method, we achieved a brachiation behavior in which the 0.74 m long extensible robot brachiates from one bar to another which are at: i) the same height, ii) the other is 0.14 m higher than the former. This was achieved without an aerial phase in both cases as the bars were in a smaller distance than the robot length. This is followed by a brachiation experiment with an aerial phase, where the bar distance is 0.79 m.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "From Object-Oriented Simulation to Model Based MPC Design - an Automated Procedure", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Chevathamanon, Patarachai", "affiliation": "RPTU University of Kaiserslautern-Landau" }, { "name": "Liu, Steven", "affiliation": "University of Kaiserslautern Landau" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "Mechatronic system estimation, identification, control", "Mechatronic system fault detection, diagnostics, hardware-in-the-loop simulation" ], "abstract": "This paper presents an automated procedure for obtaining a linearized, state-space representation for MPC design directly from an object-oriented simulation model. The method integrates structural analysis, successive linearization, and causalization. A lightweight user interface is provided to configure MPC settings, enabling closed-loop online optimization in conjunction with the object-oriented simulation while requiring minimal user intervention. A water-boosting station case study demonstrates that the automatically obtained state-space model captures the dominant system dynamics and enables efficient, energy-aware flow control.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Performance Evaluation of Embedded MPC-QP Solvers on STM32-Based Real-Time Platforms", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Jin, Duyong", "affiliation": "Inha University" }, { "name": "Gwon, Minwoo", "affiliation": "Inha University" }, { "name": "Kim, Kwangki", "affiliation": "Inha University" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "Mechatronics for robotic systems", "Task and motion planning" ], "abstract": "Model Predictive Control (MPC) has traditionally been restricted to desktop-based control systems due to its computational complexity. Recent advances in semiconductor integration have made it feasible to implement MPC on single-chip microcontrollers. Despite this progress, systematic research and practical demonstrations of MPC on embedded hardware remain relatively scarce. This paper implements linear MPC using open-source Quadratic Programming (QP) and Second-Order Cone Programming (SOCP) solvers on an STM32 NUCLEO-F767ZI (Cortex-M7) microcontroller and assess their performance through Processor-in-the-Loop Simulations (PiLS). The results highlight the distinct characteristics of each solver and demonstrate their practical applicability to embedded MPC implementations.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Motor Cost Re-Optimization in Indirect Human Movement Pattern Adaptation", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Xu, Yangmengfei", "affiliation": "The University of Melbourne" }, { "name": "Crocher, Vincent", "affiliation": "The University of Melbourne" }, { "name": "Fong, Justin", "affiliation": "University of Melbourne" }, { "name": "Tan, Ying", "affiliation": "The Univ of Melbourne" }, { "name": "Oetomo, Denny Nurjanto", "affiliation": "The University of Melbourne" } ], "keywords": [ "Medical and rehabilitation robotics", "Human-robot interaction" ], "abstract": "Human movement resolves kinematic redundancy by organizing high-dimensional joint activity into low-dimensional coordination patterns, or synergies, which are plastic and can be reshaped for rehabilitation and skill training. While explicit error correction can reduce task errors, it may also induce slacking, limiting genuine learning. Indirect shaping control (ISC) was proposed to induce movement pattern change implicitly, without explicit reference trajectories. In a previous experiment, 20 participants performed reaching tasks while a robotic system applied a hand force that varied with the arm’s swivel angle, creating an energetic bias that altered their movement patterns. Although this setup induced adaptation under ISC, the underlying motor-cost mechanisms remained unquantified. In this work, we retrospectively analyzed the same dataset using a rigid-body inverse-dynamics model to estimate motor cost associated with swivel-angle change. Motor cost was quantified using the torque-time integral (TTI) and decomposed into natural and robot-induced components, linking cost variation to swivel angle and hand velocity. This study provides a quantitative description of implicit adaptation and insights for designing effective implicit interventions.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "Adaptive Bias Adjustment of Event Cameras for Pose Estimation", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Tao, Xingyu", "affiliation": "University of Glasgow" }, { "name": "Zhao, Dezong", "affiliation": "University of Glasgow" } ], "keywords": [ "Robot perception and sensing", "Adaptive and adaptable automation", "Robotic learning and adaptation" ], "abstract": "Object pose estimation is a key task in computer vision, whose goal is to accurately obtain a representation of the object pose in the real world. Unlike traditional frame-based cameras, event cameras offer high temporal resolution, low latency, and a high dynamic range, making them well-suited for capturing fast-moving objects and handling challenging lighting conditions. The accurate estimation of pose of objects using event cameras is highly influenced by the system's ability to adapt to changing environmental conditions, particularly variations in lighting. The Bias of event camera refers to a set of configuration parameters that control the sensitivity and behavior of the individual pixels in the sensor. Traditional methods with fixed bias settings often struggle to maintain precision in dynamic environments. To address this, an adaptive bias adjustment mechanism is proposed which dynamically responds to light intensity fluctuations, enhancing the reliability of pose estimation. This real-time adjustment ensures that the event camera can capture relevant data without being affected by external changes, leading to more stable and accurate tracking. The real-world experiment shows that the system achieves precise pose estimation in various lighting conditions, with errors under 5%.", "url": "" }, { "id": "Mo-MoC38", "code": "MoC38", "title": "HRNet Pose Estimation of Target AUVs", "day": "Monday", "date": "August 24, 2026", "time": "15:30-17:30", "sessionCode": "MoC38", "sessionTitle": "Poster Session Monday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Uth, Esben Thomsen", "affiliation": "Aalborg University" }, { "name": "Mai, Christian", "affiliation": "Aalborg University" }, { "name": "Liniger, Jesper", "affiliation": "Aalborg University" }, { "name": "Pedersen, Simon", "affiliation": "Aalborg University" } ], "keywords": [ "Robot perception and sensing", "Autonomous navigation", "Aerial, field, and marine robotics" ], "abstract": "This study presents a proof-of-concept framework for keypoint-based pose estimation of Autonomous Underwater Vehicles (AUVs) using deep learning, addressing the growing demand for reliable perception in underwater missions. A high-resolution architecture, HRNet-W32, originally developed for human pose estimation, is adapted to the underwater domain through a custom semantic keypoint model representing nine structural features of a survey-type AUV. Due to the absence of publicly available underwater keypoint datasets, a synthetic dataset of 1,400 images is generated using physically-based rendering in seven Jerlov water types, spanning clear oceanic to turbid coastal conditions. The dataset provides controlled variability in visibility, viewpoint, and illumination, enabling systematic evaluation of domain-transfer performance. The adapted HRNet model is fine-tuned on this dataset and evaluated using Object Keypoint Similarity (OKS), mean Average Precision (mAP), and pose-estimation accuracy derived from front–rear geometric cues. Results show strong keypoint detection performance with reliable pose estimation achievable in 64% of test images, despite substantial visibility degradation in high-turbidity water. The proposed synthetic-to-real pipeline and keypoint formulation provide a foundation for future onboard AUV perception and embedded real-time implementation.", "url": "" }, { "id": "Mo-MoNSP1.1", "code": "MoNSP1.1", "title": "Robust and Data-Efficient Inverse Reinforcement Learning: A Control-Theoretic Perspective", "day": "Monday", "date": "August 24, 2026", "time": "17:40-18:30", "sessionCode": "MoNSP1", "sessionTitle": "Robust and Data-Efficient Inverse Reinforcement Learning: A Control-Theoretic Perspective", "sessionType": "Semi-Plenary Session", "room": "Auditorium", "authors": [ { "name": "Xie, Lihua", "affiliation": "Nanyang Technological University" } ], "keywords": [ "Linear system identification" ], "abstract": "Inverse reinforcement learning (IRL) aims to infer an underlying reward function from expert demonstrations, thereby eliminating the need for manually crafted rewards required in conventional reinforcement learning (RL). From a control-theoretic perspective, IRL can be viewed as a modern formulation of the inverse optimal control problem originally posed by Kalman. Despite substantial progress in recent years, several fundamental challenges must be addressed before IRL can be reliably deployed in real-world systems. These include robustness to noisy or suboptimal demonstrations, efficient learning from limited or low-quality data, and the incorporation of safety constraints in practical implementations. In this talk, we present a control- and optimization-based framework to address these challenges. First, we introduce a differential dynamic programming (DDP)-based IRL approach for reward learning from expert demonstrations and develop a closed-loop loss formulation to improve robustness against noise in the demonstrations. Second, to enhance data efficiency, we propose online and model-free IRL algorithms that adaptively refine the reward function using real-time data. We further discuss the interplay among safety, reinforcement learning, and inverse optimality, and analyze the robustness properties of safe IRL-induced controllers. Finally, we demonstrate the application of IRL to active perception, where sensing, estimation, and control are tightly integrated through a hybrid MPC-IRL architecture.", "url": "" }, { "id": "Mo-MoNSP2.1", "code": "MoNSP2.1", "title": "Smarter Decisions for a Better World", "day": "Monday", "date": "August 24, 2026", "time": "17:40-18:30", "sessionCode": "MoNSP2", "sessionTitle": "Smarter Decisions for a Better World", "sessionType": "Semi-Plenary Session", "room": "Convention Hall - Room 205", "authors": [ { "name": "Albert, Laura", "affiliation": "University of Wisconsin-Madison" } ], "keywords": [ "Adaptive control design" ], "abstract": "Optimization, control, and systems engineering solve many of our world's most complex challenges, boosting the global economy. This talk explores the immense power and untapped potential of these tools to have a positive, visible impact on our world. Advancing industrial engineering and operations research through societally relevant applications has been the central theme of Dr. Laura Albert's academic career. In this talk, she will explore the boundless possibilities that industrial engineering and operations research offer as well as the latest trends shaping the future of the field. From emergency response and public safety to critical infrastructure protection and election resilience, she will share stories of how technical rigor translates into policy impact. Attendees will gain insight into identifying problems worthy of study, overcoming modeling challenges, creating data-driven modeling frameworks, and influencing policy. The 21st century presents a new frontier of \"wicked\" problems, ranging from global supply chain disruptions to the ethical integration of artificial intelligence into complex industrial systems. This community is uniquely positioned to tackle these challenges. This talk will explore how our tools can make a positive impact on the world and provide lasting benefits for human flourishing.", "url": "" }, { "id": "Tu-TuM00.1", "code": "TuM00.1", "title": "Nonlinear Optimal Control and Filtering Beyond the HJB Equation", "day": "Tuesday", "date": "August 25, 2026", "time": "08:30-09:30", "sessionCode": "TuM00", "sessionTitle": "Nonlinear Optimal Control and Filtering Beyond the HJB Equation", "sessionType": "Plenary Session", "room": "Auditorium", "authors": [ { "name": "Astolfi, Alessandro", "affiliation": "King Abdullah University of Science and Technology (KAUST)" } ], "keywords": [ "Optimal control theory" ], "abstract": "Optimal control and filtering problems have a rich history that can be traced back to Queen Dido’s solution to the isoperimetric problem in 814 BC, the solution to the brachistochrone problem by Johann Bernoulli in 1696, and the introduction of the least-squares method by Legendre and Gauss in the late 18th and early 19th centuries. Modern solutions exploit the calculus of variations and its control perspectives, Pontryagin’s Minimum Principle, Bellman’s Principle of Optimality, and the Wiener and Kalman filters. While for linear systems all roads lead to the Algebraic Riccati Equation and its dual, we demonstrate that for nonlinear systems one can go well beyond the HJB equation by judiciously exploiting properties of the stable invariant manifold, the associated invariant distribution, and adapted coordinates for the underlying Hamiltonian system. These, in turn, yield PDEs that differ dramatically from the HJB equation and have a deep geometrical meaning. Finally, we propose an ansatz for developing global solutions based on a fixed-point characterization of optimal feedback strategies and on a geometric relation between the tangent space to the stable manifold and the first order approximation of the Hamiltonian vector field away from its equilibrium.", "url": "" }, { "id": "Tu-TuA01.1", "code": "TuA01.1", "title": "Introduction to Noncooperative Games and Incentive Designs (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:20", "sessionCode": "TuA01", "sessionTitle": "Game-Theoretic Control Paradigms for Socio-Technical Networks", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Basar, Tamer", "affiliation": "Univ. of Illinois Urbana-Champaign" } ], "keywords": [ "Adaptive control design" ], "abstract": "This is the first presentation in the IFAC WC’26 tutorial session on “Game-Theoretic Control Paradigms for Socio-Technical Networks”, which will introduce the audience to the foundations of game-theoretic methods for control, including static and dynamic games, stochastic games, information structures, and equilibrium solution concepts, with an emphasis on their relevance to socio-technical networks. Two classes of equilibria will be elaborated on, namely Nash and Stackelberg, as well as a mix of the two, where the latter will lead to design of incentives (or “soft” inducement mechanisms) and control of outcome of a hierarchical decision-making process resulting from strategic interactions among multiple agents. In this context, an introduction to “Bayesian persuasion” will be provided, which captures a game-theoretic framework where an informed sender influences a receiver’s actions by designing an appropriate information structure under which a Bayesian receiver ends up with actions that benefit the sender.", "url": "" }, { "id": "Tu-TuA01.2", "code": "TuA01.2", "title": "Incentive Mechanism for Noncooperative Dynamical Systems (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:20-10:50", "sessionCode": "TuA01", "sessionTitle": "Game-Theoretic Control Paradigms for Socio-Technical Networks", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Hayakawa, Tomohisa", "affiliation": "Tokyo Institute of Technology" } ], "keywords": [ "Adaptive control design" ], "abstract": "", "url": "" }, { "id": "Tu-TuA01.3", "code": "TuA01.3", "title": "Control of Epidemic Diseases and Opinion Dynamics (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:20", "sessionCode": "TuA01", "sessionTitle": "Game-Theoretic Control Paradigms for Socio-Technical Networks", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Ishii, Hideaki", "affiliation": "University of Tokyo" } ], "keywords": [ "Adaptive control design" ], "abstract": "", "url": "" }, { "id": "Tu-TuA01.4", "code": "TuA01.4", "title": "Cooperative and Noncooperative Paradigms for Game-Theoretic Control of Socio-Technical Systems (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:20-11:50", "sessionCode": "TuA01", "sessionTitle": "Game-Theoretic Control Paradigms for Socio-Technical Networks", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Basar , Tamer", "affiliation": "Univ. of Illinois Urbana-Champaign" }, { "name": "Hayakawa, Tomohisa", "affiliation": "Tokyo Institute of Technology" }, { "name": "Ishii, Hideaki", "affiliation": "University of Tokyo" }, { "name": "Zhu, Quanyan", "affiliation": "New York University" } ], "keywords": [ "Adaptive control design" ], "abstract": "This tutorial presents cooperative and noncooperative game-theoretic frameworks for modeling, learning, and control in socio-technical systems, where human behavior, incentives, institutions, and social interactions are coupled with cyber-physical and networked infrastructures. The paper reviews strategic, dynamic, cooperative, matching, learning, and feedback-control approaches for analyzing how local decision-making, adaptation, and strategic interactions shape collective system outcomes. The tutorial further develops feedback-learning and incentive-design perspectives that connect equilibrium analysis with adaptation, distributed control, and mechanism design under information and coordination constraints.We also examine resilience and security challenges arising from adversarial behavior, misinformation, disruptions, and cascading failures in interconnected socio-technical networks. Finally, we discuss emerging research directions at the intersection of game theory, control, learning, and network science for resilient and adaptive socio-technical systems.", "url": "" }, { "id": "Tu-TuA02.1", "code": "TuA02.1", "title": "An Integrated Perspective for Modelling Cyber-Physical Systems Interoperability", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-09:55", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Torres Ricaurte, Diana Maria", "affiliation": "Imt Mines Ales" }, { "name": "Daclin, Nicolas", "affiliation": "IMT Mines Alès" }, { "name": "Zacharewicz, Gregory", "affiliation": "IMT - Mines Ales" } ], "keywords": [ "Cyber-physical-social systems in enterprises", "Enterprise interoperability", "Model-driven enterprise-system engineering" ], "abstract": "Cyber-physical systems (CPS) embrace cybernetic and physical components in dynamic interactions. CPS modelling involved multiple views of the system from different disciplines. Interoperability of CPS comprises coordinating data exchange and operation between heterogeneous components and systems. Due to the multidisciplinary nature of CPS, the independence of its components, and its complex behavior, interoperability approaches tend to focus on a specific level of abstraction and a single type of interoperability. Whereas a holistic view is expected to provide a more accurate representation of reality. The aim of this paper is to highlight the lack of an integrated perspective on CPS interoperability. First, we identify how the usage of different models contributes to achieving CPS interoperability. Then, we propose a pre-conceptual schema to show CPS elements and its relationships involved in interoperability from a general perspective. A complete characterization of CPS interoperability is required to include essential aspects in a unified model abstraction.", "url": "" }, { "id": "Tu-TuA02.2", "code": "TuA02.2", "title": "Accurate Temporal Calibration of a Digital Twin for Sorting Machine Synchronization Using Event-Based Vision", "day": "Tuesday", "date": "August 25, 2026", "time": "09:55-10:00", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Kombaya Touckia, Jesus Vital", "affiliation": "Université Claude Bernard Lyon 1, INSA Lyon, Université Lumière Lyon 2, Université Jean Monnet Saint-Etienne, DISP UR4570," }, { "name": "Cheutet, Vincent", "affiliation": "Université De Lyon, INSA Lyon, Laboratoire DISP (EA4570)" }, { "name": "Henry, Sébastien", "affiliation": "DISP Laboratory, University of Lyon, University Lyon 1" } ], "keywords": [ "Digital transformation", "Intelligent manufacturing systems" ], "abstract": "A digital twin is defined as an organised set of models that accurately represent a physical entity in the real world in order to meet specific industrial uses. Continuously updated using real data, it offers a level of precision and granularity tailored to operational needs. This virtual model can integrate the shapes, states, functions, processes, behaviours and dynamic data of the equipment under study, while reflecting its environment. However, precise calibration between the virtual twin and its physical counterpart remains a major challenge, mainly due to the limitations of current industrial IoT approaches, which are often costly, complex and unreliable. To overcome these constraints, this research proposes the integration of neuromorphic machine vision, a technology characterised by high temporal resolution and low latency, enabling automatic synchronisation of the digital twin via discrete event system modelling. This approach aims to reduce the gap between the virtual and the real, improve calibration accuracy and optimise operational efficiency in complex industrial environments. The study highlights the potential of event-based vision systems, combined with machine learning algorithms, to capture and interpret the behaviour of physical equipment in real time. By replacing heavy IoT instrumentation with intelligent visual observation, this method offers a more economical, robust and adaptable solution, contributing to the emergence of a more connected and efficient industry of the future.", "url": "" }, { "id": "Tu-TuA02.3", "code": "TuA02.3", "title": "Towards Inclusive Industry 5.0: A Systematic Mapping on Cobot Applications for Workers with Disabilities", "day": "Tuesday", "date": "August 25, 2026", "time": "10:00-10:05", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Leoni, Leonardo", "affiliation": "ECampus University" }, { "name": "Mancusi, Francesco", "affiliation": "Università Degli Studi Della Basilicata" }, { "name": "Portaluri, Tommaso", "affiliation": "Verity AG" }, { "name": "Fruggiero, Fabio", "affiliation": "University of Basilicata" }, { "name": "De Carlo, Filippo", "affiliation": "Università Degli Studi Di Firenze" } ], "keywords": [ "Human-technology integration in manufacturing", "Robotics in manufacturing systems" ], "abstract": "International organizations report concerning statistics regarding the inclusion of people with disabilities in the labor market, underscoring the need for effective inclusive solutions. Industry 4.0 has accelerated technological advances, including collaborative robots (cobots), whose design enables safer and improved interaction with human workers than non-collaborative solutions. Hence, cobots have the potential to support workers with disabilities (DWs), reinforcing the human-centric orientation emphasized in Industry 5.0 and contributing to more inclusive workplaces. This topic has attracted growing scholarly interest, with studies addressing diverse goals such as developing cobot-based assistance systems for DWs or examining user acceptance. Research also varies in the categories of disabilities and impairments, industrial applications, or cobot technologies involved. Such heterogeneity has resulted in a fragmented body of knowledge that may hinder broader implementation efforts. To address this gap, this study conducts a Systematic Literature Mapping (SLM) to review, structure, and synthesize existing research. The review showed that developing Human-Robot Collaboration (HRC) systems and improving the human-cobot alignment are the most prevalent research goals. Assembly tasks emerge as the most common application area, with frequent focus on robotic arms. The findings can support researchers in identifying promising research directions and assist practitioners in introducing cobots to better include DWs in industrial settings.", "url": "" }, { "id": "Tu-TuA02.4", "code": "TuA02.4", "title": "Meta-Knowledge Transfer-Based Dynamic Operation Optimization for Municipal Solid Waste Incineration Process", "day": "Tuesday", "date": "August 25, 2026", "time": "10:05-10:10", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Cui, Yingying", "affiliation": "Beijing Information Science & Technology University" }, { "name": "Fan, Junfang", "affiliation": "Beijing Information Science and Technology University" }, { "name": "Qiao, Junfei", "affiliation": "Beijing University of Technology" } ], "keywords": [ "Industrial artificial intelligence", "Manufacturing plant simulation, control and optimization", "Simulation and optimization in production, operations and services" ], "abstract": "Abstract: Municipal solid waste incineration (MSWI) process is a complex industrial process characterized by high nonlinearity and nonstationary dynamics, making it difficult to achieve optimum operation. To solve this problem, a meta-knowledge transfer-based dynamic operation optimization (MKT-DOO) method is proposed for the MSWI process. First, the data stream learning is employed with online elastic weight consolidation incremental update strategy and attention mechanism to construct ensemble surrogate models. Then, the time-varying objective functions can be approximated accurately. Second, a dynamic multi-objective particle swarm optimization algorithm based on transfer learning is proposed to derive the optimal solutions of the manipulated variables. To reduce negative transfer, a meta-knowledge transfer strategy is designed to address the issue of task-specific knowledge differing significantly across transfer tasks caused by drastic fluctuations in operating conditions. Finally, the effectiveness of the proposed method operation optimization is validated by real industrial data.", "url": "" }, { "id": "Tu-TuA02.5", "code": "TuA02.5", "title": "BDI-Based Resource Agent Architecture for Adaptive Skill-Based Manufacturing Control", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:15", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Weber, Jakob", "affiliation": "Ulm University of Applied Sciences" }, { "name": "Lober, Andreas", "affiliation": "Ulm University of Applied Sciences" }, { "name": "Ollinger, Lisa", "affiliation": "Ulm University of Applied Sciences" } ], "keywords": [ "Intelligent manufacturing systems", "Cyber-physical production systems", "Smart production and logistics in manufacturing" ], "abstract": "Modern manufacturing systems require control architectures capable of bridging the gap between flexible high-level planning and the immediate low-level execution of the manufacturing process. This paper proposes a Resource agent architecture that links the planning and execution layers by integrating a Belief-Desire-Intention agent into the Skill Orchestration Agent framework. Thereby, enabling agent-based planning combined with skill-based execution. A shared knowledge base, structured by the Capability-Service-Skill model, ensures semantic coherence between capabilities and skills across all control levels. This architecture enables autonomous and decentralized production planning and execution.", "url": "" }, { "id": "Tu-TuA02.6", "code": "TuA02.6", "title": "From CAM to SAM : When Harmony Beats Accuracy", "day": "Tuesday", "date": "August 25, 2026", "time": "10:15-10:20", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Rouleau, Samuel", "affiliation": "Université Laval" }, { "name": "Gaudreault, Jonathan", "affiliation": "Universite Laval" } ], "keywords": [ "Intelligent manufacturing systems", "Smart production and logistics in manufacturing" ], "abstract": "During the design of a product, shapes are defined using complex mathematical functions. However, these must eventually be approximated by lines/arc segments. Under traditional Computer-Aided Manufacturing (CAM), this is done individually for each part. Thus, the approximations can be inconsistent, which results in poor assembly. We propose a workflow and a datamodel to generate toolpaths knowing final product assembly information. This allows parts that are meant to be assembled to share common machining toolpaths. We generated 9261 part assemblies for two use cases. Results show that the Shared Approximation Method (SAM) eliminates mismatches in assemblies regardless of the approximation quality.", "url": "" }, { "id": "Tu-TuA02.7", "code": "TuA02.7", "title": "The Problem of Constructing Local Econometric Models Based on the Maximum Correntropy Coefficient (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:20-10:25", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Chernyshov, Kirill", "affiliation": "V.A. Trapeznikov Institute of Control Sciences" }, { "name": "Jharko, Elena", "affiliation": "V.A. Trapeznikov Institute of Control Sciences" } ], "keywords": [ "Manufacturing plant simulation, control and optimization", "Complex dynamic systems", "Large-scale complex systems" ], "abstract": "Extracting knowledge from observed data regarding complex systemic behavior is closely associated with system identification methodology, where inherent uncertainty in model development necessitates stochastic formulations. Addressing stochastic identification tasks requires appropriate quantifiers of statistical association among variables. The most widely used quantifier, the ordinary (Pearson) correlation, may vanish even when a deterministic functional relationship exists between the variables of interest. Dependence measures termed “consistent”, which equal zero only when two random variables are statistically independent, provide a more comprehensive representation of inter-variable relationships. However, additional considerations such as normalization constraints and compatibility with Gaussian assumptions introduce further complexity. To address these challenges, this work adopts the maximum correntropy coefficient. This measure captures affine associations between pairs of random variables and enables computationally tractable procedures for stochastic system identification. Since an affine mapping constitutes a nonlinear transformation, the systems considered should be classified as nonlinear, despite their relatively simple nonlinearity. The nonlinear behavior examined arises primarily from the complex probabilistic interdependencies among model variables. This study develops a framework for constructing piecewise-affine stochastic models, aiming to identify and precisely quantify the stochastic relationships between model inputs and outputs.", "url": "" }, { "id": "Tu-TuA02.8", "code": "TuA02.8", "title": "Technical an Economical Indexes of Nuclear Power Plants: Results and Prospective Studies (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:25-10:30", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Jharko, Elena", "affiliation": "V.A. Trapeznikov Institute of Control Sciences" }, { "name": "Abdulova, Ekaterina", "affiliation": "V.A. Trapeznikov Institute of Control Sciences" } ], "keywords": [ "Manufacturing plant simulation, control and optimization", "Manufacturing engineering and management", "Advanced manufacturing and remanufacturing technologies" ], "abstract": "This paper provides a detailed examination of the calculation of technical and economic indicators (TEI) for nuclear power plants, focusing on methodology, algorithms, and implementation as a specialized software module for analyzing and quantifying the thermal efficiency of nuclear power plant units. The paper presents the theoretical foundations and practical aspects of using TEI to monitor the efficiency of thermodynamic conversion of thermal energy generated in the core of a nuclear reactor. Methodological approaches to TEI calculation, data processing algorithms, and methods for visualizing analytical results are considered. Particular attention is paid to assessing the energy efficiency of both individual equipment and the unit as a whole.", "url": "" }, { "id": "Tu-TuA02.9", "code": "TuA02.9", "title": "Asset Administration Shell-Based OCL Validation Framework for Model-Based System Engineering", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:35", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Parkash, Om", "affiliation": "University of Applied Sciences Pforzheim" }, { "name": "Bauer, Jannik", "affiliation": "University of Applied Sciences Pforzheim" }, { "name": "Schmitt, Vincent", "affiliation": "University of Applied Sciences Pforzheim" }, { "name": "Greiner, Thomas", "affiliation": "Pforzheim University" }, { "name": "Drath, Rainer", "affiliation": "University of Applied Sciences Pforzheim" } ], "keywords": [ "Model-driven enterprise-system engineering", "Enterprise interoperability", "Digital transformation" ], "abstract": "Increasing complexity of modern enterprise systems and the demand for automation and interoperability require consistent and semantically validated models in Model-Based Systems Engineering (MBSE). The Object Constraint Language (OCL) supports formal definition of such constraint validations. However, MBSE models and OCL constraints are typically managed in separate tools, causing manual effort during model constraint application and result interpretation. To address this gap, this paper proposes an approach to managing OCL constraints and their validation results through Asset Administration Shells (a well-established technology for interoperability in enterprise systems). The methodology is demonstrated through a fictional industrial scenario, and to support reproducibility, all artifacts are publicly available in a GitHub repository. Keywords: MBSE, OCL, AAS, Semantic Constraint Modeling, AutomationML", "url": "" }, { "id": "Tu-TuA02.10", "code": "TuA02.10", "title": "Model-Based Safe Reinforcement Learning for Control Using Action Replacement Strategy", "day": "Tuesday", "date": "August 25, 2026", "time": "10:35-10:40", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Ankalugari, Rahul Yadav", "affiliation": "Indian Institute of Technology Tirupati" }, { "name": "Magbool Jan, Nabil", "affiliation": "Indian Institute of Technology Tirupati" } ], "keywords": [ "Reinforcement learning and deep learning in control", "AI-driven modeling and control", "Knowledge-based and data-driven control" ], "abstract": "Process systems often impose several state and input constraints owing to safety and environmental limitations. There is an increasing interest in deploying reinforcement learning-based controllers to achieve the goal of autonomous process systems. Standard reinforcement learning algorithms lack the provision to impose hard state constraints. This impedes their applicability in safety-critical process systems, where constraint violations can have catastrophic consequences. To this end, we characterize the concept of safe set as a maximal control invariant set, and ensure that exploration and exploitation occur within the safe set. We propose an action replacement-based reinforcement learning approach that can effectively prevent violation of state constraints while learning the control policy. More specifically, we propose a model-based safety filter that replaces the potentially unsafe control action suggested by the conventional reinforcement learning controller with the safe control action such that the replaced control input drives the system to safe states. In this work, we integrate this safety filter with the deep deterministic policy gradient algorithm to learn the control policy. We demonstrate the efficacy of the proposed approach on a double integrator system, showing that the proposed action replacement strategy provides a safety guarantee.", "url": "" }, { "id": "Tu-TuA02.11", "code": "TuA02.11", "title": "A Hybrid Reinforcement and Self-Supervised Learning Aided Benders Decomposition Algorithm", "day": "Tuesday", "date": "August 25, 2026", "time": "10:40-10:45", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Agyeman, Bernard", "affiliation": "University of Alberta" }, { "name": "Li, Zhe", "affiliation": "University of Minnesota" }, { "name": "Mitrai, Ilias", "affiliation": "The University of Texas at Austin" }, { "name": "Daoutidis, Prodromos", "affiliation": "Univ. of Minnesota" } ], "keywords": [ "Reinforcement learning and deep learning in control", "AI-driven modeling and control", "Machine learning for modeling and prediction" ], "abstract": "We propose a hybrid reinforcement and self-supervised learning approach for accelerating generalized Benders decomposition. On the master side, we employ a graph-based reinforcement learning agent that operates on a bipartite graph representation of the master problem and is equipped with a verification mechanism to either partially or fully solve it. On the subproblem side, a physics-informed neural network, trained to approximate solutions that satisfy the Karush--Kuhn--Tucker conditions via self-supervision, takes the values of the integer variables as input and produces primal--dual pairs for Benders cut construction. The proposed framework is evaluated on a mixed-integer nonlinear programming case study, where it achieves a 52% reduction in solution time relative to classical GBD while preserving convergence behavior.", "url": "" }, { "id": "Tu-TuA02.12", "code": "TuA02.12", "title": "Individual Control Barrier Functions-Guided Diffusion Model for Safe Offline Multi-Agent Reinforcement Learning", "day": "Tuesday", "date": "August 25, 2026", "time": "10:45-10:50", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Guo, Qingyun", "affiliation": "Aalto University" }, { "name": "Shi, Junyi", "affiliation": "Aalto University" }, { "name": "Huang, Jianuo", "affiliation": "Xiamen University Malaysia" }, { "name": "Shi, Tianyu", "affiliation": "University of Toronto" } ], "keywords": [ "Reinforcement learning and deep learning in control", "Control architecture for multi agent systems" ], "abstract": "Offline reinforcement learning allows control policies to be learned directly from data without online interaction, making it suitable for safety-critical tasks. Recent studies have applied diffusion models to offline reinforcement learning to leverage their strong capacity for modeling complex data distributions. However, existing approaches primarily focus on single-agent settings, leaving the safety challenges in multi-agent environments largely unexplored. In this work, we propose a safe offline multi-agent reinforcement learning algorithm that embeds neural individual control barrier functions into the diffusion model to enhance safety during trajectory generation, with control policies recovered through inverse dynamics. We evaluate our algorithm across diverse benchmarks, demonstrating substantial safety improvements while maintaining competitive rewards.", "url": "" }, { "id": "Tu-TuA02.13", "code": "TuA02.13", "title": "Primal-Dual Based Safe Multi-Agent Reinforcement Learning with Graph Information Aggregation", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-10:55", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Gou, Fandi", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Zhao, Chenyu", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Zhao, Hengyuan", "affiliation": "ShangHai Jiao Tong University" }, { "name": "Cai, Yunze", "affiliation": "Shanghai Jiaotong University" } ], "keywords": [ "Reinforcement learning and deep learning in control", "Control architecture for multi agent systems", "Safety and security in networked control" ], "abstract": "This paper proposes a primal-dual based safe multi-agent reinforcement learning (MARL) framework that integrates Transformer-driven graph neural networks (GNNs) and Lagrangian method, termed G-MATrans-Lagr, to enable safe and scalable cooperation among agents under limited communication. The approach adopts Lagrangian multipliers to optimize the reward and cost in a hybrid objective function, and a Transformer-based GNN is utilized to aggregate local observations into expressive graph representations, facilitating effective information sharing among neighboring agents. Experimental validation on multi-UAV navigation task demonstrates that G-MATrans-Lagr achieves superior performance compared with the latest MARL and safe control baselines, maintaining higher performance and lower safety costs across varying agent scales. The results showcase our method’s ability to balance efficiency and safety while enhancing scalability for complex multi-agent systems. Besides, we open source our code at https://github.com/finleygou/G-MAT-Lagr.", "url": "" }, { "id": "Tu-TuA02.14", "code": "TuA02.14", "title": "Soft Switching Expert Policies for Controlling Systems with Uncertain Parameters", "day": "Tuesday", "date": "August 25, 2026", "time": "10:55-11:00", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Ikemoto, Junya", "affiliation": "The University of Osaka" } ], "keywords": [ "Reinforcement learning and deep learning in control", "Knowledge-based and data-driven control", "AI-driven modeling and control" ], "abstract": "This paper proposes a simulation-based reinforcement learning algorithm for controlling systems with uncertain and varying system parameters. While simulators are useful for safely learning control policies, the reality gap remains a major challenge. To alleviate this challenge, we propose a two-stage algorithm. First, multiple control policies are learned for systems with different system parameters in a simulator. Second, for a real system, the control policies are adaptively switched using an online convex optimization algorithm based on observations. The proposed approach mitigates the learning difficulty of training a single policy to handle all possible system parameters and enables lightweight online adaptation.", "url": "" }, { "id": "Tu-TuA02.15", "code": "TuA02.15", "title": "State-Conditional Adversarial Learning: An Off-Policy Visual Domain Transfer Method for End-To-End Imitation Learning", "day": "Tuesday", "date": "August 25, 2026", "time": "11:00-11:05", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Liu, Yuxiang", "affiliation": "University of Califronia, Berkeley" }, { "name": "Cao, Shengfan", "affiliation": "University of California, Berkeley" } ], "keywords": [ "Reinforcement learning and deep learning in control", "Knowledge-based and data-driven control", "AI-driven modeling and control" ], "abstract": "We study visual domain transfer for end-to-end imitation learning in a realistic and challenging setting where target-domain data are strictly off-policy, expert-free, and scarce. We first provide a theoretical analysis showing that the target-domain imitation loss can be upper bounded by the source-domain loss plus a state-conditional latent KL divergence between source and target observation models. Guided by this result, we propose State- Conditional Adversarial Learning (SCAL), an off-policy adversarial framework that aligns latent distributions conditioned on system state using a discriminator-based estimator of the conditional KL term. Experiments on visually diverse autonomous driving environments built on the BARC–CARLA simulator demonstrate that SCAL achieves robust transfer and strong sample efficiency.", "url": "" }, { "id": "Tu-TuA02.16", "code": "TuA02.16", "title": "Memory-Augmented PPO-GRU for Beyond-Visual-Range Air Combat Decision-Making under Partially Observable Conditions", "day": "Tuesday", "date": "August 25, 2026", "time": "11:05-11:10", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Guo, Zheng", "affiliation": "Beihang University" }, { "name": "Li, Xiaoduo", "affiliation": "Beihang University" }, { "name": "Yu, Jianglong", "affiliation": "Beihang University" }, { "name": "Chen, Yi-Ming", "affiliation": "Beihang University" }, { "name": "Duan, Yu", "affiliation": "Nanyang Technological University" }, { "name": "Zhang, Kanghao", "affiliation": "Beihang University" } ], "keywords": [ "Reinforcement learning and deep learning in control", "Machine learning for modeling and prediction" ], "abstract": "This paper proposes a memory-enhanced PPO-GRU reinforcement learning framework for autonomous beyond-visual-range (BVR) air combat under partial observability. The BVR air-combat scenario is formulated as a partially observable Markov decision process, and the framework integrates recurrent memory, progressive curriculum learning, and an auxiliary prediction module to improve long-horizon tactical decision-making under intermittent observations. Experimental results show that the proposed agent achieves an 89.2% final win rate and outperforms feedforward PPO, SAC, and DDPG baselines under the same observation, reward, and action settings.", "url": "" }, { "id": "Tu-TuA02.17", "code": "TuA02.17", "title": "Digital Twin-Enhanced Quadruped Robot Locomotion Control: From Geometric Inverse Kinematics to Physical Prototyping", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:15", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Kuhn Fernandes, Bruno", "affiliation": "Regional Integrated University of High Uruguay and Missions - URI - Santo Angelo, Brazil" }, { "name": "Pignaton de Freitas, Edison", "affiliation": "Federal University of Rio Grande Do Sul" }, { "name": "Dos Santos Roque, Alexandre", "affiliation": "Halmstad University, Federal University of Rio Grande Do Sul - UFRGS" } ], "keywords": [ "Remote control", "Networking for internet of things", "Networking for teleoperation" ], "abstract": "This work presents a Digital Twin-enhanced tele-operated locomotion system for an articulated quadruped robot, easy-to-deploy, and designed to calibration walking movements. A geometric approach is developed to solve the inverse kinematics for a three-joint leg model, thereby accurately deriving the required joint angles from desired foot coordinates. Central to this enhancement is a digital twin implementation within CoppeliaSim software, which provides a virtual testing ground for predictive analysis and optimization of the control algorithms, significantly accelerating development and improving system robustness. Commercial servomotors, actuated based on these calculated angles, are controlled by a mobile application developed in .NET MAUI. This application facilitates remote operation and telemetry monitoring through secure MQTT communication via HiveMQ Cloud. The refined control equations, initially validated through the digital twin, are then thoroughly tested on a 3D-printed physical prototype utilizing an ESP32 microcontroller. The results show the feasibility of communication and quadruped robot calibration in runtime, while offering an integrated and scalable solution, supported by a simulation-driven physical prototyping.", "url": "" }, { "id": "Tu-TuA02.18", "code": "TuA02.18", "title": "FPGA Remote Lab: Interactive and Hands-On Online Learning Experience", "day": "Tuesday", "date": "August 25, 2026", "time": "11:15-11:20", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Patel, Ankit", "affiliation": "Laboratoire Des Technologies Innovantes, l’Université De Picardie Jules Verne" }, { "name": "Rachid, Ahmed", "affiliation": "University of Picardie Jules Verne" } ], "keywords": [ "Remote control", "Virtualized and cloud-based control architectures", "Remote data acquisition and fusion" ], "abstract": "This paper presents an FPGA Remote Laboratory that enables students and hobbyists to conduct real hardware experiments on a Digilent (2025) Arty Z7-20 board through a web interface. The platform combines MQTT based control, RDP virtual access, multi peripheral hardware, and live video feedback to provide a hands-on FPGA learning environment beyond simulation-only approaches. The system achieves 120–180 ms control latency and supports up to five concurrent sessions. It offers a scalable and low-cost model for remote FPGA and embedded systems education, supporting self-paced experimentation and practical understanding.", "url": "" }, { "id": "Tu-TuA02.19", "code": "TuA02.19", "title": "The Meaning of Cobots Implementation in the Aspect of Industry 4.0 and Industry 5.0 Transformation (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:20-11:25", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Pizoń, Jakub", "affiliation": "Lublin University of Technology" }, { "name": "Gola, Arkadiusz", "affiliation": "Faculty of Mechanical Engineering, Lublin University of Technology" }, { "name": "Rudawska, Anna", "affiliation": "Lublin University of Technology" }, { "name": "Piotrowska, Katarzyna", "affiliation": "Lublin University of Technology" }, { "name": "Paulina, Golinska-Dawson", "affiliation": "Poznan University of Technology" } ], "keywords": [ "Robotics in manufacturing systems", "Industry X.0 for production and logistics", "Human-technology integration in manufacturing" ], "abstract": "The use of collaborative robots (cobots) in production systems is no longer a vision of the future, but a practical solution for human-robot collaboration. This paper provides a literature review on the role of cobots in the transition from Industry 4.0 to Industry 5.0. The review is based on Web of Science, Scopus, and Google Scholar searches using terms related to cobots, HRC, Industry 4.0/5.0, safety, HMI, mass customization, and mass personalization. The study shows how cobots connect Industry 4.0, a digitized and automation-focused industry, with Industry 5.0, a human-centered industry, by combining AI-driven customization, safe physical interaction and HMI-based operator support. From a production management perspective, implementation is also seen as a managerial and technological enabler of mass personalization, bottleneck mitigation, and manufacturing-as-a-service models. The contribution is to merge market trends, security features, and implementation logic into a conceptual argument for cobots as a driver of contemporary production transformation.", "url": "" }, { "id": "Tu-TuA02.20", "code": "TuA02.20", "title": "Probabilistic Recursively Feasible Motion Planning under Uncertain Environments", "day": "Tuesday", "date": "August 25, 2026", "time": "11:25-11:30", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Sung, Hyeontae", "affiliation": "KAIST" }, { "name": "Ham, Hyeongchan", "affiliation": "KAIST" }, { "name": "Park, Junyoung", "affiliation": "KAIST" }, { "name": "Ren, Kai", "affiliation": "EPFL" }, { "name": "Ahn, Heejin", "affiliation": "KAIST" } ], "keywords": [ "Stochastic optimal control problems", "Model predictive control" ], "abstract": "Safe motion planning in uncertain, time-varying environments is challenging because the safe region can change unpredictably across planning steps, often causing a loss of recursive feasibility. In this work, we present a Probabilistic Recursively Feasible Model Predictive Control (PRF-MPC) framework that guarantees recursive feasibility with a specified probability. We introduce properties that an ideal predictor should satisfy to ensure distributional consistency, and use these properties to derive closed-form expressions for the means and covariances of trajectories predicted at future time steps. Building on this analysis, we construct safety constraints that ensure, with high probability, that the current safe set is contained within the safe sets at future time steps, thereby probabilistically guaranteeing recursive feasibility. Simulation results on a lane-change scenario demonstrate that the proposed method significantly improves recursive feasibility.", "url": "" }, { "id": "Tu-TuA02.21", "code": "TuA02.21", "title": "Integrating Design, Diagnosis and Recovery for Offshore Wind Turbines", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:35", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Jing Jung, Zhang", "affiliation": "School of Information Management & Engineering" }, { "name": "Simani, Silvio", "affiliation": "University of Ferrara" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" } ], "keywords": [ "Supervision and testing", "Fault detection and isolation", "Design methods for data-based control" ], "abstract": "This paper presents an integrated procedure for designing, diagnosing and recovering offshore wind turbine operation under faulty conditions. The main contribution is not a stand-alone control or diagnosis algorithm, but a reproducible co-design workflow in which controller tuning, residual-based diagnosis and recovery actions are selected together and assessed against common safety and performance requirements. The procedure is applied to a benchmark floating offshore wind farm represented by an aero-hydro-servo-elastic digital twin. Candidate supervisory settings are first obtained from an energy-load trade-off. Diagnosis thresholds and isolation rules are then tuned on separate healthy and faulty scenarios, and the resulting decisions trigger recovery actions via safe derating and command reconfiguration. The complete closed loop is tested under multiple wind conditions, noisy measurements and injected sensor and actuator faults. The results show that the integrated strategy improves availability, reduces downtime and shortens post-fault recovery episodes while preserving load-sensitive operational margins. The study also clarifies how diagnostic delay, false alarms, and missed detections affect feasible recovery, thereby making the links between design choices, diagnosability and safe operation explicit. This provides a traceable route from design intent to evidence-based operation, suitable for further validation on higher-fidelity models and field data.", "url": "" }, { "id": "Tu-TuA02.22", "code": "TuA02.22", "title": "Digital Representation of Circular Economy Data Points at the Nano Level Using Asset Administration Shell", "day": "Tuesday", "date": "August 25, 2026", "time": "11:35-11:40", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Rezapour, Mahdi", "affiliation": "German Research Center for Artificial Intelligence (DFKI)" }, { "name": "Farrukh, Abdullah", "affiliation": "German Research Center for Artificial Intelligence (DFKI)" }, { "name": "Pourjafarian, Monireh", "affiliation": "Technologie-Initiative SmartFactory KL E.V" }, { "name": "Plociennik, Christiane", "affiliation": "DFKI GmbH, Kaiserslautern" }, { "name": "Nolte, Annalisa", "affiliation": "RWTH Aachen" }, { "name": "Araujo, Juliano", "affiliation": "Pforzheim University, Institute for Industrial Ecology" }, { "name": "Berg, Holger", "affiliation": "Wuppertal Institut Fuer Klima, Umwelt, Energie" }, { "name": "Ruskowski, Martin", "affiliation": "German Research Center for Artificial Intelligence" } ], "keywords": [ "Sustainable and circular manufacturing systems", "Sustainable and circular supply chain and production", "Cyber-physical production systems" ], "abstract": "The transition to a Circular Economy (CE) requires structured, interoperable data across product life cycles. The Asset Administration Shell (AAS), as the Industry 4.0 digital representation standard, provides this foundation, yet CE-relevant data points remain insufficiently defined. This paper asks: How can nano-level CE data points be formally integrated into the AAS? We present a methodology to identify and classify nano-level CE data, map them to modular AAS submodels, and produce a reusable template for Digital Product Passports and digital twins. The approach enhances data exchange, supports future CE requirements, and is scalable to higher CE levels.", "url": "" }, { "id": "Tu-TuA02.23", "code": "TuA02.23", "title": "Observer Design for Heat PDEs with Nonuniformly-Distributed Actuator Delay", "day": "Tuesday", "date": "August 25, 2026", "time": "11:40-11:45", "sessionCode": "TuA02", "sessionTitle": "Shotgun: Design, Communications and Cyber-Physical Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Barbara, Sara", "affiliation": "University Moulay Ismail, Ensam" }, { "name": "Giri, Fouad", "affiliation": "University of Caen Normandie" }, { "name": "Krstic, Miroslav", "affiliation": "Univ. of California at San Diego" }, { "name": "Chaoui, Fatima-Zahra", "affiliation": "ENSET, Université Mohammed V" }, { "name": "Brouri, Adil", "affiliation": "ENSAM, Moulay Ismail University," } ], "keywords": [ "System identification and adaptive control of distributed parameter systems", "Backstepping control of distributed parameter systems" ], "abstract": "We are considering the problem of observer design for heat partial difference equations (PDEs) with distributed delay in actuator. Distributed delays are generally assumed to be uniformly distributed, i.e., their kernel functions are constant and perfectly known. The main novelty of this study lies in letting the actuator delay kernel function (DKF) not to be necessarily constant or known. These considerations make the observer design problem under study a new problem never studied in the past. Making use of the backstepping design method and a suitable decoupling transformation, we develop an adaptive observer that provides online estimates of the PDE state and the actuator DKF. We first show that the L^2-norm of the DKF estimation error exponentially converges to zero, under a well-defined persistent excitation (PE) depending only on the input signal. Then, we show that the PDE state estimation error in turn exponentially converges to zero.", "url": "" }, { "id": "Tu-TuA03.1", "code": "TuA03.1", "title": "Scalability of Alignment: Measuring the Maximum Number of Human Agents a Machine Intelligence Can Reliably Serve Anywhere, Anytime", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-09:55", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Tembine, Hamidou", "affiliation": "New York University" }, { "name": "Noupa Yongueng, Daryl", "affiliation": "Université Du Québec à Trois-Rivière" } ], "keywords": [ "AI-driven modeling and control", "AI tools in automation engineering and operation", "AI in networked control" ], "abstract": "We characterize the achievable satisfaction region of real-world generative machine intelligence systems under compute, architecture, training, adaptation, and budget constraints. The result defines an alignment capacity metric that quantifies how many user preferences can be met to a target quality and frequency. By expressing this capacity as an explicit resource-allocation optimization driven by user-specific expectile utility, the theorem reveals clean Pareto frontiers between coverage, quality, and reliability, and provides sharp conditions for when universality is not achievable. The framework offers actionable guidance for maximizing user satisfaction and quality-of-experience in deployed machine intelligence systems.", "url": "" }, { "id": "Tu-TuA03.2", "code": "TuA03.2", "title": "Physics Informed Neural Networks for Nonlinear Delay Differential Equations", "day": "Tuesday", "date": "August 25, 2026", "time": "09:55-10:00", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Yao, Lei", "affiliation": "University of Waterloo" }, { "name": "Kumar, Vipin", "affiliation": "Max Planck Institute for Dynamics of Complex Technical Systems" }, { "name": "Guglielmi, Roberto", "affiliation": "University of Waterloo" } ], "keywords": [ "AI-driven modeling and control", "Knowledge-based and data-driven control", "Machine learning for modeling and prediction" ], "abstract": "In this paper we propose a novel physics-informed neural network framework for solving general first-order delay differential equations. Our approach combines a differentiable history switch, a trial-solution formulation that explicitly enforces history constraints, and a segmented collocation strategy to stabilize gradient propagation across large temporal domains. The method enables a scalable and physics-consistent approximation of delay differential equation solutions while maintaining continuity across subintervals. Numerical experiments demonstrate the effectiveness of the proposed method.", "url": "" }, { "id": "Tu-TuA03.3", "code": "TuA03.3", "title": "Perron--Frobenius Operator Matching for Generative Modeling", "day": "Tuesday", "date": "August 25, 2026", "time": "10:00-10:05", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Zhang, Shiqi", "affiliation": "Peking University" }, { "name": "Wu, Wuwei", "affiliation": "City University of Hong Kong" }, { "name": "Oh, Jaemin", "affiliation": "Brown University" }, { "name": "Chen, Jie", "affiliation": "City University of Hong Kong" }, { "name": "Qian, Xiaoning", "affiliation": "Texas A&M University" } ], "keywords": [ "AI-driven modeling and control", "Machine learning for modeling and prediction", "Knowledge-based and data-driven control" ], "abstract": "We introduce Perron--Frobenius Operator Matching (PFOM), a generative framework that matches density evolution via the integral PF operator, subsuming flow, diffusion, and jump models. We prove that among Bregman divergences, only Kullback--Leibler divergence preserves equality between density-level and sample-conditioned objectives, yielding a practical loss equivalent to Koopman path matching. We further develop Nesterov-accelerated training and sampling that stabilize discretization and accelerate convergence. %On Gaussian mixtures and two-moons, PFOM achieves faster KL/W_2/MMD decrease and improved wall-clock efficiency with empirical validation. PFOM unifies operator-theoretic identification with modern generative modeling and opens paths to adaptive dictionaries and high-dimensional applications.", "url": "" }, { "id": "Tu-TuA03.4", "code": "TuA03.4", "title": "Component-Aware Pruning Framework for Neural Network Controllers Via Gradient-Based Importance Estimation", "day": "Tuesday", "date": "August 25, 2026", "time": "10:05-10:10", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Sundaram, Ganesh", "affiliation": "RPTU University Kaiserslautern-Landau, Germany" }, { "name": "Ulmen, Jonas", "affiliation": "RPTU Kaiserslautern-Landau" }, { "name": "Görges, Daniel", "affiliation": "University of Kaiserslautern" } ], "keywords": [ "AI-driven modeling and control", "Machine learning for modeling and prediction", "Reinforcement learning and deep learning in control" ], "abstract": "The transition from monolithic to multi-component neural architectures in advanced neural network controllers poses substantial challenges due to the high computational complexity of the latter. Conventional model compression techniques for complexity reduction, such as structured pruning based on norm-based metrics to estimate the relative importance of distinct parameter groups, often fail to capture functional significance. This paper introduces a component-aware pruning framework that utilizes gradient information to compute three distinct importance metrics during training: Gradient Accumulation, Fisher Information, and Bayesian Uncertainty. Experimental results with an autoencoder and a TD-MPC agent demonstrate that the proposed framework reveals critical structural dependencies and dynamic shifts in importance that static heuristics often miss, supporting more informed compression decisions.", "url": "" }, { "id": "Tu-TuA03.5", "code": "TuA03.5", "title": "Model-Free Reinforcement Learning Control for Resilient Cyber-Physical Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:15", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Garces, Hugo", "affiliation": "Universidad De Concepcion" }, { "name": "Rojas, Alejandro", "affiliation": "Universidad De Concepcion" }, { "name": "Hernandez-Vicente, Bernardo", "affiliation": "Departamento De Ingeniería Mecánica, Universidad De Concepción" }, { "name": "Escalona, Andrés", "affiliation": "Departamento De Ingeniería Mecánica, Universidad De Concepción" }, { "name": "Palma, Jonathan M.", "affiliation": "UTalca | Universidad De Talca" }, { "name": "Parvez, Md Rezwan", "affiliation": "Department of Electrical & Computer Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada" }, { "name": "Gopaluni, Bhushan", "affiliation": "University of British Columbia" }, { "name": "Shah, Sirish L.", "affiliation": "University of Alberta" } ], "keywords": [ "AI-driven modeling and control", "Reinforcement learning and deep learning in control", "Cyber physical systems" ], "abstract": "This paper presents a unified benchmarking framework to compare model-free reinforcement learning (RL) controllers on a nonlinear cyber-physical system (CPS) under false data injection and denial-of-service attacks. Four reward functions—exponential, progressive, Lyapunov-descent, and linear are analysed across two controller architectures (RL-PID,RL-MPC) and two learning algorithms (PPO, DDPG) using eight Key Performance Indicators covering tracking error, computational cost, and resilience. The Lyapunov reward yields the best resilience and lowest tracking error; the exponential mode provides a strong accuracy–robustness trade-off. Progressive and linear rewards converge faster but are less robust under attacks. RL-MPC achieves superior steady-state resilience, whereas RL-PID requires significantly less training time and is better suited for embedded deployment. These results demonstrate that reward shaping is a central design lever for model-free RL in CPS security, and provide actionable guidance for practitioners selecting controller and reward configurations.", "url": "" }, { "id": "Tu-TuA03.6", "code": "TuA03.6", "title": "Real-Time Point Cloud Data Transmission Via L4S for 5G-Edge-Assisted Robotics", "day": "Tuesday", "date": "August 25, 2026", "time": "10:15-10:20", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Damigos, Gerasimos", "affiliation": "Ericsson Research" }, { "name": "Stathoulopoulos, Nikolaos", "affiliation": "Luleå University of Technology" }, { "name": "Seisa, Achilleas Santi", "affiliation": "Ericsson Research" }, { "name": "Sandberg, Sara", "affiliation": "Ericsson AB" }, { "name": "Nikolakopoulos, George", "affiliation": "Luleå University of Technology" } ], "keywords": [ "Cloud control and robotics", "Networking for teleoperation" ], "abstract": "This article presents a novel framework for real-time Light Detection and Ranging (LiDAR) data transmission that leverages rate-adaptive technologies and point cloud encoding methods to ensure low-latency and low-loss data streaming. The proposed framework is intended for, but not limited to, robotic applications that require real-time data transmission over the internet for offloaded processing. Specifically, the Low Latency, Low Loss, Scalable Throughput (L4S)-enabled SCReAM v2 transmission framework is extended to incorporate the Draco geometry compression algorithm, enabling dynamic compression of high-bitrate 3D LiDAR data according to the sensed channel capacity and network load. The low-latency 3D LiDAR streaming system is designed to maintain minimal end-to-end delay while constraining encoding errors to meet the accuracy requirements of robotic applications. We demonstrate the effectiveness of the proposed method through real-world experiments conducted over a public 5G network across multi-kilometer urban environments. The low-latency and low-loss requirements are preserved, while real-time offloading and evaluation of 3D SLAM algorithms are used to validate the framework’s performance in practical use cases.", "url": "" }, { "id": "Tu-TuA03.7", "code": "TuA03.7", "title": "Evaluating Performance of Aperiodic Controllers", "day": "Tuesday", "date": "August 25, 2026", "time": "10:20-10:25", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Nyberg Carlsson, Max", "affiliation": "Lund University" }, { "name": "Arzen, Karl-Erik", "affiliation": "Lund Inst. of Technology" } ], "keywords": [ "Control software architecture", "Information models for control engineering", "Virtualized and cloud-based control architectures" ], "abstract": "A common assumption when designing control systems is periodic sampling and actuation. As a consequence of this periodicity, unnecessary control delays may be caused. In this paper we show how performance can be improved if, rather than waiting for periodicity, control systems actuate and sample as soon as possible. The performance evaluations are done using stochastic analysis of a large number of processes, comparisons to continuous controllers in simulations, and implementation on a ball and beam system.", "url": "" }, { "id": "Tu-TuA03.8", "code": "TuA03.8", "title": "Evaluating LLM-Based Semantic Labelling of Discrete States in Cyber-Physical Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "10:25-10:30", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Overlöper, Phillip", "affiliation": "Helmut-Schmidt-University" }, { "name": "Hildebrandt, Constantin", "affiliation": "Helmut Schmidt Universitaet" }, { "name": "Niggemann, Oliver", "affiliation": "Helmut-Schmidt-Universität / Universität Der Bundeswehr Hamburg" } ], "keywords": [ "Cyber physical systems", "AI tools in automation engineering and operation", "Knowledge-based and data-driven control" ], "abstract": "This paper evaluates the capacity of off-the-shelf Large Language Models to infer human-interpretable cyber-physical system states from multivariate time-series data in a zero-shot setting. Using the JIGSAWS surgical benchmark, we prompt the model with lightweight per-state kinematic summaries. Across tasks, these summaries produce consistent, though modest, improvements in semantic alignment, as reflected by cosine similarity and ranking metrics. The effects are strongly task-dependent, yet the observed performance gains indicate that LLMs do extract meaningful structure from kinematic time series despite the absence of domain adaptation or supervision.", "url": "" }, { "id": "Tu-TuA03.9", "code": "TuA03.9", "title": "Asset Administration Shell-Based MLOps for Adaptive Alarm Flood Classification (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:35", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Manca, Gianluca", "affiliation": "Ruhr University Bochum" }, { "name": "Rezaee Ahvanouee, Hesam", "affiliation": "Ruhr University Bochum" }, { "name": "Faubel-Teich, Leonhard", "affiliation": "University of Hildesheim" }, { "name": "Kunze, Franz Christopher", "affiliation": "Ruhr University Bochum" }, { "name": "Fay, Alexander", "affiliation": "Ruhr University Bochum" } ], "keywords": [ "Digital twins for cyber physical systems", "AI tools in automation engineering and operation" ], "abstract": "This paper presents an adaptive framework that integrates Machine Learning Operations (MLOps) with the Asset Administration Shell (AAS) to maintain the reliability of Alarm Flood Classification (AFC) models under changing alarm configurations. The AAS serves as a vendor-independent interface for semantically typed configuration revisions and change events, which automatically trigger a change-aware MLOps pipeline for AFC model evaluation, retraining, and redeployment. Alarm data are regenerated using the updated configuration and compared with prior results, while models are selectively redeployed based on performance thresholds. Experiments on two industrial datasets with 200 perturbed configurations demonstrate that static models degrade strongly with increasing configuration change, whereas the proposed method maintains stable accuracy while reducing unnecessary retraining by up to 30%.", "url": "" }, { "id": "Tu-TuA03.10", "code": "TuA03.10", "title": "Real-Time Cyber Attack Detection in Smart Spaces Using a Zonotope-Based Digital Twin Framework", "day": "Tuesday", "date": "August 25, 2026", "time": "10:35-10:40", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Agarwal, Akash", "affiliation": "Motilal Nehru National Institute of Technology Allahabad" }, { "name": "Rath, Jagat Jyoti", "affiliation": "Motilal Nehru National Institute of Technology Allahabad" }, { "name": "Purwar, Shubhi", "affiliation": "Motilal Nehru National Institute of Technology, Allahabad" }, { "name": "Sentouh, Chouki", "affiliation": "LAMIH UMR CNRS 8201, Université Polytechnique Hauts-De-France, Valenciennes, France" } ], "keywords": [ "Digital twins for cyber physical systems", "Cyber physical systems", "Remote data acquisition and fusion" ], "abstract": "A real-time method for cyber-attack detection based on zonotopic state estimation is presented in this work for a smart cyber-physical system with energy management. The proposed approach employs set-based zonotopic Kalman filtering to explicitly account for bounded process and measurement uncertainties while ensuring consistency under adversarial conditions. By combining residual bound violation with secure control logic, the method enables reliable attack detection and prevents the propagation of corrupted data into the energy management and relay actuation layer. The proposed work is validated through real-time experimental results, which demonstrate improved attack detection, reduced false alarms, and secure energy management operations in the presence of cyber attacks.", "url": "" }, { "id": "Tu-TuA03.11", "code": "TuA03.11", "title": "Digital Twins of Systems of Systems: A Systematic Literature Review", "day": "Tuesday", "date": "August 25, 2026", "time": "10:40-10:45", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Smati, Meriem", "affiliation": "INSA LYON and POLYTECHNIQUE MONTREAL" }, { "name": "Cheutet, Vincent", "affiliation": "Université De Lyon, INSA Lyon, Laboratoire DISP (EA4570)" }, { "name": "Laval, Jannik", "affiliation": "DISP Lab, Université Lumière Lyon 2" }, { "name": "Danjou, Christophe", "affiliation": "Polytechnique Montreal" } ], "keywords": [ "Digital twins for cyber physical systems", "Cyber physical systems", "Soft computing and robust intelligent control" ], "abstract": "Digital Twins (DTs) are increasingly invoked to pilot Systems-of-Systems (SoS), yet how they are built and what value they actually deliver at SoS scale remains unclear. We review 19 studies to examine scope, implementation, application domains, complexity drivers, DT roles, and supporting properties for SoS piloting. No study reports a fully implemented SoS-wide DT, i.e. most replicate only parts. Roles concentrate on experimentation–simulation and control–orchestration, with governance and assurance rising, while pure monitoring is rare. We identify interoperability, composition and SoS-level Verification and Validation (V&V) as key gaps and propose a role–capacity crosswalk and metrics to guide future deployments.", "url": "" }, { "id": "Tu-TuA03.12", "code": "TuA03.12", "title": "Multi-Criteria Evaluation of Digital Twins for Industry 5.0: Sustainability, Resilience and Human-Centricity", "day": "Tuesday", "date": "August 25, 2026", "time": "10:45-10:50", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Gataa, Achref", "affiliation": "University of Reims Champagne-Ardenne" }, { "name": "Saddem, Ramla", "affiliation": "University of Reims Champagne-Ardènne, CRESTIC" }, { "name": "Assila Ahlem, Ahlem Assila", "affiliation": "CESI LINEACT" } ], "keywords": [ "Digital twins for cyber physical systems", "Fuzzy and neural systems in control", "Knowledge-based and data-driven control" ], "abstract": "Digital twins (DTs) are a key enabler of Industry 5.0's objective to reconcile operational performance with sustainability and human well-being. However, there is no widely adopted and reproducible evaluation framework for assessing the contributions of DT to these objectives. To address this gap, we first conducted a systematic literature review to identify current practices and limitations, then present a practical, modular six-step evaluation framework that calculates a single, interpretable score for a DT instance by jointly evaluating three explicit pillars: sustainability (environmental, economic, and social), resilience, and human-centricity. The framework combines expert elicitation using a triangular fuzzy number analytical hierarchy process (TFN-AHP), objective weighting using Shannon entropy, and epistemic uncertainty modeling through spherical fuzzy sets. An optional PROMETHEE II module enables pairwise ranking across alternatives. We demonstrate the robustness of the framework through a sensitivity analysis and five synthetic case studies, with all datasets and evaluation scripts published to support reproducibility.", "url": "" }, { "id": "Tu-TuA03.13", "code": "TuA03.13", "title": "Context-Transferable Performance Measure Retrieval from Operator Preferences Using Preferential Bayesian Optimization", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-10:55", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "De Witte, Sander", "affiliation": "Ghent University" }, { "name": "Taets, Jeroen", "affiliation": "Ghent University" }, { "name": "Crevecoeur, Guillaume", "affiliation": "Ghent University" }, { "name": "Lefebvre, Tom", "affiliation": "Ghent University" } ], "keywords": [ "Expert systems and cognitive-based control", "AI tools in automation engineering and operation", "Intelligent human-machine interaction" ], "abstract": "The use of Bayesian Optimization (BO) to tune engineering systems is increasing. Conventional BO requires an objective function, which is often difficult to define and rarely captures expert judgment. Preferential Bayesian Optimization (PBO) addresses this limitation by using preference selections. We show that, after applying PBO, a data-driven cost function can be extracted that captures expert preferences, removing the human operator from the loop when safety constraints are well-defined and enabling fully automated tuning while still emulating expert decision-making. By mapping from well-defined features rather than raw control settings, this cost function becomes transferable across operating conditions, provided that the new conditions remain sufficiently covered in the feature space.", "url": "" }, { "id": "Tu-TuA03.14", "code": "TuA03.14", "title": "Sensing Pod: Integrated On-Device AI Node for Human–Robot Interaction in Indoor Environments", "day": "Tuesday", "date": "August 25, 2026", "time": "10:55-11:00", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Hwang, Sunjun", "affiliation": "Ulsan National Institute of Science and Technology" }, { "name": "Kim, Ji Soo", "affiliation": "Ulsan National Institute of Science and Technology" }, { "name": "Kim, Hyojin", "affiliation": "Ulsan National Institute of Science and Technology" }, { "name": "Kim, SungUn", "affiliation": "UNIST" }, { "name": "Hwang, Dongjoon", "affiliation": "Ulsan National Institute of Science and Technology" }, { "name": "Lee, Hui Sung", "affiliation": "UNIST(Ulsan National Institute of Science and Technology)" } ], "keywords": [ "Intelligent human-machine interaction" ], "abstract": "This paper presents the Sensing Pod, a compact on-device AI sensor node integrating fall detection, localization support, and wake-word recognition for indoor service environments. Low-resolution thermal and audio data are processed entirely on-device using lightweight learn ing pipelines, enabling real-time inference while preserving user privacy. IR-marker signaling improves robot localization without additional hardware. In addition, centroid-based thermalfeatures enable reliable identification of user falls, and a robust three-class wake-word model ensures dependable voice activation under natural pronunciation variability. These results demonstrate that practical safety monitoring and human–robot interaction can be achieved with low-cost sensors, making the Sensing Pod a scalable infrastructure component for future service-robot deployments.", "url": "" }, { "id": "Tu-TuA03.15", "code": "TuA03.15", "title": "Automatic Infrared Detection of Hypervelocity Impact Damage Via Density-Driven TTR Clustering and Multi-Objective Feature Extraction", "day": "Tuesday", "date": "August 25, 2026", "time": "11:00-11:05", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Yan, Zhongbao", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Yin, Chun", "affiliation": "University of ElectronicScience and Technology of China, Chengdu611731, P.R. China" }, { "name": "Gao, Yan", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Liu, Junyang", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Cao, Jiuwen", "affiliation": "Hangzhou Dianzi University" }, { "name": "Tan, Xutong", "affiliation": "University of Electronic Science and Technology of China" } ], "keywords": [ "Intelligent human-machine interaction", "Data fusion and mining in control", "Information models for control engineering" ], "abstract": "With the increase of space debris, efficient spacecraft damage detection and assessment have become increasingly important. This study proposes a hypervelocity impact damage identification method based on multi-objective feature extraction. An adaptive classification algorithm driven by transient thermal response (TTR) density information is first used for unsupervised separation of different damage types. A multi-objective optimization model is then established to balance intra-class representativeness and inter-class difference, where MOEA/D with dynamic weight vector adjustment is adopted to optimize typical TTRs under an irregular Pareto front Finally, the selected high-quality TTRs are used to reconstruct infrared images. Experimental results demonstrate that the proposed method enhances defect features and improves image discriminability for spacecraft damage assessment.", "url": "" }, { "id": "Tu-TuA03.16", "code": "TuA03.16", "title": "Designing a Security Support System for ICS Powered by Generative AI (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:05-11:10", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Sakata, Kousei", "affiliation": "Hitachi, Ltd" }, { "name": "Tanaka, Mayuko", "affiliation": "Hitachi, Ltd" }, { "name": "Kawaguchi, Nobutaka", "affiliation": "Hitachi, Ltd" }, { "name": "Ando, Eriko", "affiliation": "Hitachi Ltd" }, { "name": "Ishii, Hideaki", "affiliation": "University of Tokyo" }, { "name": "Takemoto, Satoshi", "affiliation": "Hitachi Ltd" } ], "keywords": [ "IT/OT-security in automation systems", "AI tools in automation engineering and operation", "Service-architectures for control systems" ], "abstract": "Industrial Control Systems (ICS) need security measures aligned with evolving regulations, but manually linking laws, standards, and threat intelligence is slow and inconsistent. We propose an automated framework integrating the Cyber Resilience Act, IEC 62443, and MITRE ATT&CK for ICS into an accountable database via Latent Dirichlet Allocation (LDA), providing the knowledge base for Retrieval-Augmented Generation (RAG) of countermeasures. On a ground truth of 3,330 candidate pairs labeled by three-LLM consensus, the LDA-based linkage achieves Recall@5 of 0.527 (law--standard) and 0.454 (standard--countermeasure), outperforming BERT-base by 11.3 and 18.5 points respectively at lower computational cost and higher interpretability.", "url": "" }, { "id": "Tu-TuA03.17", "code": "TuA03.17", "title": "Enabling Zero-Touch Certificate Management in Modular Plants through Overlay Networks (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:15", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Madsen, Marwin", "affiliation": "Karlsruhe Institute of Technology" }, { "name": "Bühlmann, Ilona", "affiliation": "Karlsruhe Institute of Technology" }, { "name": "Barth, Mike", "affiliation": "Karlsruhe Institute of Technology (KIT)" } ], "keywords": [ "IT/OT-security in automation systems", "Safety and security in networked control" ], "abstract": "Growing regulatory pressure increases the need for field‑level certificate management. In modular plants, operators typically integrate only a module-level interface, breaking the implicit assumption of direct connectivity between field devices and plant public key infrastructure assumed in current solutions. This paper examines whether overlay networks can provide a lightweight, decentralized substrate for zero‑touch certificate management within modules. Classical overlays are evaluated, and three (Chord, Kademlia, CAN) were selected for a proof of concept assessing resource efficiency and feasibility for automation systems. The results show that overlays provide a viable, protocol‑independent foundation for certificate management in modular plants.", "url": "" }, { "id": "Tu-TuA03.18", "code": "TuA03.18", "title": "Mixup Buffer: Enhancing Soft Monotonicity with Dynamic Violation Replay", "day": "Tuesday", "date": "August 25, 2026", "time": "11:15-11:20", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Visentin, Giacomo", "affiliation": "Università Di Padova" }, { "name": "Sinigaglia, Alberto", "affiliation": "Human Inspired Technology Research Center, University of Padua, 35121 Padua, Italy" }, { "name": "Sartor, Davide", "affiliation": "Università Di Padova" }, { "name": "Susto, Gian Antonio", "affiliation": "University of Padova" } ], "keywords": [ "Knowledge-based and data-driven control", "AI-driven modeling and control", "Machine learning for modeling and prediction" ], "abstract": "Monotonicity is a key requirement for trustworthy machine learning in high-stakes applications, where predictions must align with domain knowledge and human intuition. While deep neural networks excel at modeling complex non-linear relationships, they lack inherent guarantees of monotonic behavior. Existing approaches enforce monotonicity through either hard architectural constraints, which limit expressiveness, or soft regularization penalties, which lack robust guarantees. We introduce Mixup Buffer, a training technique that significantly enhances soft monotonicity enforcement by maintaining a dynamic replay buffer of synthetic constraint-violating samples. By forcing the model to repeatedly confront its worst violations through targeted retraining, Mixup Buffer drives optimization toward solutions with superior monotonic compliance. Extensive experiments across five benchmark datasets demonstrate that Mixup Buffer achieves state-of-the-art monotonicity performance for a soft optimization approach, both in-distribution and out-of-distribution, without sacrificing predictive performance.", "url": "" }, { "id": "Tu-TuA03.19", "code": "TuA03.19", "title": "Preference-Based Optimization from Noisy Pairwise Comparisons", "day": "Tuesday", "date": "August 25, 2026", "time": "11:20-11:25", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Wang, Siyi", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Wang, Zifan", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Knowledge-based and data-driven control", "Bio-inspired algorithms and optimization-based control" ], "abstract": "In interactive systems, feedback is often provided as preferences over queried options rather than precise scores. In this work, we propose a preference-based optimization algorithm that relies on noisy two-point comparisons. At each iteration, the algorithm employs a uniform-sphere perturbation to generate a perturbed action and queries the resulting loss comparison to estimate a descent direction. We demonstrate that, under standard smoothness and bounded variance assumptions, the algorithm converges to a stationary point when the smoothing and step size parameters are properly chosen. Numerical experiments on an LQG system demonstrate the effectiveness of the preference-based optimization algorithm with comparison feedback.", "url": "" }, { "id": "Tu-TuA03.20", "code": "TuA03.20", "title": "Mask-Enhanced and Regularization-Driven Semi-Supervised Learning for Industrial Soft Sensor", "day": "Tuesday", "date": "August 25, 2026", "time": "11:25-11:30", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Liu, Yonghao", "affiliation": "Yunnan University" }, { "name": "Lang, Xun", "affiliation": "Information School, Yunnan University" }, { "name": "Chen, Yiwei", "affiliation": "Yunnan University" }, { "name": "Wu, Jiande", "affiliation": "Yunnan University" }, { "name": "Lang, Yumin", "affiliation": "Information School, Yunnan University" } ], "keywords": [ "Machine learning for modeling and prediction", "AI-driven modeling and control" ], "abstract": "Due to the scarcity of labeled data and inherently nonlinear, time-varying dynamic nature of industrial processes, achieving accurate prediction of key variables remains a major challenge. To address scenarios with only a few labeled samples but numerous raw measurements, we propose a semi-supervised collaborative masking and regularization-driven (SS-CMR) model for industrial soft sensor. We first design a dual-view masked autoencoder to emulate realistic missing-data patterns and learn robust temporal representations via self-supervised learning. During fine-tuning, a random clustering-based regularization strategy is introduced to further stabilize the latent space and mitigate overfitting. In addition, a hybrid predictor combining a deep neural network and a factorization machine is constructed to jointly capture nonlinear dependencies and interactive effects among process variables. We evaluated the performance of SS-CMR on an industrial study. The results show that the proposed approach consistently outperforms existing methods, confirming its effectiveness as a promising soft sensor solution under label-limited conditions.", "url": "" }, { "id": "Tu-TuA03.21", "code": "TuA03.21", "title": "Wavelet-Dilated Net: A Steel Surface Defect Detection Network Based on Two-Level Wavelet Transform and Dilated Convolution", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:35", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Chen, Zihui", "affiliation": "Shanghai University" }, { "name": "Fei, Zixiang", "affiliation": "Shanghai University" }, { "name": "Fei, Minrui", "affiliation": "Shanghai University" }, { "name": "Wenju, Zhou", "affiliation": "Shanghai University" }, { "name": "Du, Dajun", "affiliation": "Shanghai University" }, { "name": "Peng, Chen", "affiliation": "Shanghai University" }, { "name": "Wang, Yu-Long", "affiliation": "Shanghai University" }, { "name": "Song, Yang", "affiliation": "Shanghai University" }, { "name": "Sun, Qing", "affiliation": "Shanghai University" } ], "keywords": [ "Machine learning for modeling and prediction", "AI-driven modeling and control", "Intelligent human-machine interaction" ], "abstract": "Steel-surface defect detection is crucial for quality control in industrial manufacturing. However, prevailing object detection models based on deep learning still struggle with defects with large range of scale variation, moreover, pooling-based down-sampling often erases fine details and causes missed detections, especially when the defects have high similarity to the normal background. To address these issues, we propose Wavelet-DilatedNet, a novel detection framework that introduces two plug-and-play modules on top of the DEIM-DFINE-n baseline. (i) A Multi-Layered Dilated Reparameterized Convolution (MDRC) module which captures multi-scale defect features by fusing parallel dilated convolutions with re-parameterization. (ii) A Two-Stage Wavelet Transform Down-sampling (TWTD) module that cascades Haar wavelet decomposition and inversed Haar wavelet transform to preserve weak edges and textures during feature reduction. Besides, experiments on the high-resolution public dataset GC10-DET show that Wavelet-Dilated Net achieves 37.1% mAP@50:95 and 72.1% mAP@50, surpassing the baseline by 2.6% and 5.8%, respectively, while outperforming other state-of-the-art methods.", "url": "" }, { "id": "Tu-TuA03.22", "code": "TuA03.22", "title": "Effect of Sampling‑Time Jitter on Embedded Control Dynamics", "day": "Tuesday", "date": "August 25, 2026", "time": "11:35-11:40", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Schwarzmann, Dieter", "affiliation": "Robert Bosch GmbH" }, { "name": "Käser, Simon Wilhelm", "affiliation": "Universität Stuttgart" }, { "name": "Lunze, Jan", "affiliation": "Ruhr-Universität Bochum" } ], "keywords": [ "Model driven engineering of control systems", "Information models for control engineering", "Control software architecture" ], "abstract": "This paper is aimed at practitioners and offers an analysis of the effect of sampling-time jitter, i.e. the error produced by execution-time inaccuracies. It proposes a reinterpretation of jitter-afflicted linear time-invariant systems as equivalent jitter-free analogs. By constructing a perceived system that absorbs the effects of timing perturbations into its dynamics, we find an affine scaling of the system matrices with respect to jitter. Moreover, in the Laplace domain, jitter can be interpreted as a frequency scaling. The main result of this paper shows that the effects of jitter can be transferred to a time-variation of the continuous system dynamics. Consequently, the overall system can be analysed by the standard sampled-data control theory with constant sampling period, which is demonstrated by the robustness analysis of feedback loops with jitter.", "url": "" }, { "id": "Tu-TuA03.23", "code": "TuA03.23", "title": "Leveraging Normalizing Flows for Policy Learning in the Competitive Two-Player Zero-Sum Game of Air Hockey", "day": "Tuesday", "date": "August 25, 2026", "time": "11:40-11:45", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Boscolo Meneguolo, Francesco", "affiliation": "University of Padova" }, { "name": "Sinigaglia, Alberto", "affiliation": "Human Inspired Technology Research Center, University of Padua, 35121 Padua, Italy" }, { "name": "Sartor, Davide", "affiliation": "Università Di Padova" }, { "name": "Cederle, Matteo", "affiliation": "University of Padova" }, { "name": "Susto, Gian Antonio", "affiliation": "University of Padova" } ], "keywords": [ "Reinforcement learning and deep learning in control" ], "abstract": "Normalizing Flow (NF) models have recently emerged as a powerful class of generative models capable of learning expressive probability distributions through invertible transformations. In Reinforcement Learning (RL), most of the modern algorithms rely on distributions typically parameterized as Gaussian or deterministic. While these choices facilitate tractable optimization, they can severely limit the expressiveness of learned policies. In environments where optimal behaviors require multimodal action distributions, such restrictions can hinder both learning efficiency and final performance. A promising way to address these limitations is through more flexible generative models that can accurately capture complex probability distributions. This study investigates the application of Normalizing Flow architectures to RL tasks, both in single-agent and multi-agent environments. In particular, it is assessed that NFs are capable to model policies that converge to the Nash equilibrium in a two-player zero-sum game scenario, unlike deterministic policies.", "url": "" }, { "id": "Tu-TuA03.24", "code": "TuA03.24", "title": "Hybrid LQR-TD3 Collective Pitch Control Architecture for Wind Turbines (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:45-11:50", "sessionCode": "TuA03", "sessionTitle": "Shotgun: Computers, Cognition and Communication", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Gil-Macia, Alberto", "affiliation": "Complutense University of Madrid" }, { "name": "Sierra-Garcia, Jesus Enrique", "affiliation": "University of Burgos" }, { "name": "Santos, Matilde", "affiliation": "University Complutense of Madrid (VAT ESQ2818014I)" } ], "keywords": [ "Reinforcement learning and deep learning in control", "AI-driven modeling and control", "AI tools in automation engineering and operation" ], "abstract": "Reinforcement learning (RL)-based controllers provide excellent control characteristics for power-output stabilization of wind turbines but require large training datasets, while LQR controllers are suboptimal away from the linearization point. This paper proposes a hybrid collective pitch control (CPC) architecture combining an LQR and Twin Delayed Deep Deterministic Policy Gradient (TD3) controller. The LQR controller guides the TD3 agent during training, while the TD3 controller learns to compensate for the nonlinear dynamics not captured during linearization. Results show that the LQR+TD3 hybrid controller improves performance and reduces steady-state error compared with individual LQR and TD3 controllers.", "url": "" }, { "id": "Tu-TuA04.1", "code": "TuA04.1", "title": "Safe Multi-Agent Navigation under Limited Communication Using High-Order Robust Control Barrier Functions", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-09:55", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Jia, Zhanxiao", "affiliation": "Northwestern Polytechnical University" }, { "name": "Xu, Bowen", "affiliation": "Northwestern Polytechnical University" }, { "name": "Xue, Ruihong", "affiliation": "Northwestern Polytechnical University" }, { "name": "Fan, Chengli", "affiliation": "Air Force Engineering University" }, { "name": "Fu, Qiang", "affiliation": "Air Force Engineering University" }, { "name": "Yu, Dengxiu", "affiliation": "Northwestern Polytechnical University" } ], "keywords": [ "Applications of optimal control", "Learning methods for optimal control" ], "abstract": "This paper proposes a novel framework for safe and coordinated multi-agent navigation under communication constraints. Traditional multi-agent reinforcement learning methods often struggle to ensure safety and coordination in partially observable environments with limited bandwidth. The proposed R-MADDPG–HORCBF framework integrates Recurrent Multi-Agent Deep Deterministic Policy Gradient (R-MADDPG) with High-Order Robust Control Barrier Functions (HORCBFs). Specifically, a recurrent actor-critic network is employed to capture temporal dependencies, while a differentiable RCBF layer is incorporated to enforce safety constraints in real time. Simulation results in multi-vehicle navigation scenarios demonstrate that the proposed framework significantly enhances both safety and communication efficiency, highlighting its strong potential for real-world deployment in safety-critical systems.", "url": "" }, { "id": "Tu-TuA04.2", "code": "TuA04.2", "title": "Optimal Path Planning of Airborne Wind Energy Systems in the Wake of a Horizontal Axis Wind Turbine", "day": "Tuesday", "date": "August 25, 2026", "time": "09:55-10:00", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Heydarnia, Omid", "affiliation": "Ghent University" }, { "name": "Wauters, Jolan", "affiliation": "KU Leuven" }, { "name": "Lefebvre, Tom", "affiliation": "Ghent University" }, { "name": "Crevecoeur, Guillaume", "affiliation": "Ghent University" } ], "keywords": [ "Applications of optimal control", "Numerical methods for optimal control", "Application of nonlinear analysis and design" ], "abstract": "The increasing deployment of wind turbines and the limited availability of suitable installation areas motivate the integration of multiple wind-energy-harvesting technologies. Airborne Wind Energy Systems (AWES), capable of accessing high-altitude wind resources, offer a promising complement to conventional Horizontal-Axis Wind Turbines (HAWTs). This work presents an optimal path-planning algorithm for AWES operating within the wake of HAWTs. A simplified wake model is employed to estimate wind speed deficits behind the turbine and is incorporated directly into the trajectory optimization scheme. Simulation results show that lemniscate flight paths exhibit less sensitivity to wake effects compared to circular trajectories. The results demonstrate the potential of wake-aware path planning to improve AWES performance in multi-technology wind farm environments.", "url": "" }, { "id": "Tu-TuA04.3", "code": "TuA04.3", "title": "Automatic Evaluation of Fastener Assembly Quality in Aircraft Power Distribution Boxes Using RT-DETR and Template Comparison", "day": "Tuesday", "date": "August 25, 2026", "time": "10:00-10:05", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Yan, Zhongbao", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Yin, Chun", "affiliation": "University of ElectronicScience and Technology of China, Chengdu611731, P.R. China" }, { "name": "Liu, Junyang", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Cao, Jiuwen", "affiliation": "Hangzhou Dianzi University" }, { "name": "Zhang, Yuanhao", "affiliation": "University of Electronic Science and Technology of China" } ], "keywords": [ "Applications of optimal control", "Optimal control of hybrid systems", "Fault detection and isolation" ], "abstract": "To address the low efficiency of fastener assembly inspection for aircraft power distribution boxes, the reliance on manual expertise, and the poor adaptability to small targets and diverse assembly specifications, this paper presents a two stage automatic inspection method that combines an RT-DETR based detection network with template comparison. We build a dataset of 4,125 images of power distribution box fasteners, use RT-DETR to obtain class labels and bounding box priors for each assembly position, and design a global image matching method constrained by keypoints and annotation boxes to align template boxes with detection results and perform consistency assessment. Experiments show that the RT-DETR detector achieves an mAP50 of 0.9925 on the constructed dataset, with mean precision and recall of 0.9862 and 0.9844, respectively. Experimental results on multi view inspection images show that the proposed framework can reliably identify missing and misinstalled fasteners and reduce reliance on manual inspection, indicating strong potential for engineering applications.", "url": "" }, { "id": "Tu-TuA04.4", "code": "TuA04.4", "title": "Nonlinear Control of an Asymmetric Falling Cat Model Via State-Dependent Riccati Equation (SDRE)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:05-10:10", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Xin, Xin", "affiliation": "Southeast University" }, { "name": "Fang, Dingyang", "affiliation": "Southeast University" }, { "name": "Zhou, Chi", "affiliation": "Southeast University" }, { "name": "Sampei, Mitsuji", "affiliation": "The Polytechnic University of Japan" } ], "keywords": [ "Applications of optimal control", "Real-time optimal control", "Application of nonlinear analysis and design" ], "abstract": "This paper investigates state-dependent Riccati equation (SDRE) feedback for practical self-righting of an asymmetric two-link falling-cat model. The velocity-input nonholonomic model is augmented with virtual angular-acceleration inputs to better align the control layer with torque-driven actuation. Three state-dependent coefficient (SDC) parameterizations are constructed, and their pointwise controllability conditions are characterized through a PBH-based analysis. Comparative simulations for a static-drop maneuver show that the parameterization preserving the dominant spin dynamics yields faster convergence and smoother inputs, whereas the alternatives either fail near the zero-velocity manifold or violate the bending constraint.", "url": "" }, { "id": "Tu-TuA04.5", "code": "TuA04.5", "title": "Output-Feedback Hierarchical Control Using Approximate Simulation -- towards a Data-Driven Implementation", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:15", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Niu, Zirui", "affiliation": "Imperial College London" }, { "name": "Shakib, Mohammad Fahim", "affiliation": "Eindhoven University of Technology" }, { "name": "Scarciotti, Giordano", "affiliation": "Imperial College London" } ], "keywords": [ "Control of complex systems", "Design methods for data-based control", "Linear systems" ], "abstract": "Approximate simulation-based hierarchical control (ASHC), in brief, is a technique used for simplifying the control design of a complex system with an a priori known output discrepancy bound. Current ASHC methods are based on state feedback, which hinders the possibility of developing data-driven enhancements. To overcome this difficulty, in this paper, we present a novel output-feedback ASHC framework when online state feedback is not possible. Furthermore, we propose a direct data-driven enhancement. While the proposed data-driven results still rely on the state data, the results of this paper can be seen as a stepping stone in developing a fully input-output data-driven method for solving the ASHC problem. All results are illustrated by means of a numerical example.", "url": "" }, { "id": "Tu-TuA04.6", "code": "TuA04.6", "title": "Tuning of PID/PIDD2 Controllers Via State-Space Pole Placement", "day": "Tuesday", "date": "August 25, 2026", "time": "10:15-10:20", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Tan, Wen", "affiliation": "North China University of Technology" } ], "keywords": [ "Control of complex systems", "Parametric optimization", "Robustness analysis" ], "abstract": "A state space pole placement approach is proposed to design PID controllers for high-order processes. The method makes use of a single parameter to determine the locations of the closed-loop poles, thus a (high-order) PID controller can be tuned with this parameter. Tuning rules of PID/PIDD2 controllers are then derived for typical stable, integrating and unstable process models. The tuned rules are applied to the benchmark processes. Simulation results show that the tuning rules can achieve compromise among disturbance rejection, robustness, and noise attenuation.", "url": "" }, { "id": "Tu-TuA04.7", "code": "TuA04.7", "title": "Hylomorphic Dynamic Programming", "day": "Tuesday", "date": "August 25, 2026", "time": "10:20-10:25", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Yang, Ya-Ting", "affiliation": "New York University" }, { "name": "Zhu, Quanyan", "affiliation": "New York University" } ], "keywords": [ "Differential or dynamic games", "Control of hybrid systems", "Optimal control of hybrid systems" ], "abstract": "Many real-world systems, such as robotics and cyber defense, rely on hierarchical decision processes where a strategic layer sets long-term configurations and a tactical layer executes fast-time actions, leading to a leader–follower structure with asymmetric information and temporally coupled interactions that may fall outside classical Stackelberg models. To address this gap, we introduce hylomorphic dynamic programming (HDP) for hierarchical control. HDP operates between an anamorphism, which unfolds strategic choices into tactical consequences by solving inner dynamic programs, and a catamorphism, which folds tactical outcomes into strategic values. This hylomorphic recursion provides a consistent and computationally tractable framework of the associated dynamic Stackelberg equilibrium.", "url": "" }, { "id": "Tu-TuA04.8", "code": "TuA04.8", "title": "Analysis of the Attacker-Defender-Target Differentiable Game with Faster Attackers", "day": "Tuesday", "date": "August 25, 2026", "time": "10:25-10:30", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Song, XiangYu", "affiliation": "Tongji University" }, { "name": "Lei, Jinlong", "affiliation": "Tongji University" }, { "name": "Yi, Peng", "affiliation": "Tongji University" } ], "keywords": [ "Differential or dynamic games", "Optimal control theory", "Analytic design" ], "abstract": "This paper proposes a comprehensive analysis framework and optimal strategies for the Attacker-Defender-Target (ADT) differential game. The game involves three agents with simple kinematic models, where the attacker has a speed advantage. Based on Pontryagin’s minimum principle, this paper establishes a unified Hamiltonian framework for both scenarios where the attacker wins and the defender wins. The study proves that each agent's optimal strategy manifests as constant-velocity rectilinear motion towards a specific interception point. Drawing upon the geometric theory of Apollonius circles, analytical equations for determining the optimal interception point are derived. Furthermore, by analyzing the relative positions of the two Apollonius circles—between the attacker and defender, and between the attacker and target—this paper provides strict geometric criteria for dividing the game’s winning regions.Finally, numerical simulations are implemented to validate the theoretic results.", "url": "" }, { "id": "Tu-TuA04.9", "code": "TuA04.9", "title": "A Feedback Linearization and Riccati-Based Approach to Nonlinear Zero-Sum Differential Games", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:35", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Garazha, Ilya", "affiliation": "National Research University Higher School of Economics" }, { "name": "Afanas'ev, Valery", "affiliation": "National Research University Higher School of Economics Moscow Institute of Electronics and Mathematics" } ], "keywords": [ "Differential or dynamic games", "Real-time optimal control", "Stability of nonlinear systems" ], "abstract": "This paper addresses a zero-sum differential game with a quadratic cost functional for controlling nonlinear plants under bounded disturbances, modelled by ordinary differential equations with state feedback. A diffeomorphic coordinate transformation linearizes the system, yielding a model with constant parameters and a transformed cost functional featuring state-dependent weighting matrices. Optimal strategies are derived from the Bellman–Isaacs equation, which leads to a state-dependent Riccati-type equation. In the infinite-horizon case the problem reduces to a state-dependent Riccati equation (SDRE), which is solved numerically, yielding a suboptimal regulator that guarantees asymptotic stability. The control and disturbance inputs are combined into a single regulator, and the inverse transformation recovers the original controls. An example based on the Lotka–Volterra predator–prey model illustrates the effectiveness of the proposed method.", "url": "" }, { "id": "Tu-TuA04.10", "code": "TuA04.10", "title": "Collapsed Filtering for Fault Root–Cause Identification in Nonlinear Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "10:35-10:40", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Canyakmaz, Ilayda", "affiliation": "Singapore University of Technology and Design" }, { "name": "Escudero, Cédric", "affiliation": "Laboratoire Ampère CNRS, INSA Lyon, Université De Lyon" }, { "name": "Murguia, Carlos", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Fault detection and isolation", "Observer design", "Application of nonlinear analysis and design" ], "abstract": "This paper presents a framework for fault estimation and root–cause identification (RCI) in nonlinear systems that avoids the structural difficulties of nonlinear unknown–input observers. We construct a collapsed model that merges nonlinearities and unknown faults into aggregated input channels, and propose a robust L_2 filter to estimate the resulting lifted state. We show that the lifted dynamics remain well posed and that filter existence requires only a weak zero-frequency input-observability condition, milder than full input observability. Individual fault components are then recovered through simple algebraic extractor maps. For RCI, we introduce a dictionary-based filter that compares the estimated trajectory against a library of candidate fault signatures and scores each by how well it explains the observed fault behaviour. The approach is illustrated on a three-tank benchmark.", "url": "" }, { "id": "Tu-TuA04.11", "code": "TuA04.11", "title": "Detection of Actuator Faults in Systems with Overlapped Ostensible Metzler Dynamics", "day": "Tuesday", "date": "August 25, 2026", "time": "10:40-10:45", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Krokavec, Dusan", "affiliation": "Technical University of Kosice" }, { "name": "Filasova, Anna", "affiliation": "Technical University of Kosice" } ], "keywords": [ "Fault detection and isolation", "Positive linear systems", "Observer design" ], "abstract": "The paper deals with the properties of a fault detection filter when applied to a class of continuous-time linear systems with dynamics specified by a system matrix with an overlapped ostensible Metzler structure. The proposed solution reduces to the use of diagonal stabilization in the synthesis of the state observer and uses orthogonal transformation to construct a model with reduced order dynamics in the form of an ostensible Metzler matrix and the separation principle to generate a hidden strictly Metzler matrix for the synthesis conditions. This approach creates a unified framework that covers the compactness of parametric constraints on Metzler matrices and their diagonal quadratic stability. Using a structural model of a fixed-wing unmanned aerial vehicle to validate the method shows that the proposed approach provides high sensitivity of the fault detection filter for actuator fault detection.", "url": "" }, { "id": "Tu-TuA04.12", "code": "TuA04.12", "title": "An Efficient Distributed ADMM with Local Updates for Composite Optimization", "day": "Tuesday", "date": "August 25, 2026", "time": "10:45-10:50", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Zhou, Yuan", "affiliation": "Southeast University" }, { "name": "Shi, Xinli", "affiliation": "Southeast University" }, { "name": "Xu, Xiangping", "affiliation": "Hohai University" }, { "name": "Cao, Jinde", "affiliation": "Southeast Univ" } ], "keywords": [ "Large-scale and networked optimization problems", "Convex optimization" ], "abstract": "This paper addresses distributed composite optimization, where standard algorithms suffer from significant communication overhead and computational burden. We propose DC-ADMM-LU, a novel framework that achieves both communication and computation efficiency through local updates. The key innovation is leveraging ADMM's variable splitting to decouple the expensive proximal operator from frequent local computations, while each client performs multiple lightweight, explicit update steps. An integrated variance-reduction mechanism ensures rigorous error control across local iterations. We establish the first linear convergence guarantee for multi-step local-update ADMM in the distributed stochastic setting, without restrictive assumptions. Numerical experiments confirm superior performance.", "url": "" }, { "id": "Tu-TuA04.13", "code": "TuA04.13", "title": "Optimal Safe Attitude Tracking Control for UAV System with Unknown Disturbances under Relaxed PE Conditions", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-10:55", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Chen, Chen", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Peng, Zhinan", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Luo, Rui", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Kuang, Yiqun", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Cheng, Hong", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Ghosh, Bijoy", "affiliation": "Texas Tech University" } ], "keywords": [ "Learning methods for optimal control", "Adaptive control design" ], "abstract": "This paper proposes a novel adaptive learning control approach for attitude tracking of unmanned aerial vehicles (UAVs) subject to safety constraints and unknown disturbances with relaxed persistence of excitation (PE) conditions. We first formalize the robust optimal attitude tracking problem with a zero-sum game structure. Then, a modified reward function that consists of a control barrier function (CBF) is presented, which prevents the system states from violating the prescribed safety boundaries. To solve this optimization problem, a critic adaptive dynamic programming (ADP) framework is employed to approximate the solution of Hamilton-Jacobi-Issac (HJI) equation, thus obtaining the approximated optimal control. Unlike the existing gradient-descent learning methods, we transform the weight learning problem into a parameter estimation problem, which is further solved by a novel estimator design using dynamic regression extension and mixing (DREM) and generalized parameter estimation based observer (GPEBO) techniques. The main advantage of this method lies in that it not only relaxes the strict PE conditions for parameter convergence but also provides specific implementation solutions, thereby enhancing its applicability in real-world scenarios. Rigorous theoretical analysis and numerical simulations demonstrate the effectiveness and superiority of our proposed method.", "url": "" }, { "id": "Tu-TuA04.14", "code": "TuA04.14", "title": "A Physics-Informed Neural Network Approach for Solving HJB Equations", "day": "Tuesday", "date": "August 25, 2026", "time": "10:55-11:00", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Georges, Didier", "affiliation": "Grenoble Institute of Engineering and Management - Univ. Grenoble Alpes" } ], "keywords": [ "Learning methods for optimal control", "Numerical methods for optimal control", "Applications of optimal control" ], "abstract": "A physics-informed neural network (PINN) approach for solving hyperbolic infinite-horizon Hamilton--Jacobi--Bellman (HJB) equations arising in nonlinear optimal regulator problems is proposed in this paper. The method simultaneously learns the value function and the optimal feedback control law through two coupled neural networks, trained to satisfy the continuous-time HJB equation and the optimality conditions for the control. We then apply the method to the closed-loop control of a quadrotor UAV and a high-dimensional reduced model of a nonlinear heat equation. The proposed PINN approach proves capable of overcoming the curse of dimensionality problem. Finally, the application of the proposed PINN approach is discussed for solving the optimal nonlinear estimation problem.", "url": "" }, { "id": "Tu-TuA04.15", "code": "TuA04.15", "title": "Predefined-Time Observer-Identifier-Based Optimal Tracking Control for Uncertain Robotic Systems under State Constraints", "day": "Tuesday", "date": "August 25, 2026", "time": "11:00-11:05", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Hao, Lin", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Peng, Zhinan", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Chen, Chen", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Luo, Rui", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Cheng, Hong", "affiliation": "University of Electronic Science and Technology of China" } ], "keywords": [ "Learning methods for optimal control", "Optimization-based estimation and control", "Adaptive control design" ], "abstract": "This article proposes a novel predefined--time observer--identifier--based optimal tracking control framework for robotic systems with unknown states and uncertain dynamics subject to prescribed state constraints. Till now, most of the existing results on optimal control approaches for uncertain robotic systems require full--state information in the identifier and controller design, which is often invalid in practical scenarios. To address this issue, a predefined--time dynamic regression extension and mixing (PTDREM) method is proposed to design an observer--identifier that can simultaneously estimate unmeasurable system states and uncertain model parameters. Then, a new predefined--time prescribed performance control (PTPPC) scheme is developed under the framework of optimized backstepping technique. With this scheme, the tracking error is guaranteed to be constrained to a prearranged vicinity of origin within a predefined time. In contrast to previous studies, the proposed framework not only achieves the convergence of all closed-loop signals, but also allows that the upper bounds of convergence time for the observer--identifier and controller can all be adjusted through separate design parameters, thus ensuring global predefined--time stability (GPTS). Finally, simulation results demonstrate the effectiveness of the proposed observer--identifier--based control method.", "url": "" }, { "id": "Tu-TuA04.16", "code": "TuA04.16", "title": "Towards Guaranteed Optimal PID Tuning for Uncertain Nonlinear Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "11:05-11:10", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Zhu, Jingru", "affiliation": "University of Chinese Academy of Sciences" }, { "name": "Zhao, Cheng", "affiliation": "Chinese Academy of Sciences" }, { "name": "Guo, Lei", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Learning methods for optimal control", "Stability of nonlinear systems", "Uncertain systems" ], "abstract": "Despite the widespread use of PID controllers in engineering practice, designing optimal PID parameters has long been regarded as a challenging problem in both theory and practice, particularly when faced with uncertain nonlinear dynamical systems. Based on the authors' PID control theory established recently for MIMO nonlinear uncertain systems (Zhao and Guo, 2022), which provides a concrete PID parameter set for global stability of PID controlled systems, this paper further proposes a near-optimal PID tuning method, where only input-output (zeroth-order) data on the control performance is available. The tuning method is formulated as a constrained optimization problem and solved by an iterative learning algorithm, referred to as HRS-KW algorithm, that combines a hysteretic random search with the Kiefer–Wolfowitz algorithm, aiming at utilizing the advantages of both global exploration and local gradient acceleration. This method operates without requiring precise structural knowledge of the system dynamics, yet its almost sure convergence to an epsilon-optimal solution for the PID parameters can be guaranteed in theory while ensuring closed-loop system stability. Simulation results illustrate that our HRS-KW algorithm outperforms other related optimization methods, exhibiting better convergence to the prescribed epsilon-optimal performance set.", "url": "" }, { "id": "Tu-TuA04.17", "code": "TuA04.17", "title": "Pole Placement for Static Output Feedback Systems by Continuous Pole Shifting and Its Application to PID Control Design", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:15", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Ochi, Yoshimasa", "affiliation": "National Defense Academy" }, { "name": "Totoki, Hironori", "affiliation": "National Defense Academy" } ], "keywords": [ "Linear systems" ], "abstract": "This paper proposes a computational procedure for designing a static output feedback (SOF) gain matrix for multi-input multi-output (MIMO) systems using a continuation (or homotopy) method. We regard the characteristic equations for the closed-loop SOF system as simultaneous nonlinear equations with respect to the gain elements for a given set of desired poles. We then derive differential equations from the characteristic equations based on the continuation approach. By integrating the differential equations from known initial poles to desired poles, we can obtain a gain matrix that assigns the closed-loop poles to the desired ones. From the rank of a derivative matrix in the differential equation, we can know if all or part of the designated closed-loop poles are assignable. The method is also extended to dynamic control design, particularly PID control. The effectiveness of the proposed procedure is demonstrated through flight control design for an unstable aircraft and its numerical simulation.", "url": "" }, { "id": "Tu-TuA04.18", "code": "TuA04.18", "title": "Control of Discrete-Time Linear Systems with Charge-Balanced Inputs", "day": "Tuesday", "date": "August 25, 2026", "time": "11:15-11:20", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Qin, Yuzhen", "affiliation": "Radboud University" }, { "name": "Liu, Zonglin", "affiliation": "University of Kassel" }, { "name": "Stursberg, Olaf", "affiliation": "University of Kassel" }, { "name": "van Gerven, Marcel", "affiliation": "Radboud University" } ], "keywords": [ "Linear systems", "Control in neuroscience", "Optimal control theory" ], "abstract": "Electrical brain stimulation relies on externally applied currents to modulate neural activity, but safety constraints require each stimulation cycle to be charge-balanced, enforcing a zero net injected charge. However, how such charge-balanced stimulation works remains poorly understood. This paper investigates the ability of charge-balanced inputs to steer state trajectories in discrete-time linear systems. Motivated by both open-loop and adaptive neurostimulation protocols, we study two practically relevant input structures: periodic (repetitive) charge-balanced inputs and non-repetitive charge-balanced inputs. For each case, we derive novel reachability and controllability conditions. The theoretical results are further validated through numerical demonstrations of minimum-energy control input design.", "url": "" }, { "id": "Tu-TuA04.19", "code": "TuA04.19", "title": "Re-Opening PID Controller Stability Domain in 3D Via Ruled Surface by D-Partition", "day": "Tuesday", "date": "August 25, 2026", "time": "11:20-11:25", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Tremba, Andrey", "affiliation": "Institute of Control Sciencies" } ], "keywords": [ "Linear systems", "Controller constraints and structure", "Linear time-delay systems" ], "abstract": "All stabilizing PID controllers form a set in three-dimensional space. A novel viewpoint to its boundary as a ruled surface (or surfaces) being cut with 3D planes is presented. The characterization, being not too new, contributes to an understanding of the stability set as the whole, instead of the classical view as a stack of 2D slices, say, on the P-coefficient. The viewpoint gives clear insight on the structure of the PID stability region, and, in particular, splits its boundary into continuous parts. It is followed by natural 2D unwrapping of the stability set boundary. It also correctly handles pure imaginary zeros in transfer function. A wireframe 3D visualization reveals the structure of the stability set. The presentation is valid both for ideal and filtered PID controllers, as well as for time-delay systems and other linear systems. Finally, based on the viewpoint, a simple formula for stability (fragility) radius is provided.", "url": "" }, { "id": "Tu-TuA04.20", "code": "TuA04.20", "title": "Enhanced Inverse Linear Quadratic Control for Hot Rolling Looper-Gauge Coordination", "day": "Tuesday", "date": "August 25, 2026", "time": "11:25-11:30", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Yuan, Hao", "affiliation": "Northeastern University" }, { "name": "Li, Xu", "affiliation": "Northeastern University" }, { "name": "Tian, Yong", "affiliation": "State Key Laboratory of Digital Steel, Northeastern University, Shenyang, China" }, { "name": "Li, Yong", "affiliation": "Northeastern University" } ], "keywords": [ "Linear systems", "Optimal control theory" ], "abstract": "Addressing the strong dynamic coupling between the looper and gauge control systems in hot rolling, this paper proposes a coordinated control scheme based on an enhanced inverse linear quadratic (ILQ) theory. The proposed design systematically constructs the adjustable gain matrix Π and establishes an autonomous optimization framework integrating swarm intelligence. Furthermore, disturbance observer-based robust control (DOBRC) is innovatively incorporated, forming a composite control architecture. Simulation results demonstrate that the proposed scheme significantly improves the suppression of external mismatched disturbances and enhances robustness against model uncertainties.", "url": "" }, { "id": "Tu-TuA04.21", "code": "TuA04.21", "title": "Fragility Analysis and Stabilizing Sets of PID Controllers in Frequency Domain", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:35", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Shatov, Dmitrii", "affiliation": "V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences" } ], "keywords": [ "Linear systems", "Robust estimation", "Uncertain systems" ], "abstract": "This research focuses on fragility analysis of PID controllers. The problem considered is to find a complete stabilizing set for each parameter of a given PID controller. The proposed solution is based on the classical frequency-domain stability criterion -- the Nyquist criterion. The procedure utilizes a known robust analysis method, the so-called ``breaking by parameter'' technique, which enables the study of robust (here, stabilizing) properties for an individual system parameter. Applying this technique to PID controller parameters solves the fragility analysis problem. The main result is presented as an analytical procedure for individual PID parameters.", "url": "" }, { "id": "Tu-TuA04.22", "code": "TuA04.22", "title": "Efficient Numerical Techniques for Data-Driven Approach to Geometric Control Problems", "day": "Tuesday", "date": "August 25, 2026", "time": "11:35-11:40", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "N, Naveen Mukesh", "affiliation": "Indian Institute of Technology Bombay" }, { "name": "Patil, Deepak", "affiliation": "Indian Institute of Technology Delhi" }, { "name": "Pal, Debasattam", "affiliation": "Indian Institute of Technology Bombay" } ], "keywords": [ "Linear systems", "Structural and geometric control", "Numerical methods for optimal control" ], "abstract": "This work aims to provide numerically efficient computational techniques for recent results from data-driven geometric control. First, an overview of recent results on the data-driven disturbance decoupling problem (D4P) from (Naveen Mukesh et al., 2025) is presented. These results use multiple noisy output trajectories collected from the system instead of system matrices. Then, numerically efficient subspace computational methods that use only input-output data are developed to verify the solvability condition for the disturbance decoupling problem (DDP). The proposed numerical method uses the LQ decomposition to perform the required subspace computations. Subsequently, from the ``noisy'' output data, the largest controlled invariant subspace contained in the nullspace of the output matrix and a corresponding feedback matrix that solves the DDP are also computed numerically using LQ decomposition. Lastly, efficient computation techniques for computing the largest controlled invariant subspace contained in the nullspace of the output matrix and the smallest conditioned invariant subspace containing the range space of the input matrix, from exact noise-free data collected from the system, are presented.", "url": "" }, { "id": "Tu-TuA04.23", "code": "TuA04.23", "title": "Spectrum Reconstruction for LTI Discrete-Time Delay Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "11:40-11:45", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Li, Xu", "affiliation": "Nanjing University of Posts and Telecommunications" }, { "name": "Li, Xu-Guang", "affiliation": "Northeastern University" }, { "name": "Fan, Gaoxia", "affiliation": "Northeastern University" }, { "name": "Chen, Jun-Xiu", "affiliation": "Northeastern University" }, { "name": "Zhang, Lu", "affiliation": "Northeastern University" } ], "keywords": [ "Linear time-delay systems" ], "abstract": "The spectrum of a discrete-time delay system (DTDS) with linear-time-invariant (LTI) dynamics is of the discontinuity nature, when the delay tau is treated as a free parameter. This is a long-standing obstacle for directly keeping track of the stability property in the whole delay parameter space. This work proposes an intuitive frequency-domain framework to solve this problem. First, we construct the characteristic entire function for a DTDS, whose spectrum has the equivalence relation with that of the characteristic function. Second, we propose the continuity property of unstable roots for the characteristic entire function. Therefore, the spectrum of the characteristic function is replaced by that of the characteristic entire function, and the discontinuity issue is fully solved, which allows for an available and direct way to study the stability w.r.t. a free tau. Finally, within our new framework, a general idea for analyzing the stability in the whole delay parameter space, the tau-decomposition idea for DTDS, is provided.", "url": "" }, { "id": "Tu-TuA04.24", "code": "TuA04.24", "title": "Price-And-Branch for Sweep Coverage with Mobile Sensors on Cell-Shaped Areas", "day": "Tuesday", "date": "August 25, 2026", "time": "11:45-11:50", "sessionCode": "TuA04", "sessionTitle": "Shotgun: Design Methods in Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Gusrialdi, Azwirman", "affiliation": "Tampere University" }, { "name": "Marinelli, Fabrizio", "affiliation": "Università Politecnica Delle Marche" }, { "name": "Pizzuti, Andrea", "affiliation": "Università Degli Studi ECampus" }, { "name": "Ronchini, Nicola", "affiliation": "Università Politecnica Delle Marche" } ], "keywords": [ "Task and motion planning", "Aerial, field, and marine robotics" ], "abstract": "This paper presents a path-based integer linear programming formulation for the sweep coverage problem, in which points of interest of a given area, i.e., an indoor farming field, must be covered by mobile sensors, subject to redundancy and sensing range constraints. A price-and-branch algorithm, whose pricing subproblem is formulated as a generalized orienteering problem, is employed to compute primal and dual bounds. For a simplified variant of the problem, a convex-hull-based destroy-and-repair heuristic is designed for the warm start and acceleration of column generation. The effectiveness of the proposed approach is discussed through computational experiments.", "url": "" }, { "id": "Tu-TuA05.1", "code": "TuA05.1", "title": "Model-Free Predictive Control with Sliding Mode Augmentation", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:05", "sessionCode": "TuA05", "sessionTitle": "LB: Model Predictive Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Kornmaneesang, Woraphrut", "affiliation": "National Taiwan Normal University" }, { "name": "Pratvittaya, Jiravit", "affiliation": "King Mongkut's University of Technology Thonburi" }, { "name": "Thongking, Witchuda", "affiliation": "Department of Control Systems and Instrumentation Engineering, King Mongkut’s University of Technology Thonburi" } ], "keywords": [ "Data-driven robust control", "Sliding mode control", "Model predictive control" ], "abstract": "This paper proposes a robust data-driven control for nonlinear systems by integrating model-free predictive control (MFPC) with discrete-time sliding mode control (DTSMC). While MFPC effectively optimizes control actions using historical input-output data without an explicit model and Sliding mode augmentation is introduced to ensure the system stability and to address the steady-state error issue due to the unmodeled uncertainty. Simulation results on a nonlinear system demonstrate that the proposed method significantly reduces the error compared to conventional PID and pure MFPC, yielding faster settling times and superior tracking performance.", "url": "" }, { "id": "Tu-TuA05.2", "code": "TuA05.2", "title": "A Crowd Behavior Model Reflecting Attention of Pedestrians and Its Evaluation of Fluidity", "day": "Tuesday", "date": "August 25, 2026", "time": "10:05-10:20", "sessionCode": "TuA05", "sessionTitle": "LB: Model Predictive Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Inagaki, Kenshin", "affiliation": "Tokyo Metropolitan University" }, { "name": "Kojima, Akira", "affiliation": "Tokyo Metropolitan University" } ], "keywords": [ "Model predictive control of hybrid systems", "Multi-agent systems", "Optimal control of discrete event and hybrid systems" ], "abstract": "Recently, it has become increasingly important to develop crowd behavior models which reflect the states of pedestrian attention. An example of these models is the behavior of smartphone-distracted pedestrians, whose reduced attention increases the risk of accidents. In this study, we focus on a hybrid system model which represents the pedestrian behavior with model predictive control, and reflect the state of attentions by adjusting the recalculation cycle of the model predictive control. The features of the proposed models are discussed based on simulation results.", "url": "" }, { "id": "Tu-TuA05.3", "code": "TuA05.3", "title": "Model Predictive Control for Autonomous Overtaking with Virtual Vehicle Reference", "day": "Tuesday", "date": "August 25, 2026", "time": "10:20-10:35", "sessionCode": "TuA05", "sessionTitle": "LB: Model Predictive Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Morita, Ryosuke", "affiliation": "Gifu University" }, { "name": "Yokozeki, Ko", "affiliation": "Gifu University" } ], "keywords": [ "Model predictive control", "Applications of optimal control", "Convex optimization" ], "abstract": "This paper presents a model predictive control (MPC) framework for autonomous vehicle overtaking of a slower preceding vehicle. A virtual vehicle, defined as a copy of the following vehicle assuming no slower preceding vehicle, is used to generate a nominal reference trajectory. The MPC optimizes the rates of steering and acceleration commands to obtain smooth maneuvers, while safety is enforced through a rectangular safety region constraint with slack variables to maintain feasibility. Simulation results with a linear vehicle model demonstrate smooth lane change and lane return while satisfying input-rate and safety constraints.", "url": "" }, { "id": "Tu-TuA05.4", "code": "TuA05.4", "title": "Model Predictive Control of a Class of Water Distribution Networks", "day": "Tuesday", "date": "August 25, 2026", "time": "10:35-10:50", "sessionCode": "TuA05", "sessionTitle": "LB: Model Predictive Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Das, Tarak", "affiliation": "Indian Institute of Technology, Madras" }, { "name": "Narasimhan, Sridharakumar", "affiliation": "Indian Institute of Technology, Madras" } ], "keywords": [ "Model predictive control", "Convex optimization", "Large-scale and networked optimization problems" ], "abstract": "Water Distribution Networks (WDNs) require efficient operational strategies to ensure reliable and energy-efficient water supply. Model Predictive Control (MPC) approaches for scheduling pumps in WDNs often lead to Mixed-Integer Nonlinear Programs (MINLPs) due to combinatorial pump configurations which hinder the computational tractability. This paper presents a decomposition-based framework for a class of single-sourced, branched WDNs with a single pumping station composed of multiple variable-frequency-drive pumps (VFDs). The pumping station is replaced with a discharge pressure decision variable, allowing the network to optimize for an energy-dissipation surrogate for pumping work. Under suitable relaxations, the resulting problem becomes convex and yields a well-defined optimal solution. The actual pump operation is then recovered through a set of smaller nonlinear programs corresponding to different numbers of active pumps. Such a decomposition has also been integrated into MPC experiments to enable control of WDNs.", "url": "" }, { "id": "Tu-TuA05.5", "code": "TuA05.5", "title": "Towards a Competitive-Ratio Bound for Model Predictive Control under Plant-Model Mismatch", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:05", "sessionCode": "TuA05", "sessionTitle": "LB: Model Predictive Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Liu, Changrui", "affiliation": "Delft University of Technology" }, { "name": "Shi, Shengling", "affiliation": "Delft University of Technology" }, { "name": "De Schutter, Bart", "affiliation": "Delft University of Technology" } ], "keywords": [ "Model predictive control", "Uncertain systems", "Stability of nonlinear systems" ], "abstract": "Certainty-equivalence MPC (CE-MPC) is widely used despite lacking performance guarantees under model mismatch. This paper discusses the analysis framework proposed by Liu et al. (2026), which provides theoretical stability and optimality guarantees for CE-MPC of nonlinear systems, and applies it to scenarios involving (non-smooth) additive model mismatch. The core of the analysis framework is a perturbation analysis of the MPC value function for quadratic stage costs. A sufficient condition on the mismatch level ensuring stability is presented, followed by a competitive-ratio performance bound quantifying the suboptimality of CE-MPC relative to the infinite-horizon optimal controller with perfect model knowledge. The results explicitly characterize the joint effect of the prediction horizon and the mismatch on the stability and infinite-horizon performance of CE-MPC, and they are particularly useful for designing CE-MPC using surrogate models, e.g., neural network-based models.", "url": "" }, { "id": "Tu-TuA05.6", "code": "TuA05.6", "title": "Infinite-Horizon Sparse Optimal Control by Solving a Finite-Horizon Subproblem", "day": "Tuesday", "date": "August 25, 2026", "time": "11:05-11:20", "sessionCode": "TuA05", "sessionTitle": "LB: Model Predictive Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Oishi, Yasuaki", "affiliation": "Nanzan University" }, { "name": "Iwata, Takumi", "affiliation": "Hiroshima University" }, { "name": "Nagahara, Masaaki", "affiliation": "Hiroshima University" } ], "keywords": [ "Optimal control theory", "Model predictive control", "Saturation and discontinuity" ], "abstract": "Sparse optimal control is considered in the infinite horizon. In the literature, sparse control has been considered mostly in a finite horizon so that it is formulated into a finite-dimensional optimization problem. It is shown in this paper that an optimal solution of the infinite-horizon sparse control problem can be obtained through a solution of some finite-horizon subproblem. This is due to sparsity of the optimal solution in the sense that the optimal control input is constantly equal to zero at its tail. An estimate is given on the horizon length required for this subproblem to guarantee optimality in the infinite horizon.", "url": "" }, { "id": "Tu-TuA05.7", "code": "TuA05.7", "title": "Thermal Management of Electric Vehicles Using the Neural State-Space-Based Model Predictive Control", "day": "Tuesday", "date": "August 25, 2026", "time": "11:20-11:35", "sessionCode": "TuA05", "sessionTitle": "LB: Model Predictive Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Bae, Jaehyun", "affiliation": "Kongju National University" }, { "name": "Yi, Sun", "affiliation": "North Carolina A&T State University" }, { "name": "Han, Jaeyoung", "affiliation": "Kongju National Univeristy" } ], "keywords": [ "AI and learning-based control for automotive systems", "Automotive system identification and modelling", "Electric and solar vehicles" ], "abstract": "This study proposes a Neural State-Space Model Predictive Control (NSS-MPC) strategy for integrated EV charging–thermal management under low-temperature slow-charging conditions. An NSS prediction model was identified using data generated from an AMESim-based EV charging–thermal management system model and integrated into a MATLAB-based MPC framework. The proposed controller uses compressor speed as the manipulated variable, incorporates ambient temperature and charging current as measured disturbances, and optimizes preheating timing to satisfy HVB temperature requirements without PTC heater use. To distinguish the effect of heater elimination from the effect of predictive preheating control, the proposed strategy was compared with Baseline (HP+PTC) and Baseline (HP) cases. AMESim–MATLAB co-simulation results showed that the proposed NSS-MPC achieved the highest charging efficiency and final SOC and the lowest carbon emissions among the compared cases.", "url": "" }, { "id": "Tu-TuA06.1", "code": "TuA06.1", "title": "Residual-Based Output-Feedback Data-Driven Control for Nonlinear Systems: A Model Reference Gaussian Process Regression Approach (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA06", "sessionTitle": "Data-Driven Control I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Kim, Hyuntae", "affiliation": "University of Oxford" } ], "keywords": [ "Data-driven control theory", "Machine and deep learning for system identification", "Learning methods for control" ], "abstract": "We study residual-based, data-driven control for nonlinear discrete-time systems with unknown dynamics. Rather than identifying full plant dynamics, we learn the residual between a nominal model and the true input-output map and inject this correction into a model-reference controller. A Model-Reference Gaussian Process Regression (MR-GPR) module estimates a one-step update that absorbs nominal-plant mismatch; the resulting law is an output-feedback controller built from finite input-output windows. Under finite-window reconstructability, practical internal stability, and a deterministic residual-approximation condition, the closed loop admits an explicit class- K output ultimate bound in terms of the residual approximation error. The Gaussian process is used as a smooth nonparametric approximator; its predictive variance is reported only diagnostically and is not used in the controller or theorem. On a battery-manufacturing-motivated coating example with state-dependent mismatch, the design improves over nominal-only control, and a dataset-size sweep clarifies the accuracy-computation trade-off.", "url": "" }, { "id": "Tu-TuA06.2", "code": "TuA06.2", "title": "Experiment Design Using Prior Knowledge on Controllability and Stabilizability (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA06", "sessionTitle": "Data-Driven Control I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Shakouri, Amir", "affiliation": "University of Groningen" }, { "name": "van Waarde, Henk J.", "affiliation": "University of Groningen" }, { "name": "Camlibel, Kanat", "affiliation": "University of Groningen" } ], "keywords": [ "Data-driven control theory", "Linear system identification", "Time series modeling" ], "abstract": "In this paper, we consider the problem of designing input signals for an unknown linear time-invariant system in such a way that the resulting input-state data, in the noise-free setting, is suitable for identification or stabilization. We will take into account prior knowledge on system-theoretic properties of the system, in particular, controllability and stabilizability. For this, we extend the notion of universal inputs to incorporate prior knowledge on the system. An input is called universal for identification (resp., stabilization) if, when applied to any system complying with the prior knowledge, it results in data suitable for identification (resp., stabilization) regardless of the initial condition. We provide a full characterization of such universal inputs. In addition, we discuss online experiment design using prior knowledge, and we study cases where this approach results in the shortest possible experiment for identification and stabilization.", "url": "" }, { "id": "Tu-TuA06.3", "code": "TuA06.3", "title": "Image-Driven Control with Application to Thermoforming (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA06", "sessionTitle": "Data-Driven Control I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Shim, Junyong", "affiliation": "The University of British Columbia" }, { "name": "van Heusden, Klaske", "affiliation": "University of British Columbia" } ], "keywords": [ "Data-driven control theory" ], "abstract": "This paper proposes Image-Driven Control (IDC), an approach to vision-based feedback that operates directly in the image space. IDC offers a direct end-to-end solution in the behavioral framework. Targeting a limited subset of vision-based feedback problems, including applications in manufacturing, the IDC design builds on a computationally advantageous subspace predictive control formulation and offset-free tracking to enable reference tracking in the image space under input and pixel-level constraints. IDC provides a control-theoretic alternative to vision-based feedback systems, enabling improved transparency for analysis and tuning of end-to-end control. It offers straightforward experiment design, an advantage in applications where data collection is expensive. The effectiveness of IDC is shown in a simulation example using a nonlinear high-fidelity model of the heating phase of the thermoforming process.", "url": "" }, { "id": "Tu-TuA06.4", "code": "TuA06.4", "title": "Data-Driven Synchronization for Network Systems with Noiseless Data (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA06", "sessionTitle": "Data-Driven Control I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Li, Yongzhang", "affiliation": "University of Groningen" }, { "name": "Camlibel, Kanat", "affiliation": "University of Groningen" } ], "keywords": [ "Data-driven control theory", "Consensus", "Control of networks" ], "abstract": "For a collection of homogeneous LTI systems that is interconnected by a protocol, given the network topology and the system model, one may obtain a feedback gain to synchronize the network. However, the model-based methods cannot be applied in case the system model is unknown. Therefore, in this paper, we study the data-driven synchronization problem for homogeneous networks. In particular, given a collection of LTI systems, we collect the input-state data from one individual system. Then, given the network topology, we provide data-based necessary and sufficient conditions for synchronizability. Once the conditions are satisfied, one can also obtain a feedback gain directly from data to synchronize the network with the corresponding design method provided in this paper. Finally, we illustrate our results with a numerical simulation.", "url": "" }, { "id": "Tu-TuA06.5", "code": "TuA06.5", "title": "Gain-Scheduling Data-Enabled Predictive Control for Nonlinear Systems with Linearized Operating Regions (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA06", "sessionTitle": "Data-Driven Control I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Zieglmeier, Sebastian", "affiliation": "University of Oslo" }, { "name": "Hudoba de Badyn, Mathias", "affiliation": "University of Oslo" }, { "name": "Warakagoda, Narada", "affiliation": "Norwegian Defense Research" }, { "name": "Krogstad, Thomas", "affiliation": "Norwegian Defence Research Establishment" }, { "name": "Engelstad, Paal", "affiliation": "University of Oslo" } ], "keywords": [ "Data-driven control theory", "Nonlinear system identification", "Adaptive gain scheduling autotuning control and switching control" ], "abstract": "This paper presents a Gain-Scheduled Data-Enabled Predictive Control (GS-DeePC) framework for nonlinear systems based on multiple locally linear data representations. Instead of relying on a single global Hankel matrix, the operating range of a measurable scheduling variable is partitioned into regions, and regional Hankel matrices are constructed from persistently exciting data. To ensure smooth transitions between linearization regions and suppress region-induced chattering, composite regions are introduced, merging neighboring data sets and enabling a robust switching mechanism. The proposed method maintains the original DeePC problem structure and requires locally informative data sequences. Extensive experiments on a nonlinear DC-motor with an unbalanced disc demonstrate the significantly improved control performance compared to standard DeePC.", "url": "" }, { "id": "Tu-TuA06.6", "code": "TuA06.6", "title": "A Unified Bayesian Framework for Data-Driven Smoothing, Prediction, and Control (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA06", "sessionTitle": "Data-Driven Control I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Yin, Mingzhou", "affiliation": "Leibniz University Hannover" }, { "name": "Iannelli, Andrea", "affiliation": "University of Stuttgart" }, { "name": "Nazari, Seyed Ali", "affiliation": "Leibniz Universtitaet Hannover" }, { "name": "Müller, Matthias A.", "affiliation": "Leibniz University Hannover" } ], "keywords": [ "Probabilistic and Bayesian methods for system identification", "Data-driven control theory", "Linear system identification" ], "abstract": "Extending data-driven algorithms based on Willems' fundamental lemma to stochastic data often requires empirical and customized workarounds. This work presents a unified Bayesian framework for linear systems that provides a systematic and general method for handling stochastic data-driven tasks, including smoothing, prediction, and control, via maximum a posteriori estimation. This framework formulates a unified trajectory estimation problem and solves a Bayesian problem that optimally combines trajectory knowledge with trajectory characterization from offline data. This problem generalizes existing data-driven prediction and control algorithms. Numerical examples demonstrate the performance of the unified approach for all three tasks against other data-driven and system identification approaches.", "url": "" }, { "id": "Tu-TuA07.1", "code": "TuA07.1", "title": "Observer-Based Control of Multi-Agent Systems under STL Specifications", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA07", "sessionTitle": "Advanced Control and Coordination in Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Zaccherini, Tommaso", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Liu, Siyuan", "affiliation": "Eindhoven University of Technology" }, { "name": "Dimarogonas, Dimos V.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Multi-agent systems", "Distributed control and estimation", "Control under communication constraints" ], "abstract": "This paper proposes a decentralized controller for large-scale heterogeneous multi-agent systems subject to bounded external disturbances, where agents must satisfy Signal Temporal Logic (STL) specifications requiring cooperation among non-communicating agents. To address the lack of direct communication, we employ a decentralized k-hop Prescribed Performance State Observer (k-hop PPSO) to provide each agent with state estimates of those agents it cannot communicate with. By leveraging the performance bounds on the state estimation errors guaranteed by the k-hop PPSO, we first modify the space robustness of the STL tasks to account for these errors, and then exploit the modified robustness to design a decentralized continuous-time feedback controller that ensures satisfaction of the STL tasks even under worst-case estimation errors. A simulation result is provided to validate the proposed framework.", "url": "" }, { "id": "Tu-TuA07.2", "code": "TuA07.2", "title": "Distributed Control of Nonholonomic Formations with Limited Field-Of-View and Directed Sensing", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA07", "sessionTitle": "Advanced Control and Coordination in Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Mostafa, Ahmed Fahim", "affiliation": "University of Waterloo" }, { "name": "Fidan, Baris", "affiliation": "University of Waterloo" }, { "name": "Melek, William", "affiliation": "University of Waterloo" } ], "keywords": [ "Multi-agent systems", "Control of networks", "Consensus" ], "abstract": "The practical implementation of multi-agent formation control is often hindered by physical sensing limitations. Moving beyond traditional assumptions of omnidirectional motion and undirected topologies, this paper addresses formation control for nonholonomic mobile robots constrained by directed sensing graphs and limited Field-of-View (FOV) sensors. We propose a distributed control framework that guarantees convergence to a desired geometric configuration while maintaining neighbor visibility throughout the transient phase. Specifically, the underlying nominal control law reduces the relative bearing errors under nonholonomic constraints, while the FOV limits are modeled using Control Barrier Functions (CBFs) and incorporated via a Quadratic Programming (QP) formulation. Rigorous stability analysis proves the asymptotic convergence of the constrained agent trajectories to the target formation, despite the directed sensing topology. The numerical simulations verify that the proposed framework successfully achieves the target formation without violating visual connectivity constraints.", "url": "" }, { "id": "Tu-TuA07.3", "code": "TuA07.3", "title": "Fundamental Limitations of Digital Control: Quadratic Performance and (Q, S, R)-Dissipativity", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA07", "sessionTitle": "Advanced Control and Coordination in Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Lang, Simon", "affiliation": "University of Stuttgart" }, { "name": "Allgower, Frank", "affiliation": "University of Stuttgart" } ], "keywords": [ "Control under communication constraints", "Quantized systems", "Control over networks" ], "abstract": "Digital communication and controller implementation have become ubiquitous in many modern control applications. However, digital control also imposes new challenges on the controller design because it requires that the controller has a finite control range. This raises the question of what fundamental limitations controllers with finite control range have, and whether classical performance objectives can be achieved. We investigate these questions for quadratic performance and dissipativity with quadratic supply rates. This work proves that these properties require an infinite control range if the system possesses an unstable mode affecting the performance channel. Consequently, it is not possible to design digital controllers that can guarantee these properties. The results also imply that dissipativity-based feedback theorems, such as the passivity theorem, cannot be used to guarantee stability of feedback interconnections when the individual systems within the interconnection should be controlled digitally. In view of the significance of digital control, these limitations show the importance to define new notions of control performance which can be achieved by digital controllers and which mathematically describe properties which are practically desired.", "url": "" }, { "id": "Tu-TuA07.4", "code": "TuA07.4", "title": "Distributed Estimation of the Algebraic Connectivity for Undirected Graphs", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA07", "sessionTitle": "Advanced Control and Coordination in Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Van Assche, Thomas", "affiliation": "Université Polytechnique Hauts-De-France, Università Degli Studi Dell'Aquila" }, { "name": "Defoort, Michael", "affiliation": "University of Valenciennes" }, { "name": "Pola, Giordano", "affiliation": "University of L'Aquila" }, { "name": "Djemai, Mohamed", "affiliation": "ENSEA" } ], "keywords": [ "Multi-agent systems", "Distributed control and estimation", "Consensus" ], "abstract": "The knowledge of the algebraic connectivity is central in many graph-theory related studies, such as network analysis, maintaining connectivity, and control of multi-agent systems. The computation of this variable is usually centralized, but for some applications it is necessary to obtain it in a distributed way. In this paper, a distributed scheme is proposed to compute the algebraic connectivity using dynamic average consensus algorithms converging in finite time. It is proved that the estimated value asymptotically converges toward the algebraic connectivity for undirected graphs. Numerical simulations show the performance and the advantages of the proposed scheme compared to existing distributed ones.", "url": "" }, { "id": "Tu-TuA07.5", "code": "TuA07.5", "title": "Distributed GNE Seeking Via Control Barrier Functions for Double-Integrator Agents", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA07", "sessionTitle": "Advanced Control and Coordination in Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Meng, Yihan", "affiliation": "University of Toronto" }, { "name": "Li, Weijian", "affiliation": "University of Notre Dame" }, { "name": "Pavel, Lacra", "affiliation": "Univ of Toronto" } ], "keywords": [ "Multi-agent systems", "Control over networks" ], "abstract": "In this paper, we propose a control barrier function (CBF) approach to distributed generalized Nash equilibrium (GNE) seeking problems, which ensures feasible set invariance while seeking the equilibrium. We start with singe-integrator agents, and design a CBF-based algorithm that converges asymptotically to the exact GNE of the game without violating the feasibility of the problem along the evolution. Then by introducing a coordinate transformation, we extend the approach to double-integrator agents. The algorithms are developed with full decision information setting. A simulation example is provided to illustrate the applicability of the algorithms.", "url": "" }, { "id": "Tu-TuA07.6", "code": "TuA07.6", "title": "Communication-Efficient Learning for Satellite Constellations", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA07", "sessionTitle": "Advanced Control and Coordination in Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Tudose, Ruxandra-Stefania", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Grüss, Moritz Hjalmar Wiking", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Kim, Grace Ra", "affiliation": "Stanford University" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Bastianello, Nicola", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Multi-agent systems", "Distributed optimization", "Control under communication constraints" ], "abstract": "Satellite constellations in low-Earth orbit are now widespread, enabling positioning, Earth imaging, and communications. In this paper, we address how learning problems can be solved in a distributed manner across these satellite constellations. Specifically, we focus on a federated approach, where satellites collect and locally process data, with the ground station aggregating local models. The goal is to design a novel algorithm that is jointly communication-efficient and accurate. To this end, we employ several mechanisms to reduce the number of communications with the ground station (local training) and their size (compression). We then propose an error feedback mechanism that enhances accuracy, which yields, as a byproduct, an algorithm-agnostic error feedback scheme that can be more broadly applied. We analyze the convergence of the resulting algorithm, and compare it with the state of the art through simulations in a realistic space scenario, showcasing superior performance.", "url": "" }, { "id": "Tu-TuA08.1", "code": "TuA08.1", "title": "Adaptive Control of Input-Constrained Multi-Vehicle Systems Via Constrained Reference Model (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA08", "sessionTitle": "Security, Safety, Resilience, and Privacy for Cyber-Physical Systems I", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Li, Miao", "affiliation": "Southeast University" }, { "name": "Liu, Di", "affiliation": "Southeast University" }, { "name": "Baldi, Simone", "affiliation": "Southeast University" }, { "name": "Astolfi, Alessandro", "affiliation": "King Abdullah University of Science and Technology (KAUST)" }, { "name": "Annaswamy, Anuradha", "affiliation": "Massachusetts Inst. of Tech" } ], "keywords": [ "Adaptive control of multi-agent systems", "Model reference adaptive control" ], "abstract": "Despite the fact that numerous protocols have been proposed for the longitudinal control of multi-vehicle systems, most designs disregard the inherent actuation constraints of vehicles. Ignoring such constraints may lead to disruption in the formation, e.g., loss of cohesiveness or collisions. In this study we address this challenge in the presence of uncertainties in the multi-vehicle dynamics. We solve the problem by introducing a constrained reference model: the idea is to design a reference behavior that prevents the vehicles from reaching their input constraints, so that the formation is not disrupted. Using absolute stability arguments, stability of the adaptive closed-loop system is proven for input constraints described by sector-bounded nonlinearities. The effectiveness of the proposed approach is verified via simulations.", "url": "" }, { "id": "Tu-TuA08.2", "code": "TuA08.2", "title": "Can Inherent Communication Noise Guarantee Privacy in Distributed Cooperative Control? (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA08", "sessionTitle": "Security, Safety, Resilience, and Privacy for Cyber-Physical Systems I", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Ma, Yuwen", "affiliation": "University College London" }, { "name": "Spurgeon, Sarah K.", "affiliation": "University College London" }, { "name": "Li, Tao", "affiliation": "Academy of Mathematics and Systems Science，Chinese Academy of Sciences" }, { "name": "Chen, Boli", "affiliation": "University College London" } ], "keywords": [ "Cyber security networked control", "Control over networks", "Multi-agent systems" ], "abstract": "This paper investigates privacy-preserving distributed cooperative control for multi-agent systems within the framework of differential privacy. In cooperative control, communication noise is inevitable and is typically treated as a disturbance that degrades coordination performance. This work instead reinterprets such noise as a potential privacy-preserving mechanism and develops a linear quadratic regulator (LQR)-based distributed control framework to exploit this property. In the proposed setup, agents communicate over noisy channels whose noise variance depends on the relative state differences between neighbouring agents. It is analytically shown that, under a tree-structured communication topology, the inherent communication noise guarantees bounded (ϵ,δ)-differential privacy for reference signals without requiring additional privacy injection. Meanwhile, the cooperative tracking error remains bounded and converges in both the mean-square and almost-sure senses.", "url": "" }, { "id": "Tu-TuA08.3", "code": "TuA08.3", "title": "FDI Attacks on Multi-Agent Systems: Stealthiness and Its Geometric Characterization (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA08", "sessionTitle": "Security, Safety, Resilience, and Privacy for Cyber-Physical Systems I", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Zhu, Shiyong", "affiliation": "City University of Hong Kong" }, { "name": "Wang, Miaomiao", "affiliation": "City University of Hong Kong" }, { "name": "Parisini, Thomas", "affiliation": "Imperial C., Aalborg U. & Univ. of Trieste" }, { "name": "Rantzer, Anders", "affiliation": "Lund Univ" }, { "name": "Chen, Jie", "affiliation": "City University of Hong Kong" } ], "keywords": [ "Cyber security networked control", "Multi-agent systems", "Consensus" ], "abstract": "In this paper, we investigate false data injection attacks on multi-agent systems. We consider attacks on agent sensors and the defense of agents on actuators under these attacks. We focus on the intrinsic stealthiness of attacks and the vulnerability of systems to attacks. For this purpose, we propose a signal-centric, detection-oriented stealthiness metric termed stealthiness margin, which constitutes the fundamental limit of attack stealthiness and system vulnerability, and can be computed by solving a zero-sum minimax optimization problem. We solve this problem analytically, explicitly constructing optimal attack and defense signals. The solution indicates that the stealthiness and the vulnerability are closely related to the observability and controllability Gramian matrices of multi-agent systems in terms of agent dynamics and network topology, which can be interpreted from a geometric analysis by generalizing the concept of balanced realization to multi-agent systems.", "url": "" }, { "id": "Tu-TuA08.4", "code": "TuA08.4", "title": "Maximal Energy Margin of Stealthy Attacks on Multi-Agent Systems (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA08", "sessionTitle": "Security, Safety, Resilience, and Privacy for Cyber-Physical Systems I", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Zhang, Kangkang", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Zhu, Shiyong", "affiliation": "City University of Hong Kong" }, { "name": "Lin, Yankai", "affiliation": "Wuhan Institute of Technology" }, { "name": "Xu, Yuhang", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Jiang, Bin", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Chen, Jie", "affiliation": "City University of Hong Kong" } ], "keywords": [ "Fault detection and diagnosis", "Cyber security networked control", "Resilient networked control systems" ], "abstract": "In this paper, we investigate the maximally allowable energy of an attack that can be injected into multi-agent systems with disturbances, while remaining stealthy by passing through the anomaly detector. Specifically, it is formulated as a feedback control problem in which, the attack policy is a feedback of the system disturbance. Then, the attacker maximizes the controller H∞ norm to achieve the allowed energy margin, while satisfying the stealthiness requirement that the H∞ norm of the composite disturbance–attack residual channel stays below the detector’s threshold. Our results demonstrate that the existence of stealthy attacks closely depends on a specific distance metric between the non-minimum phase zeros of the attack and disturbance channels entering the agents. Furthermore, a closed-form solution for the optimal attack policy is derived based on a generalized Hankel operator characterizing the projection of disturbance channels on the attack channels. These results quantify how the agent dynamics and network topologies confine the attack performance in the security of distributed systems.", "url": "" }, { "id": "Tu-TuA08.5", "code": "TuA08.5", "title": "On the Stealth of Unbounded Attacks under Non-Negative-Kernel Feedback (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA08", "sessionTitle": "Security, Safety, Resilience, and Privacy for Cyber-Physical Systems I", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Hassan, Kamil", "affiliation": "KTH Royal Institute of Technology, Sweden" }, { "name": "Sandberg, Henrik", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Cyber security networked control", "Fault detection and diagnosis" ], "abstract": "The stealth of false data injection attacks (FDIAs) against feedback sensors in linear time-varying (LTV) control systems is investigated. In that regard, the following notions of stealth are pursued: For some finite epsilon > 0, i) an FDIA is deemed epsilon-stealthy if the deviation it produces in the signal that is monitored by the anomaly detector remains epsilon-bounded for all time, and ii) the epsilon-stealthy FDIA is further classified as untraceable if the bounded deviation dissipates over time (asymptotically). For LTV systems that contain a chain of q geq 1 integrators and feedback controllers with non-negative impulse-response kernels, it is proved that polynomial (in time) FDIA signals of degree a—growing unbounded over time for a geq 1—will remain i) epsilon-stealthy, for some finite epsilon > 0, if a leq q, and ii) untraceable, if a < q. These results are obtained using the theory of linear Volterra integral equations.", "url": "" }, { "id": "Tu-TuA09.1", "code": "TuA09.1", "title": "Pursuit-Evasion Problem with Limited Field of View: A Partially Observable Stochastic Game Approach", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA09", "sessionTitle": "Markov Decision Process", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Sun, Yu", "affiliation": "Zhejiang University of Technology" }, { "name": "Feng, Yu", "affiliation": "Zhejiang University of Technology" }, { "name": "Li, Yongqiang", "affiliation": "Zhejiang University of Technology" }, { "name": "Luo, Biao", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Markov decision process", "Consensus and reinforcement learning control" ], "abstract": "In this paper, we consider an N-to-1 pursuit-evasion problem with a limited field of view, where multiple pursuers make decisions independently. The problem is formulated as a partially observable stochastic pursuit-evasion game with history information, and the existence of a Nash equilibrium is established by constructing a saddle point of an auxiliary two-player zero-sum game. For strategy computation, we adopt a sliding window approach based on recent observations and present a self-play reinforcement learning algorithm to compute the corresponding strategies. Moreover, the effectiveness of the proposed method is validated through a numerical example.", "url": "" }, { "id": "Tu-TuA09.2", "code": "TuA09.2", "title": "Event-Triggered Control for Discrete-Time Markovian Jump Systems Based on Data-Driven Analysis", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA09", "sessionTitle": "Markov Decision Process", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Tang, Qi", "affiliation": "The University of Hong Kong" }, { "name": "Liu, Tao", "affiliation": "The University of Hong Kong" }, { "name": "Yang, Dong", "affiliation": "Qufu Normal University" } ], "keywords": [ "Markov decision process", "Control under communication constraints", "Data-driven control theory" ], "abstract": "This paper investigates the problem of data-driven event-triggered control for discrete-time Markovian jump systems (MJSs) with generally uncertain transition probabilities (TPs). Considering the scenario where the measured state is transmitted through a network, a novel data-driven framework is developed that directly constructs stabilizing state-feedback controllers and a triggering policy from offline input-state data without requiring explicit system identification. First, ignoring the network, we derive sufficient conditions for designing mode- dependent controllers that guarantee stochastic stability solely from collected data via linear matrix inequalities (LMIs). Then, incorporating the event-triggered mechanism, a triggering policy is proposed to reduce communication load while preserving closed-loop stability. We further establish a verifiable sufficient condition under which the designed triggering policy reduces to time-triggered transmission. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.", "url": "" }, { "id": "Tu-TuA09.3", "code": "TuA09.3", "title": "Cooperation Evolution in Public Goods Games with Random Entry-Exit Mechanism", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA09", "sessionTitle": "Markov Decision Process", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Li, Kaibing", "affiliation": "Shandong University" }, { "name": "Zhang, Renren", "affiliation": "Shandong University" } ], "keywords": [ "Markov decision process", "Multi-agent systems" ], "abstract": "Collective cooperation drives natural, social, and economic systems, with its evolutionary game study a priority. Though human interactions form complex networks, current public goods game research is limited to deterministic evolution with static or instantly replaced players. In reality, interactions involve randomness and finite lifecycles, leaving cooperation in such dynamic evolutionary networks unaddressed. This paper investigates cooperation evolution through a random entry-exit mechanism, developing an overlapping generations model where finite-lived players undergo evolutionary processes influenced by cooperative dynamics. Our analysis shows that properly accounting for update randomness enables a stable cooperative equilibrium given sufficient population size and update frequency.", "url": "" }, { "id": "Tu-TuA09.4", "code": "TuA09.4", "title": "Lexicographic Multi-Objective Stochastic Shortest Path with Mixed Max–Sum Costs", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA09", "sessionTitle": "Markov Decision Process", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Zhang, Zhiquan", "affiliation": "University of Illinois Urbana-Champaign" }, { "name": "Muhammetkulyyev, Omar", "affiliation": "Iowa State University" }, { "name": "Wongpiromsarn, Tichakorn", "affiliation": "Nutonomy Asia" }, { "name": "Ornik, Melkior", "affiliation": "Univ. of Illinois Urbana-Champaign" } ], "keywords": [ "Markov decision process", "Supervisory control and automata", "Synthesis of stochastic systems" ], "abstract": "We study the Stochastic Shortest Path (SSP) problem for autonomous systems with mixed max-sum cost aggregations under Linear Temporal Logic constraints. Classical SSP formulations rely on sum-aggregated costs, which are suitable for cumulative quantities such as time or energy but fail to capture bottleneck-style objectives where performance is determined by the worst single event along a trajectory. To address this limitation, we introduce max-aggregated objectives that minimize the maximum one-step cost. We show that standard Bellman equations on the original state space are not directly applicable and propose an augmented MDP that tracks the running maximum cost. We also identify a cyclic policy phenomenon caused by zero marginal cost under max-aggregation, and resolve it via a finite-horizon formulation. To handle high-level task specifications, we construct a product MDP from the stochastic system and the automaton corresponding to the LTL formula. A lexicographic value iteration algorithm is then developed to optimize mixed max-sum objectives under lexicographic ordering. Gridworld case studies demonstrate the effectiveness of the framework.", "url": "" }, { "id": "Tu-TuA09.5", "code": "TuA09.5", "title": "Omniscient Attacker in Stochastic Security Games with Interdependent Nodes", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA09", "sessionTitle": "Markov Decision Process", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Arslantas, Yuksel", "affiliation": "Bilkent University" }, { "name": "Donmez, Ahmed Said", "affiliation": "Bilkent University" }, { "name": "Yuceel, Ege", "affiliation": "University of Illinois at Urbana-Champaign" }, { "name": "Sayin, Muhammed Omer", "affiliation": "Bilkent University" } ], "keywords": [ "Security for stochastic systems", "Markov decision process", "Stochastic control" ], "abstract": "The adoption of reinforcement learning for critical infrastructure defense introduces a vulnerability where sophisticated attackers can strategically exploit the defense algorithm's learning dynamics. While prior work addresses this vulnerability in the context of repeated normal-form games, its extension to the stochastic games remains an open research gap. We close this gap by examining stochastic security games between an RL defender and an omniscient attacker, utilizing a tractable linear influence network model. To overcome the structural limitations of prior methods, we propose and apply neuro-dynamic programming. Our experimental results demonstrate that the omniscient attacker can significantly outperform a naive defender, highlighting the critical vulnerability introduced by the learning dynamics and the effectiveness of the proposed strategy.", "url": "" }, { "id": "Tu-TuA09.6", "code": "TuA09.6", "title": "Short-Term Forecasting with Stochastic Automata Networks in Meteorology", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA09", "sessionTitle": "Markov Decision Process", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Lima de bem, Douglas", "affiliation": "Laboratoire LICIIS - LRC CEA DIGIT, Université De Reims Champagne-Ardenne, France" }, { "name": "Anabor, Vagner", "affiliation": "Universidade Federal De Santa Maria, GruMA, Brazil" }, { "name": "Steffenel, Luiz Angelo", "affiliation": "Université De Reims Champagne-Ardenne" }, { "name": "Brenner, Leonardo", "affiliation": "Université De Reims Champagne-Ardenne" } ], "keywords": [ "Statistical analysis", "Markov decision process", "Statistical inference" ], "abstract": "This study evaluates a Stochastic Automata Network (SAN) framework for short-term forecasting of sky conditions. Observations from the Reims--Prunay station were used to build a stochastic model coupling temperature, relative humidity, atmospheric pressure, and cloud cover through data-driven transition rules. Independent validation for 2025 shows that the model performs best in identifying coarse sky regimes, particularly clear-sky conditions, while intermediate cloud states remain challenging. The SAN behaves primarily as a statistical regime classifier rather than a physical cloud model. Although its accuracy is lower than that of Numerical Weather Prediction (NWP) systems, the model generates daily forecasts in seconds, highlighting its potential for forecasting in computationally constrained environments.", "url": "" }, { "id": "Tu-TuA10.1", "code": "TuA10.1", "title": "Task and Motion Planning of Dynamic Systems Using Hyperproperties for Signal Temporal Logics (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA10", "sessionTitle": "JO-NAHS: Discrete Event and Hybrid Systems II", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Zhao, Jianing", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Ye, Bowen", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Yu, Xinyi", "affiliation": "University of Southern California" }, { "name": "Majumdar, Rupak", "affiliation": "Max Planck Institute for Software Systems and University of California at Los Angeles" }, { "name": "Yin, Xiang", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Optimal control of discrete event and hybrid systems", "Discrete event modeling and simulation", "Reachability analysis, verification and abstraction of hybrid systems" ], "abstract": "We investigate the task and motion planning problem for dynamic systems under signal temporal logic (STL) specifications. Existing works on STL control synthesis mainly focus on generating plans that satisfy properties over a single executed trajectory. In this work, we consider the planning problem for hyperproperties evaluated over a set of possible trajectories, which naturally arise in information-flow control problems. Specifically, we study discrete-time dynamic systems and employ the recently developed temporal logic HyperSTL as the new objective for planning. To solve this problem, we propose a novel recursive counterexampleguided synthesis approach capable of effectively handling HyperSTL specifications with multiple alternating quantifiers. The proposed method is not only applicable to planning but also extends to HyperSTL model checking for discrete-time dynamic systems. Finally, we present case studies on security-preserving planning and ambiguity-free planning to demonstrate the effectiveness of the proposed HyperSTL planning framework.", "url": "" }, { "id": "Tu-TuA10.2", "code": "TuA10.2", "title": "Switching Constraints As a Design Tool for Predictive Control Based on K-Invariant Sets (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA10", "sessionTitle": "JO-NAHS: Discrete Event and Hybrid Systems II", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Zhixin, Zhao", "affiliation": "Univ. Paris Saclay" }, { "name": "Girard, Antoine", "affiliation": "CNRS" }, { "name": "Olaru, Sorin", "affiliation": "CentraleSupelec" } ], "keywords": [ "Optimal control of discrete event and hybrid systems", "Event-based control" ], "abstract": "This paper proposes a constraint-switching approach for Nonlinear Model Predictive Control (NMPC) to address the challenges of large prediction horizons coupled with recursively feasible constraints in particular for design frameworks which imply exploration goals. The K-invariant sets and their parameterization is introduced as a substitute for controlled invariant sets. It is shown that such sets can be effectively constructed through an external selection and verification module, making them available along the prediction horizon. The availability of K-invariant sets and their enforcement as constraint in the receding optimization for exploration purposes leads to a switching mechanism. The recursive feasibility is guaranteed under the framework, and an example of navigation in partially known environment is provided to demonstrate the advantages.", "url": "" }, { "id": "Tu-TuA10.3", "code": "TuA10.3", "title": "Securing Services Age-Based Redeployment Using Weakly Coupled Markov Decision Processes (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA10", "sessionTitle": "JO-NAHS: Discrete Event and Hybrid Systems II", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Charreaux, Pierre", "affiliation": "Lab-STICC Laboratory, IMT Atlantique" }, { "name": "Parag, Parimal", "affiliation": "Indian Institute of Science" }, { "name": "Reiffers-Masson, Alexandre", "affiliation": "IMT Atlantique - Bretagne" }, { "name": "Sailhan, Françoise", "affiliation": "Lab-STICC Laboratory, IMT Atlantique" } ], "keywords": [ "Optimal control of discrete event and hybrid systems", "Markov decision process", "Security for stochastic systems" ], "abstract": "We formalize a proactive defense that dynamically redeploys service instances (e.g., containers) based on their age to interrupt attacks. We model the problem using weakly coupled Markov decision processes with a per-time availability constraint to maintain service access through a pool of low-risk servers. We find ergodic stationary policies that minimize operational and energy costs for standalone servers and extend the result with an algorithm to control the servers' redeployment while maintaining the per-time availability constraint in expectation.", "url": "" }, { "id": "Tu-TuA10.4", "code": "TuA10.4", "title": "Data-Driven Dissipative Vehicle Lateral Control: Deep Neural Koopman Approach (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA10", "sessionTitle": "JO-NAHS: Discrete Event and Hybrid Systems II", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Lee, Yongjun", "affiliation": "Korea University, Department of Electrical and Computer Engineering" }, { "name": "Ahn, Woo Jin", "affiliation": "Inha University" }, { "name": "Jang, Sunho", "affiliation": "Korea Institute of Robotics and Technology Convergence" }, { "name": "Lim, Myo-Taeg", "affiliation": "Korea Univ" } ], "keywords": [ "Optimal control of discrete event and hybrid systems", "Neural and fuzzy adaptive control", "Data-driven control theory" ], "abstract": "Model-based lateral control for autonomous vehicles faces challenges with unmeasurable parameters and nonlinearities. This paper proposes a robust data-driven control to identify vehicle lateral dynamics from driving data. The Koopman operator lifts nonlinear dynamics into a linear space, but its observable function selection is challenging and experience-dependent. A neural network is employed to learn the embedding function and Koopman operator. Based on Serret-Frenet kinematics and deep neural Koopman model, the control design achieves dissipativity under steering constraints, with conditions formulated as linear matrix inequalities. Moreover, an adaptive sliding mode approach attenuates adverse influence of actuator uncertainties. CarSim-Simulink co-simulations validate the proposed control's effectiveness.", "url": "" }, { "id": "Tu-TuA10.5", "code": "TuA10.5", "title": "Correct-By-Design Control Synthesis of Stochastic Multi-Agent Systems: A Robust Tensor-Based Solution (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA10", "sessionTitle": "JO-NAHS: Discrete Event and Hybrid Systems II", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Wang, Ruohan", "affiliation": "Eindhoven University of Technology" }, { "name": "Liu, Siyuan", "affiliation": "Eindhoven University of Technology" }, { "name": "Sun, Zhiyong", "affiliation": "Peking University (PKU)" }, { "name": "Haesaert, Sofie", "affiliation": "TU Eindhoven" } ], "keywords": [ "Optimal control of discrete event and hybrid systems", "Reachability analysis, verification and abstraction of hybrid systems", "Stochastic hybrid systems" ], "abstract": "Discrete-time stochastic systems with continuous spaces are hard to verify and control due to the curse of dimensionality. We propose an abstraction-based framework with robust dynamic programming mappings that synthesize controllers with provable temporal-logic satisfaction lower bounds via approximate stochastic simulation relations. Exploiting decoupled dynamics, we reveal a Canonical Polyadic Decomposition tensor structure in value functions, enabling scalable dynamic programming. The method provides correct-by-design probabilistic guarantees and is validated on continuous-state linear stochastic systems.", "url": "" }, { "id": "Tu-TuA10.6", "code": "TuA10.6", "title": "A Stackelberg Game Approach for Signal Temporal Logic Motion Planning with Uncontrollable Agents (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA10", "sessionTitle": "JO-NAHS: Discrete Event and Hybrid Systems II", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Cui, Bohan", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Yu, Xinyi", "affiliation": "University of Southern California" }, { "name": "Giua, Alessandro", "affiliation": "University of Cagliari, Italy" }, { "name": "Yin, Xiang", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Optimal control of discrete event and hybrid systems", "Supervisory control and automata", "Discrete event modeling and simulation" ], "abstract": "In this paper, we investigate the motion planning problem for Signal Temporal Logic (STL) specifications in the presence of uncontrollable agents. Existing works mainly address this problem in a robust control setting by assuming the uncontrollable agents are adversarial and accounting for the worst-case scenario. While this approach ensures safety, it can be overly conservative in scenarios where uncontrollable agents have their own objectives that are not entirely opposed to the system’s goals. Motivated by this limitation, we propose a new framework for STL motion planning within the Stackelberg game setting. Specifically, we assume that the system controller, acting as the leader, first commits to a plan, after which the uncontrollable agents, acting as followers, take a best response based on the committed plan and their own objectives. Our goal is to synthesize a control sequence for the leader such that, for any rational followers producing a best response, the leader’s STL task is guaranteed to be satisfied. We present an effective solution to this problem by transforming it into a single-stage optimization problem and leveraging counter-example guided synthesis techniques. We demonstrate that the proposed approach is sound and identify conditions under which it is optimal. Simulation results are also provided to illustrate the effectiveness of the proposed framework.", "url": "" }, { "id": "Tu-TuA13.1", "code": "TuA13.1", "title": "The Continuous Steepest Descent Method with Convex-Like Potential", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA13", "sessionTitle": "Convex Optimization", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Niederlaender, Simon", "affiliation": "Technische Hochschule Ingolstadt" } ], "keywords": [ "Convex optimization", "Lyapunov methods", "Stability of nonlinear systems" ], "abstract": "In a real Hilbert space setting, we investigate the asymptotic properties of the solutions of the classical continuous steepest descent method with convex-like potential. Despite the absence of convexity, we show that the solutions preserve the remarkable minimizing properties typically associated with convex functions. In particular, we find that the values of the convex-like potential decay asymptotically at a sublinear rate. If, moreover, the potential function is weakly lower semi-continuous, we prove that the solutions weakly converge toward a minimizer. Under a quadratic growth condition on the convex-like potential, we further provide a strong convergence result for the solutions along with an exponential decay rate of the function values. Numerical experiments illustrate our theoretical findings.", "url": "" }, { "id": "Tu-TuA13.2", "code": "TuA13.2", "title": "Accelerated ADMM: Automated Parameter Tuning and Improved Linear Convergence", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA13", "sessionTitle": "Convex Optimization", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Tavakoli, Meisam", "affiliation": "Università Di Bologna" }, { "name": "Jakob, Fabian", "affiliation": "University of Stuttgart" }, { "name": "Carnevale, Guido", "affiliation": "Alma Mater Studiorum Università Di Bologna" }, { "name": "Notarstefano, Giuseppe", "affiliation": "University of Bologna" }, { "name": "Iannelli, Andrea", "affiliation": "University of Stuttgart" } ], "keywords": [ "Convex optimization", "Robustness analysis" ], "abstract": "This work studies the linear convergence of an accelerated scheme of the Alternating Direction Method of Multipliers (ADMM) for strongly convex and Lipschitz-smooth problems. We use the methodology of expressing the accelerated ADMM as a Lur'e system, i.e., an interconnection of a linear dynamical system in feedback with a slope-restricted operator, and we use Integral Quadratic Constraints to establish linear convergence. We leverage this machinery to systematically explore parameter tuning heuristics, including Nesterov-inspired choices and configurations identified via grid search, and analyze their impact on the convergence rate. Our new bounds show improved linear convergence rates compared to the vanilla algorithm and previously proposed accelerated variants, which is also empirically validated on a LASSO regression benchmark.", "url": "" }, { "id": "Tu-TuA13.3", "code": "TuA13.3", "title": "Sliding Follow-The-Ridge for Fast Finite-Time Local Minimax Optimisation", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA13", "sessionTitle": "Convex Optimization", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Zenati, Abdelhafid", "affiliation": "School of Mathematics, Computer Science and Engineering, City University of London" }, { "name": "Jamous, Will", "affiliation": "City St George's University of London" }, { "name": "Youcef-Toumi, Kamal", "affiliation": "Massachusetts Institute of Technology" } ], "keywords": [ "Convex optimization", "Optimization-based estimation and control", "Sliding mode control" ], "abstract": "This paper introduces a control-theoretic framework for solving local minimax optimisation problems by formulating invariant ridge-following dynamics within the paradigm of sliding mode control. The proposed textit{Sliding Follow-the-Ridge} (SFR) algorithm reinterprets classical Gradient Descent–Ascent (GDA) schemes as a sliding manifold control problem, where the gradient field defines the manifold representing the first-order optimality condition. The reaching law is designed in such a way that the sliding manifold remains asymptotically stable only when the second-order necessary condition for a true minimax equilibrium is satisfied, thereby preventing convergence to non-minimax stationary points. The resulting dynamics ensure finite-time convergence to local minimax solutions, while preserving numerical stability and low computational cost. Comparative evaluations against the Follow-the-Ridge (FR) algorithm demonstrate that SFR achieves faster convergence, shorter trajectory paths and improved robustness. By embedding minimax optimisation in a rigorous control-theoretic structure, SFR establishes a principled bridge between sliding mode control and nonconvex game optimisation.", "url": "" }, { "id": "Tu-TuA13.4", "code": "TuA13.4", "title": "Pursuing Optimal Stepsize in Adaptive Gradient-Based Quadratic Optimization", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA13", "sessionTitle": "Convex Optimization", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Wang, Yifan", "affiliation": "Southeast University" }, { "name": "Ballotta, Luca", "affiliation": "University of Padova" }, { "name": "Carli, Ruggero", "affiliation": "Univ of Padova" }, { "name": "Cao, Xianghui", "affiliation": "Southeast University" }, { "name": "Schenato, Luca", "affiliation": "Univ of Padova" } ], "keywords": [ "Convex optimization", "Adaptive control design", "Linear systems" ], "abstract": "In this paper, we address the problem of achieving fast convergence in gradient descent for quadratic functions without relying on a-priori knowledge of global function parameters. Inspired by adaptive stepsize algorithms for smooth convex functions, we propose a computationally lightweight strategy based on running estimates of minimal and maximal local curvatures. We prove that our proposed algorithm converges to the optimal constant stepsize which achieves the fastest convergence. Simulations show that the convergence rate achieved by our proposed algorithm is comparable or superior to recent adaptive approaches both in the quadratic case under consideration and in a preliminary test on logistic classification.", "url": "" }, { "id": "Tu-TuA13.5", "code": "TuA13.5", "title": "Linear Convergence of Proportional-Integral Projected Gradient Methods for Quadratic Programs", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA13", "sessionTitle": "Convex Optimization", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Li, Tianxun", "affiliation": "Tsinghua University" }, { "name": "You, Keyou", "affiliation": "Tsinghua University" } ], "keywords": [ "Convex optimization", "Numerical methods for optimal control", "Model predictive control" ], "abstract": "The Proportional-Integral Projected Gradient (PIPG) method has demonstrated to be an efficient first-order method for quadratic programs (QP) in practice, which only uses vector operations and one projection per iteration. However, it lacks linear convergence guarantees for strongly convex QPs, which is a key property for comparable first-order methods. This gap limits both theoretical understanding and practical confidence in its use. To this end, this paper rigorously proves that PIPG achieves global linear convergence for such problems with explicit convergence rate. To further accelerate its convergence, we propose an adaptive step-size rule with periodic restarts. Numerical experiments on a model predictive control problem show that the enhanced PIPG converges faster than the-state-of-the-art first-order methods, while maintaining its signature simplicity and low per-iteration cost.", "url": "" }, { "id": "Tu-TuA13.6", "code": "TuA13.6", "title": "A Communication-Efficient Distributed Optimization Algorithm for Constraint-Coupled Problems", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA13", "sessionTitle": "Convex Optimization", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Duan, Yuzhu", "affiliation": "Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Sha" }, { "name": "Yang, Ziwen", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Duan, Xiaoming", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Zhu, Shanying", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Convex optimization" ], "abstract": "Resource allocation is a fundamental problem in Industrial Internet of Things (IIoT) systems, in which devices work together under limited communication bandwidth to complete diverse tasks. This paper proposes a communication-efficient distributed optimization algorithm tailored for problems with coupled constraints. To tackle coupled constraints, we solve the problem via its dual counterpart, and develop a compressed version. Difference compression and a dynamic scaling factor are then introduced to mitigate compression errors. We show that the proposed algorithm converges linearly for strongly convex and smooth objectives. Numerical simulations verify the theoretical results and demonstrate the efficiency and robustness of the proposed algorithm.", "url": "" }, { "id": "Tu-TuA14.1", "code": "TuA14.1", "title": "Lyapunov Neural Ordinary Differential Equation State-Feedback Policies (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA14", "sessionTitle": "JO-EAAI: Learning Methods for Optimal Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Ip, Joshua Hang Sai", "affiliation": "University of California, Berkeley" }, { "name": "Makrygiorgos, Georgios", "affiliation": "University of California, Berkeley" }, { "name": "Mesbah, Ali", "affiliation": "University of California, Berkeley" } ], "keywords": [ "Learning methods for optimal control" ], "abstract": "Deep neural networks are increasingly used as effective parameterizations of control policies in various learning-based control paradigms. For continuous-time optimal control problems (OCPs), which are central to many decision-making tasks, control policy learning can be cast as neural ordinary differential equation (NODE) problems wherein state and control constraints are naturally accommodated. This paper presents a NODE approach to solving continuous-time OCPs for the case of stabilizing a known constrained nonlinear system around a target state. The approach, termed Lyapunov-NODE control (L-NODEC), uses a novel Lyapunov loss formulation that incorporates an exponentially-stabilizing control Lyapunov function to learn a state-feedback neural control policy, bridging the gap of solving continuous-time OCPs via NODEs with stability guarantees. The proposed Lyapunov loss allows L-NODEC to guarantee exponential stability of the controlled system, as well as its adversarial robustness to perturbations to the initial state. The performance of L-NODEC is illustrated on a double integrator, where it effectively stabilizes the controlled system around the target state despite perturbations to the initial state and it reduces the inference time necessary to reach the target.", "url": "" }, { "id": "Tu-TuA14.2", "code": "TuA14.2", "title": "Trotterized Variational Quantum Control for Spin-Chain State Transfer (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA14", "sessionTitle": "JO-EAAI: Learning Methods for Optimal Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Binandeh Dehaghani, Nahid", "affiliation": "Aalborg University" }, { "name": "Wisniewski, Rafal", "affiliation": "Aalborg University" }, { "name": "Aguiar, A. Pedro", "affiliation": "Faculty of Engineering, University of Porto (FEUP)" } ], "keywords": [ "Learning methods for optimal control" ], "abstract": "We propose a variational quantum control framework for high-fidelity state transfer in spin chains under noisy and resource-constrained quantum hardware conditions. The method maps a continuous-time optimal control problem into a Trotterized, physics-informed parameterized quantum circuit, enabling hybrid quantum-classical optimization of control parameters. We investigate two control parameterizations: a local scheme with site-wise parameters and a compact global scheme with a low-dimensional shared structure. Numerical results on XXZ spin chains show that both parameterizations achieve near-unit fidelity in the noiseless setting. Under depolarizing and dephasing noise, the global parameterization exhibits improved robustness, training stability, and parameter efficiency. These findings highlight an expressivity-robustness trade-off and demonstrate the advantages of structured low-dimensional ansätze for variational quantum control in NISQ regimes.", "url": "" }, { "id": "Tu-TuA14.3", "code": "TuA14.3", "title": "Safe and Optimal Trajectory Learning for Autonomous Racing Via Deep Reinforcement Learning and Control Barrier Functions (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA14", "sessionTitle": "JO-EAAI: Learning Methods for Optimal Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Carvalho, José P.", "affiliation": "Faculty of Engineering, University of Porto (FEUP)" }, { "name": "Aguiar, A. Pedro", "affiliation": "Faculty of Engineering, University of Porto (FEUP)" } ], "keywords": [ "Learning methods for optimal control", "Applications of optimal control" ], "abstract": "This paper presents a safety-certified reinforcement learning framework for autonomous racing that minimizes lap times while strictly enforcing safety and velocity constraints. To address the brittleness and lack of safety guarantees in standard Deep Reinforcement Learning (DRL), we propose a multi-stage architecture. In this framework, a Proximal Policy Optimization (PPO) policy optimizes racing performance, while a structured safety filtering stack combining Control Barrier Functions (CBF), Control Lyapunov Functions (CLF), and a feasibility-enforcing mechanism, enforces track limits and velocity constraints while guaranteeing episode completion. This filtering stack restricts exploration to admissible regions, stabilizing training, and preventing constraint violations. Simulations on 1:10-scale tracks demonstrate near-optimal trajectories and successful zero-shot generalization to unseen environments, without requiring computationally expensive offline trajectory optimization.", "url": "" }, { "id": "Tu-TuA14.4", "code": "TuA14.4", "title": "Meta-Reinforcement Learning for Building Temperature Control: Design and Experimental Validation (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA14", "sessionTitle": "JO-EAAI: Learning Methods for Optimal Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Ferrarini, Luca", "affiliation": "Politecnico Di Milano" }, { "name": "Palmieri, Serena", "affiliation": "Politecnico Di Milano" }, { "name": "Valentini, Alberto", "affiliation": "Politecnico Di Milano" }, { "name": "Gehrke, Oliver", "affiliation": "Department of Electrical Engineering, Intelligent Energy Systems, Risø Campus, Technical University of Denmark," } ], "keywords": [ "Learning methods for optimal control", "Applications of optimal control", "Model predictive control" ], "abstract": "This paper presents the design and experimental validation of a Meta-Reinforcement Learning approach for temperature control in a real building with not perfectly known thermal dynamics. The proposed architecture consists of three main modules: an encoder, a controller, and an adapter. In particular, the encoder projects the building’s uncertain parameters into a low-dimensional latent representation that provides contextual information to the downstream controller, enabling it to interpret the current building dynamics and apply the most suitable temperature control strategy to optimize closed-loop performance. The whole control architecture is trained in simulation on a control-oriented model across the range of dynamics induced by parameter uncertainties. Experimental results on a real house located at the SYSLAB Risø Campus of the Technical University of Denmark demonstrate that the Meta-Reinforcement Learning approach is feasible in practice and improves energy efficiency after just three days of data collection.", "url": "" }, { "id": "Tu-TuA14.5", "code": "TuA14.5", "title": "A Reinforcement Learning Based Decision Support System for Multi-Stage Rooftop PV Investment in a Renewable Energy Community (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA14", "sessionTitle": "JO-EAAI: Learning Methods for Optimal Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Joshi, Amit", "affiliation": "University of Sannio, Benevento" }, { "name": "Glielmo, Luigi", "affiliation": "University of Napoli Federico II" } ], "keywords": [ "Learning methods for optimal control", "Applications of optimal control", "Stochastic optimal control problems" ], "abstract": "In this article, we study the problem of rooftop PV installation for a renewable energy community, while considering a multi-stage, multi-investor setting, subject to uncertainty in the membership status of the community end-users. We model the evolution of the membership status as a Markov chain and formulate the investor's decision making as a stochastic optimal control problem; with coupling in the objective function due to non-linear proportional sharing mechanism. We then translate the problem as a partially observable Markov decision process and propose a reinforcement learning based solution. We perform Monte Carlo simulations using real-world household dataset and validate the proposed framework.", "url": "" }, { "id": "Tu-TuA14.6", "code": "TuA14.6", "title": "Verification and Validation of Reinforcement Learning Based Aeroelastic Control System (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA14", "sessionTitle": "JO-EAAI: Learning Methods for Optimal Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Konatala, Ramesh", "affiliation": "German Aerospace Center (DLR)" }, { "name": "Stalla, Felix", "affiliation": "German Aerospace Center (DLR)" }, { "name": "Kier, Thiemo", "affiliation": "DLR" }, { "name": "Looye, Gertjan", "affiliation": "German Aerospace Center DLR" }, { "name": "Pusch, Manuel", "affiliation": "Munich University of Applied Sciences" }, { "name": "van Kampen, Erik-Jan", "affiliation": "Delft University of Technology" } ], "keywords": [ "Learning methods for optimal control", "Design methods for data-based control", "Applications of optimal control" ], "abstract": "This paper presents the adaptation of established Verification & Validation (V&V) practices for classical flight control to a data-driven Reinforcement Learning (RL) based Gust Load Alleviation (GLA) control system, with the aim of addressing the simulation to reality gap in RL based aeroelastic control applications. The control law was developed using the Soft ActorCritic (SAC) algorithm as an end-to-end deep neural network that maps sensor measurements to actuator commands, and was trained offline using data from a linear Aeroservoelastic (ASE) model. The resulting fixed-policy controller is then subjected to a V&V workflow comprising closed-loop verification through simulation analyses on a high-fidelity nonlinear ASE model, followed by experimental validation through wind tunnel testing. Validation was conducted on a flexible wing demonstrator under harmonic gust excitation generated by cylindrical gust generators. Performance was evaluated against a set of control design specifications that cover nominal load alleviation, robustness to unstructured and parametric uncertainties, actuator and safety constraints. Wind tunnel results demonstrated that the RL controller attained a maximum Wing Root Bending Moment (WRBM) reduction of 65% and 80% using single and dual-actuator configurations, respectively, at the first wing bending mode frequency.", "url": "" }, { "id": "Tu-TuA15.1", "code": "TuA15.1", "title": "Fractional-Order TID Control of Time-Delayed Processes Via a Delayed Bode Framework (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA15", "sessionTitle": "Fractional-Order Control Systems: Advances in Theory, Optimization, and Industrial Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Guzelkaya, Mujde", "affiliation": "Istanbul Technical University" }, { "name": "Yumuk, Erhan", "affiliation": "Ghent University" } ], "keywords": [ "Linear fractional-order systems", "Linear systems", "Linear time-delay systems" ], "abstract": "In this work, a design methodology for fractional-order tilted derivative (FOTID) controllers is introduced for fractional-order systems with inherent time delay. The approach is built upon a delayed Bode transfer function (DBTF) framework, which enables systematic compensation of the phase lag induced by dead-time while ensuring a well-shaped open-loop frequency response. As a result, the proposed FOTID controllers achieve enhanced robustness against both gain and delay variations, while preserving the desired frequency-domain performance characteristics. The effectiveness of the proposed FOTID controllers is validated through simulation studies and comparative analyses against established fractional order PID controllers reported in the literature.", "url": "" }, { "id": "Tu-TuA15.2", "code": "TuA15.2", "title": "A General Ratio Fractional Control Methodology (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA15", "sessionTitle": "Fractional-Order Control Systems: Advances in Theory, Optimization, and Industrial Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Padula, Fabrizio", "affiliation": "Curtin University, School of Electrical Engineering, Computing and Mathematical Sciences" }, { "name": "Visioli, Antonio", "affiliation": "University of Brescia" } ], "keywords": [ "Controller constraints and structure", "Control of complex systems" ], "abstract": "In this paper, we present a ratio control architecture where fractional-order proportional-integral-derivative (FOPID) controllers are employed. In particular, we extend the dynamic blend station method and show that the general architecture can be successfully applied in a fractional setting, achieving perfect ratio tracking when the set-point changes and improving load disturbance rejection performance. In addition, a simplified technique is proposed to provide greater flexibility in the overall design and reduced computational cost. Simulation results demonstrate the effectiveness of the proposed methodology.", "url": "" }, { "id": "Tu-TuA15.3", "code": "TuA15.3", "title": "A Robust Fractional Order Controller for Time Delay Systems (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA15", "sessionTitle": "Fractional-Order Control Systems: Advances in Theory, Optimization, and Industrial Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Badau, Nicoleta", "affiliation": "Technical University of Cluj-Napoca" }, { "name": "De Keyser, Robin M.C.", "affiliation": "Ghent University" }, { "name": "Ben Othman, Ghada", "affiliation": "Ghent University" }, { "name": "Ionescu, Clara", "affiliation": "Ghent University" }, { "name": "Mihai, Marcian", "affiliation": "Technical University of Cluj-Napoca" }, { "name": "Muresan, Cristina Ioana", "affiliation": "Technical University of Cluj Napoca" } ], "keywords": [ "Robust control applications", "Robustness analysis", "Linear fractional-order systems" ], "abstract": "Most fractional-order PID tuning research focuses on gain robustness; however, few papers address the critical issue of robustness to time constant variations. A design procedure for robust fractional order PID controllers under time constant variations is presented in this study, with a focus on both first and second order plus dead time processes. Partial derivatives are used in the design method to specify the robustness condition. The resulting system of nonlinear equations is solved by a graphical approach. The numerical examples based on biomedical systems are employed to validate the performance of the developed method in ensuring robustness to varying time constants. Comparative closed-loop simulation results for PID controllers are presented.", "url": "" }, { "id": "Tu-TuA15.4", "code": "TuA15.4", "title": "Hypnosis–Analgesia Multivariable Framework for Decentralized Fractional PI Control in High-Risk Surgery (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA15", "sessionTitle": "Fractional-Order Control Systems: Advances in Theory, Optimization, and Industrial Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Yumuk, Erhan", "affiliation": "Ghent University" }, { "name": "Ayvaz, Bora", "affiliation": "Ghent University" }, { "name": "Ynineb, Amani Rayene", "affiliation": "Ghent University" }, { "name": "De Keyser, Robin M.C.", "affiliation": "Ghent University" }, { "name": "Birs, Isabela Roxana", "affiliation": "Technical University of Cluj-Napoca" }, { "name": "Muresan, Cristina Ioana", "affiliation": "Technical University of Cluj Napoca" }, { "name": "Copot, Dana", "affiliation": "Ghent University" } ], "keywords": [ "Linear fractional-order systems", "Linear systems", "Decentralized control" ], "abstract": "Closed-loop control of anesthesia faces fundamental limitations when modeled as multiple-input single-output (MISO) systems, where patient responses may lead to non-unique or physiologically ambiguous operating points. To ensure an interpretable multivariable formulation, this work proposes a 2×2 multiple-input multiple-output (MIMO) control framework that simultaneously regulates clinical Bispectral Index (BIS) and the Nociception Level Index (NOL) using Propofol and Remifentanil infusion as inputs. The framework leverages Response Surface Models (RSM) for sedation–nociception dose mapping and defines a unique operating point through analytical RSM-based (BIS–NOL) characterization. For induction, population-based decentralized fractional-order PI (FOPI) loops are tuned using frequency-domain specifications. For maintenance, a disturbance-benchmarking profile, specific to the high-risk procedure of liver transplantation, is proposed and designed in this work to test controller robustness against abrupt and recurrent hemodynamic perturbations. Simulation results confirm that a population-based, decentralized fractional-order PI controller ensures fast convergence, accurate reference tracking, and robustness against inter-patient variability and external disturbances across ten patient parameterizations.", "url": "" }, { "id": "Tu-TuA15.5", "code": "TuA15.5", "title": "Event-Based Control of Multivariable Anesthesia System: Reducing Dynamic Coupling through Temporal Decorrelation (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA15", "sessionTitle": "Fractional-Order Control Systems: Advances in Theory, Optimization, and Industrial Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Hegedus, Erwin", "affiliation": "Technical University of Cluj-Napoca" }, { "name": "Birs, Isabela Roxana", "affiliation": "Technical University of Cluj-Napoca" }, { "name": "Khoumeri, Bouchra", "affiliation": "Ghent University" }, { "name": "Ben Othman, Ghada", "affiliation": "Ghent University" }, { "name": "Ionescu, Clara", "affiliation": "Ghent University" }, { "name": "Mihai, Marcian", "affiliation": "Technical University of Cluj-Napoca" }, { "name": "Muresan, Cristina Ioana", "affiliation": "Technical University of Cluj Napoca" } ], "keywords": [ "Control in system biology", "Adaptive control design", "Nonlinear time-delay systems" ], "abstract": "Multivariable control using steady-state decoupling is computationally attractive but theoretically limited by residual dynamic cross-coupling at physiological frequencies. This paper demonstrates that event-based execution fundamentally alters this limitation through temporal decorrelation, achieving 41% coupling reduction in a 4 × 4 anesthesia system despite using only steady-state decoupling. Four independent fractional-order PI/PID controllers, one per decoupled loop, are compared under fixed-rate and event-based execution strategies across 24 virtual patients. Event-triggered updates decorrelate substantially faster than fixed-rate execution (threshold-based: 35.6% improvement, p < 0.001; integral timescale: 32.0% improvement, p < 0.001), disrupting temporal correlation chains that amplify cross-coupling beyond steady-state predictions. This mechanism yields 46.6% faster system-wide induction, 43.7% improved disturbance rejection, and 80% computational cost reduction compared to fixed-rate discrete-time implementation with identical controllers. Results suggest that execution strategy is a critical design variable for multi-input multi-output control, demonstrating that intelligent sampling can overcome fundamental architectural limitations without complex dynamic models.", "url": "" }, { "id": "Tu-TuA15.6", "code": "TuA15.6", "title": "A Systematic Approach to Identifying Stable Systems of Fractional-Order: The PETRA IV Fast Corrector Magnets (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA15", "sessionTitle": "Fractional-Order Control Systems: Advances in Theory, Optimization, and Industrial Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Rousselange, Lucas", "affiliation": "Deutsches Elektronen-Synchrotron DESY" }, { "name": "Eichler, Annika", "affiliation": "DESY" }, { "name": "Hespe, Christian", "affiliation": "Deutsches Elektronen-Synchrotron DESY" }, { "name": "Mirza, Sajjad Hussain", "affiliation": "DESY" }, { "name": "Pfeiffer, Sven", "affiliation": "DESY Hamburg" } ], "keywords": [ "Linear fractional-order systems" ], "abstract": "This paper proposes an improved frequency domain identification method for stable fractional-order systems of commensurate-order. The notion of normalized root stability is introduced as optimization constraint to enforce system stability. This notion is applied to the identification of models for fast corrector magnets designed for PETRA IV, the fourth generation synchrotron light source currently under development at Deutsches Elektronen-Synchrotron (DESY). The performance of the proposed method is validated against reference methods for fractional-order identification.", "url": "" }, { "id": "Tu-TuA16.1", "code": "TuA16.1", "title": "Enforcing Certainty Equivalence Via a Self-Tuning Disturbance Observer (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA16", "sessionTitle": "Recent Advances on Disturbance Observer-Based Control for Robust and Versatile Control Systems: From Theory to Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Song, Donghyeon", "affiliation": "Seoul National University" }, { "name": "Byun, Hyungjo", "affiliation": "Seoul National University" }, { "name": "Lee, Chanhwa", "affiliation": "Sejong University" }, { "name": "Shim, Hyungbo", "affiliation": "Seoul National University" } ], "keywords": [ "Adaptive control design", "Robust controller synthesis", "Linear systems" ], "abstract": "The classical self-tuning regulator (STR) relies on the certainty equivalence hypothesis, which often leads to performance degradation or even instability when parameter estimation is not sufficiently accurate. This paper proposes a self-tuning disturbance observer (DOB) as an inner-loop controller for STR, which is a variant of Q-filter-based DOB. The proposed self-tuning DOB forces the inner-loop system to behave like the model estimated by a recursive least squares algorithm instead of a nominal model, so that the certainty equivalent hypothesis actually meets dynamically from the point of view of the outer-loop STR. A stability condition is analyzed by the singular perturbation theory, which clarifies the interaction between the fast inner-loop dynamics of DOB and the slow parameter adaptation process.", "url": "" }, { "id": "Tu-TuA16.2", "code": "TuA16.2", "title": "A Variational Approach to Infinite Horizon Optimal Control Problems under External Disturbances (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA16", "sessionTitle": "Recent Advances on Disturbance Observer-Based Control for Robust and Versatile Control Systems: From Theory to Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Bai, Dongping", "affiliation": "Academy of Mathematics and Systems Science, CAS" }, { "name": "Li, Yibei", "affiliation": "Chinese Academy of Sciences" }, { "name": "Xue, Wenchao", "affiliation": "Chinese Academy of Sciences, Beijing 100190," } ], "keywords": [ "Optimal control theory", "Applications of optimal control", "Uncertain systems" ], "abstract": "This paper addresses the infinite horizon optimal control problem under time-varying external disturbances through a variational-based framework. Beyond model uncertainty and the strong coupling between the estimator and the optimal controller, additional challenges will arise in the infinite horizon problems due to the requirements for cost convergence and persistent stability. By integrating the disturbance estimation into the controller design, a complete analytical characterization of the dependence of optimality loss on estimation error is established. It is demonstrated that the variation in the optimal solution is a linear functional of the disturbance estimation error. Furthermore, both the variations in the optimal solution and the optimal cost can be quantitatively assessed by the estimation inaccuracy. Finally, the effectiveness of the proposed method is shown by numerical simulations.", "url": "" }, { "id": "Tu-TuA16.3", "code": "TuA16.3", "title": "On Extended Neighboring Optimal Control for Flight Vehicles Trajectory Optimization under Nonlinear Dynamics and Uncertain Parameter (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA16", "sessionTitle": "Recent Advances on Disturbance Observer-Based Control for Robust and Versatile Control Systems: From Theory to Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Hu, Xiaowen", "affiliation": "Chinese Academy of Sciences" }, { "name": "Xue, Wenchao", "affiliation": "Chinese Academy of Sciences, Beijing 100190," }, { "name": "Zhang, Ran", "affiliation": "Beihang University" }, { "name": "Huang, Feimin", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Optimal control theory", "Lagrangian and Hamiltonian systems", "Real-time optimal control" ], "abstract": "This paper addresses the trajectory optimization problem for flight vehicles under free-final-time conditions. The severe nonlinearity of the vehicle dynamics, together with uncertainty in the model parameters—interpreted as deviation from their nominal values—makes real-time trajectory optimization particularly challenging. To overcome this difficulty, we propose the extended neighboring optimal control (ENOC) framework that unifies initial-state deviation, terminal-condition deviation, and parameter deviation within a second-order variation analysis, thereby yielding a neighboring-optimal feedback law capable of compensating both boundary-condition and parameter deviation. Building on this framework, the extended state observer (ESO) is incorporated to estimate the parameter deviation in real time. Simulation results demonstrate that the proposed method maintains near-optimal performance and high terminal accuracy in the presence of parameter deviation as well as initial-state and terminal-condition deviations.", "url": "" }, { "id": "Tu-TuA16.4", "code": "TuA16.4", "title": "Internal-Model-Based Design of Disturbance Observers for a Class of Linear Systems with Modeled Disturbances (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA16", "sessionTitle": "Recent Advances on Disturbance Observer-Based Control for Robust and Versatile Control Systems: From Theory to Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Awai, Tersoo Samuel", "affiliation": "Korea University of Technology and Education" }, { "name": "Joo, Youngjun", "affiliation": "Sookmyung Women's University" }, { "name": "Kim, Hongkeun", "affiliation": "Korea University of Technology and Education" } ], "keywords": [ "Robust controller synthesis", "Robust linear matrix inequalities", "Uncertain systems" ], "abstract": "This paper addresses the design problem of disturbance observers for a class of uncertain linear plants affected by external disturbances. The disturbance entering the plant is assumed to be generated and modeled by a linear system whose eigenvalues all lie in the closed right-half complex plane. Under this setting, we propose a design method that asymptotically rejects the effect of the modeled disturbance on the closed-loop system, whereas conventional disturbance observers mainly attenuate it in an approximate sense. This is achieved by implicitly embedding an internal model of the disturbance into one of the low-pass filters of the disturbance observer. In contrast to existing internal-model-based designs that require the solvability of certain linear equations and thus restrict the class of disturbances to polynomials in time and/or sinusoids with distinct frequencies, our method does not impose such restrictions. With the proposed disturbance observer, we show that the closed-loop system is robustly stable and provide simulations to validate it.", "url": "" }, { "id": "Tu-TuA16.5", "code": "TuA16.5", "title": "Robust CACC for Heterogeneous Platoons Via Disturbance Observer and Dynamic Feedforward Filter (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA16", "sessionTitle": "Recent Advances on Disturbance Observer-Based Control for Robust and Versatile Control Systems: From Theory to Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Lee, Kangjun", "affiliation": "Sejong University" }, { "name": "Byun, Jaie Hyoung", "affiliation": "Sejong University" }, { "name": "Lee, Chanhwa", "affiliation": "Sejong University" } ], "keywords": [ "Robust controller synthesis", "Distributed robust controller synthesis", "Uncertain systems" ], "abstract": "This paper presents a robust cooperative adaptive cruise control (CACC) strategy for heterogeneous vehicle platoons subject to parameter uncertainties and external disturbances. A disturbance observer (DOB) is employed in the inner-loop to compensate for model mismatches, achieving nominal performance recovery. By doing so, the uncertain heterogeneous platoon is effectively treated as a homogeneous system governed by a nominal model. Based on these compensated dynamics, a simple outer-loop CACC is proposed that integrates a dynamic feedforward filter utilizing the desired acceleration of the preceding vehicle with a proportional-derivative (PD) feedback controller to theoretically guarantee string stability. Simulation results validate that the proposed method effectively maintains string stability against severe heterogeneity and disturbances.", "url": "" }, { "id": "Tu-TuA16.6", "code": "TuA16.6", "title": "Discrete-Time Disturbance Observer with Minimum-Phase Guarantees Via Robust Generalized Sampler (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA16", "sessionTitle": "Recent Advances on Disturbance Observer-Based Control for Robust and Versatile Control Systems: From Theory to Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Kim, Daehan", "affiliation": "Kwangwoon University" }, { "name": "Ha, Wonseok", "affiliation": "Inha Technical College" }, { "name": "Park, Hanbyeol", "affiliation": "Kwangwoon University" }, { "name": "Back, Juhoon", "affiliation": "Kwangwoon University" } ], "keywords": [ "Robust control applications", "Uncertain systems", "Robust controller synthesis" ], "abstract": "The inverse model-based disturbance observer (DOB) that incorporates the nominal inverse of a given uncertain plant is a kind of robust controller for estimating and compensating for the combined effect of external disturbance and model uncertainties. Despite the high performance and transparent structure, one of the limitations is that it cannot be directly applied to non-minimum phase systems. In this paper, we try to overcome this limitation by employing a generalized sampler replacing the conventional sampler used in the sampled-data control system. Applying the zero-assignment ability of this new sampler, we obtain a discrete-time model that is of minimum phase so that the disturbance observer design can be applied. In addition, a robust version of the generalized sampler together with its design is introduced to effectively cope with the plant uncertainty. A robust stability condition of the closed-loop system is also proposed, and the effectiveness of the result is validated through numerical simulations.", "url": "" }, { "id": "Tu-TuA17.1", "code": "TuA17.1", "title": "Contraction-Guaranteed Unconstrained Model Augmentation of Dynamical Systems Using Neural Networks (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA17", "sessionTitle": "Neural Networks for and within Nonlinear Control: Analysis, Design and Estimation", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Subrahamanian Moosath, Adarsh", "affiliation": "Eindhoven University of Technology" }, { "name": "Shakib, Mohammad Fahim", "affiliation": "Eindhoven University of Technology" }, { "name": "Fey, Rob H.B.", "affiliation": "PO Box 513, Eindhoven University of Technology" }, { "name": "van de Wouw, Nathan", "affiliation": "Eindhoven Univ of Technology" } ], "keywords": [ "Nonlinearity learning from data", "Stability of nonlinear systems", "Application of nonlinear analysis and design" ], "abstract": "This work proposes a novel model augmentation framework for learning discrete-time Lur’e-type systems in which the existing linear-time-invariant first-principles equations are augmented with static nonlinearities captured by neural networks. We first present conditions guaranteeing that the resulting augmented model is contractive and admits a unique, bounded steady-state solution to any bounded input. The latter property facilitates training of the neural networks directly based on steady-state responses. Unlike conventional approaches that require constrained enforcement of stability properties during training, our method employs a direct parameterisation technique, enabling scalability to large scale systems. The result is a scalable and contraction enforcing learning framework that improves model accuracy while retaining the inherent properties of the first-principle model. The effectiveness of the approach is demonstrated through a simulation case study of a nonlinear oscillator.", "url": "" }, { "id": "Tu-TuA17.2", "code": "TuA17.2", "title": "Free Parametrization of L_2-Bounded Structured State-Space Controllers for Nonlinear Control with Stability Guarantees (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA17", "sessionTitle": "Neural Networks for and within Nonlinear Control: Analysis, Design and Estimation", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Zakwan, Muhammad", "affiliation": "ETH Zurich" }, { "name": "Massai, Leonardo", "affiliation": "Ecole Polytechnique Fédérale De Lausanne (EPFL)" }, { "name": "Balta, Efe C.", "affiliation": "Inspire AG" }, { "name": "Ferrari-Trecate, Giancarlo", "affiliation": "Ecole Polytechnique Fédérale De Lausanne" } ], "keywords": [ "Learning methods for optimal control", "Output feedback nonlinear control", "Robust controller synthesis" ], "abstract": "Designing stabilizing control policies for nonlinear systems while optimizing complex objectives remains a formidable challenge. Neural Networks (NNs), despite their expressive power, can be highly sensitive to small input perturbations and can easily destabilize the closed-loop system. Existing approaches often impose explicit constraints on the controller’s parameters to ensure stability, but this typically leads to extra computational overhead. To address this issue, we leverage recently proposed Structured State-Space Models (SSMs) to parametrize discrete-time control policies for nonlinear systems. Our key contribution is a new free parametrization of Linear Time Invariant (LTI) systems with a prescribed mathcal{L}_2-gain, which we use to construct the L2-Recurrent Unit (L2RU) architecture, an SSM layer that enforces the desired mathcal{L}_2-bound emph{by design}. This result can be leveraged to guarantee closed-loop stability via the small-gain theorem or the so-called performance-boosting framework, independently of the controller’s optimization parameters, thereby enabling fully unconstrained optimization of general nonlinear objectives. Furthermore, the structure induced by the proposed parametrization allows efficient processing of long input sequences, as it is highly parallelizable through algorithms such as parallel scan. We demonstrate the effectiveness of this approach on a formation control task for mobile robots, where the L2RU-based controller ensures collision and obstacle avoidance while maintaining stability and performance.", "url": "" }, { "id": "Tu-TuA17.3", "code": "TuA17.3", "title": "A Unified Representation of Neural Networks Architectures (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA17", "sessionTitle": "Neural Networks for and within Nonlinear Control: Analysis, Design and Estimation", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Prieur, Christophe", "affiliation": "CNRS" }, { "name": "Lazar, Mircea", "affiliation": "Eindhoven Univ. of Technology" }, { "name": "Robu, Bogdan", "affiliation": "Université Grenoble Alpes" } ], "keywords": [ "Nonlinearity learning from data", "Distributed nonlinear control", "Infinite-dimensional multi-agent systems and networks" ], "abstract": "In this paper we consider the limiting case of neural networks (NNs) architectures when the number of neurons in each hidden layer and the number of hidden layers tend to infinity thus forming a continuum, and we derive approximation errors as a function of the number of neurons and/or hidden layers. Firstly, we consider the case of neural networks with a single hidden layer and we derive an infinite width integral neural representation that generalizes existing continuous neural networks (CNNs) representations. Then we extend this to deep residual CNNs that have a finite number of integral hidden layers and residual connections. Secondly, we revisit the relation between neural ODEs and deep residual NNs and we formalize approximation errors via discretization techniques. Then, we merge these two approaches into a unified homogeneous representation of NNs as a Distributed Parameter neural Network (DiPaNet) and we show that most of the existing finite and infinite-dimensional NNs architectures are related via homogenization/discretization with the DiPaNet representation.", "url": "" }, { "id": "Tu-TuA17.4", "code": "TuA17.4", "title": "Learning a Contracting KKL-Observer with Local Optimal Guarantees (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA17", "sessionTitle": "Neural Networks for and within Nonlinear Control: Analysis, Design and Estimation", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Galimberti, Clara Lucía", "affiliation": "Scuola Universitaria Professionale Della Svizzera Italiana" }, { "name": "Peralez, Johan", "affiliation": "Université De Lyon, Université Lyon 1, CNRS, LAGEP" }, { "name": "Astolfi, Daniele", "affiliation": "CNRS - Univ Lyon 1" }, { "name": "Andrieu, Vincent", "affiliation": "Université De Lyon" }, { "name": "Nadri, Madiha", "affiliation": "Universite Claude Bernard Lyon 1" } ], "keywords": [ "Nonlinear observers and filters", "Observer design", "Application of nonlinear analysis and design" ], "abstract": "The Kazantzis-Kravaris-Luenberger (KKL) observer provides a general framework for nonlinear state estimation by immersing the system dynamics into a stable linear or nonlinear latent dynamics. However, the performance of KKL observers relies heavily on the specific choice of these latent dynamics, which is often heuristic. This paper proposes a methodology to learn a KKL observer that combines global stability guarantees with local optimality. We derive a condition on the latent dynamics such that the observer locally mimics the behavior of a Minimum Energy Estimator (Mortensen observer). We then employ Deep Learning to approximate the KKL transformation and the latent dynamics, using neural network architectures that structurally enforce the contraction property. The proposed strategy is validated through numerical simulations on nonlinear benchmarks, demonstrating a good performance in the presence of state and measurement noise.", "url": "" }, { "id": "Tu-TuA17.5", "code": "TuA17.5", "title": "Stable Extrapolation in Physics-Data Hybrid Models Via Unconstrained Transition Parametrization (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA17", "sessionTitle": "Neural Networks for and within Nonlinear Control: Analysis, Design and Estimation", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Habboush, Abdullah", "affiliation": "Eindhoven University of Technology" }, { "name": "Shakib, Mohammad Fahim", "affiliation": "Eindhoven University of Technology" }, { "name": "Oomen, Tom", "affiliation": "Eindhoven University of Technology" }, { "name": "van de Wouw, Nathan", "affiliation": "Eindhoven Univ of Technology" } ], "keywords": [ "Nonlinearity learning from data", "Stability of nonlinear systems", "Lyapunov methods" ], "abstract": "Hybrid models combine physics-based models with data-driven components to achieve high accuracy while maintaining interpretability. However, their performance can degrade when extrapolating beyond the training data, often producing physically inconsistent predictions and compromising stability. The aim of this paper is to address this challenge by enforcing consistency with the underlying physics-based model away from the training dataset while maintaining accuracy on it. We propose a framework in which we modify existing hybrid models via a transition mapping that modulates the contribution of the data-driven component. Design constraints are imposed on the transition mapping to guarantee stability-preserving extrapolation based on known stability properties of the physics-based model. To enable scalable application to high-order systems, we introduce an unconstrained parametrization of the transition mapping that satisfies the design constraints by construction for any given hybrid model, regardless of model order or structure. We provide theoretical results establishing well-posedness and stability guarantees inherited from the underlying physics-based model. A simulation-based case study illustrates the effectiveness of the approach.", "url": "" }, { "id": "Tu-TuA17.6", "code": "TuA17.6", "title": "Learning the Dynamics of Nonlinear Systems with Regional Stability Guarantees through Linear Matrix Inequality Constraints (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA17", "sessionTitle": "Neural Networks for and within Nonlinear Control: Analysis, Design and Estimation", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Frank, Daniel", "affiliation": "University of Stuttgart" }, { "name": "Shakib, Mohammad Fahim", "affiliation": "Eindhoven University of Technology" }, { "name": "Staab, Steffen", "affiliation": "University of Stuttgart" } ], "keywords": [ "Nonlinearity learning from data", "Robust learning systems" ], "abstract": "This paper presents a method that learns a regionally stable recurrent neural network model from a set of input-output data generated by an unknown dynamical system. Relying on generalized sector conditions on the deadzone activation function, we first derive sufficient conditions that guarantee forward invariance on a compact set of the state space for any inputs from a given set. Such regional properties lead to less conservative conditions compared to variants that offer a global form of stability, and are in line with the system data that is only observed regionally. We then present a learning method that imposes the derived conditions for regional stability using a barrier function approach, leading to models equipped with a certificate of regional stability in a subset of the state space and for a given input set. We illustrate our theoretical result with a numerical example and compare it to methods that impose a global form of stability, which fail to identify the system, and with a method that imposes no stability constraints at all, which does not guarantee a stable behavior within any state or input set.", "url": "" }, { "id": "Tu-TuA18.1", "code": "TuA18.1", "title": "A Multi-Attribute Demand Forecasting Framework to Support Digital Twin-Based Decision-Making in Industrial Systems (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA18", "sessionTitle": "Intelligent Methods and Tools Supporting Decision Making in Manufacturing Systems and Supply Chains I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Garred, Wassim", "affiliation": "Centre Génie Industriel, IMT Mines Albi, Université De Toulouse" }, { "name": "Oger, Raphael", "affiliation": "Toulouse University, IMT Mines Albi, Industrial Engineering Center" }, { "name": "Lauras, Matthieu", "affiliation": "KEDGE Business School" }, { "name": "Lamothe, Jacques", "affiliation": "Toulouse University, Mines Albi" } ], "keywords": [ "Supply chain and logistics engineering, simulation and optimization", "Data-driven and AI-based modelling of production and logistics", "Manufacturing plant simulation, control and optimization" ], "abstract": "The increasing adoption of digital twins in industrial and logistics systems has profoundly changed how organizations plan and control their operations. However, the reliability of digital twin-based decision support critically depends on the realism of the demand forecasts used as inputs. This paper presents a structured tool-supported methodology for multi-attribute demand forecasting specifically designed to feed digital twins with coherent and simulation-ready data. The approach combines independent time-series forecasting, correlation reconstruction, and heuristic assignment to generate detailed synthetic demand datasets that preserve the interdependencies observed in historical data. The proposed framework is validated through an industrial application in a distribution warehouse, where it supports daily capacity planning decisions. Results demonstrate high forecasting accuracy across key demand attributes, structural coherence with historical distributions, and significant improvements in the realism and usefulness of simulation-based analyses. The study contributes to bridging the methodological gap between forecasting and digital twin integration and outlines research perspectives toward probabilistic and adaptive decision-support frameworks.", "url": "" }, { "id": "Tu-TuA18.2", "code": "TuA18.2", "title": "Integrated Optimization–Simulation Framework for Sustainable Sourcing Flow Allocation under Supply and Delivery Risk (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA18", "sessionTitle": "Intelligent Methods and Tools Supporting Decision Making in Manufacturing Systems and Supply Chains I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Meliani, Youssef", "affiliation": "Savoie Mont Blanc University" }, { "name": "Sahin, Evren", "affiliation": "Ecole Centrale Paris" } ], "keywords": [ "Supply chain and logistics engineering, simulation and optimization", "Supply network dynamics and control", "Supply chain management in manufacturing" ], "abstract": "This paper addresses sourcing and transportation planning in an aeronautics supply chain under cost, CO2 emissions, and delivery-risk considerations.We propose the architecture of a hybrid optimization–simulation decision-support framework in which a deterministic mixedinteger linear programming (MILP) model generates sourcing and transport plans, while a discrete-event simulation (DES) model is intended to evaluate their robustness under operational uncertainty. The present paper focuses on the formulation of the MILP decision layer and on the definition of its interaction with an existing DES model previously validated on the same industrial context. Using industrially inspired data, we illustrate how the MILP reacts to changes in service, carbon, and capacity-related parameters, and how such scenario analyses can support sourcing and transport decisions. The automated closed-loop interaction in which DES results are used to update MILP parameters is left for future work.", "url": "" }, { "id": "Tu-TuA18.3", "code": "TuA18.3", "title": "Graph Neural Network Simulation Trace Discovery for Digital Twin Services (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA18", "sessionTitle": "Intelligent Methods and Tools Supporting Decision Making in Manufacturing Systems and Supply Chains I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Shams Shemirani, Sadaf", "affiliation": "Institut National Polytechnique De Toulouse (INP Toulouse)" }, { "name": "Namaki Araghi, Sina", "affiliation": "E.N.I.T (National Engineering School of Tarbes)" }, { "name": "Karray, Hedi", "affiliation": "LGP-ENIT" }, { "name": "Archimede, Bernard", "affiliation": "Universite De Toulouse, Laboratoire GeniedeProduction, Ecole Nationale d'Ingenieurs De Tarbes" } ], "keywords": [ "Intelligent manufacturing systems", "Simulation and optimization in production, operations and services", "Data-driven and AI-based modelling of production and logistics" ], "abstract": "The dynamic nature of manufacturing systems requires agile monitoring approaches to represent and assess the system’s behavior accurately. Discrete-Event-Simulation (DES) models have been demonstrated in the literature to provide proactive insights into manufacturing operations. Despite this prospect, the design and maintenance of DES models requires substantial cost and resources. A minor alteration to the physical process flow can render a model obsolete. This paper addresses this challenge by dynamically extracting an optimized DES workflow from large manufacturing datasets, using it to run simulations on these data and identify the most advantageous improvement scenario. A Heterogeneous Graph Transformer (HGT) is trained on real and simulation-generated data to predict key performance indicators such as waiting time, service time, total duration, and resource utilization. The model achieves high accuracy, with R2 scores up to 0.98 for machine-usage prediction. The proposed pipeline serves as a scalable surrogate for system understanding, enabling integration into optimization and scheduling applications.", "url": "" }, { "id": "Tu-TuA18.4", "code": "TuA18.4", "title": "Material Supply Planning for Matrix-Structured Manufacturing Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA18", "sessionTitle": "Intelligent Methods and Tools Supporting Decision Making in Manufacturing Systems and Supply Chains I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Schumacher, Patrick", "affiliation": "Technische Universität Braunschweig" }, { "name": "Weckenborg, Christian", "affiliation": "University of Regensburg" }, { "name": "Spengler, Thomas S.", "affiliation": "TU Braunschweig" } ], "keywords": [ "Cyber-physical production systems", "Smart production and logistics in manufacturing", "Manufacturing plant simulation, control and optimization" ], "abstract": "Matrix-structured manufacturing systems (MMS), in which products flow through stations arranged in a matrix-shaped grid, have recently gained attention as an alternative to traditional assembly lines for mixed-model assembly. As MMS allow multiple possible product routes through the system even for identical products, planning material supply to stations becomes considerably more complex. Despite this, material supply planning and its interdependencies with system design have not yet been systematically addressed for MMS. This article presents an approach for material supply planning in MMS. To this end, a mathematical optimization model is presented. Based on the model's implementation as a mixed-integer programming model, numerical examples demonstrate the effectiveness of the proposed approach.", "url": "" }, { "id": "Tu-TuA18.5", "code": "TuA18.5", "title": "AI-Enabled Decision Support System for Managing Uncertainty in Circular Manufacturing: Towards Zero-Defect Re-Manufacturing (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA18", "sessionTitle": "Intelligent Methods and Tools Supporting Decision Making in Manufacturing Systems and Supply Chains I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Panagou, Sotirios", "affiliation": "NTNU" }, { "name": "Arena, Simone", "affiliation": "Università Di Cagliari" }, { "name": "Psarommatis, Foivos", "affiliation": "Univeristy of Oslo" }, { "name": "Fruggiero, Fabio", "affiliation": "University of Basilicata" } ], "keywords": [ "Sustainable and circular supply chain and production", "Supply chain management in manufacturing" ], "abstract": "Circular manufacturing systems are challenged by uncertainties in the quality, quantity, and timing of returned products, which complicate planning and condition assessment in reverse logistics. This paper proposes an AI-enabled Decision Support System (AIxDSS) designed to support decision-making under such uncertainty through predictive analytics and explainable machine-learning models. The AIxDSS evaluates component reusability, derives human-interpretable rules, and supports routing decisions for reuse, remanufacture, or recycling. A case study on electronic component recovery demonstrates how decision-tree models and feature analysis improve transparency, prediction reliability, and decision consistency. The approach contributes to Zero-Defect Re-Manufacturing (ZDRM) by enabling early defect prevention and quality-oriented process control. The results show that integrating explainable predictive models into DSS architectures can enhance uncertainty management, operator understanding, and overall efficiency in circular manufacturing environments.", "url": "" }, { "id": "Tu-TuA18.6", "code": "TuA18.6", "title": "Multi-View Component Detection for the Intelligent Production Lines: An Adaptive Inception-YOLO Framework", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA18", "sessionTitle": "Intelligent Methods and Tools Supporting Decision Making in Manufacturing Systems and Supply Chains I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Liao, Zhenxiang", "affiliation": "Ningbo University" }, { "name": "Guan, Hongtao", "affiliation": "Ningbo University" }, { "name": "Jiang, Yichen", "affiliation": "Ningbo University" }, { "name": "Wang, Rui", "affiliation": "Ningbo University" } ], "keywords": [ "Intelligent manufacturing systems", "AI-based enterprise systems", "Cyber-physical production systems" ], "abstract": "Component detection is crucial for the autonomy of intelligent production lines. However, the high-precision component detection remains challenging due to cluttered back-grounds, multi-view variability, and heterogeneous target appearances. This paper introduces Adaptive Inception-YOLO, a detection framework tailored for an intelligent manufacturing line. Firstly, an Inception-style backbone with large-kernel depthwise convolutions is built based on YOLOv11 to enlarge the effective receptive field and improve small-object perception in complex backgrounds. To improve robustness, we propose a dynamic multi-branch aggregation module with a learning gate, enabling the network to adaptively weight multi-scale branches according to input features. An experiment is carried out based on a real-world multi-view production-line dataset. Experimental results demonstrate the effectiveness of the proposed method in multi-view component detection with the mAP@50 of 0.9848, mAP@75 of 0.9786, and mAP@[0.50:0.95] of 0.8746, exceeding the performance of both the YOLOv11 baseline and a static Inception-enhanced variant.", "url": "" }, { "id": "Tu-TuA19.1", "code": "TuA19.1", "title": "WhoFi: Deep Person Re-Identification Via Wi-Fi Channel Signal Encoding for Cognitive Perception in Digital Twin Ecosystems (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Avola, Danilo", "affiliation": "Sapienza University of Rome" }, { "name": "Bernardini, Andrea", "affiliation": "Fondazione Ugo Bordoni" }, { "name": "Emam, Emad", "affiliation": "Department of Computer Science, Sapienza University of Rome," }, { "name": "Lezoche, Mario", "affiliation": "CRAN, Nancy-University, CNRS" }, { "name": "Montagnini, Dario", "affiliation": "Department of Computer Science, Sapienza University of Rome" }, { "name": "Nicolussi, Raffaele", "affiliation": "Fondazione Ugo Bordoni" }, { "name": "Pannone, Daniele", "affiliation": "Università La Sapienza" }, { "name": "Ranaldi, Amedeo", "affiliation": "Department of Computer Science, Sapienza University of Rome" } ], "keywords": [ "Digital enterprise", "Human-centered production and logistics", "Manufacturing engineering and management" ], "abstract": "Digital Twins (DTs) increasingly require robust human-centered perception to support monitoring, safety, and autonomous decision-making across diverse real-world environments, including smart industries, for reliable and continuous operation. We introduce WhoFi, a Wi-Fi–based person re-identification (Re-ID) model that offers an alternative to vision-based methods, which often suffer from occlusion, visual degradation, and privacy constraints. By extracting biometric signatures from Channel State Information (CSI) and encoding them through a Transformer-based architecture, WhoFi enables resilient and privacy-friendly Re-ID. Evaluation on the NTU-Fi dataset, currently a key reference benchmark for complex Wi-Fi sensing tasks, demonstrates its effectiveness within adaptive, human-aware DT ecosystems.", "url": "" }, { "id": "Tu-TuA19.2", "code": "TuA19.2", "title": "Understanding Digital Twin Resilience: A Conceptual Analysis (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Abdoune, Farah", "affiliation": "LS2N, Ecole Centrale De Nantes" }, { "name": "Eslami, Yasamin", "affiliation": "Ecole Centrale De Nantes" }, { "name": "da Cunha, Catherine", "affiliation": "Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004" }, { "name": "Cardin, Olivier", "affiliation": "LS2N UMR CNRS 6004 - Nantes University - IUT De Nantes" } ], "keywords": [ "Intelligent manufacturing systems", "Cyber-physical production systems", "Industrial artificial intelligence" ], "abstract": "Digital Twins (DTs) are increasingly used in manufacturing and supply chain systems to support real-time monitoring and decision-making. As these DTs become essential for operational continuity, their reliability under data, communication, or cyber disruptions becomes a critical requirement. While many studies focus on resilience by DTs how they help physical systems anticipate and recover from disruptions, there is limited attention to the resilience of DTs themselves, that is, their ability to remain accurate, functional, and trustworthy under uncertainty. This paper introduces the concept of a Resilient Digital Twin. Five resilience dimensions are identified: data, model, synchronization, architectural resilience, and cyber. These dimensions are mapped to the classical resilience capabilities of detection, response, recovery, and adaptation, providing a structured understanding of how resilience manifests within DT ecosystems.", "url": "" }, { "id": "Tu-TuA19.3", "code": "TuA19.3", "title": "Neuro-Symbolic Process Planning Supported by YOLO for Technical Drawing Classification and LLM Data Extraction (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Skrzek, Murillo", "affiliation": "Pontifical Catholic University of Paraná (PUCPR)" }, { "name": "Souza, Bruno Jose", "affiliation": "Pontifical Catholic University of Parana" }, { "name": "Bernardim Andrade, Matheus Herman", "affiliation": "Pontifical Catholic University of Parana" }, { "name": "Szejka, Anderson Luis", "affiliation": "Pontifical Catholic University of Parana, University of Lorraine, CNRS" }, { "name": "Mas, Fernando", "affiliation": "CT Engineering Group / University of Sevilla" }, { "name": "Zanetti Freire, Roberto", "affiliation": "Universidade Tecnológica Federal Do Paraná" } ], "keywords": [ "Industrial artificial intelligence", "Manufacturing engineering and management", "Digital transformation" ], "abstract": "Industry 5.0 emphasises human-centred manufacturing and the use of cognitive Artificial Intelligence to support collaboration between experts and intelligent systems. In advanced manufacturing, technical process planning still depends heavily on manual interpretation of drawings, material information, production constraints, and expert knowledge. This paper presents a neuro-symbolic framework that integrates multimodal large language models, computer vision, and ontology-based reasoning to support manufacturing plan generation. The proposed system extracts structured data from technical drawings, classifies part geometry using a YOLO-based model, and populates a domain ontology enriched with semantic rules and tacit knowledge. The results indicate that combining neural perception with symbolic reasoning can improve decision support, reduce manual effort, and generate manufacturing sequences consistent with expert-developed plans.", "url": "" }, { "id": "Tu-TuA19.4", "code": "TuA19.4", "title": "Cognitive Digital Twins in Manufacturing: Analyzing the Synergy of Semantic Web and Cognitive Processes (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Lezoche, Mario", "affiliation": "CRAN, Nancy-University, CNRS" }, { "name": "Torres, Diego", "affiliation": "National University of La Plata" } ], "keywords": [ "Cyber-physical production systems", "Digital enterprise", "Industry X.0 for production and logistics" ], "abstract": "This paper presents a systematic mapping study on how Semantic Web technologies support cognition in digital twins and Industry 4.0/5.0 systems. A SCOPUS search (2019–2025) identifies 20 studies combining ontologies, knowledge graphs, reasoning frameworks, and semantic integration tools with cognitive capabilities in industrial or robotic applications. Cognitive functions are classified using taxonomies by Metzler and Neisser. Results show strong emphasis on perception, memory, and low-level reasoning, while higher-order cognition is less developed. Semantic technologies dominate knowledge representation, but advanced reasoning and neuro-symbolic methods remain limited. The study highlights key gaps and opportunities for cognitive-ready semantic frameworks.", "url": "" }, { "id": "Tu-TuA19.5", "code": "TuA19.5", "title": "Towards a Hybrid Neuro-Symbolic and Connectivity-Driven AI for Automated Feature Extraction from STEP Models (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Bernardim Andrade, Matheus Herman", "affiliation": "Pontifical Catholic University of Parana" }, { "name": "Cavalcanti Hernandes, Leonardo", "affiliation": "Pontifical Catholic University of Parana - PUCPR" }, { "name": "Skrzek, Murillo", "affiliation": "Pontifical Catholic University of Paraná (PUCPR)" }, { "name": "Jacomini Prioli, João Paulo", "affiliation": "North Carolina A&T State University" }, { "name": "Szejka, Anderson Luis", "affiliation": "Pontifical Catholic University of Parana, University of Lorraine, CNRS" } ], "keywords": [ "Digital transformation", "Industrial artificial intelligence", "Digital enterprise" ], "abstract": "Automated Feature Recognition (AFR) is a critical enabler of the digital thread in advanced manufacturing, translating low-level geometric data from CAD models into high-level semantic information for process planning, cost estimation, and inspection. Traditional AFR methods, often relying on rule-based or graph-based algorithms, struggle with robustness and adaptability when faced with the geometric complexity and variability of modern aerospace components. This paper introduces a novel hybrid AI-symbolic framework that integrates traditional geometric analysis with advanced artificial intelligence techniques, including multi-agent systems and ensemble learning. We present a comprehensive benchmarking suite of five distinct feature extraction methodologies, ranging from simple geometric parsers to sophisticated multi-agent AI systems. These methods were evaluated on a corpus of aerospace parts represented in the STEP format. The experimental results demonstrate that our top-performing hybrid model, the \"Improved Multi-Agent AI,\" achieves a mean F1-score of 0.89 and an accuracy of 96.5%, significantly outperforming both traditional symbolic proxies and simpler AI-based extractors. This work demonstrates the synergistic potential of combining symbolic reasoning with generative AI to create a more robust, accurate, and versatile AFR solution for the manufacturing industry.", "url": "" }, { "id": "Tu-TuA19.6", "code": "TuA19.6", "title": "Data-Driven Drift Detection and Diagnosis Framework for Predictive Maintenance of Heterogeneous Production Processes: Application to a Multiple Tapping Process", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Chapelin, Julien", "affiliation": "ACESI GROUP" }, { "name": "Voisin, Alexandre", "affiliation": "Université De Lorraine, CNRS, CRAN" }, { "name": "Rose, Bertrand", "affiliation": "Université De Strasbourg" }, { "name": "Iung, Benoît", "affiliation": "Lorraine University" } ], "keywords": [ "Industrial artificial intelligence", "Manufacturing prognostics and health management", "Maintenance engineering, management and services" ], "abstract": "The rise of Industry 4.0 technologies has revolutionized industries, enabled seamless data access, and fostered data-driven methodologies for improving key production processes such as maintenance. Predictive maintenance has notably advanced by aligning decisions with real-time system degradation. However, data-driven approaches confront challenges such as data availability and complexity, particularly at the system level. Most approaches address component-level issues, but system complexity exacerbates problems. In the realm of predictive maintenance, this paper proposes a framework for addressing drift detection and diagnosis in heterogeneous manufacturing processes. The originality of the paper is twofold. First, this paper proposes algorithms for handling drift detection and diagnosing heterogeneous processes. Second, the proposed framework leverages several machine learning techniques (e.g., novelty detection, ensemble learning, and continuous learning) and algorithms (e.g., K-Nearest Neighbors, Support Vector Machine, Random Forest and Long-Short Term Memory) for enabling the concrete implementation and scalability of drift detection and diagnostics on industrial processes. The effectiveness of the proposed framework is validated through metrics such as accuracy, precision, recall, F1-score, and variance. Furthermore, this paper demonstrates the relevance of combining machine learning and deep learning algorithms in a production process of SEW USOCOME, a French manufacturer of electric gearmotors and a market leader. The results indicate a satisfactory level of accuracy in detecting and diagnosing drifts, and the adaptive learning loop effectively identifies new drift and nominal profiles, thereby validating the robustness of the framework in real industrial settings.", "url": "" }, { "id": "Tu-TuA20.1", "code": "TuA20.1", "title": "MPC Design through Inverse-Optimal PI Control", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA20", "sessionTitle": "Model Predictive Control: Theory and Algorithms", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Sundström, Emil", "affiliation": "Lund University" }, { "name": "Norlund, Frida", "affiliation": "Lund University" }, { "name": "Soltesz, Kristian", "affiliation": "Lund University" }, { "name": "Allgower, Frank", "affiliation": "University of Stuttgart" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes", "Industrial applications of process control", "Advanced process control" ], "abstract": "With the aim to lower the barrier for industries to adopt model predictive controllers (MPC), we derive analytical expressions for the terms in the cost matrices of an MPC formulation for a first-order system, such that the control signal exactly matches the output from a PI-controller when constraints are inactive. We solve this controller-matching problem with an inverse optimal control formulation by requiring no state-input cross-terms in the cost function, resulting in a MPC formulation with terminal costs. The resulting controller is validated with simulations based on a control scenario from the Swedish mining industry.", "url": "" }, { "id": "Tu-TuA20.2", "code": "TuA20.2", "title": "A Simple Quadratic Programming Algorithm for Model Predictive Control", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA20", "sessionTitle": "Model Predictive Control: Theory and Algorithms", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Wang, Liuping", "affiliation": "RMIT University" }, { "name": "Guan, Robin", "affiliation": "RMIT University" } ], "keywords": [ "Advanced process control", "Model-predictive and optimization-based control in chemical processes", "Industrial applications of chemical process control" ], "abstract": "The core computational algorithm in Model Predictive Control (MPC) is based on real-time optimization with respect to operational constraints. This optimization problem is commonly solved using a quadratic programming algorithm. This paper proposes a solution of the optimization problem using a prime-dual Hildreth's algorithm with respect to interval constraints. The utilization of interval constraints reduces the number of constraints in half. More importantly, the algorithm is exceedingly simple for real-time implementation. A MATLAB program is presented in this paper for those who wish to try the proposed approach. end{abstract}", "url": "" }, { "id": "Tu-TuA20.3", "code": "TuA20.3", "title": "Development of an Explicit Dual Adaptive MPC Scheme with Improved Disturbance Rejection", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA20", "sessionTitle": "Model Predictive Control: Theory and Algorithms", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Kumar, Kunal", "affiliation": "IIT Bombay" }, { "name": "Singh, Ashutosh Kumar", "affiliation": "Indian Institute of Technology Bombay" }, { "name": "Patwardhan, Sachin C.", "affiliation": "Indian Institute of Technology Bombay" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes", "Advanced process control", "Real-time optimization and control in chemical processes" ], "abstract": "The development of MPC schemes based on dual control framework (DMPC) has attracted significant attention over the last few years. Most of the approaches available in the literature are concerned with solving the target tracking problem rather than disturbance rejection. The unmeasured disturbances are correlated in time, and using their temporal relationships in controller synthesis can help in improving the regulatory performance. The ARMAX models provide a parsimonious representation of such autocorrelated signals. Therefore, in this work, we propose to use multiple MISO ARMAX models to improve the regulatory control performance of adaptive dual MPC (ADMPC) schemes. Using the concept of excitation horizon, the future predictions in the stochastic optimal control problem are split into the excitation and control components. For approximating the excitation term, a sampling-based approach using the unscented transformations is used to arrive at a computationally tractable ADMPC formulations. The efficacy of the proposed ADMPC schemes is evaluated by conducting simulation studies on the benchmark quadruple tank process. The simulation studies reveal that the proposed ADMPC schemes have the edge over ADMPC schemes that employ models with OE structure while dealing with unmeasured disturbances.", "url": "" }, { "id": "Tu-TuA20.4", "code": "TuA20.4", "title": "Explainable LP-MPC: Shadow Price Contributions Reveal MV-CV Pairings", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA20", "sessionTitle": "Model Predictive Control: Theory and Algorithms", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Siang, Lim C.", "affiliation": "Burnaby Refinery" }, { "name": "O'Connor, Daniel L.", "affiliation": "Control Consulting Inc" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes", "Industrial applications of chemical process control", "Advanced process control" ], "abstract": "In the process industries, MPC (Model Predictive Control) is typically implemented as a two-stage controller with a Linear Program (LP) steady-state optimizer that generates economically optimal targets for the MPC algorithm. Abnormal behaviors in industrial LP optimizers are often difficult to rationalize, especially when a large number of manipulated variables (MVs) and controlled variables (CVs) are involved. We introduce a novel, post-hoc LP explainability method by recasting the role of shadow prices in the LP solution as an attribution mechanism for MV-CV relationships. The core idea is that the shadow price of a constrained CV is not just an intrinsic property of the LP solution, but can be split into contributions from individual unconstrained MVs and resolved into one-to-one MV-CV pairings using a linear sum assignment algorithm. The proposed MV-CV pairing framework serves as a practical explainability tool for online LP-MPC systems, enabling practitioners to diagnose suboptimal constraints and verify alignment of the controller's behavior with its original design.", "url": "" }, { "id": "Tu-TuA20.5", "code": "TuA20.5", "title": "Closed-Loop System Identification under Sampling and Measurement Delay in Run-To-Run Controlled Processes", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA20", "sessionTitle": "Model Predictive Control: Theory and Algorithms", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Kim, Seunghyeon", "affiliation": "DGIST" }, { "name": "Lee, Jaeho", "affiliation": "DGIST" }, { "name": "Kim, Mike Young-Han", "affiliation": "Gauss Labs Inc" }, { "name": "Eun, Yongsoon", "affiliation": "Daegu Gyeongbuk Institute of Science and Technology" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Advanced process control" ], "abstract": "This paper investigates a closed-loop system identification method for run-to-run controlled semiconductor processes in the presence of sampled and delayed metrology. It is well known that system identification under closed-loop operation can lead to biased estimates if the effect of feedback is ignored. However, we derive a condition involving sampling interval and measurement delay under which accurate identification is possible under closed-loop environment. We assume a static process model with an integrated first-order moving average (IMA(1,1)) disturbance and an exponentially weighted moving average (EWMA) controller. A mathematical analysis is first carried out for a single-input single-output (SISO) control system, from which an identifiability condition that depends on the sampling interval and measurement delay is derived. Furthermore, simulation studies of both SISO and multiple-input multiple-output (MIMO) control systems are presented to demonstrate the validity of the derived condition.", "url": "" }, { "id": "Tu-TuA20.6", "code": "TuA20.6", "title": "Fusion of Vision-Based High-Gain Observer and Model Predictive Control for Vehicle Adaptive Cruise Control (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA20", "sessionTitle": "Model Predictive Control: Theory and Algorithms", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Jacques, William", "affiliation": "University of Southampton" }, { "name": "Bessafa, Hichem", "affiliation": "University of Minnesota" }, { "name": "Zemouche, Ali", "affiliation": "CRAN UMR CNRS 7039, University of Lorraine" }, { "name": "Belkhatir, Zehor", "affiliation": "University of Southampton" } ], "keywords": [ "Motion control for AVs", "Autonomous vehicles", "Trajectory tracking and path following for AVs" ], "abstract": "Adaptive Cruise Control (ACC) is an Advanced Driver Assistance System (ADAS) that enhances vehicle safety by regulating ego throttle and brake inputs to sustain a desired speed or a safe following distance. Conventional ACC often depends on costly radar sensors or computationally intensive learning-based perception to track surrounding vehicles. This paper proposes a low-cost, vision-based ACC framework that relies solely on a monocular camera for vehicle detection, tracking, and ego control. The method uses a model-based high-gain observer with the YOLO computer-vision algorithm to estimate surrounding vehicles’ trajectories directly from ego-vehicle camera frames. These estimates are incorporated into a Model Predictive Control (MPC) scheme to achieve real-time ACC functionality. The proposed vision-based observer–MPC technique is validated in the CARLA simulation environment by demonstrating stability, real-time feasibility, and applicability to practical real-world driving scenarios.", "url": "" }, { "id": "Tu-TuA21.1", "code": "TuA21.1", "title": "Distributed Voltage and Phase Angle Estimation of Power System and Its Calculation Procedures", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA21", "sessionTitle": "Emerging Hybrid Heuristics for Optimal Design of Assessment and Control Functionalities in IBR Dominated Energy Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Akutsu, Hikaru", "affiliation": "Toyama Prefectural University" }, { "name": "Terasaki, Hayato", "affiliation": "Hitachi Solutions Create" }, { "name": "Hirata, Kenji", "affiliation": "University of Toyama" }, { "name": "Hespanha, Joao", "affiliation": "University of California, Santa Barbara" } ], "keywords": [ "Distributed optimization for smart grids", "Distributed optimization and control for smart cities" ], "abstract": "This paper proposes a distributed estimation method for the voltage and phase angle of power systems. We assume that each node measures active and reactive power with Gaussian noise. We construct an optimization-based approach to state estimation, based on maximum likelihood. The first-order necessary conditions of the optimization problems are equivalent to systems of non-linear equations. To calculate these equations, we use a sequential procedure inspired by the Gauss-Seidel method and a parallel procedure inspired by the Jacobi method. In addition, we propose a modified parallel procedure such that each node uses the two previous steps’ estimates of its two-hop neighbors. We evaluate the effectiveness of the proposed method through numerical experiments using five IEEE bus system models.", "url": "" }, { "id": "Tu-TuA21.2", "code": "TuA21.2", "title": "Dynamic Droop Adaptation for Inverter Functionality Transitions in Power Networks", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA21", "sessionTitle": "Emerging Hybrid Heuristics for Optimal Design of Assessment and Control Functionalities in IBR Dominated Energy Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Park, Jaesang", "affiliation": "University of Illinois Urbana-Champaign" }, { "name": "Askarian, Alireza", "affiliation": "University of Illinois Urbana Champaign" }, { "name": "Salapaka, Srinivasa", "affiliation": "Univ of Illinois" } ], "keywords": [ "Electrical distribution systems", "Electrical transmission systems", "Control and management of energy systems" ], "abstract": "High penetration of renewable energy resources increases operating variability and challenges the regulation capability of grid-following inverters, placing greater burden on synchronous generators. While grid-forming inverters enhance stability, their droop coefficients are typically tuned heuristically, remain fixed in operation, and do not account for temporally varying uncertainty in power sources and loads. This paper derives the steady-state relationship between droop parameters and network dynamics and introduces a sensitivity metric for bounded, time-varying load perturbations. A worst-case robust optimization with adaptive droop updating is developed. The proposed framework enables seamless temporal functional adjustment and improves voltage-frequency regulation and robustness under dynamically evolving uncertainty. In simulations, we evaluate two test cases with different perturbation levels to illustrate time-varying uncertainties. The proposed method achieves performance improvements of 27.08% and 4.76% compared with a fixed-droop baseline.", "url": "" }, { "id": "Tu-TuA21.3", "code": "TuA21.3", "title": "Nonlinear Model Predictive Control of Permanent Magnet Synchronous Generators Using Feedback Linearising Terminal Control", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA21", "sessionTitle": "Emerging Hybrid Heuristics for Optimal Design of Assessment and Control Functionalities in IBR Dominated Energy Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Zhao, RuoChen", "affiliation": "The University of Sheffield" }, { "name": "Drummond, Ross", "affiliation": "University of Sheffield" }, { "name": "Trodden, Paul", "affiliation": "University of Sheffield" } ], "keywords": [ "Power plant control", "Control and management of energy systems" ], "abstract": "Permanent magnet synchronous generators (PMSGs) are widely used in microgrid, wind, and tidal power systems to provide reliable and efficient renewable energy generation. However, their inherent nonlinear dynamics and state constraints, arising from physical and safety limits, pose significant challenges for conventional controllers. Proportional–Integral–Derivative (PID) control cannot explicitly handle this complexity, and traditional model predictive control (MPC) approaches with fixed weighting matrices often fail to ensure stability when the operating conditions of the PMSG vary. To address these issues, we propose a novel terminal control law and terminal cost function design for the nonlinear MPC (NMPC) that explicitly accounts for the nonlinear dynamics and constraints of the PMSG. Simulation results verify the effectiveness of the proposed scheme and highlight its advantages over linear MPC, showing the potential to go beyond linear MPC for micro-grid control while still guaranteeing performance.", "url": "" }, { "id": "Tu-TuA21.4", "code": "TuA21.4", "title": "Negative Imaginary and Passivity Properties of Synchronous Machine Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA21", "sessionTitle": "Emerging Hybrid Heuristics for Optimal Design of Assessment and Control Functionalities in IBR Dominated Energy Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Khodabakhshloo, Maryam", "affiliation": "Australian National University" }, { "name": "Ratnam, Elizabeth Louise", "affiliation": "The Australian National University" }, { "name": "Petersen, Ian R", "affiliation": "The Australian National University" } ], "keywords": [ "Power systems stability" ], "abstract": "The recent rapid proliferation of renewable energy is fundamentally changing the dynamic operations of power systems, necessitating new approaches to assess stability for these highly nonlinear systems. In this paper, we prove that synchronous machine systems, modeled in the nonlinear dq frame, possess fundamental dissipativity properties. Specifically, we show passivity from current input to voltage output and a nonlinear negative imaginary property from torque input to rotor angle output. For the nonlinear system shifted around an equilibrium point, we derive explicit conditions for both passivity and the NI property to hold. Finally, we demonstrate that interconnection with passive droop controllers preserves these dissipativity properties with identical supply rates, thereby ensuring closed-loop stability.", "url": "" }, { "id": "Tu-TuA21.5", "code": "TuA21.5", "title": "Equilibrium Points and Stability of Synchronous Machine Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA21", "sessionTitle": "Emerging Hybrid Heuristics for Optimal Design of Assessment and Control Functionalities in IBR Dominated Energy Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Khodabakhshloo, Maryam", "affiliation": "Australian National University" }, { "name": "Ratnam, Elizabeth Louise", "affiliation": "The Australian National University" }, { "name": "Petersen, Ian R", "affiliation": "The Australian National University" } ], "keywords": [ "Power systems stability" ], "abstract": "This paper investigates equilibrium points and stability in two synchronous machine configurations: (i) a single generator with an impedance load and (ii) two interconnected machines with co-located loads. We consider both abc and dq reference frames to show that the equilibrium condition reduces to a cubic polynomial in the single-machine case and to an 18th-degree polynomial in the two-machine case. For the single-machine system, Lyapunov stability analysis and linearization based stability analysis are carried out. For the two-machine system, local stability is assessed through linearization and eigenvalue analysis. Illustrative examples confirm the existence of multiple equilibria and illustrate the impact of parameter variation on stability. Our results provide insight into the stability of synchronous machine systems.", "url": "" }, { "id": "Tu-TuA21.6", "code": "TuA21.6", "title": "Event-Triggered Global SMC Approach for a DFIG-Based WECS", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA21", "sessionTitle": "Emerging Hybrid Heuristics for Optimal Design of Assessment and Control Functionalities in IBR Dominated Energy Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Islam, Mohammad Zohurul", "affiliation": "University of Louisiana at Lafayette" }, { "name": "Musarrat, Md Nafiz", "affiliation": "University of Louisiana at Lafayette" }, { "name": "Fekih, Afef", "affiliation": "Univ of Louisiana at Lafayette" } ], "keywords": [ "Power systems stability", "Wind power", "Fault-tolerant control methods" ], "abstract": "This paper proposes an event-triggered global sliding mode control (ET-GSMC) strategy for the grid side converter (GSC) of a doubly fed induction generator (DFIG)-based wind energy conversion system (WECS). Unlike the standard sliding mode control (SMC) approach, ET-GSMC establishes the DC-link voltage stability from the initial moment by eliminating the reaching phase and mitigates the chattering phenomenon by reducing the switching frequency. Hence, the proposed controller allows the DC-link capacitor to assist the rotor side converter (RSC) for seamless bidirectional power transfer with the grid. The effectiveness of the proposed ET-GSMC is assessed by computer experiments in the MATLAB/Simulink environment. The results are further compared with those of the standard SMC. The assessment was performed under various faulty conditions, including single-line-to-ground fault, three phase symmetrical fault and balanced load variation. The results confirmed the effectiveness and improved dynamic performance of the DFIG-based WECS in the presence of faulty conditions.", "url": "" }, { "id": "Tu-TuA22.1", "code": "TuA22.1", "title": "A Stacking Ensemble Framework with Federated Learning for Robust Non-Intrusive Load Monitoring", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA22", "sessionTitle": "Learning, Control and Stability for Power and Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Li, Ding", "affiliation": "Wuhan Institute of Technology" }, { "name": "Xu, Jinghao", "affiliation": "China University of Geosciences" } ], "keywords": [ "Energy management systems", "Forecasting of power supply and demand", "Big data and machine learning applied to smart cities" ], "abstract": "Non-intrusive load monitoring (NILM) faces significant challenges in maintaining performance when deployed to unseen users or new environments due to limited generalization capability. Accordingly, this paper proposes FedStacking-NILM, an enhanced framework that integrates federated learning with stacking ensemble methodology for non-intrusive load monitoring. The framework employs three federated learning algorithms, including FedAvg, FedProx, and FedAC, as base learners to capture diverse load characteristics while preserving data privacy. A Kolmogorov-Arnold Network (KAN) based meta learner then effectively integrates the base learners' outputs through sophisticated nonlinear fusion. Comprehensive evaluations on public datasets demonstrate the effectiveness of FedStacking-NILM, which enhances identification accuracy when tested on unseen households. The framework maintains robust performance across diverse appliance types while ensuring data privacy protection.", "url": "" }, { "id": "Tu-TuA22.2", "code": "TuA22.2", "title": "Tractable Convex Hull Pricing Approximation Via Continuous Relaxation of Time-Dependent Constraints", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA22", "sessionTitle": "Learning, Control and Stability for Power and Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Mohamad, Judy", "affiliation": "Institute of Science Tokyo" }, { "name": "Ishizaki, Takayuki", "affiliation": "Tokyo Institute of Technology" } ], "keywords": [ "Energy market" ], "abstract": "We propose a tractable convex hull pricing (CHP) approximation for multi-period electricity pricing. The method represents single-period non-convexities using the exact convex biconjugate of the bus-level cost function, while intertemporal constraints are handled via continuous relaxation of commitment variables, as in Extended Locational Marginal Pricing (ELMP). Simulations on a modified PJM 5-bus system show that the hybrid formulation can reduce lost opportunity cost uplift relative to ELMP in test cases with nonlinear variable generation costs, while remaining more tractable than a discretized CHP benchmark.", "url": "" }, { "id": "Tu-TuA22.3", "code": "TuA22.3", "title": "End-To-End Learning for Robust Economic Dispatch with Statistical Feasibility Guarantee", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA22", "sessionTitle": "Learning, Control and Stability for Power and Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Li, Jiayi", "affiliation": "Peking University" }, { "name": "You, Pengcheng", "affiliation": "Zhejiang University" } ], "keywords": [ "Energy market", "Power systems stability", "Energy management systems" ], "abstract": "This paper presents a learning framework for economic dispatch in a day-ahead electricity market subject to two main sources of uncertainty in (net) loads and cost bids. Uncertain loads add significant challenges to power balance, threatening power system safety, while uncertain cost bids may render system operation economically inefficient. To jointly tackle the two issues, we propose a robust economic dispatch formulation that hedges against worst-case bid deviations from actual costs and further provide statistical feasibility guarantee for solutions to satisfy all physical and operational constraints. Without the knowledge of true distributions of these randomness, we develop a data-driven approach that in parallel learns feasible regions and robust optimal decisions. In particular, we employ conformal prediction with an affine recourse policy to ensure feasibility with statistical validity. We then adopt an end-to-end learning framework that embed a robust optimization layer in training to acquire dispatch decisions directly from historical data of realized marginal costs. This approach mitigates cost bid risks, respects constraints (with high probability), and, more importantly, represents a full paradigm shift from prediction-based optimization to task-oriented learning. Numerical simulations on the IEEE 39-bus system validate that our framework robustly reduces system costs in a variety of scenarios with feasible dispatch decisions.", "url": "" }, { "id": "Tu-TuA22.4", "code": "TuA22.4", "title": "Empirical Fusion Transformer Integrated with Grey Wolf Optimizer for 24-Hour Ahead Load Forecasting", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA22", "sessionTitle": "Learning, Control and Stability for Power and Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Hong, Ying Yi", "affiliation": "Chung Yuan Christian University" }, { "name": "Rioflorido, Christian Lian Paulo Perez", "affiliation": "Chung Yuan Christian University" }, { "name": "Chen, Chien Mao", "affiliation": "Chung Yuan Christian University" }, { "name": "Yang, Chia Jui", "affiliation": "Chung Yuan Christian University" }, { "name": "Centeno, Juan Miguel", "affiliation": "Mapua University" }, { "name": "Limjoco, Hanah", "affiliation": "Mapua University" }, { "name": "Gulmatico, Leonyl", "affiliation": "Mapua University" }, { "name": "Manalo, Mary Joyce Nicole", "affiliation": "Mapua University" }, { "name": "Onia, John Laurence", "affiliation": "Mapúa University" } ], "keywords": [ "Forecasting of power supply and demand" ], "abstract": "This paper proposes a 24-hour ahead load forecasting framework that integrates the Temporal Fusion Transformer (TFT) with the Grey Wolf Optimizer (GWO) to address forecasting challenges in Taiwan’s power system, which is affected by renewable energy integration, industrial variability, and extreme weather. The TFT component captures multi-scale temporal dependencies and provides interpretability, while the GWO is employed for systematic hyperparameter optimization to improve accuracy and robustness. The framework is validated using nine years (2017~2025) of Taiwan Power Company operational data, including load profiles, meteorological variables, and economic indicators. Compared with benchmark models, the proposed TFT-GWO achieves superior results, with MAE (mean absolute error), RMSE (root mean squared error), and R2 (coefficient of determination) of 0.0145, 0.0220, and 0.9390, respectively. The results demonstrate that the proposed approach supports more reliable unit commitment, reserve allocation, and day-ahead market operations, highlighting the effectiveness of combining transformer-based architectures with metaheuristic optimization in power system forecasting.", "url": "" }, { "id": "Tu-TuA22.5", "code": "TuA22.5", "title": "Slow Converter-Driven Stability Analysis Via Directional and Relative Passivity Indices", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA22", "sessionTitle": "Learning, Control and Stability for Power and Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Kowalewski, Julia", "affiliation": "Friedrich-Alexander-Universität Erlangen-Nürnberg" }, { "name": "Lorenz, Andreas", "affiliation": "Siemens Energy Global GmbH & Co. KG" }, { "name": "Graichen, Knut", "affiliation": "Friedrich-Alexander-University Erlangen-Nuremberg" } ], "keywords": [ "Power systems stability", "Electrical transmission systems", "Electrical distribution systems" ], "abstract": "Stability phenomena induced by outer converter control loops are attributed to slow converter-driven stability. These can occur, when the converter input admittance exhibits non-passive frequency bands in the lower frequency range. This paper focuses on the two-channel negative feedback interconnection (NFI) structure of the converter-grid interaction (CGI) and evaluates suitable passivity-based stability theorems capable of addressing this lack of passivity by leveraging directional and relative passivity indices. The present work identifies the most appropriate stability theorem for systems exhibiting non-passive behavior. In particular, the so-called Small R f Theorem is extended to be applicable to a broader class of such systems, addressing a gap in existing literature. The extended theorem is then employed to analyze the stability of CGI under subsynchronous resonance (SSR), highlighting its practical relevance.", "url": "" }, { "id": "Tu-TuA22.6", "code": "TuA22.6", "title": "Benchmarking Multi-Horizon Building Energy Forecasting with Robust Uncertainty Quantification", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA22", "sessionTitle": "Learning, Control and Stability for Power and Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Mohaghegh Rad, Zahra", "affiliation": "Centrale Méditerranée" }, { "name": "Graton, Guillaume", "affiliation": "Ecole Centrale De Marseille" }, { "name": "Ben Elghali, Seifeddine", "affiliation": "Aix-Marseille University, UMR CNRS 7020 LIS, Marseille, France" } ], "keywords": [ "Forecasting of power supply and demand", "Energy management systems", "Demand response" ], "abstract": "Accurate energy forecasting is essential for reducing demand charges. This paper benchmarks eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), CNN-LSTM-Attention, and Temporal Fusion Transformer (TFT) using four uncertainty quantification methods. Validated across multiple commercial buildings, results reveal an accuracy-calibration paradox: TFT delivers state-of-the-art accuracy but severe under-coverage. In contrast, XGBoost with Quantile Regression (QR) provides the best operational trade-off with adaptive intervals. While Conformal Prediction (CP) ensures safety, it lacks sharpness. We demonstrate that operational reliability requires balancing point accuracy with calibrated uncertainty to guide risk-aware energy management.", "url": "" }, { "id": "Tu-TuA23.1", "code": "TuA23.1", "title": "Embedding Linear Equality Constraints in Probabilistic Neural Networks for Dynamic Modelling", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA23", "sessionTitle": "Hybrid and Physics-Informed Modeling for Chemical Processes", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Marsh, Matthew", "affiliation": "Imperial College London" }, { "name": "Chachuat, Benoit", "affiliation": "Imperial College London" }, { "name": "del Rio-Chanona, Ehecatl Antonio", "affiliation": "Imperial College London" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Interaction between design and control in processes", "Machine learning and artificial intelligence in chemical process control" ], "abstract": "Machine learning models are increasingly used to model chemical process systems, yet they often lack principled uncertainty quantification and mechanisms to enforce physical constraints. We propose a probabilistic neural network framework that guarantees satisfaction of linear equality constraints within a given tolerance, while capturing aleatoric uncertainty. Compared to state-of-the-art methods, our formulation demonstrates improved predictive accuracy, uncertainty calibration, and adherence to constraints on reduced data. It also demonstrates competitive performance, but with significantly faster training times when evaluated on large data regimes. We evaluated this on two batch reactor case studies, enforcing mass balances.", "url": "" }, { "id": "Tu-TuA23.2", "code": "TuA23.2", "title": "Hybrid Modeling of Vapor Compression Cycles Via Latent Parameter Estimation for Enhanced Numerical Stability", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA23", "sessionTitle": "Hybrid and Physics-Informed Modeling for Chemical Processes", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Byun, Jisung", "affiliation": "Seoul National University" }, { "name": "Hong, Seokyoung", "affiliation": "Ulsan National Institute of Science and Technology (UNIST)" }, { "name": "Lee, Jong Min", "affiliation": "Seoul National University" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Machine learning and artificial intelligence in chemical process control", "Industrial applications of chemical process control" ], "abstract": "Vapor compression cycles (VCCs) are fundamental to heating, cooling, and thermal management systems, but their phase-changing heat exchangers make dynamic modeling challenging for real-time optimization and control. Moving boundary (MB) models provide compact and physically interpretable representations, yet variations in active phase regions introduce mode-switching and non-smooth dynamics. This study proposes a phase-continuous hybrid modeling framework that combines an MB-based physical model with a multilayer perceptron (MLP)-based latent parameter estimator, yielding a single model structure without explicit switching logic. Latent parameters are estimated from the current operating condition and substituted into the reduced mass and energy balance equations. The proposed model is validated against a high-fidelity MATLAB/Simscape reference simulation. On a temporally segmented test set, the proposed model accurately reproduces the reference simulation with reduced computational time. Moreover, compared with a black-box MLP baseline, it retains physical interpretability and conservation-law consistency, while maintaining competitive prediction accuracy. This framework provides a numerically stable and physically structured basis for future optimization and control applications.", "url": "" }, { "id": "Tu-TuA23.3", "code": "TuA23.3", "title": "Learning-Based Data-Enabled Moving Horizon Estimation with Application to Membrane-Based Biological Wastewater Treatment Process", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA23", "sessionTitle": "Hybrid and Physics-Informed Modeling for Chemical Processes", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Li, Xiaojie", "affiliation": "Nanyang Technological University" }, { "name": "Yin, Xunyuan", "affiliation": "Nanyang Technological University" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Advanced process control", "Machine learning and artificial intelligence in chemical process control" ], "abstract": "In this paper, we propose a data-enabled moving horizon estimation (MHE) approach for nonlinear systems. While the approach is formulated by leveraging Koopman theory, its implementation does not require explicit Koopman modeling. Lifting functions are learned from the state and input data of the original nonlinear system to project the system trajectories into the lifted space, where the resulting trajectories implicitly describe the Koopman representation for the original nonlinear system. A convex data-enabled MHE formulation is developed to provide real-time state estimates of the Koopman representation, from which the states of the nonlinear system can be reconstructed. Sufficient conditions are derived to ensure the stability of the estimation error. The effectiveness of the proposed method is illustrated using a membrane-based biological wastewater treatment process.", "url": "" }, { "id": "Tu-TuA23.4", "code": "TuA23.4", "title": "Koopman-Based Control of Agglomerate Size and Porosity in Continuous Fluidized Bed Spray Agglomeration Processes", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA23", "sessionTitle": "Hybrid and Physics-Informed Modeling for Chemical Processes", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Otto, Eric", "affiliation": "Otto Von Guericke University Magdeburg" }, { "name": "Maksakov, Anton", "affiliation": "TU Clausthal" }, { "name": "Palis, Stefan", "affiliation": "Clausthal University of Technology" }, { "name": "Kienle, Achim", "affiliation": "University Magdeburg" } ], "keywords": [ "Control of multi-scale, distributed, and particulate systems", "Machine learning and artificial intelligence in chemical process control", "Model-predictive and optimization-based control in chemical processes" ], "abstract": "This study investigates the application of Model Predictive Control (MPC) to regulate agglomerate size and porosity in the fluidized bed spray agglomeration process. To enable effective control, a data-driven modeling approach based on Koopman theory is employed. A coordinate transformation, approximated by a neural network, is used to map measured data to a lifted space where the process dynamics are rendered linear. This facilitates the use of computationally efficient linear MPC despite the inherent non-linearity of the process system. The identified Koopman model is benchmarked against a conventional linear model obtained via N4SID subspace identification. The models are compared based on their prediction error on an independent test data set. A subsequent simulation study assesses the full MPC controller performance for setpoint tracking and disturbance attenuation tested on a nonlinear population balance model. The results demonstrate that the Koopman-based model significantly outperforms the N4SID model in both predictive accuracy and overall controller performance, validating the Koopman framework as a highly effective method for controlling complex agglomeration processes.", "url": "" }, { "id": "Tu-TuA23.5", "code": "TuA23.5", "title": "Koopman-Based Control for Thermal and Humidity Management in a PEM Fuel Cell", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA23", "sessionTitle": "Hybrid and Physics-Informed Modeling for Chemical Processes", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Golovin, Ievgen", "affiliation": "Otto Von Guericke University Magdeburg" }, { "name": "Maksakov, Anton", "affiliation": "TU Clausthal" }, { "name": "Palis, Stefan", "affiliation": "Clausthal University of Technology" }, { "name": "Kienle, Achim", "affiliation": "University Magdeburg" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Machine learning and artificial intelligence in chemical process control", "Hydrogen systems for energy generation and storage" ], "abstract": "In this paper, we propose a data-driven control strategy for thermal and water management in a proton exchange membrane fuel cell. Based on data from a mathematical model, we obtained a linear time-invariant model using the Koopman operator framework. This linearization does not depend on a specific operating point and is valid across the entire range of training data. This enables the use of linear optimal control techniques for the underlying nonlinear system. Finally, we designed a Linear Quadratic Integral controller to achieve temperature and humidity set-point tracking and to effectively reject disturbances caused by load changes.", "url": "" }, { "id": "Tu-TuA24.2", "code": "TuA24.2", "title": "Speech Mixture Effects on EEG-Based Auditory Attention Decoding", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA24", "sessionTitle": "Modeling and Control of the Human Nervous System", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Wilroth, Johanna", "affiliation": "Linköping University" }, { "name": "Enqvist, Martin", "affiliation": "Linköping University" }, { "name": "Skoglund, Martin A", "affiliation": "Linköping University" }, { "name": "Alickovic, Emina", "affiliation": "Eriksholm Research Centre" } ], "keywords": [ "Biomedical system modeling, identification, and simulation", "Biomedical signal measurement and processing" ], "abstract": "Auditory attention decoding (AAD) is typically treated as a backward system-identification problem, reconstructing speech features from electroencephalography (EEG). A central challenge is defining the “true” speech signal, since decoders often are trained on clean speech unavailable in real-world settings. We compare two ways of modeling speech mixture effects - clean attended/ignored mixtures, and four mixture methods: time-difference-of-arrival (TDOA)-based microphone estimates, room impulse response (RIR)-filtered speech, RIR-filtered EEG, and raw microphone signals. Our results show that ignored speech remains strongly represented in neural responses, with implications for future AAD models and adaptive noise-reduction strategies in closed-loop hearing aids.", "url": "" }, { "id": "Tu-TuA24.3", "code": "TuA24.3", "title": "Neurovascular Complexity Disruption across the Alzheimer’s Spectrum: A Resting-State fNIRS Study (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA24", "sessionTitle": "Modeling and Control of the Human Nervous System", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Hong, Keum-Shik", "affiliation": "Pusan National University" }, { "name": "Kang, Min-Kyoung", "affiliation": "Pusan National University" }, { "name": "Yong-Il, Shin", "affiliation": "Pusan National University Yangsan Hospital" }, { "name": "Jisoo, Baik", "affiliation": "Pusan National University Yangsan Hospital" } ], "keywords": [ "Dynamics and control of biologically motivated nonlinear systems", "Modelling, parameter identification and state estimation in biosystems", "Biological networks inference and modelling" ], "abstract": "Early and noninvasive identification of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI), is essential for enabling early clinical intervention. We propose a stage-sensitive analytical framework incorporating three nonlinear complexity measures—Higuchi's fractal dimension (HFD), spectral entropy (SE), and wavelet entropy (WE)—derived from resting-state functional near-infrared spectroscopy (fNIRS) recordings. Prefrontal fNIRS signals were acquired from 83 participants (AD: 19, MCI: 37, healthy controls (HC): 27), and complexity features were comprehensively characterized across hemoglobin types. Across groups, HFD exhibited a monotonic decline from HC through MCI to AD, while SE and WE were elevated in the AD group, indicative of increased signal irregularity. Classification models trained on a biologically informed core-channel feature set outperformed their full-channel counterparts, attaining an area under the receiver operating characteristic curve (AUC) of 0.889 with minimal fold-to-fold variability. Furthermore, the extracted complexity features showed strong associations with Mini-Mental State Examination (MMSE) scores, highlighting their clinical utility. Collectively, these results provide provide evidence that nonlinear complexity analysis of resting-state fNIRS signals can expose stage-specific neurovascular disruptions spanning the Alzheimer's continuum. The identification of robust, anatomically interpretable channel-level biomarkers positions resting-state fNIRS as a clinically applicable modality—moving beyond a supporting role toward active utility in the early diagnosis and disease staging of AD.", "url": "" }, { "id": "Tu-TuA24.4", "code": "TuA24.4", "title": "Robust Closed-Loop Control for Propofol-Induced Hypnosis During General Anesthesia", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA24", "sessionTitle": "Modeling and Control of the Human Nervous System", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Fushimi, Emilia", "affiliation": "Instituto LEICI, Facultad De Ingeniería, UNLP-CONICET" }, { "name": "Faedo, Nicolás", "affiliation": "Politecnico Di Torino" } ], "keywords": [ "Control of physiological and clinical variables", "Pharmacokinetics, tracer kinetic modelling and drug delivery" ], "abstract": "Propofol infusion during surgical procedures is a challenging task that aims to achieve and maintain clinical hypnosis: a state of unconsciousness to avoid intra-operative recollection. Within this paper, a closed-loop depth of hypnosis (DoH) controller based on H-infinity optimal control is proposed. Inter-patient variability is identified and utilized for the controller design to ensure robustness to modeling errors. The strategy is evaluated in silico considering modeling errors and external disturbances due to common surgical events. Results suggest the proposed controller is able to induce and maintain an appropriate level of hypnosis in the face of inter-patient and intra-operative variability.", "url": "" }, { "id": "Tu-TuA25.1", "code": "TuA25.1", "title": "Signal-Based Monitoring for Tissue Oxygenation & Diabetes Characterization (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA25", "sessionTitle": "Engineering Diabetes Technologies II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Guir, Abdelbaki", "affiliation": "Inria" }, { "name": "French, Chloe", "affiliation": "ARU Campus Chelmsford, UK" }, { "name": "Robbins, Dan", "affiliation": "ARU Campus Chelmsford, UK" }, { "name": "Gordon, Dan", "affiliation": "ARU Campus Chelmsford, UK" }, { "name": "Gernigon, Marie", "affiliation": "Université Paris-Saclay, CIAMS, Gif-Sur-Yvette 91190 FR" }, { "name": "Laleg, Taous-Meriem", "affiliation": "Inria" } ], "keywords": [ "Healthcare management, disease control, critical care", "Real time monitoring and control of environmental systems" ], "abstract": "This study investigates the correlation between Near-Infrared Spectroscopy (NIRS) and transcutaneous oxygen pressure (T cP O2) for monitoring tissue oxygenation and wound heal- ing progression in diabetic feet. Signal-derived features, including area under the curve (AUC), reperfusion recovery speed, and peak amplitudes, were extracted and analyzed longitudinally to assess healing trajectories. The results demonstrate that NIRS and T cP O2 measurements provide complementary insights into microvascular function and can serve as reliable indicators of wound healing status. These findings highlight the potential of integrating non-invasive, signal-based monitoring techniques into personalized diabetic foot care and clinical decision- making.", "url": "" }, { "id": "Tu-TuA25.2", "code": "TuA25.2", "title": "Exploring the Robustness of Reinforcement Learning in Standardized Blood Glucose Management for Type 1 Diabetes (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA25", "sessionTitle": "Engineering Diabetes Technologies II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Dénes-Fazakas, Lehel", "affiliation": "Óbuda University" }, { "name": "Dulf, Eva Henrietta", "affiliation": "Technical University of Cluj Napoca" }, { "name": "László, Szász", "affiliation": "Óbuda University" }, { "name": "Hartveg, Adam", "affiliation": "Obuda University" }, { "name": "Eigner, György", "affiliation": "Óbuda University" }, { "name": "Kovacs, Levente", "affiliation": "Obuda University" } ], "keywords": [ "Artificial pancreas or organs", "Healthcare management, disease control, critical care", "Decision support and control in medicine" ], "abstract": "Diabetes affects millions globally, especially in type 1 cases where precise blood glucose control is crucial. Current methods, relying on patient monitoring and insulin administration, often fall short. Hence, there's interest in using reinforcement learning (RL) to optimize management. RL, a type of machine learning, shows promise in adjusting insulin dosages based on feedback. Our study examines a Proximal Policy Optimization (PPO) agent trained on average patient data, showing its effectiveness across diverse patient profiles. Our findings highlight the adaptability of PPO-based controllers in managing blood glucose levels effectively.", "url": "" }, { "id": "Tu-TuA25.3", "code": "TuA25.3", "title": "Observed Eating Behaviors in the T1DEXI Cohort and Their Impact on an Advanced Automated Insulin Delivery System (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA25", "sessionTitle": "Engineering Diabetes Technologies II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Kaykayoglu, Ceren Asli", "affiliation": "University of Bern" }, { "name": "Manzoni, Eleonora", "affiliation": "University of Bern" }, { "name": "Naik, Vihangkumar Vinaykumar", "affiliation": "University of Bern" }, { "name": "Witthauer, Lilian", "affiliation": "University of Bern" }, { "name": "García-Tirado, José Fernando", "affiliation": "University of Bern" } ], "keywords": [ "Decision support and control in medicine", "Artificial pancreas or organs", "Control of physiological and clinical variables" ], "abstract": "Meal timing is a critical behavioral determinant of glycemic control in type 1 diabetes (T1D), yet its characterization in free-living conditions remains limited. Understanding daily eating patterns may provide insights into glucose variability and inform personalized therapeutic strategies. This study aimed to derive eating behavior (meal timing and carbohydrate content) in adults with T1D using real-world dietary logs from the T1DEXI dataset and evaluate the effect of these behaviors on glucose control via in-silico testing. A total of 477 participants provided free-living records of breakfast, lunch, and dinner over an average of 16 days. Meal timing distributions were analyzed, and stratification was performed using k-means clustering based on temporal meal variability. Identified clusters were subsequently incorporated into an in-silico framework to assess their impact on simulated glycemic control. Fifteen distinct meal-timing phenotypes emerged. As a proof of concept, three representative phenotypes, nominal meal spacing (S1), compressed daytime (S2), and late dinner (S3), were evaluated in the UVa/Padova simulator using the UniBE hybrid closed-loop controller (10 virtual adults for 10 days per phenotype). Glycemic safety remained high across scenarios, with median time in range of 94.8% for S1, 92.1% for S2, and 95.3% for S3, and time below range approximating 0%. Modest but consistent increases in time above range and glucose variability were observed in S2 compared to S1 and S3. Behavioral phenotyping of meal timing revealed distinct clusters with quantifiable differences in glycemic outcomes. In-silico validation underscores their potential utility for personalized diabetes care and supports the integration of behavioral metrics into digital therapeutic strategies.", "url": "" }, { "id": "Tu-TuA25.4", "code": "TuA25.4", "title": "A Machine Learning Approach for Fully Automated Meal Bolus Delivery in Subjects with Type 1 Diabetes (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA25", "sessionTitle": "Engineering Diabetes Technologies II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Mongini, Paolo Alberto", "affiliation": "University of Pavia" }, { "name": "Magni, Lalo", "affiliation": "Univ. of Pavia" }, { "name": "Toffanin, Chiara", "affiliation": "University of Pavia" } ], "keywords": [ "Artificial pancreas or organs", "Biomedical system modeling, identification, and simulation", "Decision support and control in medicine" ], "abstract": "Accurate meal detection and carbohydrate content (CHO) estimation are key aspects for effective closed-loop insulin delivery in Type 1 diabetes. In literature, effective approaches rely on the estimates of glycemia and its derivatives via a Kalman Filter (KF), evaluated with conditional logic. Recently, also machine learning techniques have shown promising results for this application. This work proposes an algorithm enabling fully automated meal bolus delivery. To estimate CHO, the KF estimations have been evaluated leveraging a Convolutional Neural Network (CNN) thus avoiding any case-specific conditional logic. Then, CHO estimations have been used to calculate and inject insulin meal boluses. The proposed CNN approach is compared with a reference reported in literature using the 100 in silico adults patients of the UVA/Padova simulator. Results demonstrate superior performance both in meal estimation capability (F1 score of 84.21% vs 64.15%) and in closed-loop performance (Tr 83.43% vs 75.58%, p-value < 0.001), highlighting the strong potential of the proposed method for meal detection and CHO estimation in fully automated closed loop applications, avoiding datasetspecific tuning..", "url": "" }, { "id": "Tu-TuA25.5", "code": "TuA25.5", "title": "Intrinsic Dimensionality Estimation of Automated Insulin Delivery State Representations Via β-Variational Autoencoders (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA25", "sessionTitle": "Engineering Diabetes Technologies II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Shen, Jiaxin", "affiliation": "University of Virginia" }, { "name": "El Fathi, Anas", "affiliation": "University of Virginia" }, { "name": "Breton, Marc D", "affiliation": "University of Virginia" } ], "keywords": [ "Artificial pancreas or organs", "Biomedical system modeling, identification, and simulation", "Control of physiological and clinical variables" ], "abstract": "The next-generation neural-network-based automated insulin delivery (AID) systems rely on compact and informative state representations to achieve safe and efficient closed-loop control. Thus, understanding the intrinsic dimensionality of these AID state vectors is critical in explaining, validating, and improving NN-based insulin dosage algorithms. In this work, we apply beta-Variational Autoencoders (beta-VAEs) as a theoretically grounded probabilistic method to estimate this intrinsic dimensionality. By sweeping over a range of regularization coefficients beta, we analyze the reconstruction error, the total Kullback--Leibler (KL) divergence, and the degree of latent dimension collapse. This combined way allows us to identify an appropriate balance between reconstruction fidelity and the latent space regularization. The beta-sweep result shows an optimal region, beta in [10^{-3}, 10^{-1}]. Focusing on this range, we observe a clear emph{elbow point} when encoding the original state space, an 8-dimensional Kalman-filtered state representation inferred from the past 6 hours of glucose and insulin dynamics. Our findings indicate that these states lie on an approximately 4-dimensional nonlinear manifold. Thus, the proposed beta-VAE framework provides a compact, manageable, and generalizable latent representation for data-driven biomedical control systems.", "url": "" }, { "id": "Tu-TuA25.6", "code": "TuA25.6", "title": "Nonlinear Pharmacokinetics of Subcutaneous Glucagon Absorption (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA25", "sessionTitle": "Engineering Diabetes Technologies II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Furió Novejarque, Clara", "affiliation": "Universitat Politècnica De València" }, { "name": "Sala-Mira, Iván", "affiliation": "Universitat Politècnica De València" }, { "name": "Ranjan, Ajenthen G.", "affiliation": "Steno Diabetes Center Copenhagen" }, { "name": "Nørgaard, Kirsten", "affiliation": "Steno Diabetes Center Copenhagen" }, { "name": "Jorgensen, John Bagterp", "affiliation": "Technical University of Denmark" }, { "name": "Díez, José Luis", "affiliation": "Universitat Politècnica De València" }, { "name": "Bondia Company, Jorge", "affiliation": "Universitat Politècnica De València" } ], "keywords": [ "Pharmacokinetics, tracer kinetic modelling and drug delivery", "Biomedical system modeling, identification, and simulation", "Artificial pancreas or organs" ], "abstract": "Most glucagon pharmacokinetics mathematical models consider a linear dose-plasma concentration relationship. However, evidence from clinical data suggests a nonlinear linkage between subcutaneous glucagon and plasma glucagon concentration. This work explores this connection using data from four different clinical trials, including doses from 100 to 500 ug, and a total of 44 participants with type 1 diabetes. To this end, first, a pharmacokinetics model is identified. Then, statistical mixed-effects models are exploited to characterize the relation between the identified parameters and glucagon doses. The results highlight a nonlinear relationship between the dose amount and the glucagon clearance from plasma. Taking this nonlinearity into consideration could improve mathematical models of glucagon pharmacokinetics or help characterize these patterns in other drugs.", "url": "" }, { "id": "Tu-TuA26.1", "code": "TuA26.1", "title": "An Exact Bundled Redistributed Control Allocation Method for Over-Actuated Thrust-Vectoring UAV: Application to a Quadrotor with Rotatable Thrusters", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA26", "sessionTitle": "Fault-Tolerant, Safety-Critical and Estimation-Based Aerospace Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Fang, Kai-Cheng", "affiliation": "National Taiwan University" }, { "name": "Chu, Yen-Cheng", "affiliation": "National Taiwan University" }, { "name": "Liao, Teh-Lu", "affiliation": "National Cheng Kung Univ" }, { "name": "Lian, Feng-Li", "affiliation": "National Taiwan Univ" } ], "keywords": [ "Aerial and space robotics", "Aerospace mission control and operations", "Urban air mobility" ], "abstract": "This paper investigates the flight control of an over-actuated thrust-vectoring UAV equipped with four 2-DoF rotatable thrusters. To enable thrust vector control, a full-pose controller is employed as the high-level controller, and a novel Exact Bundled Redistributed (EBR) control allocation method is proposed to allocate the desired wrench command exactly to each actuator. The algorithm then determines the required thrust vectors, force magnitudes, and deflection angles for all thrusters. The proposed EBR control allocation method is developed based on the admissible force space of four vectored thrusters, where each actuator computes intersections on the bundled local admissible force space (LAFS). The overall framework integrates Truncated Wrench Allocation (TWA) and Post-Torque Enhancement (PTE), ultimately obtaining an exact allocation solution while ensuring direction preservation. Simulation results illustrate the feasibility of the proposed method by comparing feasible and infeasible control wrench commands with respect to the attainable force space, as well as through maneuver tracking along a square trajectory, thereby validating the performance of the EBR control allocation method in thrust-vectoring UAV applications.", "url": "" }, { "id": "Tu-TuA26.2", "code": "TuA26.2", "title": "Guidance and Control Co-Design for Enhanced Performance of Fixed-Wing UAVs", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA26", "sessionTitle": "Fault-Tolerant, Safety-Critical and Estimation-Based Aerospace Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Wisbacher, Sabine", "affiliation": "Munich University of Applied Sciences HM" }, { "name": "Berenguer Bertran, Roser", "affiliation": "Technische Universität Dresden" }, { "name": "Guist, Martin", "affiliation": "Technische Universität Dresden" }, { "name": "Ossmann, Daniel", "affiliation": "Munich University of Applied Sciences HM" }, { "name": "Pfifer, Harald", "affiliation": "Technische Universität Dresden" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Aerospace mission control and operations", "Aerial and space robotics" ], "abstract": "This paper presents the design and flight test validation of a co-designed guidance and control system for a fixed-wing unmanned aerial vehicle (UAV). The co-design of all integrated control loops enables increased performance capabilities which are especially important for highly dynamic UAV missions. The control design is based on a mixed-sensitivity control approach to enable robust inner loop controls in the presence of uncertainties. The guidance design implements a look-ahead path following algorithm which enables superior performance metrics while considering the computational constraints present for small UAV systems. The co-designed guidance and control approach is applied to a small fixed-wing UAV and flight tested mimicking a highly dynamic urban flight trajectory. The flight-test results not only validate the seamless implementation of the developed algorithms on the available flight-control computer, but also demonstrate their robust performance during real-world operations.", "url": "" }, { "id": "Tu-TuA26.3", "code": "TuA26.3", "title": "Fault-Tolerant Attitude Control for UAVs Via Adaptive INDI with Event-Triggered CEM Estimation", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA26", "sessionTitle": "Fault-Tolerant, Safety-Critical and Estimation-Based Aerospace Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Chen, Yuteng", "affiliation": "Xidian University" }, { "name": "Chang, Jing", "affiliation": "Xidian University" }, { "name": "Shang, Chunyiding", "affiliation": "Xidian University" }, { "name": "Chen, Weisheng", "affiliation": "Xidian University" }, { "name": "Guo, Zongyi", "affiliation": "Northwestern Polytechnical University" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft" ], "abstract": "Incremental Nonlinear Dynamic Inversion (INDI) is effective for UAV attitude control but remains highly vulnerable to Control Effectiveness Matrix (CEM) inaccuracies during actuator faults. {Furthermore, conventional continuous adaptation schemes impose prohibitive computational loads on embedded processors.} To overcome these challenges, this paper presents a resource-aware fault-tolerant attitude control framework based on Adaptive INDI (AINDI). A data-driven CEM reconstruction method is developed using pseudo-partial derivatives together with Recursive Least Squares (RLS) to compensate for aerodynamic uncertainties and actuator faults in real time. To reduce computational overhead and inherently prevent parameter drift, an Event-Triggered Mechanism (ETM) is incorporated to update the estimator only when the tracking error exceeds a prescribed threshold. Crucially, a Lyapunov-based robust auxiliary control law with a dynamic adaptive gain is designed to rigorously guarantee Uniformly Ultimately Bounded (UUB) stability, explicitly accounting for event-triggered residual errors. Numerical simulations demonstrate that the proposed approach delivers superior tracking accuracy and robustness against severe gusts and actuator jamming compared to conventional methods.", "url": "" }, { "id": "Tu-TuA26.4", "code": "TuA26.4", "title": "Magnetic and Image Information-Based GNSS Independent Attitude Estimation for Aerial Vehicles", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA26", "sessionTitle": "Fault-Tolerant, Safety-Critical and Estimation-Based Aerospace Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Bauer, Peter", "affiliation": "HUN-REN Institute for Computer Science and Control" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Robotic vision for AVs", "Autonomous vehicles" ], "abstract": "This paper presents an IMU, magnetic and image measurement-based attitude estimator to improve previous results with an IMU and magnetic measurement-based one. As image-based estimated rotation is a relative information, a local-global representation of the attitude is formulated presenting also local observability. The newly introduced method greatly improves attitude estimation precision but still tends to drift for longer time horizons. However, having GNSS spoofing detection in mind, a moving window technique should be feasible running parallel instants of the proposed algorithm initialized with accurate attitude from a more complex (considering also GNSS information) estimator at different times.", "url": "" }, { "id": "Tu-TuA26.5", "code": "TuA26.5", "title": "Safety-Assured Arrival Scheduling in Sequential UAM Corridor Sections under Speed and Separation Constraints", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA26", "sessionTitle": "Fault-Tolerant, Safety-Critical and Estimation-Based Aerospace Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Pruekprasert, Sasinee", "affiliation": "The University of Tokyo" }, { "name": "Nakadai, Shinji", "affiliation": "Intent Exchange, Inc" }, { "name": "Nishinari, Katsuhiro", "affiliation": "The University of Tokyo" } ], "keywords": [ "Urban air mobility", "Automatic control, optimization, real-time operations in transportation", "Modeling and simulation of transportation systems" ], "abstract": "This paper presents a safety-assured arrival-scheduling framework for Urban Air Mobility (UAM) corridor operations. We propose an analytical method to compute a sufficient ETA gap at Constrained Waypoints (CWPs) that guarantees longitudinal separation along sequential corridor sections with heterogeneous speed limits. The resulting ETA-gap condition depends on section-specific speed bounds and the required separation distance, providing an efficiently computable rule suitable for integration into future digital ETA-scheduling and air traffic management systems. We show that the computed ETA gap ensures safe separation across all corridor sections under prescribed section travel times and speed limits. Numerical simulations for a decreasing-speed corridor confirm that vehicles coordinated with the proposed mechanism adjust their speeds to maintain the required spacing, avoid potential collisions, and support improved traffic flow compared with unscheduled operations.", "url": "" }, { "id": "Tu-TuA27.1", "code": "TuA27.1", "title": "Coupled Aero-Hydro-Elastic Modeling and Adaptive Coordinated Control of Floating Offshore Wind Turbines (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA27", "sessionTitle": "JO-CEP: Modelling, Identification and Control in Marine Systems I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Li, Shuzhen", "affiliation": "Qingdao University" }, { "name": "Li, Xian", "affiliation": "Qingdao University" }, { "name": "Hong, Keum-Shik", "affiliation": "Pusan National University" } ], "keywords": [ "Marine renewable energy systems", "Modelling, identification and control in marine systems" ], "abstract": "Due to coupled aero-hydro-elastic dynamics and harsh marine environments, the floating offshore wind turbines (FOWTs) suffer from power loss and structural fatigue. To deal with these issues, an integrated modeling and coordinated control framework is proposed by combining a nonlinear five-degree-of-freedom model with a dual-loop control strategy, where the outer adaptive super-twisting sliding-mode controller stabilizes the platform motion, while the inner feedback-feedforward pitch controller maintains rotor speed regulation. A Lyapunov based analysis ensures its closed-loop stability. Simulation results under harsh marine conditions show that, compared with traditional PID control, the proposed method significantly reduces platform motion and structural load, thereby improving the overall stability and reliability of FOWTs.", "url": "" }, { "id": "Tu-TuA27.2", "code": "TuA27.2", "title": "Wave Excitation Forecasts in Model Predictive Control of Wave Energy Converters: A Processor-In-The-Loop Validation (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA27", "sessionTitle": "JO-CEP: Modelling, Identification and Control in Marine Systems I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Cavanini, Luca", "affiliation": "Università Politecnica Delle Marche" }, { "name": "Felicetti, Riccardo", "affiliation": "Università Politecnica Delle Marche" }, { "name": "Ferracuti, Francesco", "affiliation": "Universita' Politecnica Delle Marche" }, { "name": "Monteriù, Andrea", "affiliation": "Università Politecnica Delle Marche" }, { "name": "Campos-Gaona, David", "affiliation": "The University of Strathclyde" }, { "name": "Du, Feng", "affiliation": "University of Strathclyde" }, { "name": "Forehand, David", "affiliation": "University of Edinburgh" }, { "name": "McCallum, Peter", "affiliation": "The University of Edinburgh" }, { "name": "Price, Alexandra", "affiliation": "Heriot Watt University" }, { "name": "Stock, Adam", "affiliation": "Heriot Watt University" } ], "keywords": [ "Marine renewable energy systems", "Modelling, identification and control in marine systems", "AI and embodied-AI in marine systems" ], "abstract": "This paper presents the Processor-in-the-Loop (PiL) evaluation of an advanced control system designed to optimize the performance of a wave energy converter. The advanced controller is composed of a model predictive control and a support vector machine wave prediction algorithm. This machine learning algorithm is integrated within the controller to provide a suitable prediction of the wave excitation force dynamics over a future time horizon of interest. The data-driven method is trained on data representing an interesting operation condition and a self-adaptation policy has been integrated within the algorithm to adapt the performance to varying sea conditions, achieving, on average, a 38% increase in extracted power relative to a baseline spring-damper controller. The approach has been validated on a high-fidelity PiL testing benchmark, thus validating both the control performance and the computational complexity.", "url": "" }, { "id": "Tu-TuA27.3", "code": "TuA27.3", "title": "Stochastic Output-Feedback MPC for Safe and Energy-Efficient SOFC Operation Subject to Marine Disturbances (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA27", "sessionTitle": "JO-CEP: Modelling, Identification and Control in Marine Systems I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "de Lange, Matthis Hendrik", "affiliation": "Delft University of Technology" }, { "name": "Segovia, Pablo", "affiliation": "Universitat Politècnica De Catalunya" }, { "name": "Negenborn, Rudy", "affiliation": "Delft University of Technology" }, { "name": "van Biert, Lindert", "affiliation": "Delft University of Technology" } ], "keywords": [ "Marine renewable energy systems", "Power and propulsion in marine systems" ], "abstract": "This paper introduces a stochastic output-feedback MPC approach for energy-efficient operation of a solid oxide fuel cell (SOFC) system in a maritime environment with disturbances. The MPC optimises load tracking within a tightened operating space, where the constraints are adjusted based on closed-loop propagation of disturbances and measurement noise. Specific marine disturbances are considered, allowing their dynamics to be leveraged in the prediction and estimation model. The results compare the proposed approach with a nominal tracking and economic MPC, highlighting trade-offs. In the two presented scenarios, the method achieves a balanced performance between energy efficient and safe operation.", "url": "" }, { "id": "Tu-TuA27.4", "code": "TuA27.4", "title": "Bringing Airborne Wind Energy Offshore: A Hardware-In-The-Loop Framework for Closed-Loop Experimental Testing (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA27", "sessionTitle": "JO-CEP: Modelling, Identification and Control in Marine Systems I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Trombini, Sofia", "affiliation": "Politecnico Di Milano" }, { "name": "Cecchin, Leonardo", "affiliation": "Politecnico Di Milano" }, { "name": "Lucarelli, Alessia", "affiliation": "National Research Council-Institute of Marine Engineering (CNR-INM)" }, { "name": "Bardazzi, Andrea", "affiliation": "CNR" }, { "name": "Lugni, Claudio", "affiliation": "CNR" }, { "name": "Fagiano, Lorenzo", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Marine renewable energy systems", "Simulation and digital-twin in marine systems", "Modelling, identification and control in marine systems" ], "abstract": "The first documented wave tank testing setup of a floating platform for Airborne Wind Energy Systems (AWES) is presented, featuring a novel hardware-in-the-loop (HIL) experimental methodology for the controlled reproduction of aerodynamic and hydrodynamic interactions. The proposed setup combines a real-time kite simulation with a physical spar platform, subjected to both wave excitation and three-dimensional kite-induced force. The latter is applied by four actuators linked to the platform by tethers. The design of the actuation system and of its control logic is described. The hierarchical control approach includes a real-time, optimization-based allocation technique of the force setpoints to the actuators, to cope with the nonlinearity of the tethers' geometry, and a force feedback loop at each actuator to track the corresponding setpoint. The experimental results showcase the performance of the actuation system in reproducing at scale the kite forces computed by the real-time simulator coupled to the physical platform. The setup establishes a framework for future experimental investigations of floating AWE systems, supporting numerical model validation, control design, and performance assessment for offshore deployment.", "url": "" }, { "id": "Tu-TuA27.5", "code": "TuA27.5", "title": "ARMs-Sailboat: Architecture, Implementation and Validation (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA27", "sessionTitle": "JO-CEP: Modelling, Identification and Control in Marine Systems I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Huang, Yi", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Liu, Zongyang", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Chen, HaoJie", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Zhu, Yanji", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Yang, Shaolong", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Xiang, Gong", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Xiang, Xianbo", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Zhang, Qin", "affiliation": "Huazhong University of Science and Technology" } ], "keywords": [ "Marine robotics", "Autonomous marine systems and vehicles", "Marine renewable energy systems" ], "abstract": "Autonomous sailboats are becoming important equipment for maritime observation due to their unique advantages of long sailing time, green and low-carbon. This paper introduces ARMs-Sailboat, an autonomous sailboat, for long-term ocean observation. At the hardware level, the ARMs-Sailboat is actuated and energized by wind and solar energy, and poses distributed hardware architecture, which enhances the fault-tolerant performance. At the software level, the ARMs-Sailboat has multilayer software architecture with over-the-air (OTA) programming capabilities, enabling flexible firmware upgrades and scalable algorithm deployment. The feasibility and performance of the ARMs-Sailboat are verified with sea trials, including multi-waypoint tracking, upwind navigation, and station keeping.", "url": "" }, { "id": "Tu-TuA27.6", "code": "TuA27.6", "title": "CNN-Guided UAV Recovery of Autonomous Underwater Vehicles with Dual-Camera Hook Localization (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA27", "sessionTitle": "JO-CEP: Modelling, Identification and Control in Marine Systems I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Li-Fan, Wu", "affiliation": "Purdue University" }, { "name": "Demaria, Lorenzo", "affiliation": "KTH" }, { "name": "Özer, Özkahraman", "affiliation": "KTH" }, { "name": "Zihan, Wang", "affiliation": "Purdue University" }, { "name": "Folkesson, John", "affiliation": "KTH" }, { "name": "Stenius, Ivan", "affiliation": "KTH" }, { "name": "Rastgaar, Mo", "affiliation": "Purdue University" }, { "name": "Mahmoudian, Nina", "affiliation": "Purdue University" } ], "keywords": [ "Marine robotics", "Marine system guidance, navigation and control", "Robotic vision for AVs" ], "abstract": "Autonomous aerial recovery of underwater vehicles remains a key challenge in achieving persistent multi-domain marine operations, where complex rope geometries, wave disturbances, and limited sensing hinder robust performance. This paper presents a unified UAV-assisted AUV recovery framework integrating a suspended–hook mechanism with perception-driven trajectory planning. A convolutional neural network (CNN) is developed to interpret dynamic buoy–rope configurations and predict optimal catch points and recovery directions, increasing retrieval success rates from 30% to 80% in high-fidelity Unity simulations. Complementarily, a dual-camera visual estimator localizes the swinging hook beneath the UAV without fiducial markers, achieving a three-dimensional root-mean-square error of 0.012 m in field experiments. The proposed system eliminates dependence on external motion-capture systems and reduces the need for highly agile UAV hardware, enabling adaptive, safe, and autonomous AUV recovery in realistic marine environments.", "url": "" }, { "id": "Tu-TuA28.1", "code": "TuA28.1", "title": "Covering the Pareto Frontier with LLM-Coordinated Interpretable Policy Library (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA28", "sessionTitle": "Control and Optimization for Smart Cities II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Mu, Ni", "affiliation": "Tsinghua University" }, { "name": "Luan, Yao", "affiliation": "Tsinghua University" }, { "name": "Jia, Qing-Shan", "affiliation": "Tsinghua University" } ], "keywords": [ "AI for smart cities", "Smart city control and optimization", "Decision making under uncertainty" ], "abstract": "Industrial control systems require diverse policy libraries to balance multiple objectives. Expert-designed policies demand substantial domain expertise and manual effort, while learning-based methods such as reinforcement learning often lack interpretability. To address these limitations, we propose a novel LLM-based framework that autonomously generates interpretable policy libraries through iterative cycles of code generation, evaluation, and refinement. Specifically, a Policy Generator produces candidate policies, while a Coordinator analyzes the Pareto frontier to identify unexplored regions, guiding the generator toward diverse trade-offs. Our method achieves competitive performance on two industrial tasks, efficiently approximating the Pareto frontier without extensive training and providing transparent, interpretable solutions.", "url": "" }, { "id": "Tu-TuA28.2", "code": "TuA28.2", "title": "Experimental Validation of Resilient Multi-UAV Control against Agent Failures (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA28", "sessionTitle": "Control and Optimization for Smart Cities II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Murakami, Takuya", "affiliation": "Keio University" }, { "name": "Namerikawa, Toru", "affiliation": "Keio University" } ], "keywords": [ "Smart city security and resilience", "Smart city control and optimization", "Cyber-physical urban systems" ], "abstract": "This paper proposes a resilient formation control framework for leader-follower multi-UAV systems subject to non-compensable actuator faults. We develop a scheme that extends resilient consensus theory to second-order linear dynamics in three-dimensional space, integrated with a coordinated leader replacement mechanism. Through numerical simulations and indoor flight experiments, we demonstrate that non-faulty agents maintain formation via autonomous leader replacement, even in the presence of severe faults. These results confirm that multi-UAV formations can recover from non-compensable failures.", "url": "" }, { "id": "Tu-TuA28.3", "code": "TuA28.3", "title": "Efficient Model-Based Reinforcement Learning through Optimal Computing Budgets Allocation (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA28", "sessionTitle": "Control and Optimization for Smart Cities II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Tao, Zhikun", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Xiong, Gang", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Zhang, Xiaotong", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Jin, Xiaoqiang", "affiliation": "Center for Intelligent and Networked Systems, Tsinghua University" }, { "name": "Lv, Yisheng", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Han, Yunjun", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Shen, Zhen", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Decision making under uncertainty" ], "abstract": "Model-based reinforcement learning (MBRL) promises superior sample efficiency by planning with learned dynamics models. However, obtaining trustworthy uncertainty estimates for learned models and incorporating them into decision making remains difficult. In particular, existing methods lack allocation rules that balance model quality and uncertainty, leading to redundant rollouts and reliance on unreliable predictions. To address these issues, we propose an uncertainty-aware sampling strategy that allocates synthetic rollouts via Optimal Computing Budget Allocation (OCBA) in the model space. At rollout time, we allocate each candidate model's budget based on its suboptimality gap and uncertainty, where the uncertainty is divided into epistemic variance estimated by Monte-Carlo dropout and aleatoric variance produced by the probabilistic heads of dynamic models. On continuous control benchmarks, our method achieves higher returns with fewer interactions than other baselines, indicating improved sample efficiency and a more reliable exploration-exploitation balance.", "url": "" }, { "id": "Tu-TuA28.4", "code": "TuA28.4", "title": "Stability Analysis of Sampled-Data Load Frequency Control for Cyber-Physical Power Systems with Coordinated Cyber Attacks (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA28", "sessionTitle": "Control and Optimization for Smart Cities II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Guo, Weiru", "affiliation": "School of Automation, Central South University, Changsha" }, { "name": "Wang, Yixiao", "affiliation": "Central South University" }, { "name": "Liu, Fang", "affiliation": "Central South University" } ], "keywords": [ "Cyber-physical urban systems" ], "abstract": "As the power grids are evolving from traditional power systems into cyber-physical power systems (CPPSs), maintaining the stability of systems becomes more challenging with the complex operating environment. This paper focuses on the stability problem of the sampled data load frequency control (LFC) for CPPSs with coordinated time delay attacks and false data injection (TD-FDI) attacks. First, the model of sampled-data LFC system with TD-FDI attacks are established. Then, a novel LKF is constructed with the looped-functional and the stability criterion is derived. Moreover, an H∞ controller design method is proposed. Finally, a numerical example is given to investigate the stability problem by utilizing the proposed criterion. The impact of coordinated TD-FDI attacks is discussed and the controller is designed by the proposed method. The simulation results are given to demonstrate the robustness of the designed controller.", "url": "" }, { "id": "Tu-TuA28.5", "code": "TuA28.5", "title": "Energy-Efficient Last-Mile Delivery Via Truck-Drone-Bus Collaboration (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA28", "sessionTitle": "Control and Optimization for Smart Cities II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Wu, Hongcai", "affiliation": "Zhengzhou University" }, { "name": "Xin, Jianbin", "affiliation": "Zhengzhou University" }, { "name": "Wang, Yihui", "affiliation": "Delft University of Technology" } ], "keywords": [ "Smart city control and optimization", "Smart city design and planning", "AI for smart cities" ], "abstract": "The rapid expansion of e-commerce has intensified the challenges of last-mile delivery, particularly regarding operational efficiency and energy constraints. To address these issues, this paper proposes a novel collaborative multi-modal delivery framework integrating trucks, drones, and a two-way public transportation network. In this hierarchical two-echelon system, trucks function as mobile depots that transport drones to cluster centroids, while drones execute last-mile deliveries by opportunistically leveraging public buses as transportation backbones to overcome their inherent battery endurance limitations. We formulate the problem by constructing a discrete topological network based on bus stops and developing a flow-based Mixed-Integer Linear Programming (MILP) model to determine energy-optimal routes within the static bus network. Validated through a real-world case study, the experimental results demonstrate that this collaborative approach significantly extends the operational range of drones and offers substantial energy-saving potential compared to traditional single-mode delivery systems, providing a robust solution for sustainable urban delivery.", "url": "" }, { "id": "Tu-TuA28.6", "code": "TuA28.6", "title": "Predictive-Reset Hybrid Control for Robust Tracking and Targeting with Markov Chain-Based Re-Acquisition (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA28", "sessionTitle": "Control and Optimization for Smart Cities II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Liu, Jinze", "affiliation": "Dalian University of Technology" }, { "name": "Yang, Shuai", "affiliation": "Dalian University of Technology, Dalian, China" }, { "name": "Wang, Tianyu", "affiliation": "Dalian University of Technology" }, { "name": "Zhao, Jun", "affiliation": "Dalian University of Technology" }, { "name": "Wang, Wei", "affiliation": "Dalian University of Technology" } ], "keywords": [ "AI for smart cities", "Smart city control and optimization", "Decision making under uncertainty" ], "abstract": "Intelligent tracking and targeting control systems play a crucial role in numerous fields. To address challenges such as response delay, target loss, and poor transient performance faced by these systems in complex dynamic environments, this paper proposes a hybrid control framework that integrates a Markov chain-based position probability prediction method with an intelligent re-acquisition (MPPIR) strategy, as well as a Model Predictive Reset Control (Reset-MPC) mechanism. By constructing a hierarchical intelligent control architecture, the upper layer utilizes probability prediction to generate optimal observation viewpoints and reset trigger signals, while the lower layer employs Reset-MPC based on Linear Matrix Inequality (LMI) optimization to achieve precise control with optimal transient performance. Experimental results demonstrate that the proposed framework improves the overall performance index by approximately 42% over conventional methods in complex environments with obstacles. Specifically, target retention rate and transient adjustment time are significantly enhanced, along with significantly improved robustness and re-acquisition capability in scenarios involving highly maneuvering targets.", "url": "" }, { "id": "Tu-TuA29.1", "code": "TuA29.1", "title": "Modeling and Decoupling Control of Axis Coupling Caused by Rotational Center Deviation in a 6-DOF Maglev Planar Motor", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA29", "sessionTitle": "Applications of Mechatronic Principles", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Nakata, Keigo", "affiliation": "The University of Tokyo" }, { "name": "Ohnishi, Wataru", "affiliation": "The University of Tokyo" }, { "name": "Koseki, Takafumi", "affiliation": "The University of Tokyo" }, { "name": "Nakamura, Yuichiro", "affiliation": "Mitsubishi Electric Corp" }, { "name": "Takahashi, Kenji", "affiliation": "Mitsubishi Electric Corp" }, { "name": "Sekiguchi, Hiroyuki", "affiliation": "Mitsubishi Electric" } ], "keywords": [ "Application of mechatronic principles" ], "abstract": "Magnetically levitated planar motors have recently been in the spotlight; however, many challenges remain in practical applications, as the systems are inherently unstable and multi-input--multi-output. In particular, the center of rotation of the mover varies with the payload or sensor setup, resulting in strong axis coupling. This coupling can make the effects of right-half-plane zeros and unstable poles more pronounced, thereby intensifying the sensitivity limitation associated with the waterbed effect and restricting the achievable control bandwidth. In this study, a model-based decoupling controller is proposed to mitigate axis coupling and the resulting sensitivity limitation. The proposed controller improves the stability and control performance of the planar motor. The effectiveness of the controller is experimentally evaluated through two-dimensional trajectory tracking and the Direct Nyquist Array method.", "url": "" }, { "id": "Tu-TuA29.2", "code": "TuA29.2", "title": "Experimental Investigation and Control of a Hybrid Reluctance Actuator with a Tunable Magnet", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA29", "sessionTitle": "Applications of Mechatronic Principles", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Ronaes, Endre Peder", "affiliation": "TU Delft" }, { "name": "Isgandarov, Huseyn", "affiliation": "Delft University of Technology" }, { "name": "van Ostayen, Ron", "affiliation": "Delft Universtiy of Technology" }, { "name": "Hunt, Andres", "affiliation": "Delft University of Technology" }, { "name": "HosseinNia, S Hassan", "affiliation": "Delft University of Technology" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Application of mechatronic principles" ], "abstract": "Joule heating in electromagnetic actuators can degrade positioning accuracy through thermal expansion. This paper investigates a hybrid reluctance actuator with a tunable magnet, comprising an AlNiCo magnet that is magnetised in situ, to provide tunable offsets in actuation force without a proportional steady-state current. Two different magnetisation-state tuning methods are explored. One method relies on an estimate of the magnetisation state and magnetic reversal curves, while the other leverages machine learning to predict the duration of magnetisation pulses from previous remnant magnetisation states. A separate coil generates additional reluctance forces, providing the actuator with two modes of operation that can be combined to minimise heat generation. The performance of the concept is experimentally demonstrated through force-reference tracking and energy-consumption measurements for a selected input sequence. The results demonstrate the potential for energy-efficient force control in magnetically actuated systems.", "url": "" }, { "id": "Tu-TuA29.3", "code": "TuA29.3", "title": "Pressure Fluctuation Suppression and Precise Flow Rate Control through Simultaneous Control for Series-Connected Two-Port Valves", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA29", "sessionTitle": "Applications of Mechatronic Principles", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Hattori, Koki", "affiliation": "The University of Tokyo" }, { "name": "Ohnishi, Wataru", "affiliation": "The University of Tokyo" }, { "name": "Koseki, Takafumi", "affiliation": "The University of Tokyo" } ], "keywords": [ "High-performance motion control systems", "Mechatronic system estimation, identification, control", "Application of mechatronic principles" ], "abstract": "The increasing demand for fast and precise pneumatic control requires improved pressure regulator performance to ensure a constant supply pressure. This study aims to mitigate pressure fluctuations caused by abrupt changes in downstream flow rate within an accumulator tank located between the regulator and the flow control valve. Feedforward control is introduced for the pressure regulator, assuming prior knowledge of the downstream flow rate reference. The proposed control system integrates cascaded feedback controllers for tank pressure and poppet position with feedforward control based on iterative learning. Performance improvements are experimentally demonstrated in tank pressure regulation and downstream flow-rate step responses, achieving an 85% reduction in maximum pressure error and a 92% reduction in flow-rate settling time.", "url": "" }, { "id": "Tu-TuA29.4", "code": "TuA29.4", "title": "Modeling and Detection of Wheel Wear for Autonomous Mobile Robots", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA29", "sessionTitle": "Applications of Mechatronic Principles", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Ribeiro, Warley F. R.", "affiliation": "Aix-Marseille Universite" }, { "name": "Hellani, Hassanein", "affiliation": "Aix-Marseille Univ, CNRS, LIS" }, { "name": "Azari, Hamidreza", "affiliation": "Aix-Marseille Univ" }, { "name": "Chauchat, Paul", "affiliation": "Aix-Marseille Université" }, { "name": "Graton, Guillaume", "affiliation": "Ecole Centrale De Marseille" } ], "keywords": [ "Mechatronic system fault detection, diagnostics, hardware-in-the-loop simulation", "Mechatronic system modeling, design, optimization", "Mechatronics for robotic systems" ], "abstract": "This work presents a simplified model to calculate the change in the wheel radius of mobile robot resulting from friction between the wheel and the floor. Simulations are conducted to assess the potential impact of radius reduction on the mission the robot needs to perform, particularly the trajectory tracking performance. An Extended Kalman Filter is implemented to estimate the radius to detect wheel wear. Numerical simulations demonstrate the effectiveness of the proposed method in estimating a degraded radius and following its wear progress.", "url": "" }, { "id": "Tu-TuA29.5", "code": "TuA29.5", "title": "Closed Loop Reference Optimization for Extrusion Additive Manufacturing", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA29", "sessionTitle": "Applications of Mechatronic Principles", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Hoteit, Rawan", "affiliation": "ETH Zürich" }, { "name": "Balestra, Andrea", "affiliation": "Inspire AG" }, { "name": "Mingard, Nathan", "affiliation": "Inspire AG" }, { "name": "Balta, Efe C.", "affiliation": "Inspire AG" }, { "name": "Lygeros, John", "affiliation": "ETH Zurich" } ], "keywords": [ "Mechatronics for advanced manufacturing and energy systems", "Mechatronic system estimation, identification, control", "Mechatronic system modeling, design, optimization" ], "abstract": "Various defects occur during material extrusion additive manufacturing processes that degrade the quality of the 3D printed parts and lead to significant material waste. This motivates feedback control of the extrusion process to mitigate defects and prevent print failure. We propose a linear quadratic regulator (LQR) for closed-loop control with force feedback to provide accurate width tracking of the extruded filament. Furthermore, we propose preemptive optimization of the reference force given to the LQR that accounts for the performance of the LQR and generates the optimal reference for the closed loop extrusion dynamics and machine constraints. Simulation results demonstrate the improved tracking performance and response time. Experiments on a Fused Filament Fabrication 3D printer showcase a root mean square error improvement of 39.57% compared to tracking the unmodified reference as well as an 83.7% shorter settling time.", "url": "" }, { "id": "Tu-TuA29.6", "code": "TuA29.6", "title": "A Modular IoT-Enabled Remote Laboratory Platform for Hybrid Energy System Research and Engineering Education", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA29", "sessionTitle": "Applications of Mechatronic Principles", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Chalal, Lamine", "affiliation": "Icam" }, { "name": "Olivier, Louis", "affiliation": "Icam" }, { "name": "Liennard, Pierre", "affiliation": "Icam" }, { "name": "Saadane, Allal", "affiliation": "Icam" }, { "name": "Rachid, Ahmed", "affiliation": "University of Picardie Jules Verne" } ], "keywords": [ "Remote control", "Remote data acquisition and fusion", "Digital twins for cyber physical systems" ], "abstract": "Remote laboratory systems improve accessibility in engineering education and research by enabling Internet-based interaction with physical equipment. This paper presents a modular IoT-enabled remote laboratory platform for hybrid energy system studies, combining renewable energy emulators, battery storage, and programmable loads within a three-interface architecture based on a web HMI, TIA Portal, and MATLAB/Simulink, all connected through a Talk2M VPN cloud. An industrial PLC and IoT gateway provide deterministic local control as well as secure remote access and monitoring. A hierarchical energy-management algorithm is validated by comparing local and remote executions under identical wind and irradiance profiles. The results show small differences in the energy balances of the renewable sources, battery, and load, while typical communication delays are on the order of 100 ms. Consequently, the platform supports research-grade remote experimentation and project-based learning in control and energy systems engineering.", "url": "" }, { "id": "Tu-TuA30.1", "code": "TuA30.1", "title": "Estimating Semantic Ambiguity Via Gaussian Context Distributions for VLM-Driven Traversability Analysis", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA30", "sessionTitle": "AI-Powered Robotics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Häuselmann, Ramona", "affiliation": "Luleå University of Technology" }, { "name": "Valdes Saucedo, Mario Alberto", "affiliation": "Lulea University of Technology" }, { "name": "Kanellakis, Christoforos", "affiliation": "Luleå University of Technology" }, { "name": "Nikolakopoulos, George", "affiliation": "Luleå University of Technology" } ], "keywords": [ "AI-powered robotics", "Robot perception and sensing", "Aerial, field, and marine robotics" ], "abstract": "Autonomous navigation in unstructured environments requires robust scene under- standing, yet Vision-Language Models (VLMs) often suffer from semantic ambiguity, where conflicting predictions can lead to dangerous failures. To address this, we present a novel pipeline for vision-based traversability estimation that explicitly models contextual uncertainty. Our approach utilizes ”Conceptual Anchoring” to ground open-vocabulary VLM predictions onto a continuous physical traversability scale. By formulating the model’s responses as a Gaussian Context Distribution (GCD), we derive both a dense traversability map and a dense uncertainty map based on the statistical properties of the distribution. Experimental validation on the real-world GOOSE dataset demonstrates that our proposed uncertainty metric effectively correlates with sources of ambiguity, such as visual artifacts and mixed terrain overlap. The method exhibits competitive performance while offering the distinct advantage of providing statistical uncertainty estimates to address semantic ambiguity, enabling safer and more reliable autonomous behavior in complex outdoor settings.", "url": "" }, { "id": "Tu-TuA30.2", "code": "TuA30.2", "title": "Symmetry-Aware Steering of Equivariant Diffusion Policies: Benefits and Limits", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA30", "sessionTitle": "AI-Powered Robotics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Park, Minwoo", "affiliation": "Yonsei University" }, { "name": "Chang, Junwoo", "affiliation": "Yonsei University" }, { "name": "Choi, Jongeun", "affiliation": "Yonsei University" }, { "name": "Horowitz, Roberto", "affiliation": "Univ. of California at Berkeley" } ], "keywords": [ "Robotic learning and adaptation", "AI-powered robotics", "Robotic grasping and manipulation" ], "abstract": "Equivariant diffusion policies (EDPs) combine the expressivity of diffusion models with the generalization and sample efficiency afforded by geometric symmetries. While steering these policies with reinforcement learning (RL) offers a promising fine-tuning mechanism beyond demonstration data, directly applying standard (non-equivariant) RL ignores the symmetries that EDPs are designed to exploit, leading to sample inefficiency and instability. We theoretically establish that the diffusion process of an EDP is equivariant, inducing a group-invariant latent-noise MDP which is well-suited for equivariant diffusion steering. Building on this, we introduce a principled symmetry-aware steering framework and compare standard, equivariant, and approximately equivariant RL strategies across tasks with varying degrees of symmetry. While we identify the practical boundaries of strict equivariance under symmetry breaking, exploiting symmetry during the steering yields substantial benefits---enhancing sample efficiency, preventing value divergence, and achieving strong policy improvements even when EDPs are trained from extremely limited demonstrations.", "url": "" }, { "id": "Tu-TuA30.3", "code": "TuA30.3", "title": "Joint Optimization of Defense Allocations and Surveillance Strategies against Random Intruders", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA30", "sessionTitle": "AI-Powered Robotics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Wang, Weizhen", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Duan, Xiaoming", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Task and motion planning" ], "abstract": "We study the joint optimization of defense allocations and Markov-chain-based surveillance strategies against random intruders over a graph environment, where the defense resources at a location determine the required durations for an intruder to complete the attack at the location. We adopt the capture probability as the objective and propose a coordinate-descent-based algorithm to optimize it, where the defense allocations and the surveillance strategies are updated alternately. We first derive an explicit formula for the directional derivative of the capture probability with respect to the Markov chain, which can then be employed in gradient descent algorithms to optimize the surveillance strategy. Then we show that a greedy algorithm optimally solves the defense allocation problem with a fixed surveillance strategy. Finally, we establish a connection between the capture probability and the Kemeny's constant, justifying using the latter as a proxy for the design of stochastic surveillance strategies.", "url": "" }, { "id": "Tu-TuA30.4", "code": "TuA30.4", "title": "A Neural Signed Configuration Distance Function for Path Planning of Picking Manipulators", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA30", "sessionTitle": "AI-Powered Robotics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Wullt, Bernhard", "affiliation": "Uppsala University" }, { "name": "Mattsson, Per", "affiliation": "Uppsala University" }, { "name": "Schön, Thomas Bo", "affiliation": "Uppsala University" }, { "name": "Norrlöf, Mikael", "affiliation": "ABB AB" } ], "keywords": [ "Task and motion planning", "AI-powered robotics", "Robotic learning and adaptation" ], "abstract": "Picking manipulators are task specific robots, with fewer degrees of freedom compared to general-purpose manipulators, and are heavily used in industry. The efficiency of the picking robots is highly dependent on the path planning solution, which is commonly based on sampling-based multi-query methods. The planner is robustly able to solve the problem, but its heavy use of collision-detection limits the planning capabilities for online use. We approach this problem by presenting a novel implicit obstacle representation for path planning, a neural signed configuration distance function (nSCDF), which allows us to form collision-free balls in the configuration space. We use the ball representation to re-formulate a state of the art multi-query path planner, i.e., instead of points, we use balls in the graph. Our planner returns a collision-free corridor, which allows us to use convex programming to produce optimized paths. From our numerical experiments, we observe that our planner produces paths that are close to those from an asymptotically optimal path planner, in significantly less time.", "url": "" }, { "id": "Tu-TuA30.5", "code": "TuA30.5", "title": "Decentralised Sample Threshold Task Allocation for Multiple Robots", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA30", "sessionTitle": "AI-Powered Robotics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Li, Teng", "affiliation": "Cranfield University" }, { "name": "Shin, Hyo-Sang", "affiliation": "Korea Advanced Institute of Science and Technology" }, { "name": "Sun, Mengwei", "affiliation": "Cranfield University" }, { "name": "Tsourdos, Antonios", "affiliation": "Cranfield University" } ], "keywords": [ "Task and motion planning", "Decision support systems" ], "abstract": "This paper considers large-scale decentralised task allocation with submodular objectives, where both computation and communication demands are NP-hard. This paper proposes a decentralised sample threshold task allocation (STTA) algorithm by leveraging a random sampling strategy and a decreasing threshold technique to handle the NP-hardness. The proposed algorithm can provide an approximation guarantee of 1/2-epsilon for maximising monotone submodular objective functions and 1/4-epsilon for the non-monotone case on average with polynomial computational complexity when the sampling probability equals 0.5. Monte-Carlo simulation results indicate that the algorithm matches state-of-the-art performance for monotone objectives and outperforms them for non-monotone ones, with much lower computation and communication costs.", "url": "" }, { "id": "Tu-TuA30.6", "code": "TuA30.6", "title": "Dual-Mode Mecanum Robot: Roller Locking for Energy-Efficient Mobility", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA30", "sessionTitle": "AI-Powered Robotics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Zakharov, Dmitrii", "affiliation": "ITMO University" }, { "name": "Iaremenko, Andrei", "affiliation": "ITMO University" }, { "name": "Panin, Aleksandr", "affiliation": "ITMO University" }, { "name": "Aliev, Dima", "affiliation": "ITMO" }, { "name": "Borisov, Oleg", "affiliation": "ITMO University" }, { "name": "Gromov, Vladislav", "affiliation": "ITMO University" } ], "keywords": [ "Task and motion planning", "Robotic learning and adaptation", "Mechatronics for mobility systems" ], "abstract": "Omnidirectional mobile robots, known for their excellent maneuverability in confined spaces, often struggle with energy efficiency due to their roller-based wheel design. Building on previous work that introduced a reconfigurable robot capable of switching between omnidirectional and conventional modes via a roller-locking mechanism, this paper presents further advancements aimed at enhancing versatility and efficiency. We propose: (1) a novel scalable and compact locking mechanism, validated through a redesigned robot prototype, (2) refined kinematic, dynamic, and energy models, (3) an experimental analysis of energy consumption across three modes—conventional, omnidirectional, and hybrid, and (4) a quasi-optimal mode-switching algorithm that dynamically selects configurations during trajectory tracking to optimize both energy efficiency and accuracy. Experimental results demonstrate that our approach reduces energy consumption by 8% on our test trajectory under ideal conditions. Our system maintains high maneuverability where needed, ensuring efficient navigation in complex environments. These innovations enable our platform to achieve a crucial balance between mobility, efficiency, and control accuracy, paving the way for the practical deployment of reconfigurable robots in real-world service applications.", "url": "" }, { "id": "Tu-TuA32.1", "code": "TuA32.1", "title": "Deadlock Escape Via Level-Set Pseudo-Goal Switching under CBF-QP Control", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA32", "sessionTitle": "Autonomous Navigation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Zhao, Shanshan", "affiliation": "University of Technology Sydney" }, { "name": "Wu, Lan", "affiliation": "University of Technology Sydney" }, { "name": "Vidal-Calleja, Teresa", "affiliation": "University of Technology Sydney" } ], "keywords": [ "Autonomous navigation" ], "abstract": "This paper presents a Poisson-based construction of control barrier functions (CBFs) that integrates perception-derived boundary geometry with strong analytical regularity guarantees. Leveraging elliptic PDE theory, the proposed safety function is shown to be continuously differentiable with Lipschitz gradients,ensuring that Lie derivatives are well defined for control-affine systems.Building on this foundation, we introduce a deadlock detection mechanism and a level-set–based pseudo–goal (PG) switching strategy to resolve stagnation caused by overly conservative CBF constraints in nonconvex environments. A geodesic-distance criterion is further developed to rank PG candidates on level sets, enabling robust reference switching without compromising safety. The resulting framework maintains forward invariance of the safe set under CBF-QP optimization while significantly improving task reachability.Simulation results on a complex perception-derived map demonstrate that the proposed approach eliminates local deadlocks, reduces failed PG switches, and achieves reliable obstacle avoidance and target completion.", "url": "" }, { "id": "Tu-TuA32.2", "code": "TuA32.2", "title": "Safe and Adaptive Collaborative Transportation for Quadrotor Swarms in Dynamic Environments", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA32", "sessionTitle": "Autonomous Navigation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Bao, Yuhan", "affiliation": "Beijing Institute of Technology" }, { "name": "Li, Hongzeng", "affiliation": "Beijing Institute of Technology" }, { "name": "Wang, Qiang", "affiliation": "Beijing Institute of Technology" }, { "name": "Dou, Li-Hua", "affiliation": "Beijing Institute of Technology" }, { "name": "Deng, Fang", "affiliation": "Beijing Institute of Technology" }, { "name": "Lu, Maobin", "affiliation": "Beijing Institute of Technology" } ], "keywords": [ "Autonomous navigation", "High-performance motion control systems", "Task and motion planning" ], "abstract": "Collaborative transportation by aerial swarms offers high efficiency, flexibility, and scalability. However, the practical deployment is challenged by two critical challenges: dynamic obstacles, such as pedestrians and vehicles, and intermittent communication networks. To address these issues, we develop a framework combining obstacle-velocity-aware dynamic plan- ning and communication-adaptive formation control. Specifically, a pedestrian-first trajectory planner guides quadrotor swarms to bypass pedestrians opposite to their motion, improving safety and social acceptance. In addition, a model predictive control (MPC)-based leader- following formation controller is integrated with a distributed observer for real-time leader- state estimation. Simulations and real-world experiments demonstrate the effectiveness of the proposed framework.", "url": "" }, { "id": "Tu-TuA32.3", "code": "TuA32.3", "title": "What Matters for Real-World Long-Horizon Robot Navigation?: An Experimental Study of Implicit Goals and Sparse Memory (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA32", "sessionTitle": "Autonomous Navigation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Suh, Bokeon", "affiliation": "DGIST" }, { "name": "Ju, Hyoseok", "affiliation": "DGIST" }, { "name": "Kim, Giseop", "affiliation": "DGIST" } ], "keywords": [ "Autonomous navigation", "Human-robot interaction", "AI-powered robotics" ], "abstract": "For autonomous robots to operate effectively in the real world, they must simultaneously possess two distinct capabilities: interpreting implicit human instructions and managing long-horizon memory efficiently. While recent Retrieval-Augmented Generation based approaches have shown promise, prior studies have not fully addressed the performance tradeoffs between query ambiguity and memory sparsity. We identify two underexplored factors: (i) implicit goals, in which queries omit explicit object names, and (ii) sparse memory, in which only selected keyframes are stored. We present a paired dataset of explicit and implicit queries and a keyframe-based memory policy. Implicit phrasing drops success rate by up to 40 percentage points versus matched explicit queries (Short), and the keyframe policy reduces stored entities by 31% versus a fixed-interval baseline (Long).", "url": "" }, { "id": "Tu-TuA32.4", "code": "TuA32.4", "title": "A Unified Approach for Robot–Obstacle Distance Computation Using Conformal Geometric Algebra for NMPC", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA32", "sessionTitle": "Autonomous Navigation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Chel Puc, Niger Abram", "affiliation": "LAAS-CNRS" }, { "name": "Mujica, Martin", "affiliation": "LAAS-CNRS, University of Toulouse" }, { "name": "Rangaradjou, Harendra", "affiliation": "LAAS-CNRS" }, { "name": "Porée, Rémi", "affiliation": "LAAS-CNRS" }, { "name": "Cadenat, Viviane", "affiliation": "LAAS/CNRS" } ], "keywords": [ "Autonomous navigation", "Mechatronic system estimation, identification, control" ], "abstract": "This paper introduces a collision avoidance framework for robotic manipulators that combines Conformal Geometric Algebra (CGA) with a Non-linear model predictive control (NMPC) scheme. CGA is employed to model geometric entities (such as points, planes, and spheres) using a single algebraic representation, allowing for simpler analytical expressions of distance constraints based on the inner product defined in CGA. This inner product is directly incorporated as a hard constraint into the NMPC formulation, ensuring safe motions in environments with obstacles. The proposed approach is evaluated in simulation on a 6-DoF manipulator, showing effective collision avoidance within the unified CGA representation. The NMPC performance is also examined in an ill-conditioned case, when the robot’s end-effector lies on the boundary of a sphere. In such a case, CGA-based modeling exhibits superior performance compared to some classical Euclidean formulations of distances.", "url": "" }, { "id": "Tu-TuA32.5", "code": "TuA32.5", "title": "Efficient Computation of a Continuous Topological Model of the Configuration Space of Tethered Mobile Robots", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA32", "sessionTitle": "Autonomous Navigation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Battocletti, Gianpietro", "affiliation": "Delft University of Technology" }, { "name": "Boskos, Dimitris", "affiliation": "Delft University of Technology" }, { "name": "De Schutter, Bart", "affiliation": "Delft University of Technology" } ], "keywords": [ "Task and motion planning", "Autonomous navigation" ], "abstract": "Despite the attention that the problem of path planning for tethered robots has garnered in the past few decades, the approaches proposed to solve it typically rely on a discrete representation of the configuration space and do not exploit a model that can simultaneously capture the topological information of the tether and the continuous location of the robot. In this work, we explicitly build a topological model of the configuration space of a tethered robot. To do so, we establish a link between the configuration space of the tethered robot and the universal covering space of the workspace, which we then exploit to develop an algorithm to compute a simplicial complex model of the configuration space. The proposed model can be computed in a fraction of the time required to build traditional homotopy-augmented graphs, and is continuous, allowing to solve the path planning task for tethered robots using a broad set of path planning algorithms.", "url": "" }, { "id": "Tu-TuA32.6", "code": "TuA32.6", "title": "LaCAM-AA: A Complete and Efficient Algorithm for Asynchronous Multi-Agent Path Finding", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA32", "sessionTitle": "Autonomous Navigation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Wu, Xinning", "affiliation": "National University of Defense Technology" }, { "name": "Shu, Xin", "affiliation": "National University of Denfense Technology" }, { "name": "Yu, Huangchao", "affiliation": "National University of Defense Technology" }, { "name": "Wang, Xiangke", "affiliation": "National University of Defense Technology" } ], "keywords": [ "Task and motion planning", "Autonomous navigation" ], "abstract": "This paper addresses the Multi-Agent Path Finding with Asynchronous Actions (MAPF-AA) problem by proposing LaCAM-AA, an extension of the Lazy Constraints Addition Search (LaCAM) framework that incorporates Loosely Synchronized Search (LSS). The original LaCAM algorithm was specifically designed for synchronous environments, relying on fixed time-step assumptions. To overcome these limitations, LaCAM-AA introduces a temporal state to represent asynchronous agent actions. The high-level search generates constraints with temporal dimensions and optimizes the search space through state comparisons. While the low-level search ensures spatio-temporal continuity by expanding nodes at specific timestamps. The experimental results indicate that LaCAM-AA significantly outperforms the classical asynchronous planner CCBS. Specifically, it solves a greater number of benchmark instances within fixed time constraints while maintaining scalable performance as problem complexity increases. This work provides an effective solution for MAPF-AA while preserving the completeness guarantees of the original LaCAM framework.", "url": "" }, { "id": "Tu-TuA33.1", "code": "TuA33.1", "title": "An Integrated Lyapunov-Constrained Reinforcement Learning Framework for Observer-Based Robust Control", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA33", "sessionTitle": "Reinforcement Learning and Deep Learning in Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Yavari, Ali", "affiliation": "University of Alberta" }, { "name": "Fazeli, Seyed", "affiliation": "University of Alberta" }, { "name": "Wang, Haihan", "affiliation": "University of Alberta" }, { "name": "Zhao, Qing", "affiliation": "Univ. of Alberta" } ], "keywords": [ "AI-driven modeling and control", "Reinforcement learning and deep learning in control" ], "abstract": "In many practical data-driven control systems, only partial and noisy measurements are available, making control policy design more difficult. Although reinforcement learning (RL) has achieved strong performance in control of complex systems, most formulations assume fully observed (system) states, which rarely hold in real-world applications. In this work, we propose an integrated constrained RL framework that learns a state observer and a robust actor–critic controller. In particular, estimated states drive a robust actor-critic controller that incorporates sliding-mode compensation to handle uncertain dynamics. The proposed approach supports data-only (offline) training. We provide theoretical results that establish closed-loop stability of the combined observer–controller. Experiments on a real system demonstrate the effectiveness of the method under realistic sensing conditions.", "url": "" }, { "id": "Tu-TuA33.2", "code": "TuA33.2", "title": "DataToSequence: A Novel Reward Machine Learning Approach for RL", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA33", "sessionTitle": "Reinforcement Learning and Deep Learning in Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Yu, Feiyu", "affiliation": "Beijing Institute of Technology" }, { "name": "Wu, Qizhen", "affiliation": "Beihang University" }, { "name": "Chen, Lei", "affiliation": "Beijing Institude of Technology" } ], "keywords": [ "Knowledge-based and data-driven control", "Reinforcement learning and deep learning in control", "Data fusion and mining in control" ], "abstract": "Inferring reward structures from observational data is challenging in reinforcement learning and process mining, particularly in non-Markovian environments where distinguishing temporal task progression from spurious correlations is difficult. We propose DataToSequence, an attention-based framework that infers reward machines from successful event traces through three components: attention-based event-transition scoring, dual-coverage chain selection, and two RM constructions, SBPTRM and SBLRM. Self-attention highlights task-advancing transitions, while greedy selection removes redundant events. Experiments show that DataToSequence reliably identifies temporal structures, accelerates RL convergence, and maintains interpretability, outperforming plain Proximal Policy Optimization (PPO) and a learning-based RM baseline.", "url": "" }, { "id": "Tu-TuA33.3", "code": "TuA33.3", "title": "Reinforcement Learning Stabilization Control with Safety Constraint", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA33", "sessionTitle": "Reinforcement Learning and Deep Learning in Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Wang, Haihan", "affiliation": "University of Alberta" }, { "name": "Bo, Song", "affiliation": "University of Alberta" }, { "name": "Fazeli, Seyed", "affiliation": "University of Alberta" }, { "name": "Yavari, Ali", "affiliation": "University of Alberta" }, { "name": "Liu, Brian", "affiliation": "University of Toronto" }, { "name": "Zhao, Qing", "affiliation": "Univ. of Alberta" } ], "keywords": [ "Reinforcement learning and deep learning in control", "AI-driven modeling and control", "Knowledge-based and data-driven control" ], "abstract": "This paper presents a general design method for safe reinforcement learning (RL) controllers that enforce instantaneous safety constraints. Applied to stabilization, we propose a dual-objective controller, namely, the State-wise Constrained Policy Optimization based Stabilization controller (SCPO-S), where stabilization and constraint satisfaction are optimized jointly. On a lab-scale Rotary Pendulum (ROTPEN) benchmark, SCPO-S operates at the low-level of control, issuing direct motor-voltage commands in real time, and successfully performs swing-up and stabilization while satisfying input and state constraints. It further outperforms Deep Deterministic Policy Gradient (DDPG) and nonlinear Model Predictive Control (MPC) in handling constraints and noises.", "url": "" }, { "id": "Tu-TuA33.4", "code": "TuA33.4", "title": "Constrained Policy Optimization Via Sampling-Based Weight-Space Projection", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA33", "sessionTitle": "Reinforcement Learning and Deep Learning in Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Cao, Shengfan", "affiliation": "University of California, Berkeley" }, { "name": "Borrelli, Francesco", "affiliation": "University of California" }, { "name": "Joa, Eunhyek", "affiliation": "University of California at Berkeley" } ], "keywords": [ "Reinforcement learning and deep learning in control", "AI-driven modeling and control", "Soft computing and robust intelligent control" ], "abstract": "Safety-critical learning requires policies that improve performance without leaving the safe operating regime. We study constrained policy learning where model parameters must satisfy rollout-based safety constraints that can be evaluated but not differentiated analytically. We propose SCPO, a sampling-based weight-space projection method that enforces safety directly in parameter space without requiring gradient access to the constraint functions. SCPO constructs a local safe region by combining rollout-based safety evaluations with smoothness bounds relating parameter perturbations to changes in safety metrics, and projects each gradient update via a convex QCQP. We establish a safe-by-induction guarantee: starting from any safe initialization, all intermediate policies remain safe given feasible projections. In constrained control settings with a stabilizing backup policy, SCPO further ensures closed-loop stability while enabling safe adaptation beyond the conservative backup. Experiments on constrained regression with harmful supervision and double-integrator imitation with a malicious expert show that SCPO rejects unsafe updates, maintains feasibility throughout training, and achieves meaningful objective improvement.", "url": "" }, { "id": "Tu-TuA33.5", "code": "TuA33.5", "title": "Priority-Driven Control and Communication in Decentralized Multi-Agent Systems Via Reinforcement Learning", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA33", "sessionTitle": "Reinforcement Learning and Deep Learning in Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Guo, Qingyun", "affiliation": "Aalto University" }, { "name": "Shi, Junyi", "affiliation": "Aalto University" }, { "name": "Kucner, Tomasz Pitor", "affiliation": "Aalto University" }, { "name": "Baumann, Dominik", "affiliation": "Aalto University" } ], "keywords": [ "Reinforcement learning and deep learning in control", "Control architecture for multi agent systems", "AI in networked control" ], "abstract": "Event-triggered control provides a mechanism for avoiding excessive use of constrained communication bandwidth in networked multi-agent systems. However, most existing methods rely on accurate system models, which may be unavailable in practice. In this work, we propose a model-free, priority-driven reinforcement learning algorithm that learns communication priorities and control policies jointly from data in decentralized multi-agent systems. By learning communication priorities, we circumvent the hybrid action space typical in event-triggered control with binary communication decisions. We evaluate our algorithm on benchmark tasks and demonstrate that it outperforms the baseline method.", "url": "" }, { "id": "Tu-TuA33.6", "code": "TuA33.6", "title": "Continuous Preference-Based Reinforcement Learning for Batch Process Quality Optimization", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA33", "sessionTitle": "Reinforcement Learning and Deep Learning in Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Wang, Xindong", "affiliation": "China University of Petroleum(East China)" }, { "name": "Shao, Weiming", "affiliation": "China University of Petroleum (East China)" }, { "name": "Chen, Junghui", "affiliation": "Chung-Yuan Christian Univ" } ], "keywords": [ "Reinforcement learning and deep learning in control", "Knowledge-based and data-driven control", "AI-driven modeling and control" ], "abstract": "In batch manufacturing processes, sparse and delayed quality measurements present significant challenges for real-time optimization, hindering the ability to establish direct correlations between control actions and final product quality. This study presents a continuous preference-based reinforcement learning (continuous-PbRL) framework that addresses these challenges by mapping terminal product quality to continuous preference labels, thereby generating a more informative and smoother supervisory signal. The proposed approach employs a Transformer-based reward model to optimize within-batch temperature setpoints without requiring human-annotated discrete preference labels. Simulation studies demonstrate that the proposed method outperforms discrete PbRL baselines, achieving up to 21% improvement in final product quality under process disturbances while maintaining smoother and more stable control trajectories.", "url": "" }, { "id": "Tu-TuA34.1", "code": "TuA34.1", "title": "Sharding-DAG: A Novel Federated Learning Framework Based on DAG Sharding with a Reputation Mechanism (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA34", "sessionTitle": "Blockchain Intelligence and Knowledge Automation", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Zhong, Dingzhi", "affiliation": "Renmin University of China" }, { "name": "Xie, Shengyuan", "affiliation": "School of Mathematics, Renmin University of China" }, { "name": "Liu, Tao", "affiliation": "Renmin University" }, { "name": "Yuan, Yong", "affiliation": "Renmin University of China" } ], "keywords": [ "Blockchain intelligence", "Decentralized economics/ecosystems (DeEco)", "Social computing" ], "abstract": "Blockchain-based Federated Learning (BCFL) is a promising approach to address privacy and security concerns in distributed machine learning scenarios. However, existing BCFL frameworks typically face two key challenges, i.e., limited scalability and lack of incentives. In large-scale systems, single-chain-based architectures might suffer from low blockchain throughput, severely restricting performance. While many studies have adopted sharding in BCFL to improve scalability, they often lack effective incentives to motivate active participation and curb malicious behavior. Therefore, in this paper, we propose a novel federated learning framework that integrates blockchain sharding with a reputation mechanism. More specifically, we use a Directed Acyclic Graph (DAG) to serve as the mainchain, and an asynchronous FL structure is employed within every subchain shard. Additionally, a Beta-Bernoulli-based time-frequency-sensitive reputation model is adopted to evaluate participants’ reputation based on their performance, providing a reference for model aggregation and DAG tip selection. Experimental results demonstrate that our framework outperforms other baselines with higher convergence speed and scalability.", "url": "" }, { "id": "Tu-TuA34.2", "code": "TuA34.2", "title": "Deep Deterministic Policy Gradient-Based RIS-Assisted Physical Layer Security Method for Vehicular Networks (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA34", "sessionTitle": "Blockchain Intelligence and Knowledge Automation", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Bai, Yongqiang", "affiliation": "University of Science and Technology Beijing" }, { "name": "Han, Shuangshuang", "affiliation": "University of Science and Technology Beijing" }, { "name": "Zhang, Xiaoyan", "affiliation": "Shenzhen University" }, { "name": "Lv, Yisheng", "affiliation": "Institute of Automation, Chinese Academy of Sciences" } ], "keywords": [ "Agent & AI technology for business and economy", "Blockchain intelligence" ], "abstract": "In urban vehicular networks, communications often face blockage, channel fading, and eavesdropping threats, leading to unreliable links and security vulnerabilities. This paper proposes a reconfigurable intelligent surface (RIS)-assisted physical layer security method, where RIS is optimally deployed to control wireless propagation. A deep deterministic policy gradient (DDPG)-based algorithm adaptively configures RIS phases to jointly enhance link quality and secrecy performance. Simulation results show that the proposed method achieves superior reliability and security compared with conventional fixed-RIS schemes, offering a robust and efficient solution for autonomous driving and intelligent transportation systems. In future work, this study explores the application of blockchain technology for distributed trust management in RIS-assisted networks. By utilizing an immutable ledger to store RIS configurations and channel state information, it significantly enhances system transparency and attack resilience, while providing a decentralized solution for collaborative security authentication.", "url": "" }, { "id": "Tu-TuA34.3", "code": "TuA34.3", "title": "A Web3-Based Identity Management System for Decentralized Collaboration (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA34", "sessionTitle": "Blockchain Intelligence and Knowledge Automation", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Pi, Peiding", "affiliation": "Macau University of Science and Technology" }, { "name": "Ni, Qinghua", "affiliation": "Macau University of Science and Technology" }, { "name": "Xie, Yunlong", "affiliation": "China Unicom Data Intelligence Co., Ltd" }, { "name": "Ouyang, Liwei", "affiliation": "China Unicom Data Intelligence Co., Ltd" }, { "name": "Wang, Fei-Yue", "affiliation": "Institute of Automation, Chinese Academy of Sciences" } ], "keywords": [ "Blockchain intelligence", "Knowledge automation" ], "abstract": "Effective decentralized collaboration relies on a reliable framework for identity management. While Decentralized Identifier (DID) technology can establish user-controlled identity anchoring, it faces inherent limitations within complex collaboration context due to a lack of capacity for the structured management and dynamic verification of multi-faceted credentials. To bridge this gap, this paper proposes a Web3-based identity management system (WIMS), mainly composed of identity registration contract (IRC), profile management contract (PMC), credential authorization contract (CAC), and governance contract (GC). It first establishes the basic architecture of WIMS, which separates the generic identity infrastructure from application-specific business logic. Then, it presents the operational mechanism of WIMS. Furthermore, it conducts a simulation experiment within the Decentralized Science (DeSci) scenario, to empirically validate the proposed WIMS. The results offer supportive evidence for the effectiveness of WIMS, as they confirm it can enforce dynamically earned permissions and ensure resilience against malicious actors.", "url": "" }, { "id": "Tu-TuA34.4", "code": "TuA34.4", "title": "Systematic Analysis and Empirical Study of the Decentralized Social Network: The Nostr Case (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA34", "sessionTitle": "Blockchain Intelligence and Knowledge Automation", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Ding, Wendy", "affiliation": "Obuda University" }, { "name": "Rouabah, Younes", "affiliation": "Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Macao 999078," }, { "name": "Mitchell, Sean", "affiliation": "Sandwich Farm LLC" }, { "name": "Henshaw-Plath, Evan", "affiliation": "The Inclusive Design Institute" }, { "name": "Odell, Matt", "affiliation": "TEN31" }, { "name": "Zheng, Jademont", "affiliation": "Aterdrip Investment Limited, Hong Kong 999077, China" }, { "name": "Sweigart, Elizabeth", "affiliation": "The Inclusive Design Institute" }, { "name": "Gao, Shaun", "affiliation": "YAKIHONNE" }, { "name": "Kovacs, Levente", "affiliation": "Obuda University" } ], "keywords": [ "Decentralized economics/ecosystems (DeEco)", "Social computing", "Computational economics" ], "abstract": "Web2 platforms encounter fundamental obstacles which decentralized social protocols work to overcome by stopping user account trapping and breaking information barriers and removing centralized content oversight. The main challenge for researchers who want to evaluate decentralized systems involves determining their actual level of infrastructure-based decentralization. The research examines Nostr through architectural and empirical approaches to analyze its basic design which depends on cryptographic identities and operates independently through multiple relays. The research assesses relay diversity and geographic and provider concentration and failure resilience through two large-scale data collection efforts. The research shows that relays function independently while presenting typical distribution patterns which help developers create better decentralized social networks.", "url": "" }, { "id": "Tu-TuA34.5", "code": "TuA34.5", "title": "On-Chain LLMs: Architectures, Applications, and Challenges (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA34", "sessionTitle": "Blockchain Intelligence and Knowledge Automation", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Liang, Xiaolong", "affiliation": "Chinese Academy of Sciences" }, { "name": "Qin, Rui", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Li, Juanjuan", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Pan, BaiXi", "affiliation": "Macau University of Science and Technology" }, { "name": "Lin, Fei", "affiliation": "Macau University of Science and Technology" }, { "name": "Guan, Sangtian", "affiliation": "Macau University of Science and Technology" }, { "name": "Li, Cheng", "affiliation": "Renmin University of China" }, { "name": "Hao, Jiayang", "affiliation": "Institute of Automation CAS" } ], "keywords": [ "Agent & AI technology for business and economy", "Blockchain intelligence", "Knowledge automation" ], "abstract": "Large Language Models (LLMs) have evolved to demonstrate unprecedented capabilities in language understanding and reasoning. Their integration into blockchain systems offers the potential to transcend the deterministic rule-based logic that makes them truly intelligent. However, bridging the gap between the massive computational demands of LLMs and the resource-constrained environment of blockchains presents a significant challenge. While numerous solutions have been proposed to tackle this issue, there is a notable absence of a comprehensive survey that systematically reviews and categorizes existing approaches. This paper aims to bridge this gap by providing a structured overview of this field. First, we investigate prevailing methods for on-chain LLM execution and classify them based on their underlying architecture. Second, we investigate the potential applications. Finally, we identify key open challenges and outline promising future directions. This work contributes to providing a clear roadmap for researchers and practitioners in this rapidly evolving domain.", "url": "" }, { "id": "Tu-TuA34.6", "code": "TuA34.6", "title": "An ACP-Based Lifecycle Risk Monitoring Method for DAO Governance Smart Contracts (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA34", "sessionTitle": "Blockchain Intelligence and Knowledge Automation", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Hao, Jiayang", "affiliation": "Institute of Automation CAS" }, { "name": "Qin, Rui", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Liang, Xiaolong", "affiliation": "Chinese Academy of Sciences" }, { "name": "Pi, Peiding", "affiliation": "Macau University of Science and Technology" } ], "keywords": [ "Blockchain intelligence", "Parallel intelligence", "Social computing" ], "abstract": "Smart contract security risks are a critical challenge for Decentralized Autonomous Organizations (DAOs). Existing analyses often focus on isolated vulnerabilities, neglecting dynamic monitoring of cross-stage risks. This paper proposes an ACP-based lifecycle risk monitoring method for DAO governance smart contracts. By constructing a high-fidelity artificial DAO system and a multi-level computational experiment framework, it enables systematic monitoring and assessment spanning anomaly detection, attack simulation and attack chain analysis. This method offers new insights and perspectives for the security analysis of DAO governance smart contracts.", "url": "" }, { "id": "Tu-TuA35.1", "code": "TuA35.1", "title": "JuPCHS: A Julia Packages for Port-Hamiltonian Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA35", "sessionTitle": "Advanced Teaching Methodologies", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Marin-Silva, Kenneth", "affiliation": "Universidad Tecnológica De Pereira" }, { "name": "Garces, Alejandro", "affiliation": "Universidad Tecnologica De Pereira" }, { "name": "Avila-Becerril, Sofia", "affiliation": "Universidad Nacional Autonoma De Mexico" }, { "name": "Espinosa-Perez, Gerardo", "affiliation": "Universidad Nacional Autonoma De Mexico" } ], "keywords": [ "Control education laboratories", "Control education learning analytics", "Continuing control education" ], "abstract": "Major advances in control systems pose significant challenges from an educational perspective, as there is a need to combine attractive and efficient teaching methodologies with the foundations of control theory to capture the attention of both control students and industrial practitioners. Control education must account for computational resources to simplify concept application and foster systematic thinking. In this paper, this problem is approached by presenting a new open-source Julia-based package for controlling, simulating, and analyzing Port-Controlled Hamiltonian Systems. The aim is to provide a means to consolidate learning and understanding of this control field using a modular computational tool that facilitates the analysis and design of control schemes, enabling the user to easily formulate different practical scenarios.", "url": "" }, { "id": "Tu-TuA35.2", "code": "TuA35.2", "title": "PID Control of a Multi-Agent System: A Rabbits Ecosystem Case Study", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA35", "sessionTitle": "Advanced Teaching Methodologies", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Moura Oliveira, Paulo", "affiliation": "Univ. De Tras Os Montes E Alto Douro" }, { "name": "Vrancic, Damir", "affiliation": "Jozef Stefan Institute" } ], "keywords": [ "Control education laboratories", "Generative AI in control education", "Control engineering curricula" ], "abstract": "Facilitating active student engagement in control engineering courses presents a significant challenge. The strong mathematical content of most feedback control techniques means that traditional teaching methods may demotivate students and even contribute to dropout rates. Developing complementary teaching strategies to address this issue can therefore be valuable. This work proposes using an agent-based modelling and simulation approach to teach proportional, integral, and derivative (PID) control. A NetLogo multi-agent system that models an artificial rabbit ecosystem is extended to include PID con-trol, providing an engaging tool to demonstrate both open-loop and closed-loop control in a complex sys-tem. Preliminary results are presented to illustrate the benefits of the proposed approach.", "url": "" }, { "id": "Tu-TuA35.3", "code": "TuA35.3", "title": "Boost-Glide Vehicles and Drones: Enlightening Military Examples for Control Education", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA35", "sessionTitle": "Advanced Teaching Methodologies", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Vermeulen, Arthur", "affiliation": "Netherlands Defence Academy" }, { "name": "Savelsberg, Ralph", "affiliation": "Netherlands Defence Academy" } ], "keywords": [ "Control engineering curricula" ], "abstract": "Knowledge of control engineering is a requisite for all technical officers of the armed forces. The present paper presents two typical military examples of a control system which can be used in the officers’ academic curriculum at an introductory level. These examples illustrate not only the importance of control engineering but they also teach the basics of the underlying systems that have to be controlled: (1) a boost-glide vehicle, as launched from a ballistic missile, and (2) the guidance loop of a missile or a drone with remote control and its inherently occurring time delay. Advantages of feedback control over feedforward control and the impact of time delay on the stability of a control system and its performance are highlighted for students.", "url": "" }, { "id": "Tu-TuA35.4", "code": "TuA35.4", "title": "A Backward Design Approach for Integrating Control Systems Education across Engineering Programs: The ABET Experience at Universidad Pontificia Bolivariana", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA35", "sessionTitle": "Advanced Teaching Methodologies", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Vasquez, Rafael E.", "affiliation": "Universidad Pontificia Bolivariana" }, { "name": "Castrillon, Fabio", "affiliation": "Universidad Pontificia Bolivariana" }, { "name": "Ramirez-Macias, Juan A.", "affiliation": "Universidad Pontificia Bolivariana" }, { "name": "Reina-Alzate, Jackson", "affiliation": "Universidad Pontificia Bolivariana" }, { "name": "Taborda, Elkin", "affiliation": "Universidad Pontificia Bolivariana" }, { "name": "Arenas-Castiblanco, Erika", "affiliation": "Universidad Pontificia Bolivariana" }, { "name": "Hincapie-Reyes, Roberto", "affiliation": "Universidad Pontificia Bolivariana" } ], "keywords": [ "Control engineering curricula", "Continuing control education", "Internationalization of control education" ], "abstract": "This paper presents the design of a unified Control Systems course for multiple undergraduate engineering programs at Universidad Pontificia Bolivariana (UPB), Medellín, Colombia. Within ABET accreditation and outcomes-based education (OBE), the School of Engineering initiated a curricular transformation based on Backward Design, moving from capstone courses and constituent commission inputs (industry needs) toward foundational courses. Historically, Control Systems at UPB was offered independently in each program. Through alignment guided by the seven Student Outcomes (SOs) established by the Engineering Accreditation Commission (EAC), the course was redesigned as a transversal component for Aeronautical, Chemical, Electrical/Electronic, and Mechanical Engineering programs. The main achievement was reaching faculty consensus to define common Performance Indicators (PIs) mapped to capstone competencies, leading to unified learning outcomes, educational experiences, and computational tools. Overall, this work presents a school-level curriculum-integration model for Control Systems education, supported by preliminary implementation evidence, assessment alignment, and transferable design principles. The study demonstrates how Backward Design, aligned with ABET outcomes, can unify control systems education while promoting systems thinking, interdisciplinary integration, and practical application.", "url": "" }, { "id": "Tu-TuA35.5", "code": "TuA35.5", "title": "Describing Function: Analysis and Implications for Control Education", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA35", "sessionTitle": "Advanced Teaching Methodologies", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Dormido, Sebastián", "affiliation": "UNED" }, { "name": "Lampón Diestre, Cristina", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "Díaz, Jose Manuel", "affiliation": "UNED" }, { "name": "Costa-Castelló, Ramon", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "Miguel-Escrig, Oscar", "affiliation": "Universitat Jaume I" } ], "keywords": [ "Control engineering curricula", "Control curriculum in elementary/secondary education" ], "abstract": "The describing function (DF) method was introduced in the early 1950s as a natural extension of the Nyquist criterion, allowing for the graphical calculation of limit cycles in nonlinear control systems. DF is an approximate method theoretically based on the harmonic balance method of Krylov and Bogoliuvov. Given the practical importance of limit cycles, it is considered that the study of DF should be included in introductory nonlinear control courses. This paper proposes a method for carrying it using the dual-input describing function (DIDF), which allows for the study of non-autonomous systems. Two feedback structures NL-C(s)-G(s) and C(s)-NL-G(s) are presented that, in certain cases, lead to different calculations of the associated limit cycles. A simple example demonstrates that it is necessary to consider the effective nonlinearity seen by the linear part of the loop, not the actual nonlinearity.", "url": "" }, { "id": "Tu-TuA35.6", "code": "TuA35.6", "title": "Teaching Control Engineering in the Era of AI", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA35", "sessionTitle": "Advanced Teaching Methodologies", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Albertos, Pedro", "affiliation": "Univ. Politecnica De Valencia" }, { "name": "Lu, Shaowen", "affiliation": "Northeastern University" }, { "name": "Fan, Jialu", "affiliation": "Northeastern University" } ], "keywords": [ "Generative AI in control education", "Control engineering curricula", "Continuing control education" ], "abstract": "Control Engineering (CE) has been a matter of research and teaching since more than a century. Both, the subject as well as the way to teach it, have been evolving along the time, with typical characteristics at each moment. Nowadays, the irruption of the Artificial Intelligence (AI) is changing everything and in control it is influencing the subject and the way it should be taught. In this paper, a review of different situations is carried out, analyzing the topics to be learned and how this knowledge should be transferred to the students. Some conclusions are drafted about the current situation, where AI is pervading both, the knowledge and its transmission.", "url": "" }, { "id": "Tu-TuA36.1", "code": "TuA36.1", "title": "Robust Data-Driven Nash Equilibrium Seeking under Disturbances and Equality Constraints (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA36", "sessionTitle": "Next-Generation Control for Urban Systems: Planning, Safety and Resilience", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Wang, Linqi", "affiliation": "Beijing Institute of Technology" }, { "name": "Li, Yifei", "affiliation": "Beijing Institute of Technology" }, { "name": "Liu, Wenjie", "affiliation": "Nanyang Technological University, Singapore" }, { "name": "Wei, Yuzhou", "affiliation": "Beijing Institute of Technology" }, { "name": "Xiao, Wei", "affiliation": "Beijing Institute of Technology" }, { "name": "Wang, Gang", "affiliation": "Beijing Institute of Technology" } ], "keywords": [ "Game theories", "System dynamics and control in CPHS", "Social networks and opinion dynamics" ], "abstract": "This paper addresses the Nash Equilibrium (NE) seeking problem for multi-agent networks characterized by unknown linear dynamics, subject to constant disturbances, partial-decision information, and equality constraints. To tackle this, a novel data-driven framework is proposed. By reformulating the NE seeking problem as a cooperative output regulation task, we design distributed integral controllers directly from noisy input-state data via linear matrix inequalities. Theoretical analysis guarantees both closed-loop stability and asymptotic convergence to the NE. Numerical simulations further validate the robustness and effectiveness of the proposed approach.", "url": "" }, { "id": "Tu-TuA36.2", "code": "TuA36.2", "title": "Resilient Control of Multi-Energy Power Generation in Smart Cities (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA36", "sessionTitle": "Next-Generation Control for Urban Systems: Planning, Safety and Resilience", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Hu, Zhijian", "affiliation": "LAAS-CNRS" }, { "name": "Wang, Changshuo", "affiliation": "University College London" }, { "name": "Ye, Hefu", "affiliation": "University of Macau" }, { "name": "Wang, Mengxin", "affiliation": "Harbin Institute of Technology, Weihai" }, { "name": "Xu, Zeyuan", "affiliation": "University of Pavia" }, { "name": "Ma, Renjie", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Smart city control and optimization", "Smart city security and resilience" ], "abstract": "The growing deployment of renewable and distributed resources in smart cities accelerates the transition toward multi-energy power generation, where electrical, thermal, and storage units are tightly coupled. This integration, however, increases the vulnerability of urban energy systems to renewable fluctuations, load uncertainties, and cyber-physical disruptions. To address these challenges, this paper proposes a resilient control framework for multi-energy power generation in smart cities. The design extends automatic generation control principles to a multi-area, multi-energy context, where local controllers regulate frequency and tie-line exchanges while accounting for inter-energy couplings. By incorporating resilient control techniques, the proposed approach guarantees the system stability in the presence of random load disturbances and feedback signal intermittency. Simulation studies on a four-area smart city system demonstrate that the resilient control strategy enables fast frequency recovery, reduced tie-line oscillations, and effective disturbance rejection compared to conventional counterparts.", "url": "" }, { "id": "Tu-TuA36.3", "code": "TuA36.3", "title": "Differentiable Optimization Layered Safety-Critical Control for Risk-Aware Navigation Via Conformal Prediction (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA36", "sessionTitle": "Next-Generation Control for Urban Systems: Planning, Safety and Resilience", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Dong, Jinyang", "affiliation": "Nankai University" }, { "name": "Wu, Shizhen", "affiliation": "Nankai University" }, { "name": "Fang, Yongchun", "affiliation": "Nankai Univ" } ], "keywords": [ "Safety-critical and resilient systems" ], "abstract": "Risk-aware navigation in unknown environments remains a fundamental challenge for autonomous vehicles in complex urban systems. To address this issue, this paper presents a differentiable optimization layered safety-critical control method based on conformal prediction. Specifically, conformal prediction is used to construct risk-aware obstacle ellipsoids under sensor noise. Then, two nested differentiable optimization layers are introduced to formulate control barrier functions for obstacle avoidance and persistent feasibility, respectively. Finally, a QP-based controller integrates these barrier constraints with input constraints. The effectiveness of the proposed framework is validated through numerical simulations.", "url": "" }, { "id": "Tu-TuA36.4", "code": "TuA36.4", "title": "Open Distributed Task Allocation for Multi-Robot Urban Delivery with Time Windows (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA36", "sessionTitle": "Next-Generation Control for Urban Systems: Planning, Safety and Resilience", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Wu, Xiaoyu", "affiliation": "Zhejiang Normal University" }, { "name": "Zhang, Yan", "affiliation": "Zhejiang Normal University" }, { "name": "Lin, Jie", "affiliation": "Hunan University" }, { "name": "Yang, Mo", "affiliation": "Hunan University" }, { "name": "Zhong, Hang", "affiliation": "Hunan University" }, { "name": "Zhang, Hui", "affiliation": "Hunan University" }, { "name": "Wang, Yaonan", "affiliation": "Hunan University" }, { "name": "Zhang, Wentao", "affiliation": "Hunan University" } ], "keywords": [ "Decision making under uncertainty", "Smart city control and optimization", "Social transportation and social energy" ], "abstract": "This paper studies the multi-robot urban delivery problem when suffering new demand arrival and time-window constraints. To achieve this, a primal decomposition-based open distributed resource allocation mechanism is proposed for handling a large-scale dynamic mixed integer linear programming that is required to be solved without a centralized coordinator. It is shown that pickup-and-delivery tasks can be cooperatively completed while respecting the time window of the delivered demand, thus providing a scalable and flexible routing solution for modern urban logistics. A promising feature of the proposed solution scheme is that robots are enabled to autonomously schedule routes as new demands arrive without violating the time-window constraints that remain challenging using a fleet of autonomous robots. Finally, the effectiveness of the proposed solution scheme is validated with transportation demand data for robot fleets in smart city and low-altitude logistics scenarios.", "url": "" }, { "id": "Tu-TuA36.5", "code": "TuA36.5", "title": "Vulnerability of Open Multi-Agent Systems to Sybil Attacks (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA36", "sessionTitle": "Next-Generation Control for Urban Systems: Planning, Safety and Resilience", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Gao, Jinming", "affiliation": "Tianjin University" }, { "name": "Wang, Yijing", "affiliation": "Tianjin Univ" }, { "name": "Zuo, Zhiqiang", "affiliation": "Tianjin University" }, { "name": "Zhao, Rui", "affiliation": "Tianjin University" }, { "name": "Zheng, Li", "affiliation": "Tianjin University" } ], "keywords": [ "Smart city security and resilience", "Smart city control and optimization", "Cyber-physical urban systems" ], "abstract": "This paper investigates the security issue of smart city model on open multi-agent systems under Sybil attacks. Within the framework of open multi-agent systems, a Sybil attack model is first formulated, where malicious virtual joining agents are introduced. Algebraic connectivity is then employed as a metric to evaluate the effect of Sybil attacks. Quantitative bounds relating the number of Sybil agents to algebraic connectivity are derived. The analysis shows that Sybil attacks cause scale expansion and decrease algebraic connectivity. For path and cycle topologies, we prove that algebraic connectivity approaches zero as the number of Sybil agents tends to infinity, which severely degrades some convergence performance. Simulation results corroborate the theoretical findings.", "url": "" }, { "id": "Tu-TuA36.6", "code": "TuA36.6", "title": "A Privacy-Preserving Distributed Seeking Algorithm for Higher-Order Systems in Smart Cities Game (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:30-11:50", "sessionCode": "TuA36", "sessionTitle": "Next-Generation Control for Urban Systems: Planning, Safety and Resilience", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Wang, Mengxin", "affiliation": "Harbin Institute of Technology, Weihai" }, { "name": "Ma, Baitao", "affiliation": "Harbin Institute of Technology, Weihai" }, { "name": "Qin, Sitian", "affiliation": "Department of Mathematics, Harbin Institute of Technology, Weihai" }, { "name": "Guangliang, Liu", "affiliation": "Bohai University" }, { "name": "Hu, Zhijian", "affiliation": "LAAS-CNRS" }, { "name": "Ye, Hefu", "affiliation": "Nanyang Technological University" } ], "keywords": [ "Smart city control and optimization" ], "abstract": "Multi-agent noncooperative games are pivotal in the construction of smart cities, yet information security during their communication process remains the primary bottleneck. This paper addresses a continuous-time distributed algorithm based on dynamic privacy masking. First, the high-order dynamic attributes of the agents are explicitly considered, and feedback linearization is employed to handle the high-order system. Second, within a continuous-time framework, a privacy-preserving mechanism is developed by designing a time-varying masking function to generate dynamic output perturbations. This mechan is mensures players can effectively conceal their true intentions during strategy updates, thereby enabling information security. Third, the convergence of traditional privacy-preserving algorithms are improved by establishing precise convergence analysis, and enables accurate Nash equilibrium seeking in noncooperative games of high-order player. The noncooperative game among charging stations in a smart-city setting demonstrate the algorithm’s effectiveness.", "url": "" }, { "id": "Tu-TuA37.1", "code": "TuA37.1", "title": "Robust Loop Shaping for HDD Actuator Control under Stroke Constraints (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "09:50-10:10", "sessionCode": "TuA37", "sessionTitle": "High-Performance and Precision Control System Design in HDD Benchmark Models", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Tokuyama, Ryutaro", "affiliation": "Chiba Institute of Technology" }, { "name": "Atsumi, Takenori", "affiliation": "Chiba Institute of Technology" } ], "keywords": [ "High-performance motion control systems" ], "abstract": "With the continuous increase in data-storage capacity requirements for hard disk drives (HDDs), precise and stable magnetic-head positioning has become essential. This study presents a control-design approach for dual-stage actuator systems. The method employs Bode plot analysis to integrate the stroke limitations of Lead Zirconate Titanate (PZT) actuators directly into controller synthesis. These limitations are expressed as forbidden regions on the Bode diagram, providing an intuitive means to assess controller feasibility. The physical stroke constraints are mathematically formulated as inequality conditions and embedded within the design framework. The proposed approach is evaluated on the HDD benchmark problem, and the results demonstrate that the designed controllers satisfy the stroke constraints while maintaining high positioning accuracy and favorable dynamic performance.", "url": "" }, { "id": "Tu-TuA37.2", "code": "TuA37.2", "title": "High-Speed Harmonic Estimation for Asynchronous RRO Compensation Using AFC in HDD Production (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:10-10:30", "sessionCode": "TuA37", "sessionTitle": "High-Performance and Precision Control System Design in HDD Benchmark Models", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Yabui, Shota", "affiliation": "Tokyo City University" }, { "name": "Oswald, Robin", "affiliation": "ETH Zurich" }, { "name": "Atsumi, Takenori", "affiliation": "Chiba Institute of Technology" } ], "keywords": [ "High-performance motion control systems", "Mechatronic system estimation, identification, control", "Mechatronic system modeling, design, optimization" ], "abstract": "This paper proposes a fast harmonic estimation method for asynchronous Repeatable Runout (RRO) compensation in hard disk drive (HDD) manufacturing, using adaptive feed-forward cancellation (AFC) with negative damping. Asynchronous RRO, which occurs at high frequencies and lacks correlation across tracks, poses a risk of track misregistration (TMR) if followed during control. To address this, we introduce a learning algorithm that accelerates the convergence of AFC parameters by applying negative damping, while ensuring system stability through closed-loop design. A total of 59 AFCs are deployed in parallel to learn harmonic components from 5040~Hz to 12000~Hz. Simulation and experimental results demonstrate that the proposed method achieves faster convergence compared to conventional AFC, effectively suppresses asynchronous RRO, and improves manufacturing throughput. Additionally, a learning termination algorithm is introduced to prevent excessive compensation and ensure robust performance.", "url": "" }, { "id": "Tu-TuA37.3", "code": "TuA37.3", "title": "Final-State Control for Track Seeking in Dual-Stage Hard Disk Drives (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:30-10:50", "sessionCode": "TuA37", "sessionTitle": "High-Performance and Precision Control System Design in HDD Benchmark Models", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Hirata, Mitsuo", "affiliation": "Utsunomiya University" } ], "keywords": [ "High-performance motion control systems", "Micro and nano mechatronic systems", "Application of mechatronic principles" ], "abstract": "This study proposes a final-state-control-based method for track-seeking in hard disk drives equipped with a dual-stage actuator, enabling cooperative motion between the VCM and PZT. The VCM is allowed to move over a slightly longer duration than the given seek time, which shifts the feedforward input spectrum toward lower frequencies and reduces resonance excitation. The PZT actuator compensates during the final portion of the seek so that the recording head reaches the target track within the given seek time. The effectiveness of the proposed method is demonstrated using an HDD benchmark model with a dual-stage actuator.", "url": "" }, { "id": "Tu-TuA37.4", "code": "TuA37.4", "title": "Multirate Multi-Modal Model Inversion for Short-Span Track Seeking Control in Dual-Stage Actuator Hard Disk Drives (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "10:50-11:10", "sessionCode": "TuA37", "sessionTitle": "High-Performance and Precision Control System Design in HDD Benchmark Models", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Mae, Masahiro", "affiliation": "The University of Tokyo" } ], "keywords": [ "Mechatronic system estimation, identification, control", "High-performance motion control systems", "Micro and nano mechatronic systems" ], "abstract": "Track seeking control of the magnetic-head positioning system in Hard Disk Drives (HDD) is fundamental for reducing read and write times as well as increasing data storage capacity. The aim of this paper is to design a multirate feedforward controller based on mode decomposition to improve the robust performance of track seeking in HDD. Compared to conventional single-rate feedforward control, the intersample behavior and robust performance against unmodeled dynamics above Nyquist frequency can be improved by the multirate feedforward control with mode decomposition. The performance improvement is validated by a dual-stage actuator HDD benchmark problem in track seeking control.", "url": "" }, { "id": "Tu-TuA37.5", "code": "TuA37.5", "title": "PyHDD Benchmark: A Python-Based Framework for Magnetic-Head Positioning Control Systems in Hard Disk Drives (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "11:10-11:30", "sessionCode": "TuA37", "sessionTitle": "High-Performance and Precision Control System Design in HDD Benchmark Models", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Liu, Zidong", "affiliation": "University of Washington" }, { "name": "Wan, Yusen", "affiliation": "University of Washington" }, { "name": "Lu, Richard", "affiliation": "University of Washington" }, { "name": "Santoso, Amy", "affiliation": "University of Washington" }, { "name": "Hu, Xiaohai", "affiliation": "University of Washington" }, { "name": "Chu, Thomas", "affiliation": "University of Washington" }, { "name": "Jiang, Tianyu", "affiliation": "Western Digital Corporation" }, { "name": "Guo, Guoxiao", "affiliation": "Western Digital Technologies, Inc" }, { "name": "Atsumi, Takenori", "affiliation": "Chiba Institute of Technology" }, { "name": "Chen, Xu", "affiliation": "University of Washington" } ], "keywords": [ "High-performance motion control systems", "Mechatronic system estimation, identification, control", "Mechatronic system modeling, design, optimization" ], "abstract": "Hard disk drives (HDDs) require ultra-precise magnetic-head positioning to meet the demands of high-density data storage, posing significant challenges for control design. This paper presents the first Python-based open-source framework for simulating dual-stage actuator control in HDD servo systems. The framework reproduces key modeling features of practical dual-stage HDDs and introduces reduced-order modeling support. It includes high-fidelity actuator dynamics, realistic disturbance modeling, and multi-rate digital control. Nine configurable benchmark cases are provided to enable robust and reproducible testing of advanced control strategies in precision storage systems. The code is publicly available at: https://github.com/macs-lab/PyHDDBenchmark.", "url": "" }, { "id": "Tu-TuB01.1", "code": "TuB01.1", "title": "Learning and Control in Game Dynamics with Heterogeneous Agents: Introduction (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:20", "sessionCode": "TuB01", "sessionTitle": "Learning and Control in Game Dynamics with Heterogeneous Agents", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Sayin, Muhammed Omer", "affiliation": "Bilkent University" } ], "keywords": [ "Linear system identification" ], "abstract": "Autonomous and adaptive agents are increasingly deployed in shared environments where their decisions are coupled through strategic, informational, and dynamical interactions. While much of multi-agent reinforcement learning and learning-in-games theory assumes that agents follow similar learning rules, real-world systems often involve substantial heterogeneity: agents may differ in their objectives, information access, update rates, computational capabilities, and strategic sophistication. This introductory part motivates the need to study learning algorithms as dynamical systems interacting within games. We will outline why heterogeneity is central in modern cyber-physical and socio-technical systems, including markets, traffic systems, robotic teams, and security-critical infrastructures. The talk will frame the tutorial around a central question: how do differences in learning rules and strategic capabilities affect convergence, performance, and vulnerability? We will then preview the tutorial’s five main themes: convergence of heterogeneous learning dynamics, rationality of learning algorithms, and strategic interactions with fictitious play, no-regret learning, and Q-learning.", "url": "" }, { "id": "Tu-TuB01.5", "code": "TuB01.5", "title": "Strategic Interaction with No Regret Learning (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:20-14:40", "sessionCode": "TuB01", "sessionTitle": "Learning and Control in Game Dynamics with Heterogeneous Agents", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Mu, Yifen", "affiliation": "Academy of Mathematics and Systems Science, Chinese Academy of Sciences" }, { "name": "Dong, Hongcheng", "affiliation": "The Chinese University of Hong Kong, Shenzhen" }, { "name": "Guo, Xinxiang", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Linear system identification" ], "abstract": "Repeated strategic interaction with algorithms is a recent thread of topic in the literature and researchers have shown that characterizing the optimal strategy against even simple algorithms is not a simple task. In this work, we consider the repeated interaction between a learning algorithm and a rational opponent, who aims to optimize his/her long-run utility. We aim to solve explicitly the human’s optimal strategy against two classical and popular learning algorithms, the fictitious play (FP) and Hedge (a.k.a. MWU) in repeated normal-form games. We will construct and prove the globally optimal strategy of the human for some games. We also investigate the corresponding system behavior and show the periodicity of the dynamical systems. Such periodicity can provide a novel asymmetric paradigm to solve the Nash equilibrium and facilitates the study of a broader class of heterogeneous learning dynamics in repeated games.", "url": "" }, { "id": "Tu-TuB01.7", "code": "TuB01.7", "title": "Learning and Control in Game Dynamics with Heterogeneous Agents: Concluding Remarks (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:00-15:10", "sessionCode": "TuB01", "sessionTitle": "Learning and Control in Game Dynamics with Heterogeneous Agents", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Sayin, Muhammed Omer", "affiliation": "Bilkent University" } ], "keywords": [ "Linear system identification" ], "abstract": "This concluding part synthesizes the main lessons from the tutorial on learning and control in game dynamics with heterogeneous agents. Across the tutorial, we have seen that heterogeneity is not merely a technical complication but a defining feature of many adaptive multi-agent systems. Differences in learning rules, information structures, update mechanisms, and strategic sophistication can preserve convergence in some settings, but can also create incentives for deviation, asymmetric advantages, periodic behavior, and exploitable vulnerabilities. The concluding remarks will connect the tutorial’s individual components into a unified perspective: learning algorithms should be analyzed not only as optimization procedures, but also as dynamical systems embedded in strategic environments. We will discuss how control-theoretic tools can help predict, stabilize, or strategically influence such dynamics, and we will highlight open research directions on robust algorithm design, incentive-aware learning, safe autonomy, and resilient multi-agent control. The session will close by emphasizing the need for principled frameworks that account for heterogeneity when designing future learning-enabled control systems.", "url": "" }, { "id": "Tu-TuB02.1", "code": "TuB02.1", "title": "Design of a Performance-Driven Control System Using Database-Driven Approach", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:15", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Li, Zhifeng", "affiliation": "Hiroshima University" }, { "name": "Kinoshita, Takuya", "affiliation": "Hiroshima University" }, { "name": "Yamamoto, Toru", "affiliation": "Hiroshima Univ" }, { "name": "Shah, Sirish L.", "affiliation": "University of Alberta" } ], "keywords": [ "Adaptive control design", "Design methods for data-based control", "Nonlinear time-delay systems" ], "abstract": "Most process systems are difficult to control due to nonlinearity, leading to the proposal of database-driven control for sequential reference trajectory tracking and regulation. However, adjusting PID control parameters at each sampling interval is unnecessary and causes inefficiency and potential safety issues. This paper first introduces control performance evaluation using generalized minimum variance and proposes a control system that accounts for the variance of both the reference trajectory and the manipulative variable. The effectiveness of the proposed method is quantitatively verified using a simulated example of a nonlinear system with a time delay and varying process gain plus time constant.", "url": "" }, { "id": "Tu-TuB02.2", "code": "TuB02.2", "title": "Extremum Seeking Control Design for a Class of Second-Order Nonlinear Systems with Unknown Control Direction", "day": "Tuesday", "date": "August 25, 2026", "time": "13:15-13:20", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Guay, Martin", "affiliation": "Queen's Univ" }, { "name": "Wang, Shimin", "affiliation": "Massachusetts Institute of Technology" } ], "keywords": [ "Adaptive control design", "Design methods for data-based control", "Optimization-based estimation and control" ], "abstract": "Fast extremum seeking is difficult for second-order plants when the control direction, the drift dynamics, and the optimizer are all unknown. This paper develops a dynamic output-feedback design for this setting using only measurements of the objective function. The proposed controller extends the dual-mode extremum-seeking idea to a class of second-order nonlinear systems by combining an observer-based dynamic extension with a Lie-bracket averaged dither transformation. The averaged closed loop has a simple cascade structure: the optimizer coordinate is driven by a gradient-like term, while the unknown plant dynamics enter through a stabilizable observer-error subsystem. Under explicit gain conditions, the averaged closed loop is shown to be globally exponentially stable. For the exact high-frequency realization, the result is stated as semiglobal practical uniform asymptotic stability with respect to a moving corrected set, which accounts for the fast oscillatory components introduced by fixed-amplitude dithering. This yields practical regulation of the optimizer coordinate and of the measured objective without requiring the sign of the input gain. An attenuated unbiased variant is also discussed as a route toward asymptotic convergence. Simulations illustrate the controller behaviour and the expected fast oscillations in the physical velocity.", "url": "" }, { "id": "Tu-TuB02.3", "code": "TuB02.3", "title": "Integral Concurrent Learning for Natural Adaptive Control of Robotic Manipulators", "day": "Tuesday", "date": "August 25, 2026", "time": "13:20-13:25", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Kaufmann, Tom", "affiliation": "TU Ilmenau" }, { "name": "Reger, Johann", "affiliation": "TU Ilmenau" } ], "keywords": [ "Adaptive control design", "Lyapunov methods" ], "abstract": "Natural adaptive control enables tracking with an estimation regime that respects physical constraints. Here, we provide a more detailed characterization of natural adaptation, proving its matrix estimates to be uniformly physically consistent and upper bounded. For certain kinematic layouts, these newly established properties guarantee the desirable existence of finite, positive uniform bounds of the estimated mass matrix. Moreover, we propose a data-driven augmentation of the natural update law so that—provided a finite excitation condition is fulfilled—estimation errors converge to zero, leading to uniformly physically consistent, precise estimation. Simulation of a 3-dof robotic manipulator with 2 rigid bodies verifies the theoretical findings.", "url": "" }, { "id": "Tu-TuB02.4", "code": "TuB02.4", "title": "Adaptive Parameter Identification of Indoor Microclimate Model", "day": "Tuesday", "date": "August 25, 2026", "time": "13:25-13:30", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Rassadin, Yuriy M", "affiliation": "Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences" }, { "name": "Orlov, Yury", "affiliation": "CICESE" } ], "keywords": [ "Adaptive control design", "Lyapunov methods", "Sliding mode control" ], "abstract": "A refined model of air temperature dynamics is considered for more efficient control of indoor microclimate. Along with air temperature dynamics, normally available to direct measurement, average temperature of enclosing surfaces (walls, ceiling, floor, etc.), referred to as mean radiant temperature, is involved into modelling. Since radiant temperature measurements are not as common as traditional air temperature measurements, while heat transfer coefficient between indoor air and surfaces, generating the mean radiant temperature, is neither available, their online estimation is a challenging problem. This problem is addressed in the present work. Based on the air temeprature measurmenets, a sliding mode observer of the mean radiant temperature and an adaptive plant parameter identifier are developed for the underlying indoor microclimate model. Capabilities of the proposed design and its robustness features are further illustrated in a numerical study.", "url": "" }, { "id": "Tu-TuB02.5", "code": "TuB02.5", "title": "Selection of Design Variables and Durability Improvement for a 55 kW Compound Planetary Geartrain Electric Tractor", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:35", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Park, Minjong", "affiliation": "Chungnam National University" }, { "name": "Jeong, Gubin", "affiliation": "Chungnam National University" }, { "name": "Kim, Yong-Joo", "affiliation": "Chungnam National University" } ], "keywords": [ "Analytic design", "Design methods for data-based control" ], "abstract": "This study optimized a 55-kW electric tractor powertrain by fixing the gear geometry and varying the design parameters, including planet gear material grade, heat treatment, surface roughness, spiral bevel module, and face width. We used Latin hypercube sampling to generate feasible candidates, and simulations were conducted to evaluate contact and bending safety factors under a measured load-duration spectrum. Three planet gear configurations improved contact safety by approximately 10% and bending safety by 4-6% across both planetary stages. Combinations with significant degradations were eliminated using a minimum safety factor of 1.10. At the system level, the spiral bevel pair was identified as the bottleneck; the optimal configuration enhanced contact safety by about 6-7% and bending safety by approximately 10%, achieving the highest overall ranking. These improvements resulted from changes in material, heat treatment, and surface finish, which strengthened surface and root durability without altering geometry or increasing meshing losses, thus ensuring robust performance across various load conditions.", "url": "" }, { "id": "Tu-TuB02.6", "code": "TuB02.6", "title": "Behavioral Stability Certification of Koopman-Lifted Controllers from Persistently Exciting Data", "day": "Tuesday", "date": "August 25, 2026", "time": "13:35-13:40", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Jain, Tushar", "affiliation": "Indian Institute of Technology Mandi" } ], "keywords": [ "Analytic design", "Design methods for data-based control", "Lyapunov methods" ], "abstract": "A data-driven framework is proposed for certifying static state-feedback stabilisers of control-affine nonlinear systems without identifying a parametric model. The state is lifted into a finite-dimensional observable space via a fixed Koopman dictionary, and persistently exciting open-loop experiments yield Hankel matrices that parametrise local closed-loop trajectories. For any candidate feedback gain, a data-induced closed-loop matrix is extracted and its Schur stability is verified via a discrete Lyapunov equation, whose solution constitutes a contraction metric in the lifted space. The framework is validated on an inverted pendulum, achieving local exponential stabilisation purely from experimental data.", "url": "" }, { "id": "Tu-TuB02.7", "code": "TuB02.7", "title": "Model-Free Practical PI-Lead Control Design by Ultimate Sensitivity Principle", "day": "Tuesday", "date": "August 25, 2026", "time": "13:40-13:45", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Ruderman, Michael", "affiliation": "University of Agder" } ], "keywords": [ "Analytic design", "Structured linear systems", "Real-time optimal control" ], "abstract": "Practical design and tuning of feedback controllers has often to get by without a model of the dynamic process at hand. Only some general assumptions about the system dynamics, in this work type-one stable, can be available for engineers, for instance in motion control applications and many others. This paper proposes a practical and simple in realization procedure for designing a robust PI-Lead control without modeling. The developed method derives from the ultimate sensitivity principles, known in empirical Ziegler–Nichols tuning of PID controllers, and makes use of some general characteristics of the loop shaping. A three-steps procedure is proposed to determine the integration time constant, control gain, and Lead-element in a way to guarantee a sufficient phase margin, while all steps are served by only experimental monitoring of the output value. Proposed method is demonstrated and discussed with experiments accomplished on a noise-perturbed electro-mechanical actuator system.", "url": "" }, { "id": "Tu-TuB02.8", "code": "TuB02.8", "title": "Necessary and Sufficient PID Gain Regions for Global Stabilization of Uncertain Second-Order MIMO Nonlinear Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "13:45-13:50", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Xiang, Tianyou", "affiliation": "AMSS, Chinese Academy of Science" }, { "name": "Zhao, Cheng", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Analytic design", "Uncertain systems", "Lyapunov methods" ], "abstract": "As is well known, classical PID control is ubiquitous in industrial processes, yet a rigorous and explicit design theory for nonlinear uncertain MIMO second-order systems remains underdeveloped. In this paper we consider a class of such systems with both uncertain dynamics and an unknown but strictly positive input gain, where the nonlinear uncertainty is characterized by bounds on the Jacobian with respect to the state variables. We explicitly construct a three-dimensional region for the PID gains that is sufficient to guarantee global stability and asymptotic tracking of constant references for all nonlinearities satisfying these Jacobian bounds. We then derive a corresponding necessary region, thereby revealing the inherent conservatism required to cope with worst-case uncertainties. Moreover, under additional structural assumptions on the nonlinearities, these sufficient and necessary regions coincide, yielding a precise necessary-and-sufficient characterization of all globally stabilizing PID gains. All these regions are given in closed form and depend only on the prescribed Jacobian bounds and the known lower bound of the input gain, in contrast to many qualitative tuning methods in the literature.", "url": "" }, { "id": "Tu-TuB02.9", "code": "TuB02.9", "title": "Adaptive Iterative Learning Control for Underactuated Surface Vessel under Constrained Uncertain Environments (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-13:55", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Huang, Xiuying", "affiliation": "Sun Yat-Sen University" }, { "name": "Li, Xuefang", "affiliation": "Sun Yat-Sen University" }, { "name": "Li, Xiaodong", "affiliation": "Sun Yat-Sen University" } ], "keywords": [ "Control barrier functions and state space constraints", "Adaptive control design", "Uncertain systems" ], "abstract": "In this paper, an adaptive iterative learning control method is proposed to address the trajectory tracking problem for underactuated surface vessel under constrained uncertain environments. In order to achieve the high-precision tracking tasks while ensuring the satisfaction of physical constraints, two different parametric updating laws and an iteration dependent barrier Lyapunov function are introduced, which are effective to deal with the system uncertainties and constraints. The convergence of the proposed control strategy is rigorously analyzed through the composite energy function method. Numerical simulations are provided to demonstrate the effectiveness and robustness of the proposed control method.", "url": "" }, { "id": "Tu-TuB02.10", "code": "TuB02.10", "title": "Closed-Loop State Estimation from Spiking-Neuron Populations", "day": "Tuesday", "date": "August 25, 2026", "time": "13:55-14:00", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Göral, Erdem", "affiliation": "Hacettepe University" }, { "name": "Boyacioglu, Burak", "affiliation": "Middle East Technical University" }, { "name": "Uyanik, Ismail", "affiliation": "Hacettepe University" } ], "keywords": [ "Control in neuroscience", "Observer design" ], "abstract": "Biological nervous systems perform estimation and control using sensory feedback encoded as sparse spike trains rather than continuous-valued measurements. Inspired by this principle, we develop a closed-loop state estimation framework that reconstructs task-related state variables directly from spiking-neuron populations. The proposed architecture decomposes relative position and velocity signals into complementary subpopulations of Leaky Integrate-and-Fire neurons, whose spike timings are converted into causal firing-rate estimates. These neural responses are decoded using a maximum-likelihood population estimator, and subsequently fused through a Kalman Filter to yield smooth estimates of the underlying tracking error suitable for feedback control. We evaluate the framework in a reference-tracking task modeled after the refuge-tracking behavior of weakly electric fish. Simulation results demonstrate that spiking-neuron populations provide sufficient information to estimate both position and velocity values and enable stable closed-loop performance using a conventional proportional–derivative controller. By showing how spike-based sensory representations can be transformed into actionable state estimates, this work establishes a control-theoretic foundation for integrating neural encoding mechanisms into state observers, with implications for neuromorphic sensing, active perception, and brain–machine interface design.", "url": "" }, { "id": "Tu-TuB02.11", "code": "TuB02.11", "title": "Uncertain Anesthesia Dynamics Control with Stochastic Optimization and Data Stratification", "day": "Tuesday", "date": "August 25, 2026", "time": "14:00-14:05", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Ajami, Mohamad", "affiliation": "GIPSA-Lab" }, { "name": "Dang, Thao", "affiliation": "VERIMAG" }, { "name": "Fiacchini, Mirko", "affiliation": "GIPSA-Lab, CNRS" } ], "keywords": [ "Control in system biology", "Probabilistic robustness" ], "abstract": "This paper presents a stochastic optimization framework with data stratification for the control of uncertain anesthesia systems. The proposed approach enables control design with probabilistic performance guarantees under minimal distributional assumptions. To mitigate interpatient variability, patients are stratified into relatively homogeneous subgroups, and a dedicated controller is optimized for each. In this study, PID controllers are optimized for propofol infusion during the induction phase, using a delayed and noisy BIS feedback signal. Chance constraints are incorporated to limit the probability of BIS undershoot.", "url": "" }, { "id": "Tu-TuB02.12", "code": "TuB02.12", "title": "Spatiotemporal Tubes Based Controller Synthesis against Omega-Regular Specifications for Unknown Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "14:05-14:10", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Das, Ratnangshu", "affiliation": "Indian Institute of Science, Bangalore" }, { "name": "Bayezeed, Aiman Aatif", "affiliation": "Indian Institute of Science, Bengaluru" }, { "name": "Jagtap, Pushpak", "affiliation": "Indian Institute of Science" } ], "keywords": [ "Control of hybrid systems", "Controller constraints and structure" ], "abstract": "This paper provides a discretization-free solution to the synthesis of approximation-free closed-form controllers for unknown nonlinear systems to enforce complex properties expressed by omega-regular languages, as recognized by Non-deterministic B{\"u}chi Automata (NBA). In order to solve this problem, we first decompose NBA into a sequence of reach-avoid (RA) problems, which are solved using the Spatiotemporal Tubes (STT) approach. Controllers for each RA task are then integrated into a hybrid policy that ensures the fulfillment of the desired omega-regular properties. We validate our method through case studies on omnidirectional robot navigation and manipulator control.", "url": "" }, { "id": "Tu-TuB02.13", "code": "TuB02.13", "title": "H∞ Fault-Compensation Control with Transients for Continuous-Time Markovian Jump Linear", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:15", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "de Oliveira, André Marcorin", "affiliation": "UNIFESP" }, { "name": "Costa, Oswaldo Luiz do Valle", "affiliation": "Univ. of Sao Paulo" } ], "keywords": [ "Control of hybrid systems", "Stochastic optimal control problems", "Robust linear matrix inequalities" ], "abstract": "This paper presents an H∞ fault-compensation control strategy considering transient behavior for continuous-time Markovian Jump Linear Systems (MJLS). A dual-controller architecture is employed, where a nominal controller governs normal operation and an auxiliary dynamic controller compensates for faults when they occur. The proposed design guarantees mean-square stability (MSS) and H∞ performance, including transient effects, by solving a set of Linear Matrix Inequality (LMI) conditions. Unlike traditional fault-tolerant control schemes, the approach explicitly incorporates nominal control information into the compensation design, so that the resulting controller activates only under faulty modes. Simulation results demonstrate the method’s effectiveness and potential for reliable operation in fault-prone networked and industrial systems.", "url": "" }, { "id": "Tu-TuB02.14", "code": "TuB02.14", "title": "Dual Mode-Dependent Stabilization Control for Continuous-Time Hybrid Switched Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "14:15-14:20", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Zhang, Jian", "affiliation": "Southeast University, Shandong University of Science and Technology" }, { "name": "Zhu, Yanzheng", "affiliation": "Shandong University of Science and Technology" }, { "name": "Yang, Rongni", "affiliation": "Shandong University" }, { "name": "Zhi, Xiyang", "affiliation": "Harbin Institute of Technology" }, { "name": "Zhang, Lixian", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Control of hybrid systems", "Switching stability and control", "Switching linear systems" ], "abstract": "This paper further studies the stabilization problem for hybrid switched linear systems with state-dependent switching and dwell time constraint. Based on the previous mode information, the dual mode-dependent (DMD) controller is designed instead of the existing mode-dependent controller, resulting in the DMD Lyapunov function and DMD switching signals, which can enhance the control performance and design freedom. Moreover, a multiple discontinuous Lyapunov function (MDLF) is developed to overcome the restriction of existing results that require the Lyapunov function to be continuous during the dwell time stage. Meanwhile, without the discontinuous control gain behavior accompanying the existing MDLF methods, the designed control gain is time-varying and continuous during the dwell time stage, which avoids the problem of frequent control bumps. Then, the stabilization criterion and the solvability conditions are derived to ensure the stability of the system. Finally, the simulation results are presented to show the benefits of the proposed method.", "url": "" }, { "id": "Tu-TuB02.15", "code": "TuB02.15", "title": "Reachability-Based Decoupling Control Scheme of Periodic Time-Varying Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "14:20-14:25", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Ling, Zhaoji", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Xie, Xiaochen", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Wang, Binbin", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Lam, James", "affiliation": "Univ of Hong Kong" } ], "keywords": [ "Controller constraints and structure", "Lyapunov methods", "Optimization-based estimation and control" ], "abstract": "This paper investigates the control of continuous-time periodic systems from the perspective of reachability. Compared with existing studies relying on piecewise linear models of periodic dynamics, our approach can relax the demands on modeling accuracy. It is proposed as a continuous-function-based framework to model time-varying dynamics, offering greater flexibility for practical applications. While the existing approaches primarily focus on guaranteeing asymptotic stability, they generally neglect transient performance. To address this limitation, we introduce a procedure inspired by reachable set estimation to impose explicit time-varying constraints on the closed-loop system's state trajectory, further employing a multi-affine approach to derive equivalent linear matrix inequality constraints. Finally, our proposed approach is validated in an equivalent magnetic levitation demonstration system.", "url": "" }, { "id": "Tu-TuB02.16", "code": "TuB02.16", "title": "Safety Control of Second-Order Nonlinear Systems under DoS Attacks", "day": "Tuesday", "date": "August 25, 2026", "time": "14:25-14:30", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Song, Ruolin", "affiliation": "Tongji University" }, { "name": "Wang, Tianqi", "affiliation": "The Hong Kong Polytechnic University" }, { "name": "Xin, Bin", "affiliation": "Beijing Institute of Technology" }, { "name": "Wang, Qing", "affiliation": "Beijing Institute of Technology" }, { "name": "Dong, Yi", "affiliation": "Tongji University" }, { "name": "Chen, Xi", "affiliation": "The Chinese University of Hong Kong" } ], "keywords": [ "Controller constraints and structure", "Output regulation and tracking", "Stability of nonlinear systems" ], "abstract": "In this paper, we study the safety and security control problem of a class of second-order nonlinear systems with output constraint and denial-of-service (DoS) attacks. By incorporating an internal model-based controller, a barrier function-based framework is incorporated to enforce the output to a prescribed safety set. Then, a DoS-resilient compensation mechanism is devised to mitigate the impact of communication interruptions on closed-loop behavior. A novel series of sufficient conditions is derived to guarantee the boundedness of the closed-loop trajectories, the satisfaction of constraints, and the convergence of the tracking error. A numerical example is provided to illustrate the effectiveness of the proposed control scheme.", "url": "" }, { "id": "Tu-TuB02.17", "code": "TuB02.17", "title": "Combining Extensional and Intensional Approaches for Logic Controller Design: Application to Tasks Synchronization", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:35", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Roisin, Mathieu", "affiliation": "Université De Reims Champagne Ardenne CReSTIC EA3804" }, { "name": "Annebicque, David", "affiliation": "University of Reims - URCA - IUT De Troyes" }, { "name": "Riera, Bernard", "affiliation": "Université De Reims Champagne Ardenne CReSTIC EA3804" }, { "name": "Pierre-Alain, Yvars", "affiliation": "ISAE-Supmeca" } ], "keywords": [ "Controller constraints and structure", "Robust controller synthesis" ], "abstract": "This paper focuses on controller synthesis and the automatic generation of IEC 61131-3 Structured Text (ST) code. Usually, the control engineer uses an extensional approach to specify the logic controller. The principle consists of explicitly modelling the solution (e.g., with GRAFCET or Petri nets). This approach does not enable the engineer to validate the solution. Another approach for solving a problem is to define the solution space through rules or constraints having to be satisfied. This intensional approach, is less used today in industry to design controllers. In this paper, we argue that combining both approaches could be more efficient and robust for control design. Although a workflow exists to integrate them and generate ST code, it lacks a clear definition and methodology. To address this, we propose a structured approach to model the synthesis problem using the DEPS language that can be connected to the existing approach to generate ST code. The approach is illustrated by a case study of the control of a converging conveyor system.", "url": "" }, { "id": "Tu-TuB02.18", "code": "TuB02.18", "title": "Asymmetric Saturation Handling in Fixed-Tilt Hexarotors Via Optimized Shifted Stabilizer", "day": "Tuesday", "date": "August 25, 2026", "time": "14:35-14:40", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Jayanna, Dharani", "affiliation": "Politecnico Di Milano" }, { "name": "Invernizzi, Davide", "affiliation": "Politecnico Di Milano" }, { "name": "Lovera, Marco", "affiliation": "Politecnico Di Milano" }, { "name": "Zaccarian, Luca", "affiliation": "LAAS-CNRS and University of Trento" } ], "keywords": [ "Controller constraints and structure", "Saturation and discontinuity", "Lyapunov methods" ], "abstract": "This paper presents an anti-windup (AW) strategy for fixed-tilt hexarotors operating under direction-dependent thrust constraints that lead to actuator saturation. The proposed method augments a baseline pose controller with a shifted-equilibrium mechanism that enlarges the region of attraction through feasible non-zero equilibria under saturation. A discrete-time AW synthesis is developed by combining a Lyapunov-based direct linear AW design with a convex quadratically constrained quadratic program (QCQP) for selecting equilibrium shifts consistent with the asymmetric actuator limits. The resulting closed-loop system achieves local exponential stability over an enlarged region-of-attraction estimate while limiting attitude transients, which is essential for contact-rich aerial interaction. Simulations on a fully modeled fixed-tilt hexarotor demonstrate improved tracking and reduced attitude deviations compared with a conventional AW scheme.", "url": "" }, { "id": "Tu-TuB02.19", "code": "TuB02.19", "title": "On the Stabilization of Rigid Formations on Regular Curves", "day": "Tuesday", "date": "August 25, 2026", "time": "14:40-14:45", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Elobaid, Mohamed", "affiliation": "King Abdullah University of Science and Technology" }, { "name": "Park, Shinkyu", "affiliation": "King Abdullah University of Science and Technology" }, { "name": "Feron, Eric", "affiliation": "King Abdullah University of Science and Technology" } ], "keywords": [ "Decentralized control", "Application of nonlinear analysis and design" ], "abstract": "This work deals with the problem of stabilizing a multi-agent rigid formation on a general class of planar curves. Namely, we seek to stabilize an equilateral polygonal formation on closed planar differentiable curves after a path sweep. The task of finding an inscribed regular polygon centered at the point of interest is solved via a randomized multi-start Newton-Like algorithm for which one is able to ascertain the existence of a minimizer. Then we design a continuous feedback law that guarantees convergence to, and sufficient sweeping of the curve, followed by convergence to the desired formation vertices while ensuring inter-agent avoidance. The proposed approach is validated through numerical simulations for different classes of curves and different rigid formations. Code: https://github.com/mebbaid/paper-elobaid-ifacwc-2026", "url": "" }, { "id": "Tu-TuB02.20", "code": "TuB02.20", "title": "A Resilient Distributed Personalized Optimization Algorithm against Byzantine Attacks", "day": "Tuesday", "date": "August 25, 2026", "time": "14:45-14:50", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Shen, Yigao", "affiliation": "Zhejiang University" }, { "name": "Zhao, Chengcheng", "affiliation": "Zhejiang University" } ], "keywords": [ "Decentralized control", "Convex optimization", "Optimization-based estimation and control" ], "abstract": "Distributed personalized optimization (DPO) has demonstrated significant potential in distributed learning where each agent maintains a global variable capturing shared features and a local variable reflecting personalization. However, whether and how we can design resilient algorithms for distributed personalized optimization against Byzantine attacks in fully distributed scenarios remains an open issue. To solve this issue, we propose a resilient gradient descent DPO algorithm, utilizing Local Filtering (LF) dynamics which discards the F (F is the maximum tolerable number of the compromised agents) largest and F smallest state values from in-neighbor agents for each dimension to update the global variable iteratively. We derive novel sufficient conditions to guarantee the linear convergence of the proposed algorithm for the cases with a strongly convex objective function. Numerical results are presented to validate the theoretical findings.", "url": "" }, { "id": "Tu-TuB02.21", "code": "TuB02.21", "title": "A Data-Based System Representation: The Stabilization Problem", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-14:55", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Szabo, Zoltan", "affiliation": "HUN-REN SZTAKI" }, { "name": "Bokor, Jozsef", "affiliation": "Hungarian Academy of Sciences" }, { "name": "Gaspar, Peter", "affiliation": "HUN-REN SZTAKI, Institute for Computer Science and Control, Hungarian Research Network" }, { "name": "Bauer, Peter", "affiliation": "HUN-REN Institute for Computer Science and Control" } ], "keywords": [ "Design methods for data-based control", "Linear systems", "Observer design" ], "abstract": "In our previous work a system representation formed by a minimal collection of sufficiently long restricted trajectories generated by an observable discrete time LTI system was proposed and conditions were given under which such a collection is a system representation. This paper addresses the problem of stabilizability in terms of the proposed data-based representation, and the construction of the stabilizing controller is also provided. It turns out that the entire problem can be reduced to a suitable state feedback design. A method for state reconstruction and observer design is also proposed.", "url": "" }, { "id": "Tu-TuB02.22", "code": "TuB02.22", "title": "Repowering Obsolete Helicopter Testbeds: A Reproducible Framework for Modern Control Education and Applications", "day": "Tuesday", "date": "August 25, 2026", "time": "14:55-15:00", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Salazar, Carlos Alberto", "affiliation": "Escuela Superior Politecnica Del Litoral, ESPOL" }, { "name": "Aguirre, Adriana", "affiliation": "Escuela Superior Politécnica Del Litoral" }, { "name": "Rodriguez Gonzalez, Mario Gustavo", "affiliation": "Escuela Superior Politecnica Del Litoral" }, { "name": "Suárez Matias, José Santiago", "affiliation": "Escuela Superior Politécnica Del Litoral" } ], "keywords": [ "Digital implementation", "Model validation" ], "abstract": "Obsolescence of didactic control platforms is a growing challenge in academic laboratories, limiting their use in both teaching and research. This paper presents a reproducible framework for repowering and optimizing a two-axis helicopter testbed, transforming an inoperative setup into a real-time compatible platform for modern control education and experimentation. The proposed methodology combines hardware reengineering, embedded electronics, and software integration through an ESP32-based acquisition system, custom PCBs, high-resolution sensors, and bidirectional serial communication with MATLAB® and SIMULINK®. Experimental validation demonstrates significant improvements in operating range, measurement robustness, sampling frequency, and communication latency compared with the legacy configuration. These enhancements enable the implementation of advanced control techniques, including state-space feedback, observer-based control, and model predictive control (MPC), which require accurate sensing and deterministic real-time operation. Beyond restoring functionality, the proposed framework provides a transferable modernization strategy for other obsolete laboratory platforms, such as inverted pendulums, rotary arms, gimbal systems, and underactuated robotic testbeds. The approach therefore bridges theory and practice while extending the useful life of educational platforms and supporting next-generation training and research in automatic control.", "url": "" }, { "id": "Tu-TuB02.23", "code": "TuB02.23", "title": "Bee Hive Monitoring System Based on Capacitive Sensors (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:00-15:05", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Zebrowski, Tomasz", "affiliation": "Warsaw University of Technology" }, { "name": "Domanski, Pawel Dariusz", "affiliation": "Warsaw University of Technology" } ], "keywords": [ "Digital implementation", "Supervision and testing", "Sampled-data/digital control" ], "abstract": "This paper presents a simple, low-cost bee hive monitoring system based on capacitive sensors for reliably detecting and counting individual bees. The system employs a novel approach to signal acquisition using a microcontroller to approximate the charging time of two ring capacitors within a bee tunnel, which form the core of the sensor. The change in capacitance, caused by a bee's high relative electrical permittivity, allows for the determination of its presence and direction of movement (entering or leaving the hive). The system's hardware design avoids complex, high-cost signal-measurement circuits, making it accessible to smaller apiaries. Two bee detection algorithms were developed and tested. Validation, including laboratory tests with bee models and site testing against video-annotated ground truth, demonstrated the functionality of the proposed sensor and algorithms. While the device successfully approximates the intensity of forager traffic, its overall accuracy is limited by abnormal bee behaviours (grouping, stopping, or turning within the sensor tunnel). Future research will explore multi-gate designs and data fusion techniques to improve counting reliability and provide a more precise estimate of colony population.", "url": "" }, { "id": "Tu-TuB02.24", "code": "TuB02.24", "title": "PGOA-MN: A Multiscale Network with Physics-Guided Orthogonal Attention for Aluminum Leakage Detection", "day": "Tuesday", "date": "August 25, 2026", "time": "15:05-15:10", "sessionCode": "TuB02", "sessionTitle": "Shotgun: Control Design", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Peng, Junhui", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Liu, Qi", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Liu, Yuxiang", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Yang, Bo", "affiliation": "Department of Automation, Shanghai Jiao Tong University, Shanghai" } ], "keywords": [ "Fault detection and isolation" ], "abstract": "Industrial AI solutions for molten aluminum leakage detection face challenges in maintaining long-term stability across dynamic factory environments and generalizing across multiple facilities. This paper proposes PGOA-MN, a multiscale network with physics-guided orthogonal attention that integrates physical knowledge with deep learning. The architecture employs dual-channel spectrogram processing with multiscale temporal modeling for comprehen\u0002sive feature extraction. Physics-guided attention leverages domain-specific features to focus on anomaly patterns, while orthogonal attention captures complementary temporal and energetic characteristics. This approach maintains detection accuracy despite environmental variations in single-factory deployments and achieves strong cross-factory generalization without retraining. Extensive validation in real aluminum production environments demonstrates that PGOA-MN effectively resolves critical challenges and provides a reliable industrial safety monitoring solution.", "url": "" }, { "id": "Tu-TuB03.1", "code": "TuB03.1", "title": "Fault Tolerant Control of Mecanum Wheeled Mobile Robots", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:15", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Ma, Xuehui", "affiliation": "Xi'an University of Technology" }, { "name": "Zhang, Shiliang", "affiliation": "University of Oslo" }, { "name": "Zhou, Panpan", "affiliation": "University of Galway" }, { "name": "Sun, Zhiyong", "affiliation": "Peking University (PKU)" } ], "keywords": [ "Adaptive and robust control of automotive systems", "Autonomous mobile robots" ], "abstract": "Mecanum wheeled mobile robots (MWMRs) are highly susceptible to actuator faults that degrade performance and risk mission failure. Current fault tolerant control (FTC) schemes for MWMRs target complete actuator failures like motor stall, ignoring partial faults e.g., in torque degradation. We propose an FTC strategy handling both fault types, where we adopt posterior probability to learn real-time fault parameters. We derive the FTC law by aggregating probability-weighed control laws corresponding to predefined faults. This ensures the robustness and safety of MWMR control despite varying levels of fault occurrence. Simulation results demonstrate the effectiveness of our FTC under diverse scenarios.", "url": "" }, { "id": "Tu-TuB03.2", "code": "TuB03.2", "title": "Active Disturbance Rejection Control of a Pneumatically Actuated Clutch", "day": "Tuesday", "date": "August 25, 2026", "time": "13:15-13:20", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Prabel, Robert", "affiliation": "University of Rostock" }, { "name": "Aschemann, Harald", "affiliation": "University of Rostock" } ], "keywords": [ "Adaptive and robust control of automotive systems", "Engine and powertrain modeling and control", "Automotive system identification and modelling" ], "abstract": "The paper presents a model-free robust control approach for the position of a pneumatically actuated clutch that is used in trucks. For simulation purposes, an overall system model is established based on physical principals addressing the dynamics of the pneumatic subsystem as well as the mechanical system part. Here, characteristics are identified for the pneumatic valves as well as the clutch spring. The proposed control structure is cascaded and involves a fast pressure control in the inner loop. The outer loop is affected by model uncertainty due to a pronounced hysteresis of the clutch spring. Therefore, a model-free active disturbance rejection control (ADRC) based on an extended state observer (ESO) is employed in the outer loop and provides robustness as emphasized by both simulations and experimental results at a test rig.", "url": "" }, { "id": "Tu-TuB03.3", "code": "TuB03.3", "title": "Vehicle Parameter Estimation Using Deep Neural Networks with Long Short-Term Memory", "day": "Tuesday", "date": "August 25, 2026", "time": "13:20-13:25", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Hain, Sören", "affiliation": "University of Stuttgart" }, { "name": "Beyer, Kimon", "affiliation": "University of Stuttgart" }, { "name": "Sawodny, Oliver", "affiliation": "Univ of Stuttgart" } ], "keywords": [ "AI and learning-based control for automotive systems", "Automotive system identification and modelling", "Electric and solar vehicles" ], "abstract": "Longitudinal vehicle parameter estimation of the mass, rolling resistance coefficient and drag area (cd*A) are of crucial importance for energy consumption prediction. Energy consumption prediction is especially important for electric vehicles (EV), since EVs have a smaller range and longer charging time compared to gasoline powered vehicles. This paper proposes an iterative machine learning algorithm for longitudinal vehicle parameter estimation. The validation is carried out with real-world measurement data from test drives with different vehicle configurations that highlight the applicability.", "url": "" }, { "id": "Tu-TuB03.4", "code": "TuB03.4", "title": "Physics-Informed Machine Learning for Integrated Longitudinal and Lateral Dynamics Modeling of Liquid Tank Trucks", "day": "Tuesday", "date": "August 25, 2026", "time": "13:25-13:30", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Tian, Liheng", "affiliation": "Southeast University" }, { "name": "Wei, Wenpeng", "affiliation": "Southeast University" } ], "keywords": [ "AI and learning-based control for automotive systems", "Automotive system identification and modelling", "Vehicle dynamic systems" ], "abstract": "Liquid sloshing in partially filled tanks poses a major challenge to accurately model liquid tank truck (LTT) dynamics. Traditional physics-based methods often require time- consuming and costly offline calibration, while recent data-driven methods lack interpretability and struggle to generalize across operating cases. This paper introduces a physics-informed machine learning (PIML) framework for integrated longitudinal and lateral dynamics modeling of a LTT. The framework connects a structured physical parameters estimator and a single- track vehicle dynamics model in series, enabling online joint estimation of time-varying physical parameters and vehicle states due to irregular motion introduced by liquid sloshing. To collect sufficient and diverse data for PIML training, a high-fidelity co-simulation platform integrating TruckSim, COMSOL Multiphysics, and Simulink is developed. Model evaluations across five liquid fill ratios show that the PIML model achieves comparable or better performance than the physical models, with the most significant improvement observed in lateral velocity. The results suggest the framework’s strong ability to capture the complex vehicle-fluid coupled dynamics.", "url": "" }, { "id": "Tu-TuB03.5", "code": "TuB03.5", "title": "Robust Deterministic Policy Gradient for Disturbance Attenuation and Its Application to Quadrotor Control", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:35", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Lee, Taeho", "affiliation": "Korea Advanced Institute of Science and Technology" }, { "name": "Lee, Donghwan", "affiliation": "Korea Advanced Institute of Science and Technology" } ], "keywords": [ "AI and learning-based control for automotive systems", "Learning and adaptation in autonomous vehicles", "Trajectory tracking and path following for AVs" ], "abstract": "This paper presents a robust reinforcement learning algorithm, robust deterministic policy gradient (RDPG), which reformulates the H ∞ control problem as a two-player zero-sum dynamic game between a user and an adversary. The user minimizes the objective while the adversary maximizes it by injecting disturbances. This formulation enables the learning of disturbance-resilient policies under worst-case scenarios. The RDPG is extended to high-dimensional continuous control by integrating it into a deep reinforcement learning framework, resulting in robust deep deterministic policy gradient (RDDPG). Simulation results on a quadrotor demonstrate improved robustness and tracking performance under external disturbances.", "url": "" }, { "id": "Tu-TuB03.6", "code": "TuB03.6", "title": "Neural Network-Based Virtual Wheel-Speed Sensor for Enhanced Low-Velocity State Estimation", "day": "Tuesday", "date": "August 25, 2026", "time": "13:35-13:40", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Schäfke, Hendrik", "affiliation": "Leibniz University Hannover, Institute of Mechatronic Systems" }, { "name": "Weber, Daniel Oliver Martin", "affiliation": "Gottfried Wilhelm Leibniz Universität Hannover" }, { "name": "Vagapov, Askar", "affiliation": "IAV GmbH (Ingenieurgesellschaft Auto Und Verkehr)" }, { "name": "Schweers, Christoph", "affiliation": "IAV GmbH (Ingenieurgesellschaft Auto Und Verkehr)" }, { "name": "Seel, Thomas", "affiliation": "Leibniz Universität Hannover" }, { "name": "Ehlers, Simon F. G.", "affiliation": "Leibniz University Hannover" } ], "keywords": [ "Automotive system identification and modelling", "AI and learning-based control for automotive systems", "Electric and solar vehicles" ], "abstract": "Accurate wheel speed information is crucial for vehicle control and state estimation. Conventional sensors suffer from quantization and latency, especially at low velocities, while motor-speed signals in electric vehicles are distorted by drivetrain torsion. This work presents a neural-network-based virtual wheel-speed sensor that fuses wheel-speed and motor-speed signals to reduce errors from both sources. Validated on real-world Volkswagen ID.7 data, the real-time-capable model achieves an error reduction of up to 85% compared to the production sensor and 47% compared to an optimized zero-phase filter, providing a smooth signal for driver-assistance functions. The results demonstrate robust generalization across diverse real-world maneuvers within the vehicle platform.", "url": "" }, { "id": "Tu-TuB03.7", "code": "TuB03.7", "title": "Constrained Physics-Informed GRU for Robust Vehicle Motion Prediction", "day": "Tuesday", "date": "August 25, 2026", "time": "13:40-13:45", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Kwon, Solyeon", "affiliation": "Hanyang University" }, { "name": "Jin, Yongsik", "affiliation": "Daegu Gyeongbuk Institute of Science and Technology (DGIST)" }, { "name": "Han, Kyoungseok", "affiliation": "Hanyang University" } ], "keywords": [ "Automotive system identification and modelling", "Modeling, supervision, control and diagnosis of automotive systems", "Vehicle dynamic systems" ], "abstract": "Physics-based vehicle models are interpretable but suffer from parametric and tire--road uncertainty, whereas purely data-driven predictors generalize poorly and may violate physical laws. We propose a constrained physics-informed gated recurrent unit (CPIGRU) that combines vehicle dynamics residuals with a penalty-based admissibility constraint and an adaptive residual-weighting schedule. A constrained universal approximation theorem establishes that the CPIGRU achieves epsilon-accurate approximation of the true dynamics on the admissible set. In high-fidelity CarMaker to CarSim cross-simulator tests, CPIGRU outperforms both a nominal 3-DOF model and an unconstrained physics-informed GRU in terms of accuracy and stability.", "url": "" }, { "id": "Tu-TuB03.8", "code": "TuB03.8", "title": "A Generalized String-Stability Criteria for Consensus Protocols", "day": "Tuesday", "date": "August 25, 2026", "time": "13:45-13:50", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Mudhangulla, Sridhar Babu", "affiliation": "FSU" }, { "name": "Anubi, Olugbenga", "affiliation": "Florida State University" } ], "keywords": [ "Control architectures in automotive control", "Automatic control, optimization, real-time operations in transportation", "Vehicle dynamic systems" ], "abstract": "This paper develops a unified frequency-domain framework for string-stability analysis of leader--follower multi-agent systems governed by first-, second-, and general m^{text{th}}-order consensus protocols over an r-predecessor directed communication topology. Existing string-stability results are often tied to specific vehicle models, protocol orders, or information structures, which obscures the mechanism that fundamentally governs disturbance amplification. Under the adopted mathcal{H}_infty disturbance-propagation definition, we show that the decisive quantity is the communication richness r: for every consensus order, the low-frequency propagation gain is 1/r. Consequently, within the proposed framework, string stability is achieved if and only if rgeq 2. The consensus order m does not alter this structural limit; instead, it shapes the transient and mid-to-high-frequency response through additional dynamic degrees of freedom. The results establish a structural--dynamic separation principle: topology determines whether disturbances attenuate along the string, whereas protocol order and gain selection determine the quality of the closed-loop response. Numerical simulations for first-, second-, and third-order protocols corroborate the analysis and illustrate the distinct roles of r and m in disturbance propagation.", "url": "" }, { "id": "Tu-TuB03.9", "code": "TuB03.9", "title": "Robust Data-Driven Control for Vehicle Merging in Mixed Traffic", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-13:55", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Bang, Heeseung", "affiliation": "Yeungnam University" }, { "name": "Dave, Aditya Deepak", "affiliation": "Cornell University" }, { "name": "Malikopoulos, Andreas", "affiliation": "Cornell University" } ], "keywords": [ "Control architectures in automotive control", "Learning and adaptation in autonomous vehicles", "Guidance, navigation and control for AVs" ], "abstract": "In this paper, we present an approach for learning human driving behavior, without relying on specific model structures or prior distributions, in a mixed-traffic environment where connected and automated vehicles (CAVs) coexist with human-driven vehicles (HDVs). We employ conformalized quantile regression to obtain statistical guarantees on the human-driving-prediction accuracy. Then, we design a controller that effectively merges CAVs with HDVs while maintaining non-disrupting distance. We provide numerical simulations to illustrate the efficacy of the control approach.", "url": "" }, { "id": "Tu-TuB03.10", "code": "TuB03.10", "title": "Design of Nonlinear Observer for EV Powertrain Vibration Suppression", "day": "Tuesday", "date": "August 25, 2026", "time": "13:55-14:00", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Kawasaki, Manato", "affiliation": "Nanzan University" }, { "name": "Sakamoto, Noboru", "affiliation": "Nanzan University" }, { "name": "Nakashima, Akira", "affiliation": "Nanzan University" } ], "keywords": [ "Engine and powertrain modeling and control", "Hybrid, electric and alternative drive vehicles", "Modeling, supervision, control and diagnosis of automotive systems" ], "abstract": "This study proposes a nonlinear observer for estimating internal states of electric vehicle (EV) powertrains with gear backlash and driveshaft torsion. The proposed observer explicitly incorporates backlash-induced nonlinear switching dynamics and estimates the motor-side and load-side angular velocities, torsional torque, backlash angle, and backlash angular velocity. The observer was evaluated using an Exact Backlash Simulator under realistic sensing conditions, including observation noise, communication delay, and sensor quantization. Compared with a conventional torsional-torque disturbance observer, the proposed method achieved high estimation accuracy, particularly for torsional torque estimation. The mode-transition timing between free rotation and tooth engagement was estimated with an average error of approximately 0.1 ms, which is sufficiently small compared with a typical 1 ms EV control cycle.", "url": "" }, { "id": "Tu-TuB03.11", "code": "TuB03.11", "title": "Personalized Energy-Aware Regenerative Braking Control Minimizing Driver Interventions", "day": "Tuesday", "date": "August 25, 2026", "time": "14:00-14:05", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Kim, Beomchang", "affiliation": "Hanyang University" }, { "name": "Lee, Jae Hwan", "affiliation": "Hanyang University" }, { "name": "Kim, Dongryul", "affiliation": "Hanyang University" }, { "name": "Kim, Dohee", "affiliation": "Hyundai Motor Company" }, { "name": "Lee, Sangho", "affiliation": "Hyundai Motor Company" }, { "name": "Han, Kyoungseok", "affiliation": "Hanyang University" } ], "keywords": [ "Hybrid, electric and alternative drive vehicles", "AI and learning-based control for automotive systems", "Nonlinear and optimal automotive control" ], "abstract": "Conventional automatic regenerative braking (ARB) systems in electrified vehicles prioritize energy efficiency but often conflict with driver preferences, leading to frequent manual interventions that reduce energy efficiency. This paper proposes a personalized ARB control framework that co-optimizes regenerative energy recovery and driver acceptance. In particular, using Gaussian process (GP) regression, the system learns individual driver braking preferences and intervention thresholds online, then selects optimal braking distances by balancing energy gains against intervention probability. Experimental results demonstrate that the proposed approach reduces driver interventions while improving net energy recovery, providing a practical solution for personalized automated braking.", "url": "" }, { "id": "Tu-TuB03.12", "code": "TuB03.12", "title": "Trajectory-Linked Nonlinear Model Predictive Control Energy Management for Hybrid UAVs in Urban Low Altitude Flight Missions", "day": "Tuesday", "date": "August 25, 2026", "time": "14:05-14:10", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Li, Jie", "affiliation": "Aalborg University" }, { "name": "Shen, Ming", "affiliation": "Aalborg University" }, { "name": "Stoustrup, Jakob", "affiliation": "Aalborg University" } ], "keywords": [ "Hybrid, electric and alternative drive vehicles", "Automatic control, optimization, real-time operations in transportation" ], "abstract": "With the opening of low altitude urban airspace, energy efficient dynamic obstacle avoidance for hybrid unmanned aerial vehicles (HUAV) has become critical. Unlike existing methods that decouple route planning and energy management, this work instead proposes a trajectory linked framework where the planned 3D path directly determines time varying propulsion demand for hydrogen–battery energy scheduling. A cost weighted 3D A* planner generates safe and energy aware paths by penalizing altitude variations to suppress power intensive climbs and descents. A segmented accelerate, cruise, and decelerate velocity model, combined with simplified flight dynamics, provides time varying propulsion power estimates that more accurately capture aerodynamic effects compared with constant velocity assumptions. Based on the trajectory induced dynamic load, a constrained Nonlinear Model Predictive Control(NMPC) strategy assigns fuel cell(FC) and battery power under slope and state of charge(SOC) constraints, reducing fuel cell stress and overall energy use. Simulation results show hydrogen consumption reductions of 12.5% compared with Equivalent Consumption Minimization Strategy(ECMS) and 9.3% compared with Equivalent Energy Management Strategy(EEMS), demonstrating the advantage of planning driven energy management over post planning optimization.", "url": "" }, { "id": "Tu-TuB03.13", "code": "TuB03.13", "title": "Interaction-Aware Multi-Modal Adaptive Unscented Kalman Filter for Safe Navigation of Autonomous Vehicles", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:15", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Heyi, Muluneh Hailu", "affiliation": "Université Bourgogne Europe" }, { "name": "Hima, Salim", "affiliation": "ESME-SUDRIA Engineering School" }, { "name": "Chaibet, Ahmed", "affiliation": "Université Bourgogne Europe" } ], "keywords": [ "Kalman filtering techniques in automotive control", "Autonomous vehicles", "Multi-vehicle systems" ], "abstract": "Safe navigation in dense highway traffic requires accurate prediction of surrounding vehicles' maneuvers while ensuring passenger safety. This paper proposes an Interaction-Aware Multi-Modal Adaptive Unscented Kalman Filter (IA-MM-AUKF) that jointly estimates maneuver intentions and future trajectories of neighboring vehicles. A bank of mode-specific AUKFs, combined with Bayesian-adaptive Markov transition probabilities and probabilistic mode fusion, captures multi-modal maneuver uncertainty under nonlinear dynamics. A trajectory uncertainty quantification module further characterizes prediction confidence. Validated on the highD naturalistic dataset, the framework achieves a lateral RMS error of 0.022m, a 59% reduction over EKF, enabling anticipatory, collision-safe motion planning.", "url": "" }, { "id": "Tu-TuB03.14", "code": "TuB03.14", "title": "Adaptive Fault-Tolerant Multi-Modal Localization of Autonomous Vehicles", "day": "Tuesday", "date": "August 25, 2026", "time": "14:15-14:20", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "AlMousawi, Ali", "affiliation": "Universite De Haute Alsace" }, { "name": "Duthay, Flavie", "affiliation": "Université De Haute-Alsace" }, { "name": "Mourllion, Benjamin", "affiliation": "UHA" }, { "name": "Lauffenburger, Jean-Philippe", "affiliation": "Université De Haute-Alsace" } ], "keywords": [ "Kalman filtering techniques in automotive control", "Guidance, navigation and control for AVs", "Trajectory tracking and path following for AVs" ], "abstract": "This paper develops and evaluates a robust multi-modal vehicle localization framework using an Extended Information Filter (EIF). The approach integrates a kinematic bicycle model (KBM) for prediction, enhanced with gyroscope angular rate measurements, and GNSS observations for update. To address faulty measurements and non-stationary sensor noise, a Fault Detection and Exclusion (FDE) mechanism and fuzzy logic system (FLS) were implemented. The FDE isolates corrupted measurements, while the FLS dynamically adjusts measurement noise covariance. Experiments across multiple trajectories demonstrate significant reductions in mean and maximum absolute position and heading errors, highlighting the effectiveness of fault handling and adaptive measurement weighting in real-world navigation.", "url": "" }, { "id": "Tu-TuB03.15", "code": "TuB03.15", "title": "Hybrid Attack Modeling for Position Deviation in Autonumous Systems: A Semi Markov Approach", "day": "Tuesday", "date": "August 25, 2026", "time": "14:20-14:25", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Yan Tingli, Tingli", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Wu, Jing", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Long, Chengnian", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Kalman filtering techniques in automotive control", "Motion control for AVs", "Automatic control, optimization, real-time operations in transportation" ], "abstract": "研究提出了一个非指数持续时间的半马尔可夫混合攻击模型，与现有指数分布的马尔可夫链形成对比。在基于卡尔曼滤波器的定位框架内，它推导了攻击强度与位置偏移之间的相关性，并提供了隐蔽攻击能量消耗的上界，并结合了最优攻击序列的算法。实验结果表明，在相同的隐蔽性约束下，Weibull分布的半马尔可夫模型在保持低误报率的同时实现了更大的位置偏移，验证了其优于传统指数模型的优势。", "url": "" }, { "id": "Tu-TuB03.16", "code": "TuB03.16", "title": "Reduced-Complexity Vehicle Mass Estimation Using Series-Production Sensors Validated with Static and Dynamic Experimental Data", "day": "Tuesday", "date": "August 25, 2026", "time": "14:25-14:30", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Wübbeler, Carlos", "affiliation": "University of Applied Sciences, Osnabrück" }, { "name": "Ehlers, Simon F. G.", "affiliation": "Leibniz University Hannover" }, { "name": "Seel, Thomas", "affiliation": "Leibniz Universität Hannover" }, { "name": "Westerkamp, Clemens", "affiliation": "Osnabrück University of Applied Sciences" }, { "name": "Böhse, Frederic", "affiliation": "ZF Friedrichshafen AG" }, { "name": "Lundberg, Alexander", "affiliation": "ZF Friedrichshafen AG" }, { "name": "Weber, Daniel Oliver Martin", "affiliation": "Gottfried Wilhelm Leibniz Universität Hannover" } ], "keywords": [ "Kalman filtering techniques in automotive control", "Vehicle dynamic systems", "Automotive system identification and modelling" ], "abstract": "Accurate and robust knowledge of vehicle mass is important for advanced driver assistance systems (ADAS) and autonomous driving. Current estimation methods, such as longitudinal 1-degree-of-freedom (DOF) models, deliver inaccurate mass estimates in driving modes near or at a standstill. Conversely, complex multi-DOF models require detailed, parameter- and signal-intensive subsystem modeling. This paper presents a novel, reduced complexity approach to vehicle mass estimation that combines a 3-DOF vehicle body model with an Unscented Kalman Filter (UKF). Inertial Measurement Unit (IMU) measurements are directly used as inputs to the simplified 3-DOF body model, reducing subsystem and parameter dependencies for a more efficient application. The algorithm is extensively validated using real world vehicle data with 13 different masses, covering various driving situations and public road tests with varying slopes. Results demonstrate high accuracy with a relative root mean square error <3.87%.", "url": "" }, { "id": "Tu-TuB03.17", "code": "TuB03.17", "title": "Sequential Quadratic Programming for Nonlinear Eco-Driving: A Proximal Primal-Dual Approach", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:35", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Heuts, Y.J.J.", "affiliation": "Eindhoven University of Technology" }, { "name": "Donkers, M.C.F. (Tijs)", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Modeling, supervision, control and diagnosis of automotive systems", "Electric and solar vehicles" ], "abstract": "This paper presents a real-time optimization approach for the eco-driving optimal control problem using a Sequential Quadratic Programming (SQP) formulation. By discretizing the dynamics in the spatial domain and applying convex relaxations and regularization, the problem is reformulated into a structure suitable for embedded implementation. Two solvers, OSQP and a proposed Heavy-Ball Projected Primal-Dual Method (HBPPDM), are employed to solve the SQP subproblems, enabling a comparison of convergence behavior and computational efficiency. Numerical results demonstrate that the SQP-based approach significantly outperforms a Second-Order Cone Programming (SOCP) formulation solved by MOSEK, particularly for long prediction horizons. While the SOCP method can solve the problem in a single shot, its complexity limits real-time feasibility. In contrast, the SQP approach achieves prediction horizons up to 6000 steps within one second, and solves a realistic 60 km route in 0.18 s, confirming its scalability and suitability for real-time eco-driving applications.", "url": "" }, { "id": "Tu-TuB03.18", "code": "TuB03.18", "title": "Development of Accelerated Life Testing Method for a 47 kW Class Agricultural Tractor Using Axle Torque During Plow Tillage", "day": "Tuesday", "date": "August 25, 2026", "time": "14:35-14:40", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Lee, Minha", "affiliation": "Chungnam National University" }, { "name": "Jeong, Gubin", "affiliation": "Chungnam National University" }, { "name": "Kim, Yong-Joo", "affiliation": "Chungnam National University" } ], "keywords": [ "Modeling, supervision, control and diagnosis of automotive systems", "Engine and powertrain modeling and control", "Automotive system identification and modelling" ], "abstract": "Due to the ongoing shortage of rural labor and the aging farming population, the farm size per farmer has increased, requiring durable and reliable agricultural equipment. This study developed an accelerated life test (ALT) methodology for tractor axles based on load data measured during actual plow tillage operations. Axle torque and rotational speed were measured using telemetry torque sensors installed on both front and rear axles. The measured time–torque data were used to construct a Load Duration Distribution (LDD), from which equivalent torque was calculated using the Palmgren–Miner linear cumulative damage rule with a fatigue damage exponent of 8.738. The equivalent torque was 6,310.99 Nm, while the selected test torque was 1.2 times the rated torque (8,170.08 Nm). The acceleration factor was computed as 9.545, reducing the required durability test time for a 3,000‑hour target life to 314.3 hours. The proposed method provides an efficient and reproducible approach for evaluating axle fatigue life under realistic operating environments.", "url": "" }, { "id": "Tu-TuB03.19", "code": "TuB03.19", "title": "Input-To-State Stability of Safe MPC in Unknown Environments with Applications to Autonomous Driving", "day": "Tuesday", "date": "August 25, 2026", "time": "14:40-14:45", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Guo, Yuxuan", "affiliation": "IMT School for Advanced Studies Lucca" }, { "name": "Quan, Yingshuai", "affiliation": "Chalmers University of Technology" }, { "name": "Falcone, Paolo", "affiliation": "Chalmers University of Technology" }, { "name": "Villanueva, Mario Eduardo", "affiliation": "IMT School for Advanced Studies Lucca" }, { "name": "Zanon, Mario", "affiliation": "IMT Institute for Advanced Studies Lucca" } ], "keywords": [ "Nonlinear and optimal automotive control", "Adaptive and robust control of automotive systems" ], "abstract": "We study the stability of safe model predictive control (MPC) in unknown environments, where safety constraints come from online perception or estimation and may tighten abruptly as new information appears. Conservative worst-case predictions ensure recursive feasibility, but changing, a priori unknown constraints cause deviations from the nominal trajectory. By modeling the evolution of environment information with a continuous parameter and assuming non-sudden activation, we show that the closed loop is input-to-state stable (ISS) with respect to disturbances entering through the safety constraints, so deviations from the nominal plan remain bounded. We demonstrate this on an autonomous-driving scenario with a pedestrian crossing under limited visibility, where simulations with perception-driven constraint updates confirm the predicted bounded deviations.", "url": "" }, { "id": "Tu-TuB03.20", "code": "TuB03.20", "title": "Finite-Time Safe Sliding Mode Control for Trajectory Tracking of Wheeled Mobile Robot", "day": "Tuesday", "date": "August 25, 2026", "time": "14:45-14:50", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Diana, Baby", "affiliation": "IIT(BHU) Varanasi" }, { "name": "Taslima, Eram", "affiliation": "Indian Institute of Technology (BHU)" }, { "name": "Kamal, Shyam", "affiliation": "Indian Institute of Technology (BHU), Varanasi" }, { "name": "Singh, Bhawana", "affiliation": "Indian Institute of Technology (ism) Dhanbad" }, { "name": "Singh, Priyanka", "affiliation": "Indian Institute of Technology (BHU), Varanasi" } ], "keywords": [ "Nonlinear and optimal automotive control", "Autonomous mobile robots", "Guidance, navigation and control for AVs" ], "abstract": "This paper presents a finite-time control barrier function (FCBF) based sliding mode control (SMC) framework for the trajectory tracking of a wheeled mobile robot (WMR) operating in the presence of static obstacle and matched disturbances. The WMR is modelled using a double-integrator representation, and a circular trajectory is defined as the reference path. To achieve robust trajectory tracking under disturbances, an SMC-based controller is designed. To ensure safety during motion, a novel finite-time high-order control barrier function (FHOCBF) is developed to address the safety constraint associated with the position-based obstacle avoidance task. Specifically, for the second-order WMR model, a finite-time second-order CBF is formulated to ensure collision-free navigation while maintaining finite-time convergence to the safety region. The effectiveness of the proposed FCBF–SMC framework is validated through both simulation and hardware experiments conducted on the Quanser QBot platform, demonstrating accurate trajectory tracking and reliable obstacle avoidance under disturbances.", "url": "" }, { "id": "Tu-TuB03.21", "code": "TuB03.21", "title": "Model Predictive Control for Dynamic Speed Planning-Based Cruise Control in Mid-Sized BEVs", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-14:55", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Kayacan, Mehmet Aygen", "affiliation": "MAN Truck and Bus Turkey" }, { "name": "Ergezer, Halit", "affiliation": "Ankara Yildirim Beyazit University" } ], "keywords": [ "Nonlinear and optimal automotive control", "Electric and solar vehicles", "Vehicle dynamic systems" ], "abstract": "This paper proposes a nonlinear discrete supervisory Model Predictive Control (MPC) strategy for mid-sized battery electric vehicles (BEVs) to minimize traction and braking energy requirements at the wheel level. The system adaptively modulates the vehicle’s set speed based on ahead road topography, aiming to reduce mechanical energy expenditure while maintaining reference speed adherence. The controller utilizes an asymmetric cost function at each horizon to leverage road slopes for energy gains, ensuring the optimized speed profile remains aligned with driver intent. A primary focus of this research is the systematic investigation of weighting factor effects on the trade-off between energy conservation and tracking performance. The proposed approach is validated in MATLAB, demonstrating significant energy savings across various control priorities compared to conventional constant-speed cruise control systems.", "url": "" }, { "id": "Tu-TuB03.22", "code": "TuB03.22", "title": "Byzantine-Resilient Leaderless Formation Control in Open Multi-Agent Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "14:55-15:00", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Wang, Xince", "affiliation": "Southeast University" }, { "name": "Gong, Xin", "affiliation": "The University of Hong Kong" } ], "keywords": [ "Trajectory tracking and path following for AVs", "Digital twins and IoT for aerospace systems control and monitoring" ], "abstract": "This paper presents a fixed-time leaderless formation control framework for open multi-agent systems under Byzantine edge attacks. A coordinate-independent vector MSR estimation layer and a nonlinear control law are integrated to ensure resilient and predictable convergence. The method guarantees stability under topology switching, agent membership variation, and persistent adversarial behaviors.", "url": "" }, { "id": "Tu-TuB03.23", "code": "TuB03.23", "title": "Stabilizing Traffic without Autonomous Vehicles", "day": "Tuesday", "date": "August 25, 2026", "time": "15:00-15:05", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Koşay, Arda", "affiliation": "Bilkent University" }, { "name": "Kara, Arda", "affiliation": "Bilkent University" }, { "name": "Sayin, Muhammed Omer", "affiliation": "Bilkent University" } ], "keywords": [ "Vehicle dynamic systems", "Modeling and simulation of transportation systems" ], "abstract": "This paper investigates whether \"Human Protocols\" (HPs), simple cognitive heuristics executed by a fraction of drivers, can mitigate phantom traffic jams as effectively as Autonomous Vehicles (AVs). Specifically, we study speed-matching rules in which compliant drivers either match the speed of the vehicle immediately ahead or the speed of the vehicle two positions ahead. Using a standard Flow/SUMO ring-road benchmark, we vary protocol compliance and penetration, comparing HPs against a benchmark AV controller in terms of stabilization time, throughput, and fuel economy. Our results show that HPs can yield superior fuel economy and throughput, although they generally require time longer to stabilize traffic than AV controllers. We conclude that such modest behavior, when adopted by a fraction of drivers, can yield macroscopic benefits competitive with hardware-based automation.", "url": "" }, { "id": "Tu-TuB03.24", "code": "TuB03.24", "title": "Dynamic One-Time Delivery of Critical Data by Small and Sparse UAV Swarms: A Model Problem for MARL Scaling Studies", "day": "Tuesday", "date": "August 25, 2026", "time": "15:05-15:10", "sessionCode": "TuB03", "sessionTitle": "Shotgun: Transportation and Vehicle Systems - Automotive Control", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Persson, Mika", "affiliation": "Saab AB, Chalmers Univ. of Technology and Univ. of Gothenburg" }, { "name": "Lidman, Jonas", "affiliation": "Swedish Defence Research Agency (FOI)" }, { "name": "Ljungberg, Jacob", "affiliation": "Saab AB" }, { "name": "Sandelius, Samuel", "affiliation": "Saab" }, { "name": "Andersson, Adam", "affiliation": "Saab AB, Chalmers Univ. of Technology and Univ. of Gothenburg" } ], "keywords": [ "Multi-agent systems", "Distributed reinforcement learning", "Learning methods for control" ], "abstract": "This work studies the application of Multi-Agent Reinforcement Learning (MARL) to decentralized control of unmanned aerial vehicles to relay a critical data package to a known position. For this purpose, a family of deterministic games is introduced, designed for MARL scaling studies. A robust baseline policy is proposed which restricts agent motion and applies Dijkstra’s shortest path algorithm. Computational experiment results show that two off-the-shelf MARL algorithms perform competitively with the baseline for a small number of agents, but face scalability issues as the number of agents increases. Source code and animations are available online at https://github.com/mikapersson/Information-Relaying.", "url": "" }, { "id": "Tu-TuB04.1", "code": "TuB04.1", "title": "LPV Model-Based Adaptive CBFs for Safety-Critical Motion Control of 4WID-4WIS Electric Vehicles (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:15", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Li, Zongxuan", "affiliation": "Tongji University" }, { "name": "Dong, Rui", "affiliation": "Tongji University" }, { "name": "Li, Yang", "affiliation": "Tongji University" }, { "name": "Chu, Hongqing", "affiliation": "Tongji University" }, { "name": "Gao, Bingzhao", "affiliation": "Tongji University" }, { "name": "Chen, Hong", "affiliation": "Tongji University" } ], "keywords": [ "Adaptive control design", "Linear parameter-varying systems", "Real-time optimal control" ], "abstract": "Control barrier functions (CBFs) based methods for four-wheel independently driving/steering electric vehicles (4WID-4WIS EV) face a fundamental modeling limitation. Due to the nonlinear characteristics of tire, non-affine models ensure high-fidelity safety constraints but induce non-convex optimization, whereas time-invariant affine models preserve convex safety constraints but lose fidelity in nonlinear regions. To achieve high-fidelity safety constraints and real-time optimization, this work proposes a safety-critical motion controller using a linear parameter-varying (LPV) model. A high-fidelity dynamics model is online linearized at each sampling instant, generating a LPV affine model that adapts to nonlinear dynamics while satisfying the affine form of the CBF-CLF quadratic program (QP) framework. To address time-varying parameter feasibility challenges, safety constraints are transformed into adaptive CBFs (ACBFs), explicitly accommodating parameter variations without relaxation. The control problem is formulated as an ACBF-CLF-QP and solved in real-time. CarSim/Simulink co-simulations demonstrate the controller's effectiveness and superiority over baselines, resolving the fundamental modeling limitation in CBFs based methods for 4WID-4WIS EV.", "url": "" }, { "id": "Tu-TuB04.2", "code": "TuB04.2", "title": "Sliding Mode Control for a Parabolic–Elliptic PDE System with Boundary Perturbation", "day": "Tuesday", "date": "August 25, 2026", "time": "13:15-13:20", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Labbadi, Moussa", "affiliation": "Bretagne INP UBO, IRDL" }, { "name": "Ilyasse, Lamrani", "affiliation": "Faculty of Sciences Meknes" } ], "keywords": [ "Control of distributed parameter systems", "Sliding mode control" ], "abstract": "In this paper, we address the robustness of parabolic–elliptic systems under boundary control. A sliding mode control strategy is proposed to reject matched perturbations. The stability analysis establishes finite-time convergence of the sliding manifold and exponential stability of the closed-loop system. Since the closed-loop system is discontinuous, we also prove its well-posedness. A numerical example is provided to validate the effectiveness of the proposed approach.", "url": "" }, { "id": "Tu-TuB04.3", "code": "TuB04.3", "title": "Robust H2 and H∞ Tuning of PID-Based Optimization and Frequency-Domain Comparison with Adam", "day": "Tuesday", "date": "August 25, 2026", "time": "13:20-13:25", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Jain, Vishesh", "affiliation": "Indian Institute of Technology, Bombay" }, { "name": "Baranwal, Mayank", "affiliation": "Tata Consultancy Services Ltd" } ], "keywords": [ "Convex optimization", "Robust control applications", "Robust learning systems" ], "abstract": "PID-based optimization algorithms (PIDAO) have recently demonstrated empirical robustness against gradient noise in machine learning. However, a theoretical framework for tuning these algorithms to guarantee stability and noise rejection is lacking. In this work, we formulate PIDAO as a discrete-time Lur’e system and utilize Integral Quadratic Constraints (IQCs) to analyze its robustness. We propose an mathcal{H}_2/mathcal{H}_infty synthesis framework to optimally tune PIDAO gains, balancing convergence speed with disturbance attenuation. Furthermore, we introduce a fixed-point linearization of the Adam optimizer, enabling a comparative control-theoretic analysis. Frequency-domain results and neural network training experiments demonstrate that PIDAO, when tuned via our robust control framework, achieves superior noise attenuation and stability margins compared to Adam.", "url": "" }, { "id": "Tu-TuB04.4", "code": "TuB04.4", "title": "Economically Optimal Sparse Controller for Constrained Processes: With Application to the Williams-Otto Reactor", "day": "Tuesday", "date": "August 25, 2026", "time": "13:25-13:30", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Magbool Jan, Nabil", "affiliation": "Indian Institute of Technology Tirupati" }, { "name": "Ankalugari, Rahul Yadav", "affiliation": "Indian Institute of Technology Tirupati" }, { "name": "Narasimhan, Sridharakumar", "affiliation": "Indian Institute of Technology, Madras" } ], "keywords": [ "Convex optimization", "Robust linear matrix inequalities", "Optimal control theory" ], "abstract": "In this paper, we address the problem of stabilizing sparse controller design for constrained processes using the notion of profit control. We propose an optimization formulation for the simultaneous selection of stabilizing state feedback controller that is row sparse and economic backoff operating point. As the proposed formulation is not computationally tractable owing to a non-convexity constraint, we develop an iterative solution technique that first determines the sparse controller by utilizing the idea of minimum variance for the active constrained variables, and then determining the economically optimal backoff operating point. Finally, we illustrate the efficacy of our proposed approach in a Williams-Otto reactor.", "url": "" }, { "id": "Tu-TuB04.5", "code": "TuB04.5", "title": "Neural Network-Based Model Error Compensator with Relative Degree for Quadcopter Control", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:35", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Koseki, Yosuke", "affiliation": "Tokyo City University" }, { "name": "Sekiguchi, Kazuma", "affiliation": "Tokyo City University" }, { "name": "Nonaka, Kenichiro", "affiliation": "Tokyo City University" } ], "keywords": [ "Data-driven robust control", "Nonlinearity learning from data", "Robust control applications" ], "abstract": "NN (Neural Network) is an excellent data-driven method for modeling nonlinear systems, but NN models face challenges related to instability and uncertainty. In this paper, NN-MEC (Neural Network-Model Error Compensator) is proposed as a data-driven robust control, which minimizes the effect of model error in model-based control. The proposed NN-MEC overcomes NN's challenges primarily through its learning rule, which incorporates the dynamics and relative degree information of the quadcopters. Furthermore, NN-MEC eases the difficulty of designing MEC for nonlinear systems by using NN. In numerical simulation, the robustness against the model errors of the NN-MEC is confirmed.", "url": "" }, { "id": "Tu-TuB04.6", "code": "TuB04.6", "title": "Cooperative Preview Feedforward and DOB-Based Hybrid Control for Dual-Frame Gimbals (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:35-13:40", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Li, Wenhao", "affiliation": "Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science" }, { "name": "Wang, Yutang", "affiliation": "Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science" }, { "name": "Tian, Dapeng", "affiliation": "Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science" } ], "keywords": [ "Disturbance rejection and input-to-state stability", "Control of distributed parameter systems", "Control of hybrid systems" ], "abstract": "Aerial vehicles operating in complex environments encounter various disturbances that severely affect Line-of-Sight (LOS) stabilization accuracy. Although multi-frame stabilization systems can isolate partial disturbances, the kinematic coupling between frames and nonlinear factors induce high-frequency coupling disturbances, posing a challenge to high-precision stabilization. Traditional Disturbance Observer (DOB)-based methods struggle to effectively suppress such high-frequency disturbances due to the phase lag introduced by low-pass filtering. Therefore, this paper proposes a hybrid control strategy combining Cooperative Preview Feedforward and a Disturbance Observer (DOB). First, a refined dynamic model incorporating inertial coupling, viscous friction, and nonlinear Coulomb friction is established. Based on this, a cooperative feedforward control law utilizing the previewed states of the outer frame is developed to implement \"anticipatory\" physical compensation before disturbances affect the inner frame. Simultaneously, the DOB is retained to suppress residual model uncertainties and random disturbances. Based on Lyapunov theory, the Uniformly Ultimately Bounded (UUB) stability of the closed-loop system, in the presence of preview errors and parameter mismatches, is rigorously proven. Simulation results demonstrate that, compared with traditional methods, the proposed approach significantly enhances the capability to suppress LOS jitter in the inner frame and notably improves the dynamic disturbance rejection performance of the system.", "url": "" }, { "id": "Tu-TuB04.7", "code": "TuB04.7", "title": "GPC-Based PID Tuning for Stable or Unstable First Order Plus Dead Time Processes", "day": "Tuesday", "date": "August 25, 2026", "time": "13:40-13:45", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Silva, Lucian Ribeiro da", "affiliation": "Universidade Federal De Santa Catarina" }, { "name": "Flesch, Rodolfo C. C.", "affiliation": "Federal University of Santa Catarina" }, { "name": "Normey-Rico, Julio Elias", "affiliation": "Federal Univ of Santa Catarina" }, { "name": "Schwedersky, Bernardo Barancelli", "affiliation": "Federal University of Pelotas (UFPel)" } ], "keywords": [ "Linear time-delay systems", "Model predictive control", "Optimal control theory" ], "abstract": "This study proposes a method for tuning proportional-integral-derivative (PID) controllers based on generalized predictive control (GPC), suitable for processes that can be modeled by a first-order transfer function with dead time. The proposed method applies to systems with stable, unstable, or integrating dynamics. The method builds on the equivalent structure of the unconstrained GPC and incorporates an approximation of the dead time, resulting in a two-degree-of-freedom PID controller. A detailed analysis of performance and robustness is provided, illustrating that when tuned for robustness, PID and GPC controllers exhibit similar behavior. Furthermore, a case study of an integrating system with dead time is included, demonstrating that both controllers achieve comparable results in reference tracking and disturbance rejection, even in scenarios considering input constraints.", "url": "" }, { "id": "Tu-TuB04.8", "code": "TuB04.8", "title": "Partial Shading Conditions: A Hierarchical MPC Scheme for Global Flexible Power Point Tracking in Photovoltaic Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "13:45-13:50", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Liu, Xiangjie", "affiliation": "North China Electric Power Univ" }, { "name": "Zhang, Pengyu", "affiliation": "North China Electric Power University" }, { "name": "Kong, Xiaobing", "affiliation": "North China Electric Power University" }, { "name": "Zhang, Jukai", "affiliation": "North China Electric Power University" }, { "name": "Lee, Kwang Y.", "affiliation": "Baylor University" } ], "keywords": [ "Model predictive control", "Adaptive control design", "Applications of optimal control" ], "abstract": "As the capacity of photovoltaic (PV) generating units increases, flexible power point tracking (FPPT) technology flourishes as an effective method of grid-connected PV. In practice, the movement of clouds often leads to partial shading conditions, which significantly reduces the effectiveness of FPPT technology. The global FPPT (GFPPT) technology has been proposed to address partial shading conditions. However, the conventional GFPPT method searches with a fixed strategy fails to remain efficient under all working conditions, while intelligent methods increase the complexity of the algorithm. To improve the performance of GFPPT, a hierarchical model predictive control (HMPC) strategy is proposed. The upper layer utilizes an adaptive control strategy to determine the optimal voltage reference, thus enhancing the performance of GFPPT under different operating conditions (i.e., operating point and environmental conditions). A maximum power point estimation method is also proposed to improve the performance of the maximum power output of the PV system. The lower layer, focusing on PV voltage control, utilizes model predictive control (MPC) to track this voltage reference, which addresses the issue of multiple variables and physical constraints inherent in PV power generation systems. Simulation demonstrates the effectiveness of the proposed strategy in five representative scenarios.", "url": "" }, { "id": "Tu-TuB04.9", "code": "TuB04.9", "title": "Nonlinear Model Predictive Control for UAV Navigation in GPS-Denied Environments Using UWB Localization and Reinforcement Learning Path Planning", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-13:55", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Hanum, Zalma Zahara", "affiliation": "Institut Teknologi Bandung" }, { "name": "Nazaruddin, Yul Yunazwin", "affiliation": "Institut Teknologi Bandung (ITB)" }, { "name": "Burohman, Azka Muji", "affiliation": "Institut Teknologi Bandung" } ], "keywords": [ "Model predictive control", "Application of nonlinear analysis and design", "Design methods for data-based control" ], "abstract": "This paper proposes a closed-loop UAV navigation framework for GPS-denied environments using Ultra-Wideband (UWB) localization, Reinforcement Learning (RL)-based path planning, and Nonlinear Model Predictive Control (NMPC). In the proposed framework, UWB localization provides real-time state feedback for both the RL planner and NMPC controller, forming an integrated estimation–planning–control loop. The RL module generates collision-free trajectories, while NMPC compensates for nonlinear UAV dynamics and localization uncertainty during trajectory tracking. In addition, the RL reward–penalty formulation is modified to account for localization uncertainty, improving robustness under noisy state observations. The UAV system is modeled using nonlinear quadrotor dynamics with constrained control inputs. Numerical simulations are conducted in a GPS-denied environment with obstacle avoidance scenarios and UWB localization disturbances. The results show that the proposed framework can maintain stable and accurate trajectory tracking despite localization errors, demonstrating the effectiveness of the tightly coupled UWB–RL–NMPC architecture for autonomous UAV navigation in uncertain environments.", "url": "" }, { "id": "Tu-TuB04.10", "code": "TuB04.10", "title": "C3A-TAB: A Cross-Domain, Conditioned, Calibrated and Aligned Tabular Framework for Ordinal Odor-Level Prediction with Electronic Nose Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "13:55-14:00", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Lv, Jinziyuan", "affiliation": "North China University of Technology" }, { "name": "Wang, Jing", "affiliation": "North China University of Technology (NCUT)" }, { "name": "Zhou, Meng", "affiliation": "North China University of Technology" }, { "name": "Lou, Zhijiang", "affiliation": "Shenzhen Polytechnic University" }, { "name": "Lu, Shan", "affiliation": "Shenzhen Polytechnic University" } ], "keywords": [ "Model predictive control", "Applications of optimal control" ], "abstract": "Traditional panel sniffing is subjective and costly, whereas electronic noses enable automation but are sensitive to sensor drift and environmental variation, causing cross-domain shifts and unstable predictions. We propose the cross-domain, conditioned, calibrated, and aligned TabTransformer (C3A-TAB) for ordinal odor-level prediction. It integrates population stability index guided drift-aware gating; feature-wise linear modulation for environmental conditioning; prototype alignment and separation; and an ordinal objective combining negative log-likelihood, kullback–leibler divergence, and earth mover’s distance, followed by temperature scaling for probability calibration. Experiments show C3A-TAB consistently surpasses TabTransformer across all metrics, and ablations confirm each component’s contribution and their structural complementarity. Comparative experiments also demonstrated the advantages.", "url": "" }, { "id": "Tu-TuB04.11", "code": "TuB04.11", "title": "Shrinking Horizon MPC with Computation Preallocated Along the Trajectory", "day": "Tuesday", "date": "August 25, 2026", "time": "14:00-14:05", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "van Leeuwen, Steven", "affiliation": "University of Michigan Ann Arbor" }, { "name": "Kolmanovsky, Ilya V.", "affiliation": "University of Michigan" } ], "keywords": [ "Model predictive control", "Numerical methods for optimal control", "Real-time optimal control" ], "abstract": "A strategy for offline allocation of the online computations in Shrinking Horizon Model Predictive Control (SH-MPC) is proposed when steering a discrete-time linear system with control constraints into a target terminal set over a prescribed number of time steps despite unmeasured disturbances, for which time-varying disturbance bounds are available. Specifically, assuming adjustable terminal penalty weights, an offline optimization problem aimed at minimizing the weighted sum of the number of optimizer iterations along the trajectory is proposed. Simulation results for a bicopter are reported to illustrate the proposed approach.", "url": "" }, { "id": "Tu-TuB04.12", "code": "TuB04.12", "title": "Decentralized Invariant Sets for Safe Control of Partially-Decomposable Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "14:05-14:10", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Nenchev, Vladislav", "affiliation": "University of the Bundeswehr Munich" } ], "keywords": [ "Model predictive control", "Optimal control of hybrid systems", "Applications of optimal control" ], "abstract": "This paper presents a decentralized computation method for control invariant sets of discrete‑time systems whose state contains a shared part and loosely coupled parts, e.g., timers, filters, uncertainties. Computing the centralized invariant becomes intractable with a growing state dimension. We compute decentralized invariants of low‑dimensional auxiliary subsystems that contain the shared and a single loosely coupled part. We show that the maximal control invariant set of the partially-decomposable system equals the intersection of invariants of the auxiliary subsystems. Case studies using the decentralized invariants on a servomotor and persistent surveillance by a mobile robot demonstrate scalability of offline invariant computation, maintaining feasibility under set constraints with short planning horizons, and competitive online computation costs for model predictive control and for safeguarding a learned policy.", "url": "" }, { "id": "Tu-TuB04.13", "code": "TuB04.13", "title": "Stochastic Nonlinear Model Predictive Control for Closed-Loop Optimization of Subsurface Flow Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:15", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Hannanu, Muhammad Iffan", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Hovd, Morten", "affiliation": "Norwegian University of Technology and Science" }, { "name": "Camponogara, Eduardo", "affiliation": "Federal University of Santa Catarina" }, { "name": "Silva, Thiago Lima", "affiliation": "SINTEF AS" } ], "keywords": [ "Model predictive control", "Optimization-based estimation and control", "Stochastic optimal control problems" ], "abstract": "We consider the implementation of Stochastic Model Predictive Control (SMPC) in the framework of Closed-Loop Reservoir Management (CLRM) for optimization of subsurface flow systems. The problem of Buckley-Leverette is investigated, where the objective is to maximize the expected value of the net present value from an ensemble of equally probable realizations, as well as minimizing the mismatch between the ensemble and the true model. The uncertainty is represented by the perturbation of the relative permeability curves. The results indicate that SMPC is capable of producing near-optimal control under uncertainty and is well-suited for reservoir management problems.", "url": "" }, { "id": "Tu-TuB04.14", "code": "TuB04.14", "title": "MPC Based Orbit Insertion and Uniform Distribution for LEO Satellite Constellation", "day": "Tuesday", "date": "August 25, 2026", "time": "14:15-14:20", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Kim, Seongheon", "affiliation": "Gyeongsang National University" }, { "name": "Kim, Yoonsoo", "affiliation": "Gyeongsang National University" }, { "name": "Vande Wouwer, Alain", "affiliation": "Université De Mons" } ], "keywords": [ "Model predictive control", "Robust control applications", "Distributed nonlinear control" ], "abstract": "This study tackles the problem of uniformly distributing satellites in circular low Earth orbits (LEO). To enable safe and reliable constellation deployment, we develop a distributed model predictive control (DMPC) framework that explicitly handles thrust constraints and inter-satellite collision avoidance. The proposed phase-based scheme consists of three steps: (i) a transfer maneuver from a parking orbit to the reference orbit, (ii) a DMPC-based phasing maneuver in which each satellite uses only the position of its preceding neighbor to achieve uniform angular spacing, and (iii) a steady-state phase where robust servomechanism MPC (RS-MPC) ensures accurate orbit tracking under persistent disturbances including atmospheric drag and the Earth’s J2 effect . Simulations with three satellites confirm that the method achieves uniform spacing and substantially improves steady-state tracking performance compared with existing approaches.", "url": "" }, { "id": "Tu-TuB04.15", "code": "TuB04.15", "title": "Integral Sliding Model Predictive Control for Wheeled Biped Robots under Uncertainties", "day": "Tuesday", "date": "August 25, 2026", "time": "14:20-14:25", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "McMullan, Rhyss", "affiliation": "Queen's University Belfast" }, { "name": "Van, Mien", "affiliation": "Queen's University Belfast" }, { "name": "McConnellogue, Peter James", "affiliation": "Queen's University Belfast" }, { "name": "Zhou, Yibo", "affiliation": "Queen's University Belfast" }, { "name": "Dianati, Mehrdad", "affiliation": "Queen's University Belfast" } ], "keywords": [ "Model predictive control", "Sliding mode control", "Application of nonlinear analysis and design" ], "abstract": "This paper presents a combined control technique of a nonlinear model predictive controller (NMPC) and integral sliding mode control (ISMC) for a wheeled biped robot, utilising dynamic modelling and the wheeled inverted pendulum model (WIPM). A rollover index via the load transfer ratio (LTR) analyses lateral dynamics and defines a tunable limit. The performance of this ISM-NMPC is investigated in simulation on the TRON1A wheeled biped, demonstrating how the biped prioritises stability during high-speed and complex turns, and how ISMC improves overall performance by rejecting matched uncertainty terms.", "url": "" }, { "id": "Tu-TuB04.16", "code": "TuB04.16", "title": "Hybrid Physics-Based and Data-Driven Identification of a Two-Axis Helicopter Testbed with Real-Time Control Applications", "day": "Tuesday", "date": "August 25, 2026", "time": "14:25-14:30", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Salazar, Carlos Alberto", "affiliation": "Escuela Superior Politecnica Del Litoral, ESPOL" }, { "name": "Aguirre, Adriana", "affiliation": "Escuela Superior Politécnica Del Litoral" }, { "name": "Rodriguez Gonzalez, Mario Gustavo", "affiliation": "Escuela Superior Politecnica Del Litoral" } ], "keywords": [ "Model validation", "Controller constraints and structure" ], "abstract": "This paper presents a hybrid system identification approach for a two-axis didactic helicopter testbed, combining physics-based modeling with experimental data-driven estimation. The main contribution is methodological: a grey-box framework that integrates Newton–Euler dynamics with experimental identification to obtain compact low-order models with physically interpretable parameters such as inertias, damping, and aerodynamic couplings. Experimental datasets were fitted to second-order transfer functions for pitch and yaw; interaction metrics (Relative Gain Array and Niederlinski Index) confirmed diagonal dominance within the operating envelope, justifying a decentralized SISO control design. Discrete-time PID controllers with derivative filtering and anti-windup achieved stable tracking in step and pulse tests. Beyond reproducing the essential nonlinear dynamics, the workflow—data acquisition, grey-box identification, controller design, and real-time validation—provides a reproducible instructional pipeline that bridges system identification theory with hands-on control practice.", "url": "" }, { "id": "Tu-TuB04.17", "code": "TuB04.17", "title": "Moment Matching in Discrete-Time for Time-Varying and Periodic Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:35", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Bhattacharjee, Debraj", "affiliation": "Imperial College London" }, { "name": "Moreschini, Alessio", "affiliation": "Imperial College London" }, { "name": "Astolfi, Alessandro", "affiliation": "King Abdullah University of Science and Technology (KAUST)" } ], "keywords": [ "Model validation", "Linear systems" ], "abstract": "We study the moment matching problem for linear time-varying and linear time-periodic systems in a discrete-time setting. We derive a class of reduced-order models that replicate the steady-state response of the underlying system when driven by a signal generator with time-varying dynamics. We illustrate our results through a simple numerical example.", "url": "" }, { "id": "Tu-TuB04.18", "code": "TuB04.18", "title": "Hierarchical Control of Inerter-Enhanced MRD Seat Suspension (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:35-14:40", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Yu, Xiaohui", "affiliation": "Jilin University" }, { "name": "Yu, Xinze", "affiliation": "Jilin University" }, { "name": "Yu, Shuyou", "affiliation": "Jilin University" }, { "name": "Yang, Jun", "affiliation": "Jilin University" }, { "name": "Chen, Hong", "affiliation": "Tongji University" } ], "keywords": [ "Nonlinearity learning from data", "Robust linear matrix inequalities", "Application of nonlinear analysis and design" ], "abstract": "Low-frequency vibrations significantly affect ride comfort, yet conventional seat suspensions struggle to suppress them. This paper proposes a novel parallel seat suspension combining a spring, MRD, and inerter, with the inerter optimized for low-frequency isolation. A hierarchical control framework is developed: The lower layer first develops a recurrent neural network (RNN) to capture the MRD's complex dynamics. Subsequently, the Koopman operator framework is applied to construct a lifted linear representation of this data-driven RNN model, enabling accurate force tracking, while the upper layer employs an H_infty output-feedback controller balancing comfort and robustness. Simulations demonstrate substantial improvements in force tracking and comfort-related metrics, providing a systematic simulation-based framework for robust semi-active seat suspension control.", "url": "" }, { "id": "Tu-TuB04.19", "code": "TuB04.19", "title": "A Numerical Approach to Incentive Stackelberg Games for Stochastic Mean-Field Games with Delay", "day": "Tuesday", "date": "August 25, 2026", "time": "14:40-14:45", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Ito, Yuki", "affiliation": "Hiroshima University" }, { "name": "Tian, Zihang", "affiliation": "Hiroshima University" }, { "name": "Mukaidani, Hiroaki", "affiliation": "Hiroshima University" }, { "name": "Sato, Masayuki", "affiliation": "Kyushu Institute of Technology" }, { "name": "Sagara, Muneomi", "affiliation": "Kochi University" } ], "keywords": [ "Numerical methods for optimal control", "Differential or dynamic games", "Robust time-delay systems" ], "abstract": "This paper investigates a numerical method for solving incentive Stackelberg games in stochastic mean-field systems with time delay. In this framework, the leader designs strategies and incentive mechanisms to guide non-cooperative followers-who play a Nash equilibrium-toward a team-optimal solution. Compared with existing results, we establish a new sufficient condition for the solvability of this game via a parametrization technique. To address the intractability of high-dimensional equations as the population size tends to infinity, we adopt a reduced-order computational approach that exploits the asymptotic properties of the coupled higher-order Lyapunov-like equations (CHLEs). The core simplified Newton method uses a fixed approximate Jacobian that is independent of the population size and is shown to achieve linear convergence. A numerical example demonstrates the effectiveness of the proposed algorithm, showing that its computational time can be reduced by an average of 40% compared to other existing typical algorithms when the number of followers is sufficiently large.", "url": "" }, { "id": "Tu-TuB04.20", "code": "TuB04.20", "title": "Momentum-Based Differential Dynamic Programming", "day": "Tuesday", "date": "August 25, 2026", "time": "14:45-14:50", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Mahmoudi Filabadi, Mohammad", "affiliation": "Ghent University" }, { "name": "Crevecoeur, Guillaume", "affiliation": "Ghent University" }, { "name": "Lefebvre, Tom", "affiliation": "Unversity of Ghent" } ], "keywords": [ "Numerical methods for optimal control", "Optimal control theory", "Applications of optimal control" ], "abstract": "Differential Dynamic Programming (DDP) is a prominent trajectory optimization method for deterministic nonlinear systems. Due to its dependency on local gradient information it is sometimes plagued by slow convergence and sensitivity to local minima. This paper introduces a momentum-based Differential Dynamic Programming (MB-DDP) algorithm, leveraging information from previous iterations to achieve faster convergence rate. The proposed algorithm is derived from a Soft Dynamic Programming framework that integrates information-theoretic measures into the optimization problem, which facilitate a principled balance between exploration and numerical stability. Our simulation results, on benchmark nonlinear control problems, demonstrate that MB-DDP achieves a faster convergence rate than standard DDP without increasing computational complexity.", "url": "" }, { "id": "Tu-TuB04.21", "code": "TuB04.21", "title": "Differentiable Material Point Method for the Control of Deformable Objects", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-14:55", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Bolliger, Diego", "affiliation": "ZHAW Zurich University for Applied Sciences" }, { "name": "Fadini, Gabriele", "affiliation": "ZHAW" }, { "name": "Bambach, Markus", "affiliation": "ETH Zürich" }, { "name": "Rupenyan, Alisa", "affiliation": "ZHAW Zurich University for Applied Sciences" } ], "keywords": [ "Numerical methods for optimal control", "Optimization-based estimation and control", "Application of nonlinear analysis and design" ], "abstract": "Controlling the deformation of flexible objects is challenging due to their non- linear dynamics and high-dimensional configuration space. This work presents a differentiable Material Point Method (MPM) simulator targeted at control applications. We exploit the differentiability of the simulator to optimize a control trajectory in an active damping problem for a hyperelastic rope. The simulator effectively minimizes the kinetic energy of the rope around 2× faster than a baseline Model Predictive Path Integral (MPPI) controller and to a 20 % lower energy level, while using about 3 % of the computation time.", "url": "" }, { "id": "Tu-TuB04.22", "code": "TuB04.22", "title": "NDO-Based Spatio-Temporal Cooperation Guidance for Multi-Missile System with Input Constraints (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:55-15:00", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Sun, Haoxuan", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Chen, Mou", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Zhou, Tongle", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Han, Zengliang", "affiliation": "Nanjing University of Aeronautics and Astronautics" } ], "keywords": [ "Observer design", "Cooperative nonlinear control", "Backstepping control of distributed parameter systems" ], "abstract": "This paper proposes a spatio-temporal cooperation guidance law for multi-missile systems with input constraints and unknown target maneuvers. The temporal cooperation objective, defined as simultaneous arrival, is formulated through consensus on both relative distance and relative velocities. The radial basis function neural network is employed to approximate system uncertainties, while a nonlinear disturbance observer (NDO) estimates and compensates for composite disturbances. For spatial cooperation objective, the backstepping-based spatial cooperation guidance law is developed. The NDO is designed based on the transformed system to directly estimate the target's maneuver. To address input constraints, auxiliary systems are designed to mitigate the adverse effects of input constraints. Lyapunov-based stability analysis guarantees the stability of all closed-loop signals. Finally, numerical simulation is used to verify the effectiveness of the guidance law.", "url": "" }, { "id": "Tu-TuB04.23", "code": "TuB04.23", "title": "On Batch Estimation for BOTMA Problem", "day": "Tuesday", "date": "August 25, 2026", "time": "15:00-15:05", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Ambit Brao, Isaac", "affiliation": "INRIA" }, { "name": "Efimov, Denis", "affiliation": "Inria" } ], "keywords": [ "Observer design", "Nonlinear observers and filters", "Convex optimization" ], "abstract": "This paper considers two-dimensional bearing-only target motion analysis for an observer platform moving at constant speed and course while the target performs a constant turn. The relative motion is modelled as a linear discrete-time state equation with a nonlinear, perspective-type bearing measurement equation. We characterise observability conditions for this scenario and design a batch estimator based on a suitable loss functional, which is proved to be convex (and to admit a unique minimiser) under explicit conditions. The performance of the convex batch estimator is evaluated via Monte-Carlo simulations and compared with an ad hoc batch estimator and an extended Kalman filter, showing improved estimation accuracy and robustness to initialisation errors.", "url": "" }, { "id": "Tu-TuB04.24", "code": "TuB04.24", "title": "Fuzzy Reduced-Order Interval Observer-Based Consensus Control of Muti-Agent Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:05-15:10", "sessionCode": "TuB04", "sessionTitle": "Shotgun: Design Methods in Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Song, Lei", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Xue, Hong", "affiliation": "University of Electronic Science and Technology" }, { "name": "Liang, Hongjing", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Yang, Jin", "affiliation": "University of Electronic Science and Technology of China" } ], "keywords": [ "Observer design", "Robust linear matrix inequalities", "Lyapunov methods" ], "abstract": "本文探讨了缩减阶区间 高木-菅野的基于观察者的共识控制问题 （T-S） 模糊多智能体系统 （MASs）受未知影响 动态和测量中的输入扰动 方程。首先，一种新颖的表示形式 不可测量扰动矢量构造为 有效解决 系统测量中的未知输入扰动。这 表示有助于建立 等效系统模型，使完整的 解耦与消除无法测量的干扰 从输出映射中获得。基于此，一个降阶 区间观察者仅利用界限 构建 不确定性，并且可以估计系统状态 计算资源显著减少。随后，基于分布式控制器的构建 在设计的降阶观察者和共识上建立了T-S模糊MAS的条件。最终， 提供模拟结果以验证其疗效 以及所提方法的优越性。", "url": "" }, { "id": "Tu-TuB05.1", "code": "TuB05.1", "title": "Adaptive Vehicle Following Via Actor–Critic Reinforcement Learning", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:25", "sessionCode": "TuB05", "sessionTitle": "LB: Multi-Agent and Network Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Matsumura, Naruaki", "affiliation": "Tokyo Metropolitan University" }, { "name": "Oguchi, Toshiki", "affiliation": "Tokyo Metropolitan University" } ], "keywords": [ "Consensus and reinforcement learning control", "Adaptive control of multi-agent systems", "Multi-agent systems" ], "abstract": "This paper proposes an online gain adaptation method for vehicle-following control based on an Actor–Critic reinforcement learning framework. The control gains are adjusted in real time using tracking errors and input variations, enabling the controller to adapt to changing driving conditions. The proposed method is integrated into a vehicle-following scheme based on vehicle-to-vehicle (V2V) communication and is validated through numerical simulations and experiments using physical robots. The results demonstrate that the adaptive gain tuning method improves tracking accuracy, reduces the amplification of tracking errors along the platoon, and suppresses excessive oscillations in the control inputs.", "url": "" }, { "id": "Tu-TuB05.2", "code": "TuB05.2", "title": "Algebraic Construction of Contractive Subspaces in Non-Contractive Systems Via Compound Matrices", "day": "Tuesday", "date": "August 25, 2026", "time": "13:25-13:40", "sessionCode": "TuB05", "sessionTitle": "LB: Multi-Agent and Network Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Dalin, Omri", "affiliation": "Tel Aviv University" } ], "keywords": [ "Control of networks", "Consensus", "Multi-agent systems" ], "abstract": "Standard contraction theory requires system trajectories to converge to a unique equilibrium. However, many physical systems (e.g., marginal oscillators, frustrated networks) are globally non-contractive but possess a stable invariant subspace. This paper proposes a constructive algebraic method to identify such subspaces. By lifting the system to its k-th compound dynamics (A^{[k]}), we show that if the compound system admits a conservation law (a left eigenvector with eigenvalue 0), this law encodes a topological obstruction matrix M. We prove that the kernel of M explicitly defines the contractive subspace in the original state space. We demonstrate the method by explicitly constructing the synchronization manifold of a cascaded frustrated ring network.", "url": "" }, { "id": "Tu-TuB05.3", "code": "TuB05.3", "title": "A Distributed Asynchronous Process Model under Initial Local Information", "day": "Tuesday", "date": "August 25, 2026", "time": "13:40-13:55", "sessionCode": "TuB05", "sessionTitle": "LB: Multi-Agent and Network Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Lee, Hyung-Gon", "affiliation": "GIST" }, { "name": "Ahn, Hyo-Sung", "affiliation": "Gwangju Institute of Science and Technology (GIST)" } ], "keywords": [ "Control under communication constraints", "Multi-agent systems", "Resilient networked control systems" ], "abstract": "We propose a distributed asynchronous process model that enables each node to determine its node-wise objective network parameters (NONPs) under an initial-localinformation constraint. A preceding transition computes prerequisite network parameters for decomposition, followed by parallel transitions that compute node-wise components. The components are then propagated and integrated to determine the NONPs, while preserving the key dependencies implied by initial local information.", "url": "" }, { "id": "Tu-TuB05.4", "code": "TuB05.4", "title": "Multi-Scale Control of Large Agent Populations: From Density Dynamics to Individual Actuation", "day": "Tuesday", "date": "August 25, 2026", "time": "13:55-14:10", "sessionCode": "TuB05", "sessionTitle": "LB: Multi-Agent and Network Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "di Bernardo, Mario", "affiliation": "University of Naples Federico II" } ], "keywords": [ "Multi-agent systems" ], "abstract": "We review a body of recent work by the author and collaborators on controlling the spatial organisation of large agent populations across multiple scales. A central theme is the systematic bridging of microscopic agent-level dynamics and macroscopic density descriptions, enabling control design at the most natural level of abstraction and subsequent translation across scales. We show how this multi-scale perspective provides a unified approach to both emph{direct control}, where every agent is actuated, and emph{indirect control}, where few leaders or herders steer a larger uncontrolled population. The review covers continuification-based control with robustness under limited sensing and decentralised implementation via distributed density estimation; leader--follower density regulation with dual-feedback stability guarantees and bio-inspired plasticity; optimal-transport methods for coverage control and macro-to-micro discretisation; nonreciprocal field theory for collective decision-making; mean-field control barrier functions for population-level safety; and hierarchical reinforcement learning for settings where closed-form solutions are intractable. Together, these results demonstrate the breadth and versatility of a multi-scale control framework that integrates analytical methods, learning, and physics-inspired approaches for large agent populations.", "url": "" }, { "id": "Tu-TuB05.5", "code": "TuB05.5", "title": "Reformulating the Blended Dynamics to Reveal the Effect of Heterogeneous Rank-Deficient Coupling in MASs", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:25", "sessionCode": "TuB05", "sessionTitle": "LB: Multi-Agent and Network Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Koo, Sunghyun", "affiliation": "Seoul National University" }, { "name": "Lee, Jin Gyu", "affiliation": "Seoul National University" }, { "name": "Shim, Hyungbo", "affiliation": "Seoul National University" } ], "keywords": [ "Multi-agent systems", "Consensus", "Distributed control and estimation" ], "abstract": "The blended dynamics theorem, which characterizes the behavior of heterogeneous multi-agent systems under strong coupling, has prompted the development of various distributed algorithms. In this paper, we develop a more intuitive representation of the blended dynamics for the case of possibly heterogeneous rank-deficient coupling. It is expected that this new representation can stimulate further advances in distributed algorithm design.", "url": "" }, { "id": "Tu-TuB05.6", "code": "TuB05.6", "title": "Transformer's Self-Attention As Multiagent Dynamics on the Sphere", "day": "Tuesday", "date": "August 25, 2026", "time": "14:25-14:40", "sessionCode": "TuB05", "sessionTitle": "LB: Multi-Agent and Network Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Altafini, Claudio", "affiliation": "Linkoping University" } ], "keywords": [ "Multi-agent systems", "Machine and deep learning for system identification", "Consensus and reinforcement learning control" ], "abstract": "At the core of a transformer lies a so-called self-attention mechanism. In this paper we study self-attention mechanisms as continuous-time multiagent-like dynamical systems living on a sphere. In the ``single-head'' time-invariant case, the equilibria of a self-attention dynamics can be classified into four classes: consensus, bipartite consensus, clustering and polygonal equilibria. For this simplified dynamics, multiple asymptotically stable equilibria from the first three classes often coexist. Interestingly, equilibria from the first two classes are always aligned with the eigenvectors of the value matrix, often but not exclusively with the principal eigenvector.", "url": "" }, { "id": "Tu-TuB05.7", "code": "TuB05.7", "title": "Exponentially Convergent Nash Equilibrium-Seeking Controller for Linear Agents in Aggregative Games", "day": "Tuesday", "date": "August 25, 2026", "time": "14:40-14:55", "sessionCode": "TuB05", "sessionTitle": "LB: Multi-Agent and Network Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Matsuda, Yuuki", "affiliation": "Ritsumeikan University" }, { "name": "Hirano, Kota", "affiliation": "Ritsumeikan University" }, { "name": "Namba, Takumi", "affiliation": "Ritsumeikan University" }, { "name": "Takaba, Kiyotsugu", "affiliation": "Ritsumeikan University" } ], "keywords": [ "Multi-agent systems", "Control over networks" ], "abstract": "This late-breaking paper studies a Nash equilibrium (NE) seeking problem for aggregative games over a network of linear agents. Each agent’s objective function depends on its own output and on an aggregation term of all agents’ outputs. Since the aggregation term is not directly available, each agent has to seek the NE through communication with its neighboring agents. In this paper, we propose a novel NE-seeking controller for linear agents, and provide a sufficient condition for exponential convergence.", "url": "" }, { "id": "Tu-TuB05.8", "code": "TuB05.8", "title": "Observer-Based Stabilization for Linear Multi-Agent Dynamical Systems Using Generalized Frequency Variables", "day": "Tuesday", "date": "August 25, 2026", "time": "14:55-15:10", "sessionCode": "TuB05", "sessionTitle": "LB: Multi-Agent and Network Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Tran, G. Q. Bao", "affiliation": "University of Illinois Urbana-Champaign" }, { "name": "Hori, Yutaka", "affiliation": "Keio University" }, { "name": "Hara, Shinji", "affiliation": "Tokyo Institute of Technology" } ], "keywords": [ "Multi-agent systems", "Control of networks", "Estimation and filtering" ], "abstract": "We address the conditions and design of controllers and observers for homogeneous networks of linear MIMO agents. We develop networked controllers and observers that ensure the stability of both the system state and the estimation error, leveraging the concept of generalized frequency variables. A separation principle for networks is then established, showing that the observer and controller can be designed independently and combined to achieve a stable output feedback. Our results are illustrated via a highly unstable, oscillatory network of locally actuated pendulums on carts. Finally, necessary conditions for controllability and observability—derived from agent properties and network structure—are established and discussed.", "url": "" }, { "id": "Tu-TuB06.1", "code": "TuB06.1", "title": "Zero-Shot Regulation of Nonlinear Systems with Contextual Controllers (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB06", "sessionTitle": "Data-Driven Control II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Busetto, Riccardo", "affiliation": "IDSIA USI-SUPSI" }, { "name": "Breschi, Valentina", "affiliation": "Eindhoven University of Technology" }, { "name": "Forgione, Marco", "affiliation": "SUPSI-USI" }, { "name": "Piga, Dario", "affiliation": "SUPSI-USI" }, { "name": "Formentin, Simone", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Learning methods for control" ], "abstract": "Recent advances in in-context learning and success stories with architectures like transformers suggest that it may become increasingly feasible to deploy pre-designed controllers on unknown systems, achieving reasonable performance. In this work, we propose an in-context learning-based approach for constructing a contextual controller capable of adapting across a class of similar (yet not identical) dynamical systems, rather than being tailored to a single one. Our preliminary results indicate that this method might be a viable option to shift from the “one-system-one-controller” paradigm to the “many-systems-one-controller” paradigm, offering a step toward controllers that can be used on new instances of the same system class without fine-tuning or adjustments.", "url": "" }, { "id": "Tu-TuB06.2", "code": "TuB06.2", "title": "On Data-Based Nash Equilibria in LQ Nonzero-Sum Differential Games (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB06", "sessionTitle": "Data-Driven Control II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Lopez, Victor G.", "affiliation": "Leibniz University Hannover, Institute for Automatic Control" }, { "name": "Müller, Matthias A.", "affiliation": "Leibniz University Hannover" } ], "keywords": [ "Data-driven control theory", "Multi-agent systems" ], "abstract": "This paper considers data-based solutions of linear-quadratic nonzero-sum differential games. Two cases are considered. First, the deterministic game is solved and Nash equilibrium strategies are obtained by using persistently excited data from the multiagent system. Then, a stochastic formulation of the game is considered, where each agent measures a different noisy output signal and state observers must be designed for each player. It is shown that the proposed data-based solutions of these games are equivalent to known model-based procedures. The resulting data-based solutions are validated in a numerical experiment.", "url": "" }, { "id": "Tu-TuB06.3", "code": "TuB06.3", "title": "On the Effect of Quadratic Regularization in Direct Data-Driven LQR (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB06", "sessionTitle": "Data-Driven Control II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Klädtke, Manuel", "affiliation": "TU Dortmund University" }, { "name": "Zhao, Feiran", "affiliation": "ETH Zurich" }, { "name": "Dorfler, Florian", "affiliation": "Swiss Federal Institute of Technology (ETH) Zurich" }, { "name": "Schulze Darup, Moritz", "affiliation": "TU Dortmund University" } ], "keywords": [ "Data-driven control theory" ], "abstract": "This paper proposes an explainability concept for direct data-driven linear quadratic regulation (LQR) with quadratic regularization. Our perspective follows the parametric effect of regularization, an analysis approach that translates regularization costs from auxiliary variables to system quantities, enabling intuitive interpretations. The framework further enables the elimination of auxiliary variables, thereby reducing computational complexity. We demonstrate the effectiveness of our approach and the identified effect of regularization via simulations.", "url": "" }, { "id": "Tu-TuB06.4", "code": "TuB06.4", "title": "System Identification for Dynamic Modeling of Large Steering Angle Vehicles (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB06", "sessionTitle": "Data-Driven Control II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Petri, Tobias", "affiliation": "RWTH Aachen University" }, { "name": "Baratto, Simone", "affiliation": "EPFL" }, { "name": "Ferrari-Trecate, Giancarlo", "affiliation": "Ecole Polytechnique Fédérale De Lausanne" } ], "keywords": [ "Physics informed and grey box model identification", "Nonlinear system identification", "Machine and deep learning for system identification" ], "abstract": "This paper presents the modeling of autonomous vehicles with high maneuverability used in an experimental framework for educational purposes. Since standard bicycle models typically neglect wide steering angles, we develop modified planar bicycle models and combine them with both parametric and non-parametric identification techniques that progressively incorporate physical knowledge. The resulting models are systematically compared to evaluate the tradeoff between model accuracy and computational requirements, showing that physics-informed neural network models surpass the purely physical baseline in accuracy at lower computational cost.", "url": "" }, { "id": "Tu-TuB06.5", "code": "TuB06.5", "title": "Data-Driven Optimal Distributed Controller Synthesis Via Spatial Regret (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB06", "sessionTitle": "Data-Driven Control II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Gupta, Vaibhav", "affiliation": "EPFL" }, { "name": "Martinelli, Daniele", "affiliation": "EPFL" }, { "name": "Ferrari-Trecate, Giancarlo", "affiliation": "Ecole Polytechnique Fédérale De Lausanne" }, { "name": "Furieri, Luca", "affiliation": "University of Oxford" }, { "name": "Karimi, Alireza", "affiliation": "Ecole Polytechnique Federale De Lausanne" } ], "keywords": [ "Distributed control and estimation", "Data-driven control theory", "Control under communication constraints" ], "abstract": "In this paper, we present a novel method for synthesising an optimal distributed spatial regret controller using experimentally obtained frequency-response data. Spatial regret provides a measure of the performance gap between a structured distributed controller and an oracle with enhanced communication topology. We relax assumptions on the communication topology, allowing the oracle to adopt any enhanced structure. While this generalisation requires an iterative solution rather than a single convex program, we provide a tractable algorithm that synthesises optimal controllers from frequency-response data while preserving stability and the desired communication structure. Numerical examples demonstrate superior performance of the spatial regret controller compared to classical H2/Hinf designs, underscoring the effectiveness of the proposed methodology.", "url": "" }, { "id": "Tu-TuB06.6", "code": "TuB06.6", "title": "Prescribed Performance Event-Triggered Output Feedback Control of MIMO Systems Using Reinforcement Learning", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB06", "sessionTitle": "Data-Driven Control II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "You, Xingxing", "affiliation": "Sichuan University" }, { "name": "Zhang, Yufeng", "affiliation": "Sichuan University" }, { "name": "Xiang, Guofei", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Liao, Yiwei", "affiliation": "Sichuan University" }, { "name": "Fang, Hongwei", "affiliation": "Sichuan University" }, { "name": "Guo, Bin", "affiliation": "Sichuan University" }, { "name": "Dian, Songyi", "affiliation": "Sichuan University" } ], "keywords": [ "Nonlinear adaptive control" ], "abstract": "This paper studies the adaptive output-feedback optimal control problem for uncertain MIMO nonlinear systems with unmeasurable states, unknown disturbances, and limited communication resources. To enhance optimal control performance, this paper develops a reinforcement learning algorithm featuring an actor-critic architecture with integrated command filtering within the backstepping framework, utilizing state estimates provided by a neural network observer. By constructing a nonlinear error transformation function, the prescribed performance control problem with asymmetric initial constraints is transformed into an equivalent unconstrained problem, thereby reducing it to a design parameter selection task. Subsequently, a novel prescribed performance adaptive event-triggered output feedback optimal controller is proposed. This controller ensures the boundedness of the system signals while keeping the tracking error within a prescribed performance range, significantly alleviating the communication burden. A simulation study further validates that the method is effective.", "url": "" }, { "id": "Tu-TuB07.1", "code": "TuB07.1", "title": "Privacy-Preserving Sign Gossip for Constrained Communication", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB07", "sessionTitle": "Consensus and Coordination in Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Fioravanti, Camilla", "affiliation": "University Campus Bio-Medico of Rome" }, { "name": "Oliva, Gabriele", "affiliation": "University Campus Bio-Medico of Rome" }, { "name": "Setola, Roberto", "affiliation": "Università Campus Biomedico" } ], "keywords": [ "Consensus", "Control of networks", "Multi-agent systems" ], "abstract": "In communication-constrained multi-agent networks (e.g., underwater systems with acoustic modems), communication is often low-bandwidth, high-latency, and asynchronous. These conditions make classical consensus schemes impractical, while the observability of transmissions makes confidentiality essential. This paper proposes a privacy-preserving gossip algorithm tailored to this setting. When activated, a node communicates with its local neighbors and performs secure pairwise comparisons through Yao’s protocol, only revealing the sign of quantized state differences while never disclosing the actual values. A multi-neighbor sign-based update rule is then executed, combined with a fully decentralized vanishing step-size mechanism where the active neighborhood constructs the update parameter from local counters. The proposed scheme is completely distributed, asynchronous, and achieves practical consensus in an activation-weighted sense with an accuracy floor induced by the fixed quantization step. Simulation results validate the protocol’s applicability for secure multi-agent coordination.", "url": "" }, { "id": "Tu-TuB07.2", "code": "TuB07.2", "title": "A Leader-Follower Approach for the Attitude Synchronization of Multiple Rigid Body Systems on SO(3)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB07", "sessionTitle": "Consensus and Coordination in Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Li, Yiliang", "affiliation": "Shandong University" }, { "name": "Feng, Jun-e", "affiliation": "Shandong University" }, { "name": "Tayebi, Abdelhamid", "affiliation": "Lakehead University" } ], "keywords": [ "Consensus", "Distributed control and estimation", "Multi-agent systems" ], "abstract": "This paper deals with the leader-follower attitude synchronization problem for a group of heterogeneous rigid body systems on SO(3) under an undirected, connected, and acyclic graph communication topology. The proposed distributed control strategy, endowed with almost global asymptotic stability guarantees, allows the synchronization of the rigid body systems to a constant desired orientation known only to a single rigid body. Some simulation results are also provided to validate the theoretical developments and illustrate the performance of the proposed control strategy.", "url": "" }, { "id": "Tu-TuB07.3", "code": "TuB07.3", "title": "The Link between Equitable Partitions and Local Agreements in Multi-Agent Systems with Nonlinear Interactions", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB07", "sessionTitle": "Consensus and Coordination in Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Couthures, Anthony", "affiliation": "University of Lorraine, CNRS UMR7039" }, { "name": "Satheeskumar Varma, Vineeth", "affiliation": "CRAN - Université De Lauraine" }, { "name": "Lasaulce, Samson", "affiliation": "CNRS - Centrale Supelec - Universite Paris Sud" }, { "name": "Morarescu, Irinel Constantin", "affiliation": "Universite De Lorraine" } ], "keywords": [ "Consensus", "Multi-agent systems" ], "abstract": "Classically, global consensus is achieved in linear multi-agent systems that interact over a connected unsigned graph. However, when the interactions are non-linear, agents may get polarized, i.e., they synchronize locally within communities while the communities do not reach a consensus with each other. In this context, we demonstrate that local synchronizations strongly rely on the existence of equitable partitions in the graph. Specifically, if some agents synchronize independently of the initial conditions, we prove that these agents must belong to the same cell of an equitable partition. On top of that, based on forward invariance properties, we are able to characterize the stability of local synchronization equilibria in terms of the stability of equilibria of a quotient graph defined by an equitable partition.", "url": "" }, { "id": "Tu-TuB07.4", "code": "TuB07.4", "title": "On Undesired Equilibria in Attitude Consensus of Multiple Rigid Bodies", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB07", "sessionTitle": "Consensus and Coordination in Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Zhou, Junyu", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Li, Xianwei", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Consensus", "Multi-agent systems", "Control over networks" ], "abstract": "This paper investigates the issue of undesired equilibria in the attitude consensus problem for multiple rigid body systems. Undesired equilibria refer to the system states where an equilibrium is reached but the attitudes of rigid bodies fail to align. The prevalence of this issue is well-documented in studies that utilize relative attitude information for control design, as it arises fundamentally from the compact and boundaryless nature of the attitude manifold. Extending the results of Markdahl et al. (2017) to second-order rigid body systems, this work proves that when attitudes are represented by unit quaternions, all undesired equilibria are unstable. Furthermore, numerical experiments confirm that this instability leads to the achievement of almost global consensus of second-order rigid body systems.", "url": "" }, { "id": "Tu-TuB07.5", "code": "TuB07.5", "title": "Towards Lag Consensus with Noisy Digital Twins Perception in Second-Order Multi-Agent Cyber-Physical Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB07", "sessionTitle": "Consensus and Coordination in Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Zhang, Zhicheng", "affiliation": "Kyoto University" }, { "name": "Lizzio, Fausto Francesco", "affiliation": "Politecnico Di Torino" }, { "name": "Ma, Zhongjun", "affiliation": "Guilin University of Electronic Technology" }, { "name": "Nagahara, Masaaki", "affiliation": "Hiroshima University" } ], "keywords": [ "Consensus", "Multi-agent systems", "Synthesis of stochastic systems" ], "abstract": "In this paper, we study second-order lag consensus in multi-agent cyber-physical networks subject to random noise and input failures, within a framework modeling the interactions and perceptions between physical twins and digital twins. We propose a lag consensus protocol and establish sufficient conditions for the mean-square (exponential) stability of the resulting stochastic lag error dynamics. The consensus criteria are derived via Lyapunov analysis using the Ito formula, ensuring robustness to random perturbations and intermittent input failures. Numerical examples illustrate the effectiveness of the proposed method.", "url": "" }, { "id": "Tu-TuB07.6", "code": "TuB07.6", "title": "Finite-Time Distributed Control for Distance-Based Formation Tracking of Multi-Agent Systems under Unknown Leader's Velocity", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB07", "sessionTitle": "Consensus and Coordination in Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Wang, Yiqun", "affiliation": "Xiamen University" }, { "name": "Ma, Ji", "affiliation": "Xiamen University" }, { "name": "Guan, Jinting", "affiliation": "Xiamen University" }, { "name": "Yu, Xiao", "affiliation": "Xiamen University" } ], "keywords": [ "Multi-agent systems", "Consensus", "Distributed control and estimation" ], "abstract": "This paper addresses the distance-based formation tracking problem for multi-agent systems (MASs) under unknown leader's velocity. A distributed sliding mode control (SMC) scheme is proposed, treating the leader's motion as a matched disturbance and leveraging the robustness of SMC to achieve finite-time convergence. A key feature is a barrier function-based adaptive gain mechanism, which obviates the prior knowledge of the velocity bound while actively suppressing control chattering. The control law relies solely on local relative position measurements and guarantees that the formation tracking error converges to a prescribed neighborhood of zero in finite time. The theoretical results are illustrated by numerical simulations.", "url": "" }, { "id": "Tu-TuB08.1", "code": "TuB08.1", "title": "Safety Verification of Interconnected Systems: An Angular Approach (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB08", "sessionTitle": "Security, Safety, Resilience, and Privacy for Cyber-Physical Systems II", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Zhang, Weihao", "affiliation": "Tongji University" }, { "name": "Chen, Chao", "affiliation": "The University of Manchester" }, { "name": "Chen, Jianqi", "affiliation": "Nanjing University" }, { "name": "Zhao, Di", "affiliation": "Nanjing University" } ], "keywords": [ "Control of networks", "Distributed control and estimation", "Resilient networked control systems" ], "abstract": "This paper addresses the problem of safety verification for interconnected systems through an angular-sector approach. The desired safety property is characterized within the angular sector, where the notions of soft safety and singular angle are integrated to describe a physically interpretable form of safety. Verification criteria are developed for both nonlinear systems and their large-scale interconnections, leveraging the angular-sector information of each subsystem. A numerical example demonstrates the applicability of the proposed framework.", "url": "" }, { "id": "Tu-TuB08.2", "code": "TuB08.2", "title": "Opacity Verification for Multi-Agent Cyber-Physical Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB08", "sessionTitle": "Security, Safety, Resilience, and Privacy for Cyber-Physical Systems II", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Wu, Jing", "affiliation": "Xidian University" }, { "name": "Raïssi, Tarek", "affiliation": "Conservatoire National Des Arts Et Métiers" }, { "name": "Li, Zhiwu", "affiliation": "Institute of Systems Engineering, Macau University of Science and Technology" } ], "keywords": [ "Control over networks", "Multi-agent systems", "Iterative and repetitive learning control" ], "abstract": "This paper addresses the opacity verification problem for multi-agent cyber-physical systems governed by P-type iterative learning control. The system’s confidentiality and opacity are formally defined by linking the anti-interference capability to the output error. A verification framework is developed based on the attacker’s observation capability, and sufficient conditions are established to ensure opacity preservation. Unlike discrete abstraction methods, the proposed approach directly analyzes continuous-time dynamics, simplifying verification while retaining intrinsic system characteristics. Theoretical analysis and simulations demonstrate that the P-type ILC scheme enables multi-agent systems to achieve accurate tracking performance while maintaining opacity against potential intrusions.", "url": "" }, { "id": "Tu-TuB08.3", "code": "TuB08.3", "title": "Sniffing Attacks on Competing Users in Remote State Estimation: The Scalar Case (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB08", "sessionTitle": "Security, Safety, Resilience, and Privacy for Cyber-Physical Systems II", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Jia, Fanlin", "affiliation": "University of Alberta" }, { "name": "Shang, Jun", "affiliation": "Tongji University" }, { "name": "Chen, Tongwen", "affiliation": "University of Alberta" } ], "keywords": [ "Estimation and filtering", "Kalman filtering", "Cyber security networked control" ], "abstract": "We consider the problem of remote state estimation (RSE) with competing users for a scalar process, where each user's measurements are transmitted to a remote estimator via a wireless communication channel. There is a malicious user who seeks to achieve the best RSE performance among all users. We introduce a sniffing attack strategy to the RSE of the malicious user by feeding back forged channel state information such that a linear combination of its own measurements and targeted users'measurements can be obtained. We develop an attack coefficient design method and derive a closed-form expression of the optimal coefficients, thereby minimizing the estimation error variance for the malicious user. Finally, simulation examples demonstrate the effectiveness of the sniffing attack strategy.", "url": "" }, { "id": "Tu-TuB08.4", "code": "TuB08.4", "title": "A Systematic Intermittent Fault Detection and Isolation Methodology for Nonlinear Dynamical Systems (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB08", "sessionTitle": "Security, Safety, Resilience, and Privacy for Cyber-Physical Systems II", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Shahvali, Milad", "affiliation": "University of Cyprus" }, { "name": "Kasis, Andreas", "affiliation": "University of Cyprus" }, { "name": "Polycarpou, Marios M.", "affiliation": "University of Cyprus" } ], "keywords": [ "Fault detection and diagnosis", "Nonlinear adaptive control" ], "abstract": "This paper considers the problem of model-based analytical detection and isolation of unknown intermittent faults in a class of nonlinear dynamical systems subject to modeling uncertainty. Unlike existing approaches, which typically address intermittent faults that remain constant when active, this work introduces a systematic methodology for determining whether nonlinear intermittent faults are active or inactive at specific time instants. Furthermore, a novel adaptive fault isolation architecture is proposed, that enables the exclusion of intermittent faults. In addition, rigorous stability analysis is conducted to establish the boundedness of all variables involved in the proposed scheme. Finally, the effectiveness and applicability of the proposed method are validated through numerical simulations.", "url": "" }, { "id": "Tu-TuB08.5", "code": "TuB08.5", "title": "A Local-Partition Algorithm for Detecting Siphon Overlapping in Petri Nets (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB08", "sessionTitle": "Security, Safety, Resilience, and Privacy for Cyber-Physical Systems II", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Wang, Xiaotian", "affiliation": "Imperial College London" }, { "name": "Angeli, David", "affiliation": "Imperial College" } ], "keywords": [ "Petri nets", "Multi-agent systems", "Consensus" ], "abstract": "In recent studies of consensus in multi-agent systems, the classical 1-to-1 interaction framework has been generalized to the N-to-1 type, known as joint-agent interactions. Such interactions enhance robustness and preserve privacy. A Petri Net approach has been proposed in the literature to model joint-agent interactions, where consensus conditions are characterized by the siphon overlapping property of the associated Petri Net. In this paper, we propose, for the first time, an efficient algorithm to verify this property for general Petri Nets. The method combines a local-partition strategy with a residual-siphon search, significantly reducing computational complexity. We provide correctness proofs and pseudocode, and present numerical experiments demonstrating both the effectiveness and the computational efficiency of the proposed algorithm compared with alternative variants.", "url": "" }, { "id": "Tu-TuB08.6", "code": "TuB08.6", "title": "Mitigating Stealthy Integrity Attacks in Cyber-Physical Systems Via Moving Target Defense and Interval Observers", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB08", "sessionTitle": "Security, Safety, Resilience, and Privacy for Cyber-Physical Systems II", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Tagne Mogue, Ruth Line", "affiliation": "Univ. Orleans" }, { "name": "Becis, Yasmina", "affiliation": "Université D'Orléans" }, { "name": "Courtial, Estelle", "affiliation": "Université D'Orléans" }, { "name": "Meslem, Nacim", "affiliation": "INP De Grenoble / CNRS" }, { "name": "Ramdani, Nacim", "affiliation": "University of Orleans" } ], "keywords": [ "Resilient networked control systems" ], "abstract": "This paper addresses secure state estimation and closed-loop stabilization for Cyber-Physical Systems (CPS) subject to bounded disturbances and additive sensor-to-estimator integrity attacks. We combine an interval observer (IO), a set-based residual detector, and a Moving Target Defense (MTD) sensor-selection policy for continuous-time systems under self-triggered measurement sampling. First, stealthiness is defined through measurement–prediction inclusion, leading to an explicit mode-dependent stealth window that bounds the attack amplitudes compatible with the IO prediction envelopes. Second, the IO is coupled with an interval-based feedback law, allowing a separation-like design of the controller and observer gains and ensuring bounded closed-loop behavior under bounded exogenous inputs. Third, we propose a probabilistic MTD policy that balances attack revealability, jump contraction, and exogenous-input sensitivity. The role of MTD is to reshape the active sensor configuration so as to shrink the admissible stealth set while preserving estimation performance. Numerical results illustrate that attacks remaining undetected under a fixed sensor configuration can be revealed by the proposed policy.", "url": "" }, { "id": "Tu-TuB09.1", "code": "TuB09.1", "title": "A New Intrinsic Mean-Covariance Estimator for Lie Group Observations: Application to SE(2)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB09", "sessionTitle": "Statistical Inference", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Labsir, Samy", "affiliation": "IPSA" }, { "name": "Renaux, Alexandre", "affiliation": "CNRS-Supelec - Universite Paris-Sud" } ], "keywords": [ "Estimation and filtering", "Statistical inference", "Statistical analysis" ], "abstract": "In this communication, we propose to derive a novel estimator of both mean and covariance matrix of observations following a Gaussian distribution on Lie groups. The originality of the approach is to estimate the covariance by using its Lie group structure. To achieve this, we use an intrinsic descent gradient algorithm minimizing a criterion based on the log-likelihood. We derive novel expressions of the gradient of this criterion and we establish that, under suitable assumptions, it converges to a unique solution. Consistency of the proposed estimator is validated numerically by comparison with state-of-the-art approaches.", "url": "" }, { "id": "Tu-TuB09.2", "code": "TuB09.2", "title": "Convex Computations for Controlled Safety Invariant Sets of Black-Box Discrete-Time Dynamical Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB09", "sessionTitle": "Statistical Inference", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Wu, Taoran", "affiliation": "Institute of Software, Chinese Academy of Sciences" }, { "name": "Xue, Yiling", "affiliation": "KLSS and SKLCS, ISCAS, Beijing, China" }, { "name": "Pan, Jingduo", "affiliation": "KLSS and SKLCS, ISCAS, Beijing, China" }, { "name": "Ren, Dejin", "affiliation": "KLSS and SKLCS, ISCAS, Beijing, China" }, { "name": "Easwaran, Arvind", "affiliation": "Nanyang Technological University" }, { "name": "Xue, Bai", "affiliation": "Institute of Software" } ], "keywords": [ "Statistical inference" ], "abstract": "Identifying controlled safety invariant sets (CSISs) is essential for safety-critical systems. This paper addresses the problem of computing CSISs for black-box discrete-time systems, where the dynamics are unknown and only limited simulation data are available. Classical CSISs require that for every state in the set, there exists a control input that keeps the system within the set at the next step, which is often overly restrictive or impractical for black-box systems. To address this, we introduce the notion of a Probably Approximately Correct (PAC) CSIS, in which, with prescribed confidence, there exists a suitable control input to keep the system within the set at the next step for at least a specified fraction of the states. Our approach leverages barrier functions and scenario optimization, yielding a tractable linear programming method for estimating PAC CSISs. Several illustrative examples demonstrate the effectiveness of the proposed framework.", "url": "" }, { "id": "Tu-TuB09.3", "code": "TuB09.3", "title": "Riemannian Gradient Based Localization Method for Range-Difference Measurements with Non-Gaussian Noise", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB09", "sessionTitle": "Statistical Inference", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Zhao, Jishu", "affiliation": "Tongji University" }, { "name": "Lei, Jinlong", "affiliation": "Tongji University" }, { "name": "Yi, Peng", "affiliation": "Tongji University" } ], "keywords": [ "Statistical inference", "Estimation and filtering", "Learning methods for control" ], "abstract": "This article aims to investigate the source localization problem based on range-difference measurements from sensor signals. In previous studies, this problem was generally modeled as a least squares problem, since the maximum likelihood estimator is equivalent to the least squares one under Gaussian noise. However, when the noises violate the Gaussian model, estimating the source position using least squares may be biased and inefficient. We assume that the distribution of random noise variables belongs to the scale family (including Gaussian, student-t, and Laplace distributions, among others) and model the problem based on maximum likelihood estimation. We propose an iterative algorithm utilizing the Riemannian gradient of statistical manifolds to approximate the optimal solution of the maximization problem. When the coordinates of sensors do not belong to a line nor a hyperbola, the iterates are proved to converge to the maximum likelihood estimator (MLE), which is consistent and Fisher efficient. Finally, numerical experiments are implemented to demonstrate the theoretical results under different noise models and show the empirical performance of the algorithm.", "url": "" }, { "id": "Tu-TuB09.4", "code": "TuB09.4", "title": "Density Estimation from Weighted Samples with the Localized Cumulative Distribution", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB09", "sessionTitle": "Statistical Inference", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Frisch, Daniel", "affiliation": "KIT" }, { "name": "Hanebeck, Uwe", "affiliation": "Karlsruhe Institute of Technology (KIT)" } ], "keywords": [ "Statistical inference", "Estimation and filtering", "Statistical analysis" ], "abstract": "We propose a novel method for nonparametric density estimation from weighted samples. Thereby the density is represented in discretized form as a regular grid of square roots of probabilities or density values. We define a distance measure between samples and density grid by computing the Localized Cumulative Distributions of both and then a modified Cramér–von Mises distance between them. We achieve smoothness with an additional Fisher Information regularization. The square root representation helps twofold: it enforces the nonnegativity constraint for the resulting density and simplifies computation of the Fisher Information. In a numerical evaluation, we compute the Kullback-Leibler divergence of our result to the ground truth density and demonstrate our method outperforming a conventional kernel density estimator (KDE) even for its best bandwidth choice. Julia source code is available here: https://github.com/KIT-ISAS/IFAC26_Frisch", "url": "" }, { "id": "Tu-TuB09.5", "code": "TuB09.5", "title": "Bayesian Pulse Reconstruction under Pile-Up Using Reversible Jump MCMC", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB09", "sessionTitle": "Statistical Inference", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Hosseini Dastja, Seyed Amir", "affiliation": "University of Melbourne" }, { "name": "Manton, Jonathan H.", "affiliation": "The Australian National Univ" } ], "keywords": [ "Statistical inference", "Statistical analysis" ], "abstract": "This paper investigates reconstructing a pulse train from noisy samples, where overlapping arrivals, known as pile‑up, hinder the estimation of the number of pulses and their arrival times and amplitudes. We employ reversible jump Markov chain Monte Carlo (RJ-MCMC) to sample the posterior distribution and derive parameter estimates incorporating novel merge and split moves to account for closely arrived pulses. We evaluate the performance using two metrics: the mean squared error (MSE) and the Wasserstein-2 distance. Numerical simulations demonstrate that the proposed RJ-MCMC accurately estimates the true number of pulses and their corresponding parameters. These results also indicate that RJ-MCMC outperforms maximum likelihood estimation (MLE) in both robustness and precision for pulse processing under pile-up.", "url": "" }, { "id": "Tu-TuB09.6", "code": "TuB09.6", "title": "Conditional Mean-Field Langevin Algorithm for Large-Scale Nonconvex Optimization", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB09", "sessionTitle": "Statistical Inference", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Chen, Yan", "affiliation": "Academy of Mathematics and Systems Science, Chinese Academy of Sciences" }, { "name": "Li, Tao", "affiliation": "Academy of Mathematics and Systems Science，Chinese Academy of Sciences" } ], "keywords": [ "Randomized algorithms in stochastic systems", "Distributed optimization", "Multi-agent systems" ], "abstract": "We study a distributed nonconvex optimization problem with a continuum of homogeneous weakly interacting nodes. All nodes’ cost functions are identical. Representing the interactions among nodes in terms of a conditional mean-field term, we propose a conditional mean-field Langevin algorithm. The evolution of its state is jointly driven by the conditional mean-field term, the gradient of the cost function and a noise term. By the classical conditional law of large numbers and the theory of convergence of measures, we prove the conditional law of large numbers of the algorithm, which reveals that the algorithm characterizes the limiting behavior of a class of large-scale interacting particle systems with common noise. Besides, by choosing algorithm gains properly, we prove that the distribution of the state in the algorithm weakly converges to a limiting distribution which concentrates on the set of the global minima of the cost function. We conduct numerical experiments to demonstrate the consistency of the results with our theoretical analysis.", "url": "" }, { "id": "Tu-TuB10.1", "code": "TuB10.1", "title": "Resource Allocation and Scheduling for Flexible Manufacturing Systems Based on Timed Petri Nets (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB10", "sessionTitle": "JO-NAHS: Discrete Event and Hybrid Systems III", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "He, Zhou", "affiliation": "Shaanxi University of Science and Technology" }, { "name": "Li, Ning", "affiliation": "Shaanxi University of Science & Technology" }, { "name": "Li, Liang", "affiliation": "Wuhan University of Science and Technology" }, { "name": "Ran, Ning", "affiliation": "Hebei University" }, { "name": "Seatzu, Carla", "affiliation": "Univ. of Cagliari" } ], "keywords": [ "Discrete event modeling and simulation", "Petri nets", "Optimal control of discrete event and hybrid systems" ], "abstract": "This paper addresses the resource allocation and scheduling problem for flexible manufacturing systems, aiming to find an initial resource allocation scheme and its corresponding scheduling scheme to minimize the system makespan while ensuring the total resource cost does not exceed a given budget. We propose an improved simulated annealing algorithm (for resource allocation) combined with a generation filtered beam search (for scheduling) based on timed Petri nets. Experimental results show that the proposed method achieves significantly higher solution quality compared to existing approaches.", "url": "" }, { "id": "Tu-TuB10.2", "code": "TuB10.2", "title": "Detectability, Opacity and Declassification of Timed DESs with Release Observation Mechanism (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB10", "sessionTitle": "JO-NAHS: Discrete Event and Hybrid Systems III", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Lefebvre, Dimitri", "affiliation": "Univ Le Havre" } ], "keywords": [ "Discrete event modeling and simulation", "Supervisory control and automata" ], "abstract": "This paper investigates timed discrete event systems operating under a Release Observation Mechanism, where delayed observations are stored and selectively released during system evolution. A formal model called Labeled Automaton with Time Intervals and Release states is introduced, along with its associated Clock Interval Automaton with Release mechanism and timed observers. Building on these models, several notions of detectability, including tick detectability and release detectability, are defined. The results show how delayed and released observations can enhance state estimation in networked or privacy-sensitive systems. Applications to opacity and declassification are discussed, illustrating how controlled release of information can preserve or relax confidentiality.", "url": "" }, { "id": "Tu-TuB10.3", "code": "TuB10.3", "title": "Dynamic Trust-Based Fault Isolation for Multi-Agent Descriptor Systems Using Interval Observers (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB10", "sessionTitle": "JO-NAHS: Discrete Event and Hybrid Systems III", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Shijian, Luo", "affiliation": "Southeast University" }, { "name": "Cao, Yang", "affiliation": "Southeast University" }, { "name": "Zhang, Jing", "affiliation": "Southeast University" } ], "keywords": [ "Distributed control and estimation", "Fault detection and diagnosis", "Multi-agent systems" ], "abstract": "Recent literature highlights the increasingly prominent network security and reliability issues driven by the rapid development of intelligent interconnected systems. This vulnerability is particularly critical in cooperative Multi-Agent Systems (MASs) because an agent with an actuator fault broadcasting erroneous data can severely cross-contaminate healthy neighbors and trigger cascading failures. To address this challenge and fundamentally enhance network security, this paper proposes a fully decentralized, dynamic trust evaluation mechanism for multi-agent descriptor systems subject to Lipschitz nonlinearities. By employing Nonlinear Robust Interval Observers (NRIOs), each agent computes strict, mathematically guaranteed upper and lower state bounds under unknown-but-bounded disturbances. Based on the interval divergence, a novel continuous trust metric is designed. This metric allows healthy agents to endogenously evaluate neighbor reliability and instantly assign a near-zero communication weight to a compromised node. Rigorous theoretical proofs and simulations demonstrate that the proposed method structurally prevents false alarms and securely severs erroneous data links, ensuring the overall resilience and security of the network.", "url": "" }, { "id": "Tu-TuB10.4", "code": "TuB10.4", "title": "Feasibility-Aware Hybrid Control for Motion Planning under Signal Temporal Logics (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB10", "sessionTitle": "JO-NAHS: Discrete Event and Hybrid Systems III", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Rousseas, Panagiotis", "affiliation": "National Technical University of Athens" }, { "name": "Dimarogonas, Dimos V.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Event-based control" ], "abstract": "Task planning problems have become increasingly relevant in recent years. Systems nowadays are not only required to carry out a prespecified series of tasks successfully, but importantly to plan for which tasks they may perform and when to execute them. This necessitates merging two fundamentally different paradigms, namely low-level continuous control with high-level discrete decision-making. Towards this direction, we propose a novel hybrid scheme where a continuous, simplified robot model is combined with a discrete variable that encodes which task-related constraints the robot is obeying at each time instance. Even though the robot model is simple, the system's workspace is non-convex while crucially, the proposed method is based on feasibility analysis that enables satisfying multiple overlapping constraints.", "url": "" }, { "id": "Tu-TuB10.5", "code": "TuB10.5", "title": "Analysis and Design of Adaptive Neuromorphic Control for Periodic Oscillation (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB10", "sessionTitle": "JO-NAHS: Discrete Event and Hybrid Systems III", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Zhang, Xinxin", "affiliation": "Delft University of Technology" }, { "name": "Vinagre, B. M.", "affiliation": "Univ. De Extremadura" }, { "name": "Tejado, Inés", "affiliation": "Universidad De Extremadura" } ], "keywords": [ "Event-based control" ], "abstract": "This work presents the analysis and design of a neuromorphic control system based on a dual half-center oscillator (HCO) architecture. We first perform a theoretical nullcline analysis to characterize the effects of intrinsic neural parameters (including the time constants, coupling gains, and inputs) on HCO dynamics. Based on this analysis, we then propose a design procedure that integrates the specification of key HCO parameters with adaptive control algorithms for rhythmic oscillation with desired amplitude and frequency regulation. Finally, the proposed HCO control system is validated through case studies on a pendulum through simulations. Results demonstrate the controller's capacity to induce and maintain periodic oscillations across varying damping ratios and a wide frequency range from 1 Hz to 100 Hz.", "url": "" }, { "id": "Tu-TuB10.6", "code": "TuB10.6", "title": "Predictive Event-Triggered Control for String-Stable Platooning (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB10", "sessionTitle": "JO-NAHS: Discrete Event and Hybrid Systems III", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Gorski, Etienne", "affiliation": "University of Lorraine" }, { "name": "Morarescu, Irinel Constantin", "affiliation": "Universite De Lorraine" }, { "name": "Satheeskumar Varma, Vineeth", "affiliation": "CRAN - Université De Lauraine" }, { "name": "Busoniu, Lucian", "affiliation": "Technical University of Cluj-Napoca" } ], "keywords": [ "Event-based control", "Stability and stabilization of hybrid systems", "Control over networks" ], "abstract": "This paper presents an event-triggered control strategy for vehicle platoons that use Cooperative Adaptive Cruise Control (CACC). In contrast to classical CACC, which relies on continuous communication of each vehicle's control input to its next follower, we propose a framework in which each vehicle intermittently communicates a longer-horizon prediction of its control trajectory. A non-standard, predictive flavor of event-triggered control results, in which these more informative predictions are used instead of the usual zero- or first-order-hold signal reconstruction. Communications are triggered by a dynamic rule, when the accumulated discrepancy between the real input trajectory and the predicted one becomes negative. By exploiting model-based predictions, we achieve a significantly reduced number of communications, while guaranteeing individual and string stability through a Lyapunov-based analysis. Numerical simulations with instantaneous and sustained perturbations on a seven-vehicle platoon illustrate the effectiveness of the proposed framework.", "url": "" }, { "id": "Tu-TuB13.1", "code": "TuB13.1", "title": "Asymptotic Optimal Synthesis for Motion Planning Complexity for Control-Affine Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB13", "sessionTitle": "Optimal Control Theory", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Motta, Michele", "affiliation": "SISSA" }, { "name": "Prandi, Dario", "affiliation": "Université Paris-Saclay, CentraleSupélec, CNRS" } ], "keywords": [ "Optimal control theory", "Application of nonlinear analysis and design", "Analytic design" ], "abstract": "We present an asymptotic optimal synthesis for the motion planning problem in the case of control-affine systems on 3-dimensional manifolds. This is based on a fine analysis of the complexity of the tracking problem of trajectories that are non-admissible for the control system, for which we provide a precise asymptotic estimate. Our result extends to the control-affine case the sharp asymptotic estimate and explicit asymptotic optimal synthesis known for the control-linear (sub-Riemannian) case, showing how the alignment of the drift with the reference trajectory determines three qualitatively different regimes.", "url": "" }, { "id": "Tu-TuB13.2", "code": "TuB13.2", "title": "Geometric Optimal Control of Nonlinear Systems Via Bilinear Embedding", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB13", "sessionTitle": "Optimal Control Theory", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Kuan, Yuan-Hung", "affiliation": "Washington University in St. Louis" }, { "name": "Li, Jr-Shin", "affiliation": "Washington University in St. Louis" } ], "keywords": [ "Optimal control theory", "Application of nonlinear analysis and design", "Numerical methods for optimal control" ], "abstract": "This paper addresses geometric optimal control problems for a class of nonlinear systems that are emph{exactly bilinearizable}. We introduce the Exact Bilinearization Iterative Form (EBIF), which transforms a nonlinear system into a dynamically equivalent higher-dimensional bilinear system. We show that this EBIF bilinearization procedure is not merely a structural change but also induces a Hamiltonian equivalence between the nonlinear and its embedded bilinear optimal control problems. Specifically, we demonstrate that the cotangent lift of the EBIF embedding defines a symplectic equivalence, ensuring that the corresponding Hamiltonian vector fields generate conjugate flows. This preservation of geometric structures ensures that the optimal solution derived from the embedded bilinear system is optimal for the original nonlinear system, facilitating feasible derivations of explicit, closed-form control laws. The effectiveness of the EBIF-based optimal control framework is demonstrated through optimal steering and trajectory-tracking tasks for a kinematic bicycle model.", "url": "" }, { "id": "Tu-TuB13.3", "code": "TuB13.3", "title": "A Case Study in Ensemble Optimal Control for Bayesian Input Design", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB13", "sessionTitle": "Optimal Control Theory", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Sacchelli, Ludovic", "affiliation": "Inria" }, { "name": "Scagliotti, Alessandro", "affiliation": "Department of Mathematics, CIT School, Technical University of Munich" } ], "keywords": [ "Optimal control theory", "Infinite-dimensional multi-agent systems and networks", "Optimization-based estimation and control" ], "abstract": "We discuss the problem of input design for uncertainty reduction in a parameter estimation procedure. Assuming a linear continuous-time control system with noisy measurements, we formulate an objective of variance reduction in a Bayesian Gaussian setting as an optimal control problem and analyze it from a geometric control perspective. The resulting cost functional depends on the unknown parameter, we compare the optimal control approach with a non-standard alternative inspired by ensemble control, where the cost is averaged over the prior distribution after computation, rather than before. This requires the statement of a generalized Pontryagin's maximum principle adapted to Gaussian distributions.", "url": "" }, { "id": "Tu-TuB13.4", "code": "TuB13.4", "title": "Jumping Extremals in State-Constrained Problems: Sufficient Optimality Conditions", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB13", "sessionTitle": "Optimal Control Theory", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Chittaro, Francesca Carlotta", "affiliation": "Università Di Trento" }, { "name": "Poggiolini, Laura", "affiliation": "Universita' Di Firenze" } ], "keywords": [ "Optimal control theory", "Non-smooth and discontinuous optimal control", "Lagrangian and Hamiltonian systems" ], "abstract": "In this article we establish new sufficient strong-local optimality conditions for a class of single-input control-affine problems subject to a second-order scalar state constraint. We deal in particular with the case in which the adjoint covector is discontinuous at the junction points.", "url": "" }, { "id": "Tu-TuB13.5", "code": "TuB13.5", "title": "Feedback Synthesis for Nonlinear Systems Via Convex Control Lyapunov Functions", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB13", "sessionTitle": "Optimal Control Theory", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Villanueva, Mario Eduardo", "affiliation": "IMT School for Advanced Studies Lucca" }, { "name": "Oravec, Juraj", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Paulen, Radoslav", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Houska, Boris", "affiliation": "ShanghaiTech University" } ], "keywords": [ "Optimal control theory", "Lyapunov methods", "Robust controller synthesis" ], "abstract": "This paper introduces computationally efficient methods for synthesizing explicit piecewise affine (PWA) feedback laws for nonlinear discrete-time systems, ensuring robustness and performance guarantees. The approach proceeds by optimizing a configuration-constrained PWA approximation of the value function of an infinite-horizon min–max Hamilton–Jacobi–Bellman equation. Here, robustness and performance are maintained by enforcing the PWA approximation to be a generalized control Lyapunov function for the given nonlinear system. This enables the generation of feedback laws with configurable storage complexity and pre-determined evaluation times, based on a selected configuration template. The framework's effectiveness is demonstrated through a constrained Van der Pol oscillator case study, where an explicit PWA controller with certified ergodic performance and specified complexity is synthesized over a large operational domain.", "url": "" }, { "id": "Tu-TuB13.6", "code": "TuB13.6", "title": "A Semi-Smooth Newton Method for the Constrained Optimal Control of Continuous-Time Linear Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB13", "sessionTitle": "Optimal Control Theory", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Jones, Simon J.", "affiliation": "University of Colorado Boulder" }, { "name": "Liao-McPherson, Dominic", "affiliation": "The University of British Columbia" }, { "name": "Nicotra, Marco M.", "affiliation": "University of Colorado Boulder" } ], "keywords": [ "Optimal control theory", "Numerical methods for optimal control", "Non-smooth and discontinuous optimal control" ], "abstract": "This paper details a novel indirect method for solving constrained optimal control problems (OCPs) directly in continuous-time function space. The KKT conditions are embedded in a a non-smooth complementarity function, which enables their reformulation as a rootfinding problem in Banach space. This problem is then solved using a non-smooth Newton method. Finally, the paper shows that the Newton update can be obtained by solving a modified differential Riccati equation, where the cost terms are reweighted at every iteration based on the constraint multipliers. Numerical simulations show the effectiveness of the method, which converges superlinearly up to the tolerance of the ODE solver.", "url": "" }, { "id": "Tu-TuB14.1", "code": "TuB14.1", "title": "Data-Driven Stochastic Optimal Control in Reproducing Kernel Hilbert Spaces (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB14", "sessionTitle": "JO-EAAI: Learning Methods for Optimal Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Hoischen, Nicolas", "affiliation": "Technical University of Munich" }, { "name": "Bevanda, Petar", "affiliation": "TU Munich" }, { "name": "Sosnowski, Stefan", "affiliation": "Technical University of Munich (TUM)" }, { "name": "Hirche, Sandra", "affiliation": "Technical University of Munich" }, { "name": "Houska, Boris", "affiliation": "ShanghaiTech University" } ], "keywords": [ "Learning methods for optimal control", "Design methods for data-based control", "Stochastic optimal control problems" ], "abstract": "This paper proposes a fully data-driven approach for optimal control of nonlinear control-affine systems represented by a stochastic diffusion. The focus is on the scenario where both the nonlinear dynamics and stage cost functions are unknown, while only a control penalty function and constraints are provided. To this end, we embed state probability densities into a reproducing kernel Hilbert space (RKHS) to leverage recent advances in operator regression, thereby identifying Markov transition operators associated with controlled diffusion processes. This operator learning approach integrates naturally with convex operator-theoretic Hamilton-Jacobi-Bellman recursions that scale linearly with state dimensionality, effectively solving a wide range of nonlinear control problems. Numerical results demonstrate its ability to address diverse nonlinear control problems, including the depth control of an autonomous underwater vehicle.", "url": "" }, { "id": "Tu-TuB14.2", "code": "TuB14.2", "title": "Robust Reachability within Deep Reinforcement Learning Framework (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB14", "sessionTitle": "JO-EAAI: Learning Methods for Optimal Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Marthi, Satya Vinay Chavan", "affiliation": "University of Lorraine" }, { "name": "Jha, Mayank Shekhar", "affiliation": "University of Lorraine" }, { "name": "Theilliol, Didier", "affiliation": "University of Lorraine" }, { "name": "Ponsart, Jean-Christophe", "affiliation": "CRAN - Université De Lorraine" } ], "keywords": [ "Learning methods for optimal control", "Differential or dynamic games", "Data-driven robust control" ], "abstract": "We propose a novel deep reinforcement learning based approach to solve Hamilton--Jacobi reachability (HJ-R) problem for nonlinear control-affine systems with external disturbance. By recasting reachability as an optimal control problem, we solve it within a Deep Deterministic Policy Gradient (DDPG) framework: the critic learns the HJ-R value function and the actor synthesizes the optimal policy. We propose a Telescopic Incentive Reward Function that makes learning process efficient, promotes finite-time convergence to the target set, and reduces control oscillations near constraint boundaries. Disturbance is incorporated through agent-environment interaction, enabling robust optimal policy learning without an explicit disturbance model. The proposed approach fares well against classical grid-based dynamic programming approach and mitigates the curse of dimensionality through deep neural approximation, yielding scalability to higher-dimensional states. Numerical studies demonstrate target reach across diverse initial conditions, smooth control inputs relative to dynamic programming baselines, and resilience to worst-case disturbances. These results establish the proposed Robust Reachability-DDPG framework as an efficient, scalable, and robust alternative for HJ--R controller synthesis in continuous state--action spaces. The efficacy of the approach is assessed in simulation.", "url": "" }, { "id": "Tu-TuB14.3", "code": "TuB14.3", "title": "Meta-Neural Predictive Control (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB14", "sessionTitle": "JO-EAAI: Learning Methods for Optimal Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Zhu, Guanyu", "affiliation": "Tokyo University of Agriculture and Technology" }, { "name": "Ding, Xuanling", "affiliation": "The University of Osaka" }, { "name": "Barreiro-Gomez, Julian", "affiliation": "Khalifa University" }, { "name": "Wang, Ye", "affiliation": "The University of Melbourne" }, { "name": "Hashimoto, Kazumune", "affiliation": "Osaka University" }, { "name": "Takai, Shigemasa", "affiliation": "The University of Osaka" }, { "name": "Arima, Takuji", "affiliation": "Tokyo University of Agriculture and Technology" }, { "name": "Shen, Xun", "affiliation": "Tokyo University of Agriculture and Technology" } ], "keywords": [ "Learning methods for optimal control", "Model predictive control" ], "abstract": "This paper presents a neural predictive control approach enhanced by meta-learning (MNPC). It is designed for applications with limited samples and requires rapid adaptation to changing scenarios. MNPC employs a deep neural network policy, meta-trained offline via bilevel optimization to derive an optimal initial parameter vector. This vector facilitates rapid online fine-tuning with minimal samples in new scenarios. Theoretical guarantees on uniform convergence and finite task coverage are also discussed. The effectiveness of the proposed method is demonstrated in the relevant simulations, where MNPC outperforms supervised learning baselines in terms of data efficiency and control accuracy.", "url": "" }, { "id": "Tu-TuB14.4", "code": "TuB14.4", "title": "Parameter-Modulation State Space Model for Quadrotor Control in Windy Environments (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB14", "sessionTitle": "JO-EAAI: Learning Methods for Optimal Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Kim, KyungSoo", "affiliation": "Pohang Univ. of Sci. & Tech" }, { "name": "Park, PooGyeon", "affiliation": "Pohang Univ. of Sci. & Tech" } ], "keywords": [ "Learning methods for optimal control", "Model predictive control", "Data-driven robust control" ], "abstract": "Quadrotor dynamics are highly sensitive to aerodynamic disturbances induced by wind. Despite its significant influence, most existing studies either neglect wind effects or treat them as random disturbances, without leveraging wind information. This paper presents a wind-adaptive dynamics learning framework that combines the Mamba model with parameter modulation technique, enabling adaptive modeling of wind-conditioned dynamics. The learned model is integrated into a model predictive control scheme for robust trajectory tracking under varying wind conditions. Validation through high-fidelity Rotorpy simulations demonstrates the proposed method’s superior ability to capture aerodynamic effects and maintain stable control compared to conventional baselines.", "url": "" }, { "id": "Tu-TuB14.5", "code": "TuB14.5", "title": "Decision Transformer-Based Tuning for Model Predictive Control (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB14", "sessionTitle": "JO-EAAI: Learning Methods for Optimal Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Güzelkaya, Nehir", "affiliation": "Technical University of Munich" }, { "name": "Leibold, Marion", "affiliation": "Technical University of Munich" }, { "name": "Buss, Martin", "affiliation": "Technische Universitaet Muenchen" } ], "keywords": [ "Learning methods for optimal control", "Model predictive control", "Optimal control theory" ], "abstract": "In this work, we propose a novel Decision Transformer (DT)-based framework for tuning the parameters of Model Predictive Control (MPC). First, we show that an MPC scheme with a quadratic cost, linear constraints, and a nominal linear model can reproduce the optimal solution of an infinite-horizon nonlinear regulation problem when the cost and constraint parameters are tuned based on the history of states and control inputs. Then, we formulate parameter tuning as a sequence modeling problem and develop a DT-based framework, referred to as MPC-Decisioner, which leverages the attention mechanism of DT to exploit historical and contextual information and generate MPC parameters online, conditioned on trajectories of costs, states, and past parameters. This framework offers interpretability through the attention scores of the Transformer and achieves improved closed-loop performance compared to baseline MPC parameter tuning methods. Its effectiveness is demonstrated through simulation studies.", "url": "" }, { "id": "Tu-TuB15.1", "code": "TuB15.1", "title": "A Douglas-Rachford Splitting for Solving Monotone Affine Variational Inequalities in Linear-Quadratic Dynamic Games", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB15", "sessionTitle": "Differential or Dynamic Games", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Rahimi Baghbadorani, Reza", "affiliation": "Erasmus University Rotterdam" }, { "name": "Benenati, Emilio", "affiliation": "KTH Stockholm" }, { "name": "Grammatico, Sergio", "affiliation": "Delft Univ. of Tech" } ], "keywords": [ "Differential or dynamic games", "Applications of optimal control", "Optimal control theory" ], "abstract": "This paper considers constrained linear dynamic games with quadratic objective functions, which can be cast as affine variational inequalities. By leveraging the problem structure, we apply the Douglas-Rachford splitting, which generates a solution algorithm with linear convergence rate. The fast convergence of the method enables receding-horizon control architectures. Furthermore, we demonstrate that the associated VI admits a closed-form solution within a neighborhood of the attractor, thus allowing for a further reduction in computation time. Finally, we benchmark the proposed method via numerical experiments in an automated driving application.", "url": "" }, { "id": "Tu-TuB15.2", "code": "TuB15.2", "title": "Cooperative Surrounding Control of Heterogeneous UAV-USV Systems Via Safety Constrained Stackelberg Game", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB15", "sessionTitle": "Differential or Dynamic Games", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Zhang, Hongye", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Chen, Yong", "affiliation": "Uestc" }, { "name": "Ali, Ikram", "affiliation": "Shenzhen University" } ], "keywords": [ "Differential or dynamic games", "Control barrier functions and state space constraints", "Cooperative nonlinear control" ], "abstract": "This article investigates the cooperative surrounding control for heterogeneous unmanned surface vehicles (USVs) and unmanned aerial vehicles (UAVs). To achieve optimal surrounding of a target USV, Stackelberg games are constructed, where USVs are treated as the upper layer of the hierarchical architecture and UAVs serve as the lower layer, implementing sequential control. Then a composed control barrier function (CBF) describing the multiple safety constraints is established and the corresponding constrained Stackelberg game is extended. Safety-critical optimal policies are designed, and the safety as well as the Stackelberg-Nash equilibrium (SNE) are theoretically proven under both constrained and unconstrained conditions. Subsequently, critic neural networks(NN) are employed to learn the optimal policies. Finally, result analysis further verified the effectiveness of the method.", "url": "" }, { "id": "Tu-TuB15.3", "code": "TuB15.3", "title": "A Neural Network Based Distributed Algorithm for Seeking Generalized Nash Equilibrium", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB15", "sessionTitle": "Differential or Dynamic Games", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Ma, Jian", "affiliation": "Xinjiang University" }, { "name": "Cai, Xin", "affiliation": "Xinjiang University" } ], "keywords": [ "Differential or dynamic games", "Distributed nonlinear control" ], "abstract": "This paper investigates distributed generalized Nash equilibrium (GNE) seeking for aggregative games with global coupling constraints. During the distributed search process for GNE, agents' dynamics are subject to unknown nonlinear dynamics and external disturbances, which are approximated by neural networks. Thus, this paper proposes a neural network-based distributed GNE seeking algorithm. Under weight-balanced directed communication graphs, a second-order nonlinear multi-agent system can achieve asymptotically GNE with a small error according to the designed algorithm, which is proved by the Lyapunov stability analysis. Finally, a numerical case is presented to validate the effectiveness of the proposed method.", "url": "" }, { "id": "Tu-TuB15.4", "code": "TuB15.4", "title": "Policy Gradient Methods for Continuous-Time Linear Quadratic Games", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB15", "sessionTitle": "Differential or Dynamic Games", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Thömmes, Felix", "affiliation": "Karlsruhe Institute of Technology" }, { "name": "Günther, Lucas", "affiliation": "Institute of Control Systems, Karlsruhe Institute of Technology" }, { "name": "Handwerker, Karl", "affiliation": "Institute of Control Systems, Karlsruhe Insitute of Technology" }, { "name": "Krüger, Paul", "affiliation": "Karlsruhe Institute of Technology" }, { "name": "Varga, Balint", "affiliation": "Karlsruhe Institute of Technology (KIT), Campus South" }, { "name": "Hohmann, Soeren", "affiliation": "KIT" } ], "keywords": [ "Differential or dynamic games", "Learning methods for optimal control", "Optimal control theory" ], "abstract": "This paper presents the vanilla, natural, and quasi-Newton policy gradients for continuous-time linear quadratic games, providing the first formulation of gradient-based adaptation in this setting. We show that all variants share exactly the set of feedback Nash equilibria as their stationary points and complement the theory with a numerical comparison of convergence rates and computational costs. We further construct counterexamples exhibiting saddle-induced divergence and attracting limit cycles known from discrete-time games, demonstrating that such non-convergent phenomena are not artifacts of temporal discretization but inherent to multi-agent gradient-based learning itself.", "url": "" }, { "id": "Tu-TuB15.5", "code": "TuB15.5", "title": "From Open-Loop Representations to Closed-Loop Feedback Implementations in Differential Games: A Numerical Case Study", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB15", "sessionTitle": "Differential or Dynamic Games", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Braun, Philipp", "affiliation": "The Australian National University" }, { "name": "Molloy, Timothy L.", "affiliation": "Monash University" }, { "name": "Barkai, Gal", "affiliation": "Université De Lorraine, CNRS," }, { "name": "Shames, Iman", "affiliation": "The University of Melbourne" } ], "keywords": [ "Differential or dynamic games", "Non-smooth and discontinuous optimal control" ], "abstract": "Solutions to pursuit-evasion and surveillance-evasion differential games are typically computed and expressed using open-loop representations, with the synthesis of feedback strategies significantly less common. We propose a numerical scheme for obtaining feedback strategies for the recently introduced prying-pedestrian surveillance-evasion differential game. The scheme involves computing feedback strategies as input-output maps approximated via neural networks trained using data obtained from open-loop representations of solutions. Simulations show the effectiveness of neural networks trained with an appropriate learning-loss function. Since optimal feedback strategies are discontinuous, as a second contribution, the potential loss/gain of individual players is subsequently studied for players using sample-and-hold feedback compared to continuous-time feedback.", "url": "" }, { "id": "Tu-TuB15.6", "code": "TuB15.6", "title": "Inverse Linear–Quadratic Gaussian Differential Games", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB15", "sessionTitle": "Differential or Dynamic Games", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Günther, Lucas", "affiliation": "Institute of Control Systems, Karlsruhe Institute of Technology" }, { "name": "Thömmes, Felix", "affiliation": "Karlsruhe Institute of Technology" }, { "name": "Handwerker, Karl", "affiliation": "Institute of Control Systems, Karlsruhe Insitute of Technology" }, { "name": "Varga, Balint", "affiliation": "Karlsruhe Institute of Technology (KIT), Campus South" }, { "name": "Hohmann, Soeren", "affiliation": "KIT" } ], "keywords": [ "Differential or dynamic games", "Optimization-based estimation and control", "Stochastic optimal control problems" ], "abstract": "This paper presents a method for solving the Inverse Stochastic Differential Game (ISDG) problem in finite-horizon linear–quadratic Gaussian (LQG) differential games. The objective is to recover cost function parameters of all players, as well as noise scaling parameters of the stochastic system, consistent with observed trajectories. The proposed framework combines (i) estimation of the feedback strategies, (ii) identification of the cost function parameters via a novel reformulation of the coupled Riccati differential equations, and (iii) maximum likelihood estimation of the noise scaling parameters. Simulation results demonstrate that the approach recovers parameters, yielding trajectories that closely match the observed trajectories.", "url": "" }, { "id": "Tu-TuB16.1", "code": "TuB16.1", "title": "Modular Adaptive Backstepping for Compensation of Unmatched Disturbances in Uncertain Nonlinear Plants", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB16", "sessionTitle": "Adaptive Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Gerasimov, Dmitry", "affiliation": "ITMO University" }, { "name": "Paramonov, Aleksei", "affiliation": "ITMO University" }, { "name": "Nikiforov, Vladimir O.", "affiliation": "ITMO University" }, { "name": "Pashenko, Artem", "affiliation": "ITMO University" } ], "keywords": [ "Adaptive control design", "Disturbance rejection and input-to-state stability" ], "abstract": "The paper deals with the problem of modular adaptive backstepping design for compensation of unmatched disturbances in nonlinear systems with unknown parameters. The disturbance is represented as a vector of unmeasured multisinusoidal functions with a priori unknown amplitudes, frequencies, and phases. The novelty of the proposed solution consists in a new modular adaptive backstepping with dynamic compensation term resulting in a relatively simple static error model without employment of the swapping technique. This model allows one to design an algorithm of adaptation with improved transient performance. The theoretical statements are illustrated by simulation results.", "url": "" }, { "id": "Tu-TuB16.2", "code": "TuB16.2", "title": "Gradient Descent-Based Adaptive State Tracking Control for Fully Uncertain Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB16", "sessionTitle": "Adaptive Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Liu, Sixin", "affiliation": "Qufu Normal University" }, { "name": "Zhang, Zhengqiang", "affiliation": "Qufu Normal University" } ], "keywords": [ "Adaptive control design", "Linear systems", "Lyapunov methods" ], "abstract": "This article proposes a gradient descent-based adaptive state tracking control scheme for uncertain linear systems. To address the uncertainty in the control coefficients, the Nussbaum gain technique is incorporated into a direct model reference adaptive control (MRAC) framework. Similar to the output feedback design, the gradient descent-based design scheme can also be applied to the adaptive state feedback control problems. The proposed scheme guarantees that all closed-loop signals are bounded and that the state tracking error converges to zero. Ultimately, the effectiveness of the scheme is validated by simulation results.", "url": "" }, { "id": "Tu-TuB16.3", "code": "TuB16.3", "title": "Adaptive Partial State Feedback Trajectory Tracking Control for Linear Time-Invariant Systems with Reference Model Uncertainties", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB16", "sessionTitle": "Adaptive Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Sang, Yingli", "affiliation": "Qufu Normal University" }, { "name": "Zhang, Zhengqiang", "affiliation": "Qufu Normal University" } ], "keywords": [ "Adaptive control design", "Linear systems", "Lyapunov methods" ], "abstract": "A model reference adaptive control strategy is presented for systems with unknown reference model parameters. The proposed control scheme does not need to impose additional assumption on the reference model compared to the traditional model reference adaptive control approach. It can solve the parametric uncertainties in both the controlled system and reference model simultaneously. Additionally, partial-state feedback method increases the possibility of the selection of feedback information. This scheme ensures asymptotic state tracking and solves the parametric uncertainties in both the controlled plant and reference model. The simulation study indicates the validity of the proposed scheme.", "url": "" }, { "id": "Tu-TuB16.4", "code": "TuB16.4", "title": "Model Reference Adaptive Control without High-Frequency Gain Knowledge Via Derivative Injection and Global HOSM Differentiators", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB16", "sessionTitle": "Adaptive Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Wang, Jiayi", "affiliation": "ShanghaiTech University" }, { "name": "Ji, Chenyang", "affiliation": "ShanghaiTech University" }, { "name": "Gong, Yizhou", "affiliation": "ShanghaiTech University" }, { "name": "Zhang, Yanjun", "affiliation": "Beijing Institute of Technology" }, { "name": "Wang, Yang", "affiliation": "Shanghaitech University" } ], "keywords": [ "Adaptive control design", "Linear systems", "Uncertain systems" ], "abstract": "This paper addresses the removal of the assumption of prior knowledge of the high-frequency gain in the model reference adaptive control (MRAC) problem for plants of arbitrary relative degree, where persistent excitation cannot be applied. Particularly, the proposed solution aims to avoid the impractical transient behavior typical of classical Nussbaum-based methods and overcome the steady-state-related limitations of existing state-of-the-art approaches. Inspired by parameter input normalization (PIN), the scheme introduces an auxiliary signal to transform the parametric error model into a relative-degree-one form, which admits a simple closed-form controller design within the PIN framework. A globally convergent higher-order sliding mode (HOSM) differentiator, enhanced with a novel state-norm-driven adaptive gain, is co-designed to reconstruct the auxiliary signal exactly and in finite time. Rigorous stability analysis guarantees uniform boundedness of all closed-loop signals and global asymptotic convergence of the tracking error. Comparative simulations against benchmark Nussbaum-based and DREM-based methods, along with validation on a ship motion model, demonstrate the improved performance and practical relevance of the proposed approach.", "url": "" }, { "id": "Tu-TuB16.5", "code": "TuB16.5", "title": "Adaptive Control of a Generalized Hill Equation", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB16", "sessionTitle": "Adaptive Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Gerasimov, Dmitry", "affiliation": "ITMO University" }, { "name": "Salina, Elizaveta", "affiliation": "ITMO University" }, { "name": "Ngo, Dang Hien", "affiliation": "ITMO University" }, { "name": "Nikiforov, Vladimir O.", "affiliation": "ITMO University" } ], "keywords": [ "Adaptive control design", "Nonlinear observers and filters", "Linear parameter-varying systems" ], "abstract": "The paper considers the control problem for a class of nonlinear parametrically uncertain systems described by the Hill equation extended by a superposition of unknown parameters multiplied by nonlinear functions of the systems state. The periodic time-varying (TV) parameter of this equation is representable by a multisinusoidal function of time with unknown amplitudes, frequencies, and phases of harmonics (sinusoids). The maximum number of harmonics is known. The control gain of the system is unknown, however its sign is known. Based on the structure of the system, a special observer of the TV parameter is proposed. Using this observer, an adapitve backstepping controller with modular identifiers and nonlinear damping ensuring the input-to-state stability property of the closed-loop system is designed. The tuning of the controller is provided by a robust adaptation algorithm.", "url": "" }, { "id": "Tu-TuB16.6", "code": "TuB16.6", "title": "Modular Adaptive Backstepping Design with Dynamic Compensation for Nonlinear Plants with Input Constraints", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB16", "sessionTitle": "Adaptive Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Gerasimov, Dmitry", "affiliation": "ITMO University" }, { "name": "Pashenko, Artem", "affiliation": "ITMO University" }, { "name": "Malysheva, Anna", "affiliation": "ITMO University" }, { "name": "Nikiforov, Vladimir O.", "affiliation": "ITMO University" } ], "keywords": [ "Adaptive control design", "Saturation and discontinuity" ], "abstract": "The paper addresses the problem of adaptive control of nonlinear systems with input constraints and violated matching conditions. A novel modification of the modular adaptive backstepping approach is proposed, incorporating dynamic compensation terms (DCT) into the control law, which results in a relatively simple static closed-loop error model. This model allows the design of adaptation algorithms with improved parametric convergence while avoiding initial swings in the tracking error by employing high-order time derivatives (HOTD) of the adjustable parameters. Simulation studies illustrate the effectiveness of the proposed method and demonstrate improved transient performance under unmatched uncertainties.", "url": "" }, { "id": "Tu-TuB17.1", "code": "TuB17.1", "title": "A 2-Contraction Framework for Initialization Analysis in Non-Convex Optimization", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB17", "sessionTitle": "Contraction Analysis for Stability and Optimality", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Bora, Riddhi Mohan", "affiliation": "Indian Institute of Technology Delhi" }, { "name": "Kar, Indra Narayan", "affiliation": "Indian Institute of Technology, Delhi" } ], "keywords": [ "Application of nonlinear analysis and design", "Stability of nonlinear systems", "Optimization-based estimation and control" ], "abstract": "Non-convex optimization problems often exhibit multiple local minima, maxima, and saddle points, making gradient-based methods sensitive to initialization. This paper applies the 2-contraction theory to the gradient-flow dynamics dot{mathbf{x}} =-nabla f(mathbf{x}) induced by a continuously differentiable non-convex objective function. Using Hessian spectral properties, we characterize the 2-contraction region and remove the saddle region to obtain a candidate region for stable equilibria. A forward-invariant sublevel-set condition is then imposed to construct a certified initialization set. The resulting method provides emph{a-priori} initialization guidance for standard gradient descent without modifying its update rule. While focusing on low-dimensional, continuous-time problems, this work also addresses scalability issues and discrete-time implementation for completeness.", "url": "" }, { "id": "Tu-TuB17.2", "code": "TuB17.2", "title": "Contraction Analysis of Filippov Solutions in Multi-Modal Piecewise Smooth Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB17", "sessionTitle": "Contraction Analysis for Stability and Optimality", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Liu, Zonglin", "affiliation": "University of Kassel" }, { "name": "Borchhardt, Kyra Leoni", "affiliation": "University Kassel" }, { "name": "Stursberg, Olaf", "affiliation": "University of Kassel" } ], "keywords": [ "Nonlinear control of switched & hybrid systems", "Switching stability and control", "Stability of nonlinear systems" ], "abstract": "This paper provides conditions to ensure contractive behavior of Filippov solutions generated by multi-modal piecewise smooth (PWS) systems. These conditions are instrumental in analyzing the asymptotic behavior of PWS systems, such as convergence towards an equilibrium point or a limit cycle. The work is motivated by a known principle for contraction analysis of bimodal PWS systems which ensures that the flow dynamics of each mode and the sliding dynamics on the switching manifold are contracting. This approach is extended first to PWS systems with multiple non-intersecting switching manifolds in R^n, and then to two intersecting switching manifolds in R^2.", "url": "" }, { "id": "Tu-TuB17.3", "code": "TuB17.3", "title": "Using Seminorms to Analyze Contraction of Switched Systems with Only Non-Contracting Modes", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB17", "sessionTitle": "Contraction Analysis for Stability and Optimality", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Baum, Edwin", "affiliation": "University of Kassel" }, { "name": "Liu, Zonglin", "affiliation": "University of Kassel" }, { "name": "Qin, Yuzhen", "affiliation": "Radboud University" }, { "name": "Stursberg, Olaf", "affiliation": "University of Kassel" } ], "keywords": [ "Nonlinear control of switched & hybrid systems", "Switching stability and control", "Stability of nonlinear systems" ], "abstract": "This paper investigates contraction properties of switched dynamical systems for the case that all modes are non-contracting, thereby extending existing results that require at least one mode to be contracting. Leveraging the property that unstable systems may still exhibit stable behavior within certain subspaces, conditions are provided which ensure contracting evolution within a given subspace of the state space of the switched system. These conditions are derived using the concepts of seminorms and semi-contracting systems. Then, by selecting a set of subspaces whose corresponding seminorms form a separating family of the state space, and by verifying whether a given mode is contracting in each subspace, conditions on the activation time of each mode are provided by which contraction on the complete state space is guaranteed. Numerical examples are presented for illustration.", "url": "" }, { "id": "Tu-TuB17.4", "code": "TuB17.4", "title": "Contraction Analysis of Monotone Systems with Time Delay", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB17", "sessionTitle": "Contraction Analysis for Stability and Optimality", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Vacchini, Edoardo", "affiliation": "University of Pavia" }, { "name": "Kawano, Yu", "affiliation": "Hiroshima University" }, { "name": "Cucuzzella, Michele", "affiliation": "University of Groningen" }, { "name": "van der Schaft, Arjan J.", "affiliation": "Univ. of Groningen" }, { "name": "Ferrara, Antonella", "affiliation": "University of Pavia" } ], "keywords": [ "Nonlinear time-delay systems", "Stability of nonlinear systems" ], "abstract": "In this paper, we show that monotonicity simplifies the incremental stability analysis of nonlinear time-delay systems. We first extend the concept of monotonicity for time-delay systems from the positive orthant cone to a general proper polyhedral cone. We then generalize the time-delay version of the Kamke condition, providing a necessary and sufficient criterion for monotonicity. Finally, as the main result, we derive a delay-independent sufficient condition for uniform incremental asymptotic stability by combining a linear Finsler-Lyapunov function for monotone delay-free systems with a Lyapunov-Krasovskii functional for time-delay systems.", "url": "" }, { "id": "Tu-TuB17.5", "code": "TuB17.5", "title": "On Contraction Conditions for Incremental Input-To-State Stability", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB17", "sessionTitle": "Contraction Analysis for Stability and Optimality", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Arkhis, Mohamed Yassine", "affiliation": "Inria" }, { "name": "Efimov, Denis", "affiliation": "Inria" } ], "keywords": [ "Stability of nonlinear systems", "Lyapunov methods" ], "abstract": "This paper establishes a necessary condition and a sufficient condition for incremental input-to-state stability. These conditions are motivated by the well-known relationship between incremental stability and contraction, a connection that has attracted increasing attention over the past two decades. This relationship allows the analysis of incremental stability for a nonlinear system to be reformulated as the stability analysis of the origin of an associated linear system. Building on this perspective, we derive contraction-like conditions that are both necessary and sufficient for incremental input-to-state stability, thereby reducing the study of this property for a nonlinear system to the verification of an ISS-like condition for a linear counterpart.", "url": "" }, { "id": "Tu-TuB17.6", "code": "TuB17.6", "title": "Scalable Formal Verification of Incremental Stability in Large-Scale Systems Using Graph Neural Networks", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB17", "sessionTitle": "Contraction Analysis for Stability and Optimality", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Basu, Ahan", "affiliation": "Indian Institute of Science" }, { "name": "Anand, Mahathi", "affiliation": "Technical University of Munich" }, { "name": "Jagtap, Pushpak", "affiliation": "Indian Institute of Science" } ], "keywords": [ "Interconnected nonlinear systems", "Stability of nonlinear systems", "Lyapunov methods" ], "abstract": "This work proposes a novel distributed framework for verifying the incremental stability of large-scale systems with unknown dynamics and known interconnection structures using graph neural networks. Our proposed approach relies on the construction of local incremental Lyapunov functions for subsystems, which are then composed together to obtain a suitable Lyapunov function for the interconnected system. Graph neural networks are used to synthesize these functions in a data-driven fashion. The formal correctness guarantee is then obtained by leveraging Lipschitz bounds of the trained neural networks. Finally, the effectiveness of our approach is validated through two nonlinear case studies.", "url": "" }, { "id": "Tu-TuB18.1", "code": "TuB18.1", "title": "Online Shifting Bottleneck Detection from Activity State Change Events: Algorithm and Case Study (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB18", "sessionTitle": "Intelligent Methods and Tools Supporting Decision Making in Manufacturing Systems and Supply Chains II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Eberlein, Sebastian", "affiliation": "BIBA - Bremer Institut Für Produktion Und Logistik GmbH at the University of Bremen" }, { "name": "Freitag, Michael", "affiliation": "University of Bremen" } ], "keywords": [ "Data-driven and AI-based modelling of production and logistics", "Production and operations management", "Viable and resilient supply chain and production" ], "abstract": "Throughput in manufacturing systems is constrained by bottlenecks, making their identification essential. The shifting bottleneck detection method is well established, but existing implementations rely on partly implicit assumptions regarding temporal edge cases, and published results often have limited replicability. This paper presents an event-based algorithm for online shifting bottleneck detection that requires only timestamped activity-state changes, maintains interval-level sole and shifting bottleneck classifications, and incorporates in-progress active periods. Benchmark comparisons highlight the importance of explicit implementation semantics for online bottleneck detection, and the case study demonstrates the algorithm's suitability for event-driven industrial environments using PackML states and MQTT.", "url": "" }, { "id": "Tu-TuB18.2", "code": "TuB18.2", "title": "Decision Making and Control of Surface Quality in Additive Manufacturing Products through Pre-Processing, Processing, and Post Processing (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB18", "sessionTitle": "Intelligent Methods and Tools Supporting Decision Making in Manufacturing Systems and Supply Chains II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Gohari, Hossein", "affiliation": "UOIT" }, { "name": "Barari, Ahmad", "affiliation": "University of Ontario Institute of Technology" } ], "keywords": [ "Intelligent manufacturing systems", "Cyber-physical production systems", "Manufacturing plant simulation, control and optimization" ], "abstract": "Modern industrial production platforms demand agile, highly flexible, and accurate manufacturing setups to produce complex parts within a reasonable lead time. Additive Manufacturing (AM) has provided exceptional flexibility and efficiency in fabricating complex geometries. However, reliably achieving industry-grade surface qualities and dimensional accuracies in AM processes is challenging. In this paper, a comprehensive framework to control and optimize surface quality in AM processes is proposed. The framework integrates the methodologies developed for the three main stages of pre-processing, processing, and post-processing. In the pre-processing stage, design analysis, defining quality targets, and parameter selection are explored to identify the best practices to achieve higher surface quality. Online monitoring and adaptive control of the deposition process are investigated in the processing stage. The post-processing stage includes inspection procedures and a decision-making module for identifying a suitable finishing operation to achieve the desired surface quality. By integrating these methodologies into an interconnected framework, a reliable and efficient surface quality control system suited for industrial productions is established.", "url": "" }, { "id": "Tu-TuB18.3", "code": "TuB18.3", "title": "Managing Unobservable Degradation: An Event-Based Maintenance Policy for Industrial Systems (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB18", "sessionTitle": "Intelligent Methods and Tools Supporting Decision Making in Manufacturing Systems and Supply Chains II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Jimenez, Hanser", "affiliation": "Université De Lorraine, CNRS, CRAN" }, { "name": "Do, Phuc", "affiliation": "IMT Mines Alès" }, { "name": "Voisin, Alexandre", "affiliation": "Université De Lorraine, CNRS, CRAN" }, { "name": "Franciosi, Chiara", "affiliation": "Université De Lorraine, CNRS, CRAN, F-54000, Nancy, France" } ], "keywords": [ "Maintenance engineering, management and services", "Simulation and optimization in production, operations and services", "Manufacturing prognostics and health management" ], "abstract": "In many industrial applications, condition-based maintenance is infeasible due to limited sensing capabilities. Consequently, maintenance decisions must be made without direct observations of the degradation state and must instead rely on nominal manufacturer information. However, such information may not accurately capture the behavior of the system in real operating environments under substantial uncertainties, which could compromise the effectiveness of replacement decisions. We introduce an event-driven maintenance strategy for a repairable non-inspectable system in which repairs—with effectiveness that degrades over time—are permitted only up to a prescribed number of failures. The central decision problem is to determine when continued imperfect repairs remain cost-effective and when a full replacement should be carried out instead. The proposed policy exploits historical stoppage information to navigate the trade-off between corrective replacements and corrective repairs without requiring real-time condition monitoring. Simulation experiments show that the proposed approach achieves lower long-run costs than traditional corrective-maintenance strategies, as well as reliability-centered alternatives.", "url": "" }, { "id": "Tu-TuB18.4", "code": "TuB18.4", "title": "Reactive Disassembly Sequence Planning under Uncertain Component Conditions (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB18", "sessionTitle": "Intelligent Methods and Tools Supporting Decision Making in Manufacturing Systems and Supply Chains II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "El Morabit, Waël", "affiliation": "Imt Nord Europe" }, { "name": "Abdous, Mohammed-Amine", "affiliation": "IMT Nord Europe, Institut Mines-Télécom, Univ. Lille, Center for Digital Systems, Lille, France" }, { "name": "Lucas, Flavien", "affiliation": "IMT Nord Europe" }, { "name": "Bluvstein, German", "affiliation": "Technical University of Munich" }, { "name": "Brunnenkant, Finn-Augustin", "affiliation": "Technical University of Munich, Institute for Machine Tools and Industrial Management" }, { "name": "Streibel, Lasse", "affiliation": "Technical University of Munich" } ], "keywords": [ "Sustainable and circular manufacturing systems", "Manufacturing plant simulation, control and optimization", "Cyber-physical production systems" ], "abstract": "Although disassembly supports circular production, planning remains difficult because the true condition of components is often unknown, and precedence relations create a large combinatorial search space. We address this problem in the setting of reactive disassembly sequence planning. Products are modeled as directed acyclic graphs with processing time, cost, and recovery profit at the component level for both complete and selective disassembly. Two exact models (a MILP and CP-SAT) provide independent references on moderate-size instances, and a hybrid planner that combines Greedy Randomized Adaptive Search Procedure (GRASP) with Variable Neighborhood Descent generates a feasible sequence at scale. On 316 benchmark instances with up to 1,000 components, the hybrid planner reaches a median optimality gap of 0.2% against the exact references, with 90% of solved instances within a 5% gap. To cope with deviations during execution, we add a fuzzy layer that monitors simple process signals such as force or torque spikes, repeated vision failures, timeouts, and loss of grip. Other normalized quality indicators, mechanical or electrical, can be used in place of force or torque. The layer selects among four actions: bypass, tool change and retry, controlled destruction, or local replanning on the residual graph, and updates the sequence accordingly. Failure simulations on 87 scenarios show that the adaptive pipeline recovers all targets in 97.7% of runs, with partial recovery otherwise and a median profit loss of 5.2%. Response times remain compatible with real-time use, indicating that optimized plans can be translated into explicit shop-floor decisions when execution deviates from nominal conditions.", "url": "" }, { "id": "Tu-TuB18.5", "code": "TuB18.5", "title": "Requirements for Human-Machine Interaction in Mixed Traffic Automated Driving on Automotive Terminals (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB18", "sessionTitle": "Intelligent Methods and Tools Supporting Decision Making in Manufacturing Systems and Supply Chains II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Rolfs, Lennart", "affiliation": "BIBA - Bremer Institut Für Produktion Und Logistik" }, { "name": "Panter, Lars", "affiliation": "BIBA – Bremer Institut Für Produktion Und Logistik" }, { "name": "Freitag, Michael", "affiliation": "University of Bremen" } ], "keywords": [ "Human-centered production and logistics" ], "abstract": "Automotive roll-on/roll-off (RoRo) terminals face increasing vehicle volumes, labour shortages, and operational volatility, motivating the automation of vehicle movements within mixed traffic (automated and manual driving) environments. As human workers will remain involved during foreseeable transition phases, effective human–machine interaction (HMI) is essential for safe and efficient operations. This study develops an initial set of HMI requirements for automated driving on automotive terminals. A three-stage approach was used, combining a targeted literature review, semi-structured expert interviews, and on-site observations, followed by stakeholder validation. The results highlight the need for transparent communication, structured training, clear process interfaces, visible operating zones, and traffic management measures that support predictable automated behaviour in mixed traffic.", "url": "" }, { "id": "Tu-TuB18.6", "code": "TuB18.6", "title": "A Time Series Similarity Measurement Approach Based on Circular Information Granules", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB18", "sessionTitle": "Intelligent Methods and Tools Supporting Decision Making in Manufacturing Systems and Supply Chains II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Zhou, Yi", "affiliation": "Wuhan Institute of Technology" }, { "name": "Huang, Peng", "affiliation": "Wuhan Institute of Technology" }, { "name": "Du, Sheng", "affiliation": "China University of Geosciences" }, { "name": "Liu, Hao", "affiliation": "Wuhan Institute of Technology" }, { "name": "Huang, Zixin", "affiliation": "Wuhan Institute of Technology" } ], "keywords": [ "Cyber-physical production systems", "Manufacturing prognostics and health management" ], "abstract": "A time series similarity measurement approach based on circular information granules is proposed in this paper. Firstly, a one-dimensional time series is differenced and concatenated to obtain a two-dimensional time series; subsequently, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is employed to partition two-dimensional time series into a number of data clusters. Then taking the maximum sum of the volumes of circular granules as the optimization objective, the Gravitational Search Algorithm (GSA) is applied to iteratively find the optimal shape of circular information granules. Lastly, the similarity result of time series is assessed through the calculation of the geometric properties of circular information granules derived from distinct time series, and experiments are conducted using public datasets. The experimental findings demonstrate that the circular information granules method is capable of effectively assessing the similarity of time series.", "url": "" }, { "id": "Tu-TuB19.1", "code": "TuB19.1", "title": "Evolving from Semantic to Cognitive Digital Twins: A Comparative Framework for Resilience in Industry 5.0 (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Al Haj Ali, Jana", "affiliation": "University of Lorraine" }, { "name": "Lezoche, Mario", "affiliation": "CRAN, Nancy-University, CNRS" }, { "name": "Panetto, Hervé", "affiliation": "CRAN, University of Lorraine, CNRS" }, { "name": "Naudet, Yannick", "affiliation": "Luxembourg Institute of Science and Technology (LIST)" } ], "keywords": [ "Intelligent manufacturing systems", "AI-based enterprise systems", "Digital enterprise" ], "abstract": "This paper compares Semantic Digital Twins (SDTs) and Cognitive Digital Twins (CDTs) to clarify their respective roles in achieving Industry 5.0 resilience. While SDTs provide semantic interoperability and consistent knowledge representation, they remain largely static and reactive. CDTs integrate perception, semantic memory, reasoning, learning, and anticipation, enabling adaptive and proactive behaviour. A three-dimensional comparison framework and a Cognitive Resilience Model (CRM) illustrate how CDTs extend resilience from informational to behavioural and cognitive levels. Results show that cognition-enabled twins constitute a paradigm shift, supporting autonomous adaptation, human-centric collaboration, and robust operation in dynamic industrial environments.", "url": "" }, { "id": "Tu-TuB19.2", "code": "TuB19.2", "title": "Automatic Quality Assessment of Asset Administration Shell Submodel Templates (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Miny, Torben", "affiliation": "RWTH Aachen University" }, { "name": "Heppner, Sebastian", "affiliation": "RWTH Aachen University" }, { "name": "Garmaev, Igor", "affiliation": "RWTH Aachen University" }, { "name": "Ristin, Marko", "affiliation": "Zurich University of Applied Sciences" }, { "name": "Otto, Björn", "affiliation": "Otto Von Guericke University" }, { "name": "Braunisch, Nico", "affiliation": "TU Dresden" }, { "name": "Dorn, Moritz", "affiliation": "Karlsruhe Institute of Technology" }, { "name": "Kleinert, Tobias", "affiliation": "RWTH Aachen University" }, { "name": "van de Venn, Hans Wernher", "affiliation": "Zurich University of Applied Sciences ZHAW" }, { "name": "Wollschlaeger, Martin", "affiliation": "TU Dresden" }, { "name": "Langer, Tobias", "affiliation": "Conplement AG" }, { "name": "Barth, Mike", "affiliation": "Karlsruhe Institute of Technology (KIT)" } ], "keywords": [ "Industry X.0 for production and logistics", "Large-scale complex systems", "Systems-of-systems" ], "abstract": "An exhaustive analysis of the official Asset Administration Shell Submodel Templates (SMT) shows that many contain structural and semantic inconsistencies that hinder their reliable use. To tackle that, this paper introduces an automated quality assessment approach for SMTs based on a set of modular assessment functions. Our prototype systematically detects syntactic and semantic issues such as conflicting definitions, dangling references, and deprecated attributes. An evaluation demonstrates the approach’s effectiveness and the impact, and highlights the need for automated quality assurance for consistent, machine-verifiable SMTs.", "url": "" }, { "id": "Tu-TuB19.3", "code": "TuB19.3", "title": "Hybrid Digital Twin Architecture for Industrial Energy Optimization (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Othen, Rosario", "affiliation": "RWTH Aachen University" }, { "name": "Lauricella, Marco", "affiliation": "ABB AG Corporate Research Center" }, { "name": "Sejdija, Jonathan", "affiliation": "Institut NOWUM-Energy, FH Aachen" }, { "name": "Sahlab, Nada", "affiliation": "ABB AG" }, { "name": "Prinz, Marcel", "affiliation": "WEPA Hygieneprodukte GmbH" }, { "name": "Song, Chen", "affiliation": "ABB AG" }, { "name": "Schlake, Jan-Christoph", "affiliation": "ABB Corporate Research Center" }, { "name": "Andreas, Schmeiser", "affiliation": "J.M. Voith SE & Co. KG" }, { "name": "Christian, Möbitz", "affiliation": "Institut Für Textiltechnik of RWTH Aachen University" }, { "name": "Gries, Thomas", "affiliation": "RWTH Aachen University" } ], "keywords": [ "Data-driven and AI-based modelling of production and logistics", "Simulation and optimization in production, operations and services", "Sustainable and circular supply chain and production" ], "abstract": "A hybrid digital twin (DT) architecture for industrial energy optimisation is presented, combining physics-based functional mock-up units (FMUs) and data-driven neural networks (NNs) under an Asset Administration Shell (AAS)-centric integration layer. Steady-state FMUs generate training data for smooth feedforward NN surrogates embedded into nonlinear optimisation, while the AAS centralises parameters, interfaces, and model provenance. In a bi-valent tissue machine drying hood, the surrogate reproduces FMU outputs with low error and enables day-ahead, cost- and CO2eq-aware optimisation in about 13 seconds per 24-hour horizon, where direct use of co-simulation FMUs would be prohibitively slow and numerically fragile. Compared to gas-only operation, the optimised bivalent schedule reduces daily energy cost by 14.8% and emissions by 9.5%, yielding an optimisation-ready, traceable, and interoperable digital twin.", "url": "" }, { "id": "Tu-TuB19.4", "code": "TuB19.4", "title": "Modeling Interoperable Fault Diagnosis Using Asset Administration Shells in Skill-Based Production (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Rübel, Pascal", "affiliation": "Technologie-Initiative SmartFactory KL" }, { "name": "Jungbluth, Simon", "affiliation": "Technologie-Initiative SmartFactoryKL E.V" }, { "name": "Blumhofer, Benjamin", "affiliation": "DFKI" }, { "name": "Ruskowski, Martin", "affiliation": "German Research Center for Artificial Intelligence" } ], "keywords": [ "Enterprise interoperability", "Enterprise architecture", "Cyber-physical-social systems in enterprises" ], "abstract": "Faults in manufacturing are rare, limiting data availability for robust fault modeling. Small batch sizes worsen this, as faults seldom recur within the same system. In flexible production, changing contexts mean identical symptoms can represent different faults, while varied symptoms may arise from the same issue. This paper proposes an interoperable, context-aware fault model implemented in Asset Administration Shells. It integrates data across resources, processes, and products, strengthening fault classification and reuse across contexts.", "url": "" }, { "id": "Tu-TuB19.5", "code": "TuB19.5", "title": "Elastic Manufacturing in a Battery-Assembly Cell: A Digital Twin of AAS Entities (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Elshafei, Basem", "affiliation": "University of Nottingham" }, { "name": "Chaplin, Jack Christopher", "affiliation": "University of Nottingham" }, { "name": "Ratchev, Svetan", "affiliation": "University of Nottingham" } ], "keywords": [ "Cyber-physical production systems", "Digital transformation", "Intelligent manufacturing systems" ], "abstract": "Modern manufacturing requires flexible, reconfigurable integration to accommodate frequent product customisation. This calls for systems capable of rapid resource reallocation, which is hindered when equipment from different vendors and different software applications must communicate and coordinate. This paper presents an elastic manufacturing paradigm that establishes a shared cognitive dataspace where all entities—physical assets and digital apps—participate through a standardised digital representation, namely Asset Administration Shell (AAS). This decouples physical assets from supervisory control, enabling flexible communication, event-driven orchestration, and seamless reconfiguration in production lines. We validate this approach in a battery assembly cell featuring two KUKA robots, a shuttle, a Beckhoff PLC, and two cameras from different vendors, along with their corresponding robot and vision apps. Each entity possesses its own AAS, with communication strictly mediated through standardised AAS-to-AAS data exchange, establishing unified interfaces regardless of the underlying hardware vendor or software application, enabling actual Plug-and-Produce functionality. Results demonstrate automated robot task execution based on real-time inspection data through dynamic app-asset binding. We demonstrate asset reconfiguration by deliberately replacing the camera with one that uses different protocols; the robot continues to receive inspection data, achieving immediate operational readiness. Experimental validation confirms limited reconfiguration, 100% message delivery reliability, and sub-second response times from inspection to robot actuation. Principal contributions include establishing a shared cognitive dataspace for bi-directional data exchange between all resources; demonstrating seamless Plug-and-Produce substitution through AAS mediation; and validating real-time, data-driven orchestration.", "url": "" }, { "id": "Tu-TuB20.1", "code": "TuB20.1", "title": "DQN-Based Computationally Efficient Switching Model Predictive Control for Nonlinear Systems (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB20", "sessionTitle": "JO-JPC: Model-Predictive and Optimization-Based Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Huang, Liqian", "affiliation": "East China University of Science and Technology" }, { "name": "Wu, Tiantian", "affiliation": "East China University of Science and Technology" }, { "name": "Fu, Zhuofan", "affiliation": "East China University of Science and Technology" }, { "name": "Zhao, Shuheng", "affiliation": "East China University of Science and Technology" }, { "name": "Tian, Zhou", "affiliation": "East China University of Science and Technology" }, { "name": "Lu, Jingyi", "affiliation": "East China University of Science and Technology" } ], "keywords": [ "Machine learning and artificial intelligence in chemical process control", "Model-predictive and optimization-based control in chemical processes", "Advanced process control" ], "abstract": "Model predictive control (MPC) is a widely adopted control strategy in the process industries. However, its reliance on online optimization can lead to performance degradation under limited computational resources, particularly for highly nonlinear systems. Learning-based MPC addresses this issue to some extent through offline policy training, but it lacks accuracy in untrained regions. To mitigate this limitation, this paper proposes a switched MPC strategy within a reinforcement learning (RL) framework. Specifically, a Deep Q-Network (DQN) was trained to switch between an exact MPC controller and a learning-based MPC controller, using real-time system state information and uncertainty quantification (UQ) from the learning-based control policy. Numerical experiments on the control of a nonlinear continuous stirred tank reactor (CSTR) demonstrate that the proposed method achieves significantly reduced computation time and lower tracking error compared to conventional methods.", "url": "" }, { "id": "Tu-TuB20.2", "code": "TuB20.2", "title": "Data-Driven Modeling and Control of Perovskite Deposition for Solar Cells (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB20", "sessionTitle": "JO-JPC: Model-Predictive and Optimization-Based Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Masero, Eva", "affiliation": "Politecnico Di Milano" }, { "name": "Zambrano-Torres, Juan Diego", "affiliation": "Politecnico Di Milano" }, { "name": "Vollbrecht, Joachim", "affiliation": "Institute for Solar Energy Research Hamelin" }, { "name": "Maestre, Jose M.", "affiliation": "University of Seville" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes", "Process modeling, identification, and estimation techniques", "Real-time optimization and control in chemical processes" ], "abstract": "Precise control of thin-film deposition is essential in perovskite solar cell manufacturing. This work presents data-driven strategies based on Model Predictive Control (MPC) and Predictive Reference Governor (PRG) to regulate temperature and deposition rate during the perovskite thin-film deposition process while explicitly handling operational constraints. First, a grey-box model of the process is identified from experimental data and integrated into an MPC controller. In parallel, a PRG strategy enhanced with a Kalman filter is proposed to retain the built-in Proportional–Integral–Derivative (PID) controller while enforcing operational constraints and ensuring accurate setpoint tracking. Simulation results show that the proposed MPC and PRG approaches improve tracking performance compared with conventional PID control, thanks to their predictive capability. Finally, a hardware-in-the-loop implementation of the PRG in a Raspberry Pi confirms suitability for embedded deployment.", "url": "" }, { "id": "Tu-TuB20.3", "code": "TuB20.3", "title": "On Piecewise Quadratic Terminal Costs for MPC (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB20", "sessionTitle": "JO-JPC: Model-Predictive and Optimization-Based Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Mulagaleti, Sampath Kumar", "affiliation": "IMT School of Advanced Studies Lucca" }, { "name": "Houska, Boris", "affiliation": "ShanghaiTech University" }, { "name": "Zanon, Mario", "affiliation": "IMT Institute for Advanced Studies Lucca" }, { "name": "Villanueva, Mario Eduardo", "affiliation": "IMT School for Advanced Studies Lucca" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes", "Real-time optimization and control in chemical processes", "Advanced process control" ], "abstract": "This paper presents a novel approach to synthesize stabilizing terminal ingredients for linear model predictive control (MPC) schemes, with the aim of increasing the region of attraction while reducing suboptimality with respect to the solution of the infinite-horizon optimal control problem. It is based on the construction of a novel terminal region using methods from the field of configuration-constrained polytopic computing, along with a terminal cost that is exactly equal to the infinite-horizon linear-quadratic regulator cost in a nontrivial neighborhood of the steady-state. The practical performance of the controller is illustrated through various case studies.", "url": "" }, { "id": "Tu-TuB20.4", "code": "TuB20.4", "title": "A Multi-Priority NMPC Framework with Adaptive Convergence Rate Tuning Strategy (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB20", "sessionTitle": "JO-JPC: Model-Predictive and Optimization-Based Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Qiu, Ruiyu", "affiliation": "Zhejiang University" }, { "name": "Yan, Yitao", "affiliation": "University of New South Wales" }, { "name": "Shao, Zhijiang", "affiliation": "Zhejiang University" }, { "name": "Bao, Jie", "affiliation": "The University of New South Wales" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes", "Real-time optimization and control in chemical processes", "Industrial applications of chemical process control" ], "abstract": "In many industrial control applications, control objectives are naturally organized in conflicting priority levels. To solve such problems, Nonlinear Model Predictive Control (NMPC) is commonly combined with lexicographic optimization in a hierarchical sequential scheme. However, a known limitation of existing methods is that when a high-priority subproblem is convex or in conflict, feasible solutions at lower levels are overly restricted. In this paper, we propose a multi-priority NMPC framework with adaptive convergence rate tuning that enlarges the attainable solution set while preserving closed-loop stability. At each priority level, an adaptive convergence rate factor is introduced into a Lyapunov condition, which provides flexibility without violating stability guarantees. The approach is demonstrated on a cascade CSTR process with multi-priority objectives and constraints.", "url": "" }, { "id": "Tu-TuB20.6", "code": "TuB20.6", "title": "Multi-Fidelity Bayesian Optimization Framework for CFD-Based Non-Premixed Burner Design (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB20", "sessionTitle": "JO-JPC: Model-Predictive and Optimization-Based Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Lima, Patrick", "affiliation": "NTNU" }, { "name": "Reis, Paulo", "affiliation": "Cimatec" }, { "name": "Santos, Alex", "affiliation": "Cimatec" }, { "name": "del Rio-Chanona, Ehecatl Antonio", "affiliation": "Imperial College London" }, { "name": "B. R. Nogueira, Idelfonso", "affiliation": "Norwegian University of Science and Technology" } ], "keywords": [ "Thermal systems modelling", "Machine learning and artificial intelligence in chemical process control", "Hydrogen systems for energy generation and storage" ], "abstract": "Abstract: This work presents a cost-aware multi-fidelity Bayesian optimisation framework for Computational fluid dynamic driven design of an adiabatic, non-premixed industrial burner operating on H₂/CH₄ blends. Optimising such systems is challenging due to strong couplings among combustion dynamics, emissions, and mesh-dependent computational cost, motivating adaptive and time-efficient strategies. To address this, fidelity is controlled continuously by mesh element size, and a wall-time surrogate learned from design-of-experiments (DOE) data is embedded in a constrained acquisition that jointly accounts for expected improvement, probability of NOx feasibility, a penalty on low information in fidelities low, and a wall time of computational fluid dynamics (CFD) simulation penalty. The CFD model employs 2D axisymmetric, k–ω SST turbulence, and Flamelet combustion. Across an initial DOE and iterative BO, the optimiser prioritises information gain per unit time, allocating mid-fidelity runs for broad exploration and reserving high-fidelity evaluations for the most promising candidates. The method identifies geometries achieving T ≈2100 K within NOx limits while substantially reducing computational effort relative to naïve high-fidelity search.", "url": "" }, { "id": "Tu-TuB21.1", "code": "TuB21.1", "title": "Real-Time EMT Stability Analysis of the 2030 High-RES Dutch Power System (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB21", "sessionTitle": "Power Electronics Controls within Intelligent Power Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Aviles-Cedeno, Jonathan", "affiliation": "Delft University of Technology" }, { "name": "Rueda, Jose L.", "affiliation": "Delft University of Technology" } ], "keywords": [ "Power systems stability", "Real time simulators for energy systems", "Electrical transmission systems" ], "abstract": "This study examines the dynamic response of the Dutch extra-high-voltage power system under projected 2030 renewable-share scenarios using a real-time electromagnetic transient (EMT) model implemented in RSCAD/RTDS. Real-time EMT simulations preserve waveform fidelity and facilitate future operator- and controller-in-the-loop integration with digital twin frameworks. Three scenarios with 50%, 65%, and 80% renewable shares bracket 2030 projections. Following a severe three-phase fault, voltage- and frequency-based performance indicators are extracted. The maximum rate of change of frequency emerges as the cleanest indicator of inertia loss, whereas voltage and recovery indicators show more nuanced trends, highlighting the need for advanced stability support strategies.", "url": "" }, { "id": "Tu-TuB21.2", "code": "TuB21.2", "title": "Suitability Analysis of Wide-Area Stochastic Data for SSO Identifiability in HVDC-HVAC Multienergy Systems (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB21", "sessionTitle": "Power Electronics Controls within Intelligent Power Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Tapia Suárez, Estefanía Alexandra", "affiliation": "TU Delft" }, { "name": "Rueda, Jose L.", "affiliation": "Delft University of Technology" } ], "keywords": [ "Stability of nonlinear systems", "Analytic design", "Stochastic optimal control problems" ], "abstract": "HDVC-HVAC multienergy systems, as emerging power system architectures integrating multiple converter-controlled wind power plants and electrolyzer facilities, face a significant risk of critical sub-synchronous oscillations (SSOs) that can compromise system stability. Although several real-time SSO identification algorithms have been proposed, the selection of electrical measurements used to feed them remains largely arbitrary, limiting their effectiveness. This work performs a data-driven suitability analysis to determine the measurements and operating conditions that most effectively reveal SSOs. A wide stochastic database is generated, and oscillatory parameters are estimated to characterize poorly damped oscillations. Statistical results show that voltage angle and reactive power are the most sensitive indicators, while high wind speed and low electrolyzer demand create vulnerable conditions to critical SSO development.", "url": "" }, { "id": "Tu-TuB21.3", "code": "TuB21.3", "title": "Integral-Droop Control for Grid Forming Inverters", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB21", "sessionTitle": "Power Electronics Controls within Intelligent Power Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Lagunas Mercado, Alejandro", "affiliation": "Instituto De Ingeniería, UNAM" }, { "name": "Rueda-Escobedo, Juan G.", "affiliation": "Institute of Renewable Energies" }, { "name": "Moreno, Jaime A.", "affiliation": "Universidad Nacional Autonoma De Mexico-UNAM" } ], "keywords": [ "Power electronics", "Power systems stability" ], "abstract": "The global energy transition is transforming electrical power systems, replacing conventional synchronous generators with renewable energy sources. This shift introduces significant challenges to frequency stability and voltage regulation due to increased variability and reduced system inertia. Grid-forming inverters have emerged as a key solution due to their capability of autonomously establishing voltage and frequency references. Among control strategies for grid-forming inverters, droop control is widely adopted for its simplicity and decentralized power-sharing capabilities. However, its static nature limits dynamic performance and introduces steady-state errors in voltage and frequency regulation. To partially address these issues, this paper proposes an extension to droop control in two directions: (1) frequency and voltage are adjusted based on deviations in both active and reactive power, allowing full specification of the closed-loop behavior; and (2) integral action is introduced to eliminate steady-state errors. To support practical design, a decoupled tuning method is developed that enables independent design of droop and integral gains, allowing a flexible controller configuration. Numerical simulations validate the proposed approach, demonstrating improved transient response, zero steady-state error, and robust performance across the inverter’s capability region.", "url": "" }, { "id": "Tu-TuB21.4", "code": "TuB21.4", "title": "IQC-Based Small-Signal Stability Criterion for Inverter-Based Power Systems with Lossy Transmission Lines", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB21", "sessionTitle": "Power Electronics Controls within Intelligent Power Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Koizumi, Jigen", "affiliation": "Institute of Science Tokyo" }, { "name": "Nishino, Taku", "affiliation": "Tokyo Institute of Technology" }, { "name": "Ishizaki, Takayuki", "affiliation": "Tokyo Institute of Technology" } ], "keywords": [ "Power systems stability", "Electrical distribution systems" ], "abstract": "In this paper, we derive a sufficient condition for the small-signal stability of power systems with lossy transmission lines and virtual synchronous generator (VSG) inverters using the Integral Quadratic Constraints (IQC) framework. The analysis employs two system representations, namely the forward and inverse systems. The resulting stability condition comprises a circle criterion for the forward system at the DC gain and a phase condition for both systems over the remaining frequency range. Numerical results demonstrate that the proposed condition closely captures the stability boundary when the difference in the R/X ratios is small, while it becomes increasingly conservative as the difference grows.", "url": "" }, { "id": "Tu-TuB21.5", "code": "TuB21.5", "title": "Damping Ratio and Convergence Rate Analysis for Inverter-Based Power Systems Via Root Locus", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB21", "sessionTitle": "Power Electronics Controls within Intelligent Power Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Wang, Dan", "affiliation": "Nanjing University" }, { "name": "Chen, Wei", "affiliation": "Peking University" }, { "name": "Jiang, Yan", "affiliation": "The Chinese University of Hong Kong, Shenzhen" }, { "name": "Hara, Shinji", "affiliation": "Tokyo Institute of Technology" } ], "keywords": [ "Power systems stability", "Electrical transmission systems" ], "abstract": "As more renewables enter the grid, it becomes insufficient to rely solely on the center of inertia frequency to analyze the transient performance, especially for oscillation behaviors. Motivated by this, we study the transient frequency performance of inverter-based power systems. Two key performance criteria are considered: the damping ratio and the convergence rate, which are determined by the angles and the real parts of the closed-loop poles, respectively. By decomposing the network system into a set of scalar feedback systems, we show that the closed-loop poles can be analyzed using the root locus of a scalar feedback system as the gain varies from the smallest nonzero eigenvalue to the largest eigenvalue of the scaled Laplacian matrix of the interconnection network. Based on this, we derive sufficient conditions that ensure desired damping and convergence performance. Our analysis shows that the worst-case damping ratio and convergence rate occur when the gain equals the largest and the smallest nonzero eigenvalues of the scaled Laplacian, respectively. This result significantly simplifies the performance assessment. We further provide guidelines for tuning the inverter parameter. Numerical simulations are included to illustrate and validate the proposed approach.", "url": "" }, { "id": "Tu-TuB21.6", "code": "TuB21.6", "title": "Virtual Resistance-Based Control for Grid-Connected Inverters Using Persidskii Systems Approach", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB21", "sessionTitle": "Power Electronics Controls within Intelligent Power Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Chatri, Chakib", "affiliation": "Aix-Marseille University" }, { "name": "Dinesh, Ajul", "affiliation": "Inria Centre at the University of Lille" }, { "name": "Labbadi, Moussa", "affiliation": "Bretagne INP UBO, IRDL" } ], "keywords": [ "Power systems stability", "Power plant control" ], "abstract": "This work addresses virtual resistance (VR)–based control for grid-connected inverters, which enhances transient damping, reduces steady-state errors, and improves robustness to grid disturbances without requiring additional voltage sensors. Classical passivity-based VR control is robust, but limited by restrictive sector bounds on nonlinearities. We extend these bounds and model the closed-loop system as a generalized Persidskii-type nonlinear system. Using this framework, we derive input-to-state stability (ISS) conditions that account for the extended nonlinearities and external disturbances, providing a systematic and less conservative approach to VR control design under practical operating conditions, which is validated through extensive simulations.", "url": "" }, { "id": "Tu-TuB22.1", "code": "TuB22.1", "title": "Closed-Form Analysis of Constant-Voltage Charging for Anode Diffusion Characterization Via Model Equivalence", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB22", "sessionTitle": "Energy Storage Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Kwak, Kyoung Hyun", "affiliation": "University of Michigan - Dearborn" }, { "name": "Shin, Hosop", "affiliation": "Purdue University" }, { "name": "Han, Je-Heon", "affiliation": "Tech University of Korea" }, { "name": "Kim, Youngki", "affiliation": "University of Michigan-Dearborn" } ], "keywords": [ "Energy storage systems" ], "abstract": "This paper presents a closed-form analysis of constant-voltage (CV) charging in lithium-ion batteries, derived from the equivalence between electrochemical and electrical circuit models. Using an OCV–R–2RC equivalent-circuit formulation, the CV current is shown to exhibit a bi-exponential decay governed by two nonzero eigenvalues, with the slow eigenvalue simplifying to lambda_{text{slow}}approx-1/(R_2C_2). This slow dynamic behavior corresponds to a diffusion-limited relaxation process in the graphite anode. Through model equivalence with the single particle model, the slow time constant is analytically related to the solid-phase diffusion coefficient. A logarithmic-slope method is employed to identify the slow time constant from the measured CV current. To validate the framework, graphite/Li coin-cell tests were conducted to measure the diffusion coefficient from the CV tail, and the extracted values were found to be consistent with independent estimates obtained from electrochemical impedance spectroscopy (EIS). These analytical and experimental results demonstrate that the CV-tail time constant tau_2 captures the anode’s diffusion characteristics, establishing a compact foundation that can be extended to physics-based battery diagnostics.", "url": "" }, { "id": "Tu-TuB22.2", "code": "TuB22.2", "title": "Efficient Linear Parameter Varying Identification of the Equivalent Circuit Model of Lithium-Ion Batteries", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB22", "sessionTitle": "Energy Storage Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Corrini, Francesco", "affiliation": "University of Bergamo" }, { "name": "Fagiani, Lorenzo", "affiliation": "University of Bergamo" }, { "name": "Previtali, Davide", "affiliation": "University of Bergamo" }, { "name": "Mazzoleni, Mirko", "affiliation": "University of Bergamo" }, { "name": "Previdi, Fabio", "affiliation": "Universita' Degli Studi Di Bergamo" } ], "keywords": [ "Energy storage systems" ], "abstract": "The adoption of electric vehicles rapidly increased in recent years. Battery packs, in particular lithium-ion batteries, are a fundamental component in electric cars. Estimating the State Of Chaerge (SOC) is a critical task to avoid unsafe conditions of the battery. However, traditional SOC estimation techniques, such as Coulomb counting, are not suitable for electric vehicles due to measurement errors and uncertain initial conditions, which affect the accuracy of the SOC estimate. Alternatives can be found in model-based approaches, in which the SOC is estimated exploiting its relation with other variables, such as voltage and current. A commonly used model for SOC estimation is the Equivalent Circuit Model (ECM), from which a state space dynamical system can be derived. In this work, we propose to estimate the parameters of the ECM with a linear parameter varying - autoregressive with exogenous input model, which is able to exploit the relation between the parameters of the model and the state of charge of the battery. We propose to identify the ECM model in a non-parametric fashion with Least Squares - Support Vector Machines (LS-SVM). To decrease the computational complexity of the LS-SVM identification algorithm, a quadratic entropy subsetting algorithm is proposed to reduce the size of the training dataaset while maintaining a full coverage of all operating conditions.", "url": "" }, { "id": "Tu-TuB22.3", "code": "TuB22.3", "title": "EIS-Based State-Of-Charge Estimation for LFP Batteries Using PCA", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB22", "sessionTitle": "Energy Storage Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Bussios, Maxime", "affiliation": "Université Libre De Bruxelles" }, { "name": "Jacques-Jourion, Antoine", "affiliation": "Université Libre De Bruxelles, KU Leuven" }, { "name": "Goldar Davila, Alejandro", "affiliation": "Université Libre De Bruxelles" }, { "name": "Garone, Emanuele", "affiliation": "Université Libre De Bruxelles" }, { "name": "Kinnaert, Michel", "affiliation": "Université Libre De Bruxelles" } ], "keywords": [ "Energy storage systems" ], "abstract": "This work proposes a computationally cheap, data driven method to estimate the state of charge (SOC) of LiFePO4 (LFP) batteries using Electrochemical Impedance Spectroscopy (EIS) data. Dimensionality of the data is reduced via singular value decomposition and mapped to SOC through regression. A two-step frequency selection scheme identifies the most SOC-informative and least cell-dependent frequencies. Experiments on nine commercial cells show errors below 6% RMSE with cell-specific models and below 7% using a single estimator, requiring measurements only at 2 and 3 Hz. The approach uses minimal calibration and simple tools, making it suitable for large battery batches and low-cost Battery Management Systems (BMS).", "url": "" }, { "id": "Tu-TuB22.4", "code": "TuB22.4", "title": "Mechanism of Post-Charge Self-Balancing in Parallel Battery Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB22", "sessionTitle": "Energy Storage Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Jang, Byeonggwan", "affiliation": "Korea Advanced Institute of Science and Technology" }, { "name": "Seo, Hyung-Tae", "affiliation": "KAIST" }, { "name": "Han, Seungho", "affiliation": "Hanyang University, Erica" }, { "name": "Kim, Wooyong", "affiliation": "Incheon National University" }, { "name": "Kim, Kyung-Soo", "affiliation": "KAIST" } ], "keywords": [ "Energy storage systems", "Control and management of energy systems", "Multi-energy networks" ], "abstract": "Energy storage systems are essential for modern power applications but may pose fire hazards. In parallel-connected battery systems, racks are connected to increase storage capacity; however, structural differences can cause current imbalance among racks, which may contribute to fire incidents. Therefore, analyzing current flow in such configurations is important. This study proposes a dynamic-model-based method for analyzing current imbalance among battery racks after charging, where each battery system is modeled as a single cell.", "url": "" }, { "id": "Tu-TuB22.5", "code": "TuB22.5", "title": "A New and Improved Validation Framework for Kramers-Kronig Transforms", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB22", "sessionTitle": "Energy Storage Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Bohlool, Faezeh", "affiliation": "Temple University" }, { "name": "Soudbakhsh, Damoon", "affiliation": "Temple University" } ], "keywords": [ "Energy storage systems", "Control and optimization for sustainability and energy systems", "Energy management systems" ], "abstract": "Electrochemical Impedance Spectroscopy (EIS) is a commonly used technique for investigating the physicochemical properties of systems across disciplines ranging from battery diagnostics to biosensing. To ensure the reliability of the EIS data, the Kramers–Kronig transforms (KKT) have long served as a mathematical tool to validate the data based on causality, linearity, stability, and boundedness of the response. Due to the limitations of the range of frequency data, methods such as polynomial extrapolation have been used to validate these foundational requirements. In this paper, we revisit the theoretical basis of KKT validation and show its limitations and failure to satisfy conditions such as causality. We then propose a new framework (eKKT) that satisfies KKT conditions, and it is easily applicable to validate EIS data collected at any frequency range due to its linear nature. We provide two simulation case studies, showing the limitations of the current approach and how the proposed approach addresses them. These studies included: i) a non-KKT-compliant system that the current approaches falsely identify as a KKT-compliant system, and ii) a KKT-compliant system deemed as non-KKT by the current approaches. Then, we demonstrate the ability of the proposed eKKT approach as a new tool for validating impedance spectra by testing it on the collected EIS from a Li-ion battery. The framework provided in this study has significant implications for the thousands of experimental studies conducted on a daily basis across the world using impedance spectra. It offers a framework for KKT validation and provides a practical tool for ensuring the physical consistency of EIS measurements.", "url": "" }, { "id": "Tu-TuB23.1", "code": "TuB23.1", "title": "Optimization-Based Control of Methanol Steam Reforming for Hydrogen Production in a Catalytic Membrane Reactor", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB23", "sessionTitle": "Modeling, Identification and Optimization of Industrial Processes", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Arcila-Osorio, Mateo", "affiliation": "Universitat Politecnica De Catalunya" }, { "name": "Ocampo-Martinez, Carlos", "affiliation": "Universitat Politecnica De Catalunya (UPC)" }, { "name": "Llorca Pique, Jordi", "affiliation": "Universitat Politecnica De Catalunya" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes", "Process modeling, identification, and estimation techniques", "Control and optimization for sustainability and energy systems" ], "abstract": "This article presents an optimization-based control strategy for methanol steam reforming (MSR) in a catalytic membrane reactor (CMR) for on-demand hydrogen production. A phenomenological-based dynamic model is developed to capture the strongly coupled reaction kinetics, heat transfer, and selective hydrogen permeation. The model is validated against experimental data from a laboratory-scale setup, showing close agreement with measured outlet flow rates. A quadratic dynamic matrix controller (QDMC) is then designed and evaluated, achieving accurate set-point tracking, smooth actuator behavior, and fast recovery from thermal and pressure disturbances while satisfying input and input-rate constraints.", "url": "" }, { "id": "Tu-TuB23.2", "code": "TuB23.2", "title": "Further Optimization of Operating Variable for Ethylene Distillation Column Based on S-Shaped Curve Characteristics", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB23", "sessionTitle": "Modeling, Identification and Optimization of Industrial Processes", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Cao, Xinyi", "affiliation": "CNPC Research Institute of Safety&Environment Technology" }, { "name": "Luo, Fangwei", "affiliation": "CNPC Research Institute of Safety&Environment Technology" }, { "name": "Wei, Zhenqiang", "affiliation": "CNPC Research Institute of Safety&Environment Technology" }, { "name": "Zhang, Xue", "affiliation": "CNPC Research Institute of Safety&Environment Technology" }, { "name": "Liu, Deping", "affiliation": "CNPC Research Institute of Safety&Environment Technology" }, { "name": "Ding, Shucheng", "affiliation": "CNPC Research Institute of Safety&Environment Technology" }, { "name": "Guo, Zhifeng", "affiliation": "China University of Petroleum-Beijing" } ], "keywords": [ "Industrial applications of chemical process control", "Advanced process control" ], "abstract": "The frequency domain based analytical method can obtain continuous control strategies when the operating conditions fluctuate widely, but for the convenience of solving, this method also introduces some errors. This paper studies the S-shaped curve characteristics of continuous control strategies and provides the range of optimal parameter values. Meanwhile, the impact of two inherent solution errors, namely nonlinear models and control variable parameters, on the original continuous control strategy was discussed and analyzed separately. Simulation results show that, when the optimized control strategy is applied to the actual ethylene distillation column model, the control performance is enhanced.", "url": "" }, { "id": "Tu-TuB23.3", "code": "TuB23.3", "title": "Modeling and Real-Time Optimization (RTO) of an Industrial Residue Oil Hydrotreating Unit", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB23", "sessionTitle": "Modeling, Identification and Optimization of Industrial Processes", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Wang, Han", "affiliation": "Sinopec" }, { "name": "Patron, Gabriel David", "affiliation": "Imperial College London" }, { "name": "Chen, Bo", "affiliation": "Sinopec" }, { "name": "Wang, Xiaolin", "affiliation": "Sinopec" }, { "name": "Ricardez-Sandoval, Luis", "affiliation": "University of Waterloo" } ], "keywords": [ "Industrial applications of chemical process control", "Real-time optimization and control in chemical processes", "Control and optimization for sustainability and energy systems" ], "abstract": "We propose and experimentally validate a continuous lumping residue oil hydrotreating model. Based on this model, a two-step real-time optimization (RTO) scheme was formulated, which includes parameter estimation and economic optimization. The model parameters were fitted using the constrained optimization by a linear approximation algorithm to lower computational costs. We also present a novel economic objective function that reflects actual operating expenses for this industrial-scale process. The results show that the optimized residue oil hydrotreating unit can make substantial cost improvements (i.e. 61.83%, 58.80%, and 60.52%) compared to the nominal operating conditions for three different inlet composition datasets collected from an industrial unit. A sensitivity analysis on energy costs was conducted whereby successively increasing the weight of the energy terms allowed for further cost improvements.", "url": "" }, { "id": "Tu-TuB23.4", "code": "TuB23.4", "title": "Observer-Oriented Thermal Modeling for Hall-Héroult Process", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB23", "sessionTitle": "Modeling, Identification and Optimization of Industrial Processes", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Maouche, Taha Moncef", "affiliation": "Gipsa-Lab, CNRS" }, { "name": "Mattioni, Andrea", "affiliation": "Gipsa-Lab" }, { "name": "da Silva Moreira, Lucas José", "affiliation": "Rio Tinto" }, { "name": "Roustan, Herve Yves Guy Bernard Louis", "affiliation": "Rio Tinto Aluminium Pechiney LRF" }, { "name": "Fiacchini, Mirko", "affiliation": "GIPSA-Lab, CNRS" }, { "name": "Besancon, Gildas", "affiliation": "Grenoble INP - UGA" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Thermal systems modelling" ], "abstract": "Temperature measurement is essential for the control of alumina dissolution and the stability of the cell in the Hall-Héroult process. However, the deployment of conventional sensors is a challenging task due to the corrosive nature of the process. In this paper, a zerodimensional thermal model for real-time bath temperature estimation is proposed. A thermal model candidate for cell temperatures is constructed using alumina concentration estimations and available measurements. The thermal model is validated on industrial data.", "url": "" }, { "id": "Tu-TuB23.5", "code": "TuB23.5", "title": "Parameter-Interval Estimation for Cooperative Reactive Sputtering Processes", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB23", "sessionTitle": "Modeling, Identification and Optimization of Industrial Processes", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Schneider, Fabian", "affiliation": "Ruhr University Bochum" }, { "name": "Wölfel, Christian Tobias", "affiliation": "Ruhr-Universität Bochum" } ], "keywords": [ "Process modeling, identification, and estimation techniques" ], "abstract": "Reactive sputtering is a plasma-based technique to deposit a thin film on a substrate. This contribution presents a novel parameter-interval estimation method for a well-established model that describes the uncertain and nonlinear reactive sputtering process behaviour. Building on a proposed monotonicity-based model classification, the method guarantees that all parameter values within the parameter interval yield output trajectories and static characteristics consistent with the enclosure induced by the parameter interval. Correctness and practical applicability of the new method are demonstrated by an experimental validation, which also reveals inherent structural limitations of the well-established process model for state-estimation tasks.", "url": "" }, { "id": "Tu-TuB23.6", "code": "TuB23.6", "title": "Optimal Operation of Biodiesel Production Using Nonlinear Model Predictive Control and a Raman Spectroscopy Soft Sensor", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB23", "sessionTitle": "Modeling, Identification and Optimization of Industrial Processes", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Bouchkira, Ilias", "affiliation": "RWTH Aachen University" }, { "name": "Mhamdi, Adel", "affiliation": "RWTH Aachen University" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes", "Batch and semi-batch process control", "Monitoring, performance assessment, and fault detection in chemical process control" ], "abstract": "We propose a model-based operation strategy of biodiesel (fatty acid methyl ester or FAME) production via transesterification of vegetable oils with methanol to enhance process efficiency, safety, and product quality. The investigation is performed in-silico for a lab-scale semi-batch reactor. The strategy integrates inline Raman spectroscopy, extended Kalman filter (EKF)-based soft sensor, and nonlinear model predictive control (NMPC) for real-time reaction monitoring and control. Conventional offline analytical methods are replaced by Raman spectroscopy coupled with a chemometric model to provide real-time concentration estimates of key species. An EKF fuses these measurements with a first-principles dynamic model to reconstruct the full reactor state, including unmeasured intermediates and temperature. The NMPC uses these estimates to compute optimal methanol feed and heating policies, maximizing purity while respecting operational constraints. The results demonstrate that the proposed strategy outperforms conventional operation and achieves smooth, stable convergence to the target product purity. Indeed, the NMPC controller successfully reaches the desired 98.8% FAME specification and reduces by more than half the total time of the conventional operation, while adhering tightly to the operational constraints. The computation times are below the sampling time, which allow for real-time application of the strategy.", "url": "" }, { "id": "Tu-TuB24.1", "code": "TuB24.1", "title": "From Learning to Control: Data-Driven Multi-Agent Reinforcement Learning for Multivariable Control in a Microalgae Bioprocess (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB24", "sessionTitle": "Challenges in Microalgae Production Processes", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Gil, Juan Diego", "affiliation": "University of Almeria" }, { "name": "del Rio-Chanona, Ehecatl Antonio", "affiliation": "Imperial College London" }, { "name": "Guzman, Jose Luis", "affiliation": "University of Almeria" }, { "name": "Berenguel, Manuel", "affiliation": "University of Almeria (CIF Q-5450008-G)" } ], "keywords": [ "Microalgae production processes and bioenergy", "Dynamics and control of biologically motivated nonlinear systems", "Water-food-energy nexus" ], "abstract": "Effective control of bioprocesses is particularly challenging due to the intrinsic nonlinearity and dynamic variability of living-cell systems. In microalgae-based photobioreactors (PBRs), maintaining stable pH and dissolved oxygen DO levels is critical for optimal growth and productivity, yet their strong coupling and sensitivity to environmental fluctuations make multivariable control difficult. This study proposes a novel hybrid offline-online Multi-Agent Reinforcement Learning (MARL) framework for simultaneous pH and DO regulation, leveraging Deep Deterministic Policy Gradient (DDPG) agents to achieve a fully data-driven and model-free control solution. The agents are trained using historical data generated by an expert system, eliminating the need for direct experimentation with the environment. After deployment, the agents operate autonomously, continuously fine-tuning their policies daily to adapt to evolving process dynamics and reject fast transient disturbances. Experimental validation in an open, industrial-scale PBR at the University of Almería demonstrated the framework’s capability to maintain stable operation under realistic conditions. The results confirm that model-free MARL control provides a robust and adaptive alternative for complex bioprocess environments.", "url": "" }, { "id": "Tu-TuB24.2", "code": "TuB24.2", "title": "Pq-Extended Dynamic Mode Decomposition for Dynamic Modeling of Microalgal Raceway Ponds Based on Actual Experimental Data (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB24", "sessionTitle": "Challenges in Microalgae Production Processes", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "González Hernández, Jose", "affiliation": "University of Almería" }, { "name": "Garcia-Tenorio, Camilo", "affiliation": "Universite De Mons" }, { "name": "Guzman, Jose Luis", "affiliation": "University of Almeria" }, { "name": "Vande Wouwer, Alain", "affiliation": "Université De Mons" }, { "name": "Gil, Juan Diego", "affiliation": "University of Almeria" } ], "keywords": [ "Modelling, parameter identification and state estimation in biosystems", "Microalgae production processes and bioenergy" ], "abstract": "Microalgae play a key role in processes such as wastewater treatment and carbon dioxide sequestration due to their high photosynthetic efficiency. Nevertheless, accurately predicting their temporal dynamics in large-scale raceway ponds remains challenging for conventional mechanistic modeling. The strong influence of weather variability, together with the complex and inherently nonlinear biological behavior of microalgae, leads to different fluctuating operation conditions that demand frequent process adjustments. In this study, we adopt a fully data-driven framework that identifies a compact set of informative predictors directly from real raceway measurements, enabling robust forecasting of key production variables. The proposed method, pqEDMD, a variant of Extended Dynamic Mode Decomposition that incorporates a p-q quasi-norm-based pruning strategy on orthogonal-polynomial observables, exhibits rapid convergence and high predictive accuracy. The methodology is evaluated on daily time-series data sampled at one-minute intervals, where pseudorandom binary sequence (PRBS) signals are used to excite the system. A dataset from a real raceway reactor serves as the benchmarking platform, and the pqEDMD results are compared against autoregressive with exogenous input (ARX) models and recurrent neural network NARX (RNN-NARX) architectures. The pqEDMD framework delivers the most accurate forecasts of pH and achieves competitive performance for dissolved oxygen (DO) under a trend-weighted evaluation metric. These results position pqEDMD as a fast, interpretable surrogate of the underlying system dynamics and a promising foundation for the development of data-enabled hybrid control strategies.", "url": "" }, { "id": "Tu-TuB24.3", "code": "TuB24.3", "title": "Enhancing Computational Efficiency of Mixed-Integer Predictive Control for Microalgae Manufacturing Systems through Benders Decomposition Method (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB24", "sessionTitle": "Challenges in Microalgae Production Processes", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Pesquer Bagan, Martí", "affiliation": "Universitat Politècnica De Catalunya" }, { "name": "Manrique-Moreno, Andres", "affiliation": "Los Andes University" }, { "name": "Martinez-Piazuelo, Juan", "affiliation": "Universitat Politecnica De Catalunya" }, { "name": "Ocampo-Martinez, Carlos", "affiliation": "Universitat Politecnica De Catalunya (UPC)" }, { "name": "Quijano, Nicanor", "affiliation": "Universidad De Los Andes" }, { "name": "Ingimundarson, Ari", "affiliation": "Technical Univ of Catalonia" } ], "keywords": [ "Microalgae production processes and bioenergy", "Pharmaceutical processes, food engineering and industrial biotechnology" ], "abstract": "This paper addresses the scalability challenge of jointly optimizing production and maintenance scheduling in microalgae manufacturing systems. In particular, we consider a system with an arbitrary number of cultures, operational constraints, and an arbitrary demand profile, operated by a mixed-integer nonlinear model predictive controller implemented as a two-stage optimization scheme. First, a mixed-integer quadratic programming problem is derived from the original nonlinear formulation and is further decomposed into a master and a subproblem following Benders’ decomposition to determine the mixed-integer optimization variables. The approach determines the maintenance schedule and the deviations between the arbitrary demand and the minimum attainable production, using a worst-case scenario to ensure feasibility. Then, a nonlinear programming problem is solved to further maximize the production of the manufacturing system. The proposed approach improves the computational efficiency and scalability of the related mixed-integer MPC formulation, achieving speedups of up to 17 times compared with a monolithic MIQP solver, and thereby enhancing the scalability of such approaches for industrial applications.", "url": "" }, { "id": "Tu-TuB24.4", "code": "TuB24.4", "title": "Unconstrained Economic Optimization for Microalgae Production in Open Reactors (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB24", "sessionTitle": "Challenges in Microalgae Production Processes", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Otálora, Pablo", "affiliation": "University of Almería" }, { "name": "Skogestad, Sigurd", "affiliation": "Norwegian Univ. of Science & Tech" }, { "name": "Guzman, Jose Luis", "affiliation": "University of Almeria" }, { "name": "Berenguel, Manuel", "affiliation": "University of Almeria (CIF Q-5450008-G)" } ], "keywords": [ "Microalgae production processes and bioenergy", "Wastewater treatment processes", "Dynamics and control of biologically motivated nonlinear systems" ], "abstract": "This work presents a dynamic optimization approach for the economic regulation of biomass concentration in open microalgae raceway reactors. An unconstrained Economic Model Predictive Control (EMPC) framework is implemented, solving a daily optimization problem with a multi-day prediction horizon based on a first-principles process model. The objective function maximizes economic profit from harvested biomass while maintaining constant reactor volume. Simulation studies under autumn and summer conditions demonstrate improved productivity and profitability compared to traditional operation, illustrating the potential of predictive economic control for open photobioreactor systems.", "url": "" }, { "id": "Tu-TuB24.5", "code": "TuB24.5", "title": "Economic MPC and Moving Horizon Estimation for Sustainable Microalgae-Bacteria Wastewater Treatment (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB24", "sessionTitle": "Challenges in Microalgae Production Processes", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Bausa-Ortiz, Irina", "affiliation": "University of Valladolid" }, { "name": "Oliveira-Silva, Erika", "affiliation": "Universidad De Valladolid" }, { "name": "Muñoz, Raúl", "affiliation": "University of Valladolid" }, { "name": "Gutiérrez, Gloria", "affiliation": "University of Valladolid" }, { "name": "P. Cristea, Smaranda", "affiliation": "University of Valladolid" }, { "name": "de Prada, César", "affiliation": "University of Valladolid" } ], "keywords": [ "Microalgae production processes and bioenergy", "Wastewater treatment processes", "Monitoring, observers and software sensors for biosystems" ], "abstract": "The present study proposes an Economic Model Predictive Control (eMPC) framework integrated with Moving Horizon Estimation (MHE) technique for optimizing a microalgae-bacteria wastewater treatment system. The proposed approach simultaneously maintains good treatment performance and maximizes biomass production by dynamically adjusting process variables based on real-time state estimation. The MHE ensures robustness against measurement noise and model uncertainties, while eMPC promotes sustainable operation through economic efficient control actions. The simulation results demonstrate an enhanced benefit in comparison to conventional control strategies. This integration presents a promising pathway to sustainable and cost-effective wastewater management.", "url": "" }, { "id": "Tu-TuB24.6", "code": "TuB24.6", "title": "Optimizing Biomass Production in a Phototrophic Wastewater Treatment Process Using a Multi-Specific ALBA Model (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB24", "sessionTitle": "Challenges in Microalgae Production Processes", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Assis Pessi, Bruno", "affiliation": "UNESP" }, { "name": "Bernard, Olivier", "affiliation": "INRIA" }, { "name": "Casagli, Francesca", "affiliation": "INRIA" }, { "name": "Pompei, Caroline", "affiliation": "UNESP" }, { "name": "Lombardi, Ana", "affiliation": "UFSCAR" }, { "name": "Ribeiro, Gustavo", "affiliation": "UNESP" } ], "keywords": [ "Microalgae production processes and bioenergy", "Wastewater treatment processes", "Modelling and control of microbial communities" ], "abstract": "This work presents an extension of the algae-bacteria (ALBA) model, a dynamical mathematical framework for phototrophic wastewater treatment. The upgraded version refines the taxonomic description by adding three anaerobic groups, including two with pathogenic potential whose removal is assessed, and a cyanobacterial group. The model was calibrated and validated with specific bacterial dynamics by integrating microbial community data obtained from genomic analysis of an outdoor pilot-scale photobioreactor using real anaerobically digested sanitary wastewater under tropical conditions. The recalibrated model, implemented in Julia, successfully reproduces the dynamics of nitrogen and phosphorus compounds while simulating the observed microbial community composition. This represents a significant advancement in modeling phototrophic wastewater treatment, as it mechanistically links genomic-informed microbial dynamics to system performance and provides the first validated predictions of pathogen removal within the ALBA framework. To demonstrate the model utility for process design and operation, an optimal control problem was formulated and solved, targeting the maximization of different biomass products (microalgae, cyanobacteria, and total biomass) by acting on the dilution rate. This optimization study constitutes a first stage for the development of a Model Predictive Control (MPC) strategy, where the control objective can be dynamically selected based on the desired process outcome, such as biomass valorization or treatment efficiency.", "url": "" }, { "id": "Tu-TuB25.1", "code": "TuB25.1", "title": "Linear Performance Based Adaptation for a Closed-Loop Artificial Pancreas System (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB25", "sessionTitle": "Engineering Diabetes Technologies III", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Pryor, Elliott", "affiliation": "University of Virginia" }, { "name": "Villa-Tamayo, Maria", "affiliation": "University of Virginia" }, { "name": "Moscoso-Vásquez, Marcela", "affiliation": "University of Virginia" }, { "name": "El Fathi, Anas", "affiliation": "University of Virginia" }, { "name": "Breton, Marc D", "affiliation": "University of Virginia" } ], "keywords": [ "Artificial pancreas or organs", "Decision support and control in medicine", "Medical devices, systems and solutions" ], "abstract": "Closed-loop automated insulin delivery (AID) systems improve glycemic control in type 1 diabetes (T1D), but adapting to individual variability in insulin requirements remains challenging. In this work, we propose a Linear Daily Adaptation (LinDA) framework that adjusts AID system parameters using clinically relevant metrics. LinDA offers transparency in system objectives and tunable aggressiveness based on user preferences or clinical guidelines. A Generalized LinDA method (GLinDA) is also introduced to adapt dual day/night profiles to address differentiated insulin requirements during the day and overnight, balancing management of daytime glycemic variability while reducing overnight hypoglycemia. Both algorithms were implemented within the latest UVA AID system and evaluated in-silico. Results demonstrate that both algorithms achieve glycemic outcomes comparable to prior adaptation methods, while offering greater stability, simplicity, interpretability, and alignment with user preferences.", "url": "" }, { "id": "Tu-TuB25.2", "code": "TuB25.2", "title": "Multiple Bound Super Twisting Observer for Glycemic Response Quantification (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB25", "sessionTitle": "Engineering Diabetes Technologies III", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Da Rosa Jurao, Fernando Leonel", "affiliation": "Instituto De Investigaciones En Electrónica, Control Y Procesamiento De Señales - LEICI (UNLP-CONICET), Facultad De Ingeniería," }, { "name": "Saggese, Arian", "affiliation": "Institute of Research in Electronics, Control, and Signal Processing-LEICI - National University of La Plata" }, { "name": "Fushimi, Emilia", "affiliation": "Instituto LEICI, Facultad De Ingeniería, UNLP-CONICET" }, { "name": "Garelli, Fabricio", "affiliation": "University of La Plata" } ], "keywords": [ "Artificial pancreas or organs", "Biomedical signal measurement and processing", "Control of physiological and clinical variables" ], "abstract": "Automated insulin delivery (AID) systems have significantly improved glycemic control in people with type 1 diabetes mellitus (T1DM), particularly during fasting. However, fully automatic meal compensation remains the main challenge for current commercial AID systems. Meals represent the primary disturbance in T1DM management, as they cause significant blood glucose (BG) peaks that must be compensated. Most existing strategies rely on carbohydrate (CHO) counting. While CHO counting is widely used, it does not account for differences in meal composition. High and low glycemic index (GI) meals produce markedly different glycemic responses (GR), even for identical CHO amounts. This work proposes a multiple bound super twisting observer (MB-STO) to quantify the GR induced by meals in real time using only BG signal. The MB-STO employs multiple bounds on the disturbance, and the GR quantification is achieved by analyzing the observer residuals. The validation is conducted using the cohort of 10 virtual adult subjects of the UVA/Padova simulator. Meals with the same CHO amount but with different GI profiles are considered. Results show that the MB-STO successfully quantifies the GR of both high and low GI meals. Although further validation with diverse meal types is needed, the MB-STO provides a real-time signal that could improve automatic meal compensation in AID systems.", "url": "" }, { "id": "Tu-TuB25.3", "code": "TuB25.3", "title": "Real-Time Physical Activity and Acute Psychological Stress Assessment for Fully-Automated Insulin Therapy (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB25", "sessionTitle": "Engineering Diabetes Technologies III", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Cinar, Ali", "affiliation": "Illinois Inst. of Tech" }, { "name": "Rashid, Mudassir", "affiliation": "Illinois Institute of Technology" }, { "name": "Abdel-Latif, Mahmoud", "affiliation": "Illinois Institute of Technology" }, { "name": "Ahmadasas, Mohammad", "affiliation": "Illinois Institute of Technology" }, { "name": "Siket, Máté", "affiliation": "Obuda University" }, { "name": "Teleki, Julia", "affiliation": "Illinois Institute of Technology" }, { "name": "Park, Minsun", "affiliation": "University of Illinois at Chicago" }, { "name": "Sharp, Lisa", "affiliation": "University of Illinois Chicago" }, { "name": "Quinn, Lauretta", "affiliation": "University of Illinois at Chicago" } ], "keywords": [ "Biomedical system modeling, identification, and simulation", "Artificial pancreas or organs", "Biomedical signal measurement and processing" ], "abstract": "Automated insulin delivery (AID) systems for people with diabetes rely on control systems that can mitigate the effects of various disturbances affecting glucose concentration levels. Current commercially available AID systems rely on manual information entries to inform the AID about meals and exercise. Yet, many physical activities (PA) and acute psychological stressors (APS) are spontaneous and their mitigations is the responsibility of the feedback controller in these AID systems. Effective mitigation of such disturbances necessitates detecting and quantifying the characteristics of PA and APS events in real-time to inform the insulin dosing decisions by the AID. We developed a multi-task long short-term memory neural network with convolutional layers model for real-time assessment of PA and APS using physiological signals collected from wearable devices. The model detects and classifies independent and concurrent occurrences of PA and APS. It achieved good performance on testing data, with a weighted F1 score of 95.1% for APS classification and 97.4% for PA classification. The model also demonstrated strong generalization, achieving weighted F1 scores up to 80.8% for PA and 68.6% for APS classification under cross-validation. Energy expenditure during PA and APS intensity are quantified by additional models. The detection, discrimination, and quantification of spontaneous PA and APS in real-time with streaming data enable feedforward control in the AID control system to provide a fully automated AID system to counteract the adverse effects of impending glycemic disturbances without any manual information to the AID.", "url": "" }, { "id": "Tu-TuB25.4", "code": "TuB25.4", "title": "Physical Activity Aware Modulation for Fully Closed Loop Automated Insulin Delivery System (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB25", "sessionTitle": "Engineering Diabetes Technologies III", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Villa-Tamayo, Maria", "affiliation": "University of Virginia" }, { "name": "Moscoso-Vásquez, Marcela", "affiliation": "University of Virginia" }, { "name": "Karagoz, Meryem Altin", "affiliation": "University of Virginia" }, { "name": "Pryor, Elliott", "affiliation": "University of Virginia" }, { "name": "Breton, Marc D", "affiliation": "University of Virginia" }, { "name": "El Fathi, Anas", "affiliation": "University of Virginia" } ], "keywords": [ "Biomedical signal measurement and processing", "Biomedical system modeling, identification, and simulation", "Control of physiological and clinical variables" ], "abstract": "Physical activity (PA) challenges glucose control in type 1 diabetes, often requiring manual intervention. This work integrates PA detection into a fully closed-loop automated insulin delivery (AID) system. Using heart rate and step data from the FCL@Home trial, we developed and validated candidate PA detection models; the best model reached an F1 score of 0.685. We then assessed the feasibility of using PA detection to modulate the UVA AID system (AIDANET) via a temporary controller rate (TCR) through 14-day replay simulations on the T1DEXI dataset (N=39). PA-aware modulation produced a mean time below range of 3.14% and 0.83 hypoglycemia treatments per day, compared with 3.32% and 0.90 for the nominal controller, while time in range was preserved. These results support the feasibility of integrating wearable-derived PA signals into AIDANET for automatic TCR modulation without compromising safety and motivate further evaluation of activity-aware closed-loop control.", "url": "" }, { "id": "Tu-TuB25.6", "code": "TuB25.6", "title": "Data-Driven Control of Type 2 Diabetes Progression Via Personalized Physical Activity (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB25", "sessionTitle": "Engineering Diabetes Technologies III", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Lops, Giada", "affiliation": "Polytechnic of Bari" }, { "name": "De Paola, Pierluigi Francesco", "affiliation": "National Research Council, Politecnico of Bari" }, { "name": "Racanelli, Vito Andrea", "affiliation": "Politecnico Di Bari" }, { "name": "Manfredi, Gioacchino", "affiliation": "Politecnico Di Bari" }, { "name": "De Cicco, Luca", "affiliation": "Politecnico Di Bari" }, { "name": "Mascolo, Saverio", "affiliation": "Politecnico Di Bari" } ], "keywords": [ "Control of physiological and clinical variables", "Biomedical system modeling, identification, and simulation", "Intensive and chronic care or treatment" ], "abstract": "This work investigates long-horizon regulation of Type 2 Diabetes progression through daily physical activity using a data-driven controller based on Proximal Policy Optimization. A five-state physiological model (comprising glucose, insulin, beta-cell mass, insulin sensitivity, and IL-6 dynamics) is embedded in a custom environment enabling closed-loop simulations over a two-year horizon. The framework introduces realistic variability through parameter and initial-condition perturbations (+/-5%), circadian glucose oscillations (+/-20 mg/dL), and mid-episode degradation of the insulin-sensitivity target, providing a physiologically consistent and challenging benchmark. The Proximal Policy Optimization agent learns adaptive daily exercise policies that preserve glucose homeostasis and robustness against uncertainty. Across a 200-patient evaluation cohort, the controller achieves a 66% success rate in maintaining final glucose levels below 126 mg/dL, demonstrating the feasibility of reinforcement learning for long-term, personalized physical activity regulation and its potential to support model-based digital therapeutics in Type 2 Diabetes management.", "url": "" }, { "id": "Tu-TuB26.1", "code": "TuB26.1", "title": "GeomPlanner: Real-Time Unmanned Aerial Vehicle Trajectory Planning Via End-To-End Geometry-Guided Learning", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB26", "sessionTitle": "Learning-Enabled Autonomy and Multi-Agent Aerospace Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Su, Hang", "affiliation": "Beihang University" }, { "name": "Duan, Haibin", "affiliation": "Beihang University(formerly Beijing University of Aeronautics and Astronautics)" } ], "keywords": [ "AI for aircraft and spacecraft navigation, guidance and control", "Aerial and space robotics" ], "abstract": "This paper proposes an end-to-end geometry-guided learning-based real-time trajectory planner (GeomPlanner) to address the real-time challenge of autonomous navigation in unknown dense obstacle environments. The approach integrates depth perception and trajectory prediction into a unified network architecture. It designs a convolutional-based state feature extraction pathway to encode the vehicle's state and target direction into spatially aligned feature maps, and proposes a vision-driven attention mechanism to modulate fused multi-source information for context-aware trajectory decision-making. During training, a geometrically guided unsupervised learning paradigm is proposed, which constructs a comprehensive cost function by combining classical trajectory optimization, Special Euclidean Group (SE(3)) geometric consistency constraints, gradient-aware safety barriers, and Riemannian metric-based goal orientation. Experimental results demonstrate that GeomPlanner achieves millisecond-level planning latency and significantly outperforms state-of-the-art algorithms in terms of success rate and safety.", "url": "" }, { "id": "Tu-TuB26.2", "code": "TuB26.2", "title": "Model Predictive Planner for UAV Navigation in Non-Convex Air Corridors", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB26", "sessionTitle": "Learning-Enabled Autonomy and Multi-Agent Aerospace Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Silva Junior, Henrique", "affiliation": "Universidade Federal De Minas Gerais" }, { "name": "Santos, Marcelo Alves", "affiliation": "University of Bergamo" }, { "name": "Raffo, Guilherme Vianna", "affiliation": "Federal University of Minas Gerais" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Urban air mobility", "Aerospace mission control and operations" ], "abstract": "This work presents a motion planning framework for UAV navigation in non-convex urban air corridors. The planner is based on a mixed-integer tracking model predictive control formulation that enforces corridor feasibility and dynamic consistency within a single optimization problem. To guarantee convergence to the target and mitigate the occurrence of local minima induced by non-convex geometry, a shortest-path-based offset cost with feasibility constraints is embedded directly into the planning problem. Numerical simulations show that the proposed formulation generates dynamically valid trajectories that satisfy the corridor constraints and converge to the target without relying on external global planning stages.", "url": "" }, { "id": "Tu-TuB26.3", "code": "TuB26.3", "title": "Multi-Stream Fusion Network and CWDR-Driven Reinforcement Learning for Multi-UAV Cooperative Air Combat (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB26", "sessionTitle": "Learning-Enabled Autonomy and Multi-Agent Aerospace Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Zhang, Xiaoman", "affiliation": "Beihang University" }, { "name": "Wang, Yangzhu", "affiliation": "Beihang University" }, { "name": "Zhang, Dingyuan", "affiliation": "Beihang University" }, { "name": "Fancheng, Ding", "affiliation": "Beihang University" }, { "name": "Qiu, Huaxin", "affiliation": "Beihang University" } ], "keywords": [ "AI for aircraft and spacecraft navigation, guidance and control", "Guidance, navigation and control of aircraft and spacecraft", "Aerospace mission control and operations" ], "abstract": "多无人机协同空战要求特工在高度敌对的环境中执行协调机动。然而，现有的强化学习方法在平衡团队目标与个人行动方面存在困难，主要原因是奖励设计仍然稀少且信息量薄弱。为克服这一限制，我们提出了贡献加权伤害比（CWDR）奖励机制，该机制直接将团队层面的战斗优势与个人伤害贡献整合起来。CWDR提供密集、方向性强且信用意识强的反馈，使客服人员能够更好地将个人决策与合作目标对齐。为进一步提升协作意识，我们进一步引入了多流融合网络，由三位专家编码员处理观测数据。模拟结果显示，我们的方法在面对基于极小极大法的对手时，能带来更强的合作机动和更高的战斗效能。", "url": "" }, { "id": "Tu-TuB26.4", "code": "TuB26.4", "title": "Capabilities and Limitations of LLMs in Assembly Task Allocation (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB26", "sessionTitle": "Learning-Enabled Autonomy and Multi-Agent Aerospace Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Zhang, Congwei", "affiliation": "Chinese Academy of Sciences" }, { "name": "Wang, Mengyang", "affiliation": "Academy of Mathematics and Systems Science, Chinese Academy of Sciences" }, { "name": "Qi, Hongsheng", "affiliation": "Academy of Mathematics and Systems Science, Chinese Academy of Sciences" }, { "name": "Huang, Yi", "affiliation": "Institute of Systems Science, Chinese Academy of Sciences" } ], "keywords": [ "Aerospace mission control and operations", "Autonomous mobile robots", "Aerial and space robotics" ], "abstract": "In this paper, the capabilities of LLMs in assembly task allocation are investigated. A Task-Decomposition-Matrix (TDM) is proposed to explicitly represent subtask dependencies. Based on this representation, a TDM-LLM framework is developed and evaluated in the IKEA Furniture Assembly Environment. The feasibility and efficiency of LLM-generated allocation plans are assessed. The results show that LLMs are effective at semantic understanding and flexible allocation, but remain limited in handling complex dependency structures. The proposed TDM helps mitigate these limitations and improves the reliability of task allocation.", "url": "" }, { "id": "Tu-TuB26.5", "code": "TuB26.5", "title": "Information Density Matching Driven Optimal Transport Control for UAV Swarm Exploration in Spatiotemporal Fields", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB26", "sessionTitle": "Learning-Enabled Autonomy and Multi-Agent Aerospace Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Gao, Hao", "affiliation": "Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Gao, Yun", "affiliation": "The Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Wang, Jiawen", "affiliation": "Harbin Institute of Technology" }, { "name": "Zhou, Siyi", "affiliation": "The Hong Kong University of Science and Technology （Guangzhou）" }, { "name": "Zhang, Shiheng", "affiliation": "Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Zhou, Jinni", "affiliation": "Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Ji, Yiding", "affiliation": "Hong Kong University of Science and Technology (Guangzhou)" } ], "keywords": [ "Space exploration and transportation", "Adaptive and robust control of automotive systems", "Autonomous mobile robots" ], "abstract": "Distributed exploration and field reconstruction with UAV swarms remain challenging due to stringent scalability constraints, limited onboard computation, and requirement for uncertainty-driven coordination. We propose an information density matching (IDM) framework where each UAV maintains a sparse Gaussian Process model of the unknown spatiotemporal field and generates a variance-based information density. A distributed optimal-transport control law then drives the swarm toward the target density, enabling them to migrate toward high-uncertainty regions while preserving spatial dispersion. The swarm density forms a Wasserstein gradient flow and a Lyapunov type dissipation result is established under bounded approximation errors. Simulations in static, multi-peak, and dynamic fields demonstrate that our approach achieves fast uncertainty reduction, competitive reconstruction accuracy, stable swarm distribution, and lower communication load compared to several baseline methods.", "url": "" }, { "id": "Tu-TuB26.6", "code": "TuB26.6", "title": "Neuro-Adaptive Output Feedback Control of Payload-Varying Quadrotor UAVs without Velocity (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB26", "sessionTitle": "Learning-Enabled Autonomy and Multi-Agent Aerospace Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Selim, Erman", "affiliation": "Ege University" }, { "name": "Yilmaz, Bayram Melih", "affiliation": "University of Waterloo" }, { "name": "Tatlicioglu, Enver", "affiliation": "Ege University" }, { "name": "Fidan, Baris", "affiliation": "University of Waterloo" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Motion control for AVs", "Guidance, navigation and control for AVs" ], "abstract": "Unmanned aerial vehicles, particularly quadrotors, are increasingly deployed in missions involving payload transport and release, which cause significant variations in both the total mass and the center of gravity of the system. These variations severely degrade the performance of conventional controllers that assume fixed dynamics and full-state feedback. This paper proposes a generalized neural network based adaptive output feedback control framework for systems with variable mass properties. Unlike existing approaches, the controller requires only position measurements, thereby eliminating the need for velocity sensing. A composite error formulation combined with an adaptation law ensures uniform ultimate boundedness of the closed-loop system while effectively compensating for unknown and time-varying dynamics. Although the method is demonstrated on a quadrotor platform, the generalized design allows straightforward extension to other nonlinear systems with similar challenges. Simulation results confirm accurate trajectory tracking and reduced control effort under abrupt payload changes, highlighting the robustness, adaptability, and practicality of the proposed approach.", "url": "" }, { "id": "Tu-TuB27.1", "code": "TuB27.1", "title": "Robust Vessel Maneuvering Modelling Using Set-Membership Identification (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB27", "sessionTitle": "JO-CEP: Modelling, Identification and Control in Marine Systems II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Dhyani, Abhishek", "affiliation": "Delft University of Technology" }, { "name": "Tsolakis, Anastasios", "affiliation": "Delft University of Technology" }, { "name": "van der El, Kasper", "affiliation": "Delft University of Technology" }, { "name": "Negenborn, Rudy", "affiliation": "Delft University of Technology" }, { "name": "Reppa, Vasso", "affiliation": "Delft University of Technology" } ], "keywords": [ "Modelling, identification and control in marine systems", "Maritime transport operation and automation", "Autonomous marine systems and vehicles" ], "abstract": "System identification of full-scale surface vessels must address significant uncertainties arising from model mismatch, sensor noise, and environmental disturbances. To provide safety, robustness and constraint satisfaction guarantees, it is essential to quantify the bounds of model parametric uncertainty. This paper proposes a set-membership identification method for estimating key parameters of a nonlinear vessel maneuvering model, including inertia and added-mass terms, hydrodynamic derivatives, and actuation-related parameters. The method provides a bounded error characterisation of the uncertainties, offering a reliable framework for modelling the effects of measurement noise, wind and waves. In addition to point estimates, the approach yields a feasible parameter set that provably contains the true parameters. Validation using full-scale experimental data from a catamaran ferry demonstrates the method’s accuracy and its capability to provide bounded parameter estimates.", "url": "" }, { "id": "Tu-TuB27.2", "code": "TuB27.2", "title": "Modelling, Parameter Identification and Nonlinear Control of a Proton Exchange Membrane Fuel Cell for Maritime Use (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB27", "sessionTitle": "JO-CEP: Modelling, Identification and Control in Marine Systems II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Ceyhun, Halit Ege", "affiliation": "Delft University of Technology" }, { "name": "Shipurkar, Udai", "affiliation": "MARIN" }, { "name": "van Biert, Lindert", "affiliation": "Delft University of Technology" }, { "name": "Negenborn, Rudy", "affiliation": "Delft University of Technology" }, { "name": "Coraddu, Andrea", "affiliation": "Delft University of Technology" } ], "keywords": [ "Modelling, identification and control in marine systems", "Power and propulsion in marine systems", "Automatic control, optimization, real-time operations in transportation" ], "abstract": "Zero-emission maritime power systems require advanced control strategies for Proton Exchange Membrane (PEM) fuel cells. This study develops a physics-based, control-oriented dynamic model of a PEM fuel cell. Model parameters are optimally identified for an average cell using experimental data from a system comprising four stacks, each with 96 cells. Two nonlinear controllers — Dynamic Feedback Linearization (DFL) and an Integral Sliding Mode Controller (ISMC) — are implemented and compared. Results show faster transients with DFL, while ISMC provides improved robustness for power tracking.", "url": "" }, { "id": "Tu-TuB27.3", "code": "TuB27.3", "title": "Predictive Adaptive Reactivity-Controlled Compression Ignition for a Dual-Fuel Marine Engine: A Model-In-The-Loop Study (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB27", "sessionTitle": "JO-CEP: Modelling, Identification and Control in Marine Systems II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Storm, Xiaoguo", "affiliation": "University of Vaasa" }, { "name": "Shamekhi, Amir-Mohammad", "affiliation": "University of Vaasa" }, { "name": "Raisi Esfarjani, Mohammad", "affiliation": "University of Vaasa" }, { "name": "Modabberian, Amin", "affiliation": "Aalto University" }, { "name": "Vasudev, Aneesh", "affiliation": "University of Vaasa" }, { "name": "Zenger, Kai", "affiliation": "Aalto University School of Electrical Engineering" }, { "name": "Hyvönen, Jari", "affiliation": "Engine Research and Technology Development at Wärtsilä Marine Solutions" }, { "name": "Mikulski, Maciej", "affiliation": "University of Vaasa" } ], "keywords": [ "Modelling, identification and control in marine systems", "Power and propulsion in marine systems", "Simulation and digital-twin in marine systems" ], "abstract": "This study develops a real-time adaptive model predictive control (AMPC) framework for marine RCCI (Reactivity-Controlled Compression Ignition) engines to regulate indicated mean effective pressure (IMEP) and the crank angle at 50% mass fraction burned (CA50) by adjusting total fuel energy and blend ratio. The controller is evaluated using the Wärtsilä 31DF UVATZ simulator and benchmarked against a decentralized PID structure. While both deliver comparable tracking accuracy, the AMPC achieves a faster IMEP response (within six cycles), lower CA50 steady-state error (maximum 0.45 crank-angle-degrees), and 3.1% lower fuel consumption. Its receding-horizon and self-tuning design further enhance robustness, advancing predictive control for efficient, clean RCCI combustion.", "url": "" }, { "id": "Tu-TuB27.4", "code": "TuB27.4", "title": "An Innovation-Based Approach to Detect Stealthy Disturbance Attacks in Maritime Monitoring (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB27", "sessionTitle": "JO-CEP: Modelling, Identification and Control in Marine Systems II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Oliva, Gabriele", "affiliation": "University Campus Bio-Medico of Rome" }, { "name": "Mazzà, Bianca", "affiliation": "Università Campus Bio-Medico Di Roma" }, { "name": "Setola, Roberto", "affiliation": "Università Campus Biomedico" } ], "keywords": [ "Perception and filtering in marine systems" ], "abstract": "Modern maritime navigation and control systems rely on digital sensing, estimation, and communication pipelines that fuse GNSS, radar, inertial, and AIS data through approaches such as Kalman-filter-based estimators. While these technologies are essential for safety and efficiency, their growing interconnection also exposes vessels to faults and cyber–physical anomalies. This paper introduces a Statistical Detection Suite (SDS) to detect malicious stealthy disturbances. Specifically, the SDS operates directly on the innovations of Kalman filters, providing a lightweight yet statistically grounded layer of anomaly monitoring within maritime estimation frameworks. The SDS jointly evaluates whitened innovations through four complementary checks: (i) bias, (ii) covariance consistency via the normalized innovation squared (NIS), (iii) Gaussianity, and (iv) temporal independence via portmanteau statistics. The analysis further examines how an adversary can craft stealthy finite-impulse-response (FIR) Gaussian disturbances that can evade classical χ2 checks, formulating an optimization-based design that balances stealth and trajectory impact. An evaluation in maritime navigation scenarios illustrates how the SDS exposes colored spoofing attacks that bypass traditional methods, highlighting the role of innovation-based monitoring in strengthening maritime resilience against cyber–physical threats.", "url": "" }, { "id": "Tu-TuB27.5", "code": "TuB27.5", "title": "Optimal Observer-Based Pressure Sensor Placement for Rigid Sails (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB27", "sessionTitle": "JO-CEP: Modelling, Identification and Control in Marine Systems II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Smith, Sean", "affiliation": "Université Grenoble Alpes" }, { "name": "Witrant, Emmanuel", "affiliation": "Université Grenoble Alpes" }, { "name": "Pan, Ya-Jun", "affiliation": "Dalhousie University" } ], "keywords": [ "Sensors and actuators in marine systems", "Perception and filtering in marine systems", "Modelling, identification and control in marine systems" ], "abstract": "This paper investigates the optimal placement of pressure sensors for observer-based feedback on rigid domains, with a particular focus on rigid sails. Existing computational fluid dynamics (CFD) studies, supported by experimental validation, have shown promising results in analyzing sail aerodynamics using pressure sensors. Building on these developments, this study adapts the General Pressure Equation (GPE) into a linearized form, close to quasi-steady conditions, for pressure sensor placement analysis. Based on this model, an observer-based closed-loop strategy for optimal sensor placement is developed. A Lagrangian method is proposed to establish local optimality conditions in the infinite-dimensional setting without relying on reduced-order (lumped) models. The proposed strategy directly considers the state estimation efficiency within the optimal sensor placement process. The efficiency of the method to estimate the pressure field is illustrated by simulation results on a rigid sail with a symmetric profile and by experimental results on the jib (flexible) sail of a 6 m sailboat.", "url": "" }, { "id": "Tu-TuB27.6", "code": "TuB27.6", "title": "Data-Driven Model Predictive Control for Real-Time Combustion Balancing in Hydrogen/Diesel Dual-Fuel Engines (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB27", "sessionTitle": "JO-CEP: Modelling, Identification and Control in Marine Systems II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Mehnatkesh Ghadikolaei, Hossein", "affiliation": "University of Alberta" }, { "name": "Kheyrollahi, Javad", "affiliation": "University of Alberta" }, { "name": "Gordon, David", "affiliation": "Univ. of Alberta" }, { "name": "Koch, Charles Robert", "affiliation": "University of Alberta" } ], "keywords": [ "AI and learning-based control for automotive systems", "Engine and powertrain modeling and control", "Nonlinear and optimal automotive control" ], "abstract": "Using hydrogen as a secondary fuel in internal combustion engines is a promising approach to significantly reducing greenhouse gas emissions in the transportation sector. However, injecting secondary fuels via port injection in multi-cylinder engines introduces variability in combustion metrics, such as peak pressure (PP) and maximum pressure rise rate (MPRR), thereby increasing emissions and reducing engine durability. This variability appears as either cycle-to-cycle or cylinder-to-cylinder variation, ultimately resulting in decreased engine performance. This study presents a machine learning-based nonlinear model predictive control strategy for achieving real-time combustion balancing in a multi-cylinder hydrogen–diesel dual-fuel engine. Experimental results demonstrate a mean absolute error of 0.2 bar for tracking the indicated mean effective pressure (IMEP) reference. Differences in IMEP between cylinders are reduced by up to 87% compared to the benchmark. The coefficients of variation for PP and MPRR have decreased by 29.6% and 5.5%, respectively, among the six cylinders. The results show that the proposed controller effectively minimizes cylinder-to-cylinder variations while maintaining all combustion and safety constraints.", "url": "" }, { "id": "Tu-TuB32.1", "code": "TuB32.1", "title": "Multi-Aerial Pursuit of a Dynamic Target with Trajectory Prediction (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB32", "sessionTitle": "JO: Task and Motion Planning", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Cariño, Jossué", "affiliation": "Université Technologie De Compigégne" }, { "name": "De Souza, Cristino", "affiliation": "ARRC - Autonomous Robotic Center, Abu Dhabi, UAE" }, { "name": "Castillo, Pedro", "affiliation": "Universite De Technologie De Compiegne" }, { "name": "Vidolov, Boris", "affiliation": "Universite De Technologie De Compiegne" } ], "keywords": [ "Autonomous navigation", "Task and motion planning", "Mechatronic system estimation, identification, control" ], "abstract": "A multi-agent 3D pursuit strategy is proposed for the prediction and tracking of an intruder drone. The tracking behavior is based on a modified Deviated Pursuit Guidance (DPP) strategy that is complemented with a prediction of the target’s state. Trajectory target predictions are estimated based on a kinematic model and on the actual time-to-interception. The proposed solution, instead of using the classical repulsion and alignment terms found in other works of DPP, the pursuers have a common goal and collision-free trajectories are imposed to corral and intercept the target. In addition, the algorithm is designed considering its implementation in real time. Our strategy results in trajectories that mimics the behavior of predator animals like lions and wolves. Numerical simulations and experimental tests are carried-out to validate the developed pursuit strategy using quadcopter vehicles.", "url": "" }, { "id": "Tu-TuB32.2", "code": "TuB32.2", "title": "Bayesian Learning-Based Safe Feedback Motion Planning for Disturbed Nonlinear Systems with Differential Flatness (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB32", "sessionTitle": "JO: Task and Motion Planning", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Yang, Rui", "affiliation": "The Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Zheng, Lei", "affiliation": "National University of Singapore" }, { "name": "Ge, Shuzhi Sam", "affiliation": "National University of Singapore" }, { "name": "Ma, Jun", "affiliation": "The Hong Kong University of Science and Technology" } ], "keywords": [ "Learning methods for optimal control", "Model predictive control", "Data-driven robust control" ], "abstract": "Typically, separate designs of planning and control with different model fidelity could compromise system safety, especially when the planner fails to account for control errors amplified by disturbances. This work presents a novel safe feedback motion planning framework for differentially flat nonlinear systems subject to unknown disturbances. A Bayesian learning adaptive controller is designed to enhance control accuracy by transforming the nonlinear system into a linear system via differential flatness and utilizing Gaussian Processes (GPs) to account for disturbances. Closed-loop input-to-state stability (ISS) is guaranteed with specified high probability. Furthermore, a probabilistic control invariant set is constructed for the control error system, which serves as an adaptive tube for the planner. Subsequently, the motion planner integrates the tube as a robustness margin to tighten safety constraints, and generates smooth trajectories by planning over B'ezier control points. The proposed method ensures recursive feasibility rigorously and provides safety assurance. Its effectiveness is validated through simulations of a robot navigation task, which demonstrate improved control accuracy and safety without unnecessary conservatism in the presence of environmental disturbances.", "url": "" }, { "id": "Tu-TuB32.3", "code": "TuB32.3", "title": "Temporal Logic-Based Coverage and Path Planning for Unmanned Aerial-Ground Vehicle Systems (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB32", "sessionTitle": "JO: Task and Motion Planning", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Zhang, Shiheng", "affiliation": "Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Zhang, Yangrui", "affiliation": "Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Miao, Shaowen", "affiliation": "The Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Ji, Yiding", "affiliation": "Hong Kong University of Science and Technology (Guangzhou)" } ], "keywords": [ "Supervisory control and automata", "Optimal control of discrete event and hybrid systems", "Discrete event modeling and simulation" ], "abstract": "This paper addresses the cooperative coordination of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) in complex environments under formal task specifications. A two-layer control framework is developed, where UAVs perform coverage and monitoring tasks, while UGVs execute path planning with obstacle avoidance. The framework leverages spatial aggregation signal temporal logic to formally specify both spatial and temporal behaviors, enabling real-time monitoring of task execution. To the best of our knowledge, this work is the first to employ temporal logic for specifying multi-agent coverage tasks. To ensure feasibility, an attractive potential field approach incorporates the Eventually specifications into the path planning control objectives, while time-varying control barrier functions enforce the Always safety constraints. The proposed method ensures task satisfaction and safety at runtime, and its effectiveness is validated through numerical simulations.", "url": "" }, { "id": "Tu-TuB32.4", "code": "TuB32.4", "title": "An Integrated Robust Integral of the Sign of the Error and Repetitive Learning Approach for Accurate Trajectory Tracking in Robotic Manipulators (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB32", "sessionTitle": "JO: Task and Motion Planning", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Hindistan, Cagri", "affiliation": "Ege University" }, { "name": "Unver, Sukru", "affiliation": "Izmir Democracy University" }, { "name": "Tatlicioglu, Enver", "affiliation": "Ege University" }, { "name": "Zergeroglu, Erkan", "affiliation": "Gebze Technical University" } ], "keywords": [ "Task and motion planning" ], "abstract": "This paper presents an integrated control framework that combines the robust integral of the sign of the error (RISE) method with a repetitive learning (RL) mechanism to achieve accurate trajectory tracking in robotic manipulators. The proposed approach leverages the disturbance rejection capability of RISE and the periodic disturbance compensation potential of RL to effectively handle model uncertainties and recurring disturbances. A rigorous Lyapunov-based analysis is conducted to establish the global asymptotic stability of the closed-loop system. Numerical simulations performed on a two link robot manipulator demonstrate the superior tracking accuracy and reduced tracking error of the proposed controller compared with the standalone RISE controller, validating its effectiveness.", "url": "" }, { "id": "Tu-TuB32.5", "code": "TuB32.5", "title": "Twist-Based Constant-Speed Path-Following Controller for Robot Manipulators with Path Invariance (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB32", "sessionTitle": "JO: Task and Motion Planning", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Niaz, Hassan", "affiliation": "Texas A&M University" }, { "name": "Pagilla, Prabhakar R.", "affiliation": "Texas A&M University" } ], "keywords": [ "Task and motion planning", "Mechatronics for robotic systems", "Robotic grasping and manipulation" ], "abstract": "In this paper, we present a Constant-Speed Path Following (CSPF) controller for robotic manipulators, formulated as a velocity-mode outer-loop scheme that enforces constant tangential speed while regulating both spatial and orientation errors. From task-space waypoints, position and orientation paths are generated and reparameterized online via a timing law to achieve geometry-independent speed regulation. The outer-loop CSPF augments the resulting tangential feedforward path terms with task-space feedback, yielding regulated speed and convergence in position and orientation under standard assumptions. Experiments on a UR16e manipulator show that CSPF outperforms pose- and twist-streaming baselines, achieving sub-millimeter cross-track error, tight orientation tracking, and markedly smoother speed profiles. The controller achieves this using standard vendor interfaces without requiring joint-torque access, supporting practical deployment on industrial and commercial robotic platforms.", "url": "" }, { "id": "Tu-TuB32.6", "code": "TuB32.6", "title": "Optimization-Based Motion Synthesis for Unified Manipulation in Robot Hand-Arm Systems with Bowden Cable Transmission (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB32", "sessionTitle": "JO: Task and Motion Planning", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Chen, Lingyun", "affiliation": "Techinical University of Munich" }, { "name": "Yuan, Siqi", "affiliation": "Technical University of Munich" }, { "name": "Li, Junnan", "affiliation": "Technical University of Munich" }, { "name": "Ganguly, Amartya", "affiliation": "Technical University of Munich" }, { "name": "Haddadin, Sami", "affiliation": "Mohamed Bin Zayed University of Artificial Intelligence" } ], "keywords": [ "Task and motion planning", "Robotic grasping and manipulation", "Mechatronic system modeling, design, optimization" ], "abstract": "Unified hand–arm robot systems face critical challenges in reliable force transmission, particularly when tendon-driven dexterous hands are actuated via Bowden cables. The remote actuation introduces nonlinear friction, hysteresis, and force loss along the transmission path, which compromises coordinated control between the hand and arm. To mitigate these effects, we propose a nonlinear optimization-based motion synthesis framework that minimizes Bowden cable force variation while maintaining precise end-effector (EE) tracking. The framework incorporates multiple objectives, including EE pose matching, motion smoothness, kinematic singularity avoidance, joint torque minimization, and Bowden cable bending minimization. We evaluated this motion synthesis framework on a single-cable setup as a representative of a multi-tendon, multi-fingered hand-arm system, demonstrating effective reduction of cable force fluctuations and accurate trajectory tracking.", "url": "" }, { "id": "Tu-TuB33.1", "code": "TuB33.1", "title": "Accelerated Explainable Anomaly Detection for Semiconductor Manufacturing (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB33", "sessionTitle": "Advances in Machine Learning and Intelligent Control for Industrial Automation and Robotics", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Bertipaglia, Beatrice Sofia", "affiliation": "Università Di Padova" }, { "name": "Brunelli, Luca", "affiliation": "Statwolf" }, { "name": "Peratoner, Alessandro", "affiliation": "Statwolf" }, { "name": "Convento, Enrico", "affiliation": "Statwolf Data Science SRL" }, { "name": "Masiero, Chiara", "affiliation": "Statwolf" }, { "name": "Beghi, Alessandro", "affiliation": "Università Di Padova" }, { "name": "Susto, Gian Antonio", "affiliation": "University of Padova" } ], "keywords": [ "AI-driven modeling and control", "Data fusion and mining in control", "Machine learning for modeling and prediction" ], "abstract": "The semiconductor manufacturing industry is a complex and high-stakes field where even small errors can result in significant financial losses. Anomaly detection is crucial in this context, as identifying faulty wafers early on can prevent costly rework or scrapping. However, traditional anomaly detection methods often lack interpretability, making it difficult for industry experts to validate and trust the results. To address this challenge, we propose a novel approach that combines Isolation Forest-based anomaly detection with accelerated perturbation-based explainability techniques to identify and interpret anomalies in semiconductor process data. Our approach leverages a combination of data preprocessing and feature engineering to identify patterns and trends in the data that are indicative of anomalous behavior. The use of XAI techniques enables us to provide insights into the root causes of the anomalies, allowing industry experts to take targeted corrective actions to improve the manufacturing process. Our approach has been evaluated using real-world data from a semiconductor manufacturing facility, demonstrating its effectiveness in detecting anomalies, improving process understanding, and potentially enabling proactive process control. This work contributes a practical, interpretable, and efficient solution for enhancing quality control and reducing costs in semiconductor manufacturing", "url": "" }, { "id": "Tu-TuB33.2", "code": "TuB33.2", "title": "Fast Neural-Network Approximation of Active Target Search under Uncertainty (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB33", "sessionTitle": "Advances in Machine Learning and Intelligent Control for Industrial Automation and Robotics", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Yousuf, Bilal", "affiliation": "Technical University of Cluj-Napoca" }, { "name": "Lendek, Zsofia", "affiliation": "Technical University of Cluj-Napoca, VAT RO 22736939" }, { "name": "Busoniu, Lucian", "affiliation": "Technical University of Cluj-Napoca" } ], "keywords": [ "Machine learning for modeling and prediction", "Knowledge-based and data-driven control", "Remote data acquisition and fusion" ], "abstract": "We address the problem of searching for an unknown number of stationary targets at unknown positions with a mobile agent. A probability hypothesis density filter is used to estimate the expected number of targets under measurement uncertainty. Existing planners, such as Active Search (AS) and its Intermittent variant (ASI), achieve accurate detection but require costly online optimization. To reduce online computation, we propose to use a convolutional neural network to approximate AS or ASI decisions through direct inference. The network is trained on AS/ASI data using a multi-channel grid that encodes target beliefs, the agent position, visitation history, and boundary information. Simulations with uniform and clustered target distributions show that the network achieves detection rates comparable to AS or ASI while reducing computation by orders of magnitude.", "url": "" }, { "id": "Tu-TuB33.3", "code": "TuB33.3", "title": "ViTA-Seg: Vision Transformer for Amodal Segmentation in Robotics (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB33", "sessionTitle": "Advances in Machine Learning and Intelligent Control for Industrial Automation and Robotics", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Caramia, Donato", "affiliation": "Politecnico Di Bari" }, { "name": "Pokorny, Florian T.", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Triggiani, Giuseppe", "affiliation": "AROL S.p.A" }, { "name": "Ruffino, Denis", "affiliation": "AROL S.p.A" }, { "name": "Naso, David", "affiliation": "Politecnico Di Bari" }, { "name": "Massenio, Paolo Roberto", "affiliation": "Polytechnic University of Bari" } ], "keywords": [ "AI tools in automation engineering and operation", "AI-driven modeling and control", "Machine learning for modeling and prediction" ], "abstract": "Occlusions in robotic bin picking compromise accurate and reliable grasp planning. We present ViTA-Seg, a class-agnostic Vision Transformer framework for real-time amodal segmentation that leverages global attention to recover complete object masks, including hidden regions. We proposte two architectures: a) Single-Head for amodal mask prediction; b) Dual-Head for amodal and occluded mask prediction. We also introduce ViTA-SimData, a photo-realistic synthetic dataset tailored to industrial bin-picking scenario. Extensive experiments on two amodal benchmarks, COOCA and KINS, demonstrate that ViTA-Seg Dual Head achieves strong amodal and occlusion segmentation accuracy with computational efficiency, enabling robust, real-time robotic manipulation.", "url": "" }, { "id": "Tu-TuB33.4", "code": "TuB33.4", "title": "Generating PLC Code Directly from P&IDs: A GenAI Approach (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB33", "sessionTitle": "Advances in Machine Learning and Intelligent Control for Industrial Automation and Robotics", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Vogt, Lucas", "affiliation": "TUD Dresden University of Technology" }, { "name": "Urbas, Leon", "affiliation": "Technische Universität Dresden" } ], "keywords": [ "AI tools in automation engineering and operation", "Model driven engineering of control systems", "Cyber physical systems" ], "abstract": "Industrial automation projects require translating Piping and Instrumentation Diagrams (P&IDs) into executable control software, a process that is traditionally manual, time-consuming, and error-prone. This paper proposes a novel Generative AI (GenAI) method to automatically generate control code directly from P&ID diagrams. The approach utilizes an Large Language Model (LLM) combined with domain-specific knowledge and industry standards. We demonstrate the approach on an industrial-like case study. Results show that the generated control code was syntactically correct and captured the intended logic with minor manual modifications. This work also highlights the remaining challenges such as complex logic interpretation and the need for standardized diagram data.", "url": "" }, { "id": "Tu-TuB33.5", "code": "TuB33.5", "title": "Reinforcement Learning in Ultimate Tic-Tac-Toe: Benchmarking Strategic Complexity", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB33", "sessionTitle": "Advances in Machine Learning and Intelligent Control for Industrial Automation and Robotics", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "D'Alberton, Enrico", "affiliation": "University of Padova" }, { "name": "Sinigaglia, Alberto", "affiliation": "Human Inspired Technology Research Center, University of Padua, 35121 Padua, Italy" }, { "name": "Arcudi, Alessio", "affiliation": "University of Padova" }, { "name": "Susto, Gian Antonio", "affiliation": "University of Padova" }, { "name": "Cederle, Matteo", "affiliation": "University of Padova" } ], "keywords": [ "Machine learning for modeling and prediction" ], "abstract": "Ultimate Tic-Tac-Toe (UTTT) presents a complex, non-trivial environment for sequential decision-making due to its large state space and meta-game mechanics. We present a systematic investigation of Deep Reinforcement Learning (DRL) applied to UTTT, utilizing a framework based on self-play training, residual neural networks, and rotational data augmentation. Our best-performing DDQN-ResNet-Aug-v2m model achieves an Elo rating of 1861.4 points and an 85% win rate against all trained agents. Through game-theoretic analysis with best response training, we reveal significant vulnerabilities in self-play agents, demonstrating the importance of robust evaluation methodologies for developing competitive agents in strategically complex environments.", "url": "" }, { "id": "Tu-TuB33.6", "code": "TuB33.6", "title": "Dual-Stream Guided Diffusion Model for Long-Term Oxygen Demand Prediction", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB33", "sessionTitle": "Advances in Machine Learning and Intelligent Control for Industrial Automation and Robotics", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Liu, Yinghua", "affiliation": "Zhejiang University" }, { "name": "Xu, Zuhua", "affiliation": "Zhejiang University" }, { "name": "Zhao, Jun", "affiliation": "Zhejiang University" } ], "keywords": [ "Machine learning for modeling and prediction", "Knowledge-based and data-driven control", "Reinforcement learning and deep learning in control" ], "abstract": "In steel enterprises, conventional oxygen demand prediction models often ignore production plans, limiting long-term accuracy. To leverage this future information, we propose a Dual-Stream Guided Diffusion (DSGD) model built upon conditional diffusion models. It processes historical data and production plans through a dual-stream structure to extract embeddings. These embeddings are integrated via a conditional fusion mechanism that uses additive bias for preliminary guidance and decoupled modulation for precise control. Furthermore, a Hybrid Plan-Guided Diffusion (HPD) method is developed to address multiscale characteristics by applying specific models to different frequency components. Real-world experiments demonstrate improved performance at extended horizons.", "url": "" }, { "id": "Tu-TuB34.1", "code": "TuB34.1", "title": "Cloud Resource Scheduling: A Fast Algorithm Considering the Value of Virtual Resources (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB34", "sessionTitle": "Resource Allocation and Decision-Making in Modern Distributed Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Chai, Pengcheng", "affiliation": "‌Xi’an Jiaotong University" }, { "name": "Zhai, Qiaozhu", "affiliation": "Xi'an Jiaotong Univ" }, { "name": "Zhou, Yuzhou", "affiliation": "Xi'an Jiaotong University" }, { "name": "Zhao, Jiexing", "affiliation": "Xi'an Jiaotong University" }, { "name": "Cao, Xiaoyu", "affiliation": "Xi'an Jiaotong University" }, { "name": "Zhang, Xushen", "affiliation": "Shandong Electrical Engineering & Equipment Group Digital Technology Co., Ltd" } ], "keywords": [ "Data centers and cloud computing", "Cyber-physical urban systems" ], "abstract": "With the rapid scaling of cloud data centers, Non-Uniform Memory Access (NUMA)-aware cloud resource scheduling has become critical for efficient resource utilization, yet unreasonable Virtual Machine (VM) placement induces severe resource fragmentation. To address this, we propose a Ratio-Based Grouping Algorithm (RGA) that combines heterogeneous VMs into complementary Meta-VMs to reduce fragmentation, and introduces Virtual Virtual Machines (VVMs) as placeholders for future high-value VMs to quantify residual space value. Experimental results show our approach achieves 93.0% of Gurobi’s optimal solution quality with a 187×speedup, significantly outperforming baseline heuristics in solution quality, resource utilization balance, and fragmentation reduction, satisfying real-time scheduling’s sub-second response requirement.", "url": "" }, { "id": "Tu-TuB34.2", "code": "TuB34.2", "title": "GPU Cluster Scheduling Via Dynamic Fragment-Aware Live Migration (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB34", "sessionTitle": "Resource Allocation and Decision-Making in Modern Distributed Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Shi, Jiaxi", "affiliation": "Xi'an Jiaotong University" }, { "name": "Hu, Jianchen", "affiliation": "Xian Jiaotong University" }, { "name": "Zhang, Meng", "affiliation": "Zhejiang University" }, { "name": "Wu, Chengshuai", "affiliation": "Xi'an Jiaotong University" } ], "keywords": [ "Data centers and cloud computing", "Decision making under uncertainty" ], "abstract": "The proliferation of training requirements for generative artificial intelligence (AI) has made the graphic processing unit (GPU) scheduling a critical issue in cloud computing. Due to the existence of uncertainty in the GPU demand sequence, a poorly designed GPU scheduling algorithm can result in severe GPU fragmentation, leading to low resource utilization ratio and increased operational cost. In order to solve this problem, we propose a new scheduling algorithm that uses the intrinsic checkpointing mechanism of AI training tasks to enable dynamic fragment-aware live task migration, so that our approach can consolidates dynamic fragmented GPU resources through rescheduling tasks. Moreover, we introduce a dual-objective scheduling strategy tailored to cluster workload which minimizes the average fragmentation rate for dense workloads and minimizes the task queueing time for sparse workloads. We have verified the improvement of our algorithm in cluster throughput and efficiency through a GPU scheduling simulation example.", "url": "" }, { "id": "Tu-TuB34.3", "code": "TuB34.3", "title": "Energy Consumption Optimization for Two-Machine Geometric Serial Lines Considering Repair Costs (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB34", "sessionTitle": "Resource Allocation and Decision-Making in Modern Distributed Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Gou, Tongxin", "affiliation": "Xi'an Jiaotong University" }, { "name": "Zhang, Sheng", "affiliation": "Xi'an Jiaotong University" }, { "name": "Yan, Chao-Bo", "affiliation": "Xi'an Jiaotong University" } ], "keywords": [ "Decision making under uncertainty" ], "abstract": "In energy-intensive production systems, both the energy consumed by machines and the costs incurred from repairs represent critical components of overall operating expenses. This paper extends existing research on energy consumption optimization in two-machine geometric serial lines by incorporating repair costs into the optimization framework. The optimization problem is formulated with the objective of jointly minimizing energy and repair costs under a productivity constraint. Two nonlinearly coupled optimality equations are derived and analyzed. A bisection-based algorithm is then developed to compute their unique solution. Numerical results demonstrate that including repair costs alters optimal strategies, and that repair-related parameters can impact system performance.", "url": "" }, { "id": "Tu-TuB34.4", "code": "TuB34.4", "title": "A Novel Power Grid Frequency Support Strategy for Wind Farms (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB34", "sessionTitle": "Resource Allocation and Decision-Making in Modern Distributed Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Luo, Hao", "affiliation": "Qingdao University of Technology" }, { "name": "Jiao, Xuguo", "affiliation": "Qingdao University of Technology" }, { "name": "Fan, Bo", "affiliation": "Xi'an Jiaotong University" }, { "name": "Wang, Rui-Hua", "affiliation": "Qingdao University of Technology" }, { "name": "Xu, Yunjiao", "affiliation": "Qingdao University of Technology" } ], "keywords": [ "Smart city control and optimization", "Smart city design and planning", "Urban energy distribution systems" ], "abstract": "The random and intermittent nature of wind power poses significant challenges for wind farms in tracking dispatch commands and providing frequency support for the urban power system. Meanwhile, fatigue load optimization of key components is vital for reducing the operation and maintenance costs of wind farms. This paper proposes an urban power system frequency support strategy for wind farms that integrates farm-level decision-making and turbine-level dual-stage control with fatigue load optimization of key components. Simulation results based on a modified urban power system incorporating wind farms validate the effectiveness of the proposed approach.", "url": "" }, { "id": "Tu-TuB34.5", "code": "TuB34.5", "title": "Siting and Sizing of Battery Swapping Stations Considering Spatio-Temporal User Choices (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB34", "sessionTitle": "Resource Allocation and Decision-Making in Modern Distributed Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Wei, Yunhao", "affiliation": "Xi'an Jiaotong University" }, { "name": "Hu, Jianchen", "affiliation": "Xian Jiaotong University" }, { "name": "Li, Xingqi", "affiliation": "Xi'an Jiaotong University" }, { "name": "Guo, Chang", "affiliation": "Xi'an Jiaotong University" }, { "name": "Yang, Lun", "affiliation": "Xi'an Jiaotong University" }, { "name": "Liu, Kun", "affiliation": "Xi'an Jiaotong University" }, { "name": "Gao, Feng", "affiliation": "Xi'an Jiaotong University" } ], "keywords": [ "Urban energy distribution systems", "Smart city control and optimization", "Cyber-physical urban systems" ], "abstract": "Battery Swapping Station (BSS) enables fast and standardized energy supply for electric vehicles (EVs), which is beneficial for improving service quality. However, the rapid growth of EVs and high BSS construction costs make the siting and sizing problem very complex, particularly when dealing with large-scale station networks. Conventional siting algorithms often ignore rational user choice behavior and operational losses, which limits their effectiveness in real-world scenarios. In order to solve these issues, we present a bilevel optimization framework integrating station siting, capacity sizing, and operational dynamics. The upper-level model generates a preliminary deployment plan based on service demand coverage, while the lower-level model refines the plan by minimizing system-level operational losses. Numerical experiments based on the Beijing road network and taxi trajectory data demonstrate the effectiveness of the proposed framework in improving demand coverage and reducing operational losses.", "url": "" }, { "id": "Tu-TuB34.6", "code": "TuB34.6", "title": "Multimodal Sensing-Informed Defect Identification in PEEK Additive Manufacturing Via CNN-LSTM (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB34", "sessionTitle": "Resource Allocation and Decision-Making in Modern Distributed Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Cui, Bin", "affiliation": "Xi'an Jiaotong University" }, { "name": "Qu, Zhi", "affiliation": "Beijing Aerospace Propulsion Institute" }, { "name": "Wu, Yin", "affiliation": "Kunming University of Science and Technology" }, { "name": "Xiao, Yao", "affiliation": "Xi'an Jiaotong University" }, { "name": "Yan, Chao-Bo", "affiliation": "Xi'an Jiaotong University" } ], "keywords": [ "Data centers and cloud computing", "Decision making under uncertainty" ], "abstract": "Polyether ether ketone (PEEK) is a high-performance engineering thermoplastic widely used in aerospace and biomedical industries. However, its additive manufacturing (AM) process is highly susceptible to thermally induced defects due to a narrow processing window. Traditional single-modal sensing techniques often fail to capture the complex spatiotemporal dynamics of the sintering process, leading to high false-positive rates in defect detection. To address this issue, this paper proposes a novel multimodal sensing-informed intelligent identification framework for PEEK AM. First, a comprehensive 8-dimensional feature vector is constructed by synchronizing optical imaging, infrared thermography, and ambient temperature monitoring to capture the morphological, thermal, and environmental nuances of the process. Subsequently, a hybrid CNN-LSTM network is developed to decode the spatial characteristics of the melt pool and the temporal evolution of sintering states. The model categorizes the process into three distinct states: proper sintering, under-sintering, and over-sintering. Experimental results demonstrate that the proposed multimodal approach significantly enhances feature separability compared to single-modal methods. The CNN-LSTM model achieves an overall accuracy of 97.0%, with F1-scores exceeding 96% across all categories, proving its robustness and effectiveness. This framework provides a reliable foundation for real-time quality control and process optimization in high-performance polymer AM.", "url": "" }, { "id": "Tu-TuB35.1", "code": "TuB35.1", "title": "Interactive Matlab Tool for Automatic Offset Controllers Applied to DIPDT System (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB35", "sessionTitle": "Microlabs, Remote Labs and Virtual Tools for Control Education I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Bistak, Pavol", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Huba, Mikulas", "affiliation": "Slovak Univ. of Tech" } ], "keywords": [ "Control education laboratories" ], "abstract": "This paper presents an interactive Matlab tool designed to demonstrate the performance of Automatic Offset Controllers (AOC) applied to Double Integrator Plus Dead-Time (DIPDT) systems. While traditional PID controllers often face limitations such as measurement noise amplification and coupling between setpoint tracking and disturbance rejection, the AOC offers a robust, model-based alternative utilizing ultra-local models and disturbance observers. The newly developed software tool facilitates the comparison of AOC and Two-Degree-of-Freedom (2DoF) PID controllers by visualizing time responses and computing quantitative performance measures, including the Integral of Absolute Error (IAE) and Total Variance (TV). Simulation results illustrate that the AOC architecture significantly improves the trade-off between transient speed and control signal effort through the use of filtered higher-order derivatives, making it a viable solution for complex dynamic processes.", "url": "" }, { "id": "Tu-TuB35.2", "code": "TuB35.2", "title": "ICSTR: A Web-Based Virtual Laboratory for PID Control CSTR Processes (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB35", "sessionTitle": "Microlabs, Remote Labs and Virtual Tools for Control Education I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Kois, Roman", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Pataro, Igor M. L.", "affiliation": "Universidad De Almería" }, { "name": "Gil, Juan Diego", "affiliation": "University of Almeria" }, { "name": "Zakova, Katarina", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Guzman, Jose Luis", "affiliation": "University of Almeria" }, { "name": "Berenguel, Manuel", "affiliation": "University of Almeria (CIF Q-5450008-G)" } ], "keywords": [ "Control education laboratories", "Continuing control education", "Internet based control education" ], "abstract": "This paper presents iCSTR, a web-based virtual laboratory (VL) for control engineering education using a nonlinear Continuous Stirred-Tank Reactor (CSTR), a standard benchmark for studying nonlinear and dynamically coupled processes. The platform enables students to analyse, design, and tune Proportional-Integral-Derivative (PID) controllers, cascade structures, and feedforward compensators through real-time visualisation in a fully browser-based environment. Built with Vue.js, iCSTR provides a responsive, cross-platform interface without requiring software installation. Students can design controllers from linearised models or perform step tests on manipulated variables and disturbances, export data, and identify empirical models for tuning. The VL supports both in-class and remote learning by integrating modelling, controller design, and performance assessment. An illustrative example demonstrates how iCSTR enhances understanding of controller tuning and nonlinear process control.", "url": "" }, { "id": "Tu-TuB35.3", "code": "TuB35.3", "title": "Soft Real-Time Python–Arduino Interface for AutomationShield Experiments in Control Education (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB35", "sessionTitle": "Microlabs, Remote Labs and Virtual Tools for Control Education I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Bregman, Sander Christian", "affiliation": "Delft University of Technology" }, { "name": "van Wingerden, Jan-Willem", "affiliation": "Delft University of Technology" }, { "name": "Van den Abbeele, Bert", "affiliation": "Delft University of Technology" }, { "name": "Mulders, Sebastiaan Paul", "affiliation": "Delft University of Technology" } ], "keywords": [ "Control education laboratories", "Control engineering curricula", "Continuing control education" ], "abstract": "Traditional control engineering setups were often bulky, expensive, and difficult to deploy in classrooms, limiting live demonstrations and hands-on learning. We introduce an open-source Python interface for Arduino-based hardware, designed to integrate with AutomationShield devices. The framework removes the need for proprietary software or low-level programming, enabling soft real-time data acquisition, live plotting, and feedback control directly in Python. Leveraging Python’s rich ecosystem, the platform supports both introductory and advanced courses. Classroom demonstrations and student projects show that this approach makes practical control experimentation more accessible, scalable, and engaging.", "url": "" }, { "id": "Tu-TuB35.4", "code": "TuB35.4", "title": "Control Cabinets for Automation Training (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB35", "sessionTitle": "Microlabs, Remote Labs and Virtual Tools for Control Education I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Domínguez, Manuel", "affiliation": "Universidad De León" }, { "name": "Prada, Miguel Angel", "affiliation": "Universidad De Leon" }, { "name": "Morán Álvarez, Antonio", "affiliation": "Universidad De Leon" }, { "name": "Alonso Castro, Serafín", "affiliation": "Universidad De León" }, { "name": "Pérez, Daniel", "affiliation": "University of León" }, { "name": "Fuertes, Juan J.", "affiliation": "Universidad De Leon" } ], "keywords": [ "Control education laboratories", "Control engineering curricula", "Industry-academia collaboration in control education" ], "abstract": "This paper presents control cabinets specifically designed to address training in automation and control for higher education and continuing professional development. These cabinets replicate real conditions, enabling students to gain practical insights of realistic industrial environments. Their modular design integrates sensors, actuators, controllers, monitoring systems and communications and power supply, facilitating a seamless interaction among components. Furthermore, they support remote connectivity, allowing real-time monitoring and management of processes. These control cabinets have been employed for automation training at the School of Engineering of the University of León, as well as for the continuing professional development of industrial workers within the framework of the European DIGIS3 project (Smart, Sustainable Digitalization – Digital Innovation Hub), through which companies receive guidance on digitalization, advanced automation, and industrial cybersecurity.", "url": "" }, { "id": "Tu-TuB35.5", "code": "TuB35.5", "title": "A Multi-Experiment Virtual Laboratory for Control in Mechatronics (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB35", "sessionTitle": "Microlabs, Remote Labs and Virtual Tools for Control Education I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Matisak, Jakub", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Kois, Roman", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Zakova, Katarina", "affiliation": "Slovak University of Technology in Bratislava" } ], "keywords": [ "Control education laboratories", "Internet based control education" ], "abstract": "This paper presents a web-based virtual laboratory designed to support education and research in mechatronics and control engineering. The platform integrates multiple experiments, each defined by structured metadata linking mathematical models, simulation parameters, and 3D visualization. A unified interface enables experiment management, configurable visualization, and execution of simulations provided by a simulation engine. The system supports both individual and collaborative work, offering synchronous interaction and shared simulation sessions. The demonstrated use cases highlight the platform's ability to support practical experimentation, iterative improvement of control designs, and continuous expansion of its experiment portfolio and simulation capabilities.", "url": "" }, { "id": "Tu-TuB36.1", "code": "TuB36.1", "title": "Digital Twin-Driven Vulnerability Analysis of Urban VANET (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:10-13:30", "sessionCode": "TuB36", "sessionTitle": "Metaverse and Parallel Intelligence for Autonomous Decision-Making I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Li, Yixuan", "affiliation": "Inner Mongolia University" }, { "name": "Zhang, Hong", "affiliation": "Inner Mongolia University" }, { "name": "Lu, Lu", "affiliation": "Inner Mongolia University" }, { "name": "Wang, Le", "affiliation": "Inner Mongolia University" }, { "name": "Hu, Linlin", "affiliation": "Inner Mongolia University" }, { "name": "He, Xiaoyu", "affiliation": "Inner Mongolia University" } ], "keywords": [ "Parallel intelligence", "Cyber physical social systems (CPSS)" ], "abstract": "This paper proposes a digital-twin-driven framework to evaluate the structural vulnerability of urban vehicular ad hoc networks (VANET). Using real traffic scenarios and SUMO-based reproduction of central Hohhot, the network performance was analyzed under multiple node-removal strategies in both static and dynamic modes. Results show that VANET exhibits small-world characteristics but suffers rapid collapse when critical nodes fail. Compared with traditional centrality-based attacks, the adopted RNEL method more effectively identifies communication-critical nodes and accelerates network degradation. The findings confirm the fragility of high-density VANET, with sensitivity analysis validating robustness across different communication ranges, and demonstrate the value of digital twin technology in supporting resilient ITS design. Future work will integrate real-world data and cooperative vehicle–road control for enhanced robustness.", "url": "" }, { "id": "Tu-TuB36.2", "code": "TuB36.2", "title": "McCollar: A Multi-Chain Collaborative Architecture for Data Trading Markets (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:30-13:50", "sessionCode": "TuB36", "sessionTitle": "Metaverse and Parallel Intelligence for Autonomous Decision-Making I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Liu, Xuan", "affiliation": "Peking University" }, { "name": "Dong, ZhiYong", "affiliation": "Peking University, School of Economics" }, { "name": "Yuan, Yong", "affiliation": "Renmin University of China" } ], "keywords": [ "Blockchain intelligence", "Decentralized economics/ecosystems (DeEco)", "Agent & AI technology for business and economy" ], "abstract": "In the digital economy era, data has become the key factor of production, embracing immense economic value. However, data trading markets face trust deficits and performance bottlenecks due to the centralized frameworks and single-chain architectures. To address these challenges, this paper proposes the Multi-chain Collaborative Architecture (McCollar) for data trading markets, which decouples critical functions such as data rights verification, transaction matching, and quality assessment across independent blockchains. Each blockchain optimizes independently, i.e., the Data Chain prioritizes security for asset registration, the Trading Chain maximizes throughput for matching, and the Evaluation Chain ensures transparent governance. Experimental results validate that McCollar outperforms traditional single-chain architectures, demonstrating superior scalability, reduced latency, and cost savings.", "url": "" }, { "id": "Tu-TuB36.3", "code": "TuB36.3", "title": "SAGA: Style-Aware Garment Generation Via Multi-Modal Control (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "13:50-14:10", "sessionCode": "TuB36", "sessionTitle": "Metaverse and Parallel Intelligence for Autonomous Decision-Making I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Zhang, Xiaoyan", "affiliation": "Shenzhen University" }, { "name": "Ren, Sisi", "affiliation": "Shenzhen University" }, { "name": "Chen, Yunlai", "affiliation": "Shenzhen University" }, { "name": "Han, Shuangshuang", "affiliation": "University of Science and Technology Beijing" }, { "name": "Huo, Yongkai", "affiliation": "Shenzhen Transsion Holdings" } ], "keywords": [ "Industrial and service applications of AI and intelligent automation" ], "abstract": "Controllable garment generation aims to produce accurate structure and consistent style garment images conditioned on multi-modal guidance such as structure, style, and text. However, existing methods often struggle to maintain a balance between geometric consistency and style fidelity, leading to distorted shapes or loss of fine textures. To overcome these limitations, we introduce SAGA, a style-aware diffusion framework for fine-grained and semantically aligned garment synthesis under multi-modal control. Specifically, we propose a multi-modal conditioning framework that explicitly disentangles and hierarchically fuses structural, stylistic and semantic representations, ensuring accurate structure and faithful style transfer. To adaptively coordinate text and visual style, we further design a dynamic style injection attention module that employs spatial gating and dual-path attention fusion for context aware texture modulation. In addition, a style-guided attention alignment loss is introduced to regularize self-attention layers, reinforcing texture coherence and local consistency during generation. Extensive experiments on large-scale fashion datasets demonstrate that SAGA surpasses state-of-the-art methods, yielding garments with more accurate structure and textures. Quantitative results on FID, LPIPS and CLIP-Score confirm its advantage in high-fidelity and controllable fashion image generation.", "url": "" }, { "id": "Tu-TuB36.4", "code": "TuB36.4", "title": "ASIND: Alternating Sparse Identification for Predicting Network Dynamics without Knowledge (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:10-14:30", "sessionCode": "TuB36", "sessionTitle": "Metaverse and Parallel Intelligence for Autonomous Decision-Making I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Kang, Mingyu", "affiliation": "University of Science and Technology of China" }, { "name": "Gao, Jianxi", "affiliation": "Rensselaer Polytechnic Institute" }, { "name": "Yu, Wenwu", "affiliation": "Southeast University" }, { "name": "Lv, Linyuan", "affiliation": "University of Science and Technology of China" } ], "keywords": [ "Cyber physical social systems (CPSS)", "Social computing", "Knowledge automation" ], "abstract": "Identifying network dynamics is a critical yet challenging task to to understand the mechanism of real-world social systems. There are two types of algorithms, and one requires the knowledge of self-dynamics function, interactive function, and interactive network to sparsely identify the network dynamics. Another one does not require any knowledge, but use simple functions to universally approximate complex functions. However, this type of algorithms lack interpretability, and the functional space is too extensive to search efficiently. Thus, to address this issue, this work proposes an Alternating Sparse Identification of Network Dynamics (ASIND) algorithm to sparsely identify the self-dynamics function, interactive function and interactive network alternatively. Extensive experiments are conducted to show the state-of-the-art identification and 100-steps prediction performance compared to the baseline. The experimental results also show the weak identifiability of interactive network, that means different networks can generate highly similar trajectories of network dynamics. The code is available at https://github.com/KMY-SEU/ASIND.", "url": "" }, { "id": "Tu-TuB36.5", "code": "TuB36.5", "title": "Spot-Catching Prompts: Efficient Vision-Language Prompt Learning (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:30-14:50", "sessionCode": "TuB36", "sessionTitle": "Metaverse and Parallel Intelligence for Autonomous Decision-Making I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Zhang, Mengmeng", "affiliation": "Institute of Automation，Chinese Academy of Sciences" }, { "name": "Wang, Jing", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Wang, Fei-Yue", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Lv, Yisheng", "affiliation": "Institute of Automation, Chinese Academy of Sciences" } ], "keywords": [ "Parallel intelligence" ], "abstract": "To enhance the few-shot learning capability of Vision-Language (V-L) models in downstream tasks, existing methods merely introduce additional modules. While this improves the model's predictive performance, it comes at the expense of increased computational cost. We propose an efficient visual-language prompt learning method, EfficientSCP (Efficient Prompt Learning by Catching Spot Features) to address this issue. EfficientSCP dynamically captures essential information from images and eliminates redundant information while performing prompt learning. This effectively improves both the generalization capability and computational efficiency of V-L models. We have conducted extensive experiments on 11 datasets, showing that our method outperforms previous methods. Specifically, EfficientSCP achieves average gains of +0.38%, +0.65%, +1.65%, and +3.94% over the state-of-the-art methods on novel class accuracy in accuracy-efficiency trade-off tasks.", "url": "" }, { "id": "Tu-TuB36.6", "code": "TuB36.6", "title": "Small Object Detection Algorithm with Composite Scaling-Based Spatial Feature Fusion (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "14:50-15:10", "sessionCode": "TuB36", "sessionTitle": "Metaverse and Parallel Intelligence for Autonomous Decision-Making I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Huang, Jiale", "affiliation": "Shangdong Jiaotong University" }, { "name": "Zhu, Fenghua", "affiliation": "Chinese Academy of Sciences" }, { "name": "Xiong, Gang", "affiliation": "Institute of Automation, Chinese Academy of Sciences" } ], "keywords": [ "Knowledge automation", "Blockchain intelligence", "Agent & AI technology for business and economy" ], "abstract": "With the rapid development of the low-altitude economy, drone technology has gained significant application value across various fields. However, due to challenges such as low resolution of small objects, complex backgrounds, and dense occlusions in aerial images, traditional object detection algorithms often underperform. To address these issues, this paper proposes an improved small object detection algorithm.First, we introduce a more efficient EfficientNetV2 backbone network, employing a compound scaling strategy to jointly optimize network width, depth, and resolution, thereby exploring an optimal balance among them. Additionally, we replace the standard attention mechanism in the original PSA (Pyramid Split Attention) with MLCA (Multi-Scale Local Channel Attention) in the C2PSA layer, enhancing the network's ability to capture discriminative features.In the detection head, we incorporate an ASFF Head (Adaptive Spatial Feature Fusion), which effectively filters out conflicting information through adaptive spatial feature fusion, thereby improving scale invariance. Furthermore, we optimize the bounding box loss function using Inner-ShapeIoU, which focuses on the shape and scale of the bounding box itself, enhancing regression accuracy while employing auxiliary bounding boxes to accelerate convergence.Extensive experiments on the VisDrone2019 aerial dataset demonstrate that our proposed algorithm outperforms the original YOLOv11n, achieving a 3.3% improvement in mAP and a 6.1% increase in precision (P), confirming its superior detection performance.", "url": "" }, { "id": "Tu-TuC01.1", "code": "TuC01.1", "title": "Guaranteed Benefit Collusion Strategies for Vickrey-Clarke-Groves Mechanism (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC01", "sessionTitle": "JO-NAHS: Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Kurniawan, Joshua Levin", "affiliation": "Imperial College London" }, { "name": "Angeli, David", "affiliation": "Imperial College" } ], "keywords": [ "Multi-agent systems" ], "abstract": "Achieving socially optimal decisions requires access to agents' true preferences, which is challenging as this information is privately held. The Vickrey-Clarke-Groves (VCG) mechanism addresses this problem by incentivizing truthful reporting when agents act individually. However, the mechanism is vulnerable to collusion, where agents form coalitions to manipulate the system for their own benefit. Although this weakness has been recognized in previous research, formal methods for guaranteed beneficial manipulation have remained elusive. This paper introduces four collusion strategies that guarantee benefits to coalitions. By characterizing these manipulation approaches, we develop modified VCG mechanisms that are robust against harmful collusion. Our contributions provide both a framework for understanding potential collusion strategies and practical mechanisms that maintain efficiency while resisting manipulation, making VCG more applicable in real-world settings where coalitions may form.", "url": "" }, { "id": "Tu-TuC01.2", "code": "TuC01.2", "title": "Winners Take All: A Reverse Consensus Model (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC01", "sessionTitle": "JO-NAHS: Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Chen, Zhiyong", "affiliation": "The University of Newcastle" } ], "keywords": [ "Multi-agent systems", "Consensus" ], "abstract": "This paper introduces a nonlinear multi-agent dynamic model that characterizes the resource-seizing mechanism for a fixed amount of resources. The model demonstrates a winners-take-all behavior within a zero-sum game framework. It represents one of the simplest dynamics where equilibria correspond to states of winners and losers, with every trajectory converging to such an equilibrium. Notably, when the model operates in reverse time, it resembles a multi-agent consensus model, referred to as a reverse consensus model. The key characteristics of this model are explored through rigorous analysis.", "url": "" }, { "id": "Tu-TuC01.3", "code": "TuC01.3", "title": "Boltzmann Social Learning with Heterogeneous Rationality (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC01", "sessionTitle": "JO-NAHS: Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Chaddad, Sylvie", "affiliation": "Avignon University - Laboratoire Informatique d’Avignon (LIA)" }, { "name": "Hayel, Yezekael", "affiliation": "Avignon University" }, { "name": "Satheeskumar Varma, Vineeth", "affiliation": "CRAN - Université De Lauraine" }, { "name": "Gast, Nicolas", "affiliation": "Inria" } ], "keywords": [ "Multi-agent systems", "Consensus" ], "abstract": "This paper analyzes a novel social learning model in which, at each discrete time step, agents with private preferences repeatedly select actions via a softmax (Boltzmann) rule, and update their preferences based on public observations of others’ choices. This work addresses a critical gap by introducing rational heterogeneity through agent-specific rationality parameters γi. Unlike previous models, our approach accounts for the diverse ways individuals process social information by using a discrete-time deterministic mean-field approximation map. We establish fundamental equilibrium properties that were previously unexplored. In particular, we prove the existence of fixed points and show that, on complete graphs, every mean-field equilibrium is a consensus state, where all agents share identical preferences. We further derive sufficient conditions for the uniqueness of this equilibrium and its local asymptotic stability. Numerical simulations validate our theoretical findings and illustrate how rational heterogeneity and network structure interact to shape collective behavior in social learning systems.", "url": "" }, { "id": "Tu-TuC01.4", "code": "TuC01.4", "title": "Optimal Interventions on the Linear Threshold Model in Large-Scale Networks (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC01", "sessionTitle": "JO-NAHS: Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Cianfanelli, Leonardo", "affiliation": "Politecnico Di Torino" }, { "name": "Messina, Sebastiano", "affiliation": "Politecnico Di Torino" }, { "name": "Como, Giacomo", "affiliation": "Politecnico Di Torino" }, { "name": "Fagnani, Fabio", "affiliation": "Politecnico Di Torino" } ], "keywords": [ "Multi-agent systems", "Control over networks" ], "abstract": "We study an optimal intervention problem on the linear threshold model (LTM) in which a social planner aims to design minimal-cost interventions that modify the agents’ thresholds, under the constraint that at least a predefined fraction of agents reaches a given state after a finite number of iterations. While this problem is known to be NP-hard and its exact solution requires full knowledge of the network structure, we focus on approximate solutions for large-scale networks and assume that the planner has only statistical knowledge of the network. In particular, we build on a local mean-field approximation of the LTM that is known to hold true on large-scale random networks, and reformulate the optimal intervention problem as a linear program with an infinite set of constraints. We then show how to approximate the solutions of the latter problem by standard linear programs with finitely many constraints. Finally, our approach is validated through numerical experiments on real-world networks and compared both with optimal seeding and state-of-the-art algorithms for the least-cost influence.", "url": "" }, { "id": "Tu-TuC01.5", "code": "TuC01.5", "title": "Adaptive Bearing-Based Formation for Multiagent Systems with Unknown Disturbances (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC01", "sessionTitle": "JO-NAHS: Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Chen, Tianxing", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Ping, Zhaowu", "affiliation": "Hefei University of Technology" }, { "name": "Zhang, Hongwei", "affiliation": "Harbin Institute of Technology, Shenzhen" } ], "keywords": [ "Multi-agent systems", "Distributed control and estimation" ], "abstract": "This paper studies robust formation tracking problem for bearing-based control of multiagent systems with unknown disturbances, where each agent’s controller relies solely on the relative bearing and local velocity measurements. Existing studies mainly address external disturbances by incorporating relative position and velocity information, or rely on neighboring communication to estimate agent’s disturbances. In this paper, by incorporating local velocity with neighborhood bearing errors, an integral sliding manifold is designed to decouple agent’s disturbances from the neighborhood bearing structure. With the aid of adaptive integral sliding mode control, a novel adaptive bearing-velocity formation (ABVF) controller is developed to dispel the adverse effects of unknown external disturbances. Sufficient conditions for guaranteeing the stability of the ABVF are provided. Numerical simulations are conducted to illustrate the efficiency of the proposed ABVF.", "url": "" }, { "id": "Tu-TuC01.6", "code": "TuC01.6", "title": "Hierarchical Parameter Estimation for Distributed Networked Systems: A Dynamic Consensus Approach (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC01", "sessionTitle": "JO-NAHS: Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Méndez Castillo, Ariana Ruth", "affiliation": "Cinvestav Gdl-Mx" }, { "name": "Aldana-López, Rodrigo", "affiliation": "Universidad De Zaragoza" }, { "name": "Ramirez-Trevino, Antonio", "affiliation": "CINVESTAV-IPN" }, { "name": "Aragues, Rosario", "affiliation": "Universidad De Zaragoza" }, { "name": "Gómez-Gutiérrez, David", "affiliation": "Intel Coporation" } ], "keywords": [ "Distributed control and estimation", "Consensus", "Multi-agent systems" ], "abstract": "This work introduces a novel two-stage distributed framework to globally estimate constant parameters in a networked system, separating shared information from local estimation. The first stage uses dynamic average consensus to aggregate agents’ measurements into surrogates of centralized data. Using these surrogates, the second stage implements a local estimator to determine the parameters. By designing an appropriate consensus gain, the persistence of excitation of the regressor matrix is achieved, and thus, exponential convergence of a local Gradient Estimator (GE) is guaranteed. The framework facilitates its extension to switched network topologies, and the heterogeneous substitution of the GE with a Dynamic Regressor Extension and Mixing (DREM) estimator, which supports relaxed excitation requirements.", "url": "" }, { "id": "Tu-TuC02.1", "code": "TuC02.1", "title": "Physics-Fusion AI: A Hybrid Framework for Enhancing Model-Based Control Prediction (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:45", "sessionCode": "TuC02", "sessionTitle": "LB: AI and Learning-Based Control for Automotive System", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Kim, Jeong Woo", "affiliation": "Hyundai Motor Company" }, { "name": "Ohn, Hyungseuk", "affiliation": "Seoul National University" }, { "name": "Jeon, Byeong Wook", "affiliation": "Korea Automotive Technology Institute" } ], "keywords": [ "AI and learning-based control for automotive systems", "Automotive system identification and modelling", "Adaptive and robust control of automotive systems" ], "abstract": "This paper proposes Physics-Fusion AI (PFAI), a hybrid modeling framework that combines a physics-based model with a residual-learning AI model. Instead of learning full system dynamics, the AI learns only the prediction error of the physical model, improving data efficiency, extrapolation stability, and interpretability. Applied to vehicle longitudinal dynamics, PFAI demonstrated notable improvement in prediction accuracy relative to the baseline physics model, effectively mitigating the accumulation of prediction errors over extended horizons. Furthermore, the PFAI model exhibited superior convergence speed and robust performance in unlearned operating regions compared to end-to-end AI models. These results suggest that the PFAI framework offers a practical and reliable solution for enhancing model-based control systems, particularly in scenarios with limited training data or high-dimensional state spaces.", "url": "" }, { "id": "Tu-TuC02.2", "code": "TuC02.2", "title": "Driving Behavior Learning Algorithm Based on Online Sparse Gaussian Process Regression for Personalized Driving Energy Prediction (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:45-16:00", "sessionCode": "TuC02", "sessionTitle": "LB: AI and Learning-Based Control for Automotive System", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Jeoung, Haeseong", "affiliation": "Hyundai Motor Company" }, { "name": "Ryu, Kunhee", "affiliation": "Hyundai Motor Company" }, { "name": "Han, Minkyu", "affiliation": "Hyundai Motor Company" }, { "name": "Hyeon, Soojong", "affiliation": "Hyundai Motor Company" }, { "name": "Hwang, Daewoong", "affiliation": "Hyundai Motor Company" }, { "name": "Kim, Jinsung", "affiliation": "Hyundai Motor Company" } ], "keywords": [ "AI and learning-based control for automotive systems", "Kalman filtering techniques in automotive control", "Adaptive and robust control of automotive systems" ], "abstract": "The automotive industry is experiencing a transformative shift towards personalized mobility platforms. It requires automotive manufacturers to develop tailored services for customers. This paper proposes an online driving behavior learning algorithm utilizing sparse Gaussian process regression to improve accurate electric vehicle energy consumption prediction. By defining driving behavior based on real-time traffic speed relative to vehicle speed, the proposed method enables real-time learning, ensuring both timeliness and reliability. Through evaluations on vehicle tests, the learning algorithm demonstrates improved accuracy in driving energy prediction by incorporating customer’s personalized speed learning. This research paves the way for more sustainable and user-centered solutions in the era of the software-defined vehicles.", "url": "" }, { "id": "Tu-TuC02.3", "code": "TuC02.3", "title": "Domain Knowledge–Based Fault Diagnosis for Automotive Chassis Systems : DevOps with Domains (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:00-16:15", "sessionCode": "TuC02", "sessionTitle": "LB: AI and Learning-Based Control for Automotive System", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Ryu, Yong-hyun", "affiliation": "Hyundai Motor Group" } ], "keywords": [ "Diagnosis of automotive control systems", "Automotive system identification and modelling", "Vehicle dynamic systems" ], "abstract": "In software-defined vehicle (SDV) development, automated DevOps pipelines support rapid software iteration independent of hardware. To incorporate hardware experts’ knowledge into chassis fault diagnosis, this study introduces the concept of “DevOps with Domains.” Shock absorbers, wheel bearings, and tires are used as representative components. For vibration-based components, lightweight analysis tailored to in-vehicle sensor bandwidth was applied. For tires, CAN-derived physical quantities were combined with a wear-model-based computation to estimate tread loss. These approaches enable modular integration of domain knowledge across data acquisition, model development, validation, and deployment while maintaining reliable diagnostic performance.", "url": "" }, { "id": "Tu-TuC02.4", "code": "TuC02.4", "title": "Trajectory-Control-Based Crank Angle Alignment for Engine-Off in Hybrid Vehicles (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:15-16:30", "sessionCode": "TuC02", "sessionTitle": "LB: AI and Learning-Based Control for Automotive System", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Kim, Daeyeong", "affiliation": "Hyundai Motor Company" }, { "name": "Lim, Jong Hyun", "affiliation": "Hyundai Motor Company" }, { "name": "Lee, Sung Back", "affiliation": "Hyundai Motor Company" } ], "keywords": [ "Hybrid, electric and alternative drive vehicles", "Engine and powertrain modeling and control", "Adaptive and robust control of automotive systems" ], "abstract": ". 이 연구는 궤적 기반 크랭크 각도 제어를 제안한다 TMED-II 하이브리드에서 엔진 시동 진동을 줄이기 위한 전략 전기차. 엔진의 크랭크 각도를 다음과 같이 정렬함으로써 점화 전 목표 위치를 회피합니다 시동 단계 동안 공진 주파수를 최소화하는 방법 진동. 기존 PID 제어 비교 제안된 궤적 제어는 후자를 보여준다. 정렬 정확도를 크게 향상시키고 감소를 줄입니다 에너지 소비. 실제 차량 테스트에서 확인해 줍니다 궤적 제어는 목표 정렬 성공률을 높입니다 67%에서 97%로 증가하며, 역방향 방지도 방지합니다 회전 및 견고하고 저진동 엔진 보장 재시작.", "url": "" }, { "id": "Tu-TuC02.5", "code": "TuC02.5", "title": "Optimization of Deceleration Profiles for Electric Vehicles in V2X Environments Via a Parametric Dynamic Programming Approach (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:45", "sessionCode": "TuC02", "sessionTitle": "LB: AI and Learning-Based Control for Automotive System", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Park, Jinrak", "affiliation": "Hyundai Motor Company" }, { "name": "Kim, Dohee", "affiliation": "Hyundai Motor Company" } ], "keywords": [ "Autonomous vehicles", "Intelligent transportation systems", "Trajectory and path planning for AVs" ], "abstract": "With the rapid advancement of Intelligent Transportation Systems (ITS), the availability of data conducive to eco-driving has significantly expanded. Modern navigation systems provide road gradient profiles and speed enforcement locations, while Vehicle-to-Infrastructure (V2I) communication offers Signal Phase and Timing (SPaT) information. Furthermore, Vehicle-to-Vehicle (V2V) communication enables the acquisition of surrounding traffic states. Collectively, this ecosystem allows autonomous vehicles to utilize look-ahead information for predictive speed control, thereby enhancing energy efficiency. This study addresses a deceleration planning framework designed to optimize the energy consumption of electric vehicles (EVs) by leveraging road slope data when approaching traffic signals, speed cameras, or preceding vehicles. The problem is formulated with specific boundary conditions: initial speed, target speed, arrival time, and travel distance. The proposed algorithm was validated against a conventional Dynamic Programming (DP) approach and manual driving data using a Kia EV6 in real-world urban scenarios. The results demonstrate that the proposed method significantly reduces computational load compared to standard DP while effectively managing deceleration events.", "url": "" }, { "id": "Tu-TuC02.8", "code": "TuC02.8", "title": "Comfort-Enhanced Adaptive Cruise Control Using Model Predictive Control with Motion Sickness Dosage Value (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "17:15-17:30", "sessionCode": "TuC02", "sessionTitle": "LB: AI and Learning-Based Control for Automotive System", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Ahmed, Syed Adil", "affiliation": "University of Michigan Dearborn" }, { "name": "Kwak, Kyoung Hyun", "affiliation": "University of Michigan - Dearborn" }, { "name": "Han, Je-Heon", "affiliation": "Tech University of Korea" }, { "name": "Han, Kyoungseok", "affiliation": "Hanyang University" }, { "name": "Kim, Youngki", "affiliation": "University of Michigan-Dearborn" } ], "keywords": [ "Nonlinear and optimal automotive control", "Trajectory tracking and path following for AVs" ], "abstract": "Adaptive Cruise Control (ACC) traditionally focuses on maintaining safe spacing and desired speed, but it often neglects motion sickness (MS), a key determinant for passenger comfort. This work presents a multi-objective ACC-MS Model Predictive Control (ACC-MS MPC) that extends standard ACC goals by explicitly minimizing MS. Motion sickness is quantified using the frequency-weighted Motion Sickness Dosage Value (MSDV), computed through a simple linear filter constructed from second-order high- and low-pass filters. The resulting filter enables MSDV to be integrated efficiently into a linear MPC cost. A structured weight-selection method preserves spacing accuracy while balancing comfort and control effort. Evaluations across three standardized driving cycles show that the proposed ACC-MS MPC outperforms a benchmark MPC, achieving reductions of up to 47% in spacing deviation, 19% in jerk, and 37% in MSDV.", "url": "" }, { "id": "Tu-TuC03.1", "code": "TuC03.1", "title": "Sliding Mode Pose Control: Fully Actuated System vs. State Space", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC03", "sessionTitle": "Enriching Existing Theoretical Developments Via the FAS Theory", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Xiao, Fu-Zheng", "affiliation": "Harbin Institute of Technology Shenzhen" }, { "name": "Yu, Yong-Heng", "affiliation": "Harbin Institute of Technology" }, { "name": "Chen, Li-Qun", "affiliation": "Harbin Institute of Technology (Shenzhen)" } ], "keywords": [ "Control using FAS approach", "Fully-actuated systems in industry", "High-order strict feedback systems" ], "abstract": "Sliding mode techniques have strong robustness and anti-disturbance performance, and thus they are frequently utilized to design spacecraft pose control laws. The state space method has governed the control field during the past several decades, leading to the fact that the spacecraft pose control laws are almost designed within the state space framework. In contrast, the control design of this work is within a fully actuated system framework, and the designed pose control laws are based on a second-order fully actuated system rather than the frequently utilized kinematic and dynamic systems. Compared with the pose control laws designed within the state space framework, the control laws designed within the fully actuated framework exhibit a special property, a symmetrical structure of the control laws. This symmetry leads to the designed control laws being immune to the unwinding phenomenon of pose control, and thereby, the infinite-time, finite-time, and fixed-time controls with the unwinding-free performance are realized via the sliding mode techniques.", "url": "" }, { "id": "Tu-TuC03.2", "code": "TuC03.2", "title": "A Distributed Fully Actuated Control Strategy for Heterogeneous Air-Ground Cooperative Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC03", "sessionTitle": "Enriching Existing Theoretical Developments Via the FAS Theory", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Mo, Zhibin", "affiliation": "Sun Yat-Sat Universty" }, { "name": "Sun, Hui-Jie", "affiliation": "Sun Yat-Sen University" }, { "name": "Zhang, Bojia", "affiliation": "Sun Yat-Sen University" }, { "name": "Liu, Wanquan", "affiliation": "Sun Yat-Sen University" } ], "keywords": [ "Global fully actuated systems", "Control using FAS approach", "Fully-actuated systems in industry" ], "abstract": "In this paper, the distributed formation control problem for a class of heterogeneous air-ground cooperative system is investigated. To this end, a adaptive distributed formation controller via fully actuated system approach is proposed. By describing the dynamics of low-altitude unmanned aerial vehicles and intelligent ground vehicles into a unified fully-actuated form, the proposed control scheme substantially relaxes the requirement of heterogeneous and nonlinear consensus controllers for consistent and highly accurate dynamic models. Several simulation results demonstrate the asymptotic stability of the closed-loop system and validate the effectiveness of the proposed control strategy. To ensure the repeatability, our codes are available on Github: url{https://github.com/EzekielMok/FAS_IFAC.git}.", "url": "" }, { "id": "Tu-TuC03.3", "code": "TuC03.3", "title": "A FAS Approach for Substabilization of Lorenz System: Part I. Using Two External Inputs", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC03", "sessionTitle": "Enriching Existing Theoretical Developments Via the FAS Theory", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Duan, Guang-Ren", "affiliation": "Harbin Institute of Technology" }, { "name": "Liu, Lin", "affiliation": "Harbin Institute of Technology" }, { "name": "Chen, Zhijun", "affiliation": "Harbin Institute of Technology" }, { "name": "Liu, Weizhen", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Sub-fully actuated systems", "Control using FAS approach" ], "abstract": "In this paper, the fully actuated system (FAS) approach is applied to design control laws of the well-known Lorenz chaotic system with two external inputs. Three distinct cases are investigated, in which global exponential stabilization, asymptotic stabilization within a specific feasibility set, and substabilization are respectively realized. Simulation results are presented to demonstrate the effectiveness of the proposed method.", "url": "" }, { "id": "Tu-TuC03.4", "code": "TuC03.4", "title": "A FAS Approach for Substabilization of Lorenz System: Part II. Using Rayleigh Number As the Control Input", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC03", "sessionTitle": "Enriching Existing Theoretical Developments Via the FAS Theory", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Duan, Guang-Ren", "affiliation": "Harbin Institute of Technology" }, { "name": "Chen, Zhijun", "affiliation": "Harbin Institute of Technology" }, { "name": "Liu, Lin", "affiliation": "Harbin Institute of Technology" }, { "name": "Liu, Weizhen", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Sub-fully actuated systems", "Control using FAS approach" ], "abstract": "In this paper, substabilization of the well-known chaotic system, the Lorenz system, is investigated on the basis of the fully-actuated system (FAS) approach. The Rayleigh number is looked upon as the only input, and the Lorenz system is equivalently represented by a sub-FAS model. By designing a substabilizing controller, the closed-loop system is transformed into a linear constant system with arbitrarily prescribed eigenstructures within the feasible set. Related to the controller gains and the initial conditions of the system, a region of exponential stabilization is properly provided, meaning that all the trajectories beginning from this region converge to the origin exponentially while remaining within the set of feasibility. The standard procedures for addressing substabilization problems are displayed entirely. The numerical simulation demonstrates the effect of the presented approach.", "url": "" }, { "id": "Tu-TuC03.5", "code": "TuC03.5", "title": "A FAS Approach for Substabilization of Lorenz System: Part III. Using a Single External Input", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC03", "sessionTitle": "Enriching Existing Theoretical Developments Via the FAS Theory", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Duan, Guang-Ren", "affiliation": "Harbin Institute of Technology" }, { "name": "Liu, Weizhen", "affiliation": "Harbin Institute of Technology" }, { "name": "Chen, Zhijun", "affiliation": "Harbin Institute of Technology" }, { "name": "Liu, Lin", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Sub-fully actuated systems", "Global fully actuated systems" ], "abstract": "In this paper, a novel control framework is proposed to stabilize the Lorenz system with a single external input by employing a sub-fully actuated system (sub-FAS) approach. The primary objective is to transform the system’s steady-state behavior from chaotic motion to a stable equilibrium point. Two control scenarios are investigated: one in which the control input acts only on the fluid velocity subsystem, and another in which it acts solely on the vertical temperature subsystem. In both cases, the system dynamics are reformulated into a sub-FAS representation, and corresponding feasibility conditions for stabilization are rigorously derived. Unlike conventional chaos control methods, the proposed scheme guarantees that all trajectories of the closed-loop system, as well as the associated control signals, exponentially converge to equilibrium, except for those initialized within a small neighborhood around a specific region of singularity. Finally, a representative set of system parameters is selected to validate the proposed control law for the case involving only the vertical temperature subsystem, demonstrating the effectiveness and practicality of the developed sub-FAS-based approach.", "url": "" }, { "id": "Tu-TuC04.1", "code": "TuC04.1", "title": "Quantum Control Enables Universally Optimal State Discrimination (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC04", "sessionTitle": "Quantum Control II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Clouatre, Maison", "affiliation": "Massachusetts Institute of Technology" }, { "name": "Marano, Stefano", "affiliation": "Univ of Salerno" }, { "name": "Win, Moe Z.", "affiliation": "Massachusetts Institute of Technology" } ], "keywords": [ "Quantum control", "Quantum systems", "Quantum optimal control" ], "abstract": "The ultimate limit on binary quantum state discrimination is provided by the Helstrom bound. Achieving this bound requires an optimal measurement, which depends on the two states to be discriminated. Hence, the bound is generally unachievable in systems equipped with a fixed measurement apparatus. However, this work proves that joint Hamiltonian and Lindblad control, applied to the quantum state prior to using the fixed measurement apparatus, enables universally ϵ-optimal quantum state discrimination. Namely, for an arbitrary pair of quantum states, the proposed scheme achieves discrimination error probability within ϵ of the Helstrom bound. This note summarizes these results and provides proof sketches.", "url": "" }, { "id": "Tu-TuC04.2", "code": "TuC04.2", "title": "Quantum Disturbance Observer for Schrodinger Gate Control: Set-Membership Guarantees (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC04", "sessionTitle": "Quantum Control II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Kim, Hyuntae", "affiliation": "University of Oxford" } ], "keywords": [ "Robust quantum control", "Coherent quantum control", "Quantum control" ], "abstract": "We present a Schrodinger-picture disturbance observer for single-input Hamiltonian gate execution under a slow coherent bias aligned with the control generator, without mid-circuit projective measurements or pulse re-synthesis. The method uses a first-order differentiator and a first-order low-pass compensator driven by the nominal drift-control pair, the nominal pulse, and a real-time propagator signal. Under an ideal propagator-access assumption, introduced to separate the observer mechanism from propagator reconstruction, we prove finite-horizon well-posedness, residual and small-gain bounds, and a non-asymptotic qubit average-gate-fidelity guarantee. In the unsaturated aligned case, the certified error can be made arbitrarily small by time-scale selection. When the residual remains in the certified perturbation class, the same estimate tightens baseline Lipschitz-type robustness certificates. A one-qubit example illustrates the plug-in nature of the approach.", "url": "" }, { "id": "Tu-TuC04.3", "code": "TuC04.3", "title": "Robustness Analysis in Static and Dynamic Quantum State Tomography (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC04", "sessionTitle": "Quantum Control II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Chen, Alan Zihan", "affiliation": "Australian National University" }, { "name": "Xiao, Shuixin", "affiliation": "Australian National University" }, { "name": "Ma, Hailan", "affiliation": "The University of New South Wales" }, { "name": "Dong, Daoyi", "affiliation": "Australian National University" } ], "keywords": [ "Quantum tomography", "Quantum systems" ], "abstract": "Quantum state tomography is a core task in quantum system identification. Real experimental conditions often deviate from nominal designs, introducing errors in both the measurement devices and the Hamiltonian governing the system’s dynamics. In this paper, we investigate the robustness of quantum state tomography against such perturbations in both static and dynamic settings using linear regression estimation. We derive explicit bounds that quantify how bounded errors in the measurement devices and the Hamiltonian affect the mean squared error (MSE) upper bound in each scenario. Numerical simulations for qubit systems illustrate how these bounds scale with resources.", "url": "" }, { "id": "Tu-TuC04.4", "code": "TuC04.4", "title": "ResTT-SS: A Quantum-Inspired Framework for Industrial Soft Sensing (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC04", "sessionTitle": "Quantum Control II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Chen, Yiwei", "affiliation": "Yunnan University" }, { "name": "Lang, Xun", "affiliation": "Information School, Yunnan University" }, { "name": "Wang, Tao", "affiliation": "Yunnan University" }, { "name": "Li, Peng", "affiliation": "Yunnan University" } ], "keywords": [ "Quantum systems", "Quantum linear systems", "Quantum observers" ], "abstract": "Soft sensors are widely used in process industries to enable online monitoring and quality prediction of difficult-to-measure or infrequently sampled variables based on routinely measured process data. Data-driven approaches have shown strong potential in this context, yet most existing models still encode feature interactions using traditional, predominantly low-order mechanisms. This restricts their ability to represent multiway dependencies among process variables and hampers the provision of trustworthy, engineering-grade explanations at deployment. In this work, we propose a quantum-inspired framework, Residual Tensor Train Soft Sensing (ResTT-SS), which models multilinear correlations among process variables within a lightweight estimator. The method tensorizes process measurements and employs a rank-controlled tensor–train kernel with residual refinement, thereby enhancing predictive accuracy by explicitly capturing higher-order structures while maintaining compact parameterization. Interpretability is achieved by quantifying feature importance via node energy and bond entropy, which together provide factor-wise and interaction-wise attributions. We validate ResTT-SS on an industrial debutanizer column against state-of-the-art soft sensor approaches. The results demonstrate that ResTT-SS consistently attains the highest prediction accuracy while offering useful post-hoc interpretability that reveals the contribution of individual variables.", "url": "" }, { "id": "Tu-TuC04.6", "code": "TuC04.6", "title": "Quantum Memory Optimisation Using Finite-Horizon, Decoherence Time and Discounted Mean-Square Performance Criteria (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC04", "sessionTitle": "Quantum Control II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Vladimirov, Igor", "affiliation": "Australian National University" }, { "name": "Petersen, Ian R", "affiliation": "The Australian National University" }, { "name": "Shi, Guodong", "affiliation": "The University of Sydney" } ], "keywords": [ "Quantum systems", "Quantum optimal control", "Coherent quantum control" ], "abstract": "This paper is concerned with open quantum memory systems for approximately retaining quantum information, such as initial dynamic variables or quantum states to be stored over a bounded time interval. In the Heisenberg picture of quantum dynamics, the deviation of the system variables from their initial values lends itself to closed-form computation in terms of tractable moment dynamics for open quantum harmonic oscillators and finite-level quantum systems governed by linear or quasi-linear Hudson-Parthasarathy quantum stochastic differential equations, respectively. This tractability is used in a recently proposed optimality criterion for varying the system parameters so as to maximise the memory decoherence time when the mean-square deviation achieves a given critical threshold. The memory decoherence time maximisation approach is extended beyond the previously considered low-threshold asymptotic approximation and to Schroedinger type mean-square deviation functionals for the reduced system state governed by the Lindblad master equation. We link this approach with the minimisation of the mean-square deviation functionals at a finite time horizon and with their discounted version which quantifies the averaged performance of the quantum system as a temporary memory under a Poisson flow of storage requests.", "url": "" }, { "id": "Tu-TuC05.1", "code": "TuC05.1", "title": "Discovering Mechanistic Causality from Time Series: A Behavioral-System Approach", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:45", "sessionCode": "TuC05", "sessionTitle": "LB: Analysis and Design of Control Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Liu, Yingzhu", "affiliation": "Peking University" }, { "name": "Li, Zhongkui", "affiliation": "Peking University" }, { "name": "Mei, Wenjun", "affiliation": "Peking University" } ], "keywords": [ "Data-driven control theory", "Time series modeling" ], "abstract": "Identifying ``true causality'' is a fundamental challenge in complex systems research. Widely adopted methods, like the Granger causality test, capture statistical dependencies between variables rather than genuine driver-response mechanisms. This critical gap stems from the absence of mathematical tools that reliably reconstruct underlying system dynamics from observational time-series data. In this paper, we introduce a new control-based method for causality discovery through the behavior-system theory, which represents dynamical systems via trajectory spaces. Our core contribution is the textbf{B}ehavior-textbf{e}nabled textbf{Caus}ality test (the BeCaus test), which transforms causality discovery into solving fictitious control problems. By exploiting the intrinsic asymmetry between system inputs and outputs, the proposed method operationalizes our conceptualization of mechanistic causality: variable X is a cause of Y if X (partially) drives the evolution of Y. We establish conditions for linear time-invariant systems to be causality-discoverable, i.e., conditions for the BeCaus test to distinguish four basic causal structures (independence, full causality, partial causality, and latent-common-cause relation). Notably, our approach accommodates open systems with unobserved inputs.", "url": "" }, { "id": "Tu-TuC05.2", "code": "TuC05.2", "title": "Extremum Seeking Control Convergence: State-Dependent and Bilinear Objectives", "day": "Tuesday", "date": "August 25, 2026", "time": "15:45-16:00", "sessionCode": "TuC05", "sessionTitle": "LB: Analysis and Design of Control Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Mulders, Sebastiaan Paul", "affiliation": "Delft University of Technology" }, { "name": "Rotea, Mario", "affiliation": "The University of Texas at Dallas" }, { "name": "Gallo, Alexander J.", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Extremum seeking and model free adaptive control" ], "abstract": "Extremum seeking control (ESC) is a model-free, adaptive scheme for real-time optimization of dynamical systems. Classical ESC relies on time-scale separation between the dither frequency and the system dynamics to ensure convergence. This paper analyzes ESC convergence for state-dependent and bilinear (state-input dependent) objectives in a Wiener-type model framework. Although both objectives share the same steady-state optimum, they exhibit fundamentally different convergence behavior when the dither frequency exceeds the dominant system dynamics. Frequency-domain and signal-based analyses expose the mechanism behind this discrepancy. Two enhanced ESC methods, namely phase-gain compensation and numerical-derivative objective reconstruction, are proposed and validated in simulation, enabling consistent convergence at higher dither frequencies.", "url": "" }, { "id": "Tu-TuC05.3", "code": "TuC05.3", "title": "PPO Based Framework for Equalizer Co-Optimization", "day": "Tuesday", "date": "August 25, 2026", "time": "16:00-16:15", "sessionCode": "TuC05", "sessionTitle": "LB: Analysis and Design of Control Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Banerjee, Tathagata", "affiliation": "IIT Dharwad" }, { "name": "Lashkari, Malika E Naz", "affiliation": "IIT Dharwad" }, { "name": "Mulla, Ameer", "affiliation": "Indian Institute of Technology Dharwad" } ], "keywords": [ "Filtering and smoothing", "Adaptive observer design", "Learning methods for control" ], "abstract": "Equalizers are integral part of high-speed communication systems to overcome channel losses and preserve signal integrity. This paper showcases a co-optimization technique for multi-stage equalizers for a given channel, targeting maximization of signal integrity, with minimum equalizer complexity. The proposed method exploits Proximal Policy Optimization algorithm to simultaneously optimize the equalizer tap count and the corresponding weights. Simulations of automotive SerDes and data-center channels demonstrate the superiority of this adaptive RL-based method over Bayesian Optimization and Random Search algorithms.", "url": "" }, { "id": "Tu-TuC05.4", "code": "TuC05.4", "title": "Learning-Based Approach for Nonlinear L1 Analysis: Systematic Verification", "day": "Tuesday", "date": "August 25, 2026", "time": "16:15-16:30", "sessionCode": "TuC05", "sessionTitle": "LB: Analysis and Design of Control Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Kim, Eunsuh", "affiliation": "POSTECH" }, { "name": "Choi, Hyung Tae", "affiliation": "Chung-Ang University" } ], "keywords": [ "Learning methods for control" ], "abstract": "Motivated by the computational difficulties in nonlinear L1 analysis, this paper proposes a learning-based framework for ensuring L1 performance of a nonlinear system. A set in the state space, which ensures the bound of L∞ norm of the corresponding output, is shown to be computed by using the idea of spatial discretization. It is then shown that a barrier function associated with the above set can be computed by using neural networks. By combining these computations, an algorithm is obtained for verifying L1 performance of a system in a systematic manner. A numerical example is provided to evaluate the validity of the proposed method.", "url": "" }, { "id": "Tu-TuC05.5", "code": "TuC05.5", "title": "YALTA-Control App Designer", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:45", "sessionCode": "TuC05", "sessionTitle": "LB: Analysis and Design of Control Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Do, Duc Duy", "affiliation": "Inria Saclay Center" }, { "name": "Bonnet, Catherine", "affiliation": "Saclay Inria Centre" }, { "name": "Yegin, Mustafa Oguz", "affiliation": "Czech Technical University in Prague" }, { "name": "Ozbay, Hitay", "affiliation": "Bilkent University" } ], "keywords": [ "Linear time-delay systems", "Robust controller synthesis", "Control of complex systems" ], "abstract": "This paper introduces YALTA-Control App Designer, a new toolbox integrating YALTA with Matlab and Simulink for simulation. This is an extension of the tool mentioned for Bonnet et al. (2025), which considered retarded time delay systems. The present work illustrates the implementation of feedback control systems for neutral delay systems.", "url": "" }, { "id": "Tu-TuC05.6", "code": "TuC05.6", "title": "Characterization of Contraction Via Direct Lyapunov Method", "day": "Tuesday", "date": "August 25, 2026", "time": "16:45-17:00", "sessionCode": "TuC05", "sessionTitle": "LB: Analysis and Design of Control Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Pogromsky, A. Yu.", "affiliation": "Eindhoven Univ of Technology" }, { "name": "Matveev, Alexey S.", "affiliation": "St.Petersburg Univ" } ], "keywords": [ "Lyapunov methods", "Stability of nonlinear systems" ], "abstract": "We develop a direct Lyapunov framework for characterizing exponential d-contraction of nonlinear systems. The approach is built on a measure-theoretic generalization of k-contraction that allows non-integer d via Hausdorff d-measure. We introduce a family of P-metric elliptic d-measures pi_d, which we call Hausdorff-Riemann measures, that are comparable with the Hausdorff measure on compact, positively invariant sets. The main result establishes an equivalence between exponential d-contraction and the existence of a state-dependent metric P(cdot) such that the corresponding pi_d decays exponentially and hence this measure plays the role of Lyapunov function for the contraction theory. The theory recovers existing k-contraction as a special case and admits variable metrics beyond compound-matrix logarithmic-norm methods. As an illustration, we obtain verifiable bounds for the Langford system which guarantees exponential 2-contraction.", "url": "" }, { "id": "Tu-TuC05.7", "code": "TuC05.7", "title": "Time-Consistent Moment Certificates for Chance-Constrained Stopping", "day": "Tuesday", "date": "August 25, 2026", "time": "17:00-17:15", "sessionCode": "TuC05", "sessionTitle": "LB: Analysis and Design of Control Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Doyoung, Heo", "affiliation": "Korea Advanced Institute of Science and Technology" }, { "name": "Han, SooJean", "affiliation": "California Institute of Technology" } ], "keywords": [ "Markov decision process", "Stochastic control", "Synthesis of stochastic systems" ], "abstract": "We study finite Markov decision processes with binary continue/stop actions under a probabilistic budget constraint on the cumulative cost before stopping. To avoid intractable distributional analysis, we develop a tractable, distribution-free certification framework based on exact computation of the first two moments of the hitting cost via linear systems on non-target states. Using Cantelli's inequality, we obtain a simple sufficient condition that certifies the chance constraint using only the mean and standard deviation, and which can be re-evaluated online with the remaining budget. We also establish structural properties ensuring well-posedness and monotone stopping geometry. Experiments on an energy-constrained stochastic navigation benchmark demonstrate a stability--performance tradeoff: compared to sampling-based VaR/CVaR baselines, the proposed method achieves competitive safe utility while significantly reducing cost variability.", "url": "" }, { "id": "Tu-TuC05.8", "code": "TuC05.8", "title": "Descriptor Model Approach for Coupled PDEs-ODEs Subject to IQCs", "day": "Tuesday", "date": "August 25, 2026", "time": "17:15-17:30", "sessionCode": "TuC05", "sessionTitle": "LB: Analysis and Design of Control Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Callegari, Sara", "affiliation": "LAAS-CNRS, Université De Toulouse, INSA" }, { "name": "Peaucelle, Dimitri", "affiliation": "LAAS-CNRS" }, { "name": "Gouaisbaut, Frederic", "affiliation": "LAAS CNRS" } ], "keywords": [ "Systems theoretic properties of distributed parameter systems", "Robust linear matrix inequalities", "Robustness analysis" ], "abstract": "Analyzing systems that couple Partial Differential Equations (PDEs) and Ordinary Differential Equations (ODEs) presents a difficult modeling challenge. To address this, we introduce a descriptor-based framework that captures these interconnected dynamics under Integral Quadratic Constraints (IQCs). Rather than treating distributed dynamics, boundary conditions, and algebraic relations as separate elements, the presented methodology groups them into a single, cohesive matrix structure. By exploiting IQCs to embed both system properties and L_{2}-performance criteria, the framework maps the stability analysis directly into mathematically tractable Linear Matrix Inequalities (LMIs).", "url": "" }, { "id": "Tu-TuC06.1", "code": "TuC06.1", "title": "Uncertainty Propagation under Residual Disturbances: A Smart-Home Case Study (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC06", "sessionTitle": "Data-Driven Control III", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Pan, Guanru", "affiliation": "Hamburg University of Technology" }, { "name": "Reinhardt, Dirk Peter", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Gros, Sebastien", "affiliation": "NTNU" }, { "name": "Faulwasser, Timm", "affiliation": "Hamburg University of Technology" } ], "keywords": [ "Data-driven control theory", "Linear system identification", "Stochastic control" ], "abstract": "This paper presents a data-driven framework for uncertainty propagation under unmeasured or statistically unmodeled (unstructured) disturbances. We consider residual disturbances, which consolidate all unstructured disturbances into a single quantity that can be estimated from data. Under mild assumptions, the resulting stochastic predictor is causal and distributionally consistent, enabling efficient uncertainty quantification through polynomial chaos expansions and higher-order Chebyshev inequalities. The proposed method is validated using experimental data from a smart home in Norway.", "url": "" }, { "id": "Tu-TuC06.2", "code": "TuC06.2", "title": "Toward Federated DeePC: Borrowing Data from Similar Systems (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC06", "sessionTitle": "Data-Driven Control III", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Vankan, Gert", "affiliation": "Eindhoven University of Technology" }, { "name": "Formentin, Simone", "affiliation": "Politecnico Di Milano" }, { "name": "Breschi, Valentina", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Data-driven control theory", "Learning methods for control" ], "abstract": "Data-driven predictive control approaches, in general, and Data-enabled Predictive Control (DeePC), in particular, exploit matrices of raw input/output trajectories for control design. These data are typically gathered only from the system to be controlled. Nonetheless, the increasing connectivity and inherent similarity of (mass-produced) systems have the potential to generate a considerable amount of information that can be exploited to undertake a control task. In light of this, we propose a preliminary textit{federated} extension of DeePC that leverages a combination of input/output trajectories from multiple similar systems for predictive control. Supported by a suite of numerical examples, our analysis unveils the potential benefits of exploiting information from similar systems and its possible downsides.", "url": "" }, { "id": "Tu-TuC06.3", "code": "TuC06.3", "title": "A Time-Delay Approach of Extremum Seeking of 1D Static Maps with Filters (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC06", "sessionTitle": "Data-Driven Control III", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Pan, Gaofeng", "affiliation": "Institute of Cyber-Systems and Control, Zhejiang University" }, { "name": "Fridman, Emilia", "affiliation": "Tel-Aviv Univ" }, { "name": "Wu, Zheng-Guang", "affiliation": "Zhejiang University" }, { "name": "Zhu, Yang", "affiliation": "Zhejiang University" } ], "keywords": [ "Extremum seeking and model free adaptive control", "Data-driven control theory", "Nonlinear adaptive control" ], "abstract": "This paper extends a recently-developed time-delay approach from first-order extremum seeking (ES) based on integrators to higher-order ES with high-pass and low-pass filters. We consider classical gradient-based ES for one-dimensional (1D) quadratic static maps to be of conceptional simplicity. To analyze the ES dynamical systems with filters of dither signals, we transform the system into a time-delay model without approximation. Furthermore, we derive sufficient conditions in terms of linear matrix inequalities (LMIs) for the practical stability of the resulting time-delay system. Finally, a numerical example demonstrates the effectiveness of our proposed method.", "url": "" }, { "id": "Tu-TuC06.4", "code": "TuC06.4", "title": "Scalable Nonlinear DeePC: Bridging Direct and Indirect Methods and Basis Reduction (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC06", "sessionTitle": "Data-Driven Control III", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "de Jong, Thomas Oliver", "affiliation": "Eindhoven University of Technology" }, { "name": "Lazar, Mircea", "affiliation": "Eindhoven Univ. of Technology" }, { "name": "Weiland, Siep", "affiliation": "Eindhoven Univ. of Tech" }, { "name": "Dorfler, Florian", "affiliation": "Swiss Federal Institute of Technology (ETH) Zurich" } ], "keywords": [ "Data-driven control theory", "Nonlinear system identification", "Linear system identification" ], "abstract": "This paper studies regularized data-enabled predictive control (DeePC) within a nonlinear framework and its relationship to subspace predictive control (SPC). The Pi-regularization is extended to general basis functions and it is shown that, under suitable conditions, the resulting basis functions DeePC formulation constitutes a relaxation of basis functions SPC. To improve scalability, we introduce a Singular Value Decomposition (SVD) based dimensionality reduction that preserves equivalence with SPC, and we derive a reduced Pi-regularization. A LASSO-based sparse basis selection method is proposed to obtain a reduced basis from lifted data. The framework is evaluated on a nonlinear van der Pol oscillator, demonstrating improved tracking performance of DeePC over SPC under noisy conditions.", "url": "" }, { "id": "Tu-TuC06.5", "code": "TuC06.5", "title": "Koopman-Based LPV Control: A Data-Driven Approach Using IQCs (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC06", "sessionTitle": "Data-Driven Control III", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Eyuboglu, Mert", "affiliation": "EPFL" }, { "name": "Strässer, Robin", "affiliation": "University of Stuttgart" }, { "name": "Allgower, Frank", "affiliation": "University of Stuttgart" }, { "name": "Karimi, Alireza", "affiliation": "Ecole Polytechnique Federale De Lausanne" } ], "keywords": [ "Nonlinear system identification", "Data-driven control theory" ], "abstract": "This paper proposes a novel data-driven control framework that combines Koopman-based linear parameter-varying (LPV) surrogate models and an integral quadratic constraint (IQC)-based error characterization to achieve effective closed-loop guarantees for nonlinear systems. In particular, we employ extended dynamic mode decomposition (EDMD) to approximate nonlinear dynamics. The residual errors are characterized directly from data using non-parametric IQC multipliers in the frequency domain, providing a tight data-driven uncertainty characterization. Moreover, an IQC-based characterization of the scheduling parameter enables frequency-domain LPV controller design, ensuring robust stability and performance. An iterative algorithm optimizes both the IQC multipliers and the controller parameters, reducing conservatism and ensuring monotonic convergence of the robust performance index. Numerical simulations validate the proposed approach and demonstrate convergence to tight performance guarantees using a finite number of data trajectories.", "url": "" }, { "id": "Tu-TuC07.1", "code": "TuC07.1", "title": "Scalable H 2 / H ∞ Control for Large-Scale Systems with Time Delays Based on Chordal Decomposition and ADMM", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC07", "sessionTitle": "Control of Networked and Large-Scale Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Song, Yang", "affiliation": "Shanghai University" }, { "name": "He, Jiahua", "affiliation": "Shanghai University" }, { "name": "Li, Zixu", "affiliation": "Shanghai University" }, { "name": "Du, Dajun", "affiliation": "Shanghai University" }, { "name": "Fei, Minrui", "affiliation": "Shanghai University" } ], "keywords": [ "Distributed control and estimation", "Distributed optimization", "Control over networks" ], "abstract": "This study addresses the design of distributed H 2 / H ∞ controllers for large-scale discrete-time systems with time delays. By employing chordal graph theory and the alternating direction method of multipliers (ADMM), a scalable state-feedback controller design framework is proposed. First, introducing an auxiliary matrix allows the multiple Lyapunov functions approach to be used in H 2 / H ∞ control, which reduces the design conservatism. The state feedback controller can then be obtained by solving a set of linear matrix inequalities (LMIs). The introduction of the auxiliary variable can also enhance the flexibility in the structure of one Lyapunov matrix within the chordal decomposition. Second, a chordal decomposition-based framework for scalable H 2 / H ∞ controller design is established. Furthermore, a novel hierarchical, multi-center ADMM method, which incorporates community detection algorithms, is developed, thereby effectively reducing the computational complexity. Numerical simulations demonstrate the effectiveness of the proposed method.", "url": "" }, { "id": "Tu-TuC07.2", "code": "TuC07.2", "title": "Synchronization of Continua of Linear Systems Connected by Regular Graphons", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC07", "sessionTitle": "Control of Networked and Large-Scale Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Prisant, Raoul", "affiliation": "CNRS, GIPSA-Lab, Univ. Grenoble Alpes" }, { "name": "Casadei, Giacomo", "affiliation": "Université Grenoble Alpes" }, { "name": "Frasca, Paolo", "affiliation": "CNRS, GIPSA-Lab, Grenoble" }, { "name": "Garin, Federica", "affiliation": "INRIA" } ], "keywords": [ "Control of networks", "Multi-agent systems", "Consensus" ], "abstract": "In this paper, we consider the problem of synchronization of an infinite network of linear systems. The infinite network of interconnections is represented by a graphon, which is a limit of graphs, where the node indexes 1, . . . , N are replaced by a continuum of indexes x in the interval I = [0, 1]. The local systems are identical linear systems of finite dimension n, and are interconnected by a diffusive coupling that is described by a graphon-Laplacian operator. Our goal is to design local gains that lead the systems to synchronize. Under the assumption that the graphon is connected and regular, and that the local systems are stabilizable, we show that this design only requires knowing (an estimate of) the graphon’s algebraic connectivity and solving an n-dimensional Riccati equation.", "url": "" }, { "id": "Tu-TuC07.3", "code": "TuC07.3", "title": "Approximation Property of One-Hidden-Layer Perceptron through the Lens of Control", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC07", "sessionTitle": "Control of Networked and Large-Scale Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Che, Linxin", "affiliation": "Beijing Institute of Technology" }, { "name": "Yu, Hao", "affiliation": "Beijing Institute of Technology" }, { "name": "Shi, Dawei", "affiliation": "Beijing Institute of Technology" } ], "keywords": [ "Control of networks", "Nonlinear system identification", "Learning methods for control" ], "abstract": "In this paper, we present a novel perspective on the problem of using the one-hidden-layer perceptron to exactly memorize training data by recasting it as a stabilization problem for an ensemble control system. By restricting attention to the one-dimensional case and adopting a particular class of activation functions, we leverage properties of numerical sequences to establish that a one-hidden-layer perceptron can ensure the Lyapunov asymptotic (exponential) stability and arbitrarily desirable ultimate accuracy, provided the network is sufficiently wide. Furthermore, numerical experiments are conducted, corroborating our theoretical findings.", "url": "" }, { "id": "Tu-TuC07.4", "code": "TuC07.4", "title": "Mode Distinction and Estimation of Hidden Markov Boolean Control Networks", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC07", "sessionTitle": "Control of Networked and Large-Scale Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Ge, Xingyu", "affiliation": "Zhejiang Normal University" }, { "name": "Zhong, Jie", "affiliation": "City University of Hong Kong" }, { "name": "Pan, Qinyao", "affiliation": "Zhejiang Normal University" }, { "name": "Zhang, Kun", "affiliation": "School of Astronautics, Beihang University" } ], "keywords": [ "Control of networks", "Control over networks", "Distributed control and estimation" ], "abstract": "This paper studies mode distinction and transition-matrix estimation for hidden Markov Boolean control networks (HMBCNs). The mode-dependent logical update maps are assumed to be known, while the active mode and its Markov transition matrix are hidden. Using the semi-tensor product representation, we derive an algebraic necessary and sufficient condition for one-step mode distinguishability. A state-feedback law is then constructed so that different modes generate distinct one-step successors, which enables the active mode to be decoded from two consecutive states. Based on the decoded mode sequence, the unknown transition matrix is estimated by maximum likelihood, and a finite-sample error bound is obtained via Hoeffding's inequality. Biological examples illustrate the proposed method and the convergence of the estimator.", "url": "" }, { "id": "Tu-TuC07.5", "code": "TuC07.5", "title": "Scheduling Mode Switches in Distributed Plug-And-Play Observers", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC07", "sessionTitle": "Control of Networked and Large-Scale Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Dhullipalla, Mani Hemanth", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Dimarogonas, Dimos V.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Distributed control and estimation", "Hybrid and switched systems modeling", "Stability and stabilization of hybrid systems" ], "abstract": "In this work, we study the problem of distributed state estimation of continuous-time linear systems. However, in contrast to existing studies, we consider that each observer node in the network has the following two modes of operation: i) to plug-in and engage/play, i.e., nodes can actively access partial outputs and exchange information, or ii) to remain on standby, i.e., nodes can only propagate in an open-loop fashion. These capabilities could allow the distributed observers to ration their energy/communication resources effectively. To facilitate these modes of the observer nodes, we modify the well-known Luenberger-based observer dynamics for state estimation and establish conditions on the switching signal that schedules mode changes at the observer nodes, and by extension, the switches in the underlying communication network. Consequently, we establish asymptotic omniscience of the distributed plug-and-play observers.", "url": "" }, { "id": "Tu-TuC07.6", "code": "TuC07.6", "title": "Sparse Add-On Controller Design: A Youla Approach to System-Level Performance", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC07", "sessionTitle": "Control of Networked and Large-Scale Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "van der Hulst, Maarten", "affiliation": "Eindhoven University of Technology" }, { "name": "Dirkx, Nic", "affiliation": "ASML" }, { "name": "González, Rodrigo A.", "affiliation": "Eindhoven University of Technology" }, { "name": "Tiels, Koen", "affiliation": "Eindhoven University of Technology" }, { "name": "van de Wijdeven, Jeroen", "affiliation": "ASML" }, { "name": "Oomen, Tom", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Control of networks" ], "abstract": "The performance of high-tech systems is often dictated by a few performance objectives shared among the many closed-loop controlled subsystems operating in the machine, such as synchronization, coordination, and alignment, which necessitates control methods that explicitly address them to achieve optimal performance. The aim of this paper is to introduce a framework that improves system performance through system-level controllers designed to be implemented as add-ons to the existing subsystem control structure. The developed method parametrizes all stabilizing system-level add-on controllers using the Youla framework, enabling a convex formulation of the sparse mathcal{H}_2 synthesis problem. The result is a sparse add-on controller that achieves the optimal trade-off between combined performance and interconnection complexity, as demonstrated through numerical simulations.", "url": "" }, { "id": "Tu-TuC08.1", "code": "TuC08.1", "title": "CVaR-Based Variational Inequalities: Stochastic Approximation Using Computationally-Efficient Projections", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC08", "sessionTitle": "Stochastic Systems and Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Verbree, Jasper", "affiliation": "University of Groningen" }, { "name": "Cherukuri, Ashish", "affiliation": "University of Groningen" } ], "keywords": [ "Randomized algorithms in stochastic systems" ], "abstract": "This paper considers variational inequalities (VI) defined by the conditional value-at-risk (CVaR) of uncertain functions and provides three stochastic approximation schemes to solve them. All methods use an empirical estimate of the CVaR at each iteration. The first algorithm constrains the iterates to the feasible set using projection. To overcome the computational burden of projections, the second one handles inequality and equality constraints defining the feasible set differently. Particularly, projection onto to the affine subspace defined by the equality constraints is achieved by matrix multiplication and inequalities are handled by using penalty functions. Finally, the third algorithm discards projections altogether by introducing multiplier updates. We establish asymptotic convergence of all our schemes to any arbitrary neighborhood of the solution of the VI. A simulation example concerning a network routing game illustrates our theoretical findings.", "url": "" }, { "id": "Tu-TuC08.2", "code": "TuC08.2", "title": "Physics-Informed System Identification Using Randomized Atomic Features", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC08", "sessionTitle": "Stochastic Systems and Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Singh, Rajiv", "affiliation": "Northeastern University" }, { "name": "Sznaier, Mario", "affiliation": "Northeastern University" }, { "name": "Ljung, Lennart", "affiliation": "Linköping University" } ], "keywords": [ "Randomized algorithms in stochastic systems", "Linear system identification", "Physics informed and grey box model identification" ], "abstract": "This paper introduces a randomized atomic feature (RAF) framework for identifying stable linear dynamics from input--output data. The impulse response is represented by a sparse combination of damped rational atoms whose poles are sampled in a prescribed stability region; the residues are then estimated by a convex regularized regression with optional time- and frequency-domain constraints. The analytic viewpoint is deliberately modest: positive measures over stable poles generate positive-definite disk-moment kernels with the appropriate radius-dependent shift defect, while a converse scalar disk representation for an arbitrary kernel requires subnormality of the associated canonical shift. We also describe the radius/gain-normalized link with Nevanlinna-Pick interpolation as a set-membership certificate for stable transfer functions. The resulting method provides an explicit, scalable way to impose stability, modal, gain, settling, passivity, and error-bound priors while retaining interpretable modal structure.", "url": "" }, { "id": "Tu-TuC08.3", "code": "TuC08.3", "title": "The Wasserstein Gradient Flow Perspective of Multi-Agent Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC08", "sessionTitle": "Stochastic Systems and Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Zhao, Zhixuan", "affiliation": "East China Normal University" }, { "name": "Wang, Bing-Chang", "affiliation": "Shandong University" }, { "name": "Li, Tao", "affiliation": "Academy of Mathematics and Systems Science，Chinese Academy of Sciences" } ], "keywords": [ "Stochastic control", "Multi-agent systems", "Stochastic differential equations" ], "abstract": "We propose a Wasserstein gradient flow perspective to model the distributional dynamics of stochastic multi-agent systems. We first show that the evolution of joint and marginal distributions satisfy the Fokker-Planck (FPK) equations in both weak and strong forms. Under symmetric communication topology and drift potential conditions, we prove that the solution to the joint FPK equation follows a Wasserstein gradient flow induced by the free-energy functional, which reveals the system's inherent variational structure. Moreover, with the lambda-convex assumption on the drift potential, the joint solution exhibits monotonic free-energy dissipation and converges exponentially to a unique equilibrium Gibbs distribution in both the W_2 metric and energy sense. Finally, we give numerical simulations of UAV formation tasks to demonstrate the validity of our theoretical framework.", "url": "" }, { "id": "Tu-TuC08.4", "code": "TuC08.4", "title": "Filtering and 1/3 Power Law for Optimal Time Discretisation in Numerical Integration of Stochastic Differential Equations", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC08", "sessionTitle": "Stochastic Systems and Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Vladimirov, Igor", "affiliation": "Australian National University" } ], "keywords": [ "Stochastic differential equations", "Diffusion process", "Estimation and filtering" ], "abstract": "This paper is concerned with the numerical integration of stochastic differential equations (SDEs) which govern diffusion processes driven by a standard Wiener process. With the latter being replaced by a sequence of increments at discrete moments of time, we revisit a filtering point of view on the approximate strong solution of the SDE as an estimate of the hidden system state whose conditional probability distribution is updated using a Bayesian approach and Brownian bridges over the intermediate time intervals. For a class of multivariable linear SDEs, where the numerical solution is organised as a Kalman filter, we investigate the fine-grid asymptotic behaviour of terminal and integral mean-square error functionals when the time discretisation is specified by a sufficiently smooth monotonic transformation of a uniform grid. This leads to constrained optimisation problems over the time discretisation profile, and their solutions reveal a 1/3 power law for the asymptotically optimal grid density functions. As a one-dimensional example, the results are illustrated for the Ornstein-Uhlenbeck process.", "url": "" }, { "id": "Tu-TuC08.5", "code": "TuC08.5", "title": "The Koopmanization of a Controlled Ito System", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC08", "sessionTitle": "Stochastic Systems and Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Lambe, Amruta", "affiliation": "Indian Institute of Science Education and Research, IISER Pune" }, { "name": "Sharma, Shambhu Nath", "affiliation": "SV National Institute of Technology, Surat, Gujarat" } ], "keywords": [ "Stochastic differential equations", "Stochastic control", "Diffusion process" ], "abstract": "The Koopmanization unfolds the bilinearization property after the action of the infinitesimal stochastic Koopman operator on the eigenfunction state vector concerning the controlled nonlinear Itô stochastic differential equation. The originality of this paper is to weave a rigorous and systematic framework for the Koopmanization of the controlled nonlinear Itô stochastic differential equation. The major ingredient of the paper is a unification of the Itô calculus and eigenfunction state space associated with the Koopman operator. Then, we apply the main Koopmanization result of the paper to a non-trivial controlled nonlinear Itô stochastic differential system to show the utility of the Theorem of the paper. This paper unfolds a Koopman-Carleman dichotomy as well. Most notably, this paper reveals a greater amenability of the Koopmanization, since the Koopmanization of a class of controlled nonlinear Itô stochastic differential equations has the finite dimensionality’, on the other hand, their Carleman linearization has the curse of dimensionality.", "url": "" }, { "id": "Tu-TuC08.6", "code": "TuC08.6", "title": "Safety Verification of Continuous-Time Stochastic Systems Via Closure Certificates", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC08", "sessionTitle": "Stochastic Systems and Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Ajeleye, Daniel", "affiliation": "University of Colorado Boulder" }, { "name": "Murali, Vishnu", "affiliation": "University of Colorado Boulder" }, { "name": "Zamani, Majid", "affiliation": "University of Colorado Boulder" } ], "keywords": [ "Stochastic differential equations", "Stochastic hybrid systems", "Reachability analysis, verification and abstraction of hybrid systems" ], "abstract": "In this paper, we introduce the concept of stochastic closure certificates (SC2) for verifying continuous-time stochastic systems with respect to safety properties over an unbounded time horizon. Classical functional approaches use barrier certificates to guarantee safety for such systems, covering both finite- and infinite-horizon verification tasks. These techniques generally construct probabilistic over-approximations of the system’s reachable state set to certify safety. In contrast, we propose SC2, which are based on over-approximating the system’s reachable transitions. By focusing on transitions rather than states, SC2 yields substantially tighter bounds on satisfaction probabilities for unbounded-time safety properties. In addition, SC2 is strictly more expressive, enabling the use of simpler functional templates as safety certificates. This results in an efficient and fully automated framework for verifying continuous-time stochastic systems against infinite-horizon safety specifications. We validate the effectiveness of our method by employing sum-of-squares techniques to synthesize SC2 on a range of benchmark case studies.", "url": "" }, { "id": "Tu-TuC09.1", "code": "TuC09.1", "title": "Digital Filtering by Arc-Tangent Relation of Polynomials and Pulse Trains", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC09", "sessionTitle": "Filtering and Smoothing", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Chakraborty, Arindam", "affiliation": "Sastra University, SEEE, EIE" }, { "name": "Suresh, Sri Kamal Krishank", "affiliation": "SASTRA Deemed-To-Be-University" }, { "name": "Dutta, Rituparna", "affiliation": "CTS" } ], "keywords": [ "Filtering and smoothing", "Data-driven control theory", "Estimation and filtering" ], "abstract": "We prove that smooth algebraic functions like polynomials and a sequence of Heaviside discontinuities are connected fundamentally by inverse tangent kernels. The inverse tangent kernel {H}_k:= tan -1 (k(.))+ tan -1 ({1 - k(.)}/{1 + k(.)}] ( R to (-pi/2, pi/2), with appropriate shift k ) is an injective operator that, for a polynomial with N_p distinct, real zeros as an argument from the ring mathscr{R} of real polynomials, returns a binary state function comprising of N_p rising and falling Heaviside sequences. Further, the locations of successive jumps are the same as the succession of real zeros, in increasing order of magnitude. We show that the rising/falling nature of the initial and final transitions, occurring at the smallest and largest real zeros respectively, can be determined by the behavior of the polynomial argument at pm infty . The injective kernel map {H} with a constant polynomial argument is called an arc tangent Heaviside function (ATHF) after its capability for discontinuous system representation. For non-constant polynomials, an arc tangent Heaviside generator (ATHG) is introduced to connect smooth function spaces and the space of distributions.", "url": "" }, { "id": "Tu-TuC09.2", "code": "TuC09.2", "title": "Multi-Target Matching for Bearing-Only Sensors: A Hypothesis-Testing-Based Geometric Approach", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC09", "sessionTitle": "Filtering and Smoothing", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Liu, Yifan", "affiliation": "University of Chinese Academy of Sciences" }, { "name": "Hu, Shenghua", "affiliation": "Chinese Academy of Science" }, { "name": "Fang, Haitao", "affiliation": "AcademyofMathematicsandSystemsScience, ChineseAcademyofScien Ces" }, { "name": "Xue, Wenchao", "affiliation": "Chinese Academy of Sciences, Beijing 100190," }, { "name": "Zhang, Kun", "affiliation": "School of Astronautics, Beihang University" } ], "keywords": [ "Filtering and smoothing", "Distributed control and estimation", "Estimation and filtering" ], "abstract": "Matching measurements obtained from bearing-only sensors presents significant challenges due to the lack of direct range measurements, especially in single-frame scenarios where trajectory information is unavailable. To address this, this paper presents a hypothesis-testing-based geometric approach for multi-target measurement matching between two such sensors in a single frame. A hypothesis test is designed to identify measurement pairs that are coplanar with the sensors, first reducing the matching problem to a binary matrix formulation. Spurious intersections among coplanar candidates are then resolved using the consistent geometric ordering of targets across the two sensors' fields of view. The method recovers the correct match under mild assumptions, and simulations under both Gaussian and uniform noise confirm substantially higher accuracy than existing methods.", "url": "" }, { "id": "Tu-TuC09.3", "code": "TuC09.3", "title": "Indicator--Gaussian Sum Filtering for a Special Class of Nonlinear Systems Arising in Li-Ion Battery SoC Estimation", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC09", "sessionTitle": "Filtering and Smoothing", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Aguero, Juan C", "affiliation": "Universidad Santa Maria" }, { "name": "Castro, Trinidad Asuncion", "affiliation": "Universidad Tecnica Federico Santa Maria" }, { "name": "de Bruijn, Mart Henricus Barend Gertrudis", "affiliation": "University of Technology Eindhoven & Universidad Técnica Federico Santa María" }, { "name": "Oomen, Tom", "affiliation": "Eindhoven University of Technology" }, { "name": "Silva, Cesar", "affiliation": "Universidad Tecnica Federico Santa Maria" } ], "keywords": [ "Filtering and smoothing", "Estimation and filtering", "Kalman filtering" ], "abstract": "This paper proposes an Indicator--Gaussian Sum Filtering (Indicator--GSF) scheme for a special class of nonlinear systems arising in Li-ion battery state-of-charge (SoC) estimation. The state dynamics are linear, while the output is a nonlinear static function of the SoC corrupted by measurement noise. The method approximates the nonlinear function in the output equation by a piecewise-linear map on a bounded domain and replaces region indicators by tailored Gaussian mixtures, yielding a two-step recursion in which all integrals admit closed-form Gaussian expressions. The resulting algorithm is a Gaussian-sum filter (GSF) with controlled mixture size. Its performance is illustrated on SoC estimation and compared with an extended Kalman filter (EKF), an unscented Kalman filter (UKF) and a particle filter (PF).", "url": "" }, { "id": "Tu-TuC09.4", "code": "TuC09.4", "title": "Online Sensor Selection for Kalman Filtering under Limited Information Feedback", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC09", "sessionTitle": "Filtering and Smoothing", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Liu, Chang", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Ye, Lintao", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Du, Bin", "affiliation": "Nanjing University of Aeronautics and Astronautics" } ], "keywords": [ "Filtering and smoothing", "Estimation and filtering", "Learning methods for control" ], "abstract": "This paper investigates an online sensor selection problem for Kalman filtering when the system dynamics are unknown and only limited feedback is available. The objective is to sequentially select sensor subsets to improve state estimation performance measured by the log-determinant of the error covariance. We propose an online greedy–bandit algorithm that integrates greedy sensor selection with bandit learning via multiple parallel experts. Under a partially transparent feedback model, we establish a dynamic regret bound that grows sub-linearly with time under mild variation assumptions. The results demonstrate that near-optimal estimation performance can be achieved online despite the lack of system models.", "url": "" }, { "id": "Tu-TuC09.5", "code": "TuC09.5", "title": "Optimal State Preparation for Impulse Estimation in Gaussian Quantum Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC09", "sessionTitle": "Filtering and Smoothing", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Schmerling, Kaspar", "affiliation": "TU Wien" }, { "name": "Kugi, Andreas", "affiliation": "TU Wien" }, { "name": "Deutschmann-Olek, Andreas", "affiliation": "TU Wien" } ], "keywords": [ "Filtering and smoothing", "Kalman filtering", "Stochastic differential equations" ], "abstract": "We present an optimal control-based strategy to enhance the estimation of impulse- like disturbances in continuously monitored linear classical and quantum systems by exploiting non-equilibrium states. Using optimal estimation techniques for linear Gaussian systems to collect information from the temporal vicinity of the disturbance, we cast the minimization of disturbance estimation uncertainty as a nonlinear optimal control problem over time-dependent system parameters. The resulting method dynamically shapes the estimation covariances through parametric modulation, maximizing information gain at a known impulse time. This differs fundamentally from conventional squeezing protocols using periodic modulation that effectively degrade inference of impulse-like disturbances. Applied to nanomechanical resonators and levitated nanoparticles, optimal parametric driving reduces estimation variance by up to a factor of two relative to steady-state operation.", "url": "" }, { "id": "Tu-TuC09.6", "code": "TuC09.6", "title": "Local Stability and Gaussian Smoothing of Quantized Neural Networks", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC09", "sessionTitle": "Filtering and Smoothing", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Salishev, Sergey", "affiliation": "St. Petersburg State University" }, { "name": "Makarov, Anton", "affiliation": "St. Petersburg State University" }, { "name": "Granichin, Oleg", "affiliation": "Sirius University of Science and Technology" } ], "keywords": [ "Filtering and smoothing", "Learning methods for control", "Machine and deep learning for system identification" ], "abstract": "We investigate Gaussian averaging as a smooth surrogate for quantized neural networks, which are inherently discontinuous and challenging to train or analyze. Under a bounded local oscillation assumption on the network mapping, we derive explicit, dimension-dependent bounds on the approximation error between the quantized model and its Gaussian-smoothed counterpart, formally linking smoothing techniques to the stability analysis of discontinuous systems. We obtain closed-form expressions for the Gaussian averages of ReLU (rectified linear unit) and sign activation functions, and demonstrate the mechanism on a high-dimensional binary perceptron. We show that pre-activation aggregation under an explicit quantization-noise surrogate naturally induces a Gaussian envelope. This envelope simultaneously justifies inference-time smoothing and enables training via differentiable surrogate gradients, bridging theoretical analysis and practical optimization of quantized models.", "url": "" }, { "id": "Tu-TuC10.1", "code": "TuC10.1", "title": "Converse Lyapunov Theorem for Switched Nonlinear Systems with Constrained Switching Signals", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC10", "sessionTitle": "Hybrid and Switched Systems Stability", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Liu, Shenyu", "affiliation": "Beijing Institute of Technology" }, { "name": "Della Rossa, Matteo", "affiliation": "Politecnico of Turin" }, { "name": "Tanwani, Aneel", "affiliation": "LAAS -- CNRS, Université De Toulouse" } ], "keywords": [ "Stability and stabilization of hybrid systems", "Hybrid and switched systems modeling" ], "abstract": "This paper investigates converse Lyapunov theorems for switched nonlinear systems comprising both stable and unstable subsystems, uniformly over a constrained set of switching signals. A novel hybrid timer is introduced to quantify switching behavior, and the considered class of signals--characterized by a finite hybrid timer--encompasses known signal classes defined by mixed average dwell-time and average activation-time conditions. The main result is a necessary and sufficient condition, expressed via the existence of multiple Lyapunov functions with prescribed decay or growth rates at flows and jumps, ensuring global uniform boundedness with hybrid timer characterization uniformly over this set of switching signals. Significantly, the sufficiency part is consistent with the stability criteria in the literature and the necessity part offers a deeper understanding of stability in switched systems with both stabilizing and destabilizing dynamics.", "url": "" }, { "id": "Tu-TuC10.2", "code": "TuC10.2", "title": "Preorders of Multiple Lyapunov Functions Can Always Be Lifted to Simulation Relations", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC10", "sessionTitle": "Hybrid and Switched Systems Stability", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Jongeneel, Wouter", "affiliation": "KTH Royal Institute of Technology, Digital Futures" }, { "name": "Jungers, Raphaël M.", "affiliation": "Université Catholique De Louvain" } ], "keywords": [ "Stability and stabilization of hybrid systems" ], "abstract": "To compare multiple Lyapunov functions in the context of switched systems, several preorders have been introduced. Unfortunately, these preorders are typically intratacble. For a handful of instances, explicit lifts---of graphs that capture the multiple Lyapunov functions---have been constructed such that the preorder relation corresponds to a graphical simulation relation, after the lift. In this note we show that such a lift always exists, for any preorder.", "url": "" }, { "id": "Tu-TuC10.3", "code": "TuC10.3", "title": "On Graph-Theoretic Conditions for Stabilizing Switched Systems under Restricted Arbitrary Switching Signals", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC10", "sessionTitle": "Hybrid and Switched Systems Stability", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Kundu, Atreyee", "affiliation": "Indian Institute of Technology, Kharagpur" } ], "keywords": [ "Stability and stabilization of hybrid systems" ], "abstract": "We study input/output-to-state stability (IOSS) of continuous-time switched nonlinear systems under arbitrary switching signals that obey pre-specified restrictions on admissible switches between the subsystems and admissible dwell times on the subsystems. It is shown that if the subsystems dynamics and the restrictions on the switching signals are such that the underlying weighted directed graph of the switched system admits a class of finite walks that satisfies a certain property that we call as contractivity, then the switched system under consideration is IOSS under all switching signals obeying the given restrictions. A numerical example is presented to demonstrate our results.", "url": "" }, { "id": "Tu-TuC10.4", "code": "TuC10.4", "title": "On Stabilizability of Discrete-Time Switched Nonlinear Systems under Restricted Min-Switching Signals", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC10", "sessionTitle": "Hybrid and Switched Systems Stability", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Dutta, Sauhardya", "affiliation": "Indian Institute of Technology Kharagpur" }, { "name": "Kundu, Atreyee", "affiliation": "Indian Institute of Technology, Kharagpur" } ], "keywords": [ "Stability and stabilization of hybrid systems" ], "abstract": "This paper is concerned with stabilizability of discrete-time switched nonlinear systems whose subsystems dynamics are all unstable and the switching signals obey pre-specified restrictions on admissible switches between the subsystems and admissible dwell times on the subsystems. We propose sufficient conditions on the subsystems dynamics and the restrictions on the switching signals under which the switched system under consideration is stabilizable. Our choice of stabilizing switching signals is the so-called restricted min-switching signals and our stabilizability condition is a nonlinear counterpart of the so-called restricted Lyapunov-Metzler inequalities. A numerical example is presented to demonstrate our results.", "url": "" }, { "id": "Tu-TuC10.5", "code": "TuC10.5", "title": "On the Stability of Zeno Switched Nonlinear Systems with Reset", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC10", "sessionTitle": "Hybrid and Switched Systems Stability", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Pola, Giordano", "affiliation": "University of L'Aquila" }, { "name": "Pepe, Pierdomenico", "affiliation": "University of L'Aquila" }, { "name": "De Santis, Elena", "affiliation": "University of L'Aquila" } ], "keywords": [ "Hybrid and switched systems modeling", "Stability and stabilization of hybrid systems" ], "abstract": "In this paper we consider a fairly general class of switched nonlinear systems which include reset and exhibit possibly Zeno phenomena both genuine and chattering in their evolution. We consider the notions of Global Asymptotic Stability (GAS) and the stronger notion of Uniform Global Asymptotic Stability (UGAS). We derive sufficient conditions for the UGAS property to hold for switched systems with infinite but not Zeno trajectories, and for switched systems with genuine Zeno trajectories. We then derive sufficient conditions for the GAS property to hold for switched systems with chattering Zeno trajectories. By putting altogether these results we obtain sufficient conditions for switched systems with infinite trajectories to be GAS. A specialization of the results proposed to the linear case is discussed.", "url": "" }, { "id": "Tu-TuC13.1", "code": "TuC13.1", "title": "A Learning-Free Diffusion Framework for Stochastic Model Predictive Control", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC13", "sessionTitle": "Stochastic Optimal Control Problems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Papaioannou, Savvas", "affiliation": "KIOS CoE, University of Cyprus" }, { "name": "Kolios, Panayiotis", "affiliation": "University of Cyprus" }, { "name": "Panayiotou, Christos", "affiliation": "Univ of Cyprus" }, { "name": "Polycarpou, Marios M.", "affiliation": "University of Cyprus" } ], "keywords": [ "Model predictive control", "Stochastic optimal control problems", "Optimization-based estimation and control" ], "abstract": "Stochastic model predictive control (SMPC) problems are generally nonlinear and non-convex, making them difficult to solve efficiently with standard methods. In this work, we reformulate SMPC as sampling from a Boltzmann density whose modes correspond to global minimizers of a penalized surrogate of the underlying SMPC objective, and derive a denoising diffusion process directly in the control space that samples from this target density by transporting noisy control sequences toward high-probability regions. Unlike existing diffusion-based approaches that rely on learned score networks, the proposed method is learning-free, i.e., the reverse diffusion is guided by the control-marginal log-density gradient estimated online via Metropolis-Hastings Markov Chain Monte Carlo (MH-MCMC). The proposed approach enables global exploration at high noise diffusion levels and mode-seeking exploitation at low noise levels. Results show that the proposed diffusion-SMPC framework consistently achieves near-optimal solutions on nonlinear control problems when compared with existing solvers.", "url": "" }, { "id": "Tu-TuC13.2", "code": "TuC13.2", "title": "Implicit Dual Control for Partially Unknown Nonlinear Systems Via eKF and Koopman Linearization", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC13", "sessionTitle": "Stochastic Optimal Control Problems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Nakahara, Ritsuki", "affiliation": "The University of Electro-Communications" }, { "name": "Sadamoto, Tomonori", "affiliation": "The University of Electro-Communications" } ], "keywords": [ "Stochastic optimal control problems", "Adaptive control design" ], "abstract": "This paper proposes an implicit dual control for partially unknown nonlinear systems by extending the eKF-Koopman-LQR framework. We introduce an augmented state comprising both system states and unknown parameters to reformulate the challenging adaptive Stochastic Optimal Control (SOC) problem into a tractable Linear Quadratic Regulator (LQR) problem via Koopman linearization. We show that optimizing the standard state cost can induce probing behavior. Numerical simulations demonstrate improved regulation and estimation compared to a certainty equivalence method.", "url": "" }, { "id": "Tu-TuC13.3", "code": "TuC13.3", "title": "A Characteristic Function Framework for Chance Constraint Programming in Stochastic Model Predictive Control", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC13", "sessionTitle": "Stochastic Optimal Control Problems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Ying, Yuwei", "affiliation": "Linköping University" }, { "name": "Löfberg, Johan", "affiliation": "Linköping University" }, { "name": "Hansson, Anders", "affiliation": "Linkoping Univ" } ], "keywords": [ "Stochastic optimal control problems", "Model predictive control" ], "abstract": "The computation of chance constraints in stochastic model predictive control is often numerically challenging due to the non-Gaussian nature of the disturbances. To overcome this problem, we propose an optimization computational framework applicable to non-Gaussian disturbances. This framework employs a numerical inversion method, utilizing the characteristic function of the disturbance distribution to compute the probability in the chance constraint as well as its gradient. To improve efficiency, it vectorizes integral points and reuses intermediate computations in Gauss-Kronrod quadrature. The framework is implemented within the YALMIP toolbox to perform chance constraint calculations for arbitrary non-Gaussian disturbances, applicable to both single-component distributions and mixture models. It allows the user to simply specify a distribution type and its parameters for the disturbance and directly compute the probability and its gradient to solve the optimization problem. The method is validated through a numerical example of a stochastic model predictive control application.", "url": "" }, { "id": "Tu-TuC13.4", "code": "TuC13.4", "title": "Parametrization of the Suboptimal and γ-Optimal Anisotropic Controllers", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC13", "sessionTitle": "Stochastic Optimal Control Problems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Kustov, Arkadiy", "affiliation": "Institute of Control Sciences, Russian Academy of Sciences" } ], "keywords": [ "Stochastic optimal control problems", "Robust controller synthesis", "Linear systems" ], "abstract": "For a linear discrete time invariant system driven by a random noise sequence with statistical uncertainty described in terms of mean anisotropy, we consider a controller design problem. The controller goal is to stabilize the closed loop system and guarantee that the performance gain described as anisotropic norm of the closed loop system is less than a given number. We provide sufficient conditions in closed form for the existence of the anisotropic controller. Additionally, we show that the anisotropic controllers, if exist, can be described in parametric form.", "url": "" }, { "id": "Tu-TuC13.5", "code": "TuC13.5", "title": "Unifying Entropy Regularization in Optimal Control: From and Back to Classical Objectives Via Iterated Soft Policies and Path Integral Solutions", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC13", "sessionTitle": "Stochastic Optimal Control Problems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Bhole, Ajinkya", "affiliation": "University of Ghent" }, { "name": "Mahmoudi Filabadi, Mohammad", "affiliation": "Ghent University" }, { "name": "Crevecoeur, Guillaume", "affiliation": "Ghent University" }, { "name": "Lefebvre, Tom", "affiliation": "Ghent University" } ], "keywords": [ "Stochastic optimal control problems", "Optimal control theory", "Optimization-based estimation and control" ], "abstract": "This paper develops a unified perspective on several optimal control formulations through the lens of Kullback-Leibler (KL) regularization. We propose a central problem that separates the KL penalties on policies and transitions with independent weights, thus generalizing the standard trajectory-level KL-regularization used in probabilistic optimal control. This umbrella formulation recovers various control problems: the classical Stochastic Optimal Control (SOC), Risk-Sensitive Stochastic Optimal Control (RSOC), and their policy-based KL-regularized counterparts, termed soft-policy SOC and RSOC, which yield tractable surrogates. Beyond being regularized variants, these soft-policy formulations majorize the original SOC and RSOC, thus, iterating their solutions recovers the original objectives. We further identify a synchronized case of soft-policy RSOC where the policy and transition KL weights coincide, yielding a linear Bellman operator, path-integral solution, and compositionality---extending these computationally favourable properties to a broad class of control problems.", "url": "" }, { "id": "Tu-TuC13.6", "code": "TuC13.6", "title": "Stochastic Robust Linear W-Infinity Control Via Dynamic Output Feedback", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC13", "sessionTitle": "Stochastic Optimal Control Problems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Cardoso, Daniel Neri", "affiliation": "Federal University of Minas Gerais" }, { "name": "Raffo, Guilherme Vianna", "affiliation": "Federal University of Minas Gerais" } ], "keywords": [ "Stochastic optimal control problems", "Robust controller synthesis" ], "abstract": "This paper introduces a robust W-infinity optimal control framework for linear Itô diffusions using a weighted Sobolev-space performance measure. Because the sample paths of Itô diffusions are nondifferentiable, the formulation leverages the weak derivative of the expected state. An LMI-based semidefinite program is developed for dynamic output-feedback synthesis, and a rigorous stability analysis guarantees mean-square ultimate boundedness with minimized ultimate bound. A numerical example demonstrates that the proposed approach provides effective disturbance attenuation with fast transient performance.", "url": "" }, { "id": "Tu-TuC14.1", "code": "TuC14.1", "title": "On Polynomial Explicit Partial Estimator Design for Nonlinear Systems with Parametric Uncertainties", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC14", "sessionTitle": "Learning Methods for Nonlinear Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Alamir, Mazen", "affiliation": "Gipsa-Lab (CNRS-University of Grenoble)" } ], "keywords": [ "Nonlinear observers and filters", "Design methods for data-based control", "Observer design" ], "abstract": "This paper investigates the idea of designing data-driven partial estimators for nonlinear systems showing parametric uncertainties using sparse multivariate polynomial relationships. A general framework is first presented and then validated on two illustrative examples with comparison to different possible Machine/Deep-Learning based alternatives. The results suggests the superiority of the proposed sparse identification scheme, at least when the learning data is small.", "url": "" }, { "id": "Tu-TuC14.2", "code": "TuC14.2", "title": "Neural Network-Based Feedback Linearization for Non-Smooth Tracking", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC14", "sessionTitle": "Learning Methods for Nonlinear Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Markis, Iustin", "affiliation": "Technical University of Cluj-Napoca" }, { "name": "Mihaly, Vlad Mihai", "affiliation": "Technical University of Cluj-Napoca" }, { "name": "Susca, Mircea", "affiliation": "Technical University of Cluj-Napoca" }, { "name": "Dobra, Petru", "affiliation": "Technical Univ of Cluj" } ], "keywords": [ "Lyapunov methods", "Nonlinearity learning from data", "Stability of nonlinear systems" ], "abstract": "Although classical feedback linearization is a key technique in nonlinear control, it critically relies on textit{exact} model knowledge, which restricts its applicability in many practical settings. This paper presents a revised feedback linearization approach that softens the firm constraint of the classic version. To successfully handle unknown dynamics, this paper proposes the use of neural networks, as universal function approximators, to augment the classic feedback linearization method, that, in addition to other available neural network-based solutions, enables non-smooth reference tracking through the nature of an indirect estimation metric. Under key assumptions, the proposed method comes with guarantees, ensuring asymptotic stability of the closed-loop control system for both full and partial relative degree cases. Finally, the theoretical results are supported by a case study along with numerical simulations.", "url": "" }, { "id": "Tu-TuC14.3", "code": "TuC14.3", "title": "On the Frequency Response and Loop Shaping for Nonlinear Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC14", "sessionTitle": "Learning Methods for Nonlinear Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Moreschini, Alessio", "affiliation": "Imperial College London" }, { "name": "Scandella, Matteo", "affiliation": "University of Bergamo" }, { "name": "Astolfi, Alessandro", "affiliation": "King Abdullah University of Science and Technology (KAUST)" } ], "keywords": [ "Application of nonlinear analysis and design", "Stability of nonlinear systems", "Nonlinearity learning from data" ], "abstract": "We use the invariance principle to establish a frequency response analysis for nonlinear systems driven by a parameterized family of periodic input signals. After characterizing the response of the nonlinear system, we introduce a nonlinear gain function that describes the ratio of the response amplitude to the input amplitude in an Lp sense. This enables us to define a nonlinear analog of the Bode magnitude diagram, represented as a surface over the spaces of input amplitude and frequency. We further show that when the system admits a linearization around an equilibrium point, and the response to a low-amplitude input is sinusoidal, the Lp gain function and the Bode representation reduce to the traditional Bode magnitude diagram. This nonlinear extension of the Bode magnitude characterization, together with the nonlinear gain, prepares the ground for the formulation and solution of nonlinear loop-shaping problems.", "url": "" }, { "id": "Tu-TuC14.4", "code": "TuC14.4", "title": "Fault Detection for Nonlinear Multi-Stage Processes Based on Double Local Neighborhood Standardization and KPLS", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC14", "sessionTitle": "Learning Methods for Nonlinear Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Feng, Liwei", "affiliation": "Shenyang University of Chemical Technology" }, { "name": "Zhou, Zhengyu", "affiliation": "Shenyang University of Chemical Technology" }, { "name": "Zhang, Cheng", "affiliation": "Shenyang University of Chemical Technology" }, { "name": "Guo, Xiaoping", "affiliation": "Shenyang University of Chemical Technology" }, { "name": "Guo, Jinyu", "affiliation": "Shenyang University of Chemical Technology" }, { "name": "Li, Yuan", "affiliation": "Shenyang Institute of Chemical Technology" } ], "keywords": [ "Nonlinearity learning from data", "Nonlinear model reduction", "Nonlinear control of switched & hybrid systems" ], "abstract": "，以应对有效检测 的缺陷 非线性、多阶段过程，结合了DLNS-KPLS 通过积分 Double 提出了故障检测方法 带内核的本地邻域标准化（DLNS） 偏最小平方法（KPLS）。DLNS构造空间 通过两层结构的邻域样本集 并用均值和标准差标准化数据 这些邻域集的其中一个问题，从而解决了 可能来自不同来源的邻近样本 阶段。DLNS 可以将多阶段数据转换为 单阶段数据，近似多元正态 分布，从而消除了多级 过程数据的特征。当与 KPLS方法，可用于非线性、多级 过程故障检测，显著改善故障质量 KPLS方法的检测率。表现 DLNS-KPLS通过田纳西东曼公司认证 过程并与多种经典故障检测进行比较 方法。实验结果表明，DLNS-KPLS 展现出卓越的探测性能。", "url": "" }, { "id": "Tu-TuC14.5", "code": "TuC14.5", "title": "Intelligent Energy Management in Hybrid Power Supply Systems Using Physics-Informed Deep Learning (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC14", "sessionTitle": "Learning Methods for Nonlinear Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Li, Zhengqi", "affiliation": "Zhengzhou University" }, { "name": "Li, Fangyuan", "affiliation": "Zhengzhou University" }, { "name": "Wan, Yanni", "affiliation": "Ningxia University" }, { "name": "Liu, Yanhong", "affiliation": "Zhengzhou University" } ], "keywords": [ "Numerical methods for optimal control", "Applications of optimal control" ], "abstract": "Hybrid power supply systems (HPSS) are especially promising where conventional grids are inaccessible or in disaster scenarios. Energy management is a core enabler for the deployment and efficient operation of HPSS. Conventional rules or optimization based methods face difficulties in high-dimensional decision spaces, while purely data-driven approaches suffer from limited data and may violate basic physical constraints. To address these issues, this paper proposes an intelligent energy management framework for HPSS based on physics-informed deep learning (PIDL). The energy management problem is formulated as a constrained optimal control problem, and the associated Hamilton–Jacobi–Bellman (HJB) partial differential equation is derived. A PIDL architecture is constructed in which a deep neural network is used to approximate the value function, while the system dynamics and optimality conditions are embedded into the loss function through physics-based residuals and boundary constraints. The optimal power allocation strategy is then obtained using automatic differentiation. Numerical simulations demonstrate that the proposed method achieves fuel-efficient operation while maintaining battery and supercapacitor state constraints. The results indicate that the proposed PIDL framework provides a physically consistent and data-adaptive solution for energy management in hybrid power supply systems.", "url": "" }, { "id": "Tu-TuC14.6", "code": "TuC14.6", "title": "Designing Neural Network-Based Observersfor Discrete-Time Nonlinear Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC14", "sessionTitle": "Learning Methods for Nonlinear Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Ambit Brao, Isaac", "affiliation": "INRIA" }, { "name": "Efimov, Denis", "affiliation": "Inria" }, { "name": "Ushirobira, Rosane", "affiliation": "Inria" }, { "name": "Chakrabarty, Sohom", "affiliation": "Indian Institute of Technology Roorkee" } ], "keywords": [ "Nonlinear observers and filters", "Lyapunov methods", "Nonlinearity learning from data" ], "abstract": "This paper proposes an observer design approach for a class of nonlinear discrete-time systems that applies artificial neural networks. These neural networks are used to calculate the output injection gain and the corresponding Lyapunov function, guaranteeing the stability of the estimation error. A canonical form of Lyapunov function for Lipschitz systems is used to be represented by neural networks. We introduce a locally defined norm-like Lyapunov function when using neural networks to avoid singularity at the origin. Examples of mechanical systems with power nonlinearity illustrate the method’s efficiency.", "url": "" }, { "id": "Tu-TuC15.1", "code": "TuC15.1", "title": "A Small Gain Theorem for Well-Defined but Not-Necessarily Well-Posed LTI Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC15", "sessionTitle": "Stability of Linear Systems and Beyond: Input-Output, Spectral, and Frequency-Domain Methods", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Kowalewski, Julia", "affiliation": "Friedrich-Alexander-Universität Erlangen-Nürnberg" }, { "name": "Moor, Thomas", "affiliation": "Friedrich-Alexander Universität Erlangen-Nürnberg" } ], "keywords": [ "Linear systems" ], "abstract": "A stable open loop with gain below unity remains stable under negative feedback. This is the essence of the small gain theorem. The latter is known in many forms, ranging from scalar linear time-invariant systems with rational transfer functions to multivariable non-linear time-varying systems. The small gain theorem has been studied extensively. However, existing results do not address linear time-invariant systems with closed-loop sensitivities that are well-defined but not necessarily proper. While such systems are not physically realisable, they arise naturally as modelling artefacts in electrical network analysis. This paper develops a variant of the small gain theorem tailored to this setting.", "url": "" }, { "id": "Tu-TuC15.2", "code": "TuC15.2", "title": "Stability Results for MIMO LTI Systems Via Scaled Relative Graphs", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC15", "sessionTitle": "Stability of Linear Systems and Beyond: Input-Output, Spectral, and Frequency-Domain Methods", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Baron-Prada, Eder", "affiliation": "ETH" }, { "name": "Padoan, Alberto", "affiliation": "University of British Columbia" }, { "name": "Anta, Adolfo", "affiliation": "AIT Austrian Institute of Technology GmbH" }, { "name": "Dorfler, Florian", "affiliation": "Swiss Federal Institute of Technology (ETH) Zurich" } ], "keywords": [ "Linear systems" ], "abstract": "This paper proposes a frequency-wise approach for stability analysis of multi-input, multi-output (MIMO) Linear Time-Invariant (LTI) feedback systems through Scaled Relative Graphs (SRGs). Unlike traditional methods, such as the Generalized Nyquist Criterion (GNC), which relies on a coupled analysis that requires the multiplication of models, our approach enables the evaluation of system stability in a decoupled fashion, system by system, each of which is represented by its SRG (or an over-approximation thereof), and it provides an intuitive, visual representation of system behavior. Our results provide conditions for certifying the stability of stable and square MIMO LTI systems connected in closed loop.", "url": "" }, { "id": "Tu-TuC15.3", "code": "TuC15.3", "title": "Multi-Variable Phase and Sensitivity Results", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC15", "sessionTitle": "Stability of Linear Systems and Beyond: Input-Output, Spectral, and Frequency-Domain Methods", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Middleton, Richard", "affiliation": "The University of Newcastle" }, { "name": "Stuedli, Sonja", "affiliation": "The Univeristy of Newcastle" }, { "name": "Seron, Maria M.", "affiliation": "The University of Newcastle" }, { "name": "Donaire, Alejandro", "affiliation": "The University of Newcastle" }, { "name": "Yan, Yamin", "affiliation": "Nanyang Technological University" } ], "keywords": [ "Linear systems" ], "abstract": "A newly developed multi-variable phase theory, pioneered by Qiu Li, Chen Wei and Wang Dan, among others, is gaining traction. It mirrors in many ways the phase of a system readily used in single input single output (SISO) systems. Using the proposed definition of phase the commonly used analysis of multiple input multiple output (MIMO) systems, which relies heavily on the magnitude of the system, i.e. the singular values of the transfer matrix, can be extended to include analysis of phase. In this sense, counterparts for the small gain theorem, sectored real lemma and H∞ synthesis have been developed. In this paper, we extend these results to establish a phase counterpart to the well known relationship between positive real systems and a bound on the closed loop sensitivity. Specifically, we establish that a system that is semi-sectorial, i.e. its phases lie within the range [α, β] with 0 < β − α ≤ π for all frequencies, implies a bound on the closed loop sensitivity.", "url": "" }, { "id": "Tu-TuC15.4", "code": "TuC15.4", "title": "Location Region of Eigenvalues of Matrices. Application to Stability Analysis and Control Law Design", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC15", "sessionTitle": "Stability of Linear Systems and Beyond: Input-Output, Spectral, and Frequency-Domain Methods", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Furtat, Igor", "affiliation": "Institute of Problems of Mechanical Engineering Russian Academy of Sciences" }, { "name": "Vrazhevsky, Sergey", "affiliation": "Institute for Problems in Mechanical Engineering of the Russian Academy of Sciences" }, { "name": "Gushchin, Pavel", "affiliation": "Gubkin Russian State University of Oil and Gas (National Research University)" } ], "keywords": [ "Linear systems", "Control of complex systems", "Linear fractional-order systems" ], "abstract": "The generalization of the Gershgorin's circle (disc) theorem, Ostrovsky's circle theorem, Brauer's oval theorem and some of its corollaries for finding the location region (and its boundary) of eigenvalues of matrices is considered. Also, the proposed results are developed to obtain the location region of the eigenvalues of matrices with interval-indefinite constant or non-stationary elements. The concept of e-circles is introduced to provide more accurate estimates of these regions than using Gershgorin's circle theorem. In contrast to existing results on control law design based on Gershgorin's circle theorem, which are restricted to systems with diagonally dominated matrices, the proposed methods are applied to systems with matrices without diagonal dominance.", "url": "" }, { "id": "Tu-TuC15.5", "code": "TuC15.5", "title": "Sectorial Indices of Discrete-Time Systems in Feedback Stability Analysis", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC15", "sessionTitle": "Stability of Linear Systems and Beyond: Input-Output, Spectral, and Frequency-Domain Methods", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Liu, Yuhuan", "affiliation": "Tianjin University" }, { "name": "Liu, Mei", "affiliation": "Tianjin University" }, { "name": "Lei, Ming", "affiliation": "Tianjin University" }, { "name": "Wang, Yan", "affiliation": "Nanyang Technological University" } ], "keywords": [ "Linear systems", "Robustness analysis", "Passivity-based control" ], "abstract": "This paper investigates semi/quasi-sectorial indices (S/Q S-indices) of multi-input multi-output linear time-invariant discrete-time systems. First, S/Q S-indices are introduced to describe the sectorial excess and deficit of discrete-time systems at specific angles. Then, two linear matrix inequalities are proposed for verifying S/Q S-indices. Subsequently, it is shown that the feedback interconnection of two open-loop systems achieves stability if the sum of their S/Q S-indices is positive at specific angles. The relationship between the small phase theorem and S/Q S-indices has also been studied. Finally, an example is presented to illustrate the main results of the paper.", "url": "" }, { "id": "Tu-TuC15.6", "code": "TuC15.6", "title": "Feedback Stabilization of Switched Systems: Memory Is Not Needed", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC15", "sessionTitle": "Stability of Linear Systems and Beyond: Input-Output, Spectral, and Frequency-Domain Methods", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Alves Lima, Thiago", "affiliation": "Aeronautics Institute of Technology (ITA)" }, { "name": "Della Rossa, Matteo", "affiliation": "Politecnico of Turin" }, { "name": "Girard, Antoine", "affiliation": "CNRS" } ], "keywords": [ "Switching linear systems", "Switching stability and control" ], "abstract": "A long-standing assumption in the literature on switched linear systems is that static, homogeneous of degree one feedbacks form the most general class of controllers necessary and sufficient for stabilization. In this paper, we provide a rigorous justification. More specifically, we prove by construction that if a switched linear system admits a stabilizing full-information controller, with access to the entire history of states and switching signals, then a memoryless and homogeneous of degree one stabilizing controller also exists. Specifically, in the mode-independent setting the controller can be chosen to depend only on the current state, and in the mode-dependent setting only on the current state and active mode. Our results thus show that dynamic controllers offer no additional stabilizing capability for switched linear systems, formally validating this folklore claim.", "url": "" }, { "id": "Tu-TuC16.1", "code": "TuC16.1", "title": "Adaptive Output Feedback Control with a Guaranteed Prescribed Performance: Experimental Study", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC16", "sessionTitle": "Adaptive Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Kolesnik, Nikita", "affiliation": "Institute for Problems in Mechanical Engineering of the Russian Academy of Sciences" }, { "name": "Gukov, Artemii", "affiliation": "Institute for Problems in Mechanical Engineering of the Russian Academy of Sciences" } ], "keywords": [ "Adaptive control design" ], "abstract": "The paper presents an experimental study of adaptive output feedback control algorithm for minimal phase systems with arbitrary relative degree. The core advantage of proposed approach is keeping the target signal within developer-prescribed set at all times. The algorithm's efficiency is demonstrated through extensive experiments on an electrodynamic vibration test bench. The experimental results confirm the algorithm's robustness and versatility, making it a suitable candidate for applications requiring high transient performance.", "url": "" }, { "id": "Tu-TuC16.2", "code": "TuC16.2", "title": "Swing-Up and Stabilization of an Inverted Cart-Pendulum Via Neuromorphic Control", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC16", "sessionTitle": "Adaptive Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Zhang, Xinxin", "affiliation": "Delft University of Technology" }, { "name": "Vinagre, B. M.", "affiliation": "Univ. De Extremadura" }, { "name": "Tejado, Inés", "affiliation": "Universidad De Extremadura" } ], "keywords": [ "Adaptive control design" ], "abstract": "This study develops a neuromorphic control strategy for the swing-up and stabilization of a cart--pendulum system. The core architecture utilizes a dual half-center oscillator (HCO) network to generate the rhythmic control effort required to drive the system. The control law is driven by a composite feedback mechanism: proportional--derivative (PD) tracking errors modulate the neural excitation of the HCO, while the system energy deviation adaptively scales the output force to regulate energy injection. Comparative simulations against an established energy--PD benchmark demonstrate that the proposed neuromorphic strategy achieves faster {transient settling}, enhanced robustness to external perturbations, lower total actuation effort, and reduced peak cart displacement.", "url": "" }, { "id": "Tu-TuC16.3", "code": "TuC16.3", "title": "Finite Time Tuning in Discrete MRAC of LTI Systems with Input Saturation", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC16", "sessionTitle": "Adaptive Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Gerasimov, Dmitry", "affiliation": "ITMO University" }, { "name": "Podoshkin, Dmitry", "affiliation": "ITMO University" }, { "name": "Nikiforov, Vladimir O.", "affiliation": "ITMO University" } ], "keywords": [ "Adaptive control design", "Linear systems", "Controller constraints and structure" ], "abstract": "The paper addresses the problem of finite time parameters tuning in direct model reference adaptive control of discrete linear time-invariant systems with unmeasurable state, unknown parameters, and input constraints. New discrete adaptation algorithms with finite-time convergence (FTC) are proposed and are based on dynamics prediction of standard or so-called exemplary adaptation algorithms -- the gradient and Kreisselmeier-like adaptation algorithm. In order to provide the alertness of the algorithms with respect to slowly or step changing unknown parameters the algorithms are timely reset with the replacement of suitably recalculated initially conditions. It is shown that the FTC property of the proposed algorithm is provided for regressors satisfying the persistent excitation or even weaker interval excitation condition. The properties of the closed-loop system are illustrated via simulation.", "url": "" }, { "id": "Tu-TuC16.4", "code": "TuC16.4", "title": "Bias Estimation and Compensation for Consensus of Multi-Agent Euler-Lagrange Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC16", "sessionTitle": "Adaptive Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Dutta, Maitreyee", "affiliation": "Institute of Science Tokyo" }, { "name": "Loria, Antonio", "affiliation": "CNRS" }, { "name": "Panteley, Elena", "affiliation": "CNRS" }, { "name": "Srikant, Sukumar", "affiliation": "Indian Institute of Technology Bombay" } ], "keywords": [ "Adaptive control design", "Lyapunov methods", "Cooperative nonlinear control" ], "abstract": "This manuscript addresses the leaderless consensus problem for multi-agent Euler-Lagrangian systems under the assumption that the absolute and relative configuration coordinates measurements are tampered with unknown heterogeneous constant biases. An adaptive estimator is designed for mitigating the effect of the measurement bias, so that global leaderless consensus is achieved asymptotically. Furthermore, our theoretical findings are illustrated via the simulation of a consensus scenario involving four marine surface vessels moving on the plane.", "url": "" }, { "id": "Tu-TuC16.5", "code": "TuC16.5", "title": "Nested Optimized Control with Stability and Performance Guarantees under Incomplete Information", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC16", "sessionTitle": "Adaptive Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Zhang, Yuxiang", "affiliation": "National University of Singapore" }, { "name": "Ge, Shuzhi Sam", "affiliation": "National University of Singapore" } ], "keywords": [ "Adaptive control design", "Output feedback nonlinear control", "Optimization-based estimation and control" ], "abstract": "Optimal control is typically formulated under the assumption that the system is stabilizable, where the resulting solution simultaneously ensures closed-loop stability and optimizes a given cost function, without explicitly decoupling stabilization from performance optimization. Nevertheless, instability is a neglected hidden danger, exacerbated by the uncertainties introduced as systems become increasingly complex, especially when complete state information is inaccessible. In this paper, we would like to explicitly handle the stabilization and then consider the optimization separately in an effort to strike a balance between guaranteed stability and performance improvement when complete state information is inaccessible. More specifically, a nested optimized control scheme with adaptive output feedback is developed that exploits the system's optimized control while ensuring stability in the presence of uncertainties through adaptive mechanisms. The key to this development lies in the introduced nested framework, which enables stabilization to be handled explicitly while facilitating subsequent performance improvement, as demonstrated through analysis and examples.", "url": "" }, { "id": "Tu-TuC16.6", "code": "TuC16.6", "title": "mathcal{L}_1-PPC: A High Performance Anti-Disturbance Method for Multirotor Trajectories Tracking", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC16", "sessionTitle": "Adaptive Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Li, Wenbo", "affiliation": "National University of Defense Technology" }, { "name": "Zhao, Shulong", "affiliation": "National University of Defense Technology" }, { "name": "Wang, Xiangke", "affiliation": "National University of Defense Technology" } ], "keywords": [ "Adaptive control design", "Structural and geometric control", "Lyapunov methods" ], "abstract": "This paper proposes an mathcal{L}_1 prescribed performance control (mathcal{L}_1-PPC) strategy for high-precision trajectory tracking control problem for multirotor UAV under unmodeled dynamics and stochastic disturbances, motivated by challenging scenarios such as hanging load transport or wind conditions. By integrating PPC and mathcal{L}_1 adaptive control, the proposed mathcal{L}_1-PPC achieves precise and efficient trajectory tracking while providing rapid disturbance rejection. Specifically, the mathcal{L}_1 adaptive controller provides rapid estimation and compensation for aggregated uncertainties, while a dual-layer PPC method is employed to strictly govern transient performance and manage large initial errors, ensuring the tracking error converges within the prescribed bounds. Moreover, comprehensive simulation results demonstrate the effectiveness of the proposed control strategy.", "url": "" }, { "id": "Tu-TuC18.1", "code": "TuC18.1", "title": "Bi-Objective Partial Disassembly Line Balancing: Modeling Comparison", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC18", "sessionTitle": "Decision-Making Problems in Manufacturing Plants", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Schulze, Judith", "affiliation": "Technische Universität Braunschweig" }, { "name": "Weckenborg, Christian", "affiliation": "University of Regensburg" }, { "name": "Schmidt, Kerstin", "affiliation": "TU Braunschweig" }, { "name": "Spengler, Thomas S.", "affiliation": "TU Braunschweig" } ], "keywords": [ "Sustainable and circular manufacturing systems", "Smart production and logistics in manufacturing", "Manufacturing plant simulation, control and optimization" ], "abstract": "This study examines a bi-objective partial disassembly line balancing problem with profit maximization and minimization of workload deviation among workers. Two optimization approaches, lexicographic optimization and weighted-sum scalarization, are evaluated using a mathematical programming formulation and a set-based model. A computational study compares modeling compactness, solution quality, and runtime. The experiments show that the set-based model enables a local-search-based solver to quickly reach optimal or near-optimal solutions in lexicographic optimization, while the mathematical programming formulation proves optimality faster. By jointly addressing economic and workload-related objectives, the study contributes to efficient and socially sustainable disassembly systems in circular manufacturing.", "url": "" }, { "id": "Tu-TuC18.2", "code": "TuC18.2", "title": "Input-Adaptive Constraint Programming Decomposition for Large Job Sets in Buffer-Aware Flexible Job-Shop Scheduling Problems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC18", "sessionTitle": "Decision-Making Problems in Manufacturing Plants", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Pedrosa, Javier", "affiliation": "Universitat Politecnica De Catalunya" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "Segovia, Pablo", "affiliation": "Universitat Politècnica De Catalunya" } ], "keywords": [ "Manufacturing plant simulation, control and optimization", "Production and operations management", "Industrial artificial intelligence" ], "abstract": "This paper presents an input-adaptive Constraint Programming (CP) approach for Flexible Job-Shop Scheduling Probelm with Limited-Capacity Buffers (FJSSP-LCB) that scales to large job sets while preserving explicit buffer feasibility. Input-adaptive denotes a general data interface (shop layout, machines, routes, processing times, and buffer capacities) that requires no model retuning across plants or datasets. The core contribution is a CP decomposition that partitions the global set of jobs into manageable subproblems that are solved sequentially. After each subproblem, the resulting assignments and timings are fixed, and the plant state is propagated to the next subproblem. Buffers remain explicit through timing-buffer linking and logical state constraints, and the objective is to minimize the makespan. Adaptive discretization aligns time steps with lot sizes to reduce the number of decision variables while maintaining feasibility. On representative instances, the method substantially reduces solve time versus a single monolithic CP with buffers, while retaining capacity-feasible storage behavior and due-date performance for large job sets. The result is a practical, general-input pipeline for buffer-aware CP scheduling at industrial scale. The effectiveness of the proposed approach is validated through a case study representative of real industrial production environments.", "url": "" }, { "id": "Tu-TuC18.3", "code": "TuC18.3", "title": "Disassembly System Design: A Stochastic Programming Framework (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC18", "sessionTitle": "Decision-Making Problems in Manufacturing Plants", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Bentaha, Mohand Lounes", "affiliation": "University of Lyon 2" } ], "keywords": [ "Manufacturing plant simulation, control and optimization", "Sustainable and circular manufacturing systems", "Simulation and optimization in production, operations and services" ], "abstract": "Disassembly systems are subject to multiple sources of uncertainty. These uncertainties arise from factors such as uncertain demand, heterogeneous quality states of end-of-life (EoL) products, variability in task processing times, and the potential presence of hazardous material (e.g., batteries). Efficient operation of disassembly systems requires addressing these uncertainties across strategic, tactical, and operational decision levels. This work demonstrates the effectiveness of stochastic programming as a decision-aiding tool to tackle uncertainty in disassembly systems. Proposed stochastic model focuses in particular on the strategic (e.g. system design) and tactical (e.g., system reconfiguration, workload balancing) dimensions.", "url": "" }, { "id": "Tu-TuC18.4", "code": "TuC18.4", "title": "Marking Automation for a Module Block in Shipbuilding Manufacture: Feasibility Investigation and Prototype Implementation", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC18", "sessionTitle": "Decision-Making Problems in Manufacturing Plants", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Kim, KyungSoo", "affiliation": "Pohang Univ. of Sci. & Tech" }, { "name": "Lee, Hye Jin", "affiliation": "POSTECH" }, { "name": "Seongrok, Moon", "affiliation": "POSTECH" }, { "name": "Park, Jeongmin", "affiliation": "POSTECH" }, { "name": "SangWook, Lee", "affiliation": "Samsung Heavy Industry" }, { "name": "Doojin, Choi", "affiliation": "Samsung Heavy Industry" }, { "name": "Sunghan, Kim", "affiliation": "AIPL" }, { "name": "Park, PooGyeon", "affiliation": "Pohang Univ. of Sci. & Tech" } ], "keywords": [ "Robotics in manufacturing systems", "Manufacturing engineering and management", "Human-technology integration in manufacturing" ], "abstract": "This paper presents an automated marking system for ship module blocks to enhance productivity and consistency in shipbuilding processes. Motivated by the increasing demand for eco-friendly ships and the shortage of skilled labor, two marking approaches are proposed using dual industrial gantry robot arm systems: a direct laser marking method and an indirect laser projection method. A prototype system with a 6-DoF manipulator on a 1-DoF sliding rail is developed to validate the direct approach. The experimental results demonstrate the feasibility and potential of the proposed system for efficient and reliable shipbuilding automation.", "url": "" }, { "id": "Tu-TuC18.5", "code": "TuC18.5", "title": "A Multi-Objective Decision Support Framework for Reconfigurable Manufacturing Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC18", "sessionTitle": "Decision-Making Problems in Manufacturing Plants", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Beldar, Pedram", "affiliation": "University of Skövde" }, { "name": "Mahmoodi, Ehsan", "affiliation": "University of Skövde" }, { "name": "Linnéusson, Gary", "affiliation": "University of Skövde" }, { "name": "Ng, Amos", "affiliation": "University of Skövde" }, { "name": "Nourmohammadi, Amir", "affiliation": "University of Skövde" } ], "keywords": [ "Intelligent manufacturing systems", "Cyber-physical production systems", "Production and operations management" ], "abstract": "Reconfigurable transfer lines (RTL) in high-volume manufacturing face the challenge of balancing operational efficiency with financial performance in response to demand fluctuations. Existing methods treat reconfiguration as static, single-objective cost minimization and provide no explicit trigger for when reconfiguration should be initiated. This paper presents a decision support framework integrating cyber-physical systems with multi-objective optimization for demand-driven RTL. The framework combines a configurable capacity-gap trigger with an epsilon-constraint formulation that generates non-dominated configurations trading off cycle time (CT) and reconfiguration cost. An industrial-scale example with seven workstations and 75 tasks shows how the framework exposes the operational-financial trade-off across an investment range, revealing a low-cost configuration that reduces CT by 34.7% and a higher-investment configuration that closes the capacity gap to 6.6%, supporting managerial selection against organizational priorities.", "url": "" }, { "id": "Tu-TuC19.1", "code": "TuC19.1", "title": "Interaction Loop: An Integration Mechanism for Industrial-Intelligence Scenarios and Its Application to Construction Machinery Maintenance (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) III", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Qu, Mengjin", "affiliation": "Tsinghua University" }, { "name": "Li, Shi hong", "affiliation": "Tsinghua University, Department of Automation" }, { "name": "Li, Qing", "affiliation": "Tsinghua University" } ], "keywords": [ "Enterprise architecture", "Enterprise interoperability", "Cyber-physical-social systems in enterprises" ], "abstract": "Against the backdrop of rapid advances in industrial intelligence and related technologies, scenario-driven approaches have become a key pathway for promoting the digital and intelligent transformation of enterprises. However, cross-layer and heterogeneous elements involved in scenario-based deployment—such as human operators, cyber systems, and physical equipment—raise two fundamental questions: why to transform and how to transform. To address these challenges, this study proposes a scenario-oriented five-layer interaction loop reference model, built upon digital-twin-based architectures and inspired by cognitive-architecture principles. The model aims to provide an actionable mechanism for integrating heterogeneous agents across multiple layers. By mapping and analyzing a construction-machinery maintenance scenario, we preliminarily validate the model’s capability to systematically describe multi-agent, multi-level collaboration mechanisms, and further present a more intelligent implementation scheme for general equipment-maintenance contexts. This work enriches research on human-cyber-physical collaboration in industrial-intelligence scenarios and offers a reference framework for the design of practical maintenance systems.", "url": "" }, { "id": "Tu-TuC19.2", "code": "TuC19.2", "title": "Cognitive Interoperability Framework for Human-Centric Cyber-Physical Systems in Smart Mobility (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) III", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Caruntu, Constantin - Florin", "affiliation": "Technical University \"Gheorghe Asachi\" of Iasi" }, { "name": "Pauca, Georgiana-Sinziana", "affiliation": "Gheorghe Asachi Technical University of Iasi" } ], "keywords": [ "Interconnected dynamical systems", "Complex dynamic systems", "Decentralized and distributed control for large-scale systems" ], "abstract": "The rapid evolution of connected and autonomous vehicles (CAVs), vehicle-to-everything (V2X) communication, and smart infrastructures creates new opportunities for safer and more efficient mobility. However, most approaches focus on technical interoperability (protocols and data exchange) while overlooking cognitive interoperability, i.e., the alignment of machine reasoning with human intent, trust, and explainability. This paper introduces a cognitive interoperability framework for Cyber-Physical-Human Systems (CPHS) in smart mobility, structured into three layers: (i) the Physical Layer for vehicles, infrastructure, and environmental dynamics; (ii) the Cyber Layer for V2X communication, multi-agent coordination, and simulation; and (iii) the Cognitive Layer for intent modeling, trust calibration, explainability, and cognitive digital twins. Two scenarios, intelligent intersection management and cooperative platooning, demonstrate how cognitive interoperability improves safety, efficiency, and transparency. Overall, the framework advances human-centric smart mobility and supports scalable, trustworthy, and adaptive transportation systems", "url": "" }, { "id": "Tu-TuC19.3", "code": "TuC19.3", "title": "Advancing the Human-Centric Paradigm through Operator 5.0: Insights from an Industrial Case (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) III", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Bucci, Ilaria", "affiliation": "University of Florence" }, { "name": "Fani, Virginia", "affiliation": "University of Florence" }, { "name": "Rossi, Monica", "affiliation": "Politecnico Di Milano" }, { "name": "Bandinelli, Romeo", "affiliation": "Università Di Firenze" } ], "keywords": [ "Human-centered production and logistics", "Human-technology integration in manufacturing", "Manufacturing engineering and management" ], "abstract": "This paper examines how the Operator 5.0 paradigm can be operationalized to enable human-centric performance in industrial settings. A case study was conducted in the Field Service department of an international energy company, combining a screening survey (70 responses) and semi-structured interviews with Field Service Engineers. Findings indicate that operators value human-centric environments and hybrid competences but face limited decision authority, fragmented digital tools, and low personalization. Six guiding principles: human-centricity, personalization, empowerment, hybrid competences, resilience, and sustainability, emerge as enablers of the transition from Operator 4.0 to 5.0. A three-stage roadmap is proposed to align digital innovation with human capabilities and strengthen human-in-the-loop resilience.", "url": "" }, { "id": "Tu-TuC19.4", "code": "TuC19.4", "title": "A Formal Framework for Evaluating Cognitive Emulation Models in Human Digital Twins (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) III", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Bhattacharya, Sukriti", "affiliation": "Luxembourg Institute of Science and Technology" }, { "name": "Gaffinet, Ben", "affiliation": "Luxembourg Institute of Science and Technology" }, { "name": "Naudet, Yannick", "affiliation": "Luxembourg Institute of Science and Technology (LIST)" }, { "name": "Panetto, Hervé", "affiliation": "CRAN, University of Lorraine, CNRS" } ], "keywords": [ "Human-centered production and logistics", "Human-technology integration in manufacturing", "Cyber-physical-social systems in enterprises" ], "abstract": "Human Digital Twins (HDT) require cognitive models that accurately emulate human behaviour for effective human-machine interaction. While cognitive architectures like ACT-R have demonstrated predictive capabilities, no formal framework exists to evaluate their suitability as emulation models. We present a mathematical framework that operationalizes cognitive emulation as behavioural indistinguishability under forced state synchronization. Our framework defines precise evaluation metrics, establishes theoretical properties including fundamental identifiability limits, and specifies deployment requirements for HDT systems. We demonstrate the framework’s utility through a LEGO assembly task case study, showing how ACT-R can be systematically evaluated as a cognitive emulator.", "url": "" }, { "id": "Tu-TuC19.5", "code": "TuC19.5", "title": "Towards a Cognitive Framework for AI-Enabled Evolutionary Design of Cyber-Physical Social Systems (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) III", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Xu, Tianxiao", "affiliation": "Université Lumière Lyon 2, DISP Laboratory" }, { "name": "Moalla, Néjib", "affiliation": "University of Lyon 2" } ], "keywords": [ "Cyber-physical-social systems in enterprises", "Industrial artificial intelligence", "Model-driven enterprise-system engineering" ], "abstract": "Intelligent Transportation Systems are formed through interactions among numerous member Cyber-Physical Systems (CPS) and humans, making them typical Cyber-Physical-Social Systems (CPSS). The evolution of the services provided by a CPSS is closely tied to functional upgrades of its member systems, which poses challenges to their development and iteration cycles. This paper, by integrating Model-Based Systems Engineering (MBSE) methods, proposes a Cognitive Framework for AI-Enabled Evolutionary Design of Physics-Based Cyber-Physical Systems. A lifecycle continuum is established to ensure that both the CPSS and its constituent systems can be developed and iterated while meeting environmental constraints and top-level mission requirements. The study extends the SysML language to express the CPSS metamodel, ensuring digital continuity throughout the CPSS lifecycle, and introduces AI integration capabilities to enable AI-driven processes. The framework is validated using a priority-passing scenario for connected buses, which serve as member systems within an intelligent transportation system.", "url": "" }, { "id": "Tu-TuC20.1", "code": "TuC20.1", "title": "A Mid-Term Redesign Approach for Robust Sampling Time Design (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC20", "sessionTitle": "JO-JPC: Process Modeling, Identification, and Estimation Techniques", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Wang, Ke", "affiliation": "University of Strathclyde" }, { "name": "Yue, Hong", "affiliation": "University of Strathclyde" } ], "keywords": [ "Process modeling, identification, and estimation techniques" ], "abstract": "This work addresses robust sampling-time design for single-run dynamic experiments under parameter uncertainty and operational changes. Existing robust and sequential experimental design methods often require conservative uncertainty descriptions, repeated experiments, or impractical measurement-by-measurement redesign. To address this, a zone-based mid-term redesign framework is proposed, in which the experimental horizon is partitioned into sequential sub-experiments and sampling schedules are re-optimised using updated parameter estimates from accumulated data. Three redesign strategies are investigated: equally spaced redesign, auto-updating redesign based on practical identifiability, and condition-triggered redesign for operational changes. Case studies on an enzyme reaction system and an enzymatic biodiesel production process show that the adaptive strategies recover much of the information content of nominal OED while maintaining robustness to parameter uncertainty.", "url": "" }, { "id": "Tu-TuC20.2", "code": "TuC20.2", "title": "A Novel State-Space Model Identification Method from a Behavioral System-Theoretic Perspective (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC20", "sessionTitle": "JO-JPC: Process Modeling, Identification, and Estimation Techniques", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Liu, Qingyuan", "affiliation": "Tsinghua University" }, { "name": "Wang, Yibo", "affiliation": "Tsinghua University" }, { "name": "Liu, Tao", "affiliation": "Dalian University of Technology (DLUT)" }, { "name": "Li, Zhongmei", "affiliation": "East China University of Science and Technology" }, { "name": "He, Xiao", "affiliation": "Tsinghua University" }, { "name": "Shang, Chao", "affiliation": "Tsinghua University" } ], "keywords": [ "Process modeling, identification, and estimation techniques" ], "abstract": "As the mainstream methodology for identifying state-space models, subspace identification relies on the orthogonality assumption between data spaces, and could thus lead to unsatisfactory identification accuracy in finite-sample regime. To overcome this limitation, a novel state-space model identification method is proposed by leveraging the capability of behavioral systems theory in characterizing system dynamics with finite-length data trajectory. In virtue of the innovation-based data-driven output predictor (DDOP), the state-space model identification is converted into an innovation estimation problem followed by a model reduction step. To achieve better identification accuracy, an improved innovation estimation strategy incorporating low-rank prior is further proposed, formulated as a rank-constrained programming problem and solved via the alternating direction method of multipliers (ADMM). Numerical and industrial dataset experiments demonstrate the superior modeling accuracy of the proposed method over existing subspace identification methods in both open-loop and closed-loop cases.", "url": "" }, { "id": "Tu-TuC20.3", "code": "TuC20.3", "title": "Sparse Identification of Physically Plausible Aggregation Kernels for Wet Granulation Processes (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC20", "sessionTitle": "JO-JPC: Process Modeling, Identification, and Estimation Techniques", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Tölle, Stefan Ruben", "affiliation": "RWTH Aachen University" }, { "name": "Dörschel, Lorenz", "affiliation": "RWTH Aachen University" }, { "name": "Klinken-Uth, Stefan", "affiliation": "Heinrich-Heine-Universität Düsseldorf" }, { "name": "Delvos, Alana", "affiliation": "Heinrich-Heine-Universität Düsseldorf" }, { "name": "Breitkreutz, Jörg", "affiliation": "Heinrich-Heine-Universität Düsseldorf" }, { "name": "Vallery, Heike", "affiliation": "Delft University of Technology" }, { "name": "Stemmler, Sebastian", "affiliation": "RWTH Aachen University" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Control of multi-scale, distributed, and particulate systems", "Biological and pharmaceutical systems" ], "abstract": "Population balance models provide a mathematical framework for describing the dynamics of particulate systems. For aggregation processes, such as twin-screw wet granulation, population balance models critically depend on accurate aggregation kernels, yet first-principles derivation is often impractical, and data-driven methods can lack physical interpretability. This work presents a sparse identification framework for learning physically plausible aggregation kernels directly from data. The approach enforces physical constraints, exploits structural properties, applies a systematic scaling strategy, and extends naturally to actuated systems. Validation on experimental data from a continuous twin-screw wet granulation process confirms the method’s robustness and its ability to recover physically meaningful aggregation kernels from real-world measurement data.", "url": "" }, { "id": "Tu-TuC20.4", "code": "TuC20.4", "title": "Sample-Efficient Counterfactual Tuning for Compressor Pressure Control (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC20", "sessionTitle": "JO-JPC: Process Modeling, Identification, and Estimation Techniques", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Guerrero, Margarita A.", "affiliation": "KTH Royal Institute of Technology" }, { "name": "González, Rodrigo A.", "affiliation": "Eindhoven University of Technology" }, { "name": "Rojas, Cristian R.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Industrial applications of process control", "Reliability and safety in processes" ], "abstract": "In controlled industrial environments, ensuring safety and performance during controller tuning is a challenging and critical task. In particular, control loops in compressor–plenum–throttle systems cannot tolerate costly interruptions, and aggressive excitation may lead to unsafe operating regimes. Given the wide availability of historical data under safe operation, this paper introduces a counterfactual explainability approach for sample-efficient retuning of compressor control loops. The proposed data-driven algorithm determines, without an explicit plant model or previous control law, the smallest controller adjustment required to achieve predefined performance specifications while maintaining closed-loop stability. The effectiveness of the method is demonstrated through an extensive Monte Carlo simulation study, where the proposed approach computes low magnitude and interpretable adjustments in the controller space that lead to improved settling time and reduced overshoot.", "url": "" }, { "id": "Tu-TuC20.5", "code": "TuC20.5", "title": "Temporal-Difference Contrastive Learning for Time-Series Anomaly Detection with Application to an Ironmaking Blast Furnace (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC20", "sessionTitle": "JO-JPC: Process Modeling, Identification, and Estimation Techniques", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Guo, Yunpeng", "affiliation": "China University of Geosciences" }, { "name": "An, Jianqi", "affiliation": "China University of Geosciences" }, { "name": "Chen, Qifu", "affiliation": "China University of Geosciences (Wuhan)" }, { "name": "Wu, Min", "affiliation": "China University of Geosciences" }, { "name": "She, Jinhua", "affiliation": "Tokyo Univ. of Tech" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Monitoring, performance assessment, and fault detection in chemical process control" ], "abstract": "Time-series anomaly detection plays a crucial role in industrial process control by ensuring system safety, production quality, and operational efficiency. However, existing self-supervised anomaly detection methods often neglect temporal evolution in complex control processes, making them sensitive to abrupt changes but less effective for gradual drifts, and they usually lack forward temporal awareness. To address these challenges, this paper presents a latent temporal-difference contrastive learning framework (TDCL). The framework integrates an attention-enhanced temporal-difference operator to jointly capture long-term dependencies and local dynamic transitions, improving the discrimination of gradual drifts and sudden anomalies. A dual-branch anomaly evaluation mechanism combining reconstruction and prediction discrepancies further enhances forward temporal perception. Experiments on a real blast furnace data verify the effectiveness of TDCL for early and accurate anomaly detection.", "url": "" }, { "id": "Tu-TuC20.6", "code": "TuC20.6", "title": "Confidence Domain-Based Zonotopic and Gaussian Kalman Filter for Discrete Linear Time-Invariant Systems (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC20", "sessionTitle": "JO-JPC: Process Modeling, Identification, and Estimation Techniques", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Li, Ziling", "affiliation": "Tsinghua University" }, { "name": "Liu, Bo", "affiliation": "Tsinghua University" }, { "name": "Xu, Feng", "affiliation": "Tsinghua Univerisity" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Monitoring, performance assessment, and fault detection in chemical process control", "Process performance monitoring/statistical process control" ], "abstract": "This paper proposes a new confidence domain-based zonotopic and Gaussian Kalman filter (CDZGKF) design framework for discrete linear time-invariant (LTI) systems. First, we establish the relationship between the covariation of zonotopes and the covariance of Gaussian distributions, offering a novel perspective that bridges zonotopic and Gaussian frameworks of system uncertainties. Second, from the perspective, by modeling the Gaussian distribution as the Gaussian zonotope (G-zonotope), the CDZGKF is proposed to address the robust state estimation problem of discrete LTI systems under the effect of hybrid zonotopic and Gaussian uncertainties. Third, the observer gain of the CDZGKF is optimized by minimizing the size of the confidence domain of the state with a given confidence level, which determines the coefficient weighting the relative magnitude of hybrid zonotopic and Gaussian uncertainties. At the end of this paper, a case study is used to illustrate the effectiveness of the proposed method.", "url": "" }, { "id": "Tu-TuC21.1", "code": "TuC21.1", "title": "Two-Time-Scale Scenario-Based Stochastic Energy Management System for a Renewable Microgrid with BESS in Local Electricity Market (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC21", "sessionTitle": "Optimal Operation of Smart Multi-Energy Microgrids", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Nguyen, Ngoc Duc", "affiliation": "Korea Maritime and Ocean University" }, { "name": "Shin, Jinsu", "affiliation": "Seoul National University of Science and Technology" }, { "name": "Park, Byungkwon", "affiliation": "Soongsil University" }, { "name": "Lee, Young Il", "affiliation": "Seoul National Univ of Science and Technology" } ], "keywords": [ "Energy management systems", "Energy market", "Demand response" ], "abstract": "This paper proposes a two-time-scale hierarchical energy management system (EMS) for a renewable microgrid participating in a local electricity market. This market operates under a dynamic pricing scheme where hourly day-ahead (DA) tariffs are announced 24 hours in advance, and real-time (RT) tariffs are updated every 15 or 5 minutes, two hours prior to dispatch. These price signals reflect grid conditions, incentivizing participants to adjust their power profiles for grid stability. The proposed EMS optimizes economic benefits through two layers: a DA planning layer that minimizes MG operation costs using probabilistic scenarios to address load and generation uncertainties; an RT trading layer that leverages RT price volatility for arbitrage while tracking the scheduled state of energy (SOE) from DA planning. Both planning and trading strategies are formulated as mixed-integer quadratic programming (MIQP) models. Simulations using 10-second historical data demonstrate that the proposed trading strategy can reduce the operation cost by around 9% more compared with the case of not participating in the real-time TE market under a 30% RT price deviation from the DA system marginal price.", "url": "" }, { "id": "Tu-TuC21.2", "code": "TuC21.2", "title": "Network-Constrained Flexibility Quantification of Heat Pumps with Integrated Space Heating and Storage Tanks in Active Distribution Grids", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC21", "sessionTitle": "Optimal Operation of Smart Multi-Energy Microgrids", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Panagi, Savvas", "affiliation": "Cyprus University of Technology / Electricity Authority of Cyprus" }, { "name": "Spanias, Chrysovalantis", "affiliation": "Electricity Authority of Cyprus" }, { "name": "Aristidou, Petros", "affiliation": "Cyprus University of Technology" } ], "keywords": [ "Electrical distribution systems", "Demand response", "Smart buildings and building automation" ], "abstract": "The rapid growth in Heat Pump (HP) usage brings considerable challenges to distribution grids, yet simultaneously unlocks opportunities for more efficient and optimized operation. This paper proposes a secure and tractable method to quantify and exploit HP flexibility. The framework adopts a two-stage operational scheduling and flexibility quantification approach that integrates building thermal inertia and Domestic Hot Water (DHW) storage into a convex Optimal Power Flow (OPF) formulation. First, a baseline day-ahead scheduling problem ensures cost-effective HP operation while respecting user comfort and tank temperature limits. Second, upward and downward flexibility envelopes are derived by treating the combined building–tank system as a virtual battery, incorporating both thermal dynamics and network constraints through OPF. The methodology is validated on a realistic low-voltage distribution network with residential loads, Photovoltaic (PV) systems, and stochastic DHW demand. Results demonstrate that upward flexibility is limited by comfort and voltage constraints at higher HP penetration levels, while downward flexibility is purely limited by user comfort. Furthermore, the case study highlights that network limits become binding at and beyond 60% HP penetration, underlining the importance of the approach under realistic high adoption scenarios.", "url": "" }, { "id": "Tu-TuC21.3", "code": "TuC21.3", "title": "Physics-Informed Neural Network for Modeling the Dynamic Behavior of Grid-Forming Converters (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC21", "sessionTitle": "Optimal Operation of Smart Multi-Energy Microgrids", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Jaffal, Hussein", "affiliation": "RWTH Aachen University" }, { "name": "Fois, Arianna", "affiliation": "RWTH Aachen University (Former Affiliate)" }, { "name": "Bouchkati, Sarra", "affiliation": "RWTH Aachen University, Institute for High Voltage Equipment and Grids, Digitalization and Energy Economics" }, { "name": "Mahjoob, Amirali", "affiliation": "RWTH Aachen University" }, { "name": "Ulbig, Andreas", "affiliation": "RWTH Aachen University" } ], "keywords": [ "Electrical distribution systems", "Power systems stability" ], "abstract": "This paper investigates physics-informed neural networks for modeling the full dynamic behavior of droop-controlled grid-forming converters. The approach is trained on synthetic data generated via numerical solvers and benchmarked against both traditional integration methods and a vanilla neural network. Results show higher predictive accuracy than the vanilla network using the same training data and substantially reduced runtime compared with numerical solvers.", "url": "" }, { "id": "Tu-TuC21.4", "code": "TuC21.4", "title": "Safety-Guaranteed Distributed Resilient Secondary Control for DC Microgrids against Constant False Data Injection Attacks", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC21", "sessionTitle": "Optimal Operation of Smart Multi-Energy Microgrids", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Lu, Limin", "affiliation": "Zhejiang University" }, { "name": "Ma, Ruijie", "affiliation": "Zhejiang University" }, { "name": "Zhao, Chengcheng", "affiliation": "Zhejiang University" } ], "keywords": [ "Cybersecurity in smart grids", "Power systems stability", "Distributed optimization for smart grids" ], "abstract": "Direct-current microgrids (DCmGs) rely on communication-based secondary control to achieve current sharing and weighted voltage balancing, but communication-involved control is vulnerable to false data injection (FDI) attacks that can drive point-of-common-coupling (PCC) voltages outside safety limits. This paper studies the problem of designing a secondary controller that simultaneously achieves all-time voltage safety and stability for PI-regulated, ZIE-loaded DCmGs under constant FDI attacks. We adopt a damping-based resilient secondary control law as the nominal layer to recover the desired steady state, and augment it with a safety filter built on high-order control barrier functions (HOCBFs). To realize distributed control, we construct HOCBF constraints that depend only on local measurements and finite-difference estimators, and bound the estimation errors with robust margins. The safety filter is implemented as a quadratic program (QP) that minimally modifies the nominal secondary input while enforcing the HOCBF constraints. We show that the proposed controller guarantees voltage safety and asymptotic stability of the DCmGs against constant FDI attacks. The effectiveness of the proposed controller is demonstrated on a full-hardware 4-DGU testbed.", "url": "" }, { "id": "Tu-TuC21.5", "code": "TuC21.5", "title": "A Framework for Design and Testing of Embedded Control Applications in Power Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC21", "sessionTitle": "Optimal Operation of Smart Multi-Energy Microgrids", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Moga, Daniel", "affiliation": "Technical University of Cluj-Napoca" }, { "name": "Stroia, Nicoleta", "affiliation": "Technical University of Cluj-Napoca" }, { "name": "Muresan, Vlad", "affiliation": "Technical University of Cluj-Napoca" }, { "name": "Stancioi, Cristina-Maria", "affiliation": "Technical University of Cluj-Napoca" }, { "name": "Bondici, Cristian", "affiliation": "Facultatea De Automatica Si Calculatoare Universitatea Tehnica Cluj-Napoca" }, { "name": "Petreus, Dorin", "affiliation": "Technical University of Cluj-Napoca" }, { "name": "Lodin, Alexandru-Cristian", "affiliation": "Technical University of Cluj-Napoca" } ], "keywords": [ "Power plant control", "Power systems stability", "Power electronics" ], "abstract": "This paper presents an educational and research framework that supports the Model-Based Design paradigm in the development of control applications in power systems. The proposed approach, emphasizing MIL(Model-in-the-loop) and PIL(Processor-in-the-loop) testing, is specifically aimed at developing, implementing and testing embedded control applications for various processes associated with energy production. From a Model-Based Design perspective, MATLAB and Simulink were identified as effective environments for developing control algorithms and for automatic code generation for industrial embedded applications. Design methods and development procedures are demonstrated on embedded platforms with ARM Cortex-M microcontrollers. Various advantages are showcased: real-time performance, comprehensive toolchain support, and seamless integration with automatic code generation workflows. The paper demonstrates a path for bridging research in controller design for power systems with industrial implementation of discrete controllers on embedded control applications.", "url": "" }, { "id": "Tu-TuC21.6", "code": "TuC21.6", "title": "Dimension-Invariant Strategically Switching Metaheuristics for Scalable Battery Parameter Estimation (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC21", "sessionTitle": "Optimal Operation of Smart Multi-Energy Microgrids", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Kim, Joonhee", "affiliation": "Pohang University of Science and Technology" }, { "name": "Han, Soohee", "affiliation": "Pohang University of Science and Technology" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Energy storage systems", "Machine learning and artificial intelligence in chemical process control" ], "abstract": "Standard strategically switching metaheuristics (SSM) enhances battery parameter estimation by using a pre-trained interpreter (FuncNet) to interpret objective landscapes and select optimal optimizers. However, the original framework’s scalability is hindered by the requirement to train a specific FuncNet for each parameter dimension. To address this, we propose a dimension-invariant FuncNet architecture incorporating zero-padding and residual blocks, allowing a single network to process heterogeneous parameter dimensions. Trained on benchmarks with 10-30 decision variables, our model empowers SSM to accurately estimate parameters for diverse electrochemical model variations, including the degradation-coupled and the internal short circuit (ISC)-coupled cases with 26 and 17 parameters, respectively. This advancement eliminates the need to retrain the landscape interpreter for new battery model configurations, thereby enabling scalable and robust optimization for practical battery digital twin implementation.", "url": "" }, { "id": "Tu-TuC22.1", "code": "TuC22.1", "title": "Efficient Oscillation Compensation of Photovoltaic Energy Systems on Floating Platforms", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC22", "sessionTitle": "Solar Power and Wind Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Teixeira, Diana F.", "affiliation": "State University of Rio De Janeiro" }, { "name": "Cunha, José Paulo V. S.", "affiliation": "State University of Rio De Janeiro" }, { "name": "Bellar, Maria D.", "affiliation": "State University of Rio De Janeiro" } ], "keywords": [ "Solar energy", "Control and management of energy systems" ], "abstract": "This paper proposes a control strategy to increase the energy efficiency of a photovoltaic (PV) system onboard vessels, compensating for mechanical oscillations that reduce energy harvesting. An extremum-seeking controller (ESC) based on a second-order polynomial approximation with parameter identification is developed. The algorithm adjusts the control signal using the average power measured in each oscillation cycle. Simulations show that the proposed approach increases the net average power, that is the average difference between the generated power and the friction losses. As far as the authors know, this is the first application of ESC to compensate the movement of PV systems on oscillating floating platforms.", "url": "" }, { "id": "Tu-TuC22.2", "code": "TuC22.2", "title": "Predictive Control of a Thermocline Tank in a Concentrated Solar Plant for Heat Production", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC22", "sessionTitle": "Solar Power and Wind Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Girard, Eliott", "affiliation": "PROMES-CNRS" }, { "name": "Laaroussi, Fatima-Zahra", "affiliation": "PROMES-CNRS" }, { "name": "Eynard, Julien", "affiliation": "University of Perpignan Via Domitia" }, { "name": "Thil, Stéphane", "affiliation": "Laboratoire PROMES (UPR 8521)" }, { "name": "Grieu, Stéphane", "affiliation": "PROMES-CNRS" } ], "keywords": [ "Solar energy", "Energy storage systems", "Control and management of energy systems" ], "abstract": "Concentrated solar energy can be used to supply thermal energy to industrial processes. However, this is a challenging task due to the intermittency of solar power. The present paper addresses this issue by developing a hybrid control strategy for the management of a concentrated solar thermal plant dedicated to heat production. This control strategy relies on the combination of an energy balance-based approach based on the first law of thermodynamics to manage the solar collectors with Model-based Predictive Control (MPC) to manage the thermocline storage tank the plant is equipped with. The predictive strategy is compared to a reference Proportional-Integral-Derivative (PID) strategy in order to prove the relevance of MPC in a concentrated solar energy context. The strategies are compared according to nine scenarios defined based on Direct Normal Irradiance (DNI) and heat demand profiles. As a result, the MPC strategy manages to satisfy heat demand when there is sufficient energy available with little overshoots (<20%) whereas the reference strategy fails to satisfy heat demand and massively overshoots (620% at most). On average, across all scenarios, the MPC and PID strategies deviate from heat demand of 31.10 kWh and 77.90 kWh, respectively.", "url": "" }, { "id": "Tu-TuC22.3", "code": "TuC22.3", "title": "On Disturbance Rejection in Central Tower Solar Plants under Time-Varying Spatial Flux Distributions", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC22", "sessionTitle": "Solar Power and Wind Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Svorcan, Mihailo", "affiliation": "Politecnico Di Milano" }, { "name": "De Pascali, Matteo Luigi", "affiliation": "Politecnico Di Milano" }, { "name": "Milo, Sergio", "affiliation": "Politecnico Di Milano" }, { "name": "Casella, Francesco", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Solar energy", "Thermal systems modelling" ], "abstract": "One of the main challenges associated with central tower solar power plants is the control of the temperature of the heat transfer fluid at the outlet of the solar receiver during solar flux transients. Current solar receiver designs show evident limitations in the control of such temperature under spatio-temporal disturbances, which are caused by variation of the distributions of the incident solar radiation on the receiver due to passing clouds. This paper shows that such limitations are directly related to the design choices of state-of-the-art solar receivers. The analysis of optimally controlled transients clearly shows that even in the ideal case of a control system with perfect knowledge of the system and of the disturbance distribution in space and time, it may not be possible to effectively reject such disturbances if more than half of the solar radiation is concentrated in the first half of the receiver's length. This result calls for the development of new receiver designs that include from the very early design stages a rigorous analysis of the process controllability.", "url": "" }, { "id": "Tu-TuC22.4", "code": "TuC22.4", "title": "Power Maximization for Floating Offshore Wind Farms under Partial Operating Conditions with Reinforcement Learning", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC22", "sessionTitle": "Solar Power and Wind Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Heiskell, Casey, Edward", "affiliation": "The University of British Columbia" }, { "name": "Nagamune, Ryozo", "affiliation": "University of British Columbia" } ], "keywords": [ "Wind power" ], "abstract": "This paper proposes a nacelle yaw controller design for floating offshore wind farms to maximize power capture, with special consideration of cases where the farm is under partial operation due to a portion of the farm being disabled. Such situations are expected to occur for various reasons, including turbine maintenance and potential marine wildlife concerns. Controller design incorporates reinforcement learning in a wind farm simulator environment and is the first to utilize turbine repositioning within partially operational wind farms. Simulation results for a 30-turbine wind farm show an average improvement of 13.6% over greedy control. In the case of 10 turbines being disabled, a 1.21% improvement is seen over full-farm control, by incorporating information about the operable wind farm subset.", "url": "" }, { "id": "Tu-TuC22.5", "code": "TuC22.5", "title": "Health-Aware Set-Point Thompson Sampling for Active Power Control in Offshore Wind Farms", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC22", "sessionTitle": "Solar Power and Wind Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Daenens, Simon", "affiliation": "Vrije Universiteit Brussel" }, { "name": "Verstraeten, Timothy", "affiliation": "Vrije Universiteit Brussel" }, { "name": "Van Binsbergen, Diederik", "affiliation": "Vrije Universiteit Brussel, Norwegian University of Science and Technology" }, { "name": "Gebel, Jakob", "affiliation": "Vrije Universiteit Brussel, Norwegian University of Science and Technology" }, { "name": "Daems, Pieter-Jan", "affiliation": "Vrije Universiteit Brussel" }, { "name": "Nowe, Ann", "affiliation": "Vrije Universiteit Brussel" }, { "name": "Helsen, Jan", "affiliation": "Vrije Universiteit Brussel" } ], "keywords": [ "Wind power", "Control and management of energy systems" ], "abstract": "Active power control is increasingly mandated by transmission system operators to ensure grid stability, often requiring wind farms to operate below their maximum capacity. While this curtailment limits immediate revenue, it introduces a degree of freedom in wind farm flow control: the ability to distribute the power set-points among turbines to minimize structural damage. In this paper, we propose a health-aware control framework that exploits this opportunity to extend turbine lifetime. We extend the Set-Point Thompson Sampling (SPTS) algorithm, a Bayesian multi-agent reinforcement learning method, to explicitly optimize for fatigue load reduction while strictly adhering to farm-level power tracking constraints. The method uses coordination graphs to factorize the control problem, ensuring scalability to large offshore wind farms. We validate the approach on a realistic model of a 44-turbine offshore wind farm, coupling the Gauss-Curl Hybrid wake model (FLORIS) with physics-based load estimates (OpenFAST). Simulation results demonstrate that the proposed controller reduces farm-wide damage equivalent loads by up to 5.6% compared to standard industrial heuristics, without violating power delivery commitments. Furthermore, we analyze the trade-off between optimality and reliability under stochastic wind conditions, showing that the method maintains a performance advantage even when safety margins are applied to mitigate uncertainty.", "url": "" }, { "id": "Tu-TuC22.6", "code": "TuC22.6", "title": "Coupled Wind Farm Control and Energy Management of Hybrid Power Plants", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC22", "sessionTitle": "Solar Power and Wind Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Friis-Møller, Mikkel", "affiliation": "Technical University of Denmark" }, { "name": "Dirik, Deniz Gokhan", "affiliation": "Technical University of Denmark" }, { "name": "Réthoré, Pierre-Elouan", "affiliation": "Technical University of Denmark" } ], "keywords": [ "Wind power", "Control and management of energy systems", "Energy storage systems" ], "abstract": "Wind farm control can improve the total energy production of a wind farm by means of active flow control such as wake steering by introducing yaw offset. Revenue increases, improved flexibility, and reduced curtailment can be obtained by combining multiple renewable energy sources, energy storage or both in a hybrid power or energy plant. This work presents a novel way of introducing wake steering by means of a graph neural network surrogate into the mixed integer linear problem of managing the energy dispatch of a hybrid power plant. The method is augmented by including the potential effects of load alleviation associated with yaw misalignment and leads to a 0.75% AEP and revenue increase as well as approx. 3.8% LCOE reduction for the studied wind farm.", "url": "" }, { "id": "Tu-TuC23.1", "code": "TuC23.1", "title": "Reweighting Using the Energy-Based Model: Addressing Covariate Shift in Industrial Soft Sensing Via Density Ratio Estimation", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC23", "sessionTitle": "Soft Sensing and Data-Driven Process Modeling", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Lu, Cheng", "affiliation": "China Jiliang University" }, { "name": "Gao, Qingtong", "affiliation": "Zhejiang Normal University" }, { "name": "Zeng, Jiusun", "affiliation": "Hangzhou Normal University" } ], "keywords": [ "Process modeling, identification, and estimation techniques" ], "abstract": "Accurate estimation of difficult-to-measure key quality variables from easy-to-measure process data using soft sensors is an efficient solution in many industrial systems. However, the distribution of process data often varies across different data acquisition scenarios, resulting in covariate shift between training and testing domains and consequently degradation in the performance. To address this challenge, an effective covariate shift adaptation method is developed for industrial soft sensors, which employs a coniditonal energy-based model (CBEM) to estimate the high-dimensional conditional density ratio between data in different distribution domains. To avoid direct computation of the partition function in the CBEM, a denoising score matching strategy is applied. The learned score functions enable tractable computation of energy differences, which are used to adaptively reweight the training samples in soft sensors. The proposed method does not require explicit domain modeling or normalization and provides an efficient solution for covariate shift adaptation. Experimental results on a real-world industrial dataset demonstrate the effectiveness of the proposed reweighting method in improving the performance of a series of baseline methods.", "url": "" }, { "id": "Tu-TuC23.2", "code": "TuC23.2", "title": "Gated Stacked Information-Separation Target-Supervised Variational Autoencoder for Soft Sensing", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC23", "sessionTitle": "Soft Sensing and Data-Driven Process Modeling", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Lian, Jie", "affiliation": "Dalian University of Technology" }, { "name": "Zhu, Li", "affiliation": "Dalian University of Technology" }, { "name": "Zhang, Anyu", "affiliation": "Dalian University of Technology" }, { "name": "Chen, Junghui", "affiliation": "Chung-Yuan Christian Univ" } ], "keywords": [ "Machine learning and artificial intelligence in chemical process control", "Monitoring, performance assessment, and fault detection in chemical process control", "Industrial applications of chemical process control" ], "abstract": "The variational autoencoder (VAE) has emerged as an effective approach for soft sensing. However, when processing strongly nonlinear data, single-layer VAEs struggle to extract high-level features. To address this limitation, this research proposes a Gated Stacked Information-Separation Target-Supervised VAE for soft Sensing. The model uses a multi-layered VAE design to improve the extraction of meaningful features. During layerwise pretraining, the model partitions the latent space into output-correlated and output-irrelevant subspaces, ensuring that target-correlated information is concentrated in the output-correlated subspace, thereby enabling effective extraction of nonlinear output-correlated features. A prior encoder is introduced to derive prior information from inputs and outputs, guiding the feature learning process. To further refine the model, gated linear units are employed to regulate the information flow from all features, thereby fully leveraging multi-level information across layers. The Debutanizer Column case study highlights the superior performance of the proposed model. Moreover, compared with other stacked architectures, the proposed model exhibits enhanced interpretability.", "url": "" }, { "id": "Tu-TuC23.3", "code": "TuC23.3", "title": "Domain-Adversarial Meta-Learning for Multi-Source Soft Sensor Modeling in Industrial Processes", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC23", "sessionTitle": "Soft Sensing and Data-Driven Process Modeling", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Wang, Yongjing", "affiliation": "Zhejiang University" }, { "name": "Ding, Qing", "affiliation": "Zhejiang University" }, { "name": "Zhao, Yindan", "affiliation": "Hangzhou Underground Pipeline Development Co., Ltd" }, { "name": "He, Bocun", "affiliation": "Zhejiang University" }, { "name": "Zhang, Xinmin", "affiliation": "Zhejiang University" }, { "name": "Song, Zhihuan", "affiliation": "Zhejiang University" } ], "keywords": [ "Machine learning and artificial intelligence in MMM process control", "Machine learning and artificial intelligence in chemical process control", "Soft sensors in MMM systems" ], "abstract": "In industrial processes, domain shift is inevitable due to the changing production environment. To overcome domain shift and thus achieve reliable prediction under new or unseen conditions, we propose a novel attention-based adversarial meta-learning method for multi-source domain generalization (AAM-MDG) application in the industrial soft-sensing field. The GRU-multi-head attention feature extractor is constructed to capture the complex nonlinear dependencies in industrial time-series data. Furthermore, an adversarial training strategy with environmental label smoothing is embedded into the meta-learning framework to reinforce the learning of domain-invariant features. The effectiveness of the proposed method was verified using the real-world data of an industrial blast furnace ironmaking process. The application results show that it can achieve stronger generalization ability and improved prediction accuracy compared with some existing methods.", "url": "" }, { "id": "Tu-TuC23.4", "code": "TuC23.4", "title": "PASS: A Phase-Aware Interpretable Soft Sensor for Multiphase Batch Processes", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC23", "sessionTitle": "Soft Sensing and Data-Driven Process Modeling", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Yun, Ji Young", "affiliation": "Seoul National University" }, { "name": "Lee, Jong Min", "affiliation": "Seoul National University" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Process performance monitoring/statistical process control", "Biological and pharmaceutical systems" ], "abstract": "Real-time quality monitoring in batch processes is often hindered by intrinsic multiphase dynamics and nonlinear temporal evolution. Although deep learning-based soft sensors are widely adopted, most of them do not explicitly use phase information, which may limit their ability to model phase-dependent dynamics and interpret inter-variable relationships across operating stages. To address this issue, we propose a Phase-Aware Soft Sensor (PASS) framework that integrates phase context obtained offline using Warped K-Means (WKM) into a deep soft sensor architecture. PASS combines phase embeddings with a variable-wise attention mechanism, enabling phase-conditioned quality estimation and the analysis of learned process-variable relationships. Experimental evaluations on an industrial-scale penicillin fermentation benchmark show that PASS achieves a 20.4% reduction in root mean squared error (RMSE) compared with representative sequence-based deep learning baselines. In addition, attention map analysis suggests that the learned variable associations are qualitatively consistent with known physicochemical trends. These results indicate that phase-aware modeling can contribute to the development of more reliable and interpretable soft sensors for multiphase batch processes.", "url": "" }, { "id": "Tu-TuC23.5", "code": "TuC23.5", "title": "Teacher-Student Knowledge Adaptation for Unit-To-Process Transfer in Spatiotemporal Graph Neural Networks for Process Modeling", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC23", "sessionTitle": "Soft Sensing and Data-Driven Process Modeling", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Wanlu, Wu", "affiliation": "National University of Singapore" }, { "name": "Wu, Guoquan", "affiliation": "National University of Singapore" }, { "name": "Xiao, Ming", "affiliation": "National University of Singapore" }, { "name": "Wu, Zhe", "affiliation": "National University of Singapore" } ], "keywords": [ "Machine learning and artificial intelligence in chemical process control", "Process modeling, identification, and estimation techniques", "Industrial applications of process control" ], "abstract": "While spatiotemporal graph neural networks (STGNNs) have shown strong potential for capturing both spatial and temporal behaviors in process systems, their practical deployment remains constrained by data scarcity and the difficulty of identifying suitable source processes for transfer learning. This work introduces a teacher-student knowledge adaptation framework that leverages a single process unit as the source of transferable knowledge for modeling an entire process network. Specifically, knowledge transfer is achieved by aligning the hidden-state representations of the pretrained unit‐level teacher and process-level student models using a weighted loss function. The proposed teacher-student framework is evaluated on a process network comprising two CSTRs and a separator, with knowledge transferred from a single CSTR used as the source task. Simulation results show that incorporating teacher guidance substantially improves predictive performance when training data is scarce.", "url": "" }, { "id": "Tu-TuC23.6", "code": "TuC23.6", "title": "Robust Variance-Weighted Multimodal Sensor Fusion Using Variational Autoencoder", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC23", "sessionTitle": "Soft Sensing and Data-Driven Process Modeling", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Kodati, Venkata Raghavendra Parashara", "affiliation": "University of Alberta" }, { "name": "Bhase, Swapnil", "affiliation": "University of Alberta" }, { "name": "Huang, Biao", "affiliation": "Univ. of Alberta" } ], "keywords": [ "Machine learning and artificial intelligence in MMM process control", "Image analysis and computer vision in MMM systems", "Process modeling, identification, and estimation techniques" ], "abstract": "In industrial environments, data is available in diverse modalities such as images and process sensor readings, and integrating them using traditional sensor fusion methods can be challenging. We propose a robust variance-weighted multimodal sensor fusion framework based on a Variational Autoencoder (VAE). Latent representations from each modality are fused using inverse-variance weighting, assigning greater importance to more reliable latent means. To handle missing data, we learn modality-specific dynamic models within the latent space that predict missing latent means from short temporal histories. The framework is evaluated on a gas detection task that combines thermal images with gas sensor measurements.", "url": "" }, { "id": "Tu-TuC24.1", "code": "TuC24.1", "title": "Alternating Minimization for Time-Shifted Synergy Extraction in Human Hand Coordination", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC24", "sessionTitle": "Biomechanics and Physical Rehabilitation", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Stepp, Trevor", "affiliation": "University of Maryland, Baltimore County" }, { "name": "Olikkal, Parthan", "affiliation": "University of Maryland Baltimore County" }, { "name": "Vinjamuri, Ramana", "affiliation": "University of Maryland Baltimore County" }, { "name": "Anguluri, Rajasekhar", "affiliation": "University of Maryland, Baltimore County" } ], "keywords": [ "Biomedical system modeling, identification, and simulation", "Biomedical signal measurement and processing", "Rehabilitation engineering and healthcare delivery" ], "abstract": "Identifying motor synergies -- coordinated hand joint patterns activated at task-dependent time shifts -- from kinematic data is central to motor control and robotics. Existing two-stage methods first extract candidate waveforms (via SVD) and then select shifted templates using sparse optimization, requiring at least two datasets and complicating data collection. We introduce an optimization-based framework that jointly learns a small set of synergies and their sparse activation coefficients. The formulation enforces group sparsity for synergy selection and element-wise sparsity for activation timing. We develop an alternating minimization method in which coefficient updates decouple across tasks and synergy updates reduce to regularized least-squares problems. Our approach requires only a single data set, and simulations show accurate velocity reconstruction with compact, interpretable synergies.", "url": "" }, { "id": "Tu-TuC24.2", "code": "TuC24.2", "title": "Power Assistance for an NDT Gait Trainer Employing Clinical Tests", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC24", "sessionTitle": "Biomechanics and Physical Rehabilitation", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Yang, Deng-Chieh", "affiliation": "National Taiwan University" }, { "name": "Cheng, Hsin-Ti", "affiliation": "National Taiwan University" }, { "name": "Chung, Po-Hsuan", "affiliation": "National Taiwan University" }, { "name": "Chang, Chia-Wei", "affiliation": "National Taiwan University" }, { "name": "Chen, Szu-Fu", "affiliation": "Cheng Hsin General Hospital" }, { "name": "Wang, Fu-Cheng", "affiliation": "National Taiwan Univ" } ], "keywords": [ "Biomedical system modeling, identification, and simulation", "Clinical trial, clinical validation", "Dynamics and control of biologically motivated nonlinear systems" ], "abstract": "This paper presents a novel power-assistance system for a gait trainer based on Neuro-Developmental Treatment. The system dynamics were experimentally identified. We then applied loop-shaping techniques to design a robust controller and subsequently simplified it as a robust PI controller using particle swarm optimization. To validate the proposed system, we conducted clinical tests and accessed the training effects by two performance indices: swing-phase asymmetry and pelvic rotation amplitude. The experimental results demonstrated the efficiency of the power-assisted trainer in improving these indexes, offering a promising solution to reduce therapist workload and enhance post-stroke rehabilitation outcomes.", "url": "" }, { "id": "Tu-TuC24.3", "code": "TuC24.3", "title": "A Hammerstein Model of the Hand for Precise Control of Electrode Arrays", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC24", "sessionTitle": "Biomechanics and Physical Rehabilitation", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Hodgins, Lucy", "affiliation": "University of Southampton" }, { "name": "Freeman, Christopher Thomas", "affiliation": "University of Southampton" }, { "name": "Belkhatir, Zehor", "affiliation": "University of Southampton" } ], "keywords": [ "Biomedical system modeling, identification, and simulation", "Rehabilitation engineering and healthcare delivery" ], "abstract": "Stroke is a leading cause of disability worldwide, and current interventions are falling short, particularly regarding fine motor movement. Assistive technologies such as functional electrical stimulation offer a promising solution, but require effective control design. Difficulties in obtaining large quantities of data motivate model-based control, however there currently exist no models of the hand suitable for control design. This paper addresses this by deriving a novel Hammerstein model of the hand and wrist. This is validated on experimental data, resulting in 13% error reduction compared to existing models, with far less computational load. The proposed model is suitable for a wide range of control frameworks, facilitating transparent design, constraint handling, and robustness analysis.", "url": "" }, { "id": "Tu-TuC24.4", "code": "TuC24.4", "title": "Estimating Muscle Fibre Conduction Velocity Using Single Channel sEMG Autocorrelation and Its Potential As a Muscle Fatigue Metric", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC24", "sessionTitle": "Biomechanics and Physical Rehabilitation", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Morison, Harvey", "affiliation": "University of Canterbury" }, { "name": "Hayes, Michael", "affiliation": "University of Canterbury" }, { "name": "Pott, Peter Paul", "affiliation": "Universität Stuttgart" }, { "name": "Pretty, Christopher", "affiliation": "University of Canterbury" } ], "keywords": [ "Biomedical signal measurement and processing", "Biomedical system modeling, identification, and simulation", "Rehabilitation engineering and healthcare delivery" ], "abstract": "Muscle fatigue monitoring is important for stroke rehabilitation, especially when assistive robotic orthosis devices are used. Current sEMG-based metrics, such as RMS amplitude and mean frequency, are indirect and activity-dependent. This study explores the feasibility of using single-channel sEMG autocorrelation to estimate muscle fibre conduction velocity (MFCV) and using this as a fatigue metric. Firstly, a simulation was used to generate sEMG signals with known MFCV values to assess the theoretical accuracy of this method. The simulation showed that the accuracy is limited by electrode spacing and impulse frequency, but the method is still feasible for muscle fatigue monitoring. Next, an experimental trial recorded sEMG from the biceps brachii during intermittent isometric contractions at varying loads. Results show that the estimated MFCV responds well to the fatigue and recovery of the participant, with significant decreases at higher loads and partial recovery during rest. However, baseline MFCV variability limits standalone use. Despite these challenges, the method demonstrates potential for real-time fatigue monitoring in applications such as robotic rehabilitation and sports science. Future work will focus on refining electrode configurations and validating performance during dynamic movements.", "url": "" }, { "id": "Tu-TuC24.5", "code": "TuC24.5", "title": "ACL Tear Rates in and across Women's Football Leagues: Insights from a Unique 2-Year Database (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC24", "sessionTitle": "Biomechanics and Physical Rehabilitation", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Sewell, Jessica", "affiliation": "University of Canterbury" }, { "name": "Crespin, Vanessa", "affiliation": "Independent Researcher and Data Analyst" }, { "name": "Kryger, Katrine Okholm", "affiliation": "St Mary’s University" }, { "name": "Zhou, Cong", "affiliation": "Chinese Academy of Sciences" }, { "name": "Kaux, Jean-Francois", "affiliation": "University Hospital and University of Liège" }, { "name": "Bradley, Brendon", "affiliation": "University of Canterbury" }, { "name": "Desaive, Thomas", "affiliation": "University of Liege" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" } ], "keywords": [ "Biomedical and medical imaging, image processing, visualization", "Medical devices, systems and solutions", "Biomedical signal measurement and processing" ], "abstract": "ACL injuries carry a high burden in football and occur more frequently in women, yet epidemiological data for women’s leagues remain limited. This study uses publicly reported injury information (news, blogs, and social media) to construct a statistical framework for ACL incidence across elite women’s leagues and compare rates with the English Men’s Premier League. Poisson distributions were used to characterise league-level injury probability and estimate confidence intervals. Analyses included mean and median rates, inter-league comparisons, and estimating the number of injury-free games required for women’s leagues to match men’s incidence. Results show for the top 7 women’s premier leagues in the world based on international ranking for the country, an overall 3.2× higher ACL injury rate in women (p < 0.05), with nearly 4× variability across 7 women’s premier leagues. Sweden, the Netherlands, and Mexico showed no statistically significant difference from men, though raw incidence remained slightly higher. Findings demonstrate substantial between-league variability and highlight the need for improved, standardised injury reporting. Applying rigorous statistical methods provides new context for understanding ACL risk and guiding targeted future research.", "url": "" }, { "id": "Tu-TuC24.6", "code": "TuC24.6", "title": "Field-Based Biomechanics Screening for ACL Injury Risk in Female Footballers (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC24", "sessionTitle": "Biomechanics and Physical Rehabilitation", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Sewell, Jessica", "affiliation": "University of Canterbury" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" }, { "name": "Zhou, Cong", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Biomedical and medical imaging, image processing, visualization", "Biomedical signal measurement and processing", "Medical devices, systems and solutions" ], "abstract": "This study presents a continuous pitch-side monitoring trial for assessing knee biomechanics in female football players using a dual-camera setup and a force plate. The purpose of the trial was to continuously measure knee valgus/varus angles, knee height, time to stabilization (TTS), and peak vertical force fluctuations over a 2-month monitoring period, including a 3-day competition at the end of the 2- month training period. Six consistent athletes were monitored during training and tournament games. Data were captured pitch-side, analysed frame-by-frame in Kinovea and processed in MATLAB to generate key biomechanical metrics. Results demonstrated consistent trends across athletes and highlighted increased valgus loading and force variability during competitive play. This approach demonstrates the feasibility of rapid, non-invasive screening for ACL injury risk in real-world settings.", "url": "" }, { "id": "Tu-TuC25.1", "code": "TuC25.1", "title": "Differentiability of Classifier Decision Surface for Evaluating Faithfulness in Local Explanations (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC25", "sessionTitle": "JO-JSC: Biomedical and Environment Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Soroush, Kimia", "affiliation": "Tallinn University of Technology" }, { "name": "Nomm, Sven", "affiliation": "Tallinn University of Technology" }, { "name": "Belikov, Juri", "affiliation": "Tallinn University of Technology" } ], "keywords": [ "Decision support and control in medicine", "Biomedical system modeling, identification, and simulation", "Biomedical signal measurement and processing" ], "abstract": "Machine learning (ML) models are now widely used in areas such as healthcare, finance, and autonomous systems, but their decision-making processes are often difficult to understand. This lack of transparency makes it hard to trust these models, especially in situations where they are used in critical applications. Explainable Artificial Intelligence (XAI) offers tools to make ML models more understandable and provide explanations of predictions with popular algorithms such as LIME and SHAP. However, it is not always clear how reliable these explanations are. Among several evaluation criteria, faithfulness is one of the most important and measures whether explanations accurately capture the decision-making of the model. In this paper, we explored this metric and found that where faithfulness definition breaks often arise from intrinsic properties of the classifier, especially the smoothness of its decision surface, rather than the limitations of the explanation method. To investigate this, we introduce a smoothness metric for classifiers, propose three novel baselines for faithfulness evaluation, and provide a comprehensive implementation that extends existing libraries. These contributions provide a model-aware perspective on explanation quality and highlight the need for evaluation metrics that consider both the XAI method and the underlying classifier.", "url": "" }, { "id": "Tu-TuC25.2", "code": "TuC25.2", "title": "Dynamic Modeling and Integrated Control Framework for an Eye-Like Robot with Torsional–Positional Decoupling and Listing’s Law Enforcement (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC25", "sessionTitle": "JO-JSC: Biomedical and Environment Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Kassaeiyan, Pouya", "affiliation": "George Mason University" }, { "name": "Wei, Qi", "affiliation": "George Mason University" }, { "name": "Yao, Ningshi", "affiliation": "George Mason University" } ], "keywords": [ "Dynamics and control of biologically motivated nonlinear systems", "Biomedical system modeling, identification, and simulation" ], "abstract": "This paper presents a dynamic eye-like robot model that explicitly includes torsional motion, enabling independent control of pupil position and torsion within a quaternion-based single-rotation framework. Based on this model, two controllers are developed: a Geometrically Optimized Position Controller (GOPC) for fast, stable convergence along the optimal rotation axis, and a Neural Network–Based Inverse Dynamics Controller (NNBIDC) for data-driven approximation of nonlinear dynamics. A torsion-correction scheme enforces Listing’s law by constraining torsional motion at the torque level. MATLAB simulations validate accurate trajectory tracking and physiologically consistent torsion.", "url": "" }, { "id": "Tu-TuC25.3", "code": "TuC25.3", "title": "Motion Data-Driven Exercise Design for the Simultaneous Enhancement of Physical Capability and Psychological State (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC25", "sessionTitle": "JO-JSC: Biomedical and Environment Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Sato, Takao", "affiliation": "University of Hyogo" }, { "name": "Kawahara, Yoshiharu", "affiliation": "University of Hyogo" }, { "name": "Kawaguchi, Natsuki", "affiliation": "University of Hyogo" }, { "name": "Tsunoda, Yusuke", "affiliation": "Osaka University" } ], "keywords": [ "Healthcare management, disease control, critical care", "Medical devices, systems and solutions", "Rehabilitation engineering and healthcare delivery" ], "abstract": "This study proposes a dual-rate, data-driven system for automated ergometer load adjustment using Heart Rate (HR) and Heart Rate Variability (HRV). The system continuously collects HR and HRV data during exercise to estimate the user's real-time physiological state and dynamically adjust resistance, maintaining exercise intensity tailored to individual responses. Validation with human participants demonstrated improved HRV without compromising HR tracking performance, highlighting the potential of this approach for personalized training in clinical rehabilitation, athlete conditioning, and general fitness.", "url": "" }, { "id": "Tu-TuC25.4", "code": "TuC25.4", "title": "Tube-Based Robust Economic LPV Model Predictive Control for Pressurized Water Distribution Networks (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC25", "sessionTitle": "JO-JSC: Biomedical and Environment Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Li, Xiaohe", "affiliation": "Universitat Politècnica De Catalunya" }, { "name": "Blesa, Joaquim", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" } ], "keywords": [ "Optimal control and operation of environment systems" ], "abstract": "This paper investigates a tube-based robust economic linear parameter-varying (LPV) model predictive control for the optimal operation of pressurized water distribution networks (WDNs). A generalized network formulation incorporating nonlinear hydraulic head loss is developed to enhance the physical fidelity of the control model. In order to mitigate the resulting computational complexity, a LPV approximation is introduced, achieving an effective trade-off between accuracy and computational efficiency. Demand uncertainties are further addressed through a robust control design that ensures reliable system performance under varying consumption patterns. Moreover, the proposed approach determines which actuators should be activated and how their operations should be coordinated to achieve minimal operational cost, by optimally scheduling the pumps and valves to minimize costs while satisfying hydraulic and operational constraints. Finally, the effectiveness of the proposed strategy is demonstrated through simulation results conducted on the Richmond WDN.", "url": "" }, { "id": "Tu-TuC25.5", "code": "TuC25.5", "title": "Dissipative Boundary Control of a 2-D Navier Stokes Equation with Polytopic Uncertainties in the Form of Port-Hamiltonian Formulation (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC25", "sessionTitle": "JO-JSC: Biomedical and Environment Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Serrano, Fernando A.", "affiliation": "Universidad Nacional Autónoma De Honduras" }, { "name": "Muñoz-Pacheco, Jesús M.", "affiliation": "Universidd Autónoma De Puebla" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "Dankir, Sara", "affiliation": "Institut De Robòtica I Informàtica Industrial (CSIC-UPC), Carrer Llorens Artigas, 4-6, 08028 Barcelona, Spain , TED: AEEP, FPL, Ab" } ], "keywords": [ "Optimal control and operation of environment systems" ], "abstract": "This paper presents the derivation of a dissipative boundary control of a 2-D Navier- Stokes equation with polytopic uncertainties in the form of the port-Hamiltonian formulation. This research study aims to simulate by a numerical setup a numerical wave tank NWT in order to obtain the waves behaviour in order to analyze and protect offshore marine wind generators and wave energy converters. The main idea consist into obtaining the behavior of a water fluid in an numerical experimental tank in order to suppress the vorticity of the waves by a dissipative boundary controller. One of the main and novel contributions of this research study, is that the presence of polytopic uncertainties is found in the boundary conditions of the 2-D Navier-Stokes equation. In this case, a complete robustness analysis which consists in the robust stability and robust performance is evinced to to analyzed the sensitivity of parameters. Besides, a complete topological analysis of the polytopic uncertainties is performed in order to evince the open loop system behavior. Meanwhile, the dissipative boundary based controller is obtained by selecting an appropriate storage function. For dynamic analysis and boundary controller design purposes, the 2-D Navier-Stokes equation is established in the port-Hamiltonian formulation.", "url": "" }, { "id": "Tu-TuC26.1", "code": "TuC26.1", "title": "Control-Oriented Modelling Framework for Hypersonic Vehicles", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC26", "sessionTitle": "Advanced Guidance and Flight Control for Atmospheric Vehicles", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Poh, Clement", "affiliation": "University of Melbourne" }, { "name": "Bone, Viv", "affiliation": "University of Melbourne" }, { "name": "Manzie, Chris", "affiliation": "The University of Melbourne" }, { "name": "Nesic, Dragan", "affiliation": "Univ of Melbourne" } ], "keywords": [ "Flight dynamics modelling and identification" ], "abstract": "Hypersonic vehicle dynamics are complex and difficult to simplify for GNC purposes without introducing significant model error. Existing control-oriented modelling efforts incrementally refine initially simplified models. However, inheritance of implicit assumptions through this approach can make identification of sources of model error difficult. A full-order model incorporating vehicle ablation and a simplification framework leveraging perturbation theory is proposed for systematic synthesis of simplified models for GNC purposes based on explicitly identified assumptions. Recovery of an existing control model and its region of validity is demonstrated using the proposed framework.", "url": "" }, { "id": "Tu-TuC26.2", "code": "TuC26.2", "title": "Path-Following Control of a Small Airship Using Vector Field-Based Synergetic Synthesis", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC26", "sessionTitle": "Advanced Guidance and Flight Control for Atmospheric Vehicles", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Tang, Ning", "affiliation": "ITMO University" }, { "name": "Wang, Zeyu", "affiliation": "Faculty of Computer Science and Control Systems, BMSTU Russia" }, { "name": "Zhivitskii, Andrei", "affiliation": "ITMO University" }, { "name": "Liu, Yixin", "affiliation": "Beihang University" }, { "name": "Fu, Li", "affiliation": "School of Automation Science and Electrical Engineering, Beihang University" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Trajectory tracking and path following for AVs", "Motion control for AVs" ], "abstract": "This paper presents a path-following control strategy for a small airship, developed using the synergetic synthesis method with a vector field. The controller is designed based on the principles of synergetic synthesis, incorporating deviations in position, linear velocity, and angular velocity. Stability of the closed-loop system is rigorously analyzed using the direct Lyapunov method. Numerical simulations demonstrate the effectiveness and robustness of the proposed approach in ensuring accurate path following.", "url": "" }, { "id": "Tu-TuC26.3", "code": "TuC26.3", "title": "The E-Rocket: Low-Cost Testbed for TVC Rocket GNC Validation", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC26", "sessionTitle": "Advanced Guidance and Flight Control for Atmospheric Vehicles", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Santos, Pedro", "affiliation": "Instituto Superior Técnico" }, { "name": "Fonte, André", "affiliation": "Instituto Superior Técnico" }, { "name": "Almeida Martins, Pedro", "affiliation": "Instituto Superior Técnico" }, { "name": "Oliveira, Paulo Jorge", "affiliation": "Instituto Superior Técnico" } ], "keywords": [ "Aerial and space robotics", "Avionics and on-board equipments", "Guidance, navigation and control for AVs" ], "abstract": "This paper presents the E-Rocket, an electric-powered, low-cost rocket prototype for validation of Guidance, Navigation & Control (GNC) algorithms based on Thrust Vector Control (TVC). Relying on commercially available components and 3D printed parts, a pair of contra-rotating DC brushless motors is assembled on a servo-actuated gimbal mechanism that provides thrust vectoring capability. A custom avionics hardware and software stack is developed considering a dual computer setup which leverages the capabilities of the PX4 autopilot and the modularity of ROS 2 to accommodate for tailored GNC algorithms. The platform is validated in an indoor motion-capture arena using a baseline PID-based trajectory tracking controller. Results demonstrate accurate trajectory tracking and confirm the suitability of the E-Rocket as a versatile testbed for rocket GNC algorithms.", "url": "" }, { "id": "Tu-TuC26.4", "code": "TuC26.4", "title": "Velocity-Free Constrained Predictive Sliding Mode Control for Quadrotor Trajectory Tracking", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC26", "sessionTitle": "Advanced Guidance and Flight Control for Atmospheric Vehicles", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Gabrielyan, Yeva", "affiliation": "Center for Scientific Inovation and Education" }, { "name": "Khodaverdian, Maria", "affiliation": "National Polytechnic University of Armenia, and the Center for Scientific Innovation and Education" }, { "name": "Henry, David", "affiliation": "Université De Bordeaux" }, { "name": "Castaldi, Paolo", "affiliation": "University of Bologna" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Aerospace mission control and operations", "Aerial and space robotics" ], "abstract": "In this work, we develop a velocity-free constrained predictive sliding mode control (PSMC) scheme for trajectory tracking of a quadrotor UAV. A Kalman filter is employed to attenuate noise in position measurements and to estimate the unmeasured linear velocity as well as lumped disturbances. Leveraging the predictive structure of the proposed controller, the tracking problem is formulated as a constraint optimization program. Using Lyapunov-based analysis, we show that the observer-based sliding variable converges to a bounded neighborhood of the origin within fixed time. Close-to-reality simulations demonstrate the effectiveness of the proposed approach.", "url": "" }, { "id": "Tu-TuC26.5", "code": "TuC26.5", "title": "Flight Testing Blending-Based Active Flutter Suppression", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC26", "sessionTitle": "Advanced Guidance and Flight Control for Atmospheric Vehicles", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Pusch, Manuel", "affiliation": "Munich University of Applied Sciences" }, { "name": "Ossmann, Daniel", "affiliation": "Munich University of Applied Sciences HM" }, { "name": "Konatala, Ramesh", "affiliation": "German Aerospace Center (DLR)" }, { "name": "Wüstenhagen, Matthias", "affiliation": "German Aerospace Center" }, { "name": "Süelözgen, Özge", "affiliation": "German Aerospace Center" }, { "name": "Looye, Gertjan", "affiliation": "German Aerospace Center DLR" }, { "name": "Kier, Thiemo", "affiliation": "DLR" } ], "keywords": [ "Flight dynamics modelling and identification", "Guidance, navigation and control of aircraft and spacecraft" ], "abstract": "This paper presents flight test results of an active flutter suppression controller based on static mode decoupling with H2-optimal blending of inputs and outputs. The method isolates the critical aeroelastic modes, enabling simple single-input single-output control design. Implemented on a 7-m-span flutter demonstrator aircraft, the stabilization of the flutter modes is demonstrated by increasing modal damping. The results confirm the practical effectiveness of static mode decoupling for active flutter suppression on highly flexible aircraft, thereby extending the flight envelope.", "url": "" }, { "id": "Tu-TuC26.6", "code": "TuC26.6", "title": "Sliding Mode Control with Explicit Actuator Dynamics: Derivation and Analysis", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC26", "sessionTitle": "Advanced Guidance and Flight Control for Atmospheric Vehicles", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Rithburi, Panithan", "affiliation": "Technical University of Munich Asia" }, { "name": "Steinert, Agnes", "affiliation": "Technical University of Munich" }, { "name": "Holzapfel, Florian", "affiliation": "Technische Universität München" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft" ], "abstract": "Most conventional controller designs assume ideal actuators that track commands instantaneously but often ignore the actuator dynamics, which can typically be represented as a first-order system. This paper presents a systematic derivation of a sliding mode control (SMC) law for nonlinear systems that explicitly accounts for actuator dynamics. By appropriately formulating the state-space representation, the control input appears explicitly in the SMC formulation. The proposed SMC law is compared with a nonlinear dynamic inversion (NDI) scheme that also incorporates actuator dynamics. Numerical simulations are provided to compare performance and demonstrate the robustness of the SMC approach under different actuator bandwidths. The simulations also compare SMC and NDI under disturbances and model uncertainties.", "url": "" }, { "id": "Tu-TuC27.1", "code": "TuC27.1", "title": "3D Path Following of AUV Using Virtual Reference Point Guidance with Attitude Control", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC27", "sessionTitle": "AUV/UUV Guidance, Control and Mission Planning", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Mallipeddi, Siva Kumar", "affiliation": "University of Bologna" }, { "name": "Matous, Josef", "affiliation": "NTNU (Norwegian University of Science and Technology)" }, { "name": "Varagnolo, Damiano", "affiliation": "NTNU - Norwegian University of Science and Technology" }, { "name": "Castaldi, Paolo", "affiliation": "University of Bologna" } ], "keywords": [ "Autonomous marine systems and vehicles", "Marine system guidance, navigation and control" ], "abstract": "This work explores how Virtual Reference Point (VRP)-based guidance frameworks, that place control references ahead of vehicles, may add stabilizing effects and enable sensor-aligned navigation for 3D path-following underactuated Autonomous Underwater Vehicles (AUVs). The paper thus investigates how the forward displacement of the VRP introduces geometric coupling that helps overcome underactuation by generating sway and heavy like behavior from available surge, pitch and yaw like actuation. The work moreover builds on earlier work where Nonlinear Model Predictive Control was used to regulate in-plane motion, and extends the framework to full 3D path following, proposing thus an approach that jointly regulates cross-track and attitude errors while incorporating vertical dynamics and environmental disturbances such as ocean currents. By doing so, the proposed approach fills the gap left by previous VRP-based methods, that have shown bounded internal attitude dynamics while not guaranteeing attitude tracking.", "url": "" }, { "id": "Tu-TuC27.2", "code": "TuC27.2", "title": "Hierarchical Trajectory Tracking of Underwater Vehicle Using Improved Safe TD3 Reinforcement Learning and MFAC", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC27", "sessionTitle": "AUV/UUV Guidance, Control and Mission Planning", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Behzadi, Saadat", "affiliation": "University of Bologna" }, { "name": "Emami, Seyyed Ali", "affiliation": "Sharif University of Technology" }, { "name": "Menghini, Massimiliano", "affiliation": "UNIBO" }, { "name": "Castaldi, Paolo", "affiliation": "University of Bologna" } ], "keywords": [ "Marine system guidance, navigation and control", "AI and embodied-AI in marine systems", "Trajectory and path planning for AVs" ], "abstract": "In this paper, a hierarchical structure for AUV trajectory tracking is proposed, in which the guidance loop is based on safe deep reinforcement learning (safe DRL with safety layer) and the inner loop is based on model-free adaptive control (MFAC) with an extended state observer. To improve the tracking accuracy and policy generalization capability, the state space of DLR is enhanced by adding “lookahead curvature” feature to encode future path variations, a GRU-based actor is employed to strengthen the temporal modeling, and simultaneous training is performed in multiple environments. Simulation results show that the proposed method provides lower tracking error and more stable and smoother control commands in the presence of hydrodynamic uncertainties, noise measurements, external disturbances, and actuator faults, compared to the simple PID–MFAC, SAC, and simple TD3–MFAC structures.", "url": "" }, { "id": "Tu-TuC27.3", "code": "TuC27.3", "title": "AUV Trajectory Planning Using SAC with Adaptive Multi-Step Returns and Tangent Subgoal Planning", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC27", "sessionTitle": "AUV/UUV Guidance, Control and Mission Planning", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Tang, YuTe", "affiliation": "Hunan University of Science and Technology" }, { "name": "Chen, ChaoYang", "affiliation": "Hunan University of Science and Technology" }, { "name": "Yang, Dan", "affiliation": "Hunan University of Science and Technology" }, { "name": "He, Lei", "affiliation": "Hunan University of Science and Technology" } ], "keywords": [ "Autonomous marine systems and vehicles", "Trajectory and path planning for AVs", "Learning and adaptation in autonomous vehicles" ], "abstract": "，轨迹规划对于提升 自主水下载具的作战能力 在复杂的海洋环境中。当前的深层加固 学习方法存在着不够长远视野的问题 决策能力不足，收敛稳定性差。 本文提出了多步回报软化方案 Actor-Critic（MSR-SAC）算法。该算法增强了 通过自适应评估多步骤实现的长远规划 通过轨迹截断和动态调整返回 克服单步近视的决策视野。 整合切线子目标规划模块提供 局部路径引导并减少冗余 障碍避让路径。复合奖励函数 合并多维目标，包括任务 完备度、路径最优性、运动平滑性、能量 效率和安全避开障碍。模拟结果 证明MSR-SAC优于主流DDPG、TD3， 以及SAC算法的收敛速度和累积奖励， 任务成功率为26.7% 进步。", "url": "" }, { "id": "Tu-TuC27.4", "code": "TuC27.4", "title": "Population-Based Hybrid PSO for Multi-UUV Cooperative Path Planning", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC27", "sessionTitle": "AUV/UUV Guidance, Control and Mission Planning", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Zhao, Maozhi", "affiliation": "Harbin Engineering University" }, { "name": "Wang, Hongjian", "affiliation": "College of Automation, Harbin Engineering University, Harbin 150001" }, { "name": "Shan, Ziqi", "affiliation": "Harbin Engineering University" }, { "name": "Yan, Wen", "affiliation": "Harbin Engineering University" }, { "name": "Wu, Hao", "affiliation": "Harbin Engineering University" }, { "name": "Song, Shaozheng", "affiliation": "Harbin Engineering University" } ], "keywords": [ "Autonomous marine systems and vehicles", "Decision and support in marine systems", "Modelling, identification and control in marine systems" ], "abstract": "Multi-UUV collaborative path planning is crucial for intelligent underwater operations. This paper proposes a population-enhanced hybrid particle swarm optimization algorithm(GW-CPSO) that integrates logistic chaotic initialization, multi-learning adaptation, and grey wolf–based velocity adjustment to balance exploration and exploitation. Population diversity monitoring and stagnation detection are employed to prevent premature convergence. Simulations in dynamic underwater environments with multi-UUV swarms demonstrate that the proposed GW-CPSO achieves faster convergence and higher path quality than other algorithms, showing its effectiveness for multi-UUV cooperative decision-making tasks.", "url": "" }, { "id": "Tu-TuC27.5", "code": "TuC27.5", "title": "Adaptive Reference Control for Depth Regulation of a Biomimetic Robotic Fish (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC27", "sessionTitle": "AUV/UUV Guidance, Control and Mission Planning", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "MacLin, Gage", "affiliation": "The University of Iowa" }, { "name": "Bibuli, Marco", "affiliation": "CNR-INM" }, { "name": "Cichella, Venanzio", "affiliation": "University of Iowa" } ], "keywords": [ "Autonomous marine systems and vehicles", "Modelling, identification and control in marine systems" ], "abstract": "This paper presents an adaptive reference control strategy for depth regulation of a biomimetic robotic fish equipped with multi-actuated caudal and lateral fins and a dual-bladder buoyancy system. The proposed approach integrates an L 1 adaptive reference controller with a baseline LQR autopilot to compensate for nonlinearities, model uncertainties, and environmental disturbances. The controller design is detailed, including tuning guidelines for practical implementation. Simulation studies and experimental tests at sea demonstrate improved tracking performance, reduced overshoot, and robustness to parameter variations compared to conventional linear controllers. These results highlight the potential of adaptive augmentation for biologically inspired underwater vehicles operating in uncertain conditions.", "url": "" }, { "id": "Tu-TuC27.6", "code": "TuC27.6", "title": "LLM-Assisted Planning with Distributed Onboard Behavior Tree Execution for Multi-AUV Missions", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC27", "sessionTitle": "AUV/UUV Guidance, Control and Mission Planning", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Khorrambakht, Ehsan", "affiliation": "University of Pisa" }, { "name": "Caissutti, Cristiano", "affiliation": "University of Pisa" }, { "name": "Munafo, Andrea", "affiliation": "National Oceanography Centre" }, { "name": "Caiti, Andrea", "affiliation": "Univ. of Pisa" } ], "keywords": [ "AI and embodied-AI in marine systems", "Mission planning and decision making for AVs", "Multi-vehicle systems" ], "abstract": "This paper proposes a hierarchical framework integrating Large Language Models (LLMs) with Behavior Trees to support natural-language mission specification and distributed execution for multi-AUV operations. An LLM-based planner extracts task–robot assignments and ordering constraints from operator instructions and embeds them in a Mixed-Integer Linear Program that generates capability-aware and temporally consistent task allocations. A BT-LLM Bridge dispatches validated plans to each vehicle, where onboard BT Mission Managers execute tasks autonomously. Robotic Agents monitor local health, detect vehicle-level faults, and trigger selective replanning while allowing unaffected robots to continue their missions. Experiments in a ROS-based AUV simulator evaluate constraint-extraction accuracy using two LLMs (Qwen-3-8B and Llama-3.1-8B) and robustness to soft and hard failures.", "url": "" }, { "id": "Tu-TuC32.1", "code": "TuC32.1", "title": "A Cell-Decomposition Based Path Planner for 3D Navigation in Constrained Workspaces", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC32", "sessionTitle": "Task and Motion Planning", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Lemos Morais, Joao Pedro", "affiliation": "Federal University of Minas Gerais" }, { "name": "Pimenta, Luciano", "affiliation": "Universidade Federal De Minas Gerais" }, { "name": "Santos, Marcelo Alves", "affiliation": "University of Bergamo" }, { "name": "Raffo, Guilherme Vianna", "affiliation": "Federal University of Minas Gerais" } ], "keywords": [ "Task and motion planning", "Autonomous navigation", "Aerial, field, and marine robotics" ], "abstract": "This paper proposes a cell decomposition algorithm for binary occupancy grids that ensures mutual complete visibility from each cell to at least one adjacent cell. This decomposition establishes a simplified framework for verifying path feasibility that can be easily embedded in optimization problems. To illustrate its utility, we formulate both second-order cone programs (SOCP) and their mixed-integer variant (MISOCP) within the proposed framework. Furthermore, we propose the KSP-SOCP method, which combines Yen's k-shortest path algorithm with the SOCP, achieving improved solutions compared to a standard SOCP approach while avoiding the computational burden of MISOCP. The cell decomposition algorithm, KSP-SOCP, and MISOCP approaches were evaluated in 9 city-like workspaces. The decomposition efficiently partitioned each map, enabling both optimization methods to compute feasible paths. The proposed KSP-SOCP achieved time performance comparable to the MISOCP while requiring less memory, making it highly suitable for large-scale problems.", "url": "" }, { "id": "Tu-TuC32.2", "code": "TuC32.2", "title": "UAV Trajectory Planning and Control for 3D Gaussian Splatting-Based Reconstruction", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC32", "sessionTitle": "Task and Motion Planning", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Nakajima, Kohei", "affiliation": "Waseda University" }, { "name": "Wasa, Yasuaki", "affiliation": "Waseda University" } ], "keywords": [ "Autonomous navigation", "Task and motion planning", "Aerial, field, and marine robotics" ], "abstract": "This paper presents a trajectory planning and control framework for unmanned aerial vehicles (UAVs) that actively acquires images to compensate for under-observed and information-poor regions in 3D models generated by 3D Gaussian Splatting (3DGS). Multiple input images are fused into a 3DGS representation, in which Gaussian attributes associated with each 3D point encode transparency and uncertainty and are used to identify incomplete or unreliable regions of the scene. By interpreting these attributes as probabilistic measures of collision risk and embedding spatial continuity into the cost function, we formulate a safe and efficient trajectory-planning problem that is solved using the A* algorithm. The resulting trajectory is tracked by a model predictive controller, enabling the UAV to autonomously navigate through the environment and capture additional viewpoints that improve the fidelity of the 3DGS model. The effectiveness of the proposed control framework and the corresponding 3DGS-based reconstruction is demonstrated in simulations with Unreal Engine 5.", "url": "" }, { "id": "Tu-TuC32.3", "code": "TuC32.3", "title": "Radioactive Source Seeking Using Bayesian Optimisation with Movement Penalty", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC32", "sessionTitle": "Task and Motion Planning", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Miller, Lysander", "affiliation": "University of Melbourne" }, { "name": "Keene, Joshua", "affiliation": "The University of Melbourne" }, { "name": "Brown, Jeremy M C", "affiliation": "Swinburne University of Technology" }, { "name": "Chapman, Airlie Jane", "affiliation": "University of Melbourne" } ], "keywords": [ "Autonomous navigation", "Task and motion planning", "Aerial, field, and marine robotics" ], "abstract": "The use of mobile robotics in radioactive source seeking has become an important part of modern radiation-safety practices, supporting timely mitigation of contamination risks and helping protect public health. However, measuring radiation is often time-consuming, rendering traditional gradient-based source-seeking methods less effective due to their lower sample efficiencies. This paper proposes a sample-efficient Bayesian-optimisation source-seeking strategy that utilises a heteroscedastic Gaussian process surrogate to balance exploration and exploitation. Excessive inter-sample travel is discouraged through a movement switching cost. The strategy is shown to generate sublinear regret in the source-seeking task, while simulations demonstrate its effectiveness in localising radioactive sources.", "url": "" }, { "id": "Tu-TuC32.4", "code": "TuC32.4", "title": "An Enhanced Multi-Agent Framework Balancing Local Exploration and Global Coverage in Urban Environments", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC32", "sessionTitle": "Task and Motion Planning", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Ibarra, David Octavio", "affiliation": "Universidad of Los Andes" }, { "name": "Lopez-Jimenez, Jorge", "affiliation": "Universidad De Los Andes" }, { "name": "Quijano, Nicanor", "affiliation": "Universidad De Los Andes" } ], "keywords": [ "Autonomous navigation", "Task and motion planning", "Aerial, field, and marine robotics" ], "abstract": "This paper addresses the challenge of multi-agent exploration in unknown urban environments under energy constraints. A unified framework is proposed, integrating Heat Equation Driven Area Coverage (HEDAC) for global coordination with Rapidly-exploring Random Trees (RRT) for local obstacle navigation, mediated by a hysteresis-equipped switching automaton to avoid chattering between the two regimes. An agent repulsion mechanism implemented as Gaussian heat sinks in the HEDAC source term reduces redundancy without explicit inter-agent communication. The framework is platform-agnostic and is validated on quadrotor agents across three OpenStreetMap-derived urban scenarios with 15–45% obstacle density. A head-to-head comparison against an external ergodic-coverage baseline (Patel et al., 2021) and against the HEDAC-only and RRT-Boustrophedon ablations shows that the unified framework Pareto-dominates all three: it adds 7–18 pp of final coverage over the ergodic baseline at ∼25% lower energy, recovers 11–26 pp of coverage that local minima cost a pure HEDAC follower in dense scenarios, and cuts redundancy and energy by ∼30% with respect to a sampling-only sweep. A complementary starting-position study confirms that the heat-sink mechanism disperses arbitrary clusters, and corner initialization is the most efficient. The architecture exposes explicit control but leaves the features open to implementing learning-based controllers.", "url": "" }, { "id": "Tu-TuC32.5", "code": "TuC32.5", "title": "From DEM to Terrain‑Class Macroregions and Primitives for Complex Seafloor Coverage Planning", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC32", "sessionTitle": "Task and Motion Planning", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Candeloro, Mauro", "affiliation": "Monterey Bay Aquarium Research Institute (MBARI)" }, { "name": "Lekkas, Anastasios M.", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Baronia, Shreya", "affiliation": "American High School of Fremont" }, { "name": "Caress, David", "affiliation": "Monterey Bay Aquarium Research Institute (MBARI)" } ], "keywords": [ "Task and motion planning", "Aerial, field, and marine robotics", "Autonomous navigation" ], "abstract": "We present a digital elevation model (DEM)-based terrain-class and primitive-aware segmentation framework for near-bottom seafloor mapping. Morphometric cues, including slope, vector ruggedness measure (VRM), bathymetric position index (BPI), curvature, and aspect, support terrain-policy classification, region aggregation, and heading assignment. The DEM is classified into four terrain policies: Flat, Gentle, Steep, and Ridge/Valley. Superpixel segmentation and graph merging produce coherent macroregions, followed by cleanup into planning regions compatible with local strip coverage. Within Steep and Ridge/Valley regions, compact, high-prominence features are extracted as primitives. The resulting map provides a compact representation for downstream terrain-aware survey planning.", "url": "" }, { "id": "Tu-TuC32.6", "code": "TuC32.6", "title": "A Robotic Control System for a Drill Jumbo with One Articulated Arm: Full-Scale Testing", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC32", "sessionTitle": "Task and Motion Planning", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Tønnessen, Jonas", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Gravdahl, Jan Tommy", "affiliation": "Norwegian University of Science and Technology (NTNU)" }, { "name": "Pettersen, Kristin Y.", "affiliation": "Norwegian Univ. of Science and Tech" } ], "keywords": [ "Aerial, field, and marine robotics", "Task and motion planning", "Mechatronics for robotic systems" ], "abstract": "This paper presents a robotic control system for an industrial drill jumbo with one articulated arm, developed for the AMV ZETA X boom, aiming to enhance the accuracy, efficiency, and safety of drill-and-blast tunneling operations. The system combines a Product-of-Exponentials forward kinematics model with a Newton-Raphson numerical inverse kinematics solver to compute joint configurations for a 6-DOF manipulator. Time-optimal joint trajectories are generated using ROS2/MoveIt2, and are tracked by PID controllers acting on the hydraulic actuators. The seventh joint, a feeder telescope, is controlled separately to establish and maintain contact with the rock face during drilling. The complete control algorithm autonomously executes a given digital drill plan, moving the 7-DOF manipulator from the current blast hole to the next and drilling each hole to its specified depth. The control system was experimentally validated in full-scale tests in Flekkefjord, Norway, and the Trælen mine, Norway, where multiple blast holes were drilled autonomously, achieving an average drilled hole accuracy of 4.6 cm and 19.1 cm, respectively. These results represent the first full-scale demonstration of numerical inverse-kinematics-based automatic control for drill jumbos and demonstrate the feasibility of the approach, marking an important step toward fully autonomous underground drilling rigs.", "url": "" }, { "id": "Tu-TuC33.1", "code": "TuC33.1", "title": "Robot Modeling with Autoregressive Physics-Informed Neural Networks", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC33", "sessionTitle": "Machine Learning for Modeling and Prediction", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Fañanás-Anaya, Javier", "affiliation": "Universidad De Zaragoza" }, { "name": "Lopez-Nicolas, Gonzalo", "affiliation": "Universidad De Zaragoza" }, { "name": "Sagues, Carlos", "affiliation": "Universidad De Zaragoza" } ], "keywords": [ "Machine learning for modeling and prediction", "AI-driven modeling and control" ], "abstract": "Accurate and efficient system modeling is essential for control applications. Analytical models offer interpretability but often become unfeasible for complex systems, while data-driven neural networks require large datasets and may lack robustness. When system equations are known, Physics-Informed Neural Networks (PINNs) provide a powerful alternative by combining physics with data. In this work, we introduce AR-PINN, which addresses key limitations of PINN-based frameworks in control by handling time-varying control inputs and improving stability and accuracy over long prediction horizons. Simulations on a Two-Link Manipulator show that AR-PINN achieves high accuracy, robustness across different control scenarios, and a low computational cost.", "url": "" }, { "id": "Tu-TuC33.2", "code": "TuC33.2", "title": "Detection of Incorrect Fastener Installation in Aircraft Equipment of Different Standards", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC33", "sessionTitle": "Machine Learning for Modeling and Prediction", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Zhang, Yuanhao", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Yin, Chun", "affiliation": "University of ElectronicScience and Technology of China, Chengdu611731, P.R. China" }, { "name": "Liu, Junyang", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Cao, Jiuwen", "affiliation": "Hangzhou Dianzi University" } ], "keywords": [ "Machine learning for modeling and prediction", "Knowledge-based and data-driven control", "Intelligent human-machine interaction" ], "abstract": "The assembly quality of fasteners in aircraft power distribution equipment is critical for aviation safety but challenged by diverse standards. To bridge the gap from ``perception'' to ``process semantics,'' we propose a two-stage framework fusing deep learning with template priors. It first uses an improved YOLOv12 for high-precision, lightweight feature recognition, then employs a deformable matching algorithm to align templates with targets for logical verification. Experiments show that the framework achieves 97.67% mAP@0.5, improving mAP@0.5 by 4.32 percentage points and reducing GFLOPs from 5.8 to 3.9 over the baseline, offering a reusable paradigm for automated verification across various assembly standards.", "url": "" }, { "id": "Tu-TuC33.3", "code": "TuC33.3", "title": "Hybrid Adaptive Framework for Modeling of Industrial-Scale Primary Separation Vessels", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC33", "sessionTitle": "Machine Learning for Modeling and Prediction", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Mohammadghasemi, Hossein", "affiliation": "University of Alberta" }, { "name": "Hourfar, Farzad", "affiliation": "University of Alberta" }, { "name": "Soesanto, Jansen Fajar", "affiliation": "University of Alberta" }, { "name": "Modir Rousta, Mohammadhossein", "affiliation": "University of Alberta" }, { "name": "Huang, Biao", "affiliation": "Univ. of Alberta" } ], "keywords": [ "Machine learning for modeling and prediction", "AI-driven modeling and control", "AI tools in automation engineering and operation" ], "abstract": "Adaptive weighted hybrid modeling integrates mechanistic and data-driven models for industrial-scale Primary Separation Vessels through dynamic weight assignment mechanisms. The Weight Assignment Network adaptively learns model contributions based on operating conditions and predictions, incorporating bias correction to reconcile plant-model mismatches. The framework operates in steady-state and dynamic modes, capturing both equilibrium and transient behaviors. Validation across multiple ore grades demonstrates superior performance. The approach leverages complementary modeling strengths through output-specific and condition dependent weighting strategies, significantly outperforming individual mechanistic or data-driven models.", "url": "" }, { "id": "Tu-TuC33.4", "code": "TuC33.4", "title": "Turbidity-Driven Evaluation of Multiclass Segmentation Models for Underwater Perception", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC33", "sessionTitle": "Machine Learning for Modeling and Prediction", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Ozalla Sánchez, Miguel", "affiliation": "Aalborg University" }, { "name": "Vibild, Patrick Dominique", "affiliation": "Aalborg University" }, { "name": "Mai, Christian", "affiliation": "Aalborg University" }, { "name": "Liniger, Jesper", "affiliation": "Aalborg University" } ], "keywords": [ "Machine learning for modeling and prediction", "Data fusion and mining in control", "AI-driven modeling and control" ], "abstract": "Accurate visual perception is essential for autonomous underwater inspection, yet turbidity severely degrades image quality. This paper presents a synthetic-to-real evaluation of five segmentation models trained on underwater scenes rendered with controlled levels of turbidity. Results show that introducing turbidity during rendering significantly improves robustness when transferring to real imagery, with Mask2Former achieving the highest accuracy across all conditions. A total of 25,382 synthetic images and 180 real annotated samples were used to analyze performance across six turbidity levels. Findings highlight turbidity as a key modeling parameter for developing reliable, transferable underwater perception systems", "url": "" }, { "id": "Tu-TuC33.5", "code": "TuC33.5", "title": "Coordinated Multi-Class SVM Training Via ADMM", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC33", "sessionTitle": "Machine Learning for Modeling and Prediction", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Kim, Sohyun", "affiliation": "Korea Advanced Institute of Science and Technology (KAIST)" }, { "name": "Hur, Jik", "affiliation": "Korea Aerospace Industries, LTD. (KAI)" }, { "name": "Shin, Hyo-Sang", "affiliation": "Korea Advanced Institute of Science and Technology" } ], "keywords": [ "Machine learning for modeling and prediction", "Data fusion and mining in control", "Bio-inspired algorithms and optimization-based control" ], "abstract": "Conventional One-vs-Rest (OvR) approaches for multi-class Support Vector Machines (SVMs) train binary classifiers independently, ignoring inter-class relationships and often resulting in suboptimal decision boundaries. While joint multi-class formulations resolve this, they suffer from severe scalability limits. To bridge this gap, we propose a coordinated training framework using the Alternating Direction Method of Multipliers (ADMM). By introducing consensus variables, we structurally decouple the joint margin objective into parallelizable sub-problems while enforcing globally consistent margins across all competing classes. Experimental results demonstrate that the proposed framework achieves higher classification accuracy and greater robustness against hyperparameter variations compared to conventional methods.", "url": "" }, { "id": "Tu-TuC33.6", "code": "TuC33.6", "title": "Genetic Algorithm-Based Feature Selection for CNC Energy Consumption Time Series Prediction", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC33", "sessionTitle": "Machine Learning for Modeling and Prediction", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Kader, Hafez", "affiliation": "Autonomous Multisensor Systems Group, Otto Von Guericke University Magdeburg, Germany" }, { "name": "Ströbel, Robin", "affiliation": "Wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT)" }, { "name": "Puchta, Alexander", "affiliation": "Wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT)" }, { "name": "Fleischer, Jürgen", "affiliation": "Wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT)" }, { "name": "Noack, Benjamin", "affiliation": "Otto Von Guericke University (OVGU)" }, { "name": "Spiliopoulou, Myra", "affiliation": "Otto Von Guericke University Magdeburg" } ], "keywords": [ "Machine learning for modeling and prediction" ], "abstract": "Accurate prediction of energy consumption in computerized numerical control machines requires feature subsets that are both compact and informative. We propose a feature-selection framework based on a multiobjective genetic algorithm with relevance-guided initialization, crossover, and mutation derived from spearman-based statistics. The method jointly optimizes prediction accuracy, sparsity, relevance, and redundancy, yielding stable and interpretable subsets. Evaluated on computerized numerical control energy time-series data and compared with mutual information filtering, forward selection, and Lasso regression, the approach achieves superior predictive performance, greater robustness to noise, and improved interpretability. The results demonstrate its effectiveness for energy-aware machining and its suitability for deployment in industrial environments.", "url": "" }, { "id": "Tu-TuC34.1", "code": "TuC34.1", "title": "Risk-Sensitive Graph Reinforcement Learning for Reliable Cloud Service Upgrade Planning", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC34", "sessionTitle": "AI for Smart Cities", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Xia, Tianyi", "affiliation": "Xi'an Jiaotong University" }, { "name": "Tian, Zhaoming", "affiliation": "Xi'an Jiaotong University" }, { "name": "Cao, Xiaoyu", "affiliation": "Xi'an Jiaotong University" } ], "keywords": [ "Data centers and cloud computing", "AI for smart cities", "Smart city control and optimization" ], "abstract": "Cloud service upgrade planning is critical for maintaining the reliability and availability of interdependent microservices. This planning task is exceptionally complex and high-risk, as it must satisfy intricate topological compatibility constraints while operating in inherently stochastic execution environments, where misplanned upgrades can cause local faults to cascade into system-wide failures. In this work, we, for the first time, seek a solution from a risk-management perspective and introduce a novel risk-sensitive reinforcement learning framework. Our method integrates a Graph Neural Network (GNN) with Cross-Attention to encode complex dependencies and learns a risk-averse policy by explicitly optimizing a Conditional Value-at-Risk (CVaR) objective via Implicit Quantile Networks. Extensive experiments demonstrate that the proposed framework significantly outperforms baseline method, maintaining an 80% success rate under severe stochasticity.", "url": "" }, { "id": "Tu-TuC34.2", "code": "TuC34.2", "title": "Stochastic Scheduling of Green Data Centers Based on NASS Framework", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC34", "sessionTitle": "AI for Smart Cities", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Huang, Yiheng", "affiliation": "Xi'an Jiaotong University" }, { "name": "Chen, Mengxiao", "affiliation": "Xi'an Jiaotong University" }, { "name": "Cao, Xiaoyu", "affiliation": "Xi'an Jiaotong University" }, { "name": "Sun, Xunhang", "affiliation": "Xi'an Jiaotong University" }, { "name": "Yang, Lun", "affiliation": "Xi'an Jiaotong University" }, { "name": "Ren, Hourui", "affiliation": "Xi'an Jiaotong University" }, { "name": "Xue, Zhichao", "affiliation": "Xi'an Jiaotong University" } ], "keywords": [ "Data centers and cloud computing" ], "abstract": "Toward the goal of global carbon neutrality, data centers are increasingly relying on on-site renewable energy generation. However, intermittent and random renewable energy outputs pose significant risks on data center operation. This paper proposes a two-stage stochastic programming (SP) model for green data center cluster scheduling. The first stage determines the optimal day-ahead grid procurement strategy. The second stage captures intraday operation under a set of stochastic scenarios. To overcome the high computational burden of SP, we introduce a Neural-Adjustment for Stochastic Scheduling (NASS) framework, which leverages a neural network to refine deterministic baseline schedules and efficiently generate dayahead decisions. Numerical results demonstrate that the proposed method can significantly improve computational efficiency while preserving decision quality, providing a practical tool for managing green data centers under uncertain environment.", "url": "" }, { "id": "Tu-TuC34.3", "code": "TuC34.3", "title": "Event-Triggered-Based Adaptive Fault-Tolerant Control for Flapping Wing-UAVs in the Low-Altitude Economy Scenario", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC34", "sessionTitle": "AI for Smart Cities", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Lin, Shengping", "affiliation": "Liaoning University of Technology, Jinzhou" }, { "name": "Tang, Li", "affiliation": "Liaoning University of Technology" }, { "name": "Liu, Yan-Jun", "affiliation": "Liaoning University of Technology" } ], "keywords": [ "Low-altitude economy" ], "abstract": "This paper investigates vibration suppression and fault-tolerant control for flapping wing-UAVs (FW-UAVs) under partial actuator failures. First, a PDE-based dynamic model is established, and the boundary conditions are reconstructed to characterize actuator performance degradation. Subsequently, an adaptive event-triggered fault-tolerant controller with a relative threshold is designed. Through adaptive estimation, the proposed controller can compensate for actuator faults and the nonlinear effects induced by the event-triggered mechanism, achieving vibration suppression while reducing communication resource consumption. Lyapunov analysis proves that all closed-loop signals are semi-globally bounded and that Zeno behavior can be excluded. Numerical results show that, under partial actuator failures, the proposed method can effectively attenuate wingtip vibrations and drive the system to a steady state, thereby supporting the safe operation of FW-UAVs in urban low-altitude environments.", "url": "" }, { "id": "Tu-TuC34.4", "code": "TuC34.4", "title": "Dual-Stream Transformer-LSTM Hybrid Network with Split-Pathway Multi-Granularity for Lithium-Ion Battery SOH Estimation", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC34", "sessionTitle": "AI for Smart Cities", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Jing, Weize", "affiliation": "Xiamen University" }, { "name": "Zhang, Chen", "affiliation": "Xiamen University" }, { "name": "Zhan, Pengfei", "affiliation": "Xiamen University" }, { "name": "Chen, Tengpeng", "affiliation": "Xiamen University" } ], "keywords": [ "Urban energy distribution systems", "AI for smart cities", "Smart city control and optimization" ], "abstract": "Lithium-ion batteries constitute the primary energy storage components within municipal power networks. Consequently, evaluating their state of health (SOH) precisely is indispensable to guarantee operational security and economic efficiency. However, battery aging exhibits complex dynamics characterized by a conflict between long-term monotonic degradation and short-term capacity regeneration. Existing data-driven approaches struggle to reconcile these dual scales: Transformer-based models often prioritize global trends at the expense of local feature fidelity, while recurrent neural networks are hindered by computational inefficiency over long sequences. To address this, a hybrid local-global Transformer (HLG-Former) is proposed. A novel split-pathway multi-granularity (SPMG) strategy is introduced to process features through two distinct streams. The local pathway utilizes sliding window attention to capture fine-grained voltage fluctuations. Meanwhile, the global pathway employs ProbSparse attention to efficiently model lifecycle-spanning dependencies. Additionally, an LSTM-enhanced decoding mechanism is integrated to eliminate high-frequency noise. Validated on the CALCE dataset, HLG-Former achieves an RMSE of 0.0386 and MAPE of 3.52%. The ablation study further confirms that disentangling local and global dynamics is key to precision prognostics.", "url": "" }, { "id": "Tu-TuC34.5", "code": "TuC34.5", "title": "SDR-RR: An LLM-Driven Agent for Fault-Recovering Cloud Service Upgrades", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC34", "sessionTitle": "AI for Smart Cities", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Wu, Zhendong", "affiliation": "Xi'an Jiaotong University" }, { "name": "Zhiyuan, Zuo", "affiliation": "Xi'an Jiaotong University" }, { "name": "Tian, Zhaoming", "affiliation": "Xi'an Jiaotong University" }, { "name": "Cao, Xiaoyu", "affiliation": "Xi'an Jiaotong University" } ], "keywords": [ "Data centers and cloud computing", "AI for smart cities", "Smart city control and optimization" ], "abstract": "In modern cloud-native systems, safe and timely cloud service upgrades are central to maintaining business continuity. However, service upgrades remain a major source of operational risk, since current methods depend heavily on human expertise and brittle rule-based workflows, leading to decision latency, inconsistent handling quality, and, in severe cases, cascading failures across dependent services. To drastically reduce upgrade-induced service disruption, this paper harnesses the causal reasoning and tool-using ability of large language model (LLM) agents and proposes SDR-RR (Sense–Diagnose–Reflect–Remedy–Replan) framework. SDR-RR enables efficient recovery by coupling a shell environment with LLM-driven cognitive agents to sense system state and execute repairs in place. It employs a two-phase reasoning scheme for stable and consistent long-horizon fault handling, and integrates a hybrid control mechanism to manage complex, cascading fault chains. Experiments on representative upgrade scenarios show that SDR-RR improves success rates on complex faults by an average of 50% compared to traditional LLM agent framework, while maintaining low operational latency in a cost-effective manner.", "url": "" }, { "id": "Tu-TuC34.6", "code": "TuC34.6", "title": "Foundations for AI-Enabled Automation Adoption in Small and Medium Enterprises in East Germany", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC34", "sessionTitle": "AI for Smart Cities", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Das, Anwesha", "affiliation": "University of Applied Sciences Magdeburg-Stendal" }, { "name": "Viswanathan, Vivekanandhan", "affiliation": "Magdeburg Stendal University of Applied Sciences" }, { "name": "Kaltschmidt, Monique Nadine Karin", "affiliation": "University of Applied Sciences Magdeburg-Stendal" }, { "name": "Timm, Patrick", "affiliation": "Hochschule Magdeburg - Stendal" }, { "name": "Schmidtke, Niels", "affiliation": "Fraunhofer Institute for Factory Operation and Automation IFF" }, { "name": "Behrendt, Fabian", "affiliation": "Magdeburg-Stendal University of Applied Sciences, Germany" } ], "keywords": [ "Capacity building in less developed regions", "Digital culture", "Industrial and service applications of AI and intelligent automation" ], "abstract": "Small and medium-sized enterprises (SMEs) are the backbone of the economy, but many face major challenges when it comes to introducing digital technologies and integrating applications based on artificial intelligence (AI). This article presents a survey-based assessment (N=12, ongoing data collection) of the current state of SMEs in the Altmark region in northern Saxony-Anhalt, a predominantly rural region undergoing structural change. The study aims to establish a baseline for the digital maturity and AI readiness of regional SMEs, in order to provide actionable insights for targeted measures within the framework of the ‘synerKI’ research project, which aims to promote AI adoption among SMEs in the region. Drawing on established frameworks such as the EU Digital Economy and Society Index (DESI) and AI readiness models, we propose a tailored set of metrics capturing ICT infrastructure, digital skills, data maturity, organizational openness, and ecosystem integration. Furthermore, consolidated digital and AI readiness indices are introduced to classify SMEs into readiness categories and enable comparisons across sectors and against national benchmarks. Our preliminary results show strong positive correlation (r=0.85) between digital and AI readiness, with environmental factors explaining 95% of variance in AI readiness. We expect to acquire additional data by the time of the conference, allowing for a more comprehensive and robust analysis. The results from this study serve as a basis for the development of targeted use cases and demonstrators tailored to the specific needs of SMEs in the Altmark region, while also contributing to the discussion on regional digitalisation and innovation strategies.", "url": "" }, { "id": "Tu-TuC35.1", "code": "TuC35.1", "title": "A Quanser Aero Laboratory Suite for Teaching Modeling, PID Control, and Lead-Lag Compensation (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC35", "sessionTitle": "Microlabs, Remote Labs and Virtual Tools for Control Education II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Efjestad Fjereide, Didrik", "affiliation": "University of Stavanger" }, { "name": "Rotondo, Damiano", "affiliation": "Universitetet I Stavanger" } ], "keywords": [ "Control education laboratories" ], "abstract": "This paper presents a structured sequence of laboratory activities developed around the Quanser Aero platform for the undergraduate course ELE320 - Control Systems at the University of Stavanger. The laboratories were designed to bridge theoretical instruction with practical implementation, allowing students to engage with core control-engineering topics through hands-on experiments. The activities guide students from first-principle modeling and system identification, through PID controller design and validation, to compensator-based lead-lag control. The proposed laboratory activities have proven effective in enhancing students conceptual understanding and practical skills, and it can be adapted to similar laboratory platforms in control education.", "url": "" }, { "id": "Tu-TuC35.2", "code": "TuC35.2", "title": "Integrated Control-Circuit Hybrid Simulation and Code Generation Framework for Remote Laboratories (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC35", "sessionTitle": "Microlabs, Remote Labs and Virtual Tools for Control Education II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "He, Jianbin", "affiliation": "Wuhan University" }, { "name": "Hu, Wenshan", "affiliation": "Wuhan University" }, { "name": "Lei, Zhongcheng", "affiliation": "Wuhan University" } ], "keywords": [ "Control education laboratories", "Internet based control education", "Control engineering curricula" ], "abstract": "Building upon the control algorithm simulation foundation of Networked Control System Laboratory (NCSLab), we propose an integrated online framework for hybrid control-circuit simulation and automatic code generation. The solution incorporates a dedicated circuit solver with modularized components, rigorously analyzes electromagnetic transient process characteristics of circuit modules, and establishes a streamlined algorithm code generation workflow. The integrated framework demonstrates enhanced scalability, flexibility, and complete technical autonomy without third-party dependencies. The framework has been deployed on a 30,kW synchronous generator test rig at Wuhan University and adopted by approximately 400 undergraduates in a National First-Class Undergraduate Course, reducing the average completion time of a single generator experiment from about three hours to roughly one hour.", "url": "" }, { "id": "Tu-TuC35.3", "code": "TuC35.3", "title": "AutomationShield Online: A Web-Based Platform for Interactive Control Experiments (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC35", "sessionTitle": "Microlabs, Remote Labs and Virtual Tools for Control Education II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Repka, Matúš", "affiliation": "Slovak University of Technology" }, { "name": "Mikulášová, Anna", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Gulan, Martin", "affiliation": "Slovak University of Technology in Bratislava" } ], "keywords": [ "Control education laboratories", "Internet based control education", "Open-source tools for increased impact of control" ], "abstract": "This paper presents the latest, user-oriented addition to the open hardware and software initiative for control engineering education, AutomationShield. AutomationShield provides small, affordable, and open-source didactic devices integrating a variety of physical experiments as Arduino extension modules. Although proven valuable, their full-scale implementation in the pedagogical process has also revealed several challenges. In response, a new online graphical user interface, AutomationShield Online (ASO), has been proposed. This paper describes its features, usability, and how the platform improves accessibility and student engagement. Furthermore, this addition leverages the wireless communication of the Arduino UNO R4, extending the platform to support control via Wi-Fi.", "url": "" }, { "id": "Tu-TuC35.4", "code": "TuC35.4", "title": "FurutaShield: Learning Advanced Embedded Control Using a Low-Cost Open-Source Platform (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC35", "sessionTitle": "Microlabs, Remote Labs and Virtual Tools for Control Education II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Paučo, Michal", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Enikov, Eniko", "affiliation": "University of Arizona" }, { "name": "Gulan, Martin", "affiliation": "Slovak University of Technology in Bratislava" } ], "keywords": [ "Control education laboratories", "Open-source tools for increased impact of control", "Repositories for control education" ], "abstract": "This paper presents FurutaShield, a low-cost open-source didactic platform based on the rotary inverted (Furuta) pendulum for teaching advanced embedded control. The hardware is implemented as a compact Arduino-compatible shield combining standard electronics with 3D-printed components, while an accompanying open-source API supports both the Arduino IDE and MATLAB/Simulink. These tools enable rapid prototyping of real-time controllers on resource-limited microcontrollers. Illustrative exercises demonstrate system identification, state estimation, and the design and implementation of implicit and explicit model predictive control, highlighting the platform’s suitability for hands-on control engineering education.", "url": "" }, { "id": "Tu-TuC35.5", "code": "TuC35.5", "title": "A Web-Based Interactive Tool for Feedforward Control Design (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC35", "sessionTitle": "Microlabs, Remote Labs and Virtual Tools for Control Education II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Kois, Roman", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Pataro, Igor M. L.", "affiliation": "Universidad De Almería" }, { "name": "Zakova, Katarina", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Guzman, Jose Luis", "affiliation": "University of Almeria" }, { "name": "Hagglund, Tore", "affiliation": "Lund University" } ], "keywords": [ "Internet based control education", "Control education laboratories" ], "abstract": "This paper presents a new web-based interactive tool designed to support the teaching and learning of feedforward control for measurable disturbances. The online virtual laboratory allows students and practitioners to explore the analysis, design, and tuning of feedforward compensators through real-time interaction and visualization. The interactive environment integrates both classical and non-interactive feedforward control schemes and enables the study of all major inversion-related realizability problems, including delay, non-minimum phase, and integrating cases. Tuning rules recently reported in the literature are implemented for a visual, interactive analysis. Metrics related to integral absolute error, integral square error, overshoot, and control effort are included in the tool for comparison purposes. The tool also includes a comprehensive library of industrial process examples, providing users with an intuitive way to connect theoretical concepts with dynamic system behavior. Its fully browser-based implementation eliminates the need for local installation, promoting accessibility and scalability for academic and professional use. Several illustrative examples demonstrate how the virtual laboratory enhances conceptual understanding of feedforward control design.", "url": "" }, { "id": "Tu-TuC36.1", "code": "TuC36.1", "title": "Incentivizing Federated Learning in Internet of Vehicles with a Dynamic Reputation Mechanism: A Stackelberg Game Perspective (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC36", "sessionTitle": "Metaverse and Parallel Intelligence for Autonomous Decision-Making II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Liu, Tao", "affiliation": "Renmin University" }, { "name": "Yuan, Yong", "affiliation": "Renmin University of China" }, { "name": "Zhong, Dingzhi", "affiliation": "Renmin University of China" } ], "keywords": [ "Game theories", "Econometric models and methods", "Decentralized economics/ecosystems (DeEco)" ], "abstract": "In recent years, the Internet of Vehicles (IoV) has been widely observed to drive the evolution of intelligent transportation toward distributed and collaborative intelligence, while also posing critical concerns regarding data privacy. Although Federated Learning (FL) preserves privacy by exchanging model parameters instead of raw data, it faces two key challenges in IoV scenarios, i.e., data heterogeneity arising from low-quality participants and insufficient node engagement due to unfair incentive allocation. To address these issues, this paper proposes a novel incentive mechanism with dynamic reputations, which dynamically adjusts thresholds for updating reputations using multi-dimensional behavioral indicators. This approach can effectively filter out low-quality participants while ensuring fair reward distribution to sustain node engagement. We formulate the interaction among stakeholders using a Stackelberg game model, and theoretically prove the existence and uniqueness of its equilibrium, thereby ensuring utility maximization for all parties. Experimental results demonstrate that our mechanism outperforms the conventional FedAvg and fixed-threshold methods in convergence speed and training stability. We also validate the stability of the Stackelberg equilibrium in simulated environments, and verify the effectiveness of our approach in simultaneously mitigating data heterogeneity and addressing incentive fairness in IoV networks.", "url": "" }, { "id": "Tu-TuC36.2", "code": "TuC36.2", "title": "Secure Consensus Control on Multi-Agent Systems Using Blockchain Smart Contract (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC36", "sessionTitle": "Metaverse and Parallel Intelligence for Autonomous Decision-Making II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Zhu, Jing", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Lu, Chengfang", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Zhang, Zhang Zibei", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Zhai, Xiangping", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Fu, Shaobo", "affiliation": "Wuhan Second Ship Design and Research Institute" } ], "keywords": [ "Blockchain intelligence", "Smart city security and resilience", "Cyber physical social systems (CPSS)" ], "abstract": "This paper investigates secure consensus control in multi-agent systems with Byzantine agents that send malicious information to interfere with consensus. A novel hierarchical model of multi-agent systems (MASs) based on blockchain is established by devising a dual-layer communication system and designing a smart contract to detect and isolate Byzantine agents. Within this framework, we propose a blockchain-based secure consensus control method for MASs to identify Byzantine agents and support secure consensus. The proposed blockchain approach enables the identification of Byzantine agents with inconsistent state submissions and facilitates secure consensus among normal agents. Experimental simulations are conducted in the ARGoS robot swarm simulator, where comparative experiments validate the feasibility of the proposed framework. Moreover, the proposed algorithm requires no prior global information about the MAS, making it widely applicable to real-world industrial scenarios.", "url": "" }, { "id": "Tu-TuC36.3", "code": "TuC36.3", "title": "A Four-Quadrant Framework for Mapping LLMs Patent Impact: Diagnosing Synergistic and Antagonistic Interactions among Work Activities (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC36", "sessionTitle": "Metaverse and Parallel Intelligence for Autonomous Decision-Making II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Zhang, Gening", "affiliation": "National University of Defense Technology" }, { "name": "Yao, Feng", "affiliation": "National University of Defense Technology" }, { "name": "Zhang, Zhongshan", "affiliation": "National University of Defense Technology" }, { "name": "Lei, Shifeng", "affiliation": "HuNan Minmetals Hi-Tech Private Equity Funds" }, { "name": "Shen, Dayong", "affiliation": "National University of Defense Technology" }, { "name": "Wang, Tao", "affiliation": "National University of Defense Technology" } ], "keywords": [ "Social computing" ], "abstract": "The rapid advancement of Large Language Model (LLM) technology is profoundly impacting human occupations. This study evaluates the penetration potential of LLM-related patent technologies into different work activities and their combinations by analyzing the semantic similarity between occupational tasks and patent texts. The research employs the XGBoost model combined with SHAP values to analyze nonlinear relationships and interaction effects among work activities. Results show that combinations of information processing and cognitive activities predominantly exhibit synergistic effects, whereas interactions within interpersonal activities, and between interpersonal and physical output activities, mostly demonstrate antagonistic effects. This study indicates that current LLM innovation primarily focuses on information and cognitive domains, while its penetration remains limited in tasks requiring contextualized social interaction, physical manipulation, and non-standardized decision-making.", "url": "" }, { "id": "Tu-TuC36.4", "code": "TuC36.4", "title": "Abstraction Learning Via Decreasing Kolmogorov Complexity (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC36", "sessionTitle": "Metaverse and Parallel Intelligence for Autonomous Decision-Making II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Zhang, JunJie", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Shen, Zhen", "affiliation": "Chinese Academy of Sciences" }, { "name": "Xiong, Gang", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Wang, Fei-Yue", "affiliation": "Institute of Automation, Chinese Academy of Sciences" } ], "keywords": [ "Agent & AI technology for business and economy", "Parallel intelligence", "Cognitive and emotional control/AI systems, arts and control" ], "abstract": "抽象学习——即从高维观测中提取不变规则的能力——是分布外推广的基石。然而，标准优化范式常因缺乏强制结构简约的机制，将模型困在高熵记忆区间。虽然“grokking”的存在暗示了从记忆到泛化的潜在路径，但推动这一通过熵壁转变所需的具体正则化力量仍未被充分探讨。本研究在统一变压器测试平台内，全面评估复杂度约束——从统计范数（L1/L2）到几何谱熵到算法压缩代理。我们的实证结果表明，统计正则化因子常导致不稳定性或脆性加速。相比之下，块分解法（BDM）通过算法概率近似柯尔莫哥洛夫复杂度（KC），作为一个强有力的“控制变量”。BDM通过施加有效的熵摩擦来滤除脆弱的记忆电路，引导系统朝向热力学稳定、基于规则的局部极小值。这\u0002", "url": "" }, { "id": "Tu-TuC36.5", "code": "TuC36.5", "title": "Research on Diversified Automatic Question Generation and Quality Evaluation Based on Large Models and Structured Prompts (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC36", "sessionTitle": "Metaverse and Parallel Intelligence for Autonomous Decision-Making II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Wu, ZhenQi", "affiliation": "National University of Defense Technology" }, { "name": "Wang, Tao", "affiliation": "National University of Defense Technology" }, { "name": "Liu, Zichu", "affiliation": "National University of Defense Technology" }, { "name": "Pan, Junyi", "affiliation": "National University of Defense Technology" }, { "name": "Li, Shuxin", "affiliation": "National University of Defense Technology" }, { "name": "Xie, Yuanhan", "affiliation": "National University of Defense Technology" }, { "name": "Shen, Dayong", "affiliation": "National University of Defense Technology" } ], "keywords": [ "Agent & AI technology for business and economy" ], "abstract": "大型语言模型为自动化和个性化教育评估提供了新的范式。然而，测试生成任务仍存在挑战，包括与教学目标难以对齐以及质量评估的强烈主观性。本文介绍了ICCESOR——一个整合了布鲁姆分类法的结构化提示框架。它系统地引导大型模型通过六个维度生成多样化问题：考官身份、内容评估、认知目标、基本需求、具体需求和输出规范。我们开发了一个多维质量评估系统，评估内容质量、认知水平、表达标准、难度适应和教学价值。在数据库原理与应用课程中，对四个主流大型模型——DeepSeek-R1、Qwen-32B 等进行了实证评估。实验结果表明：1）ICCESOR框架有效生成符合特定认知水平的高质量问题;2）不同模型在题型适应性上存在显著差异——例如，DeepSeek-R1在复杂编程题\u0002", "url": "" }, { "id": "Tu-TuC36.6", "code": "TuC36.6", "title": "PEP: An Agent-Based Social Simulation Framework for Personalized Education Planning (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC36", "sessionTitle": "Metaverse and Parallel Intelligence for Autonomous Decision-Making II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Zhang, Tengchao", "affiliation": "Macau University of Science and Technology" }, { "name": "Qin, Rui", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Lin, Fei", "affiliation": "Macau University of Science and Technology" }, { "name": "Guan, Sangtian", "affiliation": "Macau University of Science and Technology" }, { "name": "Li, Juanjuan", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Liang, Xiaolong", "affiliation": "Chinese Academy of Sciences" }, { "name": "Li, Bai", "affiliation": "Hunan University" }, { "name": "Tian, Yong-Lin", "affiliation": "State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijin" }, { "name": "Wang, Fei-Yue", "affiliation": "Institute of Automation, Chinese Academy of Sciences" } ], "keywords": [ "Social computing", "Parallel intelligence" ], "abstract": "This work proposes Personalized Education Planning (PEP), a social simulation–driven intelligent system that integrates multi-agent modeling and large language models (LLMs) to enable personalized and interpretable educational strategy generation. By simulating interactions between student and teacher agents and applying adaptive knowledge retrieval, PEP achieves dynamic, holistic education planning across cognitive, social, and emotional dimensions. Experimental results on 100 simulated profiles demonstrate the system’s validity, diversity, and superiority over existing single LLM planning systems.", "url": "" }, { "id": "Tu-TuC37.1", "code": "TuC37.1", "title": "Bias-Variance Trade-Off in Kalman Filter-Based Disturbance Observers", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-15:50", "sessionCode": "TuC37", "sessionTitle": "Dissemination: Learning, Filtering, and Estimation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Li, Shilei", "affiliation": "Beijing Institute of Technology" }, { "name": "Shi, Dawei", "affiliation": "Beijing Institute of Technology" }, { "name": "Lyu, Xiaoxu", "affiliation": "Peking University" }, { "name": "Tang, Jiawei", "affiliation": "Hong Kong University of Science and Technology" }, { "name": "Shi, Ling", "affiliation": "Hong Kong University of Science and Technology" } ], "keywords": [ "Filtering and smoothing", "Adaptive observer design" ], "abstract": "The performance of disturbance observers is strongly influenced by the level of prior knowledge about the disturbance model. The simultaneous input and state estimation (SISE) algorithm is widely recognized for providing unbiased minimum-variance estimates under arbitrary disturbance models. In contrast, the Kalman filter-based disturbance observer (KF-DOB) achieves minimum mean-square error estimation when the disturbance model is fully specified. However, practical scenarios often fall between these extremes, where only partial knowledge of the disturbance model is available. This paper investigates the inherent bias-variance trade-off in KF-DOB when the disturbance model is incomplete. We reveal that SISE can be interpreted as a special case of KF-DOB, where the disturbance noise covariance tends to infinity. To address this trade-off, we propose two novel estimators: the multi-kernel correntropy Kalman filter-based disturbance observer (MKCKF-DOB) and the interacting multiple models Kalman filter-based disturbance observer (IMMKF-DOB). Simulations verify the effectiveness of the proposed methods.", "url": "" }, { "id": "Tu-TuC37.2", "code": "TuC37.2", "title": "ECCBO: An Inherently Safe Bayesian Optimization with Embedded Constraint Control for Real-Time Process Optimization", "day": "Tuesday", "date": "August 25, 2026", "time": "15:50-16:10", "sessionCode": "TuC37", "sessionTitle": "Dissemination: Learning, Filtering, and Estimation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Krishnamoorthy, Dinesh", "affiliation": "Norwegian University of Science and Technology (NTNU)" } ], "keywords": [ "Machine learning and artificial intelligence in chemical process control", "Advanced process control", "Real-time optimization and control in chemical processes" ], "abstract": "This paper presents a model-free real-time optimization (RTO) framework that leverages unconstrained Bayesian optimization (BO) embedded with constraint control to achieve optimal steady-state operation of process systems without the need for detailed models. Leveraging the vertical decomposition of information flow with timescale separation, this paper proposes two approaches to BO with embedded constraint controllers that simplifies model-free RTO with unknown cost and constraints, while ensuring steady-state constraint feasibility. The first approach employs constraint controllers that controls the constraints to some feasible setpoint in the fast timescale, and an unconstrained BO finds the optimal setpoints to these controllers in the slower timescale. The second approach uses constraint controllers as safety filters, where BO searchers over the RTO degrees of freedom, which can be overridden by the constraint controller when necessary to ensure steady-state constraint feasibility. By embedding constraint controllers with Bayesian optimization, both approaches ensure zero cumulative constraint violation without depending on specific assumptions about the Gaussian process model used in Bayesian optimization, making it inherently safe. The proposed scheme is demonstrated on several illustrative benchmark examples.", "url": "" }, { "id": "Tu-TuC37.3", "code": "TuC37.3", "title": "Artificial Intelligence Based Learning Methods for the Automatic Tuning of fixed-Parameter MIMO PID Controllers for Industrial Applications: A Review and Comparison", "day": "Tuesday", "date": "August 25, 2026", "time": "16:10-16:30", "sessionCode": "TuC37", "sessionTitle": "Dissemination: Learning, Filtering, and Estimation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "van Niekerk, Jonathan", "affiliation": "Zutari" }, { "name": "le Roux, Derik", "affiliation": "University of Pretoria" }, { "name": "Craig, Ian Keith", "affiliation": "University of Pretoria" } ], "keywords": [ "Machine learning and artificial intelligence in MMM process control", "Industrial applications of process control" ], "abstract": "This dissemination summary outlines a Control Engineering Practice journal article that reviews and compares artificial intelligence (AI) methods for automatic tuning of fixed-parameter multi-input multi-output (MIMO) PID controllers in industrial process applications. A generalised autotuning framework is proposed to unify diverse AI methods, and a Pareto-front-based weighting strategy is introduced to fairly balance tracking performance against actuator usage. Within this framework, three representative methods—particle swarm optimisation (PSO), proximal policy optimisation (PPO), and Bayesian optimisation (BO)—are evaluated against the defining criteria of an ideal autotuner: versatility, global optimality, data efficiency, and safety. A case study of an industrial plant with nonlinear dynamics demonstrates that all methods can identify high-performing controller parameters; among them, BO provides the strongest overall practical suitability due to rapid, data-efficient sampling.", "url": "" }, { "id": "Tu-TuC37.4", "code": "TuC37.4", "title": "Robust Electro-Hydraulic Control for Aircraft Anti-Skid Systems with Full Validation from Test Bench to Flight", "day": "Tuesday", "date": "August 25, 2026", "time": "16:30-16:50", "sessionCode": "TuC37", "sessionTitle": "Dissemination: Learning, Filtering, and Estimation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Mendoza Lopetegui, José Joaquín", "affiliation": "Politecnico Di Milano" }, { "name": "Tanelli, Mara", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Mechatronics for mobility systems", "Aerial, field, and marine robotics" ], "abstract": "In modern aviation, anti-skid systems are fundamental in preventing wheel-locking conditions and maximizing braking performance. To achieve airworthiness, these systems must be robust, fault-tolerant, and comply with existing standards and regulations. Existing solutions fall short in addressing important aspects for a successful practical implementation, as testified by the lack of flight testing verification in the literature. This paper proposes a novel aircraft anti-skid system that leverages robust control techniques to enhance safety and performance. The proposed architecture integrates a fault-tolerant design that accounts for measurement noise, hydraulic system asymmetries, and pressure transducer faults, while maintaining stability despite uncertainties in the electro-hydraulic brake dynamics. A cascaded control structure combining robust pressure regulation with wheel deceleration control and supervisory logic enables resilient performance under varying operating conditions. The pressure controller's stability is verified by a Kharitonov-type stability check, whereas the proposed gain-scheduled deceleration controller is analyzed under a Linear Parameter-Varying system formulation, checked for stability by a collection of Linear Matrix Inequalities under assumptions of rate-bounded variability of the involved parameters. The approach is validated on a hydraulic test bench, an aeronautic dynamometer, and flight test experiments, demonstrating practical applicability and alignment with the demands of modern hydraulic control systems.", "url": "" }, { "id": "Tu-TuC37.5", "code": "TuC37.5", "title": "Tensor Network Square Root Kalman Filter for Online Gaussian Process Regression", "day": "Tuesday", "date": "August 25, 2026", "time": "16:50-17:10", "sessionCode": "TuC37", "sessionTitle": "Dissemination: Learning, Filtering, and Estimation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Menzen, Clara", "affiliation": "TU Delft" }, { "name": "Kok, Manon", "affiliation": "Delft University of Technology" }, { "name": "Batselier, Kim", "affiliation": "Delft University of Technology" } ], "keywords": [ "Probabilistic and Bayesian methods for system identification", "Kalman filtering", "Gaussian process" ], "abstract": "The state-of-the-art tensor network Kalman filter lifts the curse of dimensionality for high-dimensional recursive estimation problems. However, the required rounding operation can cause filter divergence due to the loss of positive definiteness of covariance matrices. We solve this issue by developing, for the first time, a tensor network square root Kalman filter, and apply it to high-dimensional online Gaussian process regression. In our experiments, we demonstrate that our method is equivalent to the conventional Kalman filter when choosing a full-rank tensor network. Furthermore, we apply our method to a real-life system identification problem where we estimate 4^{14} parameters on a standard laptop. The estimated model outperforms the state-of-the-art tensor network Kalman filter in terms of prediction accuracy and uncertainty quantification.", "url": "" }, { "id": "Tu-TuC37.6", "code": "TuC37.6", "title": "Multivariable Soft Sensor with a Predictor of Mutually Dependent Errors Applied to an Industrial Fractionator", "day": "Tuesday", "date": "August 25, 2026", "time": "17:10-17:30", "sessionCode": "TuC37", "sessionTitle": "Dissemination: Learning, Filtering, and Estimation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Snegirev, Oleg", "affiliation": "Institute of Automation and Control Processes FEB RAS" }, { "name": "Klimchenko, Vladimir", "affiliation": "Institute of Automation and Control Processes FEB RAS" }, { "name": "Shtakin, Denis", "affiliation": "Institute of Automation and Control Processes FEB RAS" }, { "name": "Torgashov, Andrei", "affiliation": "Institute of Automation and Control Processes FEB RAS" }, { "name": "Yang, Fan", "affiliation": "Tsinghua University" } ], "keywords": [ "Industrial applications of chemical process control", "Industrial applications of process control", "Monitoring, performance assessment, and fault detection in chemical process control" ], "abstract": "This paper addresses the development of a multivariable soft sensor (SS) with a predictor designed to handle mutual dependencies within multivariate error series. Typically, the mutual influence in vector time series is characterized using cross-correlation. The proposed multivariable cross-correlated error predictor (MCCEP) framework effectively manages such dependencies and is compatible with any data-driven SS model. Forecasted error values are fed back into the SS output as corrections, refining the final predictions of quality indicators. The MCCEP model is constructed through statistical analysis to minimize the generalized variance – defined as the determinant of the covariance matrix – of multivariate forecast errors. Unlike conventional approaches such as bias update techniques, the MCCEP model is chosen from a broad class of predictors for multivariate linear processes, explicitly considering the dynamic relationships among the univariate components of the SS error process. For the n-dimensional case, it is analytically demonstrated that MCCEP minimizes the generalized variance of multivariate errors by leveraging the cross-correlation functions among the univariate components of the time series, thereby enhancing SS accuracy. Analytical methods for constructing MCCEP using the autocovariance generating function and the squared SS error coherence spectrum are developed. The framework’s superiority is highlighted through a case study involving an industrial fractionator, where the SS with MCCEP outperforms conventional SSs employing dynamic partial least squares and bias updates or developed sequentially without considering interdependencies among univariate components of multi-output model errors.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "An Integrated Perspective for Modelling Cyber-Physical Systems Interoperability", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Torres Ricaurte, Diana Maria", "affiliation": "Imt Mines Ales" }, { "name": "Daclin, Nicolas", "affiliation": "IMT Mines Alès" }, { "name": "Zacharewicz, Gregory", "affiliation": "IMT - Mines Ales" } ], "keywords": [ "Cyber-physical-social systems in enterprises", "Enterprise interoperability", "Model-driven enterprise-system engineering" ], "abstract": "Cyber-physical systems (CPS) embrace cybernetic and physical components in dynamic interactions. CPS modelling involved multiple views of the system from different disciplines. Interoperability of CPS comprises coordinating data exchange and operation between heterogeneous components and systems. Due to the multidisciplinary nature of CPS, the independence of its components, and its complex behavior, interoperability approaches tend to focus on a specific level of abstraction and a single type of interoperability. Whereas a holistic view is expected to provide a more accurate representation of reality. The aim of this paper is to highlight the lack of an integrated perspective on CPS interoperability. First, we identify how the usage of different models contributes to achieving CPS interoperability. Then, we propose a pre-conceptual schema to show CPS elements and its relationships involved in interoperability from a general perspective. A complete characterization of CPS interoperability is required to include essential aspects in a unified model abstraction.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Accurate Temporal Calibration of a Digital Twin for Sorting Machine Synchronization Using Event-Based Vision", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Kombaya Touckia, Jesus Vital", "affiliation": "Université Claude Bernard Lyon 1, INSA Lyon, Université Lumière Lyon 2, Université Jean Monnet Saint-Etienne, DISP UR4570," }, { "name": "Cheutet, Vincent", "affiliation": "Université De Lyon, INSA Lyon, Laboratoire DISP (EA4570)" }, { "name": "Henry, Sébastien", "affiliation": "DISP Laboratory, University of Lyon, University Lyon 1" } ], "keywords": [ "Digital transformation", "Intelligent manufacturing systems" ], "abstract": "A digital twin is defined as an organised set of models that accurately represent a physical entity in the real world in order to meet specific industrial uses. Continuously updated using real data, it offers a level of precision and granularity tailored to operational needs. This virtual model can integrate the shapes, states, functions, processes, behaviours and dynamic data of the equipment under study, while reflecting its environment. However, precise calibration between the virtual twin and its physical counterpart remains a major challenge, mainly due to the limitations of current industrial IoT approaches, which are often costly, complex and unreliable. To overcome these constraints, this research proposes the integration of neuromorphic machine vision, a technology characterised by high temporal resolution and low latency, enabling automatic synchronisation of the digital twin via discrete event system modelling. This approach aims to reduce the gap between the virtual and the real, improve calibration accuracy and optimise operational efficiency in complex industrial environments. The study highlights the potential of event-based vision systems, combined with machine learning algorithms, to capture and interpret the behaviour of physical equipment in real time. By replacing heavy IoT instrumentation with intelligent visual observation, this method offers a more economical, robust and adaptable solution, contributing to the emergence of a more connected and efficient industry of the future.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Towards Inclusive Industry 5.0: A Systematic Mapping on Cobot Applications for Workers with Disabilities", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Leoni, Leonardo", "affiliation": "ECampus University" }, { "name": "Mancusi, Francesco", "affiliation": "Università Degli Studi Della Basilicata" }, { "name": "Portaluri, Tommaso", "affiliation": "Verity AG" }, { "name": "Fruggiero, Fabio", "affiliation": "University of Basilicata" }, { "name": "De Carlo, Filippo", "affiliation": "Università Degli Studi Di Firenze" } ], "keywords": [ "Human-technology integration in manufacturing", "Robotics in manufacturing systems" ], "abstract": "International organizations report concerning statistics regarding the inclusion of people with disabilities in the labor market, underscoring the need for effective inclusive solutions. Industry 4.0 has accelerated technological advances, including collaborative robots (cobots), whose design enables safer and improved interaction with human workers than non-collaborative solutions. Hence, cobots have the potential to support workers with disabilities (DWs), reinforcing the human-centric orientation emphasized in Industry 5.0 and contributing to more inclusive workplaces. This topic has attracted growing scholarly interest, with studies addressing diverse goals such as developing cobot-based assistance systems for DWs or examining user acceptance. Research also varies in the categories of disabilities and impairments, industrial applications, or cobot technologies involved. Such heterogeneity has resulted in a fragmented body of knowledge that may hinder broader implementation efforts. To address this gap, this study conducts a Systematic Literature Mapping (SLM) to review, structure, and synthesize existing research. The review showed that developing Human-Robot Collaboration (HRC) systems and improving the human-cobot alignment are the most prevalent research goals. Assembly tasks emerge as the most common application area, with frequent focus on robotic arms. The findings can support researchers in identifying promising research directions and assist practitioners in introducing cobots to better include DWs in industrial settings.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Meta-Knowledge Transfer-Based Dynamic Operation Optimization for Municipal Solid Waste Incineration Process", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Cui, Yingying", "affiliation": "Beijing Information Science & Technology University" }, { "name": "Fan, Junfang", "affiliation": "Beijing Information Science and Technology University" }, { "name": "Qiao, Junfei", "affiliation": "Beijing University of Technology" } ], "keywords": [ "Industrial artificial intelligence", "Manufacturing plant simulation, control and optimization", "Simulation and optimization in production, operations and services" ], "abstract": "Abstract: Municipal solid waste incineration (MSWI) process is a complex industrial process characterized by high nonlinearity and nonstationary dynamics, making it difficult to achieve optimum operation. To solve this problem, a meta-knowledge transfer-based dynamic operation optimization (MKT-DOO) method is proposed for the MSWI process. First, the data stream learning is employed with online elastic weight consolidation incremental update strategy and attention mechanism to construct ensemble surrogate models. Then, the time-varying objective functions can be approximated accurately. Second, a dynamic multi-objective particle swarm optimization algorithm based on transfer learning is proposed to derive the optimal solutions of the manipulated variables. To reduce negative transfer, a meta-knowledge transfer strategy is designed to address the issue of task-specific knowledge differing significantly across transfer tasks caused by drastic fluctuations in operating conditions. Finally, the effectiveness of the proposed method operation optimization is validated by real industrial data.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "BDI-Based Resource Agent Architecture for Adaptive Skill-Based Manufacturing Control", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Weber, Jakob", "affiliation": "Ulm University of Applied Sciences" }, { "name": "Lober, Andreas", "affiliation": "Ulm University of Applied Sciences" }, { "name": "Ollinger, Lisa", "affiliation": "Ulm University of Applied Sciences" } ], "keywords": [ "Intelligent manufacturing systems", "Cyber-physical production systems", "Smart production and logistics in manufacturing" ], "abstract": "Modern manufacturing systems require control architectures capable of bridging the gap between flexible high-level planning and the immediate low-level execution of the manufacturing process. This paper proposes a Resource agent architecture that links the planning and execution layers by integrating a Belief-Desire-Intention agent into the Skill Orchestration Agent framework. Thereby, enabling agent-based planning combined with skill-based execution. A shared knowledge base, structured by the Capability-Service-Skill model, ensures semantic coherence between capabilities and skills across all control levels. This architecture enables autonomous and decentralized production planning and execution.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "From CAM to SAM : When Harmony Beats Accuracy", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Rouleau, Samuel", "affiliation": "Université Laval" }, { "name": "Gaudreault, Jonathan", "affiliation": "Universite Laval" } ], "keywords": [ "Intelligent manufacturing systems", "Smart production and logistics in manufacturing" ], "abstract": "During the design of a product, shapes are defined using complex mathematical functions. However, these must eventually be approximated by lines/arc segments. Under traditional Computer-Aided Manufacturing (CAM), this is done individually for each part. Thus, the approximations can be inconsistent, which results in poor assembly. We propose a workflow and a datamodel to generate toolpaths knowing final product assembly information. This allows parts that are meant to be assembled to share common machining toolpaths. We generated 9261 part assemblies for two use cases. Results show that the Shared Approximation Method (SAM) eliminates mismatches in assemblies regardless of the approximation quality.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "The Problem of Constructing Local Econometric Models Based on the Maximum Correntropy Coefficient (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Chernyshov, Kirill", "affiliation": "V.A. Trapeznikov Institute of Control Sciences" }, { "name": "Jharko, Elena", "affiliation": "V.A. Trapeznikov Institute of Control Sciences" } ], "keywords": [ "Manufacturing plant simulation, control and optimization", "Complex dynamic systems", "Large-scale complex systems" ], "abstract": "Extracting knowledge from observed data regarding complex systemic behavior is closely associated with system identification methodology, where inherent uncertainty in model development necessitates stochastic formulations. Addressing stochastic identification tasks requires appropriate quantifiers of statistical association among variables. The most widely used quantifier, the ordinary (Pearson) correlation, may vanish even when a deterministic functional relationship exists between the variables of interest. Dependence measures termed “consistent”, which equal zero only when two random variables are statistically independent, provide a more comprehensive representation of inter-variable relationships. However, additional considerations such as normalization constraints and compatibility with Gaussian assumptions introduce further complexity. To address these challenges, this work adopts the maximum correntropy coefficient. This measure captures affine associations between pairs of random variables and enables computationally tractable procedures for stochastic system identification. Since an affine mapping constitutes a nonlinear transformation, the systems considered should be classified as nonlinear, despite their relatively simple nonlinearity. The nonlinear behavior examined arises primarily from the complex probabilistic interdependencies among model variables. This study develops a framework for constructing piecewise-affine stochastic models, aiming to identify and precisely quantify the stochastic relationships between model inputs and outputs.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Technical an Economical Indexes of Nuclear Power Plants: Results and Prospective Studies (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Jharko, Elena", "affiliation": "V.A. Trapeznikov Institute of Control Sciences" }, { "name": "Abdulova, Ekaterina", "affiliation": "V.A. Trapeznikov Institute of Control Sciences" } ], "keywords": [ "Manufacturing plant simulation, control and optimization", "Manufacturing engineering and management", "Advanced manufacturing and remanufacturing technologies" ], "abstract": "This paper provides a detailed examination of the calculation of technical and economic indicators (TEI) for nuclear power plants, focusing on methodology, algorithms, and implementation as a specialized software module for analyzing and quantifying the thermal efficiency of nuclear power plant units. The paper presents the theoretical foundations and practical aspects of using TEI to monitor the efficiency of thermodynamic conversion of thermal energy generated in the core of a nuclear reactor. Methodological approaches to TEI calculation, data processing algorithms, and methods for visualizing analytical results are considered. Particular attention is paid to assessing the energy efficiency of both individual equipment and the unit as a whole.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Asset Administration Shell-Based OCL Validation Framework for Model-Based System Engineering", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Parkash, Om", "affiliation": "University of Applied Sciences Pforzheim" }, { "name": "Bauer, Jannik", "affiliation": "University of Applied Sciences Pforzheim" }, { "name": "Schmitt, Vincent", "affiliation": "University of Applied Sciences Pforzheim" }, { "name": "Greiner, Thomas", "affiliation": "Pforzheim University" }, { "name": "Drath, Rainer", "affiliation": "University of Applied Sciences Pforzheim" } ], "keywords": [ "Model-driven enterprise-system engineering", "Enterprise interoperability", "Digital transformation" ], "abstract": "Increasing complexity of modern enterprise systems and the demand for automation and interoperability require consistent and semantically validated models in Model-Based Systems Engineering (MBSE). The Object Constraint Language (OCL) supports formal definition of such constraint validations. However, MBSE models and OCL constraints are typically managed in separate tools, causing manual effort during model constraint application and result interpretation. To address this gap, this paper proposes an approach to managing OCL constraints and their validation results through Asset Administration Shells (a well-established technology for interoperability in enterprise systems). The methodology is demonstrated through a fictional industrial scenario, and to support reproducibility, all artifacts are publicly available in a GitHub repository. Keywords: MBSE, OCL, AAS, Semantic Constraint Modeling, AutomationML", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Model-Based Safe Reinforcement Learning for Control Using Action Replacement Strategy", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ankalugari, Rahul Yadav", "affiliation": "Indian Institute of Technology Tirupati" }, { "name": "Magbool Jan, Nabil", "affiliation": "Indian Institute of Technology Tirupati" } ], "keywords": [ "Reinforcement learning and deep learning in control", "AI-driven modeling and control", "Knowledge-based and data-driven control" ], "abstract": "Process systems often impose several state and input constraints owing to safety and environmental limitations. There is an increasing interest in deploying reinforcement learning-based controllers to achieve the goal of autonomous process systems. Standard reinforcement learning algorithms lack the provision to impose hard state constraints. This impedes their applicability in safety-critical process systems, where constraint violations can have catastrophic consequences. To this end, we characterize the concept of safe set as a maximal control invariant set, and ensure that exploration and exploitation occur within the safe set. We propose an action replacement-based reinforcement learning approach that can effectively prevent violation of state constraints while learning the control policy. More specifically, we propose a model-based safety filter that replaces the potentially unsafe control action suggested by the conventional reinforcement learning controller with the safe control action such that the replaced control input drives the system to safe states. In this work, we integrate this safety filter with the deep deterministic policy gradient algorithm to learn the control policy. We demonstrate the efficacy of the proposed approach on a double integrator system, showing that the proposed action replacement strategy provides a safety guarantee.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "A Hybrid Reinforcement and Self-Supervised Learning Aided Benders Decomposition Algorithm", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Agyeman, Bernard", "affiliation": "University of Alberta" }, { "name": "Li, Zhe", "affiliation": "University of Minnesota" }, { "name": "Mitrai, Ilias", "affiliation": "The University of Texas at Austin" }, { "name": "Daoutidis, Prodromos", "affiliation": "Univ. of Minnesota" } ], "keywords": [ "Reinforcement learning and deep learning in control", "AI-driven modeling and control", "Machine learning for modeling and prediction" ], "abstract": "We propose a hybrid reinforcement and self-supervised learning approach for accelerating generalized Benders decomposition. On the master side, we employ a graph-based reinforcement learning agent that operates on a bipartite graph representation of the master problem and is equipped with a verification mechanism to either partially or fully solve it. On the subproblem side, a physics-informed neural network, trained to approximate solutions that satisfy the Karush--Kuhn--Tucker conditions via self-supervision, takes the values of the integer variables as input and produces primal--dual pairs for Benders cut construction. The proposed framework is evaluated on a mixed-integer nonlinear programming case study, where it achieves a 52% reduction in solution time relative to classical GBD while preserving convergence behavior.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Individual Control Barrier Functions-Guided Diffusion Model for Safe Offline Multi-Agent Reinforcement Learning", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Guo, Qingyun", "affiliation": "Aalto University" }, { "name": "Shi, Junyi", "affiliation": "Aalto University" }, { "name": "Huang, Jianuo", "affiliation": "Xiamen University Malaysia" }, { "name": "Shi, Tianyu", "affiliation": "University of Toronto" } ], "keywords": [ "Reinforcement learning and deep learning in control", "Control architecture for multi agent systems" ], "abstract": "Offline reinforcement learning allows control policies to be learned directly from data without online interaction, making it suitable for safety-critical tasks. Recent studies have applied diffusion models to offline reinforcement learning to leverage their strong capacity for modeling complex data distributions. However, existing approaches primarily focus on single-agent settings, leaving the safety challenges in multi-agent environments largely unexplored. In this work, we propose a safe offline multi-agent reinforcement learning algorithm that embeds neural individual control barrier functions into the diffusion model to enhance safety during trajectory generation, with control policies recovered through inverse dynamics. We evaluate our algorithm across diverse benchmarks, demonstrating substantial safety improvements while maintaining competitive rewards.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Primal-Dual Based Safe Multi-Agent Reinforcement Learning with Graph Information Aggregation", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Gou, Fandi", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Zhao, Chenyu", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Zhao, Hengyuan", "affiliation": "ShangHai Jiao Tong University" }, { "name": "Cai, Yunze", "affiliation": "Shanghai Jiaotong University" } ], "keywords": [ "Reinforcement learning and deep learning in control", "Control architecture for multi agent systems", "Safety and security in networked control" ], "abstract": "This paper proposes a primal-dual based safe multi-agent reinforcement learning (MARL) framework that integrates Transformer-driven graph neural networks (GNNs) and Lagrangian method, termed G-MATrans-Lagr, to enable safe and scalable cooperation among agents under limited communication. The approach adopts Lagrangian multipliers to optimize the reward and cost in a hybrid objective function, and a Transformer-based GNN is utilized to aggregate local observations into expressive graph representations, facilitating effective information sharing among neighboring agents. Experimental validation on multi-UAV navigation task demonstrates that G-MATrans-Lagr achieves superior performance compared with the latest MARL and safe control baselines, maintaining higher performance and lower safety costs across varying agent scales. The results showcase our method’s ability to balance efficiency and safety while enhancing scalability for complex multi-agent systems. Besides, we open source our code at https://github.com/finleygou/G-MAT-Lagr.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Soft Switching Expert Policies for Controlling Systems with Uncertain Parameters", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ikemoto, Junya", "affiliation": "The University of Osaka" } ], "keywords": [ "Reinforcement learning and deep learning in control", "Knowledge-based and data-driven control", "AI-driven modeling and control" ], "abstract": "This paper proposes a simulation-based reinforcement learning algorithm for controlling systems with uncertain and varying system parameters. While simulators are useful for safely learning control policies, the reality gap remains a major challenge. To alleviate this challenge, we propose a two-stage algorithm. First, multiple control policies are learned for systems with different system parameters in a simulator. Second, for a real system, the control policies are adaptively switched using an online convex optimization algorithm based on observations. The proposed approach mitigates the learning difficulty of training a single policy to handle all possible system parameters and enables lightweight online adaptation.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "State-Conditional Adversarial Learning: An Off-Policy Visual Domain Transfer Method for End-To-End Imitation Learning", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Liu, Yuxiang", "affiliation": "University of Califronia, Berkeley" }, { "name": "Cao, Shengfan", "affiliation": "University of California, Berkeley" } ], "keywords": [ "Reinforcement learning and deep learning in control", "Knowledge-based and data-driven control", "AI-driven modeling and control" ], "abstract": "We study visual domain transfer for end-to-end imitation learning in a realistic and challenging setting where target-domain data are strictly off-policy, expert-free, and scarce. We first provide a theoretical analysis showing that the target-domain imitation loss can be upper bounded by the source-domain loss plus a state-conditional latent KL divergence between source and target observation models. Guided by this result, we propose State- Conditional Adversarial Learning (SCAL), an off-policy adversarial framework that aligns latent distributions conditioned on system state using a discriminator-based estimator of the conditional KL term. Experiments on visually diverse autonomous driving environments built on the BARC–CARLA simulator demonstrate that SCAL achieves robust transfer and strong sample efficiency.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Memory-Augmented PPO-GRU for Beyond-Visual-Range Air Combat Decision-Making under Partially Observable Conditions", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Guo, Zheng", "affiliation": "Beihang University" }, { "name": "Li, Xiaoduo", "affiliation": "Beihang University" }, { "name": "Yu, Jianglong", "affiliation": "Beihang University" }, { "name": "Chen, Yi-Ming", "affiliation": "Beihang University" }, { "name": "Duan, Yu", "affiliation": "Nanyang Technological University" }, { "name": "Zhang, Kanghao", "affiliation": "Beihang University" } ], "keywords": [ "Reinforcement learning and deep learning in control", "Machine learning for modeling and prediction" ], "abstract": "This paper proposes a memory-enhanced PPO-GRU reinforcement learning framework for autonomous beyond-visual-range (BVR) air combat under partial observability. The BVR air-combat scenario is formulated as a partially observable Markov decision process, and the framework integrates recurrent memory, progressive curriculum learning, and an auxiliary prediction module to improve long-horizon tactical decision-making under intermittent observations. Experimental results show that the proposed agent achieves an 89.2% final win rate and outperforms feedforward PPO, SAC, and DDPG baselines under the same observation, reward, and action settings.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Digital Twin-Enhanced Quadruped Robot Locomotion Control: From Geometric Inverse Kinematics to Physical Prototyping", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Kuhn Fernandes, Bruno", "affiliation": "Regional Integrated University of High Uruguay and Missions - URI - Santo Angelo, Brazil" }, { "name": "Pignaton de Freitas, Edison", "affiliation": "Federal University of Rio Grande Do Sul" }, { "name": "Dos Santos Roque, Alexandre", "affiliation": "Halmstad University, Federal University of Rio Grande Do Sul - UFRGS" } ], "keywords": [ "Remote control", "Networking for internet of things", "Networking for teleoperation" ], "abstract": "This work presents a Digital Twin-enhanced tele-operated locomotion system for an articulated quadruped robot, easy-to-deploy, and designed to calibration walking movements. A geometric approach is developed to solve the inverse kinematics for a three-joint leg model, thereby accurately deriving the required joint angles from desired foot coordinates. Central to this enhancement is a digital twin implementation within CoppeliaSim software, which provides a virtual testing ground for predictive analysis and optimization of the control algorithms, significantly accelerating development and improving system robustness. Commercial servomotors, actuated based on these calculated angles, are controlled by a mobile application developed in .NET MAUI. This application facilitates remote operation and telemetry monitoring through secure MQTT communication via HiveMQ Cloud. The refined control equations, initially validated through the digital twin, are then thoroughly tested on a 3D-printed physical prototype utilizing an ESP32 microcontroller. The results show the feasibility of communication and quadruped robot calibration in runtime, while offering an integrated and scalable solution, supported by a simulation-driven physical prototyping.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "FPGA Remote Lab: Interactive and Hands-On Online Learning Experience", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Patel, Ankit", "affiliation": "Laboratoire Des Technologies Innovantes, l’Université De Picardie Jules Verne" }, { "name": "Rachid, Ahmed", "affiliation": "University of Picardie Jules Verne" } ], "keywords": [ "Remote control", "Virtualized and cloud-based control architectures", "Remote data acquisition and fusion" ], "abstract": "This paper presents an FPGA Remote Laboratory that enables students and hobbyists to conduct real hardware experiments on a Digilent (2025) Arty Z7-20 board through a web interface. The platform combines MQTT based control, RDP virtual access, multi peripheral hardware, and live video feedback to provide a hands-on FPGA learning environment beyond simulation-only approaches. The system achieves 120–180 ms control latency and supports up to five concurrent sessions. It offers a scalable and low-cost model for remote FPGA and embedded systems education, supporting self-paced experimentation and practical understanding.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "The Meaning of Cobots Implementation in the Aspect of Industry 4.0 and Industry 5.0 Transformation (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Pizoń, Jakub", "affiliation": "Lublin University of Technology" }, { "name": "Gola, Arkadiusz", "affiliation": "Faculty of Mechanical Engineering, Lublin University of Technology" }, { "name": "Rudawska, Anna", "affiliation": "Lublin University of Technology" }, { "name": "Piotrowska, Katarzyna", "affiliation": "Lublin University of Technology" }, { "name": "Paulina, Golinska-Dawson", "affiliation": "Poznan University of Technology" } ], "keywords": [ "Robotics in manufacturing systems", "Industry X.0 for production and logistics", "Human-technology integration in manufacturing" ], "abstract": "The use of collaborative robots (cobots) in production systems is no longer a vision of the future, but a practical solution for human-robot collaboration. This paper provides a literature review on the role of cobots in the transition from Industry 4.0 to Industry 5.0. The review is based on Web of Science, Scopus, and Google Scholar searches using terms related to cobots, HRC, Industry 4.0/5.0, safety, HMI, mass customization, and mass personalization. The study shows how cobots connect Industry 4.0, a digitized and automation-focused industry, with Industry 5.0, a human-centered industry, by combining AI-driven customization, safe physical interaction and HMI-based operator support. From a production management perspective, implementation is also seen as a managerial and technological enabler of mass personalization, bottleneck mitigation, and manufacturing-as-a-service models. The contribution is to merge market trends, security features, and implementation logic into a conceptual argument for cobots as a driver of contemporary production transformation.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Probabilistic Recursively Feasible Motion Planning under Uncertain Environments", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Sung, Hyeontae", "affiliation": "KAIST" }, { "name": "Ham, Hyeongchan", "affiliation": "KAIST" }, { "name": "Park, Junyoung", "affiliation": "KAIST" }, { "name": "Ren, Kai", "affiliation": "EPFL" }, { "name": "Ahn, Heejin", "affiliation": "KAIST" } ], "keywords": [ "Stochastic optimal control problems", "Model predictive control" ], "abstract": "Safe motion planning in uncertain, time-varying environments is challenging because the safe region can change unpredictably across planning steps, often causing a loss of recursive feasibility. In this work, we present a Probabilistic Recursively Feasible Model Predictive Control (PRF-MPC) framework that guarantees recursive feasibility with a specified probability. We introduce properties that an ideal predictor should satisfy to ensure distributional consistency, and use these properties to derive closed-form expressions for the means and covariances of trajectories predicted at future time steps. Building on this analysis, we construct safety constraints that ensure, with high probability, that the current safe set is contained within the safe sets at future time steps, thereby probabilistically guaranteeing recursive feasibility. Simulation results on a lane-change scenario demonstrate that the proposed method significantly improves recursive feasibility.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Integrating Design, Diagnosis and Recovery for Offshore Wind Turbines", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Jing Jung, Zhang", "affiliation": "School of Information Management & Engineering" }, { "name": "Simani, Silvio", "affiliation": "University of Ferrara" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" } ], "keywords": [ "Supervision and testing", "Fault detection and isolation", "Design methods for data-based control" ], "abstract": "This paper presents an integrated procedure for designing, diagnosing and recovering offshore wind turbine operation under faulty conditions. The main contribution is not a stand-alone control or diagnosis algorithm, but a reproducible co-design workflow in which controller tuning, residual-based diagnosis and recovery actions are selected together and assessed against common safety and performance requirements. The procedure is applied to a benchmark floating offshore wind farm represented by an aero-hydro-servo-elastic digital twin. Candidate supervisory settings are first obtained from an energy-load trade-off. Diagnosis thresholds and isolation rules are then tuned on separate healthy and faulty scenarios, and the resulting decisions trigger recovery actions via safe derating and command reconfiguration. The complete closed loop is tested under multiple wind conditions, noisy measurements and injected sensor and actuator faults. The results show that the integrated strategy improves availability, reduces downtime and shortens post-fault recovery episodes while preserving load-sensitive operational margins. The study also clarifies how diagnostic delay, false alarms, and missed detections affect feasible recovery, thereby making the links between design choices, diagnosability and safe operation explicit. This provides a traceable route from design intent to evidence-based operation, suitable for further validation on higher-fidelity models and field data.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Digital Representation of Circular Economy Data Points at the Nano Level Using Asset Administration Shell", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Rezapour, Mahdi", "affiliation": "German Research Center for Artificial Intelligence (DFKI)" }, { "name": "Farrukh, Abdullah", "affiliation": "German Research Center for Artificial Intelligence (DFKI)" }, { "name": "Pourjafarian, Monireh", "affiliation": "Technologie-Initiative SmartFactory KL E.V" }, { "name": "Plociennik, Christiane", "affiliation": "DFKI GmbH, Kaiserslautern" }, { "name": "Nolte, Annalisa", "affiliation": "RWTH Aachen" }, { "name": "Araujo, Juliano", "affiliation": "Pforzheim University, Institute for Industrial Ecology" }, { "name": "Berg, Holger", "affiliation": "Wuppertal Institut Fuer Klima, Umwelt, Energie" }, { "name": "Ruskowski, Martin", "affiliation": "German Research Center for Artificial Intelligence" } ], "keywords": [ "Sustainable and circular manufacturing systems", "Sustainable and circular supply chain and production", "Cyber-physical production systems" ], "abstract": "The transition to a Circular Economy (CE) requires structured, interoperable data across product life cycles. The Asset Administration Shell (AAS), as the Industry 4.0 digital representation standard, provides this foundation, yet CE-relevant data points remain insufficiently defined. This paper asks: How can nano-level CE data points be formally integrated into the AAS? We present a methodology to identify and classify nano-level CE data, map them to modular AAS submodels, and produce a reusable template for Digital Product Passports and digital twins. The approach enhances data exchange, supports future CE requirements, and is scalable to higher CE levels.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Observer Design for Heat PDEs with Nonuniformly-Distributed Actuator Delay", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Barbara, Sara", "affiliation": "University Moulay Ismail, Ensam" }, { "name": "Giri, Fouad", "affiliation": "University of Caen Normandie" }, { "name": "Krstic, Miroslav", "affiliation": "Univ. of California at San Diego" }, { "name": "Chaoui, Fatima-Zahra", "affiliation": "ENSET, Université Mohammed V" }, { "name": "Brouri, Adil", "affiliation": "ENSAM, Moulay Ismail University," } ], "keywords": [ "System identification and adaptive control of distributed parameter systems", "Backstepping control of distributed parameter systems" ], "abstract": "We are considering the problem of observer design for heat partial difference equations (PDEs) with distributed delay in actuator. Distributed delays are generally assumed to be uniformly distributed, i.e., their kernel functions are constant and perfectly known. The main novelty of this study lies in letting the actuator delay kernel function (DKF) not to be necessarily constant or known. These considerations make the observer design problem under study a new problem never studied in the past. Making use of the backstepping design method and a suitable decoupling transformation, we develop an adaptive observer that provides online estimates of the PDE state and the actuator DKF. We first show that the L^2-norm of the DKF estimation error exponentially converges to zero, under a well-defined persistent excitation (PE) depending only on the input signal. Then, we show that the PDE state estimation error in turn exponentially converges to zero.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Scalability of Alignment: Measuring the Maximum Number of Human Agents a Machine Intelligence Can Reliably Serve Anywhere, Anytime", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Tembine, Hamidou", "affiliation": "New York University" }, { "name": "Noupa Yongueng, Daryl", "affiliation": "Université Du Québec à Trois-Rivière" } ], "keywords": [ "AI-driven modeling and control", "AI tools in automation engineering and operation", "AI in networked control" ], "abstract": "We characterize the achievable satisfaction region of real-world generative machine intelligence systems under compute, architecture, training, adaptation, and budget constraints. The result defines an alignment capacity metric that quantifies how many user preferences can be met to a target quality and frequency. By expressing this capacity as an explicit resource-allocation optimization driven by user-specific expectile utility, the theorem reveals clean Pareto frontiers between coverage, quality, and reliability, and provides sharp conditions for when universality is not achievable. The framework offers actionable guidance for maximizing user satisfaction and quality-of-experience in deployed machine intelligence systems.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Physics Informed Neural Networks for Nonlinear Delay Differential Equations", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Yao, Lei", "affiliation": "University of Waterloo" }, { "name": "Kumar, Vipin", "affiliation": "Max Planck Institute for Dynamics of Complex Technical Systems" }, { "name": "Guglielmi, Roberto", "affiliation": "University of Waterloo" } ], "keywords": [ "AI-driven modeling and control", "Knowledge-based and data-driven control", "Machine learning for modeling and prediction" ], "abstract": "In this paper we propose a novel physics-informed neural network framework for solving general first-order delay differential equations. Our approach combines a differentiable history switch, a trial-solution formulation that explicitly enforces history constraints, and a segmented collocation strategy to stabilize gradient propagation across large temporal domains. The method enables a scalable and physics-consistent approximation of delay differential equation solutions while maintaining continuity across subintervals. Numerical experiments demonstrate the effectiveness of the proposed method.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Perron--Frobenius Operator Matching for Generative Modeling", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhang, Shiqi", "affiliation": "Peking University" }, { "name": "Wu, Wuwei", "affiliation": "City University of Hong Kong" }, { "name": "Oh, Jaemin", "affiliation": "Brown University" }, { "name": "Chen, Jie", "affiliation": "City University of Hong Kong" }, { "name": "Qian, Xiaoning", "affiliation": "Texas A&M University" } ], "keywords": [ "AI-driven modeling and control", "Machine learning for modeling and prediction", "Knowledge-based and data-driven control" ], "abstract": "We introduce Perron--Frobenius Operator Matching (PFOM), a generative framework that matches density evolution via the integral PF operator, subsuming flow, diffusion, and jump models. We prove that among Bregman divergences, only Kullback--Leibler divergence preserves equality between density-level and sample-conditioned objectives, yielding a practical loss equivalent to Koopman path matching. We further develop Nesterov-accelerated training and sampling that stabilize discretization and accelerate convergence. %On Gaussian mixtures and two-moons, PFOM achieves faster KL/W_2/MMD decrease and improved wall-clock efficiency with empirical validation. PFOM unifies operator-theoretic identification with modern generative modeling and opens paths to adaptive dictionaries and high-dimensional applications.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Component-Aware Pruning Framework for Neural Network Controllers Via Gradient-Based Importance Estimation", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Sundaram, Ganesh", "affiliation": "RPTU University Kaiserslautern-Landau, Germany" }, { "name": "Ulmen, Jonas", "affiliation": "RPTU Kaiserslautern-Landau" }, { "name": "Görges, Daniel", "affiliation": "University of Kaiserslautern" } ], "keywords": [ "AI-driven modeling and control", "Machine learning for modeling and prediction", "Reinforcement learning and deep learning in control" ], "abstract": "The transition from monolithic to multi-component neural architectures in advanced neural network controllers poses substantial challenges due to the high computational complexity of the latter. Conventional model compression techniques for complexity reduction, such as structured pruning based on norm-based metrics to estimate the relative importance of distinct parameter groups, often fail to capture functional significance. This paper introduces a component-aware pruning framework that utilizes gradient information to compute three distinct importance metrics during training: Gradient Accumulation, Fisher Information, and Bayesian Uncertainty. Experimental results with an autoencoder and a TD-MPC agent demonstrate that the proposed framework reveals critical structural dependencies and dynamic shifts in importance that static heuristics often miss, supporting more informed compression decisions.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Model-Free Reinforcement Learning Control for Resilient Cyber-Physical Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Garces, Hugo", "affiliation": "Universidad De Concepcion" }, { "name": "Rojas, Alejandro", "affiliation": "Universidad De Concepcion" }, { "name": "Hernandez-Vicente, Bernardo", "affiliation": "Departamento De Ingeniería Mecánica, Universidad De Concepción" }, { "name": "Escalona, Andrés", "affiliation": "Departamento De Ingeniería Mecánica, Universidad De Concepción" }, { "name": "Palma, Jonathan M.", "affiliation": "UTalca | Universidad De Talca" }, { "name": "Parvez, Md Rezwan", "affiliation": "Department of Electrical & Computer Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada" }, { "name": "Gopaluni, Bhushan", "affiliation": "University of British Columbia" }, { "name": "Shah, Sirish L.", "affiliation": "University of Alberta" } ], "keywords": [ "AI-driven modeling and control", "Reinforcement learning and deep learning in control", "Cyber physical systems" ], "abstract": "This paper presents a unified benchmarking framework to compare model-free reinforcement learning (RL) controllers on a nonlinear cyber-physical system (CPS) under false data injection and denial-of-service attacks. Four reward functions—exponential, progressive, Lyapunov-descent, and linear are analysed across two controller architectures (RL-PID,RL-MPC) and two learning algorithms (PPO, DDPG) using eight Key Performance Indicators covering tracking error, computational cost, and resilience. The Lyapunov reward yields the best resilience and lowest tracking error; the exponential mode provides a strong accuracy–robustness trade-off. Progressive and linear rewards converge faster but are less robust under attacks. RL-MPC achieves superior steady-state resilience, whereas RL-PID requires significantly less training time and is better suited for embedded deployment. These results demonstrate that reward shaping is a central design lever for model-free RL in CPS security, and provide actionable guidance for practitioners selecting controller and reward configurations.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Real-Time Point Cloud Data Transmission Via L4S for 5G-Edge-Assisted Robotics", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Damigos, Gerasimos", "affiliation": "Ericsson Research" }, { "name": "Stathoulopoulos, Nikolaos", "affiliation": "Luleå University of Technology" }, { "name": "Seisa, Achilleas Santi", "affiliation": "Ericsson Research" }, { "name": "Sandberg, Sara", "affiliation": "Ericsson AB" }, { "name": "Nikolakopoulos, George", "affiliation": "Luleå University of Technology" } ], "keywords": [ "Cloud control and robotics", "Networking for teleoperation" ], "abstract": "This article presents a novel framework for real-time Light Detection and Ranging (LiDAR) data transmission that leverages rate-adaptive technologies and point cloud encoding methods to ensure low-latency and low-loss data streaming. The proposed framework is intended for, but not limited to, robotic applications that require real-time data transmission over the internet for offloaded processing. Specifically, the Low Latency, Low Loss, Scalable Throughput (L4S)-enabled SCReAM v2 transmission framework is extended to incorporate the Draco geometry compression algorithm, enabling dynamic compression of high-bitrate 3D LiDAR data according to the sensed channel capacity and network load. The low-latency 3D LiDAR streaming system is designed to maintain minimal end-to-end delay while constraining encoding errors to meet the accuracy requirements of robotic applications. We demonstrate the effectiveness of the proposed method through real-world experiments conducted over a public 5G network across multi-kilometer urban environments. The low-latency and low-loss requirements are preserved, while real-time offloading and evaluation of 3D SLAM algorithms are used to validate the framework’s performance in practical use cases.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Evaluating Performance of Aperiodic Controllers", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Nyberg Carlsson, Max", "affiliation": "Lund University" }, { "name": "Arzen, Karl-Erik", "affiliation": "Lund Inst. of Technology" } ], "keywords": [ "Control software architecture", "Information models for control engineering", "Virtualized and cloud-based control architectures" ], "abstract": "A common assumption when designing control systems is periodic sampling and actuation. As a consequence of this periodicity, unnecessary control delays may be caused. In this paper we show how performance can be improved if, rather than waiting for periodicity, control systems actuate and sample as soon as possible. The performance evaluations are done using stochastic analysis of a large number of processes, comparisons to continuous controllers in simulations, and implementation on a ball and beam system.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Evaluating LLM-Based Semantic Labelling of Discrete States in Cyber-Physical Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Overlöper, Phillip", "affiliation": "Helmut-Schmidt-University" }, { "name": "Hildebrandt, Constantin", "affiliation": "Helmut Schmidt Universitaet" }, { "name": "Niggemann, Oliver", "affiliation": "Helmut-Schmidt-Universität / Universität Der Bundeswehr Hamburg" } ], "keywords": [ "Cyber physical systems", "AI tools in automation engineering and operation", "Knowledge-based and data-driven control" ], "abstract": "This paper evaluates the capacity of off-the-shelf Large Language Models to infer human-interpretable cyber-physical system states from multivariate time-series data in a zero-shot setting. Using the JIGSAWS surgical benchmark, we prompt the model with lightweight per-state kinematic summaries. Across tasks, these summaries produce consistent, though modest, improvements in semantic alignment, as reflected by cosine similarity and ranking metrics. The effects are strongly task-dependent, yet the observed performance gains indicate that LLMs do extract meaningful structure from kinematic time series despite the absence of domain adaptation or supervision.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Asset Administration Shell-Based MLOps for Adaptive Alarm Flood Classification (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Manca, Gianluca", "affiliation": "Ruhr University Bochum" }, { "name": "Rezaee Ahvanouee, Hesam", "affiliation": "Ruhr University Bochum" }, { "name": "Faubel-Teich, Leonhard", "affiliation": "University of Hildesheim" }, { "name": "Kunze, Franz Christopher", "affiliation": "Ruhr University Bochum" }, { "name": "Fay, Alexander", "affiliation": "Ruhr University Bochum" } ], "keywords": [ "Digital twins for cyber physical systems", "AI tools in automation engineering and operation" ], "abstract": "This paper presents an adaptive framework that integrates Machine Learning Operations (MLOps) with the Asset Administration Shell (AAS) to maintain the reliability of Alarm Flood Classification (AFC) models under changing alarm configurations. The AAS serves as a vendor-independent interface for semantically typed configuration revisions and change events, which automatically trigger a change-aware MLOps pipeline for AFC model evaluation, retraining, and redeployment. Alarm data are regenerated using the updated configuration and compared with prior results, while models are selectively redeployed based on performance thresholds. Experiments on two industrial datasets with 200 perturbed configurations demonstrate that static models degrade strongly with increasing configuration change, whereas the proposed method maintains stable accuracy while reducing unnecessary retraining by up to 30%.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Real-Time Cyber Attack Detection in Smart Spaces Using a Zonotope-Based Digital Twin Framework", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Agarwal, Akash", "affiliation": "Motilal Nehru National Institute of Technology Allahabad" }, { "name": "Rath, Jagat Jyoti", "affiliation": "Motilal Nehru National Institute of Technology Allahabad" }, { "name": "Purwar, Shubhi", "affiliation": "Motilal Nehru National Institute of Technology, Allahabad" }, { "name": "Sentouh, Chouki", "affiliation": "LAMIH UMR CNRS 8201, Université Polytechnique Hauts-De-France, Valenciennes, France" } ], "keywords": [ "Digital twins for cyber physical systems", "Cyber physical systems", "Remote data acquisition and fusion" ], "abstract": "A real-time method for cyber-attack detection based on zonotopic state estimation is presented in this work for a smart cyber-physical system with energy management. The proposed approach employs set-based zonotopic Kalman filtering to explicitly account for bounded process and measurement uncertainties while ensuring consistency under adversarial conditions. By combining residual bound violation with secure control logic, the method enables reliable attack detection and prevents the propagation of corrupted data into the energy management and relay actuation layer. The proposed work is validated through real-time experimental results, which demonstrate improved attack detection, reduced false alarms, and secure energy management operations in the presence of cyber attacks.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Digital Twins of Systems of Systems: A Systematic Literature Review", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Smati, Meriem", "affiliation": "INSA LYON and POLYTECHNIQUE MONTREAL" }, { "name": "Cheutet, Vincent", "affiliation": "Université De Lyon, INSA Lyon, Laboratoire DISP (EA4570)" }, { "name": "Laval, Jannik", "affiliation": "DISP Lab, Université Lumière Lyon 2" }, { "name": "Danjou, Christophe", "affiliation": "Polytechnique Montreal" } ], "keywords": [ "Digital twins for cyber physical systems", "Cyber physical systems", "Soft computing and robust intelligent control" ], "abstract": "Digital Twins (DTs) are increasingly invoked to pilot Systems-of-Systems (SoS), yet how they are built and what value they actually deliver at SoS scale remains unclear. We review 19 studies to examine scope, implementation, application domains, complexity drivers, DT roles, and supporting properties for SoS piloting. No study reports a fully implemented SoS-wide DT, i.e. most replicate only parts. Roles concentrate on experimentation–simulation and control–orchestration, with governance and assurance rising, while pure monitoring is rare. We identify interoperability, composition and SoS-level Verification and Validation (V&V) as key gaps and propose a role–capacity crosswalk and metrics to guide future deployments.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Multi-Criteria Evaluation of Digital Twins for Industry 5.0: Sustainability, Resilience and Human-Centricity", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Gataa, Achref", "affiliation": "University of Reims Champagne-Ardenne" }, { "name": "Saddem, Ramla", "affiliation": "University of Reims Champagne-Ardènne, CRESTIC" }, { "name": "Assila Ahlem, Ahlem Assila", "affiliation": "CESI LINEACT" } ], "keywords": [ "Digital twins for cyber physical systems", "Fuzzy and neural systems in control", "Knowledge-based and data-driven control" ], "abstract": "Digital twins (DTs) are a key enabler of Industry 5.0's objective to reconcile operational performance with sustainability and human well-being. However, there is no widely adopted and reproducible evaluation framework for assessing the contributions of DT to these objectives. To address this gap, we first conducted a systematic literature review to identify current practices and limitations, then present a practical, modular six-step evaluation framework that calculates a single, interpretable score for a DT instance by jointly evaluating three explicit pillars: sustainability (environmental, economic, and social), resilience, and human-centricity. The framework combines expert elicitation using a triangular fuzzy number analytical hierarchy process (TFN-AHP), objective weighting using Shannon entropy, and epistemic uncertainty modeling through spherical fuzzy sets. An optional PROMETHEE II module enables pairwise ranking across alternatives. We demonstrate the robustness of the framework through a sensitivity analysis and five synthetic case studies, with all datasets and evaluation scripts published to support reproducibility.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Context-Transferable Performance Measure Retrieval from Operator Preferences Using Preferential Bayesian Optimization", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "De Witte, Sander", "affiliation": "Ghent University" }, { "name": "Taets, Jeroen", "affiliation": "Ghent University" }, { "name": "Crevecoeur, Guillaume", "affiliation": "Ghent University" }, { "name": "Lefebvre, Tom", "affiliation": "Ghent University" } ], "keywords": [ "Expert systems and cognitive-based control", "AI tools in automation engineering and operation", "Intelligent human-machine interaction" ], "abstract": "The use of Bayesian Optimization (BO) to tune engineering systems is increasing. Conventional BO requires an objective function, which is often difficult to define and rarely captures expert judgment. Preferential Bayesian Optimization (PBO) addresses this limitation by using preference selections. We show that, after applying PBO, a data-driven cost function can be extracted that captures expert preferences, removing the human operator from the loop when safety constraints are well-defined and enabling fully automated tuning while still emulating expert decision-making. By mapping from well-defined features rather than raw control settings, this cost function becomes transferable across operating conditions, provided that the new conditions remain sufficiently covered in the feature space.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Sensing Pod: Integrated On-Device AI Node for Human–Robot Interaction in Indoor Environments", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Hwang, Sunjun", "affiliation": "Ulsan National Institute of Science and Technology" }, { "name": "Kim, Ji Soo", "affiliation": "Ulsan National Institute of Science and Technology" }, { "name": "Kim, Hyojin", "affiliation": "Ulsan National Institute of Science and Technology" }, { "name": "Kim, SungUn", "affiliation": "UNIST" }, { "name": "Hwang, Dongjoon", "affiliation": "Ulsan National Institute of Science and Technology" }, { "name": "Lee, Hui Sung", "affiliation": "UNIST(Ulsan National Institute of Science and Technology)" } ], "keywords": [ "Intelligent human-machine interaction" ], "abstract": "This paper presents the Sensing Pod, a compact on-device AI sensor node integrating fall detection, localization support, and wake-word recognition for indoor service environments. Low-resolution thermal and audio data are processed entirely on-device using lightweight learn ing pipelines, enabling real-time inference while preserving user privacy. IR-marker signaling improves robot localization without additional hardware. In addition, centroid-based thermalfeatures enable reliable identification of user falls, and a robust three-class wake-word model ensures dependable voice activation under natural pronunciation variability. These results demonstrate that practical safety monitoring and human–robot interaction can be achieved with low-cost sensors, making the Sensing Pod a scalable infrastructure component for future service-robot deployments.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Automatic Infrared Detection of Hypervelocity Impact Damage Via Density-Driven TTR Clustering and Multi-Objective Feature Extraction", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Yan, Zhongbao", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Yin, Chun", "affiliation": "University of ElectronicScience and Technology of China, Chengdu611731, P.R. China" }, { "name": "Gao, Yan", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Liu, Junyang", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Cao, Jiuwen", "affiliation": "Hangzhou Dianzi University" }, { "name": "Tan, Xutong", "affiliation": "University of Electronic Science and Technology of China" } ], "keywords": [ "Intelligent human-machine interaction", "Data fusion and mining in control", "Information models for control engineering" ], "abstract": "With the increase of space debris, efficient spacecraft damage detection and assessment have become increasingly important. This study proposes a hypervelocity impact damage identification method based on multi-objective feature extraction. An adaptive classification algorithm driven by transient thermal response (TTR) density information is first used for unsupervised separation of different damage types. A multi-objective optimization model is then established to balance intra-class representativeness and inter-class difference, where MOEA/D with dynamic weight vector adjustment is adopted to optimize typical TTRs under an irregular Pareto front Finally, the selected high-quality TTRs are used to reconstruct infrared images. Experimental results demonstrate that the proposed method enhances defect features and improves image discriminability for spacecraft damage assessment.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Designing a Security Support System for ICS Powered by Generative AI (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Sakata, Kousei", "affiliation": "Hitachi, Ltd" }, { "name": "Tanaka, Mayuko", "affiliation": "Hitachi, Ltd" }, { "name": "Kawaguchi, Nobutaka", "affiliation": "Hitachi, Ltd" }, { "name": "Ando, Eriko", "affiliation": "Hitachi Ltd" }, { "name": "Ishii, Hideaki", "affiliation": "University of Tokyo" }, { "name": "Takemoto, Satoshi", "affiliation": "Hitachi Ltd" } ], "keywords": [ "IT/OT-security in automation systems", "AI tools in automation engineering and operation", "Service-architectures for control systems" ], "abstract": "Industrial Control Systems (ICS) need security measures aligned with evolving regulations, but manually linking laws, standards, and threat intelligence is slow and inconsistent. We propose an automated framework integrating the Cyber Resilience Act, IEC 62443, and MITRE ATT&CK for ICS into an accountable database via Latent Dirichlet Allocation (LDA), providing the knowledge base for Retrieval-Augmented Generation (RAG) of countermeasures. On a ground truth of 3,330 candidate pairs labeled by three-LLM consensus, the LDA-based linkage achieves Recall@5 of 0.527 (law--standard) and 0.454 (standard--countermeasure), outperforming BERT-base by 11.3 and 18.5 points respectively at lower computational cost and higher interpretability.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Enabling Zero-Touch Certificate Management in Modular Plants through Overlay Networks (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Madsen, Marwin", "affiliation": "Karlsruhe Institute of Technology" }, { "name": "Bühlmann, Ilona", "affiliation": "Karlsruhe Institute of Technology" }, { "name": "Barth, Mike", "affiliation": "Karlsruhe Institute of Technology (KIT)" } ], "keywords": [ "IT/OT-security in automation systems", "Safety and security in networked control" ], "abstract": "Growing regulatory pressure increases the need for field‑level certificate management. In modular plants, operators typically integrate only a module-level interface, breaking the implicit assumption of direct connectivity between field devices and plant public key infrastructure assumed in current solutions. This paper examines whether overlay networks can provide a lightweight, decentralized substrate for zero‑touch certificate management within modules. Classical overlays are evaluated, and three (Chord, Kademlia, CAN) were selected for a proof of concept assessing resource efficiency and feasibility for automation systems. The results show that overlays provide a viable, protocol‑independent foundation for certificate management in modular plants.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Mixup Buffer: Enhancing Soft Monotonicity with Dynamic Violation Replay", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Visentin, Giacomo", "affiliation": "Università Di Padova" }, { "name": "Sinigaglia, Alberto", "affiliation": "Human Inspired Technology Research Center, University of Padua, 35121 Padua, Italy" }, { "name": "Sartor, Davide", "affiliation": "Università Di Padova" }, { "name": "Susto, Gian Antonio", "affiliation": "University of Padova" } ], "keywords": [ "Knowledge-based and data-driven control", "AI-driven modeling and control", "Machine learning for modeling and prediction" ], "abstract": "Monotonicity is a key requirement for trustworthy machine learning in high-stakes applications, where predictions must align with domain knowledge and human intuition. While deep neural networks excel at modeling complex non-linear relationships, they lack inherent guarantees of monotonic behavior. Existing approaches enforce monotonicity through either hard architectural constraints, which limit expressiveness, or soft regularization penalties, which lack robust guarantees. We introduce Mixup Buffer, a training technique that significantly enhances soft monotonicity enforcement by maintaining a dynamic replay buffer of synthetic constraint-violating samples. By forcing the model to repeatedly confront its worst violations through targeted retraining, Mixup Buffer drives optimization toward solutions with superior monotonic compliance. Extensive experiments across five benchmark datasets demonstrate that Mixup Buffer achieves state-of-the-art monotonicity performance for a soft optimization approach, both in-distribution and out-of-distribution, without sacrificing predictive performance.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Preference-Based Optimization from Noisy Pairwise Comparisons", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Wang, Siyi", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Wang, Zifan", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Knowledge-based and data-driven control", "Bio-inspired algorithms and optimization-based control" ], "abstract": "In interactive systems, feedback is often provided as preferences over queried options rather than precise scores. In this work, we propose a preference-based optimization algorithm that relies on noisy two-point comparisons. At each iteration, the algorithm employs a uniform-sphere perturbation to generate a perturbed action and queries the resulting loss comparison to estimate a descent direction. We demonstrate that, under standard smoothness and bounded variance assumptions, the algorithm converges to a stationary point when the smoothing and step size parameters are properly chosen. Numerical experiments on an LQG system demonstrate the effectiveness of the preference-based optimization algorithm with comparison feedback.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Mask-Enhanced and Regularization-Driven Semi-Supervised Learning for Industrial Soft Sensor", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Liu, Yonghao", "affiliation": "Yunnan University" }, { "name": "Lang, Xun", "affiliation": "Information School, Yunnan University" }, { "name": "Chen, Yiwei", "affiliation": "Yunnan University" }, { "name": "Wu, Jiande", "affiliation": "Yunnan University" }, { "name": "Lang, Yumin", "affiliation": "Information School, Yunnan University" } ], "keywords": [ "Machine learning for modeling and prediction", "AI-driven modeling and control" ], "abstract": "Due to the scarcity of labeled data and inherently nonlinear, time-varying dynamic nature of industrial processes, achieving accurate prediction of key variables remains a major challenge. To address scenarios with only a few labeled samples but numerous raw measurements, we propose a semi-supervised collaborative masking and regularization-driven (SS-CMR) model for industrial soft sensor. We first design a dual-view masked autoencoder to emulate realistic missing-data patterns and learn robust temporal representations via self-supervised learning. During fine-tuning, a random clustering-based regularization strategy is introduced to further stabilize the latent space and mitigate overfitting. In addition, a hybrid predictor combining a deep neural network and a factorization machine is constructed to jointly capture nonlinear dependencies and interactive effects among process variables. We evaluated the performance of SS-CMR on an industrial study. The results show that the proposed approach consistently outperforms existing methods, confirming its effectiveness as a promising soft sensor solution under label-limited conditions.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Wavelet-Dilated Net: A Steel Surface Defect Detection Network Based on Two-Level Wavelet Transform and Dilated Convolution", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Chen, Zihui", "affiliation": "Shanghai University" }, { "name": "Fei, Zixiang", "affiliation": "Shanghai University" }, { "name": "Fei, Minrui", "affiliation": "Shanghai University" }, { "name": "Wenju, Zhou", "affiliation": "Shanghai University" }, { "name": "Du, Dajun", "affiliation": "Shanghai University" }, { "name": "Peng, Chen", "affiliation": "Shanghai University" }, { "name": "Wang, Yu-Long", "affiliation": "Shanghai University" }, { "name": "Song, Yang", "affiliation": "Shanghai University" }, { "name": "Sun, Qing", "affiliation": "Shanghai University" } ], "keywords": [ "Machine learning for modeling and prediction", "AI-driven modeling and control", "Intelligent human-machine interaction" ], "abstract": "Steel-surface defect detection is crucial for quality control in industrial manufacturing. However, prevailing object detection models based on deep learning still struggle with defects with large range of scale variation, moreover, pooling-based down-sampling often erases fine details and causes missed detections, especially when the defects have high similarity to the normal background. To address these issues, we propose Wavelet-DilatedNet, a novel detection framework that introduces two plug-and-play modules on top of the DEIM-DFINE-n baseline. (i) A Multi-Layered Dilated Reparameterized Convolution (MDRC) module which captures multi-scale defect features by fusing parallel dilated convolutions with re-parameterization. (ii) A Two-Stage Wavelet Transform Down-sampling (TWTD) module that cascades Haar wavelet decomposition and inversed Haar wavelet transform to preserve weak edges and textures during feature reduction. Besides, experiments on the high-resolution public dataset GC10-DET show that Wavelet-Dilated Net achieves 37.1% mAP@50:95 and 72.1% mAP@50, surpassing the baseline by 2.6% and 5.8%, respectively, while outperforming other state-of-the-art methods.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Effect of Sampling‑Time Jitter on Embedded Control Dynamics", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Schwarzmann, Dieter", "affiliation": "Robert Bosch GmbH" }, { "name": "Käser, Simon Wilhelm", "affiliation": "Universität Stuttgart" }, { "name": "Lunze, Jan", "affiliation": "Ruhr-Universität Bochum" } ], "keywords": [ "Model driven engineering of control systems", "Information models for control engineering", "Control software architecture" ], "abstract": "This paper is aimed at practitioners and offers an analysis of the effect of sampling-time jitter, i.e. the error produced by execution-time inaccuracies. It proposes a reinterpretation of jitter-afflicted linear time-invariant systems as equivalent jitter-free analogs. By constructing a perceived system that absorbs the effects of timing perturbations into its dynamics, we find an affine scaling of the system matrices with respect to jitter. Moreover, in the Laplace domain, jitter can be interpreted as a frequency scaling. The main result of this paper shows that the effects of jitter can be transferred to a time-variation of the continuous system dynamics. Consequently, the overall system can be analysed by the standard sampled-data control theory with constant sampling period, which is demonstrated by the robustness analysis of feedback loops with jitter.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Leveraging Normalizing Flows for Policy Learning in the Competitive Two-Player Zero-Sum Game of Air Hockey", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Boscolo Meneguolo, Francesco", "affiliation": "University of Padova" }, { "name": "Sinigaglia, Alberto", "affiliation": "Human Inspired Technology Research Center, University of Padua, 35121 Padua, Italy" }, { "name": "Sartor, Davide", "affiliation": "Università Di Padova" }, { "name": "Cederle, Matteo", "affiliation": "University of Padova" }, { "name": "Susto, Gian Antonio", "affiliation": "University of Padova" } ], "keywords": [ "Reinforcement learning and deep learning in control" ], "abstract": "Normalizing Flow (NF) models have recently emerged as a powerful class of generative models capable of learning expressive probability distributions through invertible transformations. In Reinforcement Learning (RL), most of the modern algorithms rely on distributions typically parameterized as Gaussian or deterministic. While these choices facilitate tractable optimization, they can severely limit the expressiveness of learned policies. In environments where optimal behaviors require multimodal action distributions, such restrictions can hinder both learning efficiency and final performance. A promising way to address these limitations is through more flexible generative models that can accurately capture complex probability distributions. This study investigates the application of Normalizing Flow architectures to RL tasks, both in single-agent and multi-agent environments. In particular, it is assessed that NFs are capable to model policies that converge to the Nash equilibrium in a two-player zero-sum game scenario, unlike deterministic policies.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Hybrid LQR-TD3 Collective Pitch Control Architecture for Wind Turbines (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Gil-Macia, Alberto", "affiliation": "Complutense University of Madrid" }, { "name": "Sierra-Garcia, Jesus Enrique", "affiliation": "University of Burgos" }, { "name": "Santos, Matilde", "affiliation": "University Complutense of Madrid (VAT ESQ2818014I)" } ], "keywords": [ "Reinforcement learning and deep learning in control", "AI-driven modeling and control", "AI tools in automation engineering and operation" ], "abstract": "Reinforcement learning (RL)-based controllers provide excellent control characteristics for power-output stabilization of wind turbines but require large training datasets, while LQR controllers are suboptimal away from the linearization point. This paper proposes a hybrid collective pitch control (CPC) architecture combining an LQR and Twin Delayed Deep Deterministic Policy Gradient (TD3) controller. The LQR controller guides the TD3 agent during training, while the TD3 controller learns to compensate for the nonlinear dynamics not captured during linearization. Results show that the LQR+TD3 hybrid controller improves performance and reduces steady-state error compared with individual LQR and TD3 controllers.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Design of a Performance-Driven Control System Using Database-Driven Approach", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Li, Zhifeng", "affiliation": "Hiroshima University" }, { "name": "Kinoshita, Takuya", "affiliation": "Hiroshima University" }, { "name": "Yamamoto, Toru", "affiliation": "Hiroshima Univ" }, { "name": "Shah, Sirish L.", "affiliation": "University of Alberta" } ], "keywords": [ "Adaptive control design", "Design methods for data-based control", "Nonlinear time-delay systems" ], "abstract": "Most process systems are difficult to control due to nonlinearity, leading to the proposal of database-driven control for sequential reference trajectory tracking and regulation. However, adjusting PID control parameters at each sampling interval is unnecessary and causes inefficiency and potential safety issues. This paper first introduces control performance evaluation using generalized minimum variance and proposes a control system that accounts for the variance of both the reference trajectory and the manipulative variable. The effectiveness of the proposed method is quantitatively verified using a simulated example of a nonlinear system with a time delay and varying process gain plus time constant.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Extremum Seeking Control Design for a Class of Second-Order Nonlinear Systems with Unknown Control Direction", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Guay, Martin", "affiliation": "Queen's Univ" }, { "name": "Wang, Shimin", "affiliation": "Massachusetts Institute of Technology" } ], "keywords": [ "Adaptive control design", "Design methods for data-based control", "Optimization-based estimation and control" ], "abstract": "Fast extremum seeking is difficult for second-order plants when the control direction, the drift dynamics, and the optimizer are all unknown. This paper develops a dynamic output-feedback design for this setting using only measurements of the objective function. The proposed controller extends the dual-mode extremum-seeking idea to a class of second-order nonlinear systems by combining an observer-based dynamic extension with a Lie-bracket averaged dither transformation. The averaged closed loop has a simple cascade structure: the optimizer coordinate is driven by a gradient-like term, while the unknown plant dynamics enter through a stabilizable observer-error subsystem. Under explicit gain conditions, the averaged closed loop is shown to be globally exponentially stable. For the exact high-frequency realization, the result is stated as semiglobal practical uniform asymptotic stability with respect to a moving corrected set, which accounts for the fast oscillatory components introduced by fixed-amplitude dithering. This yields practical regulation of the optimizer coordinate and of the measured objective without requiring the sign of the input gain. An attenuated unbiased variant is also discussed as a route toward asymptotic convergence. Simulations illustrate the controller behaviour and the expected fast oscillations in the physical velocity.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Integral Concurrent Learning for Natural Adaptive Control of Robotic Manipulators", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Kaufmann, Tom", "affiliation": "TU Ilmenau" }, { "name": "Reger, Johann", "affiliation": "TU Ilmenau" } ], "keywords": [ "Adaptive control design", "Lyapunov methods" ], "abstract": "Natural adaptive control enables tracking with an estimation regime that respects physical constraints. Here, we provide a more detailed characterization of natural adaptation, proving its matrix estimates to be uniformly physically consistent and upper bounded. For certain kinematic layouts, these newly established properties guarantee the desirable existence of finite, positive uniform bounds of the estimated mass matrix. Moreover, we propose a data-driven augmentation of the natural update law so that—provided a finite excitation condition is fulfilled—estimation errors converge to zero, leading to uniformly physically consistent, precise estimation. Simulation of a 3-dof robotic manipulator with 2 rigid bodies verifies the theoretical findings.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Adaptive Parameter Identification of Indoor Microclimate Model", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Rassadin, Yuriy M", "affiliation": "Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences" }, { "name": "Orlov, Yury", "affiliation": "CICESE" } ], "keywords": [ "Adaptive control design", "Lyapunov methods", "Sliding mode control" ], "abstract": "A refined model of air temperature dynamics is considered for more efficient control of indoor microclimate. Along with air temperature dynamics, normally available to direct measurement, average temperature of enclosing surfaces (walls, ceiling, floor, etc.), referred to as mean radiant temperature, is involved into modelling. Since radiant temperature measurements are not as common as traditional air temperature measurements, while heat transfer coefficient between indoor air and surfaces, generating the mean radiant temperature, is neither available, their online estimation is a challenging problem. This problem is addressed in the present work. Based on the air temeprature measurmenets, a sliding mode observer of the mean radiant temperature and an adaptive plant parameter identifier are developed for the underlying indoor microclimate model. Capabilities of the proposed design and its robustness features are further illustrated in a numerical study.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Selection of Design Variables and Durability Improvement for a 55 kW Compound Planetary Geartrain Electric Tractor", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Park, Minjong", "affiliation": "Chungnam National University" }, { "name": "Jeong, Gubin", "affiliation": "Chungnam National University" }, { "name": "Kim, Yong-Joo", "affiliation": "Chungnam National University" } ], "keywords": [ "Analytic design", "Design methods for data-based control" ], "abstract": "This study optimized a 55-kW electric tractor powertrain by fixing the gear geometry and varying the design parameters, including planet gear material grade, heat treatment, surface roughness, spiral bevel module, and face width. We used Latin hypercube sampling to generate feasible candidates, and simulations were conducted to evaluate contact and bending safety factors under a measured load-duration spectrum. Three planet gear configurations improved contact safety by approximately 10% and bending safety by 4-6% across both planetary stages. Combinations with significant degradations were eliminated using a minimum safety factor of 1.10. At the system level, the spiral bevel pair was identified as the bottleneck; the optimal configuration enhanced contact safety by about 6-7% and bending safety by approximately 10%, achieving the highest overall ranking. These improvements resulted from changes in material, heat treatment, and surface finish, which strengthened surface and root durability without altering geometry or increasing meshing losses, thus ensuring robust performance across various load conditions.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Behavioral Stability Certification of Koopman-Lifted Controllers from Persistently Exciting Data", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Jain, Tushar", "affiliation": "Indian Institute of Technology Mandi" } ], "keywords": [ "Analytic design", "Design methods for data-based control", "Lyapunov methods" ], "abstract": "A data-driven framework is proposed for certifying static state-feedback stabilisers of control-affine nonlinear systems without identifying a parametric model. The state is lifted into a finite-dimensional observable space via a fixed Koopman dictionary, and persistently exciting open-loop experiments yield Hankel matrices that parametrise local closed-loop trajectories. For any candidate feedback gain, a data-induced closed-loop matrix is extracted and its Schur stability is verified via a discrete Lyapunov equation, whose solution constitutes a contraction metric in the lifted space. The framework is validated on an inverted pendulum, achieving local exponential stabilisation purely from experimental data.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Model-Free Practical PI-Lead Control Design by Ultimate Sensitivity Principle", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ruderman, Michael", "affiliation": "University of Agder" } ], "keywords": [ "Analytic design", "Structured linear systems", "Real-time optimal control" ], "abstract": "Practical design and tuning of feedback controllers has often to get by without a model of the dynamic process at hand. Only some general assumptions about the system dynamics, in this work type-one stable, can be available for engineers, for instance in motion control applications and many others. This paper proposes a practical and simple in realization procedure for designing a robust PI-Lead control without modeling. The developed method derives from the ultimate sensitivity principles, known in empirical Ziegler–Nichols tuning of PID controllers, and makes use of some general characteristics of the loop shaping. A three-steps procedure is proposed to determine the integration time constant, control gain, and Lead-element in a way to guarantee a sufficient phase margin, while all steps are served by only experimental monitoring of the output value. Proposed method is demonstrated and discussed with experiments accomplished on a noise-perturbed electro-mechanical actuator system.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Necessary and Sufficient PID Gain Regions for Global Stabilization of Uncertain Second-Order MIMO Nonlinear Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Xiang, Tianyou", "affiliation": "AMSS, Chinese Academy of Science" }, { "name": "Zhao, Cheng", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Analytic design", "Uncertain systems", "Lyapunov methods" ], "abstract": "As is well known, classical PID control is ubiquitous in industrial processes, yet a rigorous and explicit design theory for nonlinear uncertain MIMO second-order systems remains underdeveloped. In this paper we consider a class of such systems with both uncertain dynamics and an unknown but strictly positive input gain, where the nonlinear uncertainty is characterized by bounds on the Jacobian with respect to the state variables. We explicitly construct a three-dimensional region for the PID gains that is sufficient to guarantee global stability and asymptotic tracking of constant references for all nonlinearities satisfying these Jacobian bounds. We then derive a corresponding necessary region, thereby revealing the inherent conservatism required to cope with worst-case uncertainties. Moreover, under additional structural assumptions on the nonlinearities, these sufficient and necessary regions coincide, yielding a precise necessary-and-sufficient characterization of all globally stabilizing PID gains. All these regions are given in closed form and depend only on the prescribed Jacobian bounds and the known lower bound of the input gain, in contrast to many qualitative tuning methods in the literature.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Adaptive Iterative Learning Control for Underactuated Surface Vessel under Constrained Uncertain Environments (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Huang, Xiuying", "affiliation": "Sun Yat-Sen University" }, { "name": "Li, Xuefang", "affiliation": "Sun Yat-Sen University" }, { "name": "Li, Xiaodong", "affiliation": "Sun Yat-Sen University" } ], "keywords": [ "Control barrier functions and state space constraints", "Adaptive control design", "Uncertain systems" ], "abstract": "In this paper, an adaptive iterative learning control method is proposed to address the trajectory tracking problem for underactuated surface vessel under constrained uncertain environments. In order to achieve the high-precision tracking tasks while ensuring the satisfaction of physical constraints, two different parametric updating laws and an iteration dependent barrier Lyapunov function are introduced, which are effective to deal with the system uncertainties and constraints. The convergence of the proposed control strategy is rigorously analyzed through the composite energy function method. Numerical simulations are provided to demonstrate the effectiveness and robustness of the proposed control method.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Closed-Loop State Estimation from Spiking-Neuron Populations", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Göral, Erdem", "affiliation": "Hacettepe University" }, { "name": "Boyacioglu, Burak", "affiliation": "Middle East Technical University" }, { "name": "Uyanik, Ismail", "affiliation": "Hacettepe University" } ], "keywords": [ "Control in neuroscience", "Observer design" ], "abstract": "Biological nervous systems perform estimation and control using sensory feedback encoded as sparse spike trains rather than continuous-valued measurements. Inspired by this principle, we develop a closed-loop state estimation framework that reconstructs task-related state variables directly from spiking-neuron populations. The proposed architecture decomposes relative position and velocity signals into complementary subpopulations of Leaky Integrate-and-Fire neurons, whose spike timings are converted into causal firing-rate estimates. These neural responses are decoded using a maximum-likelihood population estimator, and subsequently fused through a Kalman Filter to yield smooth estimates of the underlying tracking error suitable for feedback control. We evaluate the framework in a reference-tracking task modeled after the refuge-tracking behavior of weakly electric fish. Simulation results demonstrate that spiking-neuron populations provide sufficient information to estimate both position and velocity values and enable stable closed-loop performance using a conventional proportional–derivative controller. By showing how spike-based sensory representations can be transformed into actionable state estimates, this work establishes a control-theoretic foundation for integrating neural encoding mechanisms into state observers, with implications for neuromorphic sensing, active perception, and brain–machine interface design.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Uncertain Anesthesia Dynamics Control with Stochastic Optimization and Data Stratification", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ajami, Mohamad", "affiliation": "GIPSA-Lab" }, { "name": "Dang, Thao", "affiliation": "VERIMAG" }, { "name": "Fiacchini, Mirko", "affiliation": "GIPSA-Lab, CNRS" } ], "keywords": [ "Control in system biology", "Probabilistic robustness" ], "abstract": "This paper presents a stochastic optimization framework with data stratification for the control of uncertain anesthesia systems. The proposed approach enables control design with probabilistic performance guarantees under minimal distributional assumptions. To mitigate interpatient variability, patients are stratified into relatively homogeneous subgroups, and a dedicated controller is optimized for each. In this study, PID controllers are optimized for propofol infusion during the induction phase, using a delayed and noisy BIS feedback signal. Chance constraints are incorporated to limit the probability of BIS undershoot.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Spatiotemporal Tubes Based Controller Synthesis against Omega-Regular Specifications for Unknown Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Das, Ratnangshu", "affiliation": "Indian Institute of Science, Bangalore" }, { "name": "Bayezeed, Aiman Aatif", "affiliation": "Indian Institute of Science, Bengaluru" }, { "name": "Jagtap, Pushpak", "affiliation": "Indian Institute of Science" } ], "keywords": [ "Control of hybrid systems", "Controller constraints and structure" ], "abstract": "This paper provides a discretization-free solution to the synthesis of approximation-free closed-form controllers for unknown nonlinear systems to enforce complex properties expressed by omega-regular languages, as recognized by Non-deterministic B{\"u}chi Automata (NBA). In order to solve this problem, we first decompose NBA into a sequence of reach-avoid (RA) problems, which are solved using the Spatiotemporal Tubes (STT) approach. Controllers for each RA task are then integrated into a hybrid policy that ensures the fulfillment of the desired omega-regular properties. We validate our method through case studies on omnidirectional robot navigation and manipulator control.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "H∞ Fault-Compensation Control with Transients for Continuous-Time Markovian Jump Linear", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "de Oliveira, André Marcorin", "affiliation": "UNIFESP" }, { "name": "Costa, Oswaldo Luiz do Valle", "affiliation": "Univ. of Sao Paulo" } ], "keywords": [ "Control of hybrid systems", "Stochastic optimal control problems", "Robust linear matrix inequalities" ], "abstract": "This paper presents an H∞ fault-compensation control strategy considering transient behavior for continuous-time Markovian Jump Linear Systems (MJLS). A dual-controller architecture is employed, where a nominal controller governs normal operation and an auxiliary dynamic controller compensates for faults when they occur. The proposed design guarantees mean-square stability (MSS) and H∞ performance, including transient effects, by solving a set of Linear Matrix Inequality (LMI) conditions. Unlike traditional fault-tolerant control schemes, the approach explicitly incorporates nominal control information into the compensation design, so that the resulting controller activates only under faulty modes. Simulation results demonstrate the method’s effectiveness and potential for reliable operation in fault-prone networked and industrial systems.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Dual Mode-Dependent Stabilization Control for Continuous-Time Hybrid Switched Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhang, Jian", "affiliation": "Southeast University, Shandong University of Science and Technology" }, { "name": "Zhu, Yanzheng", "affiliation": "Shandong University of Science and Technology" }, { "name": "Yang, Rongni", "affiliation": "Shandong University" }, { "name": "Zhi, Xiyang", "affiliation": "Harbin Institute of Technology" }, { "name": "Zhang, Lixian", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Control of hybrid systems", "Switching stability and control", "Switching linear systems" ], "abstract": "This paper further studies the stabilization problem for hybrid switched linear systems with state-dependent switching and dwell time constraint. Based on the previous mode information, the dual mode-dependent (DMD) controller is designed instead of the existing mode-dependent controller, resulting in the DMD Lyapunov function and DMD switching signals, which can enhance the control performance and design freedom. Moreover, a multiple discontinuous Lyapunov function (MDLF) is developed to overcome the restriction of existing results that require the Lyapunov function to be continuous during the dwell time stage. Meanwhile, without the discontinuous control gain behavior accompanying the existing MDLF methods, the designed control gain is time-varying and continuous during the dwell time stage, which avoids the problem of frequent control bumps. Then, the stabilization criterion and the solvability conditions are derived to ensure the stability of the system. Finally, the simulation results are presented to show the benefits of the proposed method.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Reachability-Based Decoupling Control Scheme of Periodic Time-Varying Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ling, Zhaoji", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Xie, Xiaochen", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Wang, Binbin", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Lam, James", "affiliation": "Univ of Hong Kong" } ], "keywords": [ "Controller constraints and structure", "Lyapunov methods", "Optimization-based estimation and control" ], "abstract": "This paper investigates the control of continuous-time periodic systems from the perspective of reachability. Compared with existing studies relying on piecewise linear models of periodic dynamics, our approach can relax the demands on modeling accuracy. It is proposed as a continuous-function-based framework to model time-varying dynamics, offering greater flexibility for practical applications. While the existing approaches primarily focus on guaranteeing asymptotic stability, they generally neglect transient performance. To address this limitation, we introduce a procedure inspired by reachable set estimation to impose explicit time-varying constraints on the closed-loop system's state trajectory, further employing a multi-affine approach to derive equivalent linear matrix inequality constraints. Finally, our proposed approach is validated in an equivalent magnetic levitation demonstration system.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Safety Control of Second-Order Nonlinear Systems under DoS Attacks", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Song, Ruolin", "affiliation": "Tongji University" }, { "name": "Wang, Tianqi", "affiliation": "The Hong Kong Polytechnic University" }, { "name": "Xin, Bin", "affiliation": "Beijing Institute of Technology" }, { "name": "Wang, Qing", "affiliation": "Beijing Institute of Technology" }, { "name": "Dong, Yi", "affiliation": "Tongji University" }, { "name": "Chen, Xi", "affiliation": "The Chinese University of Hong Kong" } ], "keywords": [ "Controller constraints and structure", "Output regulation and tracking", "Stability of nonlinear systems" ], "abstract": "In this paper, we study the safety and security control problem of a class of second-order nonlinear systems with output constraint and denial-of-service (DoS) attacks. By incorporating an internal model-based controller, a barrier function-based framework is incorporated to enforce the output to a prescribed safety set. Then, a DoS-resilient compensation mechanism is devised to mitigate the impact of communication interruptions on closed-loop behavior. A novel series of sufficient conditions is derived to guarantee the boundedness of the closed-loop trajectories, the satisfaction of constraints, and the convergence of the tracking error. A numerical example is provided to illustrate the effectiveness of the proposed control scheme.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Combining Extensional and Intensional Approaches for Logic Controller Design: Application to Tasks Synchronization", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Roisin, Mathieu", "affiliation": "Université De Reims Champagne Ardenne CReSTIC EA3804" }, { "name": "Annebicque, David", "affiliation": "University of Reims - URCA - IUT De Troyes" }, { "name": "Riera, Bernard", "affiliation": "Université De Reims Champagne Ardenne CReSTIC EA3804" }, { "name": "Pierre-Alain, Yvars", "affiliation": "ISAE-Supmeca" } ], "keywords": [ "Controller constraints and structure", "Robust controller synthesis" ], "abstract": "This paper focuses on controller synthesis and the automatic generation of IEC 61131-3 Structured Text (ST) code. Usually, the control engineer uses an extensional approach to specify the logic controller. The principle consists of explicitly modelling the solution (e.g., with GRAFCET or Petri nets). This approach does not enable the engineer to validate the solution. Another approach for solving a problem is to define the solution space through rules or constraints having to be satisfied. This intensional approach, is less used today in industry to design controllers. In this paper, we argue that combining both approaches could be more efficient and robust for control design. Although a workflow exists to integrate them and generate ST code, it lacks a clear definition and methodology. To address this, we propose a structured approach to model the synthesis problem using the DEPS language that can be connected to the existing approach to generate ST code. The approach is illustrated by a case study of the control of a converging conveyor system.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Asymmetric Saturation Handling in Fixed-Tilt Hexarotors Via Optimized Shifted Stabilizer", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Jayanna, Dharani", "affiliation": "Politecnico Di Milano" }, { "name": "Invernizzi, Davide", "affiliation": "Politecnico Di Milano" }, { "name": "Lovera, Marco", "affiliation": "Politecnico Di Milano" }, { "name": "Zaccarian, Luca", "affiliation": "LAAS-CNRS and University of Trento" } ], "keywords": [ "Controller constraints and structure", "Saturation and discontinuity", "Lyapunov methods" ], "abstract": "This paper presents an anti-windup (AW) strategy for fixed-tilt hexarotors operating under direction-dependent thrust constraints that lead to actuator saturation. The proposed method augments a baseline pose controller with a shifted-equilibrium mechanism that enlarges the region of attraction through feasible non-zero equilibria under saturation. A discrete-time AW synthesis is developed by combining a Lyapunov-based direct linear AW design with a convex quadratically constrained quadratic program (QCQP) for selecting equilibrium shifts consistent with the asymmetric actuator limits. The resulting closed-loop system achieves local exponential stability over an enlarged region-of-attraction estimate while limiting attitude transients, which is essential for contact-rich aerial interaction. Simulations on a fully modeled fixed-tilt hexarotor demonstrate improved tracking and reduced attitude deviations compared with a conventional AW scheme.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "On the Stabilization of Rigid Formations on Regular Curves", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Elobaid, Mohamed", "affiliation": "King Abdullah University of Science and Technology" }, { "name": "Park, Shinkyu", "affiliation": "King Abdullah University of Science and Technology" }, { "name": "Feron, Eric", "affiliation": "King Abdullah University of Science and Technology" } ], "keywords": [ "Decentralized control", "Application of nonlinear analysis and design" ], "abstract": "This work deals with the problem of stabilizing a multi-agent rigid formation on a general class of planar curves. Namely, we seek to stabilize an equilateral polygonal formation on closed planar differentiable curves after a path sweep. The task of finding an inscribed regular polygon centered at the point of interest is solved via a randomized multi-start Newton-Like algorithm for which one is able to ascertain the existence of a minimizer. Then we design a continuous feedback law that guarantees convergence to, and sufficient sweeping of the curve, followed by convergence to the desired formation vertices while ensuring inter-agent avoidance. The proposed approach is validated through numerical simulations for different classes of curves and different rigid formations. Code: https://github.com/mebbaid/paper-elobaid-ifacwc-2026", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "A Resilient Distributed Personalized Optimization Algorithm against Byzantine Attacks", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Shen, Yigao", "affiliation": "Zhejiang University" }, { "name": "Zhao, Chengcheng", "affiliation": "Zhejiang University" } ], "keywords": [ "Decentralized control", "Convex optimization", "Optimization-based estimation and control" ], "abstract": "Distributed personalized optimization (DPO) has demonstrated significant potential in distributed learning where each agent maintains a global variable capturing shared features and a local variable reflecting personalization. However, whether and how we can design resilient algorithms for distributed personalized optimization against Byzantine attacks in fully distributed scenarios remains an open issue. To solve this issue, we propose a resilient gradient descent DPO algorithm, utilizing Local Filtering (LF) dynamics which discards the F (F is the maximum tolerable number of the compromised agents) largest and F smallest state values from in-neighbor agents for each dimension to update the global variable iteratively. We derive novel sufficient conditions to guarantee the linear convergence of the proposed algorithm for the cases with a strongly convex objective function. Numerical results are presented to validate the theoretical findings.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "A Data-Based System Representation: The Stabilization Problem", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Szabo, Zoltan", "affiliation": "HUN-REN SZTAKI" }, { "name": "Bokor, Jozsef", "affiliation": "Hungarian Academy of Sciences" }, { "name": "Gaspar, Peter", "affiliation": "HUN-REN SZTAKI, Institute for Computer Science and Control, Hungarian Research Network" }, { "name": "Bauer, Peter", "affiliation": "HUN-REN Institute for Computer Science and Control" } ], "keywords": [ "Design methods for data-based control", "Linear systems", "Observer design" ], "abstract": "In our previous work a system representation formed by a minimal collection of sufficiently long restricted trajectories generated by an observable discrete time LTI system was proposed and conditions were given under which such a collection is a system representation. This paper addresses the problem of stabilizability in terms of the proposed data-based representation, and the construction of the stabilizing controller is also provided. It turns out that the entire problem can be reduced to a suitable state feedback design. A method for state reconstruction and observer design is also proposed.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Repowering Obsolete Helicopter Testbeds: A Reproducible Framework for Modern Control Education and Applications", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Salazar, Carlos Alberto", "affiliation": "Escuela Superior Politecnica Del Litoral, ESPOL" }, { "name": "Aguirre, Adriana", "affiliation": "Escuela Superior Politécnica Del Litoral" }, { "name": "Rodriguez Gonzalez, Mario Gustavo", "affiliation": "Escuela Superior Politecnica Del Litoral" }, { "name": "Suárez Matias, José Santiago", "affiliation": "Escuela Superior Politécnica Del Litoral" } ], "keywords": [ "Digital implementation", "Model validation" ], "abstract": "Obsolescence of didactic control platforms is a growing challenge in academic laboratories, limiting their use in both teaching and research. This paper presents a reproducible framework for repowering and optimizing a two-axis helicopter testbed, transforming an inoperative setup into a real-time compatible platform for modern control education and experimentation. The proposed methodology combines hardware reengineering, embedded electronics, and software integration through an ESP32-based acquisition system, custom PCBs, high-resolution sensors, and bidirectional serial communication with MATLAB® and SIMULINK®. Experimental validation demonstrates significant improvements in operating range, measurement robustness, sampling frequency, and communication latency compared with the legacy configuration. These enhancements enable the implementation of advanced control techniques, including state-space feedback, observer-based control, and model predictive control (MPC), which require accurate sensing and deterministic real-time operation. Beyond restoring functionality, the proposed framework provides a transferable modernization strategy for other obsolete laboratory platforms, such as inverted pendulums, rotary arms, gimbal systems, and underactuated robotic testbeds. The approach therefore bridges theory and practice while extending the useful life of educational platforms and supporting next-generation training and research in automatic control.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Bee Hive Monitoring System Based on Capacitive Sensors (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zebrowski, Tomasz", "affiliation": "Warsaw University of Technology" }, { "name": "Domanski, Pawel Dariusz", "affiliation": "Warsaw University of Technology" } ], "keywords": [ "Digital implementation", "Supervision and testing", "Sampled-data/digital control" ], "abstract": "This paper presents a simple, low-cost bee hive monitoring system based on capacitive sensors for reliably detecting and counting individual bees. The system employs a novel approach to signal acquisition using a microcontroller to approximate the charging time of two ring capacitors within a bee tunnel, which form the core of the sensor. The change in capacitance, caused by a bee's high relative electrical permittivity, allows for the determination of its presence and direction of movement (entering or leaving the hive). The system's hardware design avoids complex, high-cost signal-measurement circuits, making it accessible to smaller apiaries. Two bee detection algorithms were developed and tested. Validation, including laboratory tests with bee models and site testing against video-annotated ground truth, demonstrated the functionality of the proposed sensor and algorithms. While the device successfully approximates the intensity of forager traffic, its overall accuracy is limited by abnormal bee behaviours (grouping, stopping, or turning within the sensor tunnel). Future research will explore multi-gate designs and data fusion techniques to improve counting reliability and provide a more precise estimate of colony population.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "PGOA-MN: A Multiscale Network with Physics-Guided Orthogonal Attention for Aluminum Leakage Detection", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Peng, Junhui", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Liu, Qi", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Liu, Yuxiang", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Yang, Bo", "affiliation": "Department of Automation, Shanghai Jiao Tong University, Shanghai" } ], "keywords": [ "Fault detection and isolation" ], "abstract": "Industrial AI solutions for molten aluminum leakage detection face challenges in maintaining long-term stability across dynamic factory environments and generalizing across multiple facilities. This paper proposes PGOA-MN, a multiscale network with physics-guided orthogonal attention that integrates physical knowledge with deep learning. The architecture employs dual-channel spectrogram processing with multiscale temporal modeling for comprehen\u0002sive feature extraction. Physics-guided attention leverages domain-specific features to focus on anomaly patterns, while orthogonal attention captures complementary temporal and energetic characteristics. This approach maintains detection accuracy despite environmental variations in single-factory deployments and achieves strong cross-factory generalization without retraining. Extensive validation in real aluminum production environments demonstrates that PGOA-MN effectively resolves critical challenges and provides a reliable industrial safety monitoring solution.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Safe Multi-Agent Navigation under Limited Communication Using High-Order Robust Control Barrier Functions", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Jia, Zhanxiao", "affiliation": "Northwestern Polytechnical University" }, { "name": "Xu, Bowen", "affiliation": "Northwestern Polytechnical University" }, { "name": "Xue, Ruihong", "affiliation": "Northwestern Polytechnical University" }, { "name": "Fan, Chengli", "affiliation": "Air Force Engineering University" }, { "name": "Fu, Qiang", "affiliation": "Air Force Engineering University" }, { "name": "Yu, Dengxiu", "affiliation": "Northwestern Polytechnical University" } ], "keywords": [ "Applications of optimal control", "Learning methods for optimal control" ], "abstract": "This paper proposes a novel framework for safe and coordinated multi-agent navigation under communication constraints. Traditional multi-agent reinforcement learning methods often struggle to ensure safety and coordination in partially observable environments with limited bandwidth. The proposed R-MADDPG–HORCBF framework integrates Recurrent Multi-Agent Deep Deterministic Policy Gradient (R-MADDPG) with High-Order Robust Control Barrier Functions (HORCBFs). Specifically, a recurrent actor-critic network is employed to capture temporal dependencies, while a differentiable RCBF layer is incorporated to enforce safety constraints in real time. Simulation results in multi-vehicle navigation scenarios demonstrate that the proposed framework significantly enhances both safety and communication efficiency, highlighting its strong potential for real-world deployment in safety-critical systems.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Optimal Path Planning of Airborne Wind Energy Systems in the Wake of a Horizontal Axis Wind Turbine", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Heydarnia, Omid", "affiliation": "Ghent University" }, { "name": "Wauters, Jolan", "affiliation": "KU Leuven" }, { "name": "Lefebvre, Tom", "affiliation": "Ghent University" }, { "name": "Crevecoeur, Guillaume", "affiliation": "Ghent University" } ], "keywords": [ "Applications of optimal control", "Numerical methods for optimal control", "Application of nonlinear analysis and design" ], "abstract": "The increasing deployment of wind turbines and the limited availability of suitable installation areas motivate the integration of multiple wind-energy-harvesting technologies. Airborne Wind Energy Systems (AWES), capable of accessing high-altitude wind resources, offer a promising complement to conventional Horizontal-Axis Wind Turbines (HAWTs). This work presents an optimal path-planning algorithm for AWES operating within the wake of HAWTs. A simplified wake model is employed to estimate wind speed deficits behind the turbine and is incorporated directly into the trajectory optimization scheme. Simulation results show that lemniscate flight paths exhibit less sensitivity to wake effects compared to circular trajectories. The results demonstrate the potential of wake-aware path planning to improve AWES performance in multi-technology wind farm environments.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Automatic Evaluation of Fastener Assembly Quality in Aircraft Power Distribution Boxes Using RT-DETR and Template Comparison", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Yan, Zhongbao", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Yin, Chun", "affiliation": "University of ElectronicScience and Technology of China, Chengdu611731, P.R. China" }, { "name": "Liu, Junyang", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Cao, Jiuwen", "affiliation": "Hangzhou Dianzi University" }, { "name": "Zhang, Yuanhao", "affiliation": "University of Electronic Science and Technology of China" } ], "keywords": [ "Applications of optimal control", "Optimal control of hybrid systems", "Fault detection and isolation" ], "abstract": "To address the low efficiency of fastener assembly inspection for aircraft power distribution boxes, the reliance on manual expertise, and the poor adaptability to small targets and diverse assembly specifications, this paper presents a two stage automatic inspection method that combines an RT-DETR based detection network with template comparison. We build a dataset of 4,125 images of power distribution box fasteners, use RT-DETR to obtain class labels and bounding box priors for each assembly position, and design a global image matching method constrained by keypoints and annotation boxes to align template boxes with detection results and perform consistency assessment. Experiments show that the RT-DETR detector achieves an mAP50 of 0.9925 on the constructed dataset, with mean precision and recall of 0.9862 and 0.9844, respectively. Experimental results on multi view inspection images show that the proposed framework can reliably identify missing and misinstalled fasteners and reduce reliance on manual inspection, indicating strong potential for engineering applications.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Nonlinear Control of an Asymmetric Falling Cat Model Via State-Dependent Riccati Equation (SDRE)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Xin, Xin", "affiliation": "Southeast University" }, { "name": "Fang, Dingyang", "affiliation": "Southeast University" }, { "name": "Zhou, Chi", "affiliation": "Southeast University" }, { "name": "Sampei, Mitsuji", "affiliation": "The Polytechnic University of Japan" } ], "keywords": [ "Applications of optimal control", "Real-time optimal control", "Application of nonlinear analysis and design" ], "abstract": "This paper investigates state-dependent Riccati equation (SDRE) feedback for practical self-righting of an asymmetric two-link falling-cat model. The velocity-input nonholonomic model is augmented with virtual angular-acceleration inputs to better align the control layer with torque-driven actuation. Three state-dependent coefficient (SDC) parameterizations are constructed, and their pointwise controllability conditions are characterized through a PBH-based analysis. Comparative simulations for a static-drop maneuver show that the parameterization preserving the dominant spin dynamics yields faster convergence and smoother inputs, whereas the alternatives either fail near the zero-velocity manifold or violate the bending constraint.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Output-Feedback Hierarchical Control Using Approximate Simulation -- towards a Data-Driven Implementation", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Niu, Zirui", "affiliation": "Imperial College London" }, { "name": "Shakib, Mohammad Fahim", "affiliation": "Eindhoven University of Technology" }, { "name": "Scarciotti, Giordano", "affiliation": "Imperial College London" } ], "keywords": [ "Control of complex systems", "Design methods for data-based control", "Linear systems" ], "abstract": "Approximate simulation-based hierarchical control (ASHC), in brief, is a technique used for simplifying the control design of a complex system with an a priori known output discrepancy bound. Current ASHC methods are based on state feedback, which hinders the possibility of developing data-driven enhancements. To overcome this difficulty, in this paper, we present a novel output-feedback ASHC framework when online state feedback is not possible. Furthermore, we propose a direct data-driven enhancement. While the proposed data-driven results still rely on the state data, the results of this paper can be seen as a stepping stone in developing a fully input-output data-driven method for solving the ASHC problem. All results are illustrated by means of a numerical example.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Tuning of PID/PIDD2 Controllers Via State-Space Pole Placement", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Tan, Wen", "affiliation": "North China University of Technology" } ], "keywords": [ "Control of complex systems", "Parametric optimization", "Robustness analysis" ], "abstract": "A state space pole placement approach is proposed to design PID controllers for high-order processes. The method makes use of a single parameter to determine the locations of the closed-loop poles, thus a (high-order) PID controller can be tuned with this parameter. Tuning rules of PID/PIDD2 controllers are then derived for typical stable, integrating and unstable process models. The tuned rules are applied to the benchmark processes. Simulation results show that the tuning rules can achieve compromise among disturbance rejection, robustness, and noise attenuation.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Hylomorphic Dynamic Programming", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Yang, Ya-Ting", "affiliation": "New York University" }, { "name": "Zhu, Quanyan", "affiliation": "New York University" } ], "keywords": [ "Differential or dynamic games", "Control of hybrid systems", "Optimal control of hybrid systems" ], "abstract": "Many real-world systems, such as robotics and cyber defense, rely on hierarchical decision processes where a strategic layer sets long-term configurations and a tactical layer executes fast-time actions, leading to a leader–follower structure with asymmetric information and temporally coupled interactions that may fall outside classical Stackelberg models. To address this gap, we introduce hylomorphic dynamic programming (HDP) for hierarchical control. HDP operates between an anamorphism, which unfolds strategic choices into tactical consequences by solving inner dynamic programs, and a catamorphism, which folds tactical outcomes into strategic values. This hylomorphic recursion provides a consistent and computationally tractable framework of the associated dynamic Stackelberg equilibrium.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Analysis of the Attacker-Defender-Target Differentiable Game with Faster Attackers", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Song, XiangYu", "affiliation": "Tongji University" }, { "name": "Lei, Jinlong", "affiliation": "Tongji University" }, { "name": "Yi, Peng", "affiliation": "Tongji University" } ], "keywords": [ "Differential or dynamic games", "Optimal control theory", "Analytic design" ], "abstract": "This paper proposes a comprehensive analysis framework and optimal strategies for the Attacker-Defender-Target (ADT) differential game. The game involves three agents with simple kinematic models, where the attacker has a speed advantage. Based on Pontryagin’s minimum principle, this paper establishes a unified Hamiltonian framework for both scenarios where the attacker wins and the defender wins. The study proves that each agent's optimal strategy manifests as constant-velocity rectilinear motion towards a specific interception point. Drawing upon the geometric theory of Apollonius circles, analytical equations for determining the optimal interception point are derived. Furthermore, by analyzing the relative positions of the two Apollonius circles—between the attacker and defender, and between the attacker and target—this paper provides strict geometric criteria for dividing the game’s winning regions.Finally, numerical simulations are implemented to validate the theoretic results.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "A Feedback Linearization and Riccati-Based Approach to Nonlinear Zero-Sum Differential Games", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Garazha, Ilya", "affiliation": "National Research University Higher School of Economics" }, { "name": "Afanas'ev, Valery", "affiliation": "National Research University Higher School of Economics Moscow Institute of Electronics and Mathematics" } ], "keywords": [ "Differential or dynamic games", "Real-time optimal control", "Stability of nonlinear systems" ], "abstract": "This paper addresses a zero-sum differential game with a quadratic cost functional for controlling nonlinear plants under bounded disturbances, modelled by ordinary differential equations with state feedback. A diffeomorphic coordinate transformation linearizes the system, yielding a model with constant parameters and a transformed cost functional featuring state-dependent weighting matrices. Optimal strategies are derived from the Bellman–Isaacs equation, which leads to a state-dependent Riccati-type equation. In the infinite-horizon case the problem reduces to a state-dependent Riccati equation (SDRE), which is solved numerically, yielding a suboptimal regulator that guarantees asymptotic stability. The control and disturbance inputs are combined into a single regulator, and the inverse transformation recovers the original controls. An example based on the Lotka–Volterra predator–prey model illustrates the effectiveness of the proposed method.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Collapsed Filtering for Fault Root–Cause Identification in Nonlinear Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Canyakmaz, Ilayda", "affiliation": "Singapore University of Technology and Design" }, { "name": "Escudero, Cédric", "affiliation": "Laboratoire Ampère CNRS, INSA Lyon, Université De Lyon" }, { "name": "Murguia, Carlos", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Fault detection and isolation", "Observer design", "Application of nonlinear analysis and design" ], "abstract": "This paper presents a framework for fault estimation and root–cause identification (RCI) in nonlinear systems that avoids the structural difficulties of nonlinear unknown–input observers. We construct a collapsed model that merges nonlinearities and unknown faults into aggregated input channels, and propose a robust L_2 filter to estimate the resulting lifted state. We show that the lifted dynamics remain well posed and that filter existence requires only a weak zero-frequency input-observability condition, milder than full input observability. Individual fault components are then recovered through simple algebraic extractor maps. For RCI, we introduce a dictionary-based filter that compares the estimated trajectory against a library of candidate fault signatures and scores each by how well it explains the observed fault behaviour. The approach is illustrated on a three-tank benchmark.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Detection of Actuator Faults in Systems with Overlapped Ostensible Metzler Dynamics", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Krokavec, Dusan", "affiliation": "Technical University of Kosice" }, { "name": "Filasova, Anna", "affiliation": "Technical University of Kosice" } ], "keywords": [ "Fault detection and isolation", "Positive linear systems", "Observer design" ], "abstract": "The paper deals with the properties of a fault detection filter when applied to a class of continuous-time linear systems with dynamics specified by a system matrix with an overlapped ostensible Metzler structure. The proposed solution reduces to the use of diagonal stabilization in the synthesis of the state observer and uses orthogonal transformation to construct a model with reduced order dynamics in the form of an ostensible Metzler matrix and the separation principle to generate a hidden strictly Metzler matrix for the synthesis conditions. This approach creates a unified framework that covers the compactness of parametric constraints on Metzler matrices and their diagonal quadratic stability. Using a structural model of a fixed-wing unmanned aerial vehicle to validate the method shows that the proposed approach provides high sensitivity of the fault detection filter for actuator fault detection.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "An Efficient Distributed ADMM with Local Updates for Composite Optimization", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhou, Yuan", "affiliation": "Southeast University" }, { "name": "Shi, Xinli", "affiliation": "Southeast University" }, { "name": "Xu, Xiangping", "affiliation": "Hohai University" }, { "name": "Cao, Jinde", "affiliation": "Southeast Univ" } ], "keywords": [ "Large-scale and networked optimization problems", "Convex optimization" ], "abstract": "This paper addresses distributed composite optimization, where standard algorithms suffer from significant communication overhead and computational burden. We propose DC-ADMM-LU, a novel framework that achieves both communication and computation efficiency through local updates. The key innovation is leveraging ADMM's variable splitting to decouple the expensive proximal operator from frequent local computations, while each client performs multiple lightweight, explicit update steps. An integrated variance-reduction mechanism ensures rigorous error control across local iterations. We establish the first linear convergence guarantee for multi-step local-update ADMM in the distributed stochastic setting, without restrictive assumptions. Numerical experiments confirm superior performance.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Optimal Safe Attitude Tracking Control for UAV System with Unknown Disturbances under Relaxed PE Conditions", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Chen, Chen", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Peng, Zhinan", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Luo, Rui", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Kuang, Yiqun", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Cheng, Hong", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Ghosh, Bijoy", "affiliation": "Texas Tech University" } ], "keywords": [ "Learning methods for optimal control", "Adaptive control design" ], "abstract": "This paper proposes a novel adaptive learning control approach for attitude tracking of unmanned aerial vehicles (UAVs) subject to safety constraints and unknown disturbances with relaxed persistence of excitation (PE) conditions. We first formalize the robust optimal attitude tracking problem with a zero-sum game structure. Then, a modified reward function that consists of a control barrier function (CBF) is presented, which prevents the system states from violating the prescribed safety boundaries. To solve this optimization problem, a critic adaptive dynamic programming (ADP) framework is employed to approximate the solution of Hamilton-Jacobi-Issac (HJI) equation, thus obtaining the approximated optimal control. Unlike the existing gradient-descent learning methods, we transform the weight learning problem into a parameter estimation problem, which is further solved by a novel estimator design using dynamic regression extension and mixing (DREM) and generalized parameter estimation based observer (GPEBO) techniques. The main advantage of this method lies in that it not only relaxes the strict PE conditions for parameter convergence but also provides specific implementation solutions, thereby enhancing its applicability in real-world scenarios. Rigorous theoretical analysis and numerical simulations demonstrate the effectiveness and superiority of our proposed method.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "A Physics-Informed Neural Network Approach for Solving HJB Equations", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Georges, Didier", "affiliation": "Grenoble Institute of Engineering and Management - Univ. Grenoble Alpes" } ], "keywords": [ "Learning methods for optimal control", "Numerical methods for optimal control", "Applications of optimal control" ], "abstract": "A physics-informed neural network (PINN) approach for solving hyperbolic infinite-horizon Hamilton--Jacobi--Bellman (HJB) equations arising in nonlinear optimal regulator problems is proposed in this paper. The method simultaneously learns the value function and the optimal feedback control law through two coupled neural networks, trained to satisfy the continuous-time HJB equation and the optimality conditions for the control. We then apply the method to the closed-loop control of a quadrotor UAV and a high-dimensional reduced model of a nonlinear heat equation. The proposed PINN approach proves capable of overcoming the curse of dimensionality problem. Finally, the application of the proposed PINN approach is discussed for solving the optimal nonlinear estimation problem.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Predefined-Time Observer-Identifier-Based Optimal Tracking Control for Uncertain Robotic Systems under State Constraints", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Hao, Lin", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Peng, Zhinan", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Chen, Chen", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Luo, Rui", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Cheng, Hong", "affiliation": "University of Electronic Science and Technology of China" } ], "keywords": [ "Learning methods for optimal control", "Optimization-based estimation and control", "Adaptive control design" ], "abstract": "This article proposes a novel predefined--time observer--identifier--based optimal tracking control framework for robotic systems with unknown states and uncertain dynamics subject to prescribed state constraints. Till now, most of the existing results on optimal control approaches for uncertain robotic systems require full--state information in the identifier and controller design, which is often invalid in practical scenarios. To address this issue, a predefined--time dynamic regression extension and mixing (PTDREM) method is proposed to design an observer--identifier that can simultaneously estimate unmeasurable system states and uncertain model parameters. Then, a new predefined--time prescribed performance control (PTPPC) scheme is developed under the framework of optimized backstepping technique. With this scheme, the tracking error is guaranteed to be constrained to a prearranged vicinity of origin within a predefined time. In contrast to previous studies, the proposed framework not only achieves the convergence of all closed-loop signals, but also allows that the upper bounds of convergence time for the observer--identifier and controller can all be adjusted through separate design parameters, thus ensuring global predefined--time stability (GPTS). Finally, simulation results demonstrate the effectiveness of the proposed observer--identifier--based control method.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Towards Guaranteed Optimal PID Tuning for Uncertain Nonlinear Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhu, Jingru", "affiliation": "University of Chinese Academy of Sciences" }, { "name": "Zhao, Cheng", "affiliation": "Chinese Academy of Sciences" }, { "name": "Guo, Lei", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Learning methods for optimal control", "Stability of nonlinear systems", "Uncertain systems" ], "abstract": "Despite the widespread use of PID controllers in engineering practice, designing optimal PID parameters has long been regarded as a challenging problem in both theory and practice, particularly when faced with uncertain nonlinear dynamical systems. Based on the authors' PID control theory established recently for MIMO nonlinear uncertain systems (Zhao and Guo, 2022), which provides a concrete PID parameter set for global stability of PID controlled systems, this paper further proposes a near-optimal PID tuning method, where only input-output (zeroth-order) data on the control performance is available. The tuning method is formulated as a constrained optimization problem and solved by an iterative learning algorithm, referred to as HRS-KW algorithm, that combines a hysteretic random search with the Kiefer–Wolfowitz algorithm, aiming at utilizing the advantages of both global exploration and local gradient acceleration. This method operates without requiring precise structural knowledge of the system dynamics, yet its almost sure convergence to an epsilon-optimal solution for the PID parameters can be guaranteed in theory while ensuring closed-loop system stability. Simulation results illustrate that our HRS-KW algorithm outperforms other related optimization methods, exhibiting better convergence to the prescribed epsilon-optimal performance set.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Pole Placement for Static Output Feedback Systems by Continuous Pole Shifting and Its Application to PID Control Design", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ochi, Yoshimasa", "affiliation": "National Defense Academy" }, { "name": "Totoki, Hironori", "affiliation": "National Defense Academy" } ], "keywords": [ "Linear systems" ], "abstract": "This paper proposes a computational procedure for designing a static output feedback (SOF) gain matrix for multi-input multi-output (MIMO) systems using a continuation (or homotopy) method. We regard the characteristic equations for the closed-loop SOF system as simultaneous nonlinear equations with respect to the gain elements for a given set of desired poles. We then derive differential equations from the characteristic equations based on the continuation approach. By integrating the differential equations from known initial poles to desired poles, we can obtain a gain matrix that assigns the closed-loop poles to the desired ones. From the rank of a derivative matrix in the differential equation, we can know if all or part of the designated closed-loop poles are assignable. The method is also extended to dynamic control design, particularly PID control. The effectiveness of the proposed procedure is demonstrated through flight control design for an unstable aircraft and its numerical simulation.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Control of Discrete-Time Linear Systems with Charge-Balanced Inputs", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Qin, Yuzhen", "affiliation": "Radboud University" }, { "name": "Liu, Zonglin", "affiliation": "University of Kassel" }, { "name": "Stursberg, Olaf", "affiliation": "University of Kassel" }, { "name": "van Gerven, Marcel", "affiliation": "Radboud University" } ], "keywords": [ "Linear systems", "Control in neuroscience", "Optimal control theory" ], "abstract": "Electrical brain stimulation relies on externally applied currents to modulate neural activity, but safety constraints require each stimulation cycle to be charge-balanced, enforcing a zero net injected charge. However, how such charge-balanced stimulation works remains poorly understood. This paper investigates the ability of charge-balanced inputs to steer state trajectories in discrete-time linear systems. Motivated by both open-loop and adaptive neurostimulation protocols, we study two practically relevant input structures: periodic (repetitive) charge-balanced inputs and non-repetitive charge-balanced inputs. For each case, we derive novel reachability and controllability conditions. The theoretical results are further validated through numerical demonstrations of minimum-energy control input design.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Re-Opening PID Controller Stability Domain in 3D Via Ruled Surface by D-Partition", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Tremba, Andrey", "affiliation": "Institute of Control Sciencies" } ], "keywords": [ "Linear systems", "Controller constraints and structure", "Linear time-delay systems" ], "abstract": "All stabilizing PID controllers form a set in three-dimensional space. A novel viewpoint to its boundary as a ruled surface (or surfaces) being cut with 3D planes is presented. The characterization, being not too new, contributes to an understanding of the stability set as the whole, instead of the classical view as a stack of 2D slices, say, on the P-coefficient. The viewpoint gives clear insight on the structure of the PID stability region, and, in particular, splits its boundary into continuous parts. It is followed by natural 2D unwrapping of the stability set boundary. It also correctly handles pure imaginary zeros in transfer function. A wireframe 3D visualization reveals the structure of the stability set. The presentation is valid both for ideal and filtered PID controllers, as well as for time-delay systems and other linear systems. Finally, based on the viewpoint, a simple formula for stability (fragility) radius is provided.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Enhanced Inverse Linear Quadratic Control for Hot Rolling Looper-Gauge Coordination", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Yuan, Hao", "affiliation": "Northeastern University" }, { "name": "Li, Xu", "affiliation": "Northeastern University" }, { "name": "Tian, Yong", "affiliation": "State Key Laboratory of Digital Steel, Northeastern University, Shenyang, China" }, { "name": "Li, Yong", "affiliation": "Northeastern University" } ], "keywords": [ "Linear systems", "Optimal control theory" ], "abstract": "Addressing the strong dynamic coupling between the looper and gauge control systems in hot rolling, this paper proposes a coordinated control scheme based on an enhanced inverse linear quadratic (ILQ) theory. The proposed design systematically constructs the adjustable gain matrix Π and establishes an autonomous optimization framework integrating swarm intelligence. Furthermore, disturbance observer-based robust control (DOBRC) is innovatively incorporated, forming a composite control architecture. Simulation results demonstrate that the proposed scheme significantly improves the suppression of external mismatched disturbances and enhances robustness against model uncertainties.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Fragility Analysis and Stabilizing Sets of PID Controllers in Frequency Domain", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Shatov, Dmitrii", "affiliation": "V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences" } ], "keywords": [ "Linear systems", "Robust estimation", "Uncertain systems" ], "abstract": "This research focuses on fragility analysis of PID controllers. The problem considered is to find a complete stabilizing set for each parameter of a given PID controller. The proposed solution is based on the classical frequency-domain stability criterion -- the Nyquist criterion. The procedure utilizes a known robust analysis method, the so-called ``breaking by parameter'' technique, which enables the study of robust (here, stabilizing) properties for an individual system parameter. Applying this technique to PID controller parameters solves the fragility analysis problem. The main result is presented as an analytical procedure for individual PID parameters.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Efficient Numerical Techniques for Data-Driven Approach to Geometric Control Problems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "N, Naveen Mukesh", "affiliation": "Indian Institute of Technology Bombay" }, { "name": "Patil, Deepak", "affiliation": "Indian Institute of Technology Delhi" }, { "name": "Pal, Debasattam", "affiliation": "Indian Institute of Technology Bombay" } ], "keywords": [ "Linear systems", "Structural and geometric control", "Numerical methods for optimal control" ], "abstract": "This work aims to provide numerically efficient computational techniques for recent results from data-driven geometric control. First, an overview of recent results on the data-driven disturbance decoupling problem (D4P) from (Naveen Mukesh et al., 2025) is presented. These results use multiple noisy output trajectories collected from the system instead of system matrices. Then, numerically efficient subspace computational methods that use only input-output data are developed to verify the solvability condition for the disturbance decoupling problem (DDP). The proposed numerical method uses the LQ decomposition to perform the required subspace computations. Subsequently, from the ``noisy'' output data, the largest controlled invariant subspace contained in the nullspace of the output matrix and a corresponding feedback matrix that solves the DDP are also computed numerically using LQ decomposition. Lastly, efficient computation techniques for computing the largest controlled invariant subspace contained in the nullspace of the output matrix and the smallest conditioned invariant subspace containing the range space of the input matrix, from exact noise-free data collected from the system, are presented.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Spectrum Reconstruction for LTI Discrete-Time Delay Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Li, Xu", "affiliation": "Nanjing University of Posts and Telecommunications" }, { "name": "Li, Xu-Guang", "affiliation": "Northeastern University" }, { "name": "Fan, Gaoxia", "affiliation": "Northeastern University" }, { "name": "Chen, Jun-Xiu", "affiliation": "Northeastern University" }, { "name": "Zhang, Lu", "affiliation": "Northeastern University" } ], "keywords": [ "Linear time-delay systems" ], "abstract": "The spectrum of a discrete-time delay system (DTDS) with linear-time-invariant (LTI) dynamics is of the discontinuity nature, when the delay tau is treated as a free parameter. This is a long-standing obstacle for directly keeping track of the stability property in the whole delay parameter space. This work proposes an intuitive frequency-domain framework to solve this problem. First, we construct the characteristic entire function for a DTDS, whose spectrum has the equivalence relation with that of the characteristic function. Second, we propose the continuity property of unstable roots for the characteristic entire function. Therefore, the spectrum of the characteristic function is replaced by that of the characteristic entire function, and the discontinuity issue is fully solved, which allows for an available and direct way to study the stability w.r.t. a free tau. Finally, within our new framework, a general idea for analyzing the stability in the whole delay parameter space, the tau-decomposition idea for DTDS, is provided.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Price-And-Branch for Sweep Coverage with Mobile Sensors on Cell-Shaped Areas", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Gusrialdi, Azwirman", "affiliation": "Tampere University" }, { "name": "Marinelli, Fabrizio", "affiliation": "Università Politecnica Delle Marche" }, { "name": "Pizzuti, Andrea", "affiliation": "Università Degli Studi ECampus" }, { "name": "Ronchini, Nicola", "affiliation": "Università Politecnica Delle Marche" } ], "keywords": [ "Task and motion planning", "Aerial, field, and marine robotics" ], "abstract": "This paper presents a path-based integer linear programming formulation for the sweep coverage problem, in which points of interest of a given area, i.e., an indoor farming field, must be covered by mobile sensors, subject to redundancy and sensing range constraints. A price-and-branch algorithm, whose pricing subproblem is formulated as a generalized orienteering problem, is employed to compute primal and dual bounds. For a simplified variant of the problem, a convex-hull-based destroy-and-repair heuristic is designed for the warm start and acceleration of column generation. The effectiveness of the proposed approach is discussed through computational experiments.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "LPV Model-Based Adaptive CBFs for Safety-Critical Motion Control of 4WID-4WIS Electric Vehicles (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Li, Zongxuan", "affiliation": "Tongji University" }, { "name": "Dong, Rui", "affiliation": "Tongji University" }, { "name": "Li, Yang", "affiliation": "Tongji University" }, { "name": "Chu, Hongqing", "affiliation": "Tongji University" }, { "name": "Gao, Bingzhao", "affiliation": "Tongji University" }, { "name": "Chen, Hong", "affiliation": "Tongji University" } ], "keywords": [ "Adaptive control design", "Linear parameter-varying systems", "Real-time optimal control" ], "abstract": "Control barrier functions (CBFs) based methods for four-wheel independently driving/steering electric vehicles (4WID-4WIS EV) face a fundamental modeling limitation. Due to the nonlinear characteristics of tire, non-affine models ensure high-fidelity safety constraints but induce non-convex optimization, whereas time-invariant affine models preserve convex safety constraints but lose fidelity in nonlinear regions. To achieve high-fidelity safety constraints and real-time optimization, this work proposes a safety-critical motion controller using a linear parameter-varying (LPV) model. A high-fidelity dynamics model is online linearized at each sampling instant, generating a LPV affine model that adapts to nonlinear dynamics while satisfying the affine form of the CBF-CLF quadratic program (QP) framework. To address time-varying parameter feasibility challenges, safety constraints are transformed into adaptive CBFs (ACBFs), explicitly accommodating parameter variations without relaxation. The control problem is formulated as an ACBF-CLF-QP and solved in real-time. CarSim/Simulink co-simulations demonstrate the controller's effectiveness and superiority over baselines, resolving the fundamental modeling limitation in CBFs based methods for 4WID-4WIS EV.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Sliding Mode Control for a Parabolic–Elliptic PDE System with Boundary Perturbation", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Labbadi, Moussa", "affiliation": "Bretagne INP UBO, IRDL" }, { "name": "Ilyasse, Lamrani", "affiliation": "Faculty of Sciences Meknes" } ], "keywords": [ "Control of distributed parameter systems", "Sliding mode control" ], "abstract": "In this paper, we address the robustness of parabolic–elliptic systems under boundary control. A sliding mode control strategy is proposed to reject matched perturbations. The stability analysis establishes finite-time convergence of the sliding manifold and exponential stability of the closed-loop system. Since the closed-loop system is discontinuous, we also prove its well-posedness. A numerical example is provided to validate the effectiveness of the proposed approach.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Robust H2 and H∞ Tuning of PID-Based Optimization and Frequency-Domain Comparison with Adam", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Jain, Vishesh", "affiliation": "Indian Institute of Technology, Bombay" }, { "name": "Baranwal, Mayank", "affiliation": "Tata Consultancy Services Ltd" } ], "keywords": [ "Convex optimization", "Robust control applications", "Robust learning systems" ], "abstract": "PID-based optimization algorithms (PIDAO) have recently demonstrated empirical robustness against gradient noise in machine learning. However, a theoretical framework for tuning these algorithms to guarantee stability and noise rejection is lacking. In this work, we formulate PIDAO as a discrete-time Lur’e system and utilize Integral Quadratic Constraints (IQCs) to analyze its robustness. We propose an mathcal{H}_2/mathcal{H}_infty synthesis framework to optimally tune PIDAO gains, balancing convergence speed with disturbance attenuation. Furthermore, we introduce a fixed-point linearization of the Adam optimizer, enabling a comparative control-theoretic analysis. Frequency-domain results and neural network training experiments demonstrate that PIDAO, when tuned via our robust control framework, achieves superior noise attenuation and stability margins compared to Adam.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Economically Optimal Sparse Controller for Constrained Processes: With Application to the Williams-Otto Reactor", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Magbool Jan, Nabil", "affiliation": "Indian Institute of Technology Tirupati" }, { "name": "Ankalugari, Rahul Yadav", "affiliation": "Indian Institute of Technology Tirupati" }, { "name": "Narasimhan, Sridharakumar", "affiliation": "Indian Institute of Technology, Madras" } ], "keywords": [ "Convex optimization", "Robust linear matrix inequalities", "Optimal control theory" ], "abstract": "In this paper, we address the problem of stabilizing sparse controller design for constrained processes using the notion of profit control. We propose an optimization formulation for the simultaneous selection of stabilizing state feedback controller that is row sparse and economic backoff operating point. As the proposed formulation is not computationally tractable owing to a non-convexity constraint, we develop an iterative solution technique that first determines the sparse controller by utilizing the idea of minimum variance for the active constrained variables, and then determining the economically optimal backoff operating point. Finally, we illustrate the efficacy of our proposed approach in a Williams-Otto reactor.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Neural Network-Based Model Error Compensator with Relative Degree for Quadcopter Control", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Koseki, Yosuke", "affiliation": "Tokyo City University" }, { "name": "Sekiguchi, Kazuma", "affiliation": "Tokyo City University" }, { "name": "Nonaka, Kenichiro", "affiliation": "Tokyo City University" } ], "keywords": [ "Data-driven robust control", "Nonlinearity learning from data", "Robust control applications" ], "abstract": "NN (Neural Network) is an excellent data-driven method for modeling nonlinear systems, but NN models face challenges related to instability and uncertainty. In this paper, NN-MEC (Neural Network-Model Error Compensator) is proposed as a data-driven robust control, which minimizes the effect of model error in model-based control. The proposed NN-MEC overcomes NN's challenges primarily through its learning rule, which incorporates the dynamics and relative degree information of the quadcopters. Furthermore, NN-MEC eases the difficulty of designing MEC for nonlinear systems by using NN. In numerical simulation, the robustness against the model errors of the NN-MEC is confirmed.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Cooperative Preview Feedforward and DOB-Based Hybrid Control for Dual-Frame Gimbals (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Li, Wenhao", "affiliation": "Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science" }, { "name": "Wang, Yutang", "affiliation": "Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science" }, { "name": "Tian, Dapeng", "affiliation": "Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science" } ], "keywords": [ "Disturbance rejection and input-to-state stability", "Control of distributed parameter systems", "Control of hybrid systems" ], "abstract": "Aerial vehicles operating in complex environments encounter various disturbances that severely affect Line-of-Sight (LOS) stabilization accuracy. Although multi-frame stabilization systems can isolate partial disturbances, the kinematic coupling between frames and nonlinear factors induce high-frequency coupling disturbances, posing a challenge to high-precision stabilization. Traditional Disturbance Observer (DOB)-based methods struggle to effectively suppress such high-frequency disturbances due to the phase lag introduced by low-pass filtering. Therefore, this paper proposes a hybrid control strategy combining Cooperative Preview Feedforward and a Disturbance Observer (DOB). First, a refined dynamic model incorporating inertial coupling, viscous friction, and nonlinear Coulomb friction is established. Based on this, a cooperative feedforward control law utilizing the previewed states of the outer frame is developed to implement \"anticipatory\" physical compensation before disturbances affect the inner frame. Simultaneously, the DOB is retained to suppress residual model uncertainties and random disturbances. Based on Lyapunov theory, the Uniformly Ultimately Bounded (UUB) stability of the closed-loop system, in the presence of preview errors and parameter mismatches, is rigorously proven. Simulation results demonstrate that, compared with traditional methods, the proposed approach significantly enhances the capability to suppress LOS jitter in the inner frame and notably improves the dynamic disturbance rejection performance of the system.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "GPC-Based PID Tuning for Stable or Unstable First Order Plus Dead Time Processes", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Silva, Lucian Ribeiro da", "affiliation": "Universidade Federal De Santa Catarina" }, { "name": "Flesch, Rodolfo C. C.", "affiliation": "Federal University of Santa Catarina" }, { "name": "Normey-Rico, Julio Elias", "affiliation": "Federal Univ of Santa Catarina" }, { "name": "Schwedersky, Bernardo Barancelli", "affiliation": "Federal University of Pelotas (UFPel)" } ], "keywords": [ "Linear time-delay systems", "Model predictive control", "Optimal control theory" ], "abstract": "This study proposes a method for tuning proportional-integral-derivative (PID) controllers based on generalized predictive control (GPC), suitable for processes that can be modeled by a first-order transfer function with dead time. The proposed method applies to systems with stable, unstable, or integrating dynamics. The method builds on the equivalent structure of the unconstrained GPC and incorporates an approximation of the dead time, resulting in a two-degree-of-freedom PID controller. A detailed analysis of performance and robustness is provided, illustrating that when tuned for robustness, PID and GPC controllers exhibit similar behavior. Furthermore, a case study of an integrating system with dead time is included, demonstrating that both controllers achieve comparable results in reference tracking and disturbance rejection, even in scenarios considering input constraints.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Partial Shading Conditions: A Hierarchical MPC Scheme for Global Flexible Power Point Tracking in Photovoltaic Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Liu, Xiangjie", "affiliation": "North China Electric Power Univ" }, { "name": "Zhang, Pengyu", "affiliation": "North China Electric Power University" }, { "name": "Kong, Xiaobing", "affiliation": "North China Electric Power University" }, { "name": "Zhang, Jukai", "affiliation": "North China Electric Power University" }, { "name": "Lee, Kwang Y.", "affiliation": "Baylor University" } ], "keywords": [ "Model predictive control", "Adaptive control design", "Applications of optimal control" ], "abstract": "As the capacity of photovoltaic (PV) generating units increases, flexible power point tracking (FPPT) technology flourishes as an effective method of grid-connected PV. In practice, the movement of clouds often leads to partial shading conditions, which significantly reduces the effectiveness of FPPT technology. The global FPPT (GFPPT) technology has been proposed to address partial shading conditions. However, the conventional GFPPT method searches with a fixed strategy fails to remain efficient under all working conditions, while intelligent methods increase the complexity of the algorithm. To improve the performance of GFPPT, a hierarchical model predictive control (HMPC) strategy is proposed. The upper layer utilizes an adaptive control strategy to determine the optimal voltage reference, thus enhancing the performance of GFPPT under different operating conditions (i.e., operating point and environmental conditions). A maximum power point estimation method is also proposed to improve the performance of the maximum power output of the PV system. The lower layer, focusing on PV voltage control, utilizes model predictive control (MPC) to track this voltage reference, which addresses the issue of multiple variables and physical constraints inherent in PV power generation systems. Simulation demonstrates the effectiveness of the proposed strategy in five representative scenarios.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Nonlinear Model Predictive Control for UAV Navigation in GPS-Denied Environments Using UWB Localization and Reinforcement Learning Path Planning", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Hanum, Zalma Zahara", "affiliation": "Institut Teknologi Bandung" }, { "name": "Nazaruddin, Yul Yunazwin", "affiliation": "Institut Teknologi Bandung (ITB)" }, { "name": "Burohman, Azka Muji", "affiliation": "Institut Teknologi Bandung" } ], "keywords": [ "Model predictive control", "Application of nonlinear analysis and design", "Design methods for data-based control" ], "abstract": "This paper proposes a closed-loop UAV navigation framework for GPS-denied environments using Ultra-Wideband (UWB) localization, Reinforcement Learning (RL)-based path planning, and Nonlinear Model Predictive Control (NMPC). In the proposed framework, UWB localization provides real-time state feedback for both the RL planner and NMPC controller, forming an integrated estimation–planning–control loop. The RL module generates collision-free trajectories, while NMPC compensates for nonlinear UAV dynamics and localization uncertainty during trajectory tracking. In addition, the RL reward–penalty formulation is modified to account for localization uncertainty, improving robustness under noisy state observations. The UAV system is modeled using nonlinear quadrotor dynamics with constrained control inputs. Numerical simulations are conducted in a GPS-denied environment with obstacle avoidance scenarios and UWB localization disturbances. The results show that the proposed framework can maintain stable and accurate trajectory tracking despite localization errors, demonstrating the effectiveness of the tightly coupled UWB–RL–NMPC architecture for autonomous UAV navigation in uncertain environments.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "C3A-TAB: A Cross-Domain, Conditioned, Calibrated and Aligned Tabular Framework for Ordinal Odor-Level Prediction with Electronic Nose Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Lv, Jinziyuan", "affiliation": "North China University of Technology" }, { "name": "Wang, Jing", "affiliation": "North China University of Technology (NCUT)" }, { "name": "Zhou, Meng", "affiliation": "North China University of Technology" }, { "name": "Lou, Zhijiang", "affiliation": "Shenzhen Polytechnic University" }, { "name": "Lu, Shan", "affiliation": "Shenzhen Polytechnic University" } ], "keywords": [ "Model predictive control", "Applications of optimal control" ], "abstract": "Traditional panel sniffing is subjective and costly, whereas electronic noses enable automation but are sensitive to sensor drift and environmental variation, causing cross-domain shifts and unstable predictions. We propose the cross-domain, conditioned, calibrated, and aligned TabTransformer (C3A-TAB) for ordinal odor-level prediction. It integrates population stability index guided drift-aware gating; feature-wise linear modulation for environmental conditioning; prototype alignment and separation; and an ordinal objective combining negative log-likelihood, kullback–leibler divergence, and earth mover’s distance, followed by temperature scaling for probability calibration. Experiments show C3A-TAB consistently surpasses TabTransformer across all metrics, and ablations confirm each component’s contribution and their structural complementarity. Comparative experiments also demonstrated the advantages.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Shrinking Horizon MPC with Computation Preallocated Along the Trajectory", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "van Leeuwen, Steven", "affiliation": "University of Michigan Ann Arbor" }, { "name": "Kolmanovsky, Ilya V.", "affiliation": "University of Michigan" } ], "keywords": [ "Model predictive control", "Numerical methods for optimal control", "Real-time optimal control" ], "abstract": "A strategy for offline allocation of the online computations in Shrinking Horizon Model Predictive Control (SH-MPC) is proposed when steering a discrete-time linear system with control constraints into a target terminal set over a prescribed number of time steps despite unmeasured disturbances, for which time-varying disturbance bounds are available. Specifically, assuming adjustable terminal penalty weights, an offline optimization problem aimed at minimizing the weighted sum of the number of optimizer iterations along the trajectory is proposed. Simulation results for a bicopter are reported to illustrate the proposed approach.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Decentralized Invariant Sets for Safe Control of Partially-Decomposable Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Nenchev, Vladislav", "affiliation": "University of the Bundeswehr Munich" } ], "keywords": [ "Model predictive control", "Optimal control of hybrid systems", "Applications of optimal control" ], "abstract": "This paper presents a decentralized computation method for control invariant sets of discrete‑time systems whose state contains a shared part and loosely coupled parts, e.g., timers, filters, uncertainties. Computing the centralized invariant becomes intractable with a growing state dimension. We compute decentralized invariants of low‑dimensional auxiliary subsystems that contain the shared and a single loosely coupled part. We show that the maximal control invariant set of the partially-decomposable system equals the intersection of invariants of the auxiliary subsystems. Case studies using the decentralized invariants on a servomotor and persistent surveillance by a mobile robot demonstrate scalability of offline invariant computation, maintaining feasibility under set constraints with short planning horizons, and competitive online computation costs for model predictive control and for safeguarding a learned policy.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Stochastic Nonlinear Model Predictive Control for Closed-Loop Optimization of Subsurface Flow Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Hannanu, Muhammad Iffan", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Hovd, Morten", "affiliation": "Norwegian University of Technology and Science" }, { "name": "Camponogara, Eduardo", "affiliation": "Federal University of Santa Catarina" }, { "name": "Silva, Thiago Lima", "affiliation": "SINTEF AS" } ], "keywords": [ "Model predictive control", "Optimization-based estimation and control", "Stochastic optimal control problems" ], "abstract": "We consider the implementation of Stochastic Model Predictive Control (SMPC) in the framework of Closed-Loop Reservoir Management (CLRM) for optimization of subsurface flow systems. The problem of Buckley-Leverette is investigated, where the objective is to maximize the expected value of the net present value from an ensemble of equally probable realizations, as well as minimizing the mismatch between the ensemble and the true model. The uncertainty is represented by the perturbation of the relative permeability curves. The results indicate that SMPC is capable of producing near-optimal control under uncertainty and is well-suited for reservoir management problems.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "MPC Based Orbit Insertion and Uniform Distribution for LEO Satellite Constellation", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Kim, Seongheon", "affiliation": "Gyeongsang National University" }, { "name": "Kim, Yoonsoo", "affiliation": "Gyeongsang National University" }, { "name": "Vande Wouwer, Alain", "affiliation": "Université De Mons" } ], "keywords": [ "Model predictive control", "Robust control applications", "Distributed nonlinear control" ], "abstract": "This study tackles the problem of uniformly distributing satellites in circular low Earth orbits (LEO). To enable safe and reliable constellation deployment, we develop a distributed model predictive control (DMPC) framework that explicitly handles thrust constraints and inter-satellite collision avoidance. The proposed phase-based scheme consists of three steps: (i) a transfer maneuver from a parking orbit to the reference orbit, (ii) a DMPC-based phasing maneuver in which each satellite uses only the position of its preceding neighbor to achieve uniform angular spacing, and (iii) a steady-state phase where robust servomechanism MPC (RS-MPC) ensures accurate orbit tracking under persistent disturbances including atmospheric drag and the Earth’s J2 effect . Simulations with three satellites confirm that the method achieves uniform spacing and substantially improves steady-state tracking performance compared with existing approaches.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Integral Sliding Model Predictive Control for Wheeled Biped Robots under Uncertainties", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "McMullan, Rhyss", "affiliation": "Queen's University Belfast" }, { "name": "Van, Mien", "affiliation": "Queen's University Belfast" }, { "name": "McConnellogue, Peter James", "affiliation": "Queen's University Belfast" }, { "name": "Zhou, Yibo", "affiliation": "Queen's University Belfast" }, { "name": "Dianati, Mehrdad", "affiliation": "Queen's University Belfast" } ], "keywords": [ "Model predictive control", "Sliding mode control", "Application of nonlinear analysis and design" ], "abstract": "This paper presents a combined control technique of a nonlinear model predictive controller (NMPC) and integral sliding mode control (ISMC) for a wheeled biped robot, utilising dynamic modelling and the wheeled inverted pendulum model (WIPM). A rollover index via the load transfer ratio (LTR) analyses lateral dynamics and defines a tunable limit. The performance of this ISM-NMPC is investigated in simulation on the TRON1A wheeled biped, demonstrating how the biped prioritises stability during high-speed and complex turns, and how ISMC improves overall performance by rejecting matched uncertainty terms.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Hybrid Physics-Based and Data-Driven Identification of a Two-Axis Helicopter Testbed with Real-Time Control Applications", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Salazar, Carlos Alberto", "affiliation": "Escuela Superior Politecnica Del Litoral, ESPOL" }, { "name": "Aguirre, Adriana", "affiliation": "Escuela Superior Politécnica Del Litoral" }, { "name": "Rodriguez Gonzalez, Mario Gustavo", "affiliation": "Escuela Superior Politecnica Del Litoral" } ], "keywords": [ "Model validation", "Controller constraints and structure" ], "abstract": "This paper presents a hybrid system identification approach for a two-axis didactic helicopter testbed, combining physics-based modeling with experimental data-driven estimation. The main contribution is methodological: a grey-box framework that integrates Newton–Euler dynamics with experimental identification to obtain compact low-order models with physically interpretable parameters such as inertias, damping, and aerodynamic couplings. Experimental datasets were fitted to second-order transfer functions for pitch and yaw; interaction metrics (Relative Gain Array and Niederlinski Index) confirmed diagonal dominance within the operating envelope, justifying a decentralized SISO control design. Discrete-time PID controllers with derivative filtering and anti-windup achieved stable tracking in step and pulse tests. Beyond reproducing the essential nonlinear dynamics, the workflow—data acquisition, grey-box identification, controller design, and real-time validation—provides a reproducible instructional pipeline that bridges system identification theory with hands-on control practice.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Moment Matching in Discrete-Time for Time-Varying and Periodic Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Bhattacharjee, Debraj", "affiliation": "Imperial College London" }, { "name": "Moreschini, Alessio", "affiliation": "Imperial College London" }, { "name": "Astolfi, Alessandro", "affiliation": "King Abdullah University of Science and Technology (KAUST)" } ], "keywords": [ "Model validation", "Linear systems" ], "abstract": "We study the moment matching problem for linear time-varying and linear time-periodic systems in a discrete-time setting. We derive a class of reduced-order models that replicate the steady-state response of the underlying system when driven by a signal generator with time-varying dynamics. We illustrate our results through a simple numerical example.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Hierarchical Control of Inerter-Enhanced MRD Seat Suspension (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Yu, Xiaohui", "affiliation": "Jilin University" }, { "name": "Yu, Xinze", "affiliation": "Jilin University" }, { "name": "Yu, Shuyou", "affiliation": "Jilin University" }, { "name": "Yang, Jun", "affiliation": "Jilin University" }, { "name": "Chen, Hong", "affiliation": "Tongji University" } ], "keywords": [ "Nonlinearity learning from data", "Robust linear matrix inequalities", "Application of nonlinear analysis and design" ], "abstract": "Low-frequency vibrations significantly affect ride comfort, yet conventional seat suspensions struggle to suppress them. This paper proposes a novel parallel seat suspension combining a spring, MRD, and inerter, with the inerter optimized for low-frequency isolation. A hierarchical control framework is developed: The lower layer first develops a recurrent neural network (RNN) to capture the MRD's complex dynamics. Subsequently, the Koopman operator framework is applied to construct a lifted linear representation of this data-driven RNN model, enabling accurate force tracking, while the upper layer employs an H_infty output-feedback controller balancing comfort and robustness. Simulations demonstrate substantial improvements in force tracking and comfort-related metrics, providing a systematic simulation-based framework for robust semi-active seat suspension control.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "A Numerical Approach to Incentive Stackelberg Games for Stochastic Mean-Field Games with Delay", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ito, Yuki", "affiliation": "Hiroshima University" }, { "name": "Tian, Zihang", "affiliation": "Hiroshima University" }, { "name": "Mukaidani, Hiroaki", "affiliation": "Hiroshima University" }, { "name": "Sato, Masayuki", "affiliation": "Kyushu Institute of Technology" }, { "name": "Sagara, Muneomi", "affiliation": "Kochi University" } ], "keywords": [ "Numerical methods for optimal control", "Differential or dynamic games", "Robust time-delay systems" ], "abstract": "This paper investigates a numerical method for solving incentive Stackelberg games in stochastic mean-field systems with time delay. In this framework, the leader designs strategies and incentive mechanisms to guide non-cooperative followers-who play a Nash equilibrium-toward a team-optimal solution. Compared with existing results, we establish a new sufficient condition for the solvability of this game via a parametrization technique. To address the intractability of high-dimensional equations as the population size tends to infinity, we adopt a reduced-order computational approach that exploits the asymptotic properties of the coupled higher-order Lyapunov-like equations (CHLEs). The core simplified Newton method uses a fixed approximate Jacobian that is independent of the population size and is shown to achieve linear convergence. A numerical example demonstrates the effectiveness of the proposed algorithm, showing that its computational time can be reduced by an average of 40% compared to other existing typical algorithms when the number of followers is sufficiently large.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Momentum-Based Differential Dynamic Programming", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Mahmoudi Filabadi, Mohammad", "affiliation": "Ghent University" }, { "name": "Crevecoeur, Guillaume", "affiliation": "Ghent University" }, { "name": "Lefebvre, Tom", "affiliation": "Unversity of Ghent" } ], "keywords": [ "Numerical methods for optimal control", "Optimal control theory", "Applications of optimal control" ], "abstract": "Differential Dynamic Programming (DDP) is a prominent trajectory optimization method for deterministic nonlinear systems. Due to its dependency on local gradient information it is sometimes plagued by slow convergence and sensitivity to local minima. This paper introduces a momentum-based Differential Dynamic Programming (MB-DDP) algorithm, leveraging information from previous iterations to achieve faster convergence rate. The proposed algorithm is derived from a Soft Dynamic Programming framework that integrates information-theoretic measures into the optimization problem, which facilitate a principled balance between exploration and numerical stability. Our simulation results, on benchmark nonlinear control problems, demonstrate that MB-DDP achieves a faster convergence rate than standard DDP without increasing computational complexity.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Differentiable Material Point Method for the Control of Deformable Objects", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Bolliger, Diego", "affiliation": "ZHAW Zurich University for Applied Sciences" }, { "name": "Fadini, Gabriele", "affiliation": "ZHAW" }, { "name": "Bambach, Markus", "affiliation": "ETH Zürich" }, { "name": "Rupenyan, Alisa", "affiliation": "ZHAW Zurich University for Applied Sciences" } ], "keywords": [ "Numerical methods for optimal control", "Optimization-based estimation and control", "Application of nonlinear analysis and design" ], "abstract": "Controlling the deformation of flexible objects is challenging due to their non- linear dynamics and high-dimensional configuration space. This work presents a differentiable Material Point Method (MPM) simulator targeted at control applications. We exploit the differentiability of the simulator to optimize a control trajectory in an active damping problem for a hyperelastic rope. The simulator effectively minimizes the kinetic energy of the rope around 2× faster than a baseline Model Predictive Path Integral (MPPI) controller and to a 20 % lower energy level, while using about 3 % of the computation time.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "NDO-Based Spatio-Temporal Cooperation Guidance for Multi-Missile System with Input Constraints (I)", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Sun, Haoxuan", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Chen, Mou", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Zhou, Tongle", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Han, Zengliang", "affiliation": "Nanjing University of Aeronautics and Astronautics" } ], "keywords": [ "Observer design", "Cooperative nonlinear control", "Backstepping control of distributed parameter systems" ], "abstract": "This paper proposes a spatio-temporal cooperation guidance law for multi-missile systems with input constraints and unknown target maneuvers. The temporal cooperation objective, defined as simultaneous arrival, is formulated through consensus on both relative distance and relative velocities. The radial basis function neural network is employed to approximate system uncertainties, while a nonlinear disturbance observer (NDO) estimates and compensates for composite disturbances. For spatial cooperation objective, the backstepping-based spatial cooperation guidance law is developed. The NDO is designed based on the transformed system to directly estimate the target's maneuver. To address input constraints, auxiliary systems are designed to mitigate the adverse effects of input constraints. Lyapunov-based stability analysis guarantees the stability of all closed-loop signals. Finally, numerical simulation is used to verify the effectiveness of the guidance law.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "On Batch Estimation for BOTMA Problem", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ambit Brao, Isaac", "affiliation": "INRIA" }, { "name": "Efimov, Denis", "affiliation": "Inria" } ], "keywords": [ "Observer design", "Nonlinear observers and filters", "Convex optimization" ], "abstract": "This paper considers two-dimensional bearing-only target motion analysis for an observer platform moving at constant speed and course while the target performs a constant turn. The relative motion is modelled as a linear discrete-time state equation with a nonlinear, perspective-type bearing measurement equation. We characterise observability conditions for this scenario and design a batch estimator based on a suitable loss functional, which is proved to be convex (and to admit a unique minimiser) under explicit conditions. The performance of the convex batch estimator is evaluated via Monte-Carlo simulations and compared with an ad hoc batch estimator and an extended Kalman filter, showing improved estimation accuracy and robustness to initialisation errors.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Fuzzy Reduced-Order Interval Observer-Based Consensus Control of Muti-Agent Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Song, Lei", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Xue, Hong", "affiliation": "University of Electronic Science and Technology" }, { "name": "Liang, Hongjing", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Yang, Jin", "affiliation": "University of Electronic Science and Technology of China" } ], "keywords": [ "Observer design", "Robust linear matrix inequalities", "Lyapunov methods" ], "abstract": "本文探讨了缩减阶区间 高木-菅野的基于观察者的共识控制问题 （T-S） 模糊多智能体系统 （MASs）受未知影响 动态和测量中的输入扰动 方程。首先，一种新颖的表示形式 不可测量扰动矢量构造为 有效解决 系统测量中的未知输入扰动。这 表示有助于建立 等效系统模型，使完整的 解耦与消除无法测量的干扰 从输出映射中获得。基于此，一个降阶 区间观察者仅利用界限 构建 不确定性，并且可以估计系统状态 计算资源显著减少。随后，基于分布式控制器的构建 在设计的降阶观察者和共识上建立了T-S模糊MAS的条件。最终， 提供模拟结果以验证其疗效 以及所提方法的优越性。", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Fault Tolerant Control of Mecanum Wheeled Mobile Robots", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ma, Xuehui", "affiliation": "Xi'an University of Technology" }, { "name": "Zhang, Shiliang", "affiliation": "University of Oslo" }, { "name": "Zhou, Panpan", "affiliation": "University of Galway" }, { "name": "Sun, Zhiyong", "affiliation": "Peking University (PKU)" } ], "keywords": [ "Adaptive and robust control of automotive systems", "Autonomous mobile robots" ], "abstract": "Mecanum wheeled mobile robots (MWMRs) are highly susceptible to actuator faults that degrade performance and risk mission failure. Current fault tolerant control (FTC) schemes for MWMRs target complete actuator failures like motor stall, ignoring partial faults e.g., in torque degradation. We propose an FTC strategy handling both fault types, where we adopt posterior probability to learn real-time fault parameters. We derive the FTC law by aggregating probability-weighed control laws corresponding to predefined faults. This ensures the robustness and safety of MWMR control despite varying levels of fault occurrence. Simulation results demonstrate the effectiveness of our FTC under diverse scenarios.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Active Disturbance Rejection Control of a Pneumatically Actuated Clutch", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Prabel, Robert", "affiliation": "University of Rostock" }, { "name": "Aschemann, Harald", "affiliation": "University of Rostock" } ], "keywords": [ "Adaptive and robust control of automotive systems", "Engine and powertrain modeling and control", "Automotive system identification and modelling" ], "abstract": "The paper presents a model-free robust control approach for the position of a pneumatically actuated clutch that is used in trucks. For simulation purposes, an overall system model is established based on physical principals addressing the dynamics of the pneumatic subsystem as well as the mechanical system part. Here, characteristics are identified for the pneumatic valves as well as the clutch spring. The proposed control structure is cascaded and involves a fast pressure control in the inner loop. The outer loop is affected by model uncertainty due to a pronounced hysteresis of the clutch spring. Therefore, a model-free active disturbance rejection control (ADRC) based on an extended state observer (ESO) is employed in the outer loop and provides robustness as emphasized by both simulations and experimental results at a test rig.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Vehicle Parameter Estimation Using Deep Neural Networks with Long Short-Term Memory", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Hain, Sören", "affiliation": "University of Stuttgart" }, { "name": "Beyer, Kimon", "affiliation": "University of Stuttgart" }, { "name": "Sawodny, Oliver", "affiliation": "Univ of Stuttgart" } ], "keywords": [ "AI and learning-based control for automotive systems", "Automotive system identification and modelling", "Electric and solar vehicles" ], "abstract": "Longitudinal vehicle parameter estimation of the mass, rolling resistance coefficient and drag area (cd*A) are of crucial importance for energy consumption prediction. Energy consumption prediction is especially important for electric vehicles (EV), since EVs have a smaller range and longer charging time compared to gasoline powered vehicles. This paper proposes an iterative machine learning algorithm for longitudinal vehicle parameter estimation. The validation is carried out with real-world measurement data from test drives with different vehicle configurations that highlight the applicability.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Physics-Informed Machine Learning for Integrated Longitudinal and Lateral Dynamics Modeling of Liquid Tank Trucks", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Tian, Liheng", "affiliation": "Southeast University" }, { "name": "Wei, Wenpeng", "affiliation": "Southeast University" } ], "keywords": [ "AI and learning-based control for automotive systems", "Automotive system identification and modelling", "Vehicle dynamic systems" ], "abstract": "Liquid sloshing in partially filled tanks poses a major challenge to accurately model liquid tank truck (LTT) dynamics. Traditional physics-based methods often require time- consuming and costly offline calibration, while recent data-driven methods lack interpretability and struggle to generalize across operating cases. This paper introduces a physics-informed machine learning (PIML) framework for integrated longitudinal and lateral dynamics modeling of a LTT. The framework connects a structured physical parameters estimator and a single- track vehicle dynamics model in series, enabling online joint estimation of time-varying physical parameters and vehicle states due to irregular motion introduced by liquid sloshing. To collect sufficient and diverse data for PIML training, a high-fidelity co-simulation platform integrating TruckSim, COMSOL Multiphysics, and Simulink is developed. Model evaluations across five liquid fill ratios show that the PIML model achieves comparable or better performance than the physical models, with the most significant improvement observed in lateral velocity. The results suggest the framework’s strong ability to capture the complex vehicle-fluid coupled dynamics.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Robust Deterministic Policy Gradient for Disturbance Attenuation and Its Application to Quadrotor Control", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Lee, Taeho", "affiliation": "Korea Advanced Institute of Science and Technology" }, { "name": "Lee, Donghwan", "affiliation": "Korea Advanced Institute of Science and Technology" } ], "keywords": [ "AI and learning-based control for automotive systems", "Learning and adaptation in autonomous vehicles", "Trajectory tracking and path following for AVs" ], "abstract": "This paper presents a robust reinforcement learning algorithm, robust deterministic policy gradient (RDPG), which reformulates the H ∞ control problem as a two-player zero-sum dynamic game between a user and an adversary. The user minimizes the objective while the adversary maximizes it by injecting disturbances. This formulation enables the learning of disturbance-resilient policies under worst-case scenarios. The RDPG is extended to high-dimensional continuous control by integrating it into a deep reinforcement learning framework, resulting in robust deep deterministic policy gradient (RDDPG). Simulation results on a quadrotor demonstrate improved robustness and tracking performance under external disturbances.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Neural Network-Based Virtual Wheel-Speed Sensor for Enhanced Low-Velocity State Estimation", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Schäfke, Hendrik", "affiliation": "Leibniz University Hannover, Institute of Mechatronic Systems" }, { "name": "Weber, Daniel Oliver Martin", "affiliation": "Gottfried Wilhelm Leibniz Universität Hannover" }, { "name": "Vagapov, Askar", "affiliation": "IAV GmbH (Ingenieurgesellschaft Auto Und Verkehr)" }, { "name": "Schweers, Christoph", "affiliation": "IAV GmbH (Ingenieurgesellschaft Auto Und Verkehr)" }, { "name": "Seel, Thomas", "affiliation": "Leibniz Universität Hannover" }, { "name": "Ehlers, Simon F. G.", "affiliation": "Leibniz University Hannover" } ], "keywords": [ "Automotive system identification and modelling", "AI and learning-based control for automotive systems", "Electric and solar vehicles" ], "abstract": "Accurate wheel speed information is crucial for vehicle control and state estimation. Conventional sensors suffer from quantization and latency, especially at low velocities, while motor-speed signals in electric vehicles are distorted by drivetrain torsion. This work presents a neural-network-based virtual wheel-speed sensor that fuses wheel-speed and motor-speed signals to reduce errors from both sources. Validated on real-world Volkswagen ID.7 data, the real-time-capable model achieves an error reduction of up to 85% compared to the production sensor and 47% compared to an optimized zero-phase filter, providing a smooth signal for driver-assistance functions. The results demonstrate robust generalization across diverse real-world maneuvers within the vehicle platform.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Constrained Physics-Informed GRU for Robust Vehicle Motion Prediction", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Kwon, Solyeon", "affiliation": "Hanyang University" }, { "name": "Jin, Yongsik", "affiliation": "Daegu Gyeongbuk Institute of Science and Technology (DGIST)" }, { "name": "Han, Kyoungseok", "affiliation": "Hanyang University" } ], "keywords": [ "Automotive system identification and modelling", "Modeling, supervision, control and diagnosis of automotive systems", "Vehicle dynamic systems" ], "abstract": "Physics-based vehicle models are interpretable but suffer from parametric and tire--road uncertainty, whereas purely data-driven predictors generalize poorly and may violate physical laws. We propose a constrained physics-informed gated recurrent unit (CPIGRU) that combines vehicle dynamics residuals with a penalty-based admissibility constraint and an adaptive residual-weighting schedule. A constrained universal approximation theorem establishes that the CPIGRU achieves epsilon-accurate approximation of the true dynamics on the admissible set. In high-fidelity CarMaker to CarSim cross-simulator tests, CPIGRU outperforms both a nominal 3-DOF model and an unconstrained physics-informed GRU in terms of accuracy and stability.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "A Generalized String-Stability Criteria for Consensus Protocols", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Mudhangulla, Sridhar Babu", "affiliation": "FSU" }, { "name": "Anubi, Olugbenga", "affiliation": "Florida State University" } ], "keywords": [ "Control architectures in automotive control", "Automatic control, optimization, real-time operations in transportation", "Vehicle dynamic systems" ], "abstract": "This paper develops a unified frequency-domain framework for string-stability analysis of leader--follower multi-agent systems governed by first-, second-, and general m^{text{th}}-order consensus protocols over an r-predecessor directed communication topology. Existing string-stability results are often tied to specific vehicle models, protocol orders, or information structures, which obscures the mechanism that fundamentally governs disturbance amplification. Under the adopted mathcal{H}_infty disturbance-propagation definition, we show that the decisive quantity is the communication richness r: for every consensus order, the low-frequency propagation gain is 1/r. Consequently, within the proposed framework, string stability is achieved if and only if rgeq 2. The consensus order m does not alter this structural limit; instead, it shapes the transient and mid-to-high-frequency response through additional dynamic degrees of freedom. The results establish a structural--dynamic separation principle: topology determines whether disturbances attenuate along the string, whereas protocol order and gain selection determine the quality of the closed-loop response. Numerical simulations for first-, second-, and third-order protocols corroborate the analysis and illustrate the distinct roles of r and m in disturbance propagation.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Robust Data-Driven Control for Vehicle Merging in Mixed Traffic", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Bang, Heeseung", "affiliation": "Yeungnam University" }, { "name": "Dave, Aditya Deepak", "affiliation": "Cornell University" }, { "name": "Malikopoulos, Andreas", "affiliation": "Cornell University" } ], "keywords": [ "Control architectures in automotive control", "Learning and adaptation in autonomous vehicles", "Guidance, navigation and control for AVs" ], "abstract": "In this paper, we present an approach for learning human driving behavior, without relying on specific model structures or prior distributions, in a mixed-traffic environment where connected and automated vehicles (CAVs) coexist with human-driven vehicles (HDVs). We employ conformalized quantile regression to obtain statistical guarantees on the human-driving-prediction accuracy. Then, we design a controller that effectively merges CAVs with HDVs while maintaining non-disrupting distance. We provide numerical simulations to illustrate the efficacy of the control approach.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Design of Nonlinear Observer for EV Powertrain Vibration Suppression", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Kawasaki, Manato", "affiliation": "Nanzan University" }, { "name": "Sakamoto, Noboru", "affiliation": "Nanzan University" }, { "name": "Nakashima, Akira", "affiliation": "Nanzan University" } ], "keywords": [ "Engine and powertrain modeling and control", "Hybrid, electric and alternative drive vehicles", "Modeling, supervision, control and diagnosis of automotive systems" ], "abstract": "This study proposes a nonlinear observer for estimating internal states of electric vehicle (EV) powertrains with gear backlash and driveshaft torsion. The proposed observer explicitly incorporates backlash-induced nonlinear switching dynamics and estimates the motor-side and load-side angular velocities, torsional torque, backlash angle, and backlash angular velocity. The observer was evaluated using an Exact Backlash Simulator under realistic sensing conditions, including observation noise, communication delay, and sensor quantization. Compared with a conventional torsional-torque disturbance observer, the proposed method achieved high estimation accuracy, particularly for torsional torque estimation. The mode-transition timing between free rotation and tooth engagement was estimated with an average error of approximately 0.1 ms, which is sufficiently small compared with a typical 1 ms EV control cycle.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Personalized Energy-Aware Regenerative Braking Control Minimizing Driver Interventions", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Kim, Beomchang", "affiliation": "Hanyang University" }, { "name": "Lee, Jae Hwan", "affiliation": "Hanyang University" }, { "name": "Kim, Dongryul", "affiliation": "Hanyang University" }, { "name": "Kim, Dohee", "affiliation": "Hyundai Motor Company" }, { "name": "Lee, Sangho", "affiliation": "Hyundai Motor Company" }, { "name": "Han, Kyoungseok", "affiliation": "Hanyang University" } ], "keywords": [ "Hybrid, electric and alternative drive vehicles", "AI and learning-based control for automotive systems", "Nonlinear and optimal automotive control" ], "abstract": "Conventional automatic regenerative braking (ARB) systems in electrified vehicles prioritize energy efficiency but often conflict with driver preferences, leading to frequent manual interventions that reduce energy efficiency. This paper proposes a personalized ARB control framework that co-optimizes regenerative energy recovery and driver acceptance. In particular, using Gaussian process (GP) regression, the system learns individual driver braking preferences and intervention thresholds online, then selects optimal braking distances by balancing energy gains against intervention probability. Experimental results demonstrate that the proposed approach reduces driver interventions while improving net energy recovery, providing a practical solution for personalized automated braking.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Trajectory-Linked Nonlinear Model Predictive Control Energy Management for Hybrid UAVs in Urban Low Altitude Flight Missions", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Li, Jie", "affiliation": "Aalborg University" }, { "name": "Shen, Ming", "affiliation": "Aalborg University" }, { "name": "Stoustrup, Jakob", "affiliation": "Aalborg University" } ], "keywords": [ "Hybrid, electric and alternative drive vehicles", "Automatic control, optimization, real-time operations in transportation" ], "abstract": "With the opening of low altitude urban airspace, energy efficient dynamic obstacle avoidance for hybrid unmanned aerial vehicles (HUAV) has become critical. Unlike existing methods that decouple route planning and energy management, this work instead proposes a trajectory linked framework where the planned 3D path directly determines time varying propulsion demand for hydrogen–battery energy scheduling. A cost weighted 3D A* planner generates safe and energy aware paths by penalizing altitude variations to suppress power intensive climbs and descents. A segmented accelerate, cruise, and decelerate velocity model, combined with simplified flight dynamics, provides time varying propulsion power estimates that more accurately capture aerodynamic effects compared with constant velocity assumptions. Based on the trajectory induced dynamic load, a constrained Nonlinear Model Predictive Control(NMPC) strategy assigns fuel cell(FC) and battery power under slope and state of charge(SOC) constraints, reducing fuel cell stress and overall energy use. Simulation results show hydrogen consumption reductions of 12.5% compared with Equivalent Consumption Minimization Strategy(ECMS) and 9.3% compared with Equivalent Energy Management Strategy(EEMS), demonstrating the advantage of planning driven energy management over post planning optimization.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Interaction-Aware Multi-Modal Adaptive Unscented Kalman Filter for Safe Navigation of Autonomous Vehicles", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Heyi, Muluneh Hailu", "affiliation": "Université Bourgogne Europe" }, { "name": "Hima, Salim", "affiliation": "ESME-SUDRIA Engineering School" }, { "name": "Chaibet, Ahmed", "affiliation": "Université Bourgogne Europe" } ], "keywords": [ "Kalman filtering techniques in automotive control", "Autonomous vehicles", "Multi-vehicle systems" ], "abstract": "Safe navigation in dense highway traffic requires accurate prediction of surrounding vehicles' maneuvers while ensuring passenger safety. This paper proposes an Interaction-Aware Multi-Modal Adaptive Unscented Kalman Filter (IA-MM-AUKF) that jointly estimates maneuver intentions and future trajectories of neighboring vehicles. A bank of mode-specific AUKFs, combined with Bayesian-adaptive Markov transition probabilities and probabilistic mode fusion, captures multi-modal maneuver uncertainty under nonlinear dynamics. A trajectory uncertainty quantification module further characterizes prediction confidence. Validated on the highD naturalistic dataset, the framework achieves a lateral RMS error of 0.022m, a 59% reduction over EKF, enabling anticipatory, collision-safe motion planning.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Adaptive Fault-Tolerant Multi-Modal Localization of Autonomous Vehicles", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "AlMousawi, Ali", "affiliation": "Universite De Haute Alsace" }, { "name": "Duthay, Flavie", "affiliation": "Université De Haute-Alsace" }, { "name": "Mourllion, Benjamin", "affiliation": "UHA" }, { "name": "Lauffenburger, Jean-Philippe", "affiliation": "Université De Haute-Alsace" } ], "keywords": [ "Kalman filtering techniques in automotive control", "Guidance, navigation and control for AVs", "Trajectory tracking and path following for AVs" ], "abstract": "This paper develops and evaluates a robust multi-modal vehicle localization framework using an Extended Information Filter (EIF). The approach integrates a kinematic bicycle model (KBM) for prediction, enhanced with gyroscope angular rate measurements, and GNSS observations for update. To address faulty measurements and non-stationary sensor noise, a Fault Detection and Exclusion (FDE) mechanism and fuzzy logic system (FLS) were implemented. The FDE isolates corrupted measurements, while the FLS dynamically adjusts measurement noise covariance. Experiments across multiple trajectories demonstrate significant reductions in mean and maximum absolute position and heading errors, highlighting the effectiveness of fault handling and adaptive measurement weighting in real-world navigation.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Hybrid Attack Modeling for Position Deviation in Autonumous Systems: A Semi Markov Approach", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Yan Tingli, Tingli", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Wu, Jing", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Long, Chengnian", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Kalman filtering techniques in automotive control", "Motion control for AVs", "Automatic control, optimization, real-time operations in transportation" ], "abstract": "研究提出了一个非指数持续时间的半马尔可夫混合攻击模型，与现有指数分布的马尔可夫链形成对比。在基于卡尔曼滤波器的定位框架内，它推导了攻击强度与位置偏移之间的相关性，并提供了隐蔽攻击能量消耗的上界，并结合了最优攻击序列的算法。实验结果表明，在相同的隐蔽性约束下，Weibull分布的半马尔可夫模型在保持低误报率的同时实现了更大的位置偏移，验证了其优于传统指数模型的优势。", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Reduced-Complexity Vehicle Mass Estimation Using Series-Production Sensors Validated with Static and Dynamic Experimental Data", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Wübbeler, Carlos", "affiliation": "University of Applied Sciences, Osnabrück" }, { "name": "Ehlers, Simon F. G.", "affiliation": "Leibniz University Hannover" }, { "name": "Seel, Thomas", "affiliation": "Leibniz Universität Hannover" }, { "name": "Westerkamp, Clemens", "affiliation": "Osnabrück University of Applied Sciences" }, { "name": "Böhse, Frederic", "affiliation": "ZF Friedrichshafen AG" }, { "name": "Lundberg, Alexander", "affiliation": "ZF Friedrichshafen AG" }, { "name": "Weber, Daniel Oliver Martin", "affiliation": "Gottfried Wilhelm Leibniz Universität Hannover" } ], "keywords": [ "Kalman filtering techniques in automotive control", "Vehicle dynamic systems", "Automotive system identification and modelling" ], "abstract": "Accurate and robust knowledge of vehicle mass is important for advanced driver assistance systems (ADAS) and autonomous driving. Current estimation methods, such as longitudinal 1-degree-of-freedom (DOF) models, deliver inaccurate mass estimates in driving modes near or at a standstill. Conversely, complex multi-DOF models require detailed, parameter- and signal-intensive subsystem modeling. This paper presents a novel, reduced complexity approach to vehicle mass estimation that combines a 3-DOF vehicle body model with an Unscented Kalman Filter (UKF). Inertial Measurement Unit (IMU) measurements are directly used as inputs to the simplified 3-DOF body model, reducing subsystem and parameter dependencies for a more efficient application. The algorithm is extensively validated using real world vehicle data with 13 different masses, covering various driving situations and public road tests with varying slopes. Results demonstrate high accuracy with a relative root mean square error <3.87%.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Sequential Quadratic Programming for Nonlinear Eco-Driving: A Proximal Primal-Dual Approach", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Heuts, Y.J.J.", "affiliation": "Eindhoven University of Technology" }, { "name": "Donkers, M.C.F. (Tijs)", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Modeling, supervision, control and diagnosis of automotive systems", "Electric and solar vehicles" ], "abstract": "This paper presents a real-time optimization approach for the eco-driving optimal control problem using a Sequential Quadratic Programming (SQP) formulation. By discretizing the dynamics in the spatial domain and applying convex relaxations and regularization, the problem is reformulated into a structure suitable for embedded implementation. Two solvers, OSQP and a proposed Heavy-Ball Projected Primal-Dual Method (HBPPDM), are employed to solve the SQP subproblems, enabling a comparison of convergence behavior and computational efficiency. Numerical results demonstrate that the SQP-based approach significantly outperforms a Second-Order Cone Programming (SOCP) formulation solved by MOSEK, particularly for long prediction horizons. While the SOCP method can solve the problem in a single shot, its complexity limits real-time feasibility. In contrast, the SQP approach achieves prediction horizons up to 6000 steps within one second, and solves a realistic 60 km route in 0.18 s, confirming its scalability and suitability for real-time eco-driving applications.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Development of Accelerated Life Testing Method for a 47 kW Class Agricultural Tractor Using Axle Torque During Plow Tillage", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Lee, Minha", "affiliation": "Chungnam National University" }, { "name": "Jeong, Gubin", "affiliation": "Chungnam National University" }, { "name": "Kim, Yong-Joo", "affiliation": "Chungnam National University" } ], "keywords": [ "Modeling, supervision, control and diagnosis of automotive systems", "Engine and powertrain modeling and control", "Automotive system identification and modelling" ], "abstract": "Due to the ongoing shortage of rural labor and the aging farming population, the farm size per farmer has increased, requiring durable and reliable agricultural equipment. This study developed an accelerated life test (ALT) methodology for tractor axles based on load data measured during actual plow tillage operations. Axle torque and rotational speed were measured using telemetry torque sensors installed on both front and rear axles. The measured time–torque data were used to construct a Load Duration Distribution (LDD), from which equivalent torque was calculated using the Palmgren–Miner linear cumulative damage rule with a fatigue damage exponent of 8.738. The equivalent torque was 6,310.99 Nm, while the selected test torque was 1.2 times the rated torque (8,170.08 Nm). The acceleration factor was computed as 9.545, reducing the required durability test time for a 3,000‑hour target life to 314.3 hours. The proposed method provides an efficient and reproducible approach for evaluating axle fatigue life under realistic operating environments.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Input-To-State Stability of Safe MPC in Unknown Environments with Applications to Autonomous Driving", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Guo, Yuxuan", "affiliation": "IMT School for Advanced Studies Lucca" }, { "name": "Quan, Yingshuai", "affiliation": "Chalmers University of Technology" }, { "name": "Falcone, Paolo", "affiliation": "Chalmers University of Technology" }, { "name": "Villanueva, Mario Eduardo", "affiliation": "IMT School for Advanced Studies Lucca" }, { "name": "Zanon, Mario", "affiliation": "IMT Institute for Advanced Studies Lucca" } ], "keywords": [ "Nonlinear and optimal automotive control", "Adaptive and robust control of automotive systems" ], "abstract": "We study the stability of safe model predictive control (MPC) in unknown environments, where safety constraints come from online perception or estimation and may tighten abruptly as new information appears. Conservative worst-case predictions ensure recursive feasibility, but changing, a priori unknown constraints cause deviations from the nominal trajectory. By modeling the evolution of environment information with a continuous parameter and assuming non-sudden activation, we show that the closed loop is input-to-state stable (ISS) with respect to disturbances entering through the safety constraints, so deviations from the nominal plan remain bounded. We demonstrate this on an autonomous-driving scenario with a pedestrian crossing under limited visibility, where simulations with perception-driven constraint updates confirm the predicted bounded deviations.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Finite-Time Safe Sliding Mode Control for Trajectory Tracking of Wheeled Mobile Robot", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Diana, Baby", "affiliation": "IIT(BHU) Varanasi" }, { "name": "Taslima, Eram", "affiliation": "Indian Institute of Technology (BHU)" }, { "name": "Kamal, Shyam", "affiliation": "Indian Institute of Technology (BHU), Varanasi" }, { "name": "Singh, Bhawana", "affiliation": "Indian Institute of Technology (ism) Dhanbad" }, { "name": "Singh, Priyanka", "affiliation": "Indian Institute of Technology (BHU), Varanasi" } ], "keywords": [ "Nonlinear and optimal automotive control", "Autonomous mobile robots", "Guidance, navigation and control for AVs" ], "abstract": "This paper presents a finite-time control barrier function (FCBF) based sliding mode control (SMC) framework for the trajectory tracking of a wheeled mobile robot (WMR) operating in the presence of static obstacle and matched disturbances. The WMR is modelled using a double-integrator representation, and a circular trajectory is defined as the reference path. To achieve robust trajectory tracking under disturbances, an SMC-based controller is designed. To ensure safety during motion, a novel finite-time high-order control barrier function (FHOCBF) is developed to address the safety constraint associated with the position-based obstacle avoidance task. Specifically, for the second-order WMR model, a finite-time second-order CBF is formulated to ensure collision-free navigation while maintaining finite-time convergence to the safety region. The effectiveness of the proposed FCBF–SMC framework is validated through both simulation and hardware experiments conducted on the Quanser QBot platform, demonstrating accurate trajectory tracking and reliable obstacle avoidance under disturbances.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Model Predictive Control for Dynamic Speed Planning-Based Cruise Control in Mid-Sized BEVs", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Kayacan, Mehmet Aygen", "affiliation": "MAN Truck and Bus Turkey" }, { "name": "Ergezer, Halit", "affiliation": "Ankara Yildirim Beyazit University" } ], "keywords": [ "Nonlinear and optimal automotive control", "Electric and solar vehicles", "Vehicle dynamic systems" ], "abstract": "This paper proposes a nonlinear discrete supervisory Model Predictive Control (MPC) strategy for mid-sized battery electric vehicles (BEVs) to minimize traction and braking energy requirements at the wheel level. The system adaptively modulates the vehicle’s set speed based on ahead road topography, aiming to reduce mechanical energy expenditure while maintaining reference speed adherence. The controller utilizes an asymmetric cost function at each horizon to leverage road slopes for energy gains, ensuring the optimized speed profile remains aligned with driver intent. A primary focus of this research is the systematic investigation of weighting factor effects on the trade-off between energy conservation and tracking performance. The proposed approach is validated in MATLAB, demonstrating significant energy savings across various control priorities compared to conventional constant-speed cruise control systems.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Byzantine-Resilient Leaderless Formation Control in Open Multi-Agent Systems", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Wang, Xince", "affiliation": "Southeast University" }, { "name": "Gong, Xin", "affiliation": "The University of Hong Kong" } ], "keywords": [ "Trajectory tracking and path following for AVs", "Digital twins and IoT for aerospace systems control and monitoring" ], "abstract": "This paper presents a fixed-time leaderless formation control framework for open multi-agent systems under Byzantine edge attacks. A coordinate-independent vector MSR estimation layer and a nonlinear control law are integrated to ensure resilient and predictable convergence. The method guarantees stability under topology switching, agent membership variation, and persistent adversarial behaviors.", "url": "" }, { "id": "Tu-TuC38", "code": "TuC38", "title": "Stabilizing Traffic without Autonomous Vehicles", "day": "Tuesday", "date": "August 25, 2026", "time": "15:30-17:30", "sessionCode": "TuC38", "sessionTitle": "Poster Session Tuesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Koşay, Arda", "affiliation": "Bilkent University" }, { "name": "Kara, Arda", "affiliation": "Bilkent University" }, { "name": "Sayin, Muhammed Omer", "affiliation": "Bilkent University" } ], "keywords": [ "Vehicle dynamic systems", "Modeling and simulation of transportation systems" ], "abstract": "This paper investigates whether \"Human Protocols\" (HPs), simple cognitive heuristics executed by a fraction of drivers, can mitigate phantom traffic jams as effectively as Autonomous Vehicles (AVs). Specifically, we study speed-matching rules in which compliant drivers either match the speed of the vehicle immediately ahead or the speed of the vehicle two positions ahead. Using a standard Flow/SUMO ring-road benchmark, we vary protocol compliance and penetration, comparing HPs against a benchmark AV controller in terms of stabilization time, throughput, and fuel economy. Our results show that HPs can yield superior fuel economy and throughput, although they generally require time longer to stabilize traffic than AV controllers. We conclude that such modest behavior, when adopted by a fraction of drivers, can yield macroscopic benefits competitive with hardware-based automation.", "url": "" }, { "id": "Tu-TuNSP1.1", "code": "TuNSP1.1", "title": "Data, Prediction, and Control", "day": "Tuesday", "date": "August 25, 2026", "time": "17:40-18:30", "sessionCode": "TuNSP1", "sessionTitle": "Data, Prediction, and Control", "sessionType": "Semi-Plenary Session", "room": "Auditorium", "authors": [ { "name": "Chiuso, Alessandro", "affiliation": "University of Padova" } ], "keywords": [ "Linear system identification" ], "abstract": "Prediction—the ability to anticipate the effect of control actions from available information—is a fundamental step in control design. When prediction must be performed under limited information, uncertainty plays a central role. In this talk I will discuss how the language of Bayesian statistics provides a natural framework for establishing a separation principle between data and control. This viewpoint clarifies the role of models and reveals connections between classical model-based approaches and stochastic behavioral perspectives. Focusing on predictive control frameworks, we revisit the open-loop versus closed-loop design issue and show how System-Level Synthesis provides a computationally attractive route from data to predictive control design.", "url": "" }, { "id": "Tu-TuNSP2.1", "code": "TuNSP2.1", "title": "Model-Guided Extremum Seeking Control: Principles and Applications", "day": "Tuesday", "date": "August 25, 2026", "time": "17:40-18:30", "sessionCode": "TuNSP2", "sessionTitle": "Model-Guided Extremum Seeking Control: Principles and Applications", "sessionType": "Semi-Plenary Session", "room": "Convention Hall - Room 205", "authors": [ { "name": "Tan, Ying", "affiliation": "The Univ of Melbourne" } ], "keywords": [ "Linear system identification" ], "abstract": "Extremum Seeking Control (ESC) is a real-time optimisation technique for steering static or dynamic systems toward the extremum of an unknown performance map. Since its introduction in 1922, ESC has evolved from predominantly model-free or data-driven schemes into a theoretically grounded framework with successful applications in energy systems, process control, and robotics. This talk briefly reviews the core principles of classical ESC, highlighting the inherent limitation of slow convergence in data-driven approaches, and then focuses on recent advances in model-guided extremum seeking control, where partial system knowledge is incorporated to improve transient performance and accelerate convergence while retaining robustness to uncertainty. The approach is illustrated through an application to human–prosthetic interfaces, demonstrating how model-guided ESC can efficiently optimise human–device interaction dynamics, enhance functional performance, and improve user experience, underscoring its potential for complex, uncertain, and human-in-the-loop control systems.", "url": "" }, { "id": "We-WeM00.1", "code": "WeM00.1", "title": "Toward Human-Level Dexterity in Robot Manipulation: Integrating Control, Learning, Geometry and Mechanics", "day": "Wednesday", "date": "August 26, 2026", "time": "08:30-09:30", "sessionCode": "WeM00", "sessionTitle": "Toward Human-Level Dexterity in Robot Manipulation: Integrating Control, Learning, Geometry and Mechanics", "sessionType": "Plenary Session", "room": "Auditorium", "authors": [ { "name": "Park, Frank", "affiliation": "Seoul National Univ" } ], "keywords": [ "Robotic learning and adaptation" ], "abstract": "The acrobatic feats of recent humanoids notwithstanding, today's robots still struggle with everyday tasks like opening jars, inserting plugs, using scissors, and other manipulation tasks that humans perform effortlessly. While hardware limitations are partly to blame — today’s robot hands still lack the strength, precision, flexibility, and sensing capabilities of human hands — it is becoming increasingly clear that traditional model-based control methods are also inadequate. Manipulation involves the coordinated control of motion, force, and compliance under uncertainty, disturbances, unmodeled dynamics, and physical constraints imposed by the task and environment. During manipulation a robot may shift between open- and closed-chain systems, and between underactuated and overactuated regimes. Data-driven methods offer a promising alternative to manually engineering such complex control laws. However, current approaches based on Vision-Language-Action (VLA) models have major shortcomings: they are often device-dependent, require enormous training data, scale poorly, and struggle to generalize across tasks. More fundamentally, they fail to fully leverage the extensive body of knowledge accumulated in control systems design and the mechanics of manipulation. This talk proposes a new control architecture for robot manipulation that merges key concepts from control theory, mechanics, geometry, and human motor control with data-driven methods. We show that Brockett's motion description language (MDL) paradigm provides a device-independent, hierarchical, and modular framework for manipulation expressed in the language of control systems. At the highest level, foundation models decompose complex tasks into subtasks, which are then encoded as sequences of robot action primitives using both state-space control and data-driven learning methods. At lower levels, we show how coordinate-invariant geometric methods can be used to construct minimum distortion latent space manifolds, equivariant learning models, and minimum attention feedforward-feedback control laws.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA01.1", "code": "WeA01.1", "title": "Distributed Control of Linear Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:20", "sessionCode": "WeA01", "sessionTitle": "Distributed Control and Optimization", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Morse, A. Stephen", "affiliation": "Yale Univ" }, { "name": "Liu, Ji", "affiliation": "Stony Brook University" } ], "keywords": [ "Linear system identification" ], "abstract": "The objective of this short tutorial is to explain why any jointly controllable, jointly observable multi-channel linear system with a strongly connected neighbor {communication} graph can be exponentially stabilized with arbitrarily fast convergence using a time-invariant distributed linear control. This fact can be established in several different ways. One way involves using a distributed observer-based control architecture analogous to the familiar centralized observer-based architecture used to control a controllable, observable, linear system. Distributed control can be thought of as a generalization of decentralized control in which communication between neighboring agents is allowed. An important consequence of this generalization is that the well-known fixed spectrum {set of fixed modes} of a linear system which arises with decentralized control is no longer an obstacle to the system's regulation with distributed rather than decentralized control. Perhaps the most important idea in automatic control is the concept of integral control. We will explain how to use integral control in a distributed setting to realize a feedback control which enables each and every agent with access to the system to independently adjust its controlled output to any desired set-point value. Often overlooked in the study of distributed control are the effects of communication network transmission delays. It will be explained why in the face of such delays, exponential stabilization at a prescribed convergence rate can still be achieved with distributed control, at least for discrete-time, multi-channel linear systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA01.2", "code": "WeA01.2", "title": "Resilient Distributed Optimization (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:20-10:50", "sessionCode": "WeA01", "sessionTitle": "Distributed Control and Optimization", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Liu, Ji", "affiliation": "Stony Brook University" } ], "keywords": [ "Linear system identification" ], "abstract": "This short tutorial discusses resilience challenges in distributed optimization when some agents in the network behave adversarially and provide unreliable information. We focus on Byzantine settings, where compromised agents can arbitrarily manipulate messages exchanged over the communication network. To address this challenge, we introduce resilience design principles based on graph redundancy and objective redundancy, which enable reliable coordination despite the presence of adversarial agents. Using distributed subgradient methods as an illustrative example, we show how these principles ensure that all non-adversarial agents asymptotically agree on an optimal solution under suitable conditions, and we briefly discuss insights into convergence rates. The talk further demonstrates that the same resilience ideas extend beyond optimization to other distributed computational tasks, including distributed linear equation solving, leading to fully resilient algorithms that tolerate Byzantine behavior. The goal is to convey general design insights that apply across a broad class of distributed control and optimization problems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA01.3", "code": "WeA01.3", "title": "Optimization and Learning in Open Multi-Agent Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:20", "sessionCode": "WeA01", "sessionTitle": "Distributed Control and Optimization", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Linear system identification" ], "abstract": "", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA01.4", "code": "WeA01.4", "title": "Resilient Trust-Based Distributed Optimization in Multi-Agent Systems with Malicious Agents (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "11:20-11:50", "sessionCode": "WeA01", "sessionTitle": "Distributed Control and Optimization", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Nedich, Angelia", "affiliation": "Arizona State University" } ], "keywords": [ "Linear system identification" ], "abstract": "", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.1", "code": "WeA02.1", "title": "Tracking Control for Fixed-Wing AAVs under Multiple Constraints: A Differential Flatness-Based Approach", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-09:55", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Zheng, Jiayi", "affiliation": "National University of Defense Technology" }, { "name": "Zhao, Shulong", "affiliation": "National University of Defense Technology" }, { "name": "Wang, Xiangke", "affiliation": "National University of Defense Technology" } ], "keywords": [ "Application of nonlinear analysis and design", "Control barrier functions and state space constraints", "Nonlinear model reduction" ], "abstract": "In this paper, we investigate the problem of differential flatness-based two-layer control strategy for fixed-wing autonomous aerial vehicles (AAVs). Firstly, the dynamics of fixed-wing AAVs is transformed through differential flatness, where all states and inputs are denoted as the functions of flat outputs and their derivatives. Based on this transformation, the multiple constraints existed in practical flights can be unified to the constraints on flat outputs. This ensures that the inherent connections among constraints are fully regarded, and the propagation of constraints occurred in dynamics of fixed-wing AAVs is resolved. Then, we design a two-layer control strategy, consisting of control commands (accelerations) and actual controllers (thrust and control surfaces). It balances the stability and practical feasibility for fixed-wing AAVs. Finally, a simulation is conducted to verify the effectiveness of the proposed method in an obstacle environment.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.2", "code": "WeA02.2", "title": "Perception-Limited Smooth Safety Filtering", "day": "Wednesday", "date": "August 26, 2026", "time": "09:55-10:00", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Smaili, Lyes", "affiliation": "Université Du Québec En Outaouais" }, { "name": "Berkane, Soulaimane", "affiliation": "Université Du Québec En Outaouais" } ], "keywords": [ "Application of nonlinear analysis and design", "Optimization-based estimation and control" ], "abstract": "This paper develops a smooth safety-filtering framework for nonlinear control-affine systems under limited perception. Classical Control Barrier Function (CBF) filters assume global availability of the safety function---its value and gradient must be known everywhere---an assumption incompatible with sensing-limited settings, and the resulting filters often exhibit nonsmooth switching when constraints activate. We propose two complementary perception-aware safety filters applicable to general control-invariant safety sets. The first introduces a smooth perception gate that modulates barrier constraints based on sensing range, yielding a closed-form Lipschitz-safe controller with forward-invariance guarantees. The second replaces the hard CBF constraint with a differentiable penalty term, leading to a smooth unconstrained optimization-based safety filter consistent with CBF principles. For both designs, we establish existence, uniqueness, and forward invariance of the closed-loop trajectories. Numerical results demonstrate that the proposed smooth filters enable the synthesis of higher-order tracking controllers for systems such as drones and second-order ground robots, offering substantially smoother and more robust safety-critical behaviors than classical CBF-based filters.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.3", "code": "WeA02.3", "title": "Set-Relaxed Disturbance-Resistant High-Order Control Barrier Functions with Reduced Conservativeness", "day": "Wednesday", "date": "August 26, 2026", "time": "10:00-10:05", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Zhang, Tianyu", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Xu, Jun", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Ma, Jie", "affiliation": "Harbin Institute of Technology" }, { "name": "Li, Jiangang", "affiliation": "Harbin Institute of Technology Shenzhen Graduate School" } ], "keywords": [ "Control barrier functions and state space constraints" ], "abstract": "This paper proposes a set-relaxed disturbance-resistant high-order control barrier function (SRDR-HOCBF) frameworks to address limitations in existing robust CBF methods under parameter uncertainties and external disturbances. The framework employs a recursive virtual constraint relaxation mechanism to systematically enlarge the forward invariant set, and theoretical proofs establish the forward invariance under bounded uncertainties and disturbances. Comparative simulations on a horizontal pendulum and a mobile navigation system validate its superiority in safety maintenance over traditional HOCBF. And it is particularly effective in reducing initial state requirements, outperforming other methods under broader initial conditions when integrated with control strategies.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.4", "code": "WeA02.4", "title": "Reciprocal-Compensated Control Barrier Function against Parametric Uncertainties (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:05-10:10", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Wang, Xinyang", "affiliation": "Harbin Institute of Technology" }, { "name": "Xiao, Wei", "affiliation": "MIT, Boston University" }, { "name": "Zhang, Hongwei", "affiliation": "Harbin Institute of Technology, Shenzhen" } ], "keywords": [ "Control barrier functions and state space constraints", "Adaptive control design" ], "abstract": "Control barrier functions (CBFs) have proven effective in guaranteeing the safety of control systems; however, accurate system model is usually required for CBF-based controller design, which is generally difficult to obtain in practice. While uncertainty estimation and compensation can enhance robustness of CBFs, existing methods typically need the bounds of uncertain term to reject residual estimation error. This paper considers a more complex scenario where the system is subject to completely unknown parametric uncertainties, including both measurement errors and parametric deviations. Such compound uncertainty poses significant challenge for existing CBF approaches, which require the bounds of both measurement error and the parameter deviation to guarantee safety. To overcome this limitation, we propose a novel class of CBFs, called the reciprocal-compensated uncertainty-aware CBF, to enforce robust safety against uncertainties without requiring any prior knowledge of these uncertainties. A simulation example demonstrates the effectiveness of our approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.5", "code": "WeA02.5", "title": "Aircraft Trajectory Management Based on Integral Control Barrier Functions: A Static Obstacle Avoidance Case Study", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:15", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Dan, Hayato", "affiliation": "Institute of Science Tokyo" }, { "name": "Kurabayashi, Daisuke", "affiliation": "Tokyo Institute of Technology" } ], "keywords": [ "Control barrier functions and state space constraints", "Application of nonlinear analysis and design" ], "abstract": "This paper proposes an integral control barrier function (I-CBF)-based safety augmentation method for aircraft trajectory management with static obstacle avoidance. We consider a point-mass model of a cruising aircraft in which thrust, bank angle, and flight-path angle are inputs, while a waypoint-based guidance law and low-level proportional controllers define input dynamics. To handle the position-based safety constraint within the I-CBF framework, we define a barrier function as the minimum safety margin to the obstacle over a short-horizon predicted trajectory. The required gradients with respect to the current state and input are computed by integrating sensitivity equations along the prediction. This yields a linear constraint on the auxiliary input and a small quadratic programming, which can be incorporated into the I-CBF framework. Simulation using a Boeing 787-8 model shows that the proposed safety augmentation keeps the aircraft away from the static obstacle with only small deviations from the nominal waypoint-tracking path.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.6", "code": "WeA02.6", "title": "Safety Critical Control for Nonlinear Affine Systems with Unknown Disturbances and Input Constraints", "day": "Wednesday", "date": "August 26, 2026", "time": "10:15-10:20", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Chai, Haoyu", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Chen, Yong", "affiliation": "Uestc" }, { "name": "Lotfy Haridy, Ahmed", "affiliation": "School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China And" }, { "name": "Ali, Tofik Seid", "affiliation": "University of Electronic Science and Technology of China" } ], "keywords": [ "Control barrier functions and state space constraints", "Application of nonlinear analysis and design", "Optimal control theory" ], "abstract": "For nonlinear affine systems affected by unknown disturbances and input constraints, this study develops a safety critical control method based on higher-order control barrier functions(HoCBF). Firstly, to suppress the persistent impact of unknown disturbances on the safety constraint performance, a disturbance observer-based tunable input-to-state-safe HoCBF is designed, further reducing the conservatism of the safety constraints. Secondly, a time-varying function is incorporated into the construction of the HoCBF to address input constraints in safety critical control. By designing an auxiliary dynamic system to dynamically adjust the safety set, the conflict between input saturation and safety constraints is mitigated, effectively preventing infeasible solutions in quadratic programming problems under multiple constraints. Finally, the effectiveness and superiority of the suggested framework are validated via experiments on unmanned ground vehicles cooperative obstacle avoidance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.7", "code": "WeA02.7", "title": "Analysis of Feasibility Margin As a Control Barrier Function under Input Constraints", "day": "Wednesday", "date": "August 26, 2026", "time": "10:20-10:25", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Xu, Shuo", "affiliation": "Peking University" }, { "name": "Gong, Zhengning", "affiliation": "Peking University" }, { "name": "Lin, Yicheng", "affiliation": "Peking University" }, { "name": "Sun, Zhiyong", "affiliation": "Peking University (PKU)" } ], "keywords": [ "Control barrier functions and state space constraints", "Controller constraints and structure", "Application of nonlinear analysis and design" ], "abstract": "Quadratic Programs (QP) subject to Control Barrier Function (CBF)-based constraints are widely employed to design safety-critical controllers. However, ensuring the feasibility of the QP under input constraints remains a significant challenge. In this work, we propose a feasibility-margin-based CBF as a proactive safety filter to guarantee the dynamic feasibility of CBF-QP with input constraints. We first characterize the feasibility margin using support functions defined by the geometry of input constraints. We then propose a novel safe control method that employs the feasibility margin as a Control Barrier Function (FMA-CBF) for safety-critical control systems subject to polytopic input constraints. Furthermore, we formulate a unified QP that enforces both the original safety constraints and the feasibility margin constraints to guarantee feasibility. The efficacy of the proposed method is validated through double-integrator systems and unicycle robots with obstacle avoidance tasks.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.8", "code": "WeA02.8", "title": "Obstacle Avoidance of a Unicycle Via First-Order Control Barrier Function and Adaptive Point Selection", "day": "Wednesday", "date": "August 26, 2026", "time": "10:25-10:30", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Zhao, Bangwei", "affiliation": "Xiamen University" }, { "name": "Guan, Jinting", "affiliation": "Xiamen University" }, { "name": "Qian, Yangyang", "affiliation": "Lingnan University" }, { "name": "Yu, Xiao", "affiliation": "Xiamen University" } ], "keywords": [ "Control barrier functions and state space constraints", "Controller constraints and structure", "Application of nonlinear analysis and design" ], "abstract": "This paper addresses the safe navigation problem for a unicycle-type mobile robot operating in obstacle-cluttered environments. Existing safe control approaches typically employ control barrier functions (CBFs) to formulate a quadratic programming (QP) problem that minimally modifies a given nominal control input to ensure safety. However, within this CBF-QP framework, the direct application of high-order or hybrid-order CBFs to unicycle-type robots remains limited in practicality. To overcome this limitation, we first analyze the relative position dynamics between the robot and obstacles and develop a novel safe control method using a first-order CBF. This formulation enables effective obstacle avoidance based directly on point cloud data from an onboard LiDAR. Furthermore, to alleviate the computational burden associated with processing dense point clouds, we propose an efficient point cloud filtering strategy that significantly reduces the number of CBF constraints in the QP without compromising safety. Finally, the efficacy of the proposed method is validated on the NVIDIA IsaacSim platform.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.9", "code": "WeA02.9", "title": "Control of Multi-Agent Systems with Input Constraints by Time-Varying Control Barrier Functions", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:35", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Chiang, Ming-Li", "affiliation": "National Taiwan University" }, { "name": "Chuang, Che-Jung", "affiliation": "National Taiwan University" } ], "keywords": [ "Control barrier functions and state space constraints", "Controller constraints and structure", "Optimization-based estimation and control" ], "abstract": "This paper considers trajectory tracking control and collision avoidance for linear multi-agent systems (MAS) with bounded input constraints based on the control barrier function (CBF) design. We identify the conflict between leader tracking performance and follower control freedom in input-constrained multi-agent systems. And then propose a uniformly time-varying CBF to cope with the state constraints. Moreover, the trade-off between the control freedom of the leader and follower agents is examined. Conservativeness about the satisfaction of the constraints is quantified as a condition on the selection of the function used for the controller design. Some simulations are provided to illustrate the effects of the virtual leader actuation on the swarm of the follower agents and to demonstrate the efficacy of our design.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.10", "code": "WeA02.10", "title": "Feasible-Set Reshaping for Constraint Qualification in Optimization-Based Control", "day": "Wednesday", "date": "August 26, 2026", "time": "10:35-10:40", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Wu, Si", "affiliation": "Northeastern University, China" }, { "name": "Liu, Tengfei", "affiliation": "Northeastern University" }, { "name": "Hong, Yiguang", "affiliation": "Chinese Academy of Sciences" }, { "name": "Jiang, Zhong-Ping", "affiliation": "Tandon School of Engineering, New York University" }, { "name": "Chai, Tianyou", "affiliation": "Northeastern Univ" } ], "keywords": [ "Control barrier functions and state space constraints", "Convex optimization" ], "abstract": "This paper presents a novel feasible-set reshaping technique to optimization-based control with ensured constraint qualification. In our problem setting, the feasible set of admissible control inputs depends on the real-time state of the plant, and the linear independence constraint qualification (LICQ) may not be satisfied in some regions of interest. By feasible-set reshaping, we project the constraints of the original feasible set onto an appropriately chosen constant matrix with its rows forming a positive span of the space of the optimization variable. It is proved that the reshaped feasible set is nonempty and satisfies LICQ, as long as the original feasible set is nonempty. The effectiveness of the proposed method is verified by constructing Lipschitz continuous quadratic-program-based controllers based on the reshaped feasible sets.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.11", "code": "WeA02.11", "title": "Computationally Efficient and Scalable Multi-Robot Collision Avoidance Via Control Barrier Proximal Dynamics", "day": "Wednesday", "date": "August 26, 2026", "time": "10:40-10:45", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Ma, Ruijie", "affiliation": "Zhejiang University" }, { "name": "Zhao, Chengcheng", "affiliation": "Zhejiang University" } ], "keywords": [ "Control barrier functions and state space constraints", "Decentralized control" ], "abstract": "Control Barrier Function based Quadratic Programs (CBF-QPs) are widely used for collision avoidance in multi-robot systems, but their real-time implementation is limited by the computational cost of online optimization. Recently, Control Barrier Proximal Dynamics (CBPD) reformulates CBF-QPs as continuous-time dynamics and offers significant computational speedups. However, existing results are restricted to affine constraints and cannot handle the nonlinear quadratic constraints arising in collision avoidance. This paper proposes a Collision Avoidance-CBPD (CA-CBPD) framework. We establish strong contraction under a time-varying metric and prove that its tracking error with respect to the QP solution remains uniformly bounded. The maximum safety violation is explicitly quantified, enabling a robust compensation strategy with guaranteed safety. Numerical results show that CA-CBPD achieves over 200× speedup compared with CBF-QP while maintaining reliable collision avoidance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.12", "code": "WeA02.12", "title": "Robust Safety Design for Strict-Feedback Nonlinear Systems Via Observer-Based Linear Time Varying Feedback", "day": "Wednesday", "date": "August 26, 2026", "time": "10:45-10:50", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Imtiaz Ur, Rehman", "affiliation": "Laboratoire ImViA EA 7535, équipe VIBOT" }, { "name": "Labbadi, Moussa", "affiliation": "Bretagne INP" }, { "name": "Abadi, Amine", "affiliation": "Laboratoire ImViA EA 7535, équipe VIBOT" }, { "name": "Lew Yan Voon, Lew Fock Chong", "affiliation": "Laboratoire ImViA EA 7535, équipe VIBOT" } ], "keywords": [ "Control barrier functions and state space constraints", "Disturbance rejection and input-to-state stability", "Stability of nonlinear systems" ], "abstract": "This paper develops a robust safety-critical control method for nonlinear strictfeedback systems with mismatched disturbances. Using a state transformation and a linear time-varying disturbance observer, the system is converted into a form that enables safe control design. The approach ensures forward invariance of the safety set and also applies to disturbancefree systems. Safety is proven for all cases, and a numerical example illustrates the results.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.13", "code": "WeA02.13", "title": "Safe Model-Based Reinforcement Learning Via Model Predictive Control and Control Barrier Functions", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-10:55", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Dzhumageldyev, Kerim", "affiliation": "Delft University of Technology" }, { "name": "Airaldi, Filippo", "affiliation": "Delft University of Technology" }, { "name": "Dabiri, Azita", "affiliation": "Delft University of Technology" } ], "keywords": [ "Control barrier functions and state space constraints", "Model predictive control", "Optimal control theory" ], "abstract": "Optimal control strategies are often combined with safety certificates to ensure both performance and safety in safety-critical systems. A prominent example is combining Model Predictive Control (MPC) with Control Barrier Functions (CBF). Yet, efficient tuning of MPC parameters and choosing an appropriate class Kappa function in the CBF is challenging and problem dependent. This paper introduces a safe model-based Reinforcement Learning (RL) framework where a parametric MPC controller incorporates a CBF constraint with a parameterized class Kappa function and serves as a function approximator to learn improved safe control policies from data. Three variations of the framework are introduced, distinguished by the way the optimization problem is formulated and the class Kappa function is parameterized, including neural architectures. Numerical experiments on a discrete double-integrator with static and dynamic obstacles demonstrate that the proposed methods improve performance while ensuring safety.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.14", "code": "WeA02.14", "title": "Efficient Verification of Neural Control Barrier Functions with Smooth Nonlinear Activations (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:55-11:00", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Zhang, Jun", "affiliation": "Shanghai University" }, { "name": "Zhang, Haibo", "affiliation": "Beijing Institute of Control Engineering" }, { "name": "Liu, Chun", "affiliation": "Shanghai University" }, { "name": "Wang, Xiaofan", "affiliation": "Shanghai University" }, { "name": "Xu, Liang", "affiliation": "Shanghai University" } ], "keywords": [ "Control barrier functions and state space constraints", "Model validation", "Learning methods for optimal control" ], "abstract": "Formal verification of neural control barrier functions (NCBFs) remains challenging, especially for neural networks with nonlinear activations like tanh. Existing CROWN- based methods rely on conservative linear relaxations for Jacobian bounds, limiting scal- ability. We propose LightCROWN, which computes tighter Jacobian bounds by exploit- ing the analytical properties of activation functions. Experiments on nonlinear control sys- tems including the inverted pendulum, Dubins car, and planar quadrotor demonstrate that LightCROWN improves verification success rates up to 100%, while enhancing speed and scalability. Our approach provides a generalizable improvement for CROWN-based frameworks,enabling more efficient verification of complex NCBFs. The code can be found at github.com/ Autonomous-Systems-and-Control-Lab/verify-neural-CBF.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.15", "code": "WeA02.15", "title": "Safe Tracking Control of High Relative Degree Nonlinear Systems Using Gaussian Processes-Adapted High-Gain Observer and Control Lyapunov and Barrier Functions", "day": "Wednesday", "date": "August 26, 2026", "time": "11:00-11:05", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Xie, Mengxu", "affiliation": "Northeastern University" }, { "name": "Ma, Tong", "affiliation": "Northeastern University" } ], "keywords": [ "Control barrier functions and state space constraints", "Nonlinear observers and filters", "Output regulation and tracking" ], "abstract": "This paper presents an integrated safe-tracking control scheme for high-relative-degree nonlinear systems with uncertain dynamics and partial measurements. A Gaussian process (GPs) model and a high-gain observer jointly estimate the full state and learn unknown dynamics, with convergence of both estimation errors under suitable gain conditions. GP-based learning alleviates the need for large observer gains, mitigating peaking. Exponential control Lyapunov and barrier functions embedded in a one-step optimization-based controller with probabilistic guarantees enforce safety and tracking while prioritizing safety. Simulations show safe outputs, improved tracking, smoother inputs, and reduced observer gains versus GP-adapted with higher observer gains and observer-only approaches.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.16", "code": "WeA02.16", "title": "Disturbance Observer-Based Robust Control Barrier Functions", "day": "Wednesday", "date": "August 26, 2026", "time": "11:05-11:10", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Li, Jinlu", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Wang, Xinyang", "affiliation": "Harbin Institute of Technology" }, { "name": "Zhang, Hongwei", "affiliation": "Harbin Institute of Technology, Shenzhen" } ], "keywords": [ "Control barrier functions and state space constraints", "Observer design" ], "abstract": "Safety assurance for autonomous systems is challenged by unmatched disturbances, especially those with non-differentiable components like sensor noise. Existing methods are either incapable of dealing with such noise or are overly conservative. This paper proposes a novel disturbance observer-based disturbance rejection control barrier function framework for high-relative-degree safety constraints under composite disturbances. We integrate a disturbance observer with a robust disturbance rejection law to achieve less conservative performance while guaranteeing safety. Theoretical analysis and simulation study demonstrate that the proposed method guarantees safety under the unmatched composite disturbances, while outperforming a state-of-the-art robust approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.17", "code": "WeA02.17", "title": "Table-Based Iterative Synthesis of Control Barrier Functions Via Safety Capacity and Expected Safety Horizon Functions", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:15", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Duan, Yue", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Cao, Yuxiao", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Zeng, Xiangrui", "affiliation": "Huazhong University of Science and Technology" } ], "keywords": [ "Control barrier functions and state space constraints", "Optimization-based estimation and control", "Numerical methods for optimal control" ], "abstract": "Control barrier functions (CBFs) provide safety filters for constrained systems, but synthesizing a useful CBF can be difficult when the safe set is nonconvex or poorly represented by a prescribed function class. This paper develops a sampled-data, table-based CBF synthesis framework that uses finite-state prediction rather than a fixed analytic parametrization. The method evaluates each grid state through an instantaneous safety capacity, which measures the fraction of admissible inputs that are one-step safe, and an expected safety horizon, which accumulates this capacity along predicted sampled trajectories. The resulting update distinguishes states that are immediately feasible but have poor future recoverability from those with longer-term safety margins. Dubins car obstacle-avoidance simulations illustrate the construction of non-polynomial safe sets in cluttered environments and compare the result with a polynomial SOS-CBF baseline.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.18", "code": "WeA02.18", "title": "Neural Network-Based Co-Design of Output-Feedback Control Barrier Function and Observer with Input Constraints", "day": "Wednesday", "date": "August 26, 2026", "time": "11:15-11:20", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Jagabathula, Vaishnavi", "affiliation": "Indian Institute of Science, Bengaluru" }, { "name": "Basu, Ahan", "affiliation": "Indian Institute of Science" }, { "name": "Jagtap, Pushpak", "affiliation": "Indian Institute of Science" } ], "keywords": [ "Control barrier functions and state space constraints", "Output feedback nonlinear control" ], "abstract": "Control Barrier Functions (CBFs) provide a powerful framework for ensuring safety in dynamical systems. However, their application typically relies on full state information, which is often violated in real-world due to the availability of partial state information. In this work, we propose a neural network-based framework for the co design of a safety controller, observer, and CBF for partially observed continuous-time systems with input constraints. By formulating barrier conditions over an augmented state space, our approach ensures safety without requiring bounded estimation errors or handcrafted barrier functions. All components are jointly trained by formulating appropriate loss functions, and we introduce a validity condition to provide formal safety guarantees beyond the training data. Finally, we demonstrate the effectiveness of the proposed approach through several case studies.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.19", "code": "WeA02.19", "title": "Barrier Certificates for Uncertain Temporal Specifications", "day": "Wednesday", "date": "August 26, 2026", "time": "11:20-11:25", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Mamduhi, Mohammad H.", "affiliation": "University of Birmingham" }, { "name": "Soudjani, Sadegh", "affiliation": "Max Planck Institute for Software Systems" } ], "keywords": [ "Control barrier functions and state space constraints", "Uncertain systems", "Analytic design" ], "abstract": "This paper studies satisfying temporal logic specifications on stochastic dynamical systems, where the predicates evolve randomly over time. Such randomness may arise from uncertain environment models or external stochastic processes causing the sets associated with predicate satisfaction to vary in a non-deterministic manner. As a result, verifying whether a stochastic dynamical system satisfies a temporal specification depends also on the uncertainty in the predicates. We develop a certificate-based framework to bound the probability of satisfying temporal logic specifications with randomly evolving predicates. We first show that temporal logic specifications with stochastic predicates can be transformed to specifications with deterministic predicates on an augmented space which is extended to include the stochastic space of predicate’s uncertainty. We then utilize barrier certificates on an augmented space to provide tractable optimization-based conditions and to avoid the computational burden of dynamic programming. Focusing on linear dynamics and safety-type specifications, we derive analytical conditions under which barrier certificates guarantee bounds on the probability of violating the stochastic safety predicates. The approach is demonstrated on numerical case studies.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.20", "code": "WeA02.20", "title": "Approximation-Free Control Barrier Functions for Prescribed-Time Reach-Avoid of Unknown Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "11:25-11:30", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Sawarkar, Shubham", "affiliation": "Indian Institute of Science, Bengaluru" }, { "name": "Jagtap, Pushpak", "affiliation": "Indian Institute of Science" } ], "keywords": [ "Control barrier functions and state space constraints", "Uncertain systems", "Lyapunov methods" ], "abstract": "We study the prescribed-time reach-avoid (PT-RA) control problem for nonlinear systems with unknown dynamics operating in environments with moving obstacles. Unlike robust or learning-based Control Barrier Function (CBF) methods, the proposed framework re- quires neither online model learning nor uncertainty bound estimation. A CBF-based Quadratic Program (CBF-QP) is solved on a simple virtual system to generate a safe reference satisfying PT-RA conditions with respect to time-varying, tightened obstacle and goal sets. The true system is confined to a Virtual Confinement Zone (VCZ) around this reference using an approximation-free feedback law. This construction guarantees real-time safety and prescribed- time target reachability under unknown dynamics and dynamic constraints without explicit model identification or offline precomputation. Simulation results illustrate reliable dynamic obstacle avoidance and timely convergence to the target set.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.21", "code": "WeA02.21", "title": "Refined Barrier Conditions for Finite-Time Safety and Reach-Avoid Guarantees in Stochastic Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:35", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Xue, Bai", "affiliation": "Institute of Software" }, { "name": "Ong, Luke", "affiliation": "College of Computing and Data Science, Nanyang Technological University, Singapore" }, { "name": "Wagner, Dominik", "affiliation": "College of Computing and Data Science, Nanyang Technological University, Singapore" }, { "name": "Wang, Peixin", "affiliation": "Software Engineering Institute, East China Normal University, China" } ], "keywords": [ "Lyapunov methods" ], "abstract": "Providing finite-time probabilistic safety and reach-avoid guarantees is crucial for safety-critical stochastic systems. Existing barrier certificate methods often rely on a restrictive boundedness assumption for auxiliary functions, limiting their applicability. This paper presents refined barrier-like conditions that remove this assumption. Specifically, we establish conditions for deriving upper bounds on finite-time safety probabilities in discrete-time systems and lower bounds on finite-time reach-avoid probabilities in continuous-time systems. This key relaxation significantly expands the class of verifiable systems, especially those with unbounded state spaces, and facilitates the application of advanced optimization techniques, such as semi-definite programming with polynomial functions. The efficacy of our approach is validated through numerical examples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.22", "code": "WeA02.22", "title": "Forward-Invariant Control of Switched Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "11:35-11:40", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Long, Lijun", "affiliation": "State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang" }, { "name": "Huang, Chunxiao", "affiliation": "Northeastern University" }, { "name": "Chen, Zhiyong", "affiliation": "The University of Newcastle" } ], "keywords": [ "Nonlinear control of switched & hybrid systems" ], "abstract": "This paper investigates forward-invariant control of switched systems, allowing different subsystems to possess different safe sets. By analyzing the influence of subsystem dynamics and switching signals on the forward invariance of the safe set, a relaxed safety condition for individual subsystems is proposed. This condition requires the sub-tangential condition to hold only on a subregion of the safe set, rather than on the entire set. Consequently, individual subsystems may be unsafe, while overall system safety is achieved through switching control. Based on these relaxed safety conditions, an extended Nagumo’s theorem is established within a switched-systems framework. A clear and intuitive proof is provided for the practical safe sets commonly used in engineering, without relying on nontrivial tools from topology or functional analysis. In a special case, a necessary and sufficient condition is provided for the forward invariance of the safe set under arbitrary switchings. Finally, a compass-like biped walking robot example is used to demonstrate the effectiveness of the proposed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.23", "code": "WeA02.23", "title": "Prescription for Bounding Inputs in Krasovskii Passivity Based Control", "day": "Wednesday", "date": "August 26, 2026", "time": "11:40-11:45", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Kawano, Yu", "affiliation": "Hiroshima University" }, { "name": "Namba, Takumi", "affiliation": "Ritsumeikan University" }, { "name": "Cucuzzella, Michele", "affiliation": "University of Groningen" } ], "keywords": [ "Passivity-based control", "Controller constraints and structure" ], "abstract": "Krasovskii passivity is a passivity property defined by selecting the time derivative of the input as the input port variable. Because of this structure, Krasovskii passivity naturally yields integral controllers which are Krasovskii passive. In this paper, we show that such integral control schemes can easily be adapted to handle input bound constraints. Our approach consists of passing the inputs through activation-like functions and modifying the controllers so as to preserve their Krasovskii passivity. We apply the proposed tailoring method to stabilization, output consensus, and input consensus problems. For consensus controllers, we additionally demonstrate how slope constraints on the inputs can be enforced.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA02.24", "code": "WeA02.24", "title": "Interconnection and Damping Assignment Passivity-Based Control Using Sparse Neural ODEs", "day": "Wednesday", "date": "August 26, 2026", "time": "11:45-11:50", "sessionCode": "WeA02", "sessionTitle": "Shotgun: Nonlinear Control Systems I", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Botteghi, Nicolo", "affiliation": "Politecnico Di Milano" }, { "name": "Brook, Owen", "affiliation": "Imperial College London" }, { "name": "Fasel, Urban", "affiliation": "Imperial College London" }, { "name": "Califano, Federico", "affiliation": "University of Twente" } ], "keywords": [ "Passivity-based control", "Learning methods for optimal control" ], "abstract": "Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) is a nonlinear control technique that assigns a port-Hamiltonian (pH) structure to a controlled system using a state-feedback law. While IDA-PBC has been extensively studied and applied to many systems, its practical implementation often remains confined to academic examples and, almost exclusively, to stabilization tasks. The main limitation of IDA-PBC stems from the complexity of analytically solving a set of partial differential equations (PDEs), referred to as the matching conditions, which enforce the pH structure of the closed-loop system. However, this is extremely challenging, especially for complex physical systems and tasks. In this work, we propose a novel numerical approach for designing IDA-PBC controllers without solving the matching PDEs exactly. We cast the IDA-PBC problem as the learning of a neural ordinary differential equation. In particular, we rely on sparse dictionary learning to parametrize the desired closed-loop system as a sparse linear combination of nonlinear state-dependent functions. Optimization of the controller parameters is achieved by solving a multi-objective optimization problem whose cost function is composed of a generic task-dependent cost and a matching condition-dependent cost. Our numerical results show that the proposed method enables (i) IDA-PBC to be applicable to complex tasks beyond stabilization, such as the discovery of periodic oscillatory behaviors, (ii) the derivation of closed-form expressions of the controlled system, including residual terms in case of approximate matching, and (iii) stability analysis of the learned controller.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.1", "code": "WeA03.1", "title": "Multi-Objective Control and Manipulability Maximization of Robot Manipulators", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-09:55", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Vargas, Lucas", "affiliation": "Norwegian Univ. of Life Sciences & Fed. Univ. of Rio De Janeiro" }, { "name": "Candea Leite, Antonio", "affiliation": "Norwegian University of Life Sciences" }, { "name": "Costa, Ramon R.", "affiliation": "Federal University of Rio De Janeiro" } ], "keywords": [ "Robotic grasping and manipulation", "Mechatronic system modeling, design, optimization", "Mechatronic system estimation, identification, control" ], "abstract": "In this work, we revisit the use of the filtered inverse algorithm to address multi-objective control of robot manipulators. The method employs the concept of dynamic inversion of the Jacobian matrix to handle kinematic singularities and augmented task-space problems, which may be ill-posed and involve conflicting goals. Herein, we evaluate different approaches for incorporating both trajectory tracking and the additional control objective of velocity manipulability maximization, as it correlates with the energetic efficiency of robotic operations. Finally, numerical simulations of a redundant planar arm demonstrate the behavior and performance of the proposed solution.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.2", "code": "WeA03.2", "title": "Towards Simulation-Based Motion Planning for Deformable Linear Objects", "day": "Wednesday", "date": "August 26, 2026", "time": "09:55-10:00", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Völz, Andreas", "affiliation": "Friedrich-Alexander-Universität Erlangen-Nürnberg" }, { "name": "Graichen, Knut", "affiliation": "Friedrich-Alexander-University Erlangen-Nuremberg" } ], "keywords": [ "Robotic grasping and manipulation", "Task and motion planning" ], "abstract": "This paper investigates the use of physics simulation for the motion planning of deformable linear objects (DLOs) like cables and ropes. Existing work has largely focused on the modeling of equilibrium configurations in such a way that standard sampling-based planners can be applied. However, these methods are difficult to extend to scenarios that require or allow contact between the DLO and the environment. Therefore, it seems attractive to directly use physics simulations like MuJoCo for the planning process instead of relying on equilibrium models. Concepts for lattice-based and tree-based planning are presented and compared to a state-of-the-art model for an intentionally simplified task to highlight advantages and challenges.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.3", "code": "WeA03.3", "title": "Spatial Event Based Adaptive Control for Rehabilitation Robotic Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "10:00-10:05", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Zhou, Shou-Han", "affiliation": "Cardiff University" }, { "name": "Mareels, Iven", "affiliation": "Federation University Australia" } ], "keywords": [ "Robotic learning and adaptation", "Adaptive and adaptable automation", "Medical and rehabilitation robotics" ], "abstract": "In fields such as rehabilitation and biomechanics, many robotic systems have been developed to interact directly with humans. However, the speed of human movement is not constant for a given task, as the time required to complete an action varies with individual decisions. To address this variability, we develop a spatially based event controller that adapts to unknown parameters while allowing for movements in multiple directions, addressing limitations of existing spatial controllers. We derive the conditions on the controller parameters and event design that ensure system stability, and then present simulation examples demonstrating the controller’s ability to track spatial paths without constraining the terminal time.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.4", "code": "WeA03.4", "title": "Map and Navigation in Unknown Environments with Brain-Inspired Learning Approach", "day": "Wednesday", "date": "August 26, 2026", "time": "10:05-10:10", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Shen, Xiangyuan", "affiliation": "Huazhong University of Scienceand Technology" }, { "name": "Hu, Bin", "affiliation": "South China University of Technology" }, { "name": "Guan, Zhi-Hong", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Chen, Long", "affiliation": "Wuhan Institute of Technology" }, { "name": "Li, Tao", "affiliation": "Hubei Normal University" } ], "keywords": [ "Robotic learning and adaptation", "Autonomous navigation", "Robot perception and sensing" ], "abstract": "Simultaneous localization and mapping (SLAM) and navigation are core capabilities for agents, yet traditional methods rely on high-precision sensors and perform poorly in rapidly changing large-scale environments. Inspired by mammals' spatial cognitive and navigation mechanisms in neuroscience, this paper proposes a novel brain-inspired computational network for learning cognitive map representations and navigation in unknown environments. The network model diverse spatial cells to integrate perception and motion information for environmental representation, establishes a dynamically growing place cell-based cognitive map, and updates synaptic strength between place cells via agent-environment interaction to restructure the map. Additionally, a place cell sequence planning algorithm is designed for navigation using the cognitive map as input. Simulation and physical-robot experiments show that the proposed method can dynamically construct and update cognitive maps during environmental interaction and can improve navigation efficiency in the tested dynamic scenarios. These results suggest a feasible brain-inspired alternative for map learning and navigation in unknown environments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.5", "code": "WeA03.5", "title": "Residual Reinforcement Learning for Robot Teleoperation under Stochastic Delays", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:15", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Deng, Kai-Ze", "affiliation": "Technische Universität München" }, { "name": "Yang, Zewen", "affiliation": "Technical University of Munich" } ], "keywords": [ "Robotic learning and adaptation", "Teleoperation", "AI-powered robotics" ], "abstract": "Stochastic communication delays in teleoperation introduce signal discontinuities that undermine control stability and degrade control performance. Consequently, the conventional reinforcement learning (RL) methods struggle with the delayed observations due to the delay-induced observations, leading to high-frequency chattering. To address this, we propose a hybrid control framework, delay-resilient RL, integrating a state estimator utilizing Long Short-Term Memory (LSTM) with a residual RL policy, which is resilient to stochastic delays. The LSTM reconstructs smooth, continuous state estimates from delayed observations, enabling the RL agent to learn a residual torque compensation policy that balances tracking accuracy with velocity smoothness. Experimental validation on Franka Panda robots demonstrates that our approach significantly outperforms the state-of-the-art baselines, ensuring robust and stable teleoperation even under high-variance stochastic delays.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.6", "code": "WeA03.6", "title": "Enhancing Attack Detection for Mobile Robots Via Parametric Final-State Distribution Modeling (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:15-10:20", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Horikoshi, Ken", "affiliation": "The University of Electro-Communications" }, { "name": "Watanabe, Yohei", "affiliation": "The University of Electro-Communications" }, { "name": "Iwamoto, Mitsugu", "affiliation": "The University of Electro-Communications" }, { "name": "Tanaka, Takashi", "affiliation": "Purdue University" }, { "name": "Sawada, Kenji", "affiliation": "The University of Osaka" } ], "keywords": [ "Security for stochastic systems" ], "abstract": "Stealthy attacks and defenses in mobile systems have been studied as zero-sum games, where an attacker covertly drives the system to an unsafe region and a defender detects attacks from noisy trajectories. This paper experimentally evaluates such a game-theoretic framework on a mobile robot. Although the framework predicts constant attacks and likelihood-ratio tests as equilibrium strategies, robot experiments show large errors in the predicted detection failure rate due to a mismatch in final-state variance. We model this effect using empirical Gaussian final-state distributions. Experiments and simulations reduce the prediction error to 5% and clarify the model's limits.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.7", "code": "WeA03.7", "title": "Adaptive Impedance Matching Control for Railway Broadband Vibration Energy Harvesting: A Machine Learning Surrogate Approach", "day": "Wednesday", "date": "August 26, 2026", "time": "10:20-10:25", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Mansattha, Muhammad", "affiliation": "University of Birmingham" }, { "name": "Dixon, Roger", "affiliation": "University of Birmingham" }, { "name": "Stewart, Edd", "affiliation": "The University of Birmingham" } ], "keywords": [ "Smart structures and vibration control", "Mechatronic system estimation, identification, control", "Mechatronics for advanced manufacturing and energy systems" ], "abstract": "Conventional Electromagnetic Vibration Energy Harvesters (EVEHs) can be inefficient when tuned to fixed impedances, particularly under the non-stationary, broadband conditions typical of railway environments. To overcome this limitation, this paper introduces an adaptive impedance matching controller driven by a Machine Learning (ML) surrogate model. By leveraging a Random Forest (RF) regression trained on statistical signal features, the proposed system predicts the optimal complex load impedance in real-time, enabling precise complex conjugate matching. Experimental validation confirms that the controller not only tracks the theoretical maximum power during sinusoidal sweeps but also significantly outperforms traditional fixed-tuning strategies in real-world benchmarks. Specifically, under non-stationary railway vibration profiles, with instantaneous power improvements exceeding 20% during off-resonance events, proving it is the most reliable power source for automated condition monitoring systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.8", "code": "WeA03.8", "title": "Varying Bundle Size Reactive Multi-Task Assignment Using Selective Cost Estimation for Multi-Agent Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "10:25-10:30", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Dahlquist, Niklas", "affiliation": "Luleå University of Technology" }, { "name": "Velhal, Shridhar", "affiliation": "Lulea Technical University" }, { "name": "Nikolakopoulos, George", "affiliation": "Luleå University of Technology" } ], "keywords": [ "Task and motion planning", "Aerial, field, and marine robotics" ], "abstract": "This paper presents a scalable framework for multi-robot task allocation in complex environments where estimating task execution costs is computationally expensive. While combinatorial auction-based approaches offer reliable solutions, the exponential complexity of bundle generation typically renders them intractable for real-time reactive applications, particularly when accurate path planning is required for cost validation. We address this through a distributed, two-stage multi-fidelity bundle generation approach. Agents utilize a local search tree guided by a low-fidelity heuristic (such as euclidean distance) to rapidly explore the bundle space, applying high-fidelity path planning only to the most promising candidates in a best-first manner. These refined bids are then submitted to a central coordinator that solves a set packing problem to ensure global feasibility and maximize the overall utility. Simulation results in multiple environments demonstrate that the framework is able to improve the performance of reactive auction-based task allocation. Overall, the presented framework is shown to enable reactive task allocation with dynamic bundle sizes in multiple settings without exposing the agents' state and internal cost estimation models.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.9", "code": "WeA03.9", "title": "Multi-Robot Allocation and Optimization in a Multi-Mission Framework", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:35", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Miloradovic, Branko", "affiliation": "Mälardalen University" }, { "name": "Frasheri, Mirgita", "affiliation": "Aarhus University" } ], "keywords": [ "Task and motion planning", "Autonomous navigation" ], "abstract": "This paper presents a framework for multi-mission multi-robot task allocation that integrates continuous-time routing with a lightweight bucketed reservation layer. Rather than collapsing all objectives into a single global mission, the framework keeps missions distinct and enables controlled sharing of robots across stakeholders with differing priorities and limited information exchange. The reservation layer overlays coarse time buckets on the planning horizon, allowing planners to specify time-phased mission quotas and enforce one-mission-per-robot commitments within each interval, all while preserving continuous task timing. This structure provides an operational control interface through which operators can adjust mission priorities over time without disclosing internal task details, enabling responsive, interpretable, and privacy-aware coordination. The results show that the proposed framework delivers feasible, continuous-time schedules that respect mission-level policies and achieve coordinated mission progress.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.10", "code": "WeA03.10", "title": "Lazy-pRRTC: Accelerating pRRTC with Coarse-To-Fine Collision Checking on GPU", "day": "Wednesday", "date": "August 26, 2026", "time": "10:35-10:40", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Lee, Ming-Hsiu", "affiliation": "Institute of Information Science, Academia Sinica" }, { "name": "Liu, Jing-Sin", "affiliation": "Academia Sinica" } ], "keywords": [ "Task and motion planning", "Autonomous navigation", "High-performance motion control systems" ], "abstract": "pRRTC is a GPU-accelerated RRT-Connect algorithm that uses parallelism for both sample expansion and collision detection. However, setting the sample size for discrete collision detection equal to the number of threads per block may not meet the finer collision resolution required by certain applications. In this paper, we leverage a lazy strategy to enhance the efficiency of pRRTC to mitigate the safety and discretization accuracy tradeoff set by default number of threads per block. Our approach reduces a significant number of fine collision detection by deferring fine full path collision check until after the initial path linking start and goal is generated by pRRTC with its default number of discretization. Simulations in environments with 35 randomly placed rectangular obstacles and walls with narrow passages show that in safety-aware fine discretization lazy-pRRTC achieves accurate tree extension with approximately 3× higher efficiency than its predecessor, pRRTC, and enables efficient waypoints generation for fast navigation in harder environments due to significantly fewer state collision checks.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.11", "code": "WeA03.11", "title": "Toward Certifiable Robotic Surgery Policy Via a Markov Decision Process Framework", "day": "Wednesday", "date": "August 26, 2026", "time": "10:40-10:45", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Zhong, Zhiyi", "affiliation": "The University of Hong Kong" }, { "name": "Lin, Lin", "affiliation": "Southern University of Science and Technology" }, { "name": "Dai, Jing", "affiliation": "The Chinese University of Hong Kong" }, { "name": "Lam, James", "affiliation": "Univ of Hong Kong" }, { "name": "Kwok, Ka Wai", "affiliation": "The Chinese University of Hong Kong" } ], "keywords": [ "Task and motion planning", "Autonomous navigation", "Robotic learning and adaptation" ], "abstract": "This paper introduces a certification framework that analyzes deep reinforcement learning policies used in autonomous surgical planning. Current learning-based controllers lack formal safety guarantees, and we address this by representing Deep Reinforcement Learning (DRL)-generated surgical plans as explicit Markov decision processes. First, the feasibility of a surgical plan is established by two conditions: absorption stability at the target state and finite-time reachability to it. After the feasibility assessment, a quantitative robustness index is derived from a reachability-layer decomposition. This index measures the resilience of the surgical plan when a single state transition is disrupted such as by tissue deformation. Finally, the theoretical approach has been implemented in an interactive visual interface for verification and evaluation. The effectiveness of this framework has been verified through an illustrative simulation on an ultrasound navigation task and identify the critical transitions required to reach the target position.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.12", "code": "WeA03.12", "title": "Teaching Learning Based GMPC Framework for Skid Steered Robot in Human Aware Environment", "day": "Wednesday", "date": "August 26, 2026", "time": "10:45-10:50", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Shekhar Sahasrabudhe, Kartik", "affiliation": "Robotics Innovation Lab, Department of Design and Manufacturing (DM), IISc" }, { "name": "Vijay Pawar, Aditya", "affiliation": "Robotics Innovation Lab, Department of Design and Manufacturing (DM), IISc" }, { "name": "K, Kalaivanan", "affiliation": "Indian Institute of Science (IISc)" }, { "name": "S, Sushmitha", "affiliation": "Robotics Innovation Lab, Department of Design and Manufacturing (DM), IISc" }, { "name": "Susri B S, Tharun", "affiliation": "Robotics Innovation Lab, Department of Design and Manufacturing (DM), IISc" }, { "name": "RoyChowdhury, Abhra", "affiliation": "Indian Institute of Science Bangalore" } ], "keywords": [ "Task and motion planning", "Autonomous navigation", "Robotic learning and adaptation" ], "abstract": "Bio inspired metaheuristic algorithms are optimization methods that mimic natural phenomena, biological evolution to solve complex problems. This paper proposes a hybrid navigation framework combining Teaching-Learning-Based optimization(TLBO) algorithm for Bézier curve path planning and Geometric Model Predictive Control (GMPC) for trajectory tracking in a dynamic environment, implemented on a skid-steered mobile robot. Experimental validation across 45 trials with varying obstacle configurations and human interaction scenarios demonstrates framework accuracy of 79.8%±2.1% in simulation and 70.7%±21.2% accuracy in real-time experiment with significant performance observed in dynamic human interaction scenarios.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.13", "code": "WeA03.13", "title": "A Full-State Constrained Real-Time Trajectory Planning Framework for Underactuated Overhead Cranes", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-10:55", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Xinghai, Xing", "affiliation": "Nankai University" }, { "name": "Lu, Biao", "affiliation": "Nankai University, Tianjin, China" }, { "name": "Zhi, Jiayi", "affiliation": "Nankai University" }, { "name": "Fang, Yongchun", "affiliation": "Nankai Univ" }, { "name": "Yang, Yan", "affiliation": "Xuzhou Heavy Machinery Co., Ltd" }, { "name": "Ding, Weili", "affiliation": "Yanshan University" } ], "keywords": [ "Task and motion planning", "Mechatronic system modeling, design, optimization", "High-performance motion control systems" ], "abstract": "Underactuated overhead cranes present significant challenges in trajectory planning due to their complex nonlinear dynamics, coupling effects between actuated and unactuated states, and the necessity of real-time feasibility. To bridge the gap between theoretical research and industrial application, this paper proposes a full-state constrained trajectory planning framework that ensures dynamic feasibility while maintaining real-time computational performance. The proposed method explicitly incorporates system dynamics and full-state constraints into the optimization process, enabling simultaneous regulation of both actuated and unactuated variables. A partial model simplification strategy is introduced to accelerate computation without sacrificing dynamic consistency, allowing real-time online trajectory generation. The framework also demonstrates robustness against modeling uncertainties and effectively balances multiple objectives, including obstacle avoidance, motion smoothness, and time efficiency. Extensive simulations and experimental validations on overhead crane systems verify the framework’s effectiveness, achieving dynamically feasible and smooth trajectories with precise control of unactuated variables under complex operating conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.14", "code": "WeA03.14", "title": "Coverage-Aware Viewpoint Refinement for Robotic Visual Inspection", "day": "Wednesday", "date": "August 26, 2026", "time": "10:55-11:00", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Staderini, Vanessa", "affiliation": "AIT Austrian Institute of Technology GmbH" }, { "name": "Alibekov, Ulugbek", "affiliation": "AIT Austrian Institute of Technology GmbH" }, { "name": "Glück, Tobias", "affiliation": "Austrian Institute of Technology" }, { "name": "Kugi, Andreas", "affiliation": "TU Wien" } ], "keywords": [ "Task and motion planning", "Robot perception and sensing", "Mechatronic system modeling, design, optimization" ], "abstract": "Automatic visual quality inspection is a critical application in modern manufacturing, leveraging robotics and computer vision to improve efficiency and precision. Previous methodologies often address the inspection challenge from a singular perspective of robotics or computer vision, which constrains the performance and generalization of the inspection performance. This work presents a new framework focused on refining the inspection pose (viewpoints) candidates to improve the overall coverage. This process integrates the sensor model, environment constraints for collision avoidance, the kinematics of the robotic system, and the model of the inspected object. The final inspection plan is computed by adopting a path planner to derive a collision-free trajectory and visit the viewpoints in the order obtained by solving the Traveling Salesman Problem. Our framework is extensively evaluated in simulation and compared to the state of the art, demonstrating superior performance in achieving extensive coverage. Real-world experiments are conducted to prove the effectiveness of our methods. In both cases, results are presented for different objects and two robotic setups: (i) a robot with 6-dof and (ii) a robotic system with 7-dof.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.15", "code": "WeA03.15", "title": "NMPC-Based Motion Planning with Adaptive Weighting for Dynamic Object Interception", "day": "Wednesday", "date": "August 26, 2026", "time": "11:00-11:05", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Cai, Chen", "affiliation": "University of Kaiserslautern" }, { "name": "Kohli, Saksham", "affiliation": "University of Kaiserslautern-Landau" }, { "name": "Liu, Steven", "affiliation": "University of Kaiserslautern Landau" } ], "keywords": [ "Task and motion planning", "Robotic grasping and manipulation" ], "abstract": "This paper presents a nonlinear Model Predictive Control (MPC) planner for dynamic object interception using cooperative manipulator systems under closed-chain constraints. We introduce an Adaptive-Terminal (AT) formulation that employs cost shaping to mitigate actuator power violations common in Primitive-Terminal (PT) approaches. Experimental validation on a physical platform demonstrates superior motion quality and robustness compared to the PT baseline. Crucially, the system exhibits excellent real-time performance, achieving an average computation time of 19ms -- less than half the 40 ms sampling interval. This establishes the framework's suitability for agile, safety-critical cooperative tasks.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.16", "code": "WeA03.16", "title": "Bridging Discrete Planning and Continuous Execution for Redundant Robot Manipulators", "day": "Wednesday", "date": "August 26, 2026", "time": "11:05-11:10", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Yan, Teng", "affiliation": "The Hong Kong University of Science and Technology" }, { "name": "Yu, Yue", "affiliation": "The Hong Kong University of Science and Technology(GUANGZHOU)" }, { "name": "Liu, Yihan", "affiliation": "The Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Zhong, Bingzhuo", "affiliation": "Hong Kong University of Science and Technology (Guangzhou)" } ], "keywords": [ "Task and motion planning", "Robotic grasping and manipulation", "AI-powered robotics" ], "abstract": "Voxel-grid reinforcement learning is commonly used for path planning in redundant manipulators due to its simplicity and reproducibility. However, direct execution through point-wise numerical inverse kinematics on 7-DoF arms often yields step-size jitter, abrupt joint transitions, and instability near singular configurations. This work proposes an offline bridging framework that enables smooth continuous execution without modifying the core discrete planning architecture. On the planning side, step-normalized 26-neighbour Cartesian actions with geometric tie-breaking reduce unnecessary turns and oscillations. On the execution side, a task-priority damped least-squares (TP-DLS) inverse kinematics (IK) solver ensures stable tracking through null-space posture regulation and joint centering under trust-region and velocity constraints. Experiments on a 7-DoF manipulator show that this bridge improves planning success in dense scenes from 0.58 to 1.00, shortens representative path length from 1.53 m to 1.10 m, and reduces peak joint accelerations by over an order of magnitude while maintaining sub-millimeter end-effector accuracy. These results demonstrate that discretely planned RL paths can be made reliably executable through principled integration with established IK techniques.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.17", "code": "WeA03.17", "title": "Parametric Identification of Linear Time-Periodic Systems in Observable Canonical Form", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:15", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Roshan Nahad, Aylar", "affiliation": "Middle East Technical University" }, { "name": "Ankarali, Mustafa Mert", "affiliation": "Middle East Technical University (METU)" } ], "keywords": [ "Time/parameter varying system identification", "Linear system identification" ], "abstract": "This paper introduces a non-iterative parametric identification algorithm for linear time-periodic (LTP) systems. The proposed method reduces the identification task to solving a set of linear equations and yields a state-space representation in the observable canonical form. This frequency-domain approach leverages periodic input test signals and enables model complexity reduction through truncation of the harmonic transfer functions. The resulting approach provides an efficient and structured framework for modeling LTP systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.18", "code": "WeA03.18", "title": "Recursive Identification of EIV-ARX Models for Time Varying SISO Processes", "day": "Wednesday", "date": "August 26, 2026", "time": "11:15-11:20", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Das, Deepanjhan", "affiliation": "Indian Institute of Technology Madras, India" }, { "name": "Narasimhan, Shankar", "affiliation": "Indian Institute of Technology, Madras, INDIA" } ], "keywords": [ "Time/parameter varying system identification", "Linear system identification" ], "abstract": "This paper proposes a recursive algorithm, rARX-DIPCA, for identifying errors-in-variables autoregressive models with exogenous input (EIV-ARX), for tracking time-varying SISO processes. Building on a recently developed recursive iterative PCA method, the proposed algorithm recursively updates model parameters and noise variances as new measurements arrive, without storing historical data beyond a specified lag window. The method enables real-time adaptation to sensor degradation, and changes in model coefficients. The algorithm simultaneously identifies process order, time delay, and noise variances while maintaining computational efficiency through online covariance updates. Simulation studies on benchmark systems demonstrate effective tracking performance and practical applicability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.19", "code": "WeA03.19", "title": "A Unified Framework for Identifying Floquet-Equivalent Models of Linear Discrete-Time Periodic Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "11:20-11:25", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Yilmaz, Onurcan", "affiliation": "Hacettepe University" }, { "name": "Sarıtaş, Serkan", "affiliation": "Middle East Technical University" }, { "name": "Ankarali, Mustafa Mert", "affiliation": "Middle East Technical University (METU)" }, { "name": "Uyanik, Ismail", "affiliation": "Hacettepe University" } ], "keywords": [ "Time/parameter varying system identification", "Linear system identification", "Data-driven control theory" ], "abstract": "This paper presents a data-driven framework for identifying linear discrete-time periodic (LDTP) systems and extracting their Floquet-equivalent models. Identification of LDTP systems is challenging due to periodically varying state-transition matrices, while Floquet reduction requires numerically sensitive matrix-root computations of the monodromy matrix. The proposed approach integrates an optimization-based estimator with a numerically robust Schur–Pad´e procedure for computing the principal P-th matrix root of the monodromy. A Monte Carlo study on randomly generated stable systems examines how system order, period length, and monodromy conditioning affect both identification accuracy and Floquet feasibility. The resulting workflow provides a reliable and systematic route for recovering periodic dynamics and their Floquet structure using only input–output data", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.20", "code": "WeA03.20", "title": "Efficient Learning of Affine and Rational Dependency LPV Models with Linear Fractional Representation", "day": "Wednesday", "date": "August 26, 2026", "time": "11:25-11:30", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Drenth, Roel", "affiliation": "Eindhoven University of Technology" }, { "name": "Hoekstra, Jan H.", "affiliation": "Eindhoven University of Technology" }, { "name": "Schoukens, Maarten", "affiliation": "Eindhoven University of Technology" }, { "name": "Tóth, Roland", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Time/parameter varying system identification", "Nonlinear system identification", "Machine and deep learning for system identification" ], "abstract": "Identifying control-friendly models of nonlinear systems remains one of the major challenges at the intersection of system identification and control. The Linear Parameter-Varying (LPV) framework offers a promising solution, but existing identification methods often rely on model structures with affine scheduling dependency. Instead, this work proposes the use of LPV models with Linear Fractional Representation (LFR) admitting a rational scheduling-dependency, capable of modelling complex nonlinear systems with fewer scheduling variables compared to affine models. This work introduces a direct parameterization to ensure well-posedness of rational LPV-LFR models, which by joint-estimation of an LPV plant and scheduling map, using only input-output data, is capable of modelling complex nonlinear systems. Accuracy of the proposed approach is shown on two simulation examples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.21", "code": "WeA03.21", "title": "Design and Control of an Asymmetric-Torque Exoskeleton for Gait Rehabilitation in Hemiparetic Patients", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:35", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Cho, Kwonseung", "affiliation": "Gwangju Institute of Science and Technology" }, { "name": "Moon, Sunwoong", "affiliation": "GIST" }, { "name": "Cha, MyeongJu", "affiliation": "Gwangju Institute of Science and Technology" }, { "name": "Sung, Jiyoon", "affiliation": "Gwangju Institute of Science and Technology" }, { "name": "Kim, Kyunghwan", "affiliation": "NT Research Inc" }, { "name": "Hur, Pilwon", "affiliation": "Gwangju Institute of Science and Technology" } ], "keywords": [ "Wearable robotics", "Human-robot interaction", "Humanoid and legged robots" ], "abstract": "This work introduces RoboWear21, an asymmetric lower-limb exoskeleton developed to accommodate the differing mechanical demands of paretic and non-paretic limbs. The device integrates side-specific actuators, passive hip DOFs, and a hierarchical controller combining gravity compensation, disturbance observer, and gait-phase-dependent torque generation. Gait state is estimated through an IMU-based swing detection scheme and an adaptive oscillator that maps hip motion to a continuous phase variable. Bench and user tests with three healthy participants demonstrated joint tracking RMSE up to 2.177°, phase estimation with an overall RMSE of 1.191 ± 0.894% ( R 2 = 0.997 ± 0.002), and gravity-compensation deviations within 0.022°, suggesting the system's suitability for individualized assistance in hemiparetic gait rehabilitation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.22", "code": "WeA03.22", "title": "Thigh-Angle–Only Gait Phase Recognition Via LSTM for Normal and Asymmetric Walking", "day": "Wednesday", "date": "August 26, 2026", "time": "11:35-11:40", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Koo, Seonmin", "affiliation": "Sangmyung University" }, { "name": "Jo, Jung-Hee", "affiliation": "Sangmyung University" }, { "name": "Choi, Hyunjin", "affiliation": "Sangmyung University" } ], "keywords": [ "Wearable robotics", "Medical and rehabilitation robotics", "Human-robot interaction" ], "abstract": "Hip-assistive wearable robots are lightweight, portable, and easy to use, but they typically lack foot-mounted sensors, making accurate identification of gait events particularly challenging in asymmetric or pathological gait. Existing approaches have either relied on additional shoe sensors or have been validated only on healthy users, limiting their applicability in sensor-minimal configurations and abnormal walking conditions. This study proposes an LSTM–based stance and swing state recognition framework using only absolute thigh angle signals obtained from a hip-assistive wearable robot. In the implemented bilateral configuration, left and right thigh-angle sequences are processed by limb-specific LSTM encoders and fused to predict stance and swing states for both limbs. Experiments on normal walking and hemiplegic-like asymmetric gait achieved approximately 87% accuracy without using foot sensors as model inputs. The full estimation pipeline was further implemented in a pseudo-online and real-time setting, demonstrating its feasibility for embedded execution on wearable robots.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA03.23", "code": "WeA03.23", "title": "Task-Aware Predictors of Visual Reliability in Underwater Robotics", "day": "Wednesday", "date": "August 26, 2026", "time": "11:40-11:45", "sessionCode": "WeA03", "sessionTitle": "Shotgun: Systems and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Rohan, Ali", "affiliation": "University of Aberdeen" }, { "name": "Njuguna, James", "affiliation": "Robert Gordon University" }, { "name": "Shayan, Hassan", "affiliation": "Kunsan National University" }, { "name": "Kim, Sun Young", "affiliation": "Kunsan National University" }, { "name": "Jo, Han-Gue", "affiliation": "Kunsan National University" } ], "keywords": [ "Autonomous marine systems and vehicles", "Marine robotics", "Perception and filtering in marine systems" ], "abstract": "Underwater robotic perception degrades with colour loss, reduced contrast, and backscatter, yet it remains unclear which cues best signal when performance will hold. We analyse matched in-air/underwater data across lighting, range, and scene complexity, relating condition-wise appearance changes to downstream robustness. A consistent pattern emerges: preserved red–green colour balance is the strongest positive indicator of reliability, while losses of green-band detail and brightness are the most reliable warnings of failure. These task-aware indicators explain most variability across conditions and provide a compact signal for onboard health monitoring, adaptive planning, and mission triage in challenging underwater environments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.1", "code": "WeA04.1", "title": "Hybrid-State MFG Approach to Decentralized Charging Strategy Design for Large Populations of EVs", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-09:55", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Guo, Wanying", "affiliation": "Dalian University of Technology" }, { "name": "Zhang, Yuexi", "affiliation": "Dalian University of Technology" }, { "name": "Shen, Tielong", "affiliation": "Dalian University of Technology" } ], "keywords": [ "Optimal control theory", "Applications of optimal control", "Differential or dynamic games" ], "abstract": "This paper investigates the energy management problem for large populations of electric vehicles (EVs) with finite-continuous hybrid states. First, a novel model is proposed that integrates continuous state of charge and discrete events triggered by on-off charging mode switches. Then, a hierarchical optimization framework is developed to cope with the hybrid system. In this framework, the upper level, managed by the grid operator, achieves macroscopic load balancing for large populations of EVs by optimizing the finite state transition rates; the lower level involves decentralized decision-making, where individual EVs adjust their charging power to optimize their respective objectives. Given the analytical challenges posed by large-scale EVs charging behaviors, this paper formulates the coordination problem of the EV population as a mean-field game (MFG), where its equilibrium solution is characterized by two coupled sets of Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck (FP) equations. Compared to conventional HJB-FP equations, these equations incorporate additional terms associated with the finite state transition behavior. Furthermore, theoretical analysis shows that the MFG provides an varepsilon-Nash equilibrium for a finite number of EVs. Finally, an efficient numerical solution is illustrated for the optimal control problem, and simulation results demonstrate the effectiveness of the proposed framework and methodology.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.2", "code": "WeA04.2", "title": "Control of a Nitrogen-Vacancy Center As a Two-Qubit System", "day": "Wednesday", "date": "August 26, 2026", "time": "09:55-10:00", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Da Silva Tinoco, David", "affiliation": "INRIA" }, { "name": "Babin, Charles", "affiliation": "Université Bourgogne Europe" }, { "name": "Beschastnyi, Ivan", "affiliation": "Inria Centre d'Université Côte D'Azur" }, { "name": "Caillau, Jean Baptiste", "affiliation": "Université Côte d'Azur, CNRS, Inria, LJAD" }, { "name": "Sugny, Dominique", "affiliation": "University of Bourgogne" } ], "keywords": [ "Optimal control theory", "Applications of optimal control", "Numerical methods for optimal control" ], "abstract": "Nitrogen-vacancy (NV) centers are promising experimental platforms for quantum information processing. In this paper, we investigate their controllability and fundamental quantum speed limit for two-qubit gates. Such a quantum system consists of two coupled spins, an electronic and a nuclear spin, where only the former can be controlled directly via microwave pulses. We discuss the various physical approximations that lead to the system model before studying its controllability. We use this control issue as an example to demonstrate how standard geometric control tools can be applied to spin networks. We complete this analysis with a computation of the quantum speed limit using known analytical techniques on Lie groups and their algebras. We finally demonstrate, thanks to preliminary optimal control numerical experiments, that this limit can be approached while keeping a reasonable energy of the control field.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.3", "code": "WeA04.3", "title": "Basis Pursuit -- a Systems Viewpoint", "day": "Wednesday", "date": "August 26, 2026", "time": "10:00-10:05", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Marmary, Maya Vered", "affiliation": "Technion" }, { "name": "Grussler, Christian", "affiliation": "Technion - Israel Institute of Technology" } ], "keywords": [ "Optimal control theory", "Linear systems" ], "abstract": "Discrete-time minimum ell_1-norm has often been suggested as a solution for sparse optimal control of linear time-invariant systems. Unlike the continuous-time case, where controllability is guaranteed to provide the sparsest solution, this is no longer true in discrete-time. We propose a deterministic understanding of failure cases, leveraging the framework of total positivity to derive conditions under which the sparsest solution cannot be recovered. Thus, providing insights into the a priori design of sparse optimal control problems, as well as some more general compressed sensing settings, explaining why such failure is to be predicted.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.4", "code": "WeA04.4", "title": "Indirect Methods in Optimal Control on Banach Spaces", "day": "Wednesday", "date": "August 26, 2026", "time": "10:05-10:10", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Chertovskih, Roman", "affiliation": "Porto University" }, { "name": "Pogodaev, Nikolay", "affiliation": "Matrosov Institute for System Dynamics and Control Theory of Siberian Branch of Russian Academy of Sciences" }, { "name": "Staritsyn, Maxim", "affiliation": "Faculdade De Engenharia, Universidade Do Porto, Porto, Portugal" }, { "name": "Aguiar, A. Pedro", "affiliation": "Faculty of Engineering, University of Porto (FEUP)" } ], "keywords": [ "Optimal control theory", "Optimal control of PDE systems", "Control of distributed parameter systems" ], "abstract": "This work focuses on indirect descent methods for optimal control problems governed by nonlinear ordinary differential equations in Banach spaces, viewed as abstract models of distributed dynamics. As a reference line, we revisit the classical schemes, rooted in Pontryagin’s maximum principle, and highlight their sensitivity to local convexity and line-search procedures. We then develop an alternative method based on exact cost-increment formulas and finite-difference probes of the terminal cost. Numerical results for an Amari-type neural field illustrate monotone decrease of the cost, obtained without solving the adjoint equation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.5", "code": "WeA04.5", "title": "Geometry of Extremals Emerging from a Local Stable Manifold with and without Conjugate Points", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:15", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Oki, Takafumi", "affiliation": "Tokyo Denki University" }, { "name": "Otsuka, Naohisa", "affiliation": "Tokyo Denki Univ" }, { "name": "Wada, Shigeo", "affiliation": "Graduate School of Engineering, Tokyo Denki University" } ], "keywords": [ "Optimal control theory", "Stability of nonlinear systems", "Lagrangian and Hamiltonian systems" ], "abstract": "This paper revisits the infinite-horizon optimal control (IOC) problem from the perspective of a family of extremals emanating from the local stable manifold of the associated Hamiltonian system. We analyze conditions under which these extremals—parameterized by their root points on the manifold—form a Lagrangian submanifold, thereby yielding a stabilizing solution to the Hamilton–Jacobi–Bellman equation (HJBE). We further investigate how the emergence of conjugate points—instances where the Riccati differential equation along an extremal blows up—destroys this geometric structure. Additionally, we explore the connection between conjugate points and the uniqueness of solutions to a two-point boundary value problem (BVP) that incorporates the local stable manifold as a terminal condition. This BVP facilitates the generation of neighboring extremals around a reference extremal. Numerical examples using a cart-inverted-pendulum system illustrate these geometric properties through families of extremals corresponding to swing-up maneuvers and extremals exhibiting conjugate points that break the embedded submanifold structure.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.6", "code": "WeA04.6", "title": "An Error Bound for Aggregation in Approximate Dynamic Programming", "day": "Wednesday", "date": "August 26, 2026", "time": "10:15-10:20", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Li, Yuchao", "affiliation": "Arizona State University" }, { "name": "Bertsekas, Dimitri P.", "affiliation": "Massachusetts Inst. of Tech" } ], "keywords": [ "Optimal control theory", "Stochastic optimal control problems", "Numerical methods for optimal control" ], "abstract": "We consider a general aggregation framework for discounted finite-state infinite horizon dynamic programming (DP) problems. It defines an aggregate problem whose optimal cost function can be obtained off-line by exact DP and then used as a terminal cost approximation for an on-line reinforcement learning (RL) scheme. We derive a bound on the error between the optimal cost functions of the aggregate problem and the original problem. This bound was first derived by Tsitsiklis and van Roy [TvR96] for the special case of hard aggregation. Our bound is similar but applies far more broadly, including to soft aggregation and feature-based aggregation schemes.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.7", "code": "WeA04.7", "title": "Two-Point Random Gradient-Free Methods for Model-Free Feedback Optimization", "day": "Wednesday", "date": "August 26, 2026", "time": "10:20-10:25", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Mehrnoosh, Amir", "affiliation": "Universite Catholique De Louvain" }, { "name": "Bianchin, Gianluca", "affiliation": "Université Catholique De Louvain" } ], "keywords": [ "Optimization-based estimation and control", "Design methods for data-based control", "Real-time optimal control" ], "abstract": "Feedback optimization steers the steady-state operation of dynamical systems to optimal operating points. However, most existing methods still require exact knowledge of the plant dynamics, which is rarely available in practice. In this paper, we introduce a randomized two-point gradient-free feedback optimization method inspired by zeroth-order optimization. Our controller evaluates plant performance at two points to estimate gradients and update control inputs in real-time. For problems with smooth, nonconvex objectives, our method achieves convergence to an ε-stationary point with iteration complexity O(pε-1), where p denotes the dimension of the input vector, thereby recovering the best-known bounds for static two-point optimization. Numerical simulations support the theoretical results.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.8", "code": "WeA04.8", "title": "Command Governor for Switched Linear Systems with Arbitrary Switching", "day": "Wednesday", "date": "August 26, 2026", "time": "10:25-10:30", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Nguyen, Hoai-Nam", "affiliation": "Telecom SudParis" } ], "keywords": [ "Optimization-based estimation and control", "Nonlinear control of switched & hybrid systems", "Control of hybrid systems" ], "abstract": "This paper proposes a new command governor (CG) scheme for the tracking of discrete-time switched linear systems subject to input and state constraints. The approach leverages a novel class of admissible sets, termed switch-dependent semi-ellipsoidal admissible sets, which exploit available information on the switching signal. These sets enable the design of a recursively feasible CG that guarantees closed-loop constraint satisfaction. The proposed approach is demonstrated through a numerical example.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.9", "code": "WeA04.9", "title": "Optimal Sensor Placement for Output Estimation Using an Artificial Bee Colony Algorithm with Pre-Filter", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:35", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Goetz, Raphael", "affiliation": "Eindhoven University of Technology, the Netherlands" }, { "name": "Dwaraga, Yuvan", "affiliation": "Eindhoven University of Technology" }, { "name": "van de Wouw, Nathan", "affiliation": "Eindhoven Univ of Technology" }, { "name": "Oomen, Tom", "affiliation": "Eindhoven University of Technology" }, { "name": "van de Wal, Marc", "affiliation": "ASML" }, { "name": "Sharif, Bardia", "affiliation": "Eindhoven University of Technology" }, { "name": "Zwart, Hans", "affiliation": "University of Twente" } ], "keywords": [ "Optimization-based estimation and control", "Observer design", "Linear systems" ], "abstract": "Sensor placement for maximizing the estimation performance of the Kalman filter is an NP-hard optimization problem. Furthermore, its feasible set grows combinatorially with the candidate locations and the number of sensors. In this paper, we study this sensor placement problem for a 3D thermoelastic system modelled as a discrete-time linear stochastic model. We use the Novel Binary Artificial Bee Colony (NBABC) algorithm with a Gramian-based pre-filter to reduce the computational complexity. Our results show the efficiency and the fast convergence of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.10", "code": "WeA04.10", "title": "Learning to Accelerate Krasnosel'skii–Mann Fixed-Point Iterations with Guarantees", "day": "Wednesday", "date": "August 26, 2026", "time": "10:35-10:40", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Martin, Andrea", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Belgioioso, Giuseppe", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Parametric optimization", "Convex optimization", "Large-scale and networked optimization problems" ], "abstract": "We introduce a principled learning to optimize (L2O) framework for solving fixed-point problems involving general nonexpansive mappings. Our idea is to deliberately inject summable perturbations into a standard Krasnosel'skii–Mann iteration to improve its average-case performance over a specific distribution of problems while retaining its convergence guarantees. Under a metric sub-regularity assumption, we prove that the proposed parametrization includes only iterations that locally achieve linear convergence—up to a vanishing bias term—and that it encompasses all iterations that do so at a sufficiently fast rate. We then demonstrate how our framework can be used to augment several widely-used operator splitting methods to accelerate the solution of structured monotone inclusion problems, and validate our approach on a best approximation problem using an L2O-augmented Douglas–Rachford splitting algorithm.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.11", "code": "WeA04.11", "title": "Wave-BO: Waveform-Aware Bayesian Optimization for Sample-Efficient Trajectory Shaping", "day": "Wednesday", "date": "August 26, 2026", "time": "10:40-10:45", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Asaki, Kyosuke", "affiliation": "Mitsubishi Electric Corporation" }, { "name": "Ito, Rin", "affiliation": "Mitsubishi Electric Corporation" }, { "name": "Takano, Naoto", "affiliation": "Mitsubishi Electric Corporation" }, { "name": "Masui, Hideyuki", "affiliation": "Mitsubishi Electric Corporation" }, { "name": "Akaho, Shotaro", "affiliation": "National Institute of Advanced Industrial Science and Technology" }, { "name": "Hirayama, Junichiro", "affiliation": "National Institute of Advanced Industrial Science and Technology" }, { "name": "Kanemura, Atsunori", "affiliation": "National Institute of Advanced Industrial Science and Technology" }, { "name": "Asoh, Hideki", "affiliation": "National Institute of Advanced Industrial Science and Technology" } ], "keywords": [ "Parametric optimization", "Design methods for data-based control", "Optimization-based estimation and control" ], "abstract": "High-precision positioning in manufacturing equipment requires fast settling with minimal vibration. The asymmetric S-curve (AS-curve) is a jerk-limited trajectory that enables high speed and precision, but its many tuning parameters make adjustment difficult. Bayesian Optimization (BO) is a well-established sample-efficient optimization method, but its performance can be improved by exploiting information closely related to control performance. We propose waveform-aware BO for sample-efficient AS-curve shaping. A Gaussian process regression (GPR) incorporating a distance metric between command waveforms yields an accurate model with few evaluations and accelerates BO convergence. Experimental results on a real-world setup demonstrate equivalent tuning using only 15% of the trials required by conventional BO.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.12", "code": "WeA04.12", "title": "Parametric Model Reduction for H2 Design Optimization", "day": "Wednesday", "date": "August 26, 2026", "time": "10:45-10:50", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Boksebeld, Niek Herman Jan", "affiliation": "Eindhoven University of Technology" }, { "name": "Terzin, Bogoljub", "affiliation": "Eindhoven University of Technology" }, { "name": "Weiland, Siep", "affiliation": "Eindhoven Univ. of Tech" } ], "keywords": [ "Parametric optimization", "Model reduction of distributed parameter systems" ], "abstract": "This paper addresses the problem of model reduction for parameter dependent systems where the reduction criterion expresses a design objective for the parameter dependent system. Specifically, we develop a reduction method for systems that are required to meet an explicit guarantee on the H 2 approximation error with respect to a design objective. This guarantee is combined with efficiency improvements on the reduction scheme and an error estimation. The performance of the method is illustrated on a thermal design optimization problem. Results indicate superior computational efficiency compared to classical methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.13", "code": "WeA04.13", "title": "Distributed Online Estimation with Momentum and Randomized Perturbations under Heavy-Tailed Noise and Dynamic Functional Drift", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-10:55", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Akinfiev, Ivan", "affiliation": "Saint Petersburg State University" }, { "name": "Tarasova, Elizaveta", "affiliation": "Saint Petersburg State University" }, { "name": "Salishev, Sergey", "affiliation": "St. Petersburg State University" }, { "name": "Granichina, Olga", "affiliation": "St. Petersburg State University" } ], "keywords": [ "Randomized algorithms in robust control", "Distributed parameters port Hamiltonian systems", "Robust estimation" ], "abstract": "This work addresses the problem of distributed online estimation in a dynamic and potentially heavy-tailed environment. The proposed distributed stochastic approximation algorithm incorporates momentum and operates under H¨older smoothness, Lyapunov strong convexity, functional drift, and sparse structural shifts. Synthetic tests on a drifting multi- dimensional Rosenbrock function with heavy-tailed noise confirm bounded tracking error and rapid recovery following abrupt changes. Equity market experiments further validate the method, yielding stable estimates for portfolio risk management and intraday mean-reversion strategies.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.14", "code": "WeA04.14", "title": "Reactive Planning Based Control for Mobile Robots in Obstacle-Cluttered Environments", "day": "Wednesday", "date": "August 26, 2026", "time": "10:55-11:00", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Tan, Li", "affiliation": "University of Science and Technology of China" }, { "name": "Xiong, Junlin", "affiliation": "University of Science and Technology of China" }, { "name": "Wang, Yan", "affiliation": "Harbin Institute of Technology" }, { "name": "Ren, Wei", "affiliation": "Dalian University of Technology" } ], "keywords": [ "Real-time optimal control", "Control barrier functions and state space constraints", "Adaptive control design" ], "abstract": "This paper addresses the motion control problem for mobile robots in obstacle-cluttered environments. The mobile robot has partial environment information only, and aims to move from an initial position to a target position without collisions. For this purpose, a reactive planning based control strategy (RPCS) is proposed. First, the initial and target positions are connected as a reference trajectory. Then, a reactive planning strategy (RPS) is developed to ensure the collision avoidance by modifying the reference trajectory locally based on the partial environment information. Next, an adaptive tracking control strategy (ATCS) is proposed to track the reference trajectory with potentially local modifications via the discretization techniques. Finally, the RPS and ATCS are combined to establish the RPCS, whose efficacy and advantages are illustrated by numerical examples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.15", "code": "WeA04.15", "title": "Trajectory Optimization by Pseudospectral Successive Convexification on Riemannian Manifolds", "day": "Wednesday", "date": "August 26, 2026", "time": "11:00-11:05", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Narumi, Tatsuya", "affiliation": "Tokyo University" }, { "name": "Sakai, Shin-ichiro", "affiliation": "Japan Aerospace Exploration Agency" } ], "keywords": [ "Real-time optimal control", "Optimal control theory", "Convex optimization" ], "abstract": "This paper proposes an intrinsic pseudospectral convexification framework for optimal control problems with manifold constraints. While pseudospectral successive convexification combines spectral collocation with successive convexification, classical pseudospectral methods are not geometry-consistent on manifolds. This is because interpolation and differentiation are performed in Euclidean coordinates. We introduce a geometry-consistent transcription that enables pseudospectral collocation without imposing manifold constraints extrinsically. The resulting method solves nonconvex manifold-constrained problems through a sequence of convex subproblems. A six-degree-of-freedom landing guidance example with unit quaternions and unit direction vectors demonstrates the practicality of the approach. The proposed method preserves manifold feasibility to machine precision and achieves significant computational speedups.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.16", "code": "WeA04.16", "title": "Strongly Alpha-Stabilizing Plug-In Tracking Controller Synthesis with Application to Magnetic Levitation System", "day": "Wednesday", "date": "August 26, 2026", "time": "11:05-11:10", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Lin, Yu-Jen", "affiliation": "National Sun Yat-Sen University" }, { "name": "Kao, Chung-Yao", "affiliation": "National Sun Yat-Sen University" }, { "name": "Khong, Sei Zhen", "affiliation": "National Sun Yat-Sen University" }, { "name": "Hara, Shinji", "affiliation": "Tokyo Institute of Technology" } ], "keywords": [ "Robust controller synthesis", "Analytic design", "Linear systems" ], "abstract": "This paper presents a stable plug-in controller design that improves the closed-loop performance of pre-stabilized single-input-single-output (SISO) linear time-invariant (LTI) systems without sacrificing inherent robustness. To ensure both controller stability and desired pole placement, the problem is reformulated via an s-domain transformation psi(s) = s - alpha (alpha > 0). This shifts the stability boundary, rendering the original system virtually unstable and converting the design into a strong stabilization problem. By analytically solving the transformed system and applying an inverse shift, the proposed non-iterative approach yields low-order controllers. Experimental validation on a magnetic levitation system demonstrates significantly improved tracking and leftward-shifted poles compared to a standalone proportional-integral-derivative controller.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.17", "code": "WeA04.17", "title": "Second-Order Hybrid Integrator-Gain System", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:15", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Weise, Christoph", "affiliation": "TU Ilmenau" }, { "name": "Wulff, Kai", "affiliation": "TU Ilmenau" }, { "name": "Hosseini, Ali", "affiliation": "TU Delft" }, { "name": "HosseinNia, S Hassan", "affiliation": "Delft University of Technology" }, { "name": "Reger, Johann", "affiliation": "TU Ilmenau" } ], "keywords": [ "Robust controller synthesis", "Switching stability and control" ], "abstract": "We introduce a second-order version of the hybrid integrator-gain system (HIGS). In the proportional mode the second state is either reset to zero or tracks the input. We derive a method for computing the describing function and higher-order harmonics in terms of a matrix exponential. In comparison to the HIGS the new element shows the amplitude response of a second order system whereas the phase drops to approximately −52°. Using a sector transformation we can show that the second-order HIGS is passive, which allows for a conservative circle-criterion-like condition to test for closed-loop stability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.18", "code": "WeA04.18", "title": "A Proportional-Integral Equivalent-Input-Disturbance Method for Enhanced Disturbance Rejection in Generalized Repetitive-Control Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "11:15-11:20", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Zhang, Manli", "affiliation": "Wuhan University of Science and Technology" }, { "name": "Lu, Shaowu", "affiliation": "Wuhan University of Science and Technology" }, { "name": "Xie, Mingyuan", "affiliation": "Huazhong University of Science and Technology" }, { "name": "She, Jinhua", "affiliation": "Tokyo Univ. of Tech" }, { "name": "Wu, Min", "affiliation": "China University of Geosciences" } ], "keywords": [ "Robust estimation", "Learning methods for optimal control", "Linear time-delay systems" ], "abstract": "This paper presents a generalized repetitive-control (GRC) framework that achieves both precise tracking of periodic signals and suppression of aperiodic disturbances. The relationship between the ideal periodic internal model and the GRC structure is analyzed. Based on this analysis, a second-order Butterworth filter and a time-delay parameter are designed to ensure accurate steady-state tracking. In addition, the inherent limitation of the conventional equivalent-input-disturbance (EID) estimator is identified. The conventional EID estimator behaves as an integrator and therefore responds slowly to disturbances. To overcome this problem, a proportional-integral EID (PI-EID) estimator is developed. The new PI-EID estimator provides fast disturbance compensation while maintaining high estimation accuracy. The stability of the control system is guaranteed. Simulation results demonstrate that the proposed method significantly improves steady-state tracking accuracy when compared with modified repetitive control and complex-coefficient-filter-based repetitive control. The proposed method also achieves superior transient and steady-state disturbance rejection when compared with the conventional EID method and the improved EID method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.19", "code": "WeA04.19", "title": "Robust High-Gain Consensus Control for Delayed Multi-Agent Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "11:20-11:25", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Panin, Aleksandr", "affiliation": "ITMO University" }, { "name": "Tomashevich, Stanislav", "affiliation": "IPME RAS; ITMO University" }, { "name": "Borisov, Oleg", "affiliation": "ITMO University" }, { "name": "Bobtsov, Alexey", "affiliation": "ITMO University" } ], "keywords": [ "Robust time-delay systems", "Decentralized control", "Analytic design" ], "abstract": "This paper addresses the consensus problem for linear multi-agent systems with heterogeneous time-varying communication delays. Existing delay-dependent approaches based on Lyapunov--Krasovskii functionals and LMIs often suffer from high computational complexity and limited analytical insight. To overcome these limitations, an explicit modal decomposition framework is developed that exploits the Laplacian eigenstructure to decouple the network dynamics into independent subsystems. For each mode, delay-dependent stability conditions are derived in closed algebraic form using Sylvester’s criterion, enabling direct characterization of admissible delays and controller gains without numerical optimization. For agents with arbitrary relative degree, a dynamic high-gain controller is introduced to ensure simultaneous stabilization of all nonzero Laplacian modes under slowly varying heterogeneous delays. The proposed approach provides scalable and analytically tractable stability conditions that explicitly reveal the influence of network topology on delay robustness. Numerical examples demonstrate convergence to consensus and bounded control effort under time-varying delays.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.20", "code": "WeA04.20", "title": "Linear Quadratic Problem for Systems with Unknown Random State Delay", "day": "Wednesday", "date": "August 26, 2026", "time": "11:25-11:30", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Odorico, Elizandra Karla", "affiliation": "University of São Paulo" }, { "name": "Terra, Marco Henrique", "affiliation": "Depto. Engenharia Elétrica - Escola De Engenharia De São Carlos" } ], "keywords": [ "Robust time-delay systems", "Robust controller synthesis", "Control of hybrid systems" ], "abstract": "This paper develops a recursive solution to the state-feedback control problem for linear discrete-time systems with unknown random state delays and norm-bounded parametric uncertainties. It is assumed that the rate of variation between consecutive delays is bounded, and an unobserved Markov chain is used to model stochastic delay behavior. By employing the lifting technique, the original state-delayed system is converted into an equivalent delay-free Markovian jump linear system formulation. Leveraging this framework, an optimization problem is formulated that accounts for the impact of delayed state while simultaneously accommodating worst-case uncertainties. The stabilizing gains are then obtained via recursive Riccati equations, which establish standard conditions for stability and convergence. The performance of the proposed robust regulator is illustrated using a model of an F-16 aircraft. We present a comparative study using robust H_{infty} state-feedback controllers to demonstrate the effectiveness of the developed recursive regulator.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.21", "code": "WeA04.21", "title": "Tube-Based Stability Analysis of Lyapunov Redesign Model-Following Control for Trajectory Tracking with Unbounded Perturbations", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:35", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Tietze, Niclas", "affiliation": "Technische Universität Ilmenau" }, { "name": "Wulff, Kai", "affiliation": "TU Ilmenau" }, { "name": "Reger, Johann", "affiliation": "TU Ilmenau" } ], "keywords": [ "Robustness analysis", "Controller constraints and structure", "Stability of nonlinear systems" ], "abstract": "For a nonlinear system in Byrnes-Isidori form, subject to unbounded perturbations, i.e. perturbationsthat satisfy a given bound only locally on a subset of the state space, we apply the continuous approximation of Lyapunov redesign within the feedback linearisation model-following control (MFC) scheme for trajectory tracking. We establish practical tracking by generalising a tube-based stability analysis proposed for single-loop control to MFC. Conceptually, we exploit that the Lyapunov function used for the Lyapunov redesign satisfies a differential inequality, thereby guaranteeing that the solution of the perturbed closed loop remains in a tube along the a-priori known solution of the model simulated in the model control loop.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.22", "code": "WeA04.22", "title": "Parametric Quadratic Stabilizability of Bimodal Piecewise Affine Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "11:35-11:40", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Zhang, Mengxuan", "affiliation": "The University of Osaka" }, { "name": "Fujisaki, Yasumasa", "affiliation": "The University of Osaka" } ], "keywords": [ "Robustness analysis", "Robust controller synthesis", "Robust linear matrix inequalities" ], "abstract": "This paper develops a linear matrix inequality (LMI) condition for the parametric quadratic stabilizability of bimodal piecewise linear systems under affine state feedback. The affine reference input induces equilibrium migration across switching regions. The proposed condition guarantees the existence and uniqueness of the equilibrium point together with quadratic stability of the closed-loop system.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.23", "code": "WeA04.23", "title": "Efficient Robustness Analysis Along a Trajectory with Uncertain Initial Conditions", "day": "Wednesday", "date": "August 26, 2026", "time": "11:40-11:45", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Robens, Johannes", "affiliation": "German Aerospace Center DLR-RM" }, { "name": "Pfifer, Harald", "affiliation": "Technische Universität Dresden" } ], "keywords": [ "Robustness analysis", "Uncertain systems", "Linear systems" ], "abstract": "Robustness analysis of uncertain nonlinear systems is often dominated by computationally expensive Monte-Carlo simulations, motivating the development of alternative approaches, including deterministic methods for worst-case assessment. An efficient solution approach is developed for a finite-horizon robustness analysis method that is based on a linear time-varying model along a nominal trajectory with quadratic constraints capturing nonlinear effects. The method leverages a transformed Riccati differential equation formulation with analytically optimized time-varying parameters to reduce computational complexity. Local quadratic constraints are iteratively refined using sparse grids. Application to Huygens' atmospheric entry flight demonstrates accurate estimation of worst-case bounds with moderate conservatism.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA04.24", "code": "WeA04.24", "title": "Convergence Rate Comparison of PI and VI Algorithms to Stochastic LQR Problems", "day": "Wednesday", "date": "August 26, 2026", "time": "11:45-11:50", "sessionCode": "WeA04", "sessionTitle": "Shotgun: Design Methods in Control Systems III", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Wang, Dong", "affiliation": "Shandong University of Science and Technology" }, { "name": "Li, Zonghan", "affiliation": "Shandong University of Science and Technology" }, { "name": "Xin, Yanyi", "affiliation": "Shandong University of Science and Technology" }, { "name": "Zhang, Weihai", "affiliation": "Shandong University of Science and Technology" }, { "name": "Wei, Wei", "affiliation": "Shandong University of Science and Technology" } ], "keywords": [ "Stochastic optimal control problems", "Learning methods for optimal control", "Optimal control theory" ], "abstract": "This paper investigates the static output feedback control problem for linear quadratic regulation (LQR) in discrete-time stochastic systems with state- and control\u0002dependent noises. To solve the stochastic LQR problem, policy iteration (PI) and value iteration (VI) algorithms are provided. Furthermore, via the provided intermediate matrix technique, a comparative analysis of the convergence rates for the given PI and VI algorithms is presented, along with a detailed proof. Finally, simulation examples of the F-16 aircraft model are conducted to verify the effectiveness of the proposed algorithms and the validity of the relevant theories.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA05.1", "code": "WeA05.1", "title": "Design of Active Vibration Absorbers for Underactuated Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:05", "sessionCode": "WeA05", "sessionTitle": "LB: Control Applications", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Anderle, Milan", "affiliation": "Institute of Information Theory and Automation of the CAS" }, { "name": "Celikovsky, Sergej", "affiliation": "Institute of Information Theory and Automation of the Czech Academy of Sciences" }, { "name": "Rehak, Branislav", "affiliation": "The Czech Academy of Sciences, Institute of Information Theory and Automation" } ], "keywords": [ "Application of nonlinear analysis and design" ], "abstract": "A design procedure of active vibration absorbers for underactuated systems is presented. The control design is based on the exact feedback linearization of the system, with the primary structure being the linearizable part. The paper focuses on two systems: the 4-link with two actuators and the Acrobot with two vibration absorbers. The results are demonstrated using two examples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA05.2", "code": "WeA05.2", "title": "Hierarchical Covariance Steering Control for Multi-Scale Thermal Environments", "day": "Wednesday", "date": "August 26, 2026", "time": "10:05-10:20", "sessionCode": "WeA05", "sessionTitle": "LB: Control Applications", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Takase, Takuya", "affiliation": "Institute of Science Tokyo" }, { "name": "Tsubakino, Daisuke", "affiliation": "Nagoya University" }, { "name": "Hara, Shinji", "affiliation": "Institute of Science Tokyo" }, { "name": "Onishi, Ryo", "affiliation": "Tokyo Institute of Technology" } ], "keywords": [ "Applications of optimal control", "Numerical methods for optimal control", "Stochastic optimal control problems" ], "abstract": "Multi-scale thermal environment control requires a fast control framework that explicitly accounts for uncertainties such as disturbances. In this study, we propose a hierarchical covariance steering (CS) method based on two spatiotemporal resolutions. First, we demonstrate the effectiveness of CS for a heat diffusion system through an indoor temperature field control problem. Then, we organize the problem formulation and theoretical foundation of hierarchical CS and discuss its effectiveness.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA05.3", "code": "WeA05.3", "title": "Integrated Soft Sensor Design and Optimal Control of Supersaturation in Batch Sugar Crystallization", "day": "Wednesday", "date": "August 26, 2026", "time": "10:20-10:35", "sessionCode": "WeA05", "sessionTitle": "LB: Control Applications", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Lohani, Ananya", "affiliation": "Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, 812 37 Bratislava, Slovakia" }, { "name": "Fedor, Adam", "affiliation": "Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, 812 37 Bratislava, Slovakia" }, { "name": "Kurucz, Julius", "affiliation": "FUZZY S.r.o., 925 81 Diakovce, Slovakia" }, { "name": "Paulen, Radoslav", "affiliation": "Slovak University of Technology in Bratislava" } ], "keywords": [ "Batch and semi-batch process control", "Process modeling, identification, and estimation techniques", "Real-time optimization and control in chemical processes" ], "abstract": "Optimal control growth in industrial sugar production requires precise supersaturation control in the metastable region. Due to limited online measurement capability, we utilized a model-driven soft sensor for real-time estimation. A feedforward-feedback control architecture was developed to regulate the process within optimal limits. The proposed strategy enables reliable control and easy deployment in industrial environments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA05.4", "code": "WeA05.4", "title": "An Industrial Perspective on Thermal Management Systems of Electric Vehicles", "day": "Wednesday", "date": "August 26, 2026", "time": "10:35-10:50", "sessionCode": "WeA05", "sessionTitle": "LB: Control Applications", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Alt, Benedikt", "affiliation": "Robert Bosch GmbH" }, { "name": "Buck, Simon", "affiliation": "Robert Bosch GmbH" } ], "keywords": [ "Control architectures in automotive control", "Electric and solar vehicles", "Nonlinear and optimal automotive control" ], "abstract": "Thermal management systems (TMS) of battery electric vehicles (BEVs) represent a key enabler for further energy savings. Such systems enhance efficiency by recovering waste heat from key powertrain components as the electric drive, power electronics, and HV battery and by employing an integrated heat pump for active thermal conditioning of the vehicle. This high complexity in system design and the growing number of vehicle variants requires a generalizable control software architecture to serve industrial projects. Concurrently, many scientific researchers are actively working on TMS and propose promising predictive and advanced control designs. This discussion paper will explain the corresponding challenges and give some best-practice solutions for bridging the gap.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA05.5", "code": "WeA05.5", "title": "A Toolkit to Support the Teaching of Control Theory to Water Management Students", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:05", "sessionCode": "WeA05", "sessionTitle": "LB: Control Applications", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "van Nooijen, Ronald Robert Paul", "affiliation": "Delft University of Technology" }, { "name": "Kolechkina, Alla G.", "affiliation": "Delft University of Technology" } ], "keywords": [ "Control engineering curricula", "Control education laboratories" ], "abstract": "Climate change and population growth pose new challenges for water management. Control theory provides additional tools to meet these challenges. To provide future water managers with insight into those tools, a course that looks at control theory from a water management perspective is essential. Examples for such a course should ideally be closely related to real water systems. To make possible more realistic examples, a toolkit is under development that allows students to use Python as a user interface to create and run hydrodynamic models in Delft3D FM, to analyze the run results from Python, and to control weirs, gates, and pumps in the model with Python code.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA05.6", "code": "WeA05.6", "title": "Advanced Data Analysis for Development of Cyber-Physical Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "11:05-11:20", "sessionCode": "WeA05", "sessionTitle": "LB: Control Applications", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Juuso, Esko Kalevi", "affiliation": "University of Oulu" } ], "keywords": [ "Data fusion and mining in control", "Digital twins for cyber physical systems", "Knowledge-based and data-driven control" ], "abstract": "In data processing chains, different types of data can be transformed into unified dimensionless indicators which include efficiently nonlinear effects of the measurements. Informative indicators, which are based on generalised norms and nonlinear scaling are the elements of the advanced deep learning. Different types of data, analysis methodologies, system structures and interactions follow the phases of cyber-physical systems and provide a feasible basis for configuration. Several small-scale models support development of cyber-physical systems (CPS). Each module can be a model, diagnostic or control unit.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA06.1", "code": "WeA06.1", "title": "Data-Driven Sub-Optimal Servomechanism Design from Noisy Data Based on Informativity (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA06", "sessionTitle": "Data-Driven Control IV", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Ogawa, Kazuma", "affiliation": "Ritsumeikan University" }, { "name": "Ayaka, Kohei", "affiliation": "Ritsumeikan University" }, { "name": "Namba, Takumi", "affiliation": "Ritsumeikan University" }, { "name": "Takaba, Kiyotsugu", "affiliation": "Ritsumeikan University" } ], "keywords": [ "Data-driven control theory", "Time series modeling", "Learning methods for control" ], "abstract": "This paper proposes a novel data-driven sub-optimal servomechanism design from noisy response data. As widely known, servomechanism controllers incorporate state feedback and integral action to achieve robust tracking without steady-state error. In line with the data informativity approach, we derive a linear matrix inequality condition for a data-driven sub-optimal servomechanism design from noisy input-state-output data taking account of the internal model structure. The resulting servomechanism controller guarantees the integral quadratic performance below a prescribed level, improving transient response. The effectiveness of the proposed method is demonstrated through a numerical example.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA06.2", "code": "WeA06.2", "title": "Data-Driven Min-Max MPC with Integral Quadratic Constraints (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA06", "sessionTitle": "Data-Driven Control IV", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Xie, Yifan", "affiliation": "University of Stuttgart" }, { "name": "Berberich, Julian", "affiliation": "University of Stuttgart" }, { "name": "Allgower, Frank", "affiliation": "University of Stuttgart" } ], "keywords": [ "Data-driven control theory" ], "abstract": "Data-driven control of nonlinear systems with rigorous guarantees is a challenging control problem. Integral quadratic constraints (IQCs) provide a powerful framework for modeling nonlinearities. This paper presents a data-driven min-max model predictive control (MPC) synthesis method for unknown systems subject to (nonlinear) uncertainties using the IQC framework. The unknown system matrices are characterized by a set-membership representation using the input-state data and the knowledge of the IQCs. We derive two semidefinite programs (SDPs) that minimize an upper bound on the worst-case cost over all possible system dynamics and uncertainties. By iteratively solving these SDPs, the proposed state-feedback control law is obtained. We further prove that the resulting closed-loop system is exponentially stable and satisfies the input and state constraints. A numerical example demonstrates the validity of the proposed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA06.3", "code": "WeA06.3", "title": "AERMANI-Diffusion: Regime-Conditioned Dynamics Learning in Aerial Manipulators (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA06", "sessionTitle": "Data-Driven Control IV", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Ujjawal, Samaksh", "affiliation": "International Institute of Information Technology, Hyderabad" }, { "name": "Singh, Shivansh Pratap", "affiliation": "International Institute of Information Technology, Hyderabad" }, { "name": "Nair, Naveen Sudheer", "affiliation": "IIIT Hyderabad" }, { "name": "Yadav, Rishabh Dev", "affiliation": "The University of Manchester" }, { "name": "Pan, Wei", "affiliation": "Newcastle University" }, { "name": "Roy, Spandan", "affiliation": "IIIT Hyderabad" } ], "keywords": [ "Machine and deep learning for system identification", "Learning methods for control", "Data-driven control theory" ], "abstract": "Aerial manipulators undergo rapid, configuration-dependent changes in inertial coupling forces and aerodynamic forces, making accurate dynamics modeling a core challenge for reliable control. Analytical models lose fidelity under these nonlinear and nonstationary effects, while standard data-driven methods such as deep neural networks and Gaussian processes cannot represent the diverse residual behaviors that arise across different operating conditions. We propose a regime-conditioned diffusion framework that models the full distribution of residual forces using a conditional diffusion process and a lightweight temporal encoder. The encoder extracts a compact summary of recent motion and configuration, enabling consistent residual predictions even through abrupt transitions or unseen payloads. When combined with an adaptive controller, the framework enables dynamics uncertainty compensation and yields markedly improved tracking accuracy in real-world tests.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA06.4", "code": "WeA06.4", "title": "Data-Driven Inverse Optimal Control: From Linear to Nonlinear Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA06", "sessionTitle": "Data-Driven Control IV", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Othmane, Amine", "affiliation": "Saarland University, Saarbrücken, Germany" }, { "name": "Maslovskaya, Sofya", "affiliation": "Paderborn University" }, { "name": "Offen, Christian", "affiliation": "University of Birmingham" }, { "name": "Ober-Blöbaum, Sina", "affiliation": "Paderborn University" }, { "name": "Flaßkamp, Kathrin", "affiliation": "Saarland University" } ], "keywords": [ "Data-driven control theory", "Learning methods for control", "Machine and deep learning for system identification" ], "abstract": "Inverse optimal control provides a principled framework for identifying the cost function of an optimal control problem (OCP) from observed state trajectories, assuming known system dynamics. This work addresses infinite-horizon OCPs for linear and nonlinear systems and proposes a neural network-based algorithm that simultaneously reconstructs the underlying cost and a stabilizing feedback law from pure state data. The approach integrates data fidelity with stability constraints to ensure physically meaningful solutions. Numerical experiments demonstrate the effectiveness of the method for different systems and under varying noise levels.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA06.5", "code": "WeA06.5", "title": "Discovery of Fully Efficient Fault Indicators Along a Data-Based Diagnosis Process", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA06", "sessionTitle": "Data-Driven Control IV", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Bezmaternykh, Igor", "affiliation": "INSA" }, { "name": "Travé-Massuyès, Louise", "affiliation": "LAAS-CNRS" }, { "name": "Chanthery, Elodie", "affiliation": "University of Toulouse, INSA" } ], "keywords": [ "Data-driven methods for FDI/FTC", "AI methods for FDI/FTC", "Fault detection and isolation methods" ], "abstract": "The integration of model-based and data-driven paradigms provides a powerful framework for fault diagnosis by combining the interpretability of analytical redundancy relations, i.e., input-output relations that are used as diagnosis indicators in model-based diagnosis, with the adaptability of learning techniques. DT4X is a recent diagnosis algorithm that uses symbolic regression to generate multivariate relations leveraging some properties of analytical redundancy relations and uses them as split functions in a decision tree. However, its symbolic regression procedure optimizes only the separation between two selected classes at each node, often fragmenting the remaining classes and degrading both interpretability and diagnosis performance. This paper introduces DT4X+, an enhanced version of DT4X that modifies the construction of training sets and the symbolic-regression loss so that expressions separate the target classes while preserving the coherence of non-target classes. The resulting relations become fully consistent with ARR properties and lead to more informative splits, improved robustness, and better performance on dynamic-system datasets. Experiments conducted on several benchmark systems demonstrate the benefits of this enhanced formulation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA06.6", "code": "WeA06.6", "title": "A Data-Driven Approach to Disturbance Compensator Design for Stochastic Linear Systems Operating in Stationary Conditions (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA06", "sessionTitle": "Data-Driven Control IV", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Ferraboli, Francesco", "affiliation": "Politecnico Di Milano" }, { "name": "Falsone, Alessandro", "affiliation": "Politecnico Di Milano" }, { "name": "Prandini, Maria", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Data-driven control theory", "Randomized algorithms in stochastic systems" ], "abstract": "We study the problem of optimizing the stationary behavior of a discrete-time linear system subject to a stochastic disturbance. Specifically, our objective is to design a disturbance compensator that shapes the stationary state and control input distribution so as to guarantee a certain performance subject to joint state-input constraints. We consider the case when a dataset of finite-length disturbance realizations is available for the compensator design, and propose a data-driven approach that integrates scenario optimization with a mechanism to reduce the stationary state to a moving average process of finite order. The proposed method is shown to outperform a competitive state-of-the-art method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA07.1", "code": "WeA07.1", "title": "Lightweight Real-Time ALADIN for Distributed Optimization", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA07", "sessionTitle": "Distributed Optimization and Learning in Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Wang, Yifei", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Feng, Xuhui", "affiliation": "Huawei" }, { "name": "Pan, Shimin", "affiliation": "Zhejiang University" }, { "name": "Zhu, Liangfan", "affiliation": "Huawei" }, { "name": "Du, Xu", "affiliation": "Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Rikos, Apostolos I.", "affiliation": "Hong Kong University of Science and Technology (Gz)" } ], "keywords": [ "Distributed optimization", "Distributed control and estimation" ], "abstract": "This paper presents a real-time computational framework for multi-node distributed optimization by extending the Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) algorithm. Our approach integrates adjoint sequential quadratic programming (SQP) techniques to enable efficient approximation of Jacobian information within the ALADIN embedded quadratic program, thereby reducing communication overhead. Furthermore, to decrease computational complexity, we design an event-driven update strategy that avoids updating Hessian and Jacobian matrices at every iteration. The proposed method maintains convergence guarantees while achieving a twofold improvement in computational speed, making it suitable for time-sensitive applications with strict timing constraints. Numerical experiments demonstrate that our approach achieves competitive performance while exhibiting superior computational efficiency in real-time scenarios, validating its practical applicability for time-sensitive distributed optimization challenges.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA07.2", "code": "WeA07.2", "title": "Optimal Parameter Design for DIGing on Minimizing Unweighted Sum of Squares", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA07", "sessionTitle": "Distributed Optimization and Learning in Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Tian, Qiuchen", "affiliation": "Zhejiang University" }, { "name": "Chai, Li", "affiliation": "Zhejiang University" }, { "name": "Xu, Jinming", "affiliation": "Zhejiang University" } ], "keywords": [ "Distributed optimization", "Multi-agent systems" ], "abstract": "There is no general method for designing proper parameters to achieve faster convergence in distributed optimization algorithms. In this paper, we consider the distributed inexact gradient tracking (DIGing) algorithm with the objective function being the unweighted sum of squares. By representing the iteration algorithm as a dynamical linear system, we decompose it into different graph frequencies and obtain a set of decoupled subsystems, on which we can easily analyze the convergence rate. By using Routh stability criterion from control theory, we derive the explicit formula of the optimal worst-case convergence rate and the corresponding parameters. We can see that the convergence rate of DIGing is slow even for the simplest objective functions, thus acceleration is necessary for general application. The proposed method can be viewed as the first step toward optimal parameter design of DIGing algorithm in solving general objective functions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA07.3", "code": "WeA07.3", "title": "Multi-Cluster Games in Open Networks", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA07", "sessionTitle": "Distributed Optimization and Learning in Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Sun, Longwen", "affiliation": "Tongji University" }, { "name": "Meng, Min", "affiliation": "Tongji University" }, { "name": "Lu, Peng", "affiliation": "The University of Hong Kong" } ], "keywords": [ "Distributed optimization", "Multi-agent systems" ], "abstract": "This paper considers the distributed Nash equilibrium (NE) seeking problem for multi-cluster games in open networks, where players can freely join or leave the game with certain probabilities. In multi-cluster games, within a same cluster, players cooperate to optimize the cost function of the cluster, and different clusters compete, which can be seen as a noncooperative game. To track the time-varying NE of this formulated game timely, based on the inter- and intra- communication networks, a distributed projected gradient tracking algorithm under open networks is designed. Through rigorous theoretical analysis, we prove that the strategy profile sequence of players generated by the proposed algorithm linearly converges to a neighborhood of the time-varying NE in expectation. Finally, a numerical simulation of Cournot competition game is presented to illustrate the theoretical result.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA07.4", "code": "WeA07.4", "title": "Predefined-Time Distributed Optimization for Heterogeneous Linear Multi-Agent Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA07", "sessionTitle": "Distributed Optimization and Learning in Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Bennacer, Amine Rami", "affiliation": "Université Polytechnique Hauts-De-France" }, { "name": "Defoort, Michael", "affiliation": "University of Valenciennes" }, { "name": "Chen, Yiwen", "affiliation": "Ecole Centrale De Lille" } ], "keywords": [ "Distributed optimization", "Multi-agent systems", "Consensus" ], "abstract": "In this paper, the predefined-time output-consensus distributed optimization is investigated for heterogeneous linear multi-agent systems. The proposed solution is an algorithm consisting of two main components: the first part uses an appropriate formulation of the output dynamics, while the second part represents a predefined-time controller which utilises sliding-mode control, zero-gradient-sum property, and predefined-time consensus functions to achieve consensus towards the global optimal solution to the optimization problem. This algorithm allows convergence under a user-defined upper-bound settling time. Finally simulation results are provided to prove its efficiency.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA07.5", "code": "WeA07.5", "title": "Privacy-Preserving Distributed Stochastic Optimization with Homomorphic Encryption and Heterogeneous Stepsizes", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA07", "sessionTitle": "Distributed Optimization and Learning in Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Zhou, Haoqiang", "affiliation": "School of Automation, Northwestern Polytechnical University" }, { "name": "Chen, Chi", "affiliation": "School of Automation, Northwestern Polytechnical University" }, { "name": "Zhi, Yongfeng", "affiliation": "Northwestern Polytechnical University" }, { "name": "Gao, Huan", "affiliation": "Northwestern Polytechnical University" } ], "keywords": [ "Distributed optimization", "Multi-agent systems", "Cyber security networked control" ], "abstract": "Distributed stochastic optimization enables multi-agent collaboration in applications such as distributed learning and sensor networks, but also raises critical privacy concerns due to the involvement of sensitive data. While existing privacy-preserving approaches often face limitations in balancing accuracy with efficiency, we propose a novel distributed stochastic gradient descent algorithm that integrates Paillier homomorphic encryption with heterogeneous and time-varying random stepsizes. The proposed algorithm provides inherent privacy protection against both internal honest-but-curious agents and external eavesdroppers, without relying on any trusted neighbors. Furthermore, we incorporate an attenuation factor to effectively mitigate quantization error induced by the encryption process, ensuring almost sure convergence to the optimal solution while maintaining privacy preservation. Numerical simulations demonstrate the effectiveness and efficiency of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA07.6", "code": "WeA07.6", "title": "Distributed Primal-Dual Optimization with Sporadic Poisson-Driven Communication", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA07", "sessionTitle": "Distributed Optimization and Learning in Multi-Agent Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Weber, Marc", "affiliation": "RWTH Aachen University" }, { "name": "Ebenbauer, Christian", "affiliation": "RWTH Aachen University" } ], "keywords": [ "Distributed optimization", "Stochastic differential equations", "Consensus" ], "abstract": "This paper addresses distributed constrained optimization over networks with sporadic, asynchronous communication. We propose a Distributed Stochastic Primal-Dual Optimization Dynamics (Saddle-Point Flow) that integrates dual consensus dynamics driven by Poisson processes. Unlike standard approaches assuming synchronous updates, we model information exchange as Poisson-driven stochastic differential equations. For the case of a quadratic programming problem, we derive an explicit design rule linking a sufficient communication rate to the system's exponential convergence rate and ultimate accuracy. Our analysis proves that nominal performance can be recovered on distributed network topologies given sufficiently frequent communication, even in the presence of channel leakage.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA08.1", "code": "WeA08.1", "title": "Impact Analysis of Hidden Faults in Nonlinear Control Systems Using Output-To-Output Gain", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA08", "sessionTitle": "Cyber-Security and Resilient Control Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Seifullaev, Ruslan", "affiliation": "Uppsala University" }, { "name": "Teixeira, André M.H.", "affiliation": "Uppsala University" } ], "keywords": [ "Cyber security networked control", "Control over networks", "Fault detection and diagnosis" ], "abstract": "Networked control systems (NCSs) are vulnerable to faults and hidden malfunctions in communication channels that can degrade performance or even destabilize the closed loop. Classical metrics in robust control and fault detection typically treat impact and detectability separately, whereas the output-to-output gain (OOG) provides a unified measure of both. While existing results have been limited to linear systems, this paper extends the OOG framework to nonlinear NCSs with quadratically constrained nonlinearities, considering false-injection attacks that can also manipulate sensor measurements through nonlinear transformations. Specifically, we provide computationally efficient linear matrix inequality conditions and complementary frequency-domain tests that yield explicit upper bounds on the OOG of this class of nonlinear systems. Furthermore, we derive frequency-domain conditions for absolute stability of closed-loop systems, generalizing the Yakubovich quadratic criterion.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA08.2", "code": "WeA08.2", "title": "Security Index from Input/Output Data: Theory and Computation", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA08", "sessionTitle": "Cyber-Security and Resilient Control Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Shinohara, Takumi", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Sandberg, Henrik", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Cyber security networked control", "Data-driven control theory", "Resilient networked control systems" ], "abstract": "The concept of a security index quantifies the minimum number of components that must be compromised to carry out a stealth attack. This metric enables system operators to assess the security risk of each component and implement countermeasures accordingly. In this paper, we introduce a data-driven security index that can be computed solely from input/output data when the system model is unknown. We show a sufficient condition under which the data-driven security index coincides with the model-based security index, which implies that the exact risk level of each component can be identified solely from data. We also provide an algorithm for computing the data-driven security index.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA08.3", "code": "WeA08.3", "title": "Duration‑Dependent Attack Strategy Design and Sliding Mode Control Countermeasure", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA08", "sessionTitle": "Cyber-Security and Resilient Control Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Tang, Tianfeng", "affiliation": "Zhejiang University of Technology" }, { "name": "Zhang, Dan", "affiliation": "Zhejiang University of Technology" }, { "name": "Feng, Gang", "affiliation": "City Univ. of Hong Kong" } ], "keywords": [ "Cyber security networked control", "Discrete event modeling and simulation", "Control under communication constraints" ], "abstract": "This paper addresses the security problem within an integrated attack-defense framework for discrete-time networked control systems with multiple sensors. Different from traditional indiscriminate denial-of-service (DoS) attacks, a novel data-importance-aware attack strategy is considered, which selectively targets critical sensor nodes to maximize impact. Unlike traditional duration-independent attacks, the attacks under consideration are modeled as a duration-dependent switching chain governed by a joint distribution function of current mode and its duration. A secure sliding mode controller is then synthesized to counter these kinds of attacks. Finally, the effectiveness of the proposed secure control strategy is validated through an RLC circuit example.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA08.4", "code": "WeA08.4", "title": "A Null Space Approach to Opacity Enforcement in Remote Monitoring Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA08", "sessionTitle": "Cyber-Security and Resilient Control Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Wang, Xuelin", "affiliation": "Donghua University" }, { "name": "Yadgar, Obaidullah", "affiliation": "University of Duisburg-Essen" }, { "name": "Zhang, Ping", "affiliation": "University of Kaiserslautern-Landau" }, { "name": "Shen, Bo", "affiliation": "Donghua University" } ], "keywords": [ "Cyber security networked control", "Fault detection and diagnosis", "Control over networks" ], "abstract": "This paper proposes an approach to opacity enforcement in parity space based remote monitoring systems. The true plant state is regarded as the secret to be protected. The attacker is not allowed to figure out the secret, even if he is able to eavesdrop both sensor output signals and control input signals transmitted over the network. The basic idea is to inject mask signals into the sensor output channels and the control input channels to alter signals transmitted over the network, so that the attacker can't obtain an accurate state estimate of the plant. In order to avoid any influence on the fault detection performance of the remote monitoring system, the mask signals are selected in the right null space of particular matrices related to the parameters of the remote monitoring system. An example is given to illustrate the proposed opacity enforcement approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA08.5", "code": "WeA08.5", "title": "A Randomized Scheduling Framework for Privacy-Preserving Multi-Robot Rendezvous Given Prior Information", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA08", "sessionTitle": "Cyber-Security and Resilient Control Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Liu, Le", "affiliation": "University of Groningen" }, { "name": "Kawano, Yu", "affiliation": "Hiroshima University" }, { "name": "Cao, Ming", "affiliation": "University of Groningen" } ], "keywords": [ "Cyber security networked control", "Multi-agent systems", "Consensus" ], "abstract": "Privacy has become a critical concern in modern multi-robot systems, driven by both ethical considerations and operational constraints. As a result, growing attention has been directed toward privacy-preserving coordination in dynamical multi-robot systems. This work introduces a randomized scheduling mechanism for privacy-preserving robot rendezvous. The proposed approach achieves improved privacy even at lower communication rates, where privacy is quantified via pointwise maximal leakage. We show that lower transmission rates provide stronger privacy guarantees and prove that rendezvous is still achieved under the randomized scheduling mechanism. Numerical simulations are provided to demonstrate the effectiveness of the method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA08.6", "code": "WeA08.6", "title": "Stealthy Sensor Attacks against Direct Data-Driven Controllers", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA08", "sessionTitle": "Cyber-Security and Resilient Control Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "C. Anand, Sribalaji", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Cyber security networked control", "Resilient networked control systems", "Fault detection and diagnosis" ], "abstract": "This paper investigates the vulnerability of discrete-time linear time-invariant systems to stealthy sensor attacks during the learning phase. In particular, we demonstrate that an adversary can inject attacks that mislead the operator into learning an {unstable} state-feedback controller. We further analyze attacks that degrade the performance of data-driven {H}_2 controllers, while ensuring that the operator can always compute a feasible controller. Numerical examples illustrate the effectiveness of the proposed attacks and underscore the importance of accounting for adversarial manipulations in data-driven controller design.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA09.1", "code": "WeA09.1", "title": "Robust Delay-Time and State Estimation for Continuous-Discrete Linear System Using Unscented H-Infinity Filter (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA09", "sessionTitle": "JO-JSC: Estimation, Identification and Filtering", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Kinjo, Noritaka", "affiliation": "University of Tsukuba" }, { "name": "Kawai, Shin", "affiliation": "University of Tsukuba" }, { "name": "Nguyen-Van, Triet", "affiliation": "University of Tsukuba" } ], "keywords": [ "Filtering and smoothing", "Estimation and filtering" ], "abstract": "This paper addresses the problem of online estimation of input time-delay in continuous–discrete (CD) linear systems. Motivated by practical applications where both continuous-time disturbances and measurement noise are present, we propose a robust delay and state estimator based on the Unscented H-infinity Filter (UHF). To extend the UHF framework to CD systems, we redesign the performance index to penalize the energy of continuous-time disturbances and derive the discrete-time state evolution under worst-case continuous-time disturbances, yielding a prediction step without probabilistic assumptions on delay variability or any disturbances. Through numerical simulations, we demonstrate that the proposed estimator achieves superior robustness and accuracy compared with the Unscented Kalman Filter (UKF).", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA09.2", "code": "WeA09.2", "title": "Observability Analysis and State Estimation of Wind Turbine Power Systems: A Novel Sensitivity-Based Approach (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA09", "sessionTitle": "JO-JSC: Estimation, Identification and Filtering", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Abdelfattah, Hesham", "affiliation": "University of Cincinnati" }, { "name": "Eisa, Sameh", "affiliation": "University of Cincinnati" }, { "name": "Stechlinski, Peter", "affiliation": "University of Maine" } ], "keywords": [ "Kalman filtering", "Estimation and filtering", "Diagnosis of discrete event and hybrid systems" ], "abstract": "In this paper, we provide a novel framework that enables a sensitivity-based observability test and state estimation algorithm for wind turbine power systems (WTPSs). The provided framework is the first of its kind in the literature, as it is able to deal with state- of-the-art WTPS models that are non-reduced, highly nonlinear differential-algebraic equation systems. Moreover, the framework includes nonsmoothness in both the dynamics and output functions to unify the operational conditions over different wind speed regions. We demonstrate the effectiveness of the proposed framework (thanks to the underlying tools from generalized derivatives theory) on a standard wind speed profile. We also illustrate how the proposed framework, by the utilization of robust observability analysis during nonsmooth transitions, enables accurate state estimation for cases when the conventional Extended Kalman Filter approach fails.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA09.3", "code": "WeA09.3", "title": "Structural Identifiability in Fractional-Order Networks (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA09", "sessionTitle": "JO-JSC: Estimation, Identification and Filtering", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Varalda, Alessandro", "affiliation": "Uppsala University" }, { "name": "Pequito, Sérgio", "affiliation": "Instituto Superior Técnico, University of Lisbon" } ], "keywords": [ "Linear system identification", "Distributed control and estimation", "Control of networks" ], "abstract": "Identifying parameters of fractional-order discrete-time dynamical networks from input-output data is crucial for modeling complex systems in neuroscience and biology. Despite existing methods, fundamental conditions ensuring structural identifiability based solely on network structure remain unexplored. This paper establishes identifiability theory for fractional-order networks by proving that a network is structurally identifiable if and only if all its subsystems are identifiable. We introduce a graph-theoretical hierarchical algorithm that systematically partitions networks into identifiable subsystems via input-output reachability, enabling decomposition-based parameter learning with provable guarantees. Extensive simulation experiments validate that this approach preserves identifiability while achieving significant computational speedup, demonstrating scalability for large-scale systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA09.4", "code": "WeA09.4", "title": "Toward Aquaponics Digital Twin: Standardized Measurement Protocols and Dynamic Modeling (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA09", "sessionTitle": "JO-JSC: Estimation, Identification and Filtering", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Korkut, Talha Batuhan", "affiliation": "University of Picardie Jules Verne" }, { "name": "Rachid, Ahmed", "affiliation": "University of Picardie Jules Verne" } ], "keywords": [ "Modeling and estimation in agriculture", "Sensing and perception in agriculture", "Real time monitoring and control of environmental systems" ], "abstract": "Aquaponics research is challenged by fragmented measurements, heterogeneous data formats, and missing consistent modeling frameworks. This paper proposes a digital infrastructure built on two pillars: (i) a unified protocol specifying parameters, frequencies, and formats, and (ii) a transient MATLAB model to simulate biomass growth and nutrient cycling. The framework demonstrates consistency with defined parameters, enables scenario analysis, and provides predictive capacity. The scenario analysis further shows that feeding intensity is the primary driver of growth, plant area limits nitrate removal, and nitrification efficiency governs residual load. These effects are synthesized into three compact key performance indicators (KPIs)—residual nitrate, uptake fraction, and feed–based efficiency—enabling comparative benchmarking and a control–ready basis for aquaponics systems. This is the first integrated data–model framework enabling reproducible calibration and providing a digital-twin foundation for aquaponics optimization.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA09.5", "code": "WeA09.5", "title": "Orthogonal-By-Construction Augmentation of Physics-Based Input-Output Models (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA09", "sessionTitle": "JO-JSC: Estimation, Identification and Filtering", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Gyorok, Bendeguz Mate", "affiliation": "Institute for Computer Science and Control" }, { "name": "Schoukens, Maarten", "affiliation": "Eindhoven University of Technology" }, { "name": "Peni, Tamas", "affiliation": "Institute for Computer Science and Control (SZTAKI)" }, { "name": "Tóth, Roland", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Physics informed and grey box model identification", "Nonlinear system identification", "Machine and deep learning for system identification" ], "abstract": "This paper proposes a novel orthogonal-by-construction parametrization for augmenting physics-based input-output models with a learning component in an additive sense. The parametrization allows to jointly optimize the parameters of the physics-based model and the learning component. Unlike the commonly applied additive (parallel) augmentation structure, the proposed formulation eliminates overlap in representation of the system dynamics, thereby preserving the uniqueness of the estimated physical parameters, ultimately leading to enhanced model interpretability. By theoretical analysis, we show that, under mild conditions, the method is statistically consistent and guarantees recovery of the true physical parameters. With further analysis regarding the asymptotic covariance matrix of the identified parameters, we also prove that the proposed structure provides a clear separation between the physics-based and learning components of the augmentation structure. The effectiveness of the proposed approach is demonstrated through simulation studies, showing accurate reproduction of the data-generating dynamics without sacrificing consistent estimation of the physical parameters.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA09.6", "code": "WeA09.6", "title": "Observability-Driven Adaptive Vehicle Velocity Estimation under Extreme Conditions", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA09", "sessionTitle": "JO-JSC: Estimation, Identification and Filtering", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Chen, Weiheng", "affiliation": "Tongji University" }, { "name": "Zhang, Lin", "affiliation": "Tongji University" }, { "name": "Sun, Haobo", "affiliation": "School of Automotive Studies, Tongji University, Shanghai" }, { "name": "Lu, Jiaxing", "affiliation": "Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University" }, { "name": "Zhang, Weizhou", "affiliation": "Tongji University" }, { "name": "Chen, Hong", "affiliation": "Tongji University" } ], "keywords": [ "Vehicle dynamic systems", "Automotive system identification and modelling" ], "abstract": "Integrating dynamics models into vehicle state estimation improves estimator performance. However, the inherent saturation characteristics of tires introduce significant state uncertainty under extreme conditions. This paper proposes an observability-driven adaptive vehicle velocity estimator. Based on a dynamic and combined tire model, an estimation framework is developed that fuses kinematic prediction and dynamics feedback. Then, an observability-driven adaptive mechanism is proposed, which adjusts the dynamic feedback weight according to the minimum singular value of the empirical Gramian matrix and supervises the estimation through innovation when kinematic prediction dominates. This mechanism enables the effective utilization of dynamic information while suppressing unreasonable feedback. Experiments on compacted snow under circular driving verify the effectiveness of the method, reducing PE and RMSE by 60.41% and 60.99%, respectively.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA10.1", "code": "WeA10.1", "title": "Formulating the Kalman-Bucy Filter for the State Space PID (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA10", "sessionTitle": "Advances in PID Methods and Applications", "sessionType": "Invited Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Abramovitch, Daniel Y.", "affiliation": "Agilent Technologies" } ], "keywords": [ "Advanced process control", "Process modeling, identification, and estimation techniques", "Industrial applications of process control" ], "abstract": "This paper builds on two recent papers which created a coherent formulation for generating proportional-integral-derivative (PID) controllers using a state-space formulation Abramovitch (2026a,b). Those papers showed the state structure, the estimator formulation, and the constraints on the estimator gains to allow us to generate a PID controller directly from a state-space structure. In this paper, we go further by enabling the use of a Kalman-Bucy filter Bryson and Ho (1975); Wikipedia (2025a); Bucy (1967) to generate the estimator gains, subject to the constraints shown in Abramovitch (2026a,b).", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA10.2", "code": "WeA10.2", "title": "A Novel Tuning Rule for the Tracking Constant Parameter in Back-Calculation Anti-Windup Scheme (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA10", "sessionTitle": "Advances in PID Methods and Applications", "sessionType": "Invited Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Caparroz, Malena", "affiliation": "University of Almería" }, { "name": "Soltesz, Kristian", "affiliation": "Lund University" }, { "name": "Hagglund, Tore", "affiliation": "Lund University" }, { "name": "Guzman, Jose Luis", "affiliation": "University of Almeria" } ], "keywords": [ "Advanced process control", "Industrial applications of process control" ], "abstract": "Actuator saturation is a critical nonlinearity that degrades the performance of feedback control systems by inducing integrator windup and prolonging recovery from disturbances. Anti-windup strategies are designed to mitigate these effects, yet their performance depends strongly on controller design choices and tuning. This paper presents a novel tuning rule for the tracking time constant of the back-calculation anti-windup scheme for first-order-plus-dead-time processes and load disturbances, derived from an extensive optimization study that minimizes the Integral of Absolute Error (IAE) under varying levels of actuator saturation and disturbance conditions. Four practical tuning rules are presented to cover cases with different levels of information about the disturbance duration. Simulation results confirm that the proposed approach improves disturbance rejection while maintaining simplicity of implementation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA10.3", "code": "WeA10.3", "title": "On the Use of Fractional Double Derivative Action for Double Integrator Processes (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA10", "sessionTitle": "Advances in PID Methods and Applications", "sessionType": "Invited Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Milanesi, Marco", "affiliation": "University of Brescia" }, { "name": "Visioli, Antonio", "affiliation": "University of Brescia" }, { "name": "Chen, YangQuan", "affiliation": "University of California, Merced" } ], "keywords": [ "Advanced process control" ], "abstract": "In this paper we analyze the performance that can be obtained by adding an integer and a fractional double derivative action to a Proportional-Integral-Derivative (PID) controller for double integrator processes. In particular, the controllers are optimized in order to minimize the integrated absolute error (IAE) subject to constraints on the maximum sensitivity. Both the set-point following and the load disturbance rejection tasks are considered separately. A fragility analysis is also performed for the fractional double derivative order. Results show the trade-off between the increased complexity of the controller and the increment of the performance. Indeed, the use of a fractional double derivative action allows a significant performance improvement, especially for the load disturbance rejection task.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA10.4", "code": "WeA10.4", "title": "Automatic Tuning of PID Controllers for Integrating Systems Via Cascade Structure (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA10", "sessionTitle": "Advances in PID Methods and Applications", "sessionType": "Invited Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Wang, Liuping", "affiliation": "RMIT University" }, { "name": "Freeman, Christopher Thomas", "affiliation": "University of Southampton" }, { "name": "Rogers, Eric", "affiliation": "Univ of Southampton" } ], "keywords": [ "Advanced process control", "Industrial applications of process control", "Control of multi-scale, distributed, and particulate systems" ], "abstract": "A common requirement for automatic control of platforms in application areas such as renewable energy, electric vehicles, and robotics is the regulation of complex integrating systems in the form of position control. A standard control strategy is to decompose the complex integrating system's dynamics into a stable subsystem and a pure integrating system to form a cascade feedback control structure. This paper develops an approach to the automatic tuning of integrating systems using a cascade control system structure. The new design's main advantages compared to existing approaches include simplifying the auto-tuner for integrating systems and much improved closed-loop disturbance rejection performance for this control system class. Experimental results from application to an electro-mechanical system demonstrate the auto-tuner's efficacy and the superior closed-loop performance of the cascade PID control system.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA10.5", "code": "WeA10.5", "title": "Addressing Improperness in Feedforward Compensator Design (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA10", "sessionTitle": "Advances in PID Methods and Applications", "sessionType": "Invited Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Guzman, Jose Luis", "affiliation": "University of Almeria" }, { "name": "Skogestad, Sigurd", "affiliation": "Norwegian Univ. of Science & Tech" }, { "name": "Hagglund, Tore", "affiliation": "Lund University" } ], "keywords": [ "Advanced process control" ], "abstract": "Feedforward control is an efficient strategy to compensate for measurable load disturbances. An ideal feedforward compensator is obtained as the ratio between the transfer function from the load disturbance to the process output and the transfer function from the control signal to the process output, with reversed sign. If applied, this ideal feedforward compensator will eliminate the response in the process output completely. The problem is that this compensator is not realizable in many cases, since the compensator may be non-causal, unstable, or improper. Tuning rules that take the first two cases, causality and stability, into account have been developed during the last decades, but the last problem of improperness has not been treated before. An improper transfer function can be made proper in two ways, by reducing the order of the numerator or by increasing the order of the denominator. This paper presents compensator structures and tuning rules that are based on both these approaches. The tuning rules are based on tradeoffs between performance in terms of IE and IAE values, and control signal activity in terms of magnitude of control signal peaks at step load disturbances. The paper includes analytical derivations as well as simulation examples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA10.6", "code": "WeA10.6", "title": "Advanced PID Architectures for Tracking Changing Active Constraints (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA10", "sessionTitle": "Advances in PID Methods and Applications", "sessionType": "Invited Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Skogestad, Sigurd", "affiliation": "Norwegian Univ. of Science & Tech" } ], "keywords": [ "Advanced process control", "Real-time optimization and control in chemical processes", "Industrial applications of process control" ], "abstract": "Advanced regulatory control (ARC), also known as advanced PID architectures, is emerging as a simple and robust way of controlling process with changing and possibly conflicting constraints, where it previously was believed - at least in academia - that model-based solutions, such as MPC, was the only effective solution. To illustrate this, ARC is applied on three case studies. The first is a gas-liquid separation process, where selectors and split-parallel control are combined to achieve bidirectional inventory control where the throughput manipulator moves automatically to the most optimal position. The two other case studies are on keeping acceptable air quality (CO2-level) and temperature in a room (in this case, a barn for cows). The CO2 and temperature constraints may be conflicting, leading to an hierarchical switching network of PID controllers.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA13.1", "code": "WeA13.1", "title": "Multi-Loop Control of Robotic Manipulator with Online Discrepancy Handling", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA13", "sessionTitle": "Optimal and Robust Control Applications", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Mashhadireza, Ali", "affiliation": "Northeastern University" }, { "name": "Ma, Tong", "affiliation": "Northeastern University" } ], "keywords": [ "Adaptive control design", "Applications of optimal control", "Uncertain systems" ], "abstract": "This paper presents a multi-loop control framework for a two-arm robotic manipulator with nonlinear dynamics and various sources of uncertainty. First, a neural network–based control policy is trained offline to drive the nominal dynamics to achieve near-optimal performance. To address discrepancies between the actual and nominal dynamics, which include modeling errors, disturbances, and actuator faults, a piecewise-constant adaptive law with radial basis functions is developed to estimate these uncertainties. An inner-loop control law then compensates for the estimated discrepancies, ensuring that the actual system behaves similarly to the nominal system under ideal conditions. By integrating the offline data-driven near-optimal policy with the online model-based adaptive control, the proposed framework guarantees near-optimal performance despite uncertainties and disturbances. Simulation results demonstrate that, under the multi-loop control scheme, the perturbed robotic manipulator maintains nearly the same tracking performance as in the nominal case.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA13.2", "code": "WeA13.2", "title": "Adaptive Robust Control of a 4-DOF Tower Crane Considering Model Uncertainties", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA13", "sessionTitle": "Optimal and Robust Control Applications", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Watanabe, Motoyasu", "affiliation": "Ibaraki University" }, { "name": "Yang, Zi-Jiang", "affiliation": "Ibaraki University" } ], "keywords": [ "Adaptive control design", "Sliding mode control", "Robust control applications" ], "abstract": "4-DOF tower cranes are known to be challenging to control due to their complex dynamics. This paper proposes a control method for a 4-DOF tower crane that reduces the controller’s model dependency and demonstrates its effectiveness via experimental validation. Specifically, the proposed method utilizes only the mass matrix of the Euler-Lagrange (EL) dynamics and introduces an integral term into the auxiliary variable. We analytically prove that this design compensates for constant unmatched disturbances, such as crane calibration errors. The stability analysis is performed using the Lyapunov method and mathematical analysis.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA13.3", "code": "WeA13.3", "title": "Vision-Based Adaptive Steering Control for Navigation of a Soft Robotic Colonoscope", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA13", "sessionTitle": "Optimal and Robust Control Applications", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Chen, Kaiwen", "affiliation": "Imperial College London" }, { "name": "Zhang, Yahui", "affiliation": "Imperial College London" }, { "name": "Shi, Jialei", "affiliation": "Imperial College London" }, { "name": "Giannarou, Stamatia", "affiliation": "Imperial College London" }, { "name": "Elson, Daniel", "affiliation": "Imperial College London" }, { "name": "Astolfi, Alessandro", "affiliation": "King Abdullah University of Science and Technology (KAUST)" } ], "keywords": [ "Adaptive control design", "Uncertain systems", "Application of nonlinear analysis and design" ], "abstract": "This paper proposes a steering control mechanism for a soft robotic endoscope to autonomously navigate through the colon. A two-parameter adaptive steering controller, for which the visual servo problem of the soft robot is recast into the classical visual servo paradigm by introducing time-varying parameters, is proposed. The steering control guarantees an adjustable uniform ultimate bound for regulation error in the presence of disturbances and parameter variations. The controller is implemented with a navigation scheme to achieve autonomous navigation. Two experiments on set-point regulation and navigation inside a colon phantom, respectively, are presented. The results show that the proposed method can perform regulation accurately without spiral detours, and the navigation performance outperforms a proficient human operator in terms of accuracy and compliance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA13.4", "code": "WeA13.4", "title": "An Application of Robust Tube MPC to General Anesthesia", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA13", "sessionTitle": "Optimal and Robust Control Applications", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Ajami, Mohamad", "affiliation": "GIPSA-Lab" }, { "name": "Karam, Carlo", "affiliation": "GIPSA-Lab, Grenoble INP, Univ. Grenoble Alpes" }, { "name": "Dang, Thao", "affiliation": "VERIMAG" }, { "name": "Fiacchini, Mirko", "affiliation": "GIPSA-Lab, CNRS" } ], "keywords": [ "Control in system biology", "Model predictive control", "Applications of optimal control" ], "abstract": "Interpatient variability is a main challenge in model-based closed-loop anesthesia, mainly due to poor modeling of peripheral compartments which are weakly informed by clinical data. This paper proposes a tube MPC framework that treats their influence as additive disturbances. Additionally, a new co-administration approach is developed, and a shrinking-horizon strategy is employed to reduce the computational burden of the MPC algorithm. The proposed controller is then evaluated against a baseline PID controller from the literature.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA13.5", "code": "WeA13.5", "title": "Persistent Coverage Control for Two-Wheeled Mobile Robots and Its Application to Cooperative Tracking", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA13", "sessionTitle": "Optimal and Robust Control Applications", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Mimura, Kotaro", "affiliation": "Tokyo Metropolitan University" }, { "name": "Kojima, Akira", "affiliation": "Tokyo Metropolitan University" } ], "keywords": [ "Decentralized control", "Control barrier functions and state space constraints", "Large-scale and networked optimization problems" ], "abstract": "In surveillance systems utilizing autonomous robots, both area exploration and tracking of detected moving targets are required. This paper presents a method which integrates persistent coverage control and cooperative tracking for multiple two-wheeled mobile robots. The proposed method enables a team of mobile robots to flexibly switch between exploration and tracking according to the situation. By employing the persistent coverage with cooperative tracking rule, it is shown that the proposed method maintains surveillance performance while responding to dynamic targets. Simulation and experimental results demonstrate that cooperative tracking improves coverage efficiency compared with single-robot tracking.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA13.6", "code": "WeA13.6", "title": "Model-Free Disturbance Observer with Online Modification: Listening to MFDOOM", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA13", "sessionTitle": "Optimal and Robust Control Applications", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Barak, Nadav", "affiliation": "Technion - Israel Institute of Technology" }, { "name": "Grussler, Christian", "affiliation": "Technion - Israel Institute of Technology" } ], "keywords": [ "Design methods for data-based control", "Adaptive control design", "Applications of optimal control" ], "abstract": "Data-Enabled Predictive Control (DeePC) has recently emerged as a framework for controlling unknown systems from data. However, its performance relies on the relevance of the collected data, and as such, disturbances lead to inevitable errors. This paper addresses this problem by proposing an augmentation of DeePC using Model-Free Disturbance Observer with Online Modification (MFDOOM). The method corrects output predictions based on previous prediction errors using a dedicated continuously updated Hankel matrix. We compare our method, both theoretically and through simulation, to other recent algorithms designed for time-varying systems in the DeePC framework. It is shown that for disturbances that can be modeled as the output of an autonomous linear time-invariant system, this approach can reduce tracking error and online-update burden compared with existing online DeePC variants.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA14.1", "code": "WeA14.1", "title": "Fragmented Data-Driven Predictive Control: Towards Smaller Datasets (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA14", "sessionTitle": "JO-EAAI: Model Predictive Control and Model Validation", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Shaiakhmetov, Ruslan", "affiliation": "Alma Mater Studiorum - Università Di Bologna" }, { "name": "Pianini, Danilo", "affiliation": "Alma Mater Studiorum - Università Di Bologna" }, { "name": "Venusti, Valter", "affiliation": "Dallara Automobili S.p.A" }, { "name": "Papadopoulos, Alessandro Vittorio", "affiliation": "Mälardalen University" } ], "keywords": [ "Model predictive control", "Design methods for data-based control" ], "abstract": "Data-driven control techniques, such as DeePC, enable controller synthesis directly from data, but remain challenging for nonlinear or stochastic systems with limited data. Existing extensions, such as S-DeePC, address this through problem decomposition, but rely on indirect formulations. This paper presents F-DeePC, a fragmented DeePC approach that builds the constraint matrices directly and splits the prediction horizon into shorter fragments. The construction supports heterogeneous fragment lengths, while this paper focuses on the homogeneous case. The resulting formulation improves flexibility and data efficiency, yielding better tracking performance under limited data and nonlinear dynamics.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA14.2", "code": "WeA14.2", "title": "Approximate Model Predictive Control for Microgrid Energy Management Via Imitation Learning (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA14", "sessionTitle": "JO-EAAI: Model Predictive Control and Model Validation", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Liu, Changrui", "affiliation": "Delft University of Technology" }, { "name": "Shi, Shengling", "affiliation": "Delft University of Technology" }, { "name": "Alan, Anil", "affiliation": "TU Delft, Delft, Netherlands" }, { "name": "Venayagamoorthy, Ganesh", "affiliation": "Clemson University" }, { "name": "De Schutter, Bart", "affiliation": "Delft University of Technology" } ], "keywords": [ "Model predictive control", "Learning methods for optimal control", "Applications of optimal control" ], "abstract": "Efficient energy management is critical for microgrids with renewable resources and energy storage systems. This paper proposes an imitation learning-based framework to approximate mixed-integer Economic Model Predictive Control (EMPC) for microgrid energy management. By training a neural network to imitate an expert EMPC policy offline, the framework enables real-time decision-making while bypassing the unpredictable computational costs of online mixed-integer optimization. To improve robustness, we apply noise injection to mitigate distribution shift. Furthermore, a constraint-tightening approach combined with a projection layer is introduced to guarantee recursive feasibility and constraint satisfaction. Simulation results show the learned policy achieves near-optimal performance while reducing computation time by one order of magnitude.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA14.3", "code": "WeA14.3", "title": "Hybrid Offline-Online Gaussian Process Regression-Based Model Predictive Control for Autonomous Vehicles Trajectory Tracking (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA14", "sessionTitle": "JO-EAAI: Model Predictive Control and Model Validation", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Chen, Yutao", "affiliation": "Fuzhou University" }, { "name": "He, Jie", "affiliation": "Fuzhou University" }, { "name": "Cheng, Jun", "affiliation": "FuZhou University" }, { "name": "Zhou, Yu", "affiliation": "Fuzhou University" }, { "name": "Huang, Jingli", "affiliation": "Fuzhou University" }, { "name": "Huang, Jie", "affiliation": "Fuzhou University" } ], "keywords": [ "Model predictive control", "Learning methods for optimal control", "Applications of optimal control" ], "abstract": "To address the critical challenge of model mismatch in Model Predictive Control (MPC) for autonomous vehicle trajectory tracking, this paper proposes a novel hybrid offline-online Gaussian Process Regression MPC (HOO-GPR-MPC) framework. While GPR-enhanced MPC can improve model fidelity by learning unmodeled dynamics, existing approaches face a trade-off: offline-learned GPR models lack adaptability to new conditions, while online learning suffers from escalating computational costs that hinder real-time application. Our method synergistically integrates offline pre-training with efficient online updates to overcome these limitations. The core contributions are threefold: first, an active data management strategy based on predictive variance to preserve a compact and informative online dataset; second, a performance-based dynamic weight assignment mechanism to adaptively fuse offline and online GPR models; and third, a real-time implementable and theoretically supported HOOGPR-MPC framework that integrates dynamic sparsification and asynchronous updates with formal guarantees on bounded uncertainty, bounded weight variation, recursive feasibility in probability, and ISS in probability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA14.4", "code": "WeA14.4", "title": "Constraint Representation through Support Vector Machines and Its Application to Model Predictive Control (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA14", "sessionTitle": "JO-EAAI: Model Predictive Control and Model Validation", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Castroviejo Fernandez, Miguel", "affiliation": "University of Michigan" }, { "name": "Do, Huu-Thinh", "affiliation": "University of Michigan" }, { "name": "Kolmanovsky, Ilya V.", "affiliation": "University of Michigan" } ], "keywords": [ "Model predictive control", "Nonlinearity learning from data" ], "abstract": "We consider the control of dynamical systems subject to constraints of arbitrary shape. In particular, we are interested in cases where some of the constraints are not described by a smooth function but are instead specified by a mix of functions and logical statements, a lookup table, or an oracle that returns whether a queried point is safe. We propose to use a Support Vector Machine (SVM) classifier to approximate the constraint boundaries and use it to define a smooth nonconvex optimal control problem (OCP). We derive tightening bounds on the classifier to ensure safety and investigate a class of kernels that lead to the OCP being a Difference-of-Convex (DC) programming problem. Moreover, we show a natural difference of convex function decomposition for the Gaussian Radial Basis Function. The approach is numerically validated for a planar robotic manipulator with obstacle avoidance constraints.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA14.5", "code": "WeA14.5", "title": "Evolvable Physics-Informed Digital Twin for Real-Time Modeling and Adaptation} (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA14", "sessionTitle": "JO-EAAI: Model Predictive Control and Model Validation", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Hu, Xin", "affiliation": "Fudan University" }, { "name": "Yao, Yuhua", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Zhang, Zihe", "affiliation": "Fudan University" }, { "name": "Sun, Yang", "affiliation": "Fudan University" }, { "name": "Zou, Zhuo", "affiliation": "Fudan University" }, { "name": "Hu, Xiaoming", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Model validation", "Digital implementation", "Parametric optimization" ], "abstract": "Experimental digital twins (EDTs) for real-time industrial control must handle sparse data, stable long-horizon prediction, and nonstationary dynamics. Static or purely data-driven models rarely satisfy all three at once. We propose an evolvable physics-informed neural network (PINN) framework built around three modules: Dymo-PINN injects physical laws into system identification; Tempo-CL, a contrastive fine-tuning scheme, curbs error accumulation in iterative prediction; and ResAdapter, a lightweight adapter that enables online adaptation without full retraining. On permanent magnet synchronous motor (PMSM) benchmarks, the framework raises R^2 by up to 1.82times under severe data scarcity and holds MSEs to 10^{-1}--10^{-2} under parameter drift, surpassing conventional PINN, contrastive, and incremental learning baselines in data efficiency, robustness, and cross-configuration generalization.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA14.6", "code": "WeA14.6", "title": "Hybrid Modeling Framework for Flow Estimation in Concrete 3D Printing Using Pump Dynamics & Machine Learning (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA14", "sessionTitle": "JO-EAAI: Model Predictive Control and Model Validation", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Mohomad, Yosef", "affiliation": "Texas A&M University" }, { "name": "Tafreshi, Reza", "affiliation": "Texas A&M University at Qatar" }, { "name": "Pagilla, Prabhakar R.", "affiliation": "Texas A&M University" } ], "keywords": [ "Observer design", "Nonlinearity learning from data", "Model validation" ], "abstract": "This work develops a control-oriented hybrid modeling framework for concrete three-dimensional (3D) printing that couples first-principles pump dynamics with machine-learning (ML) estimators of fresh slurry density (rho) and viscosity (mu) to dynamically predict the slurry mass flow rate (dot{M}). A physics-guided feature set is screened using correlation, mutual information, and permutation importance and trained under grouped cross-validation across mixes (36 nominal mixes; 97 density and 195 rheology points). In the density predictor pipeline, a non-negative least-squares (NNLS)-stacked ensemble using Ridge, Extra Trees (ExT), and Random Forest (RF) outperforms single learners (coefficient of determination (R^2) approx 0.73). For viscosity, high-capacity learners dominate; the best performance is achieved by a ridge-meta stack trained over the full base set (R^2 approx 0.83). Unlike prior screw-extrusion flow-control work, validated on a single rig without a systematic sweep of mix compositions and reporting mean absolute error (MAE) of approx 6.7%, this study targets progressive-cavity pumping and learns rho and mu from a multi-mix dataset for dynamic, control-oriented flow prediction. Embedding the learned rho and mu maps within the pump model yields simulated flow-rate curves that closely follow laboratory measurements across water-accelerator settings and motor speeds, with MAE on the order of 1--2% of mean flow. The predictor is interpretable and low-latency (milliseconds per evaluation) and integrates with Simulink for feedforward planning, soft sensing, and closed-loop use. The workflow provides a reproducible template, from data preparation and feature screening to model training and deployment, for hybrid modeling in large-format additive construction.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA15.1", "code": "WeA15.1", "title": "Lower Bound Analysis of L2 Induced Norm for Discrete-Time Linear Parameter-Varying Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA15", "sessionTitle": "Linear Parameter-Varying Systems: Analysis, Control, and Applications", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Ishikawa, Kotaro", "affiliation": "Kyushu Institute of Technology" }, { "name": "Sebe, Noboru", "affiliation": "Kyushu Institute of Technology" } ], "keywords": [ "Linear parameter-varying systems", "Robustness analysis", "Control of uncertain LPV systems" ], "abstract": "This paper investigates the lower bound of the ℓ2 induced norm of discretetime linear systems subject to rate-bounded time-varying parameters. This paper proposes a numerical method combining the lifting technique for discrete-time systems and a numerical optimization to find a worst-case parameter sequence that attains the lower bound for the ℓ2 induced norm. This approach successfully identifies a periodic worst-case parameter sequence and the corresponding input signals, attaining lower bounds that closely match LMI-based upper bounds. A numerical example suggests that the worst-case parameter variation pattern and input signals are nontrivial to identify.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA15.2", "code": "WeA15.2", "title": "A Common Lyapunov Matrix Approach to the Exponential Stability of Augmented Primal-Dual Gradient Flow As LPV Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA15", "sessionTitle": "Linear Parameter-Varying Systems: Analysis, Control, and Applications", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Li, Mengmou", "affiliation": "Hiroshima University" }, { "name": "Zhu, Lijun", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Nagahara, Masaaki", "affiliation": "Hiroshima University" } ], "keywords": [ "Linear parameter-varying systems", "Lyapunov methods", "Passivity-based control" ], "abstract": "We show that a common Lyapunov matrix exists for the convex combination of two Hurwitz matrices if and only if the intersection of the set of strict Lyapunov matrices for one matrix and the set of non-strict Lyapunov matrices for the other is nonempty. This simple relaxation is useful for the convergence analysis of the augmented primal-dual gradient flow for constrained optimization problems with affine inequality constraints, which can be viewed as a polytopic linear parameter-varying (LPV) system driven by the active-constraint selector. Under a relaxed strong convexity condition, exponential convergence is proved for the LPV system. The analysis can further be extended to the integral quadratic constraints (IQCs) framework for LPV systems to facilitate numerical search of the convergence rate.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA15.3", "code": "WeA15.3", "title": "LPV Approach with Scheduling-Informed Performance Criteria for Vehicle Control Applications", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA15", "sessionTitle": "Linear Parameter-Varying Systems: Analysis, Control, and Applications", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Nemeth, Balazs", "affiliation": "SZTAKI" }, { "name": "Gaspar, Peter", "affiliation": "HUN-REN SZTAKI, Institute for Computer Science and Control, Hungarian Research Network" } ], "keywords": [ "Linear parameter-varying systems", "Control of uncertain LPV systems", "Robust control applications" ], "abstract": "This paper proposes a new Scheduling-Informed Linear Parameter Varying (SI-LPV) approach extended with scheduling-informed performance criteria. This method is able to handle actuator nonlinearities and unknown dynamics in linear systems, and as a novel element, the precise physically-based formulation of the performance criteria can be avoided. The SI-LPV approach uses Kolmogorov-Arnold representation theorem with ultra-local model formulation to achieve the control-oriented SI-LPV form. The paper presents how the measured performance signals can be built in the control. The effectiveness of the resulted controller is illustrated through examples, focusing on vehicle control problems, illustrating the effectiveness through longitudinal and vertical control design.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA15.4", "code": "WeA15.4", "title": "Autonomous Vehicle Trajectory Tracking: An Integrated Longitudinal/Lateral LPV MPC Approach", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA15", "sessionTitle": "Linear Parameter-Varying Systems: Analysis, Control, and Applications", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Cerrito, Francesco", "affiliation": "Politecnico Di Torino" }, { "name": "Morato, Marcelo Menezes", "affiliation": "Cnrs / Gipsa-Lab / Uga" }, { "name": "Canale, Massimo", "affiliation": "Politecnico Di Torino" }, { "name": "Sename, Olivier", "affiliation": "Universite Grenoble Alpes / Grenoble INP" } ], "keywords": [ "Linear parameter-varying systems" ], "abstract": "The trajectory tracking problem for autonomous vehicles can be enhanced by means of an effective unification of longitudinal and lateral control. To this end, Nonlinear Model Predictive Control (NMPC) is highly suitable as it inherently handles multi-objetive problems and accounts for process constraints. However, a key challenge in implementing NMPC schemes lies in the involved computational burden, which can impede systems subject to stringent real-time constraints. This issue directly affects the optimization feasibility, its overall effectiveness, and closed-loop stability. To overcome these limitations, we employ a Linear Parameter-Varying (LPV) representation of the vehicle's dynamic model. This article proposes an integrated LPV MPC solution for autonomous vehicle trajectory tracking. Accordingly, we propose an integrated LPV MPC establishing guarantees for both recursive feasibility and closed-loop stability via the formulation of parameter-dependent Linear Matrix Inequalities. The proposed approach is validated through comparative simulations using a high-fidelity nonlinear dynamic model of a scaled vehicle, demonstrating similar tracking performances compared to NMPC but strongly reducing computation time, making it suitable for real time applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA15.5", "code": "WeA15.5", "title": "Learning-Based LPV Control for Autonomous Vehicles", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA15", "sessionTitle": "Linear Parameter-Varying Systems: Analysis, Control, and Applications", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Fényes, Dániel", "affiliation": "Institute for Computer Science and Control (SZTAKI)" }, { "name": "Nemeth, Balazs", "affiliation": "SZTAKI" }, { "name": "Gaspar, Peter", "affiliation": "HUN-REN SZTAKI, Institute for Computer Science and Control, Hungarian Research Network" } ], "keywords": [ "Linear parameter-varying systems", "Learning methods for optimal control", "Robust control applications" ], "abstract": "The paper presents a novel combination method for integrating an LPV-based controller with reinforcement learning. The main idea behind the combination is that the agent learns the scheduling parameters of the LPV observer to cope with the unmodeled or uncertain dynamics of the considered system. The RL aims to simultaneously minimize the error of the observer and the tracking performance of the controller. In this way, the exploration of the nonlinear system and the stability of the closed-loop system can be guaranteed. The proposed method is validated through two vehicle-oriented control problems, namely the trajectory tracking of ground vehicles and traction control of trains.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA15.6", "code": "WeA15.6", "title": "Applying Kolmogorov-Arnold Networks to Improve Linear Quadratic Control Performance", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA15", "sessionTitle": "Linear Parameter-Varying Systems: Analysis, Control, and Applications", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Hegedűs, Tamás", "affiliation": "Budapest University of Technology and Economics" }, { "name": "Nemeth, Balazs", "affiliation": "SZTAKI" }, { "name": "Fényes, Dániel", "affiliation": "Institute for Computer Science and Control (SZTAKI)" }, { "name": "Gaspar, Peter", "affiliation": "HUN-REN SZTAKI, Institute for Computer Science and Control, Hungarian Research Network" } ], "keywords": [ "Linear systems", "Nonlinearity learning from data", "Design methods for data-based control" ], "abstract": "This paper presents a control architecture in which a Linear Quadratic Regulator (LQR) is integrated with a machine learning-based nonlinear compensator realized through a Kolmogorov-Arnold Network (KAN). The goal of the proposed approach is to achieve a balance between guaranteed closed-loop stability, computational efficiency, and enhanced control performance. First, a Reinforcement Learning (RL) framework is used to learn an effective nonlinear control policy across the entire operating range of the system. Then, the trained RL-based control method serves as the teacher network for the KAN-based algorithm. During the knowledge distillation, gradient regularization and coefficient constraints are applied to achieve a smooth and Lipschitz-bounded neural network-based controller. Stability is guaranteed for the combined LQR-KAN closed-loop system through the computation of the maximum allowable Lipschitz constant of the neural network. The proposed control structure is validated on a double inverted pendulum system. The results show that the combined KAN-based controller achieves nearly the same performance level as the RL-based method, while the Lipschitz constant and the complexity of the network are significantly reduced. This property directly supports simple stability analysis and reliable real-time implementation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA16.1", "code": "WeA16.1", "title": "HOSM Control of Single-Input LTV Systems Via a State Immersion (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA16", "sessionTitle": "Homogeneous and Finite-Time Sliding Mode Design", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Meléndez-Pérez, René", "affiliation": "Universidad Nacional Autónoma De México" }, { "name": "Moreno, Jaime A.", "affiliation": "Universidad Nacional Autonoma De Mexico-UNAM" }, { "name": "Fridman, Leonid", "affiliation": "National Autonomous University of Mexico" } ], "keywords": [ "Sliding mode control", "Linear systems", "Linear parameter-varying systems" ], "abstract": "This paper presents a methodology for high-order sliding-mode (HOSM) control of perturbed single-input linear time-varying (LTV) systems. The considered class satisfies a differential uniform controllability (DUC) property characterized through an extended controllability matrix. A key contribution is the construction of a state immersion that guarantees the existence of a sliding output with full relative degree. The methodology enables the implementation of HOSM controllers acting on the extended state, thereby ensuring finite-time stability and, under suitable parameter selection, fixed-time stability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA16.2", "code": "WeA16.2", "title": "Applying Generalized Homogeneity for Finite-Time Stabilization of Linear Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA16", "sessionTitle": "Homogeneous and Finite-Time Sliding Mode Design", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Labbadi, Moussa", "affiliation": "Bretagne INP UBO, IRDL" }, { "name": "Efimov, Denis", "affiliation": "Inria" }, { "name": "Polyakov, Andrey", "affiliation": "INRIA Lille" } ], "keywords": [ "Sliding mode control", "Lyapunov methods" ], "abstract": "This paper investigates the possibilities of application of generalized homogeneity for the finite-time stabilization in linear systems. The generalized dilation is derived that establishes homogeneity of a purely oscillating linear system and a serial connection of a linear oscillator and an integrator. We present a theoretical framework based on Implicit Lyapunov Functions (ILFs) to analyze finite-time stability of linear systems. Building on these results, a novel nonlinear control law is developed to achieve robust stabilization of linear systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA16.3", "code": "WeA16.3", "title": "Optimal Gain Selection for Second-Order Homogeneous Controllers (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA16", "sessionTitle": "Homogeneous and Finite-Time Sliding Mode Design", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Calmbach, Benjamin", "affiliation": "TU Ilmenau" }, { "name": "Moreno, Jaime A.", "affiliation": "Universidad Nacional Autonoma De Mexico-UNAM" }, { "name": "Reger, Johann", "affiliation": "TU Ilmenau" } ], "keywords": [ "Sliding mode control", "Lyapunov methods" ], "abstract": "Continuous homogeneous state-feedback control of a disturbed chain of integrators is considered for the exemplary second-order case. The control takes full state information corrupted with measurement noise. Within a high-gain setup, we choose the controller gains that are optimal w.r.t. the estimated effect of the noise and a matched disturbance on the control error. This is quantified in terms of the homogeneous Lp-gain, a generalization of the classical Lp-gain for homogeneous systems recently introduced. To estimate the homogeneous Lp-gain, we construct a solution to the associated Hamilton-Jacobi-Inequality based on a Lyapunov function. This estimate is minimized leading to an optimal gain-selection that balances the effect of noise and disturbance on the control error of interest. The estimation- and minimization procedures are evaluated numerically and applied to a laboratory experiment.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA16.4", "code": "WeA16.4", "title": "Robust Stabilization of Nonlinear Systems Using Homogeneous Invariant/attractive Ellipsoid Method (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA16", "sessionTitle": "Homogeneous and Finite-Time Sliding Mode Design", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Wang, Siyuan", "affiliation": "Beihang University" }, { "name": "Ping, Xubin", "affiliation": "Xidian University" }, { "name": "Polyakov, Andrey", "affiliation": "INRIA Lille" } ], "keywords": [ "Sliding mode control", "Stability of nonlinear systems", "Lyapunov methods" ], "abstract": "The paper deals with the problem of robust stabilization of a nonlinear plant. The method of homogeneous invariant/attractive ellipsoids is developed for a robust homogeneous stabilizer design. Similarly to linear case, the control tuning is based on solving of a semi-definite programming problem. Such a design is shown to be possible for a class of nonlinear systems satisfying a special homogeneous conic constraint, which means that the nonlinear system, in some sense, is close to a linear controllable system. The optimal tuning for a class of affine-in-control systems and homogeneous sliding mode control systems is studied. Theoretical results are supported by numerical simulations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA16.5", "code": "WeA16.5", "title": "Fast Fixed-Time Convergence in Nonlinear Dynamical Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA16", "sessionTitle": "Homogeneous and Finite-Time Sliding Mode Design", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Furtat, Igor", "affiliation": "Institute of Problems of Mechanical Engineering Russian Academy of Sciences" }, { "name": "Kuznetsov, Nikolay", "affiliation": "Saint-Petersburg State Univ" }, { "name": "Vrazhevsky, Sergey", "affiliation": "Institute for Problems in Mechanical Engineering of the Russian Academy of Sciences" } ], "keywords": [ "Sliding mode control", "Stability of nonlinear systems", "Lyapunov methods" ], "abstract": "A fast convergence in a fixed-time of solutions of nonlinear dynamical systems, for which special requirements are satisfied on the derivative of a quadratic function calculated along the solutions of the system, is proposed. The conditions for the system solutions to converge to zero and to a given region within a fixed-time are obtained. To achieve fast convergence, a negative power is applied to the derivative of a quadratic function within a specific time interval during the evolution of the system. The application of the proposed results to the design of control laws for arbitrary order linear plants using the backstepping method is considered. All the main results are accompanied by numerical modelling and a comparison of the proposed solutions with some existing ones.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA16.6", "code": "WeA16.6", "title": "An Optimal Control Approach to Finite Time Stabilization of Linear Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA16", "sessionTitle": "Homogeneous and Finite-Time Sliding Mode Design", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Weng, Weihao", "affiliation": "L2S, CentralesSupelec" }, { "name": "Chitour, Yacine", "affiliation": "Universit'e Paris-Sud, CNRS, Centralesupelec" }, { "name": "Mason, Paolo", "affiliation": "L2S CentraleSupélec, CNRS" } ], "keywords": [ "Optimal control theory", "Applications of optimal control", "Sliding mode control" ], "abstract": "This paper presents a finite-time stabilization method for the integrator chain via optimal control techniques by introducing a tailored cost function. We analyze key properties of the resulting value function to derive the corresponding Hamilton-Jacobi-Bellman equation. Numerical simulations illustrate the effectiveness of the approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA17.1", "code": "WeA17.1", "title": "Low Order Stable Controllers with Guaranteed Delay Margin (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Stability Analysis and Stabilization", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Gundes, A. Nazli", "affiliation": "Univ. of California" }, { "name": "Ozbay, Hitay", "affiliation": "Bilkent University" } ], "keywords": [ "Linear systems", "Linear time-delay systems", "System structure and control" ], "abstract": "Low-order stable controllers are designed for plants satisfying the parity interlacing property with constrained right half plane poles and zero structures, for three different classes. A lower bound of the delay margin achievable by the proposed controllers is also obtained. There are several free parameters in the low-order strongly stabilizing controller design procedure. It is shown, via examples from the literature, how these parameters affect the delay margin and the sensitivity of the feedback system.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA17.2", "code": "WeA17.2", "title": "A Class of Impulsive Systems with Impulse Delays: Stability and Disturbance Decoupling (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Stability Analysis and Stabilization", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Zattoni, Elena", "affiliation": "Alma Mater Studiorum Universita' Di Bologna" }, { "name": "Bartolucci, Veronica", "affiliation": "Università Politecnica Delle Marche" }, { "name": "Scaradozzi, David", "affiliation": "Università Politecnica Delle Marche" }, { "name": "Perdon, Anna Maria", "affiliation": "Accademia Marchigiana Di Scienze, Lettere Ed Arti" }, { "name": "Conte, Giuseppe", "affiliation": "Accademia Marchigiana Di Scienze, Lettere Ed Arti" } ], "keywords": [ "Linear time-delay systems", "Impulsive linear systems", "System structure and control" ], "abstract": "This work provides a constructive sufficient solvability condition for the disturbance decoupling problem with stability in a special class of impulsive systems with impulse delays. The so-called reset-delayed linear systems are impulsive systems in which, at the jump times, some or all state variables are brought back to the values they had a given amount of time, the reset delay, before the jump. The disturbance decoupling problem addressed consists of finding a state feedback such that the output of the compensated reset-delayed linear system is unaffected by the disturbance and the closed-loop dynamics is globally asymptotically stable provided that the time interval between any two consecutive jump times is sufficiently large — dwell-time global asymptotic stability. Under the assumption that the flow dynamics is stabilizable, the condition is expressed in geometric terms using a novel subspace that has appropriate structural properties with respect to the flow and jump dynamics and is internally stabilizable subject to the dwell-time constraint.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA17.3", "code": "WeA17.3", "title": "Distributed Delay Systems: An Improved Delay Lyapunov Matrix Based Stability Test (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Stability Analysis and Stabilization", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Castaño, Alejandro", "affiliation": "Autonomous University of the State of Hidalgo (UAEH)" }, { "name": "Aleksandrova, Irina", "affiliation": "University of Bonn" }, { "name": "Mondie, Sabine", "affiliation": "Cinvestav" } ], "keywords": [ "Linear time-delay systems", "Linear functional systems", "Lyapunov methods" ], "abstract": "This paper extends recent stability criteria in terms of delay Lyapunov matrices to the case of linear systems with both pointwise and distributed delays. The approach is based on Lyapunov–Krasovskii functionals with prescribed derivatives, where the kernels are approximated by piecewise linear matrix functions. Then, Gu's discretized Lyapunov functional method is employed to transform the approximated functional to a quadratic form structure, whereas finite dimension of the stability test is calculated by quantifying the approximation error. The resulting stability criterion maintains a convenient structure that depends on evenly spaced pointwise values of the delay Lyapunov matrix. This formulation leads to a finite computation process, reduces conservatism, and allows an efficient numerical implementation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA17.4", "code": "WeA17.4", "title": "On the Construction of the Delay Lyapunov Matrix for Linear Periodic Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Stability Analysis and Stabilization", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Aleksandrova, Irina", "affiliation": "University of Bonn" }, { "name": "Velázquez, Juan J.L.", "affiliation": "University of Bonn" } ], "keywords": [ "Linear time-delay systems", "PDEs for time delay systems", "Lyapunov methods" ], "abstract": "In this paper, the problem of constructing the delay Lyapunov matrix for linear periodic time delay systems with a constant delay is considered. It is known that, in the case when the delay is an integer multiple of the period of the system matrices, the delay Lyapunov matrix is given by a solution of a matrix hyperbolic PDE problem defined on a strip. Our approach is based on constructing the matrix fundamental solution for this hyperbolic PDE problem. The representation formula for the delay Lyapunov matrix is then derived. It depends on the fundamental solution and unknown initial matrices of the PDE problem. Finally, a non-standard boundary condition given in the PDE problem is transformed to a vector Fredholm integral equation of the second kind in order to determine initial matrices.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA17.5", "code": "WeA17.5", "title": "Checkable Conditions for Exponential Stability of Linear Time-Varying Positive Continuous-Time Difference Equations (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Stability Analysis and Stabilization", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Ridolfi, Giorgio", "affiliation": "Università Degli Studi Dell'Aquila" }, { "name": "De Iuliis, Vittorio", "affiliation": "San Raffaele University of Rome" }, { "name": "Manes, Costanzo", "affiliation": "Università Dell'Aquila" }, { "name": "Pepe, Pierdomenico", "affiliation": "University of L'Aquila" } ], "keywords": [ "Positive linear systems", "Linear functional systems", "Uncertain systems" ], "abstract": "This paper deals with the stability analysis of linear continuous-time positive difference equations with multiple time-varying delays. We consider the general case of time- varying matrices, and introduce novel sufficient conditions for global exponential stability with guaranteed exponential decay rate.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA17.6", "code": "WeA17.6", "title": "On the Digital Implementation of Continuous-Time Stabilizers for Nonlinear Systems with State Delays (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Stability Analysis and Stabilization", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Di Ferdinando, Mario", "affiliation": "Università Degli Studi Dell'Aquila" }, { "name": "Borri, Alessandro", "affiliation": "Istituto Di Analisi Dei Sistemi Ed Informatica \"A. Ruberti\" (IASI), Consiglio Nazionale Delle Ricerche (CNR)" }, { "name": "Di Gennaro, Stefano", "affiliation": "University of L'Aquila" }, { "name": "Pepe, Pierdomenico", "affiliation": "University of L'Aquila" } ], "keywords": [ "Nonlinear time-delay systems", "Sampled-data/digital control", "Stability of nonlinear systems" ], "abstract": "In this paper, the digital implementation of continuous-time stabilizers is addressed for nonlinear time-delay systems. In particular, for nonlinear systems with state delays and affected by known exogenous disturbances, it is shown the existence of a suitable fast sampling and of an accurate quantization of both input and output channels such that the digital event-triggered implementation of continuous-time global asymptotic stabilizers ensures the semi-global practical stability property of the corresponding closed-loop system with arbitrarily small final target ball of the origin. In the theory here developed, aperiodic sampling and the non-uniform quantization of both input/output channels are allowed. An application is presented to validate the results.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA18.1", "code": "WeA18.1", "title": "Shaping the Smart Industry Future of Dutch High-Tech Manufacturing: The Role of NXTGEN Program (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA18", "sessionTitle": "The Future of Operations in Industrial Plants through the Advances of Smart Manufacturing I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Al Habboush, Seymanur", "affiliation": "Eindhoven University of Technology" }, { "name": "Dang, Quang-Vinh", "affiliation": "Eindhoven University of Technology" }, { "name": "Akcay, Alp", "affiliation": "Northeastern University" }, { "name": "Adan, I.J.B.F.", "affiliation": "Eindhoven University of Technology" }, { "name": "Neijhorst, Henry", "affiliation": "Brainport Industries" } ], "keywords": [ "Industry X.0 for production and logistics", "Smart production and logistics in manufacturing", "Manufacturing engineering and management" ], "abstract": "Drawing on empirical insights and real-life examples from the Autonomous Factory project within the NXTGEN program, a Dutch national initiative in the high-tech sector, this study analyzes the barriers hindering Smart Industry (SI) adoption. It examines how these barriers can be alleviated through collaboration, shared expertise, and financial support. The paper then proposes a conceptual framework that links sector characteristics, barriers, and interventions that are often examined in isolation, and shows how coordinated actions can overcome the interconnected barriers of SI adoption in high-tech manufacturing. It also offers practical insights for policymakers and industrial managers seeking to accelerate SI transformation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA18.2", "code": "WeA18.2", "title": "Model-Based Systems Engineering for Concurrent Engineering in Manufacturing Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA18", "sessionTitle": "The Future of Operations in Industrial Plants through the Advances of Smart Manufacturing I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Palmitessa, Edoardo", "affiliation": "Politecnico Di Milano" }, { "name": "Polenghi, Adalberto", "affiliation": "Politecnico Di Milano" }, { "name": "Negri, Elisa", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Intelligent manufacturing systems", "Systems-of-systems", "Manufacturing engineering and management" ], "abstract": "Concurrent Engineering (CE) is increasingly adopted to address mass customization in manufacturing. Central to CE are the multi-disciplinary collaboration and cross-domain integration, which make Model-Based Systems Engineering (MBSE) a promising approach in this regard. Although MBSE is well established in industries such as aerospace, it is not yet tailored to the specific needs of discrete manufacturing. This paper reviews the existing literature on MBSE for CE to advance the understanding of its applicability in manufacturing systems, with particular focus on formal modeling and model-based analysis. The findings support the development of a reference framework that identifies key elements and pillars required to enable CE through MBSE. Building on this framework, a research agenda is proposed, highlighting the need for integrated formal models that encompass product, process, and production system domains, including their features and interdependencies. Advancing in this direction will enhance the reusability, scalability, and flexibility of manufacturing systems, supporting more effective CE.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA18.3", "code": "WeA18.3", "title": "Reinforcement Learning for Optimal Malt Kilning (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA18", "sessionTitle": "The Future of Operations in Industrial Plants through the Advances of Smart Manufacturing I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Romanelli, Giulio", "affiliation": "Swiss Data Science Center" }, { "name": "Castello, Roberto", "affiliation": "Swiss Data Science Center EPFL/ETHZ" }, { "name": "Bachmann, David", "affiliation": "Buhler AG, Uzwil , Switzerland (now at Sensirion Connected Solutions)" }, { "name": "Graeber, Matthias", "affiliation": "Bühler Group" } ], "keywords": [ "Industrial artificial intelligence", "Manufacturing plant simulation, control and optimization", "Intelligent manufacturing systems" ], "abstract": "The kilning phase in malt processing is essential for halting germination and inducing flavor, color, and aroma development in barley. Optimizing the kilning phase presents challenges due to its dynamic nature and high energy demands. Traditional control methods often result in sub-optimal outcomes. We propose a novel framework based on Reinforcement Learning (RL) to develop customized recipes for malt kilning, aiming to improve outcomes in energy consumption, duration, cost, and CO2-eq emissions. Our approach leverages RL algorithms to generate optimized recipes based on initial conditions and specific key performance indicators (KPIs). Initial results show improvements over existing methods in cost and time, indicating the potential for broader implementation and optimization in the malt processing industry.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA18.4", "code": "WeA18.4", "title": "A Novel Framework for Prescriptive Digital Twins in Production Scheduling (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA18", "sessionTitle": "The Future of Operations in Industrial Plants through the Advances of Smart Manufacturing I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Negri, Elisa", "affiliation": "Politecnico Di Milano" }, { "name": "Ragazzini, Lorenzo", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Cyber-physical production systems", "Industrial artificial intelligence", "Intelligent manufacturing systems" ], "abstract": "Abstract: Digital Twin applications in manufacturing increasingly require autonomy in decision-making, yet existing approaches fail to develop proper prescriptive capabilities that could enhance production planning and control activities. To cope with such limitations, this paper proposes a dual-agent framework that embeds a Reinforcement Learning agent directly within the Digital Twin, while maintaining an external Genetic Algorithm for long-term planning. The internal agent operates with continuous access to production resources states during simulation execution, while the external agent optimizes schedules across extended time horizons. The framework provides guidance for developing prescriptive Digital Twins capable of autonomous scheduling decisions in dynamic manufacturing environments. Validation within a highly automated assembly line demonstrates that the dual-agent approach outperforms traditional approaches in average lead time reduction, achieving deviation from the optimal below 2% and a 33% improvement over GA alone.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA18.5", "code": "WeA18.5", "title": "Performance Assessment of Vision Language Models in Industrial Documentation Analysis (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA18", "sessionTitle": "The Future of Operations in Industrial Plants through the Advances of Smart Manufacturing I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Sabetta, Nicolò", "affiliation": "Sapienza Università Di Roma" }, { "name": "Colabianchi, Silvia", "affiliation": "Universitas Mercatorum" }, { "name": "Gentilotti, Francesco", "affiliation": "Sapienza Università Di Roma" }, { "name": "Costantino, Francesco", "affiliation": "Sapienza University of Rome" } ], "keywords": [ "Industrial artificial intelligence", "Intelligent manufacturing systems", "Smart production and logistics in manufacturing" ], "abstract": "The interpretation of complex industrial documentation remains a challenge for Artificial Intelligence in manufacturing. This paper presents an evaluation of state-of-the-art Vision Language Models (VLMs) (GPT-5, Gemini 2.5 Pro, Claude Sonnet 4.5, Claude Opus 4, Qwen VL Max, Llama 4 Maverick) as support assistants. Using a dataset from an industrial machine programming manual, performance on images, diagrams, graphs and tables at different cognitive difficulty levels is tested. A multidimensional metrics methodology is employed. Results show strong performance on images and schematics but notable degradation on quantitative elements, highlighting the need for dedicated benchmarks for VLMs in industrial contexts.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA19.1", "code": "WeA19.1", "title": "A New Approach for Failure Detection at the Final Quality Inspection Line in the Automotive Manufacturing Industry", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) IV", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "García Martínez, Mario", "affiliation": "UPC/SEAT" }, { "name": "Cembrano, Gabriela", "affiliation": "CSIC-UPC" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "Vicente Ferreira, Jessica", "affiliation": "SEAT" } ], "keywords": [ "Data-driven and AI-based modelling of production and logistics", "Intelligent manufacturing systems", "Industrial artificial intelligence" ], "abstract": "Final quality testing in automotive manufacturing is crucial to ensure standards before delivery. This study applies sequential pattern mining to sequences of defects and reworks, extracting frequent patterns as features in classification models that predict final test outcomes. The approach enables a reduction in input data dimensionality and the selection of representative variables. Results show that using mined patterns as predictors lowers misclassifications of defective vehicles compared to using all variables, with only a slight decrease in overall accuracy. This method enhances the efficiency and reliability of final quality assurance processes.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA19.2", "code": "WeA19.2", "title": "An Agent Model Abstraction for Human-AI Teaming Cognitive Coupling (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) IV", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Kottagaha, Kolitha", "affiliation": "University of Groningen" }, { "name": "Bokhorst, J.A.C.", "affiliation": "University of Groningen" }, { "name": "Gaffinet, Ben", "affiliation": "Luxembourg Institute of Science and Technology" }, { "name": "Emmanouilidis, Christos", "affiliation": "Univeristy of Groningen" } ], "keywords": [ "Cyber-physical-social systems in enterprises", "AI-based enterprise systems", "Enterprise interoperability" ], "abstract": "Industrial environments increasingly rely on collaboration between humans and AI-enabled agents. Effective teamwork requires aligning how agents perceive situations, plan actions to pursue goals, and adapt to changing conditions, yet existing systems lack mechanisms for cross-agent cognitive processes coupling. This paper presents a conceptual cognitive agent model that formalises cognitive coupling through eight components: Input, Process, Output, State, Value, Memory, World Model, and Goal. The model abstracts how agents coordinate and co-regulate their cognitive cycles, providing a basis for analysing distributed cognition and designing cognitively interoperable human–AI systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA19.3", "code": "WeA19.3", "title": "Ontology-Guided Cognitive Digital Twin with Cognitive Architecture for Autonomous Ability Learning in HRC (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) IV", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Al Haj Ali, Jana", "affiliation": "University of Lorraine" }, { "name": "Pannequin, Rémi", "affiliation": "CNRS & Lorraine University" }, { "name": "Zimmermann, Emmanuel", "affiliation": "CRAN" }, { "name": "Lezoche, Mario", "affiliation": "CRAN, Nancy-University, CNRS" }, { "name": "Panetto, Hervé", "affiliation": "CRAN, University of Lorraine, CNRS" }, { "name": "Naudet, Yannick", "affiliation": "Luxembourg Institute of Science and Technology (LIST)" } ], "keywords": [ "Robotics in manufacturing systems", "Cyber-physical-social systems in enterprises", "Human-technology integration in manufacturing" ], "abstract": "Human-Robot Collaboration (HRC) requires robots to interpret task semantics, understand human actions, and adapt autonomously when humans deviate from expected workflows. This paper describes how the cognition and simulation functions of a Cognitive Digital Twin (CDT) can be implemented by combining ontology-based reasoning with the CLARION cognitive architecture. A modular ontology (AI4C2PS) encodes tasks, affordances, capabilities, and abilities, enabling SWRL-based inference to determine when a robot can perform an action. These inferences are fed into CLARION, where the explicit subsystem handles semantic knowledge and the implicit subsystem learns procedural skills through Q-learning in simulation. A collaborative screwing scenario demonstrates how a cobot that was not initially programmed for screwing can infer the helical-motion capability and learn to execute it autonomously.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA19.4", "code": "WeA19.4", "title": "Cognitive Human–AI Collaboration for Microclimate Improvement: The GAIA Conceptual Architecture (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) IV", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Bafna, Nayan", "affiliation": "Otto-Von-Guericke University, Magdeburg" }, { "name": "Reider, Richard", "affiliation": "Otto-Von-Guericke-University" }, { "name": "Reggelin, Tobias", "affiliation": "Otto Von Guericke University Magdeburg" }, { "name": "Lang, Sebastian", "affiliation": "OVGU" } ], "keywords": [ "Large-scale complex systems", "Complex dynamic systems", "Interconnected dynamical systems" ], "abstract": "As urban heat island (UHI) effects—where urban areas experience significantly higher temperatures than surrounding rural regions—intensify under climate change, urban heat mitigation is becoming a central challenge for cities. Urban microclimate‑sensitive planning, however, still relies on fragmented tools, specialized expertise, and workflows that are difficult to integrate into early design phases. Generative AI offers new opportunities to ease this complexity, but its practical use in urban microclimate improvement planning remains largely unexplored. This paper presents GAIA, a conceptual generative‑AI planning assistant built on a multilayer architecture that integrates LLM‑based reasoning, geospatial context, domain‑knowledge retrieval, microclimate simulation feedback, and digital‑twin visualization. We detail the architectural design and illustrate its operation through a planner‑centred workflow. The concept provides a technically grounded foundation for next‑generation, AI‑supported urban microclimate planning.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA19.5", "code": "WeA19.5", "title": "An Interactive Cognition Framework for Human-Systems Collaborative Decision-Making (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) IV", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Wang, Lei", "affiliation": "Alibaba Group" } ], "keywords": [ "AI-based enterprise systems", "Cyber-physical-social systems in enterprises", "Enterprise interoperability" ], "abstract": "As enterprise systems evolve toward more autonomous, adaptive, and context-aware capabilities, understanding how humans and systems can effectively interact, co-operate and collaborate is critical. This paper proposes an interactive cognition framework for constructing human-like cognition models, following the human-like cognitive processes, but also reasoning mechanisms, as well as methods for interpreting human intent, adapting machine behavior, creating common understanding or mindset, and fostering trust and transparency. By integrating large language model (LLM) with mathematical computation models of physical entities and enterprise business operation processes, and traditional optimization and machine learning algorithms, autonomous agents are developed for human-systems collaborative decision-making. A real-world industrial application case study is presented to illustrate the method for developing the interactive cognition agents, and specify the challenging factors and problems that should be considered and solved in the future.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA19.6", "code": "WeA19.6", "title": "Market-Based Mechanisms Assessment for Carbon Capture and Utilization: Optimal Supply Chain Design for Green Olefin Production", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA19", "sessionTitle": "Cognition for Human-Systems Collaboration: Models and Applications (C-HSC) IV", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Crîstiu, Daniel", "affiliation": "The Sargent Centre for Process Systems Engineering, Imperial College London" }, { "name": "Klosterhalfen, Steffen", "affiliation": "BASF" }, { "name": "Walz, Olga", "affiliation": "RWTH Aachen UniversityOlga" }, { "name": "Holtze, Christian", "affiliation": "BASF SE, 37063 Ludwigshafen Am Rhein" }, { "name": "del Rio-Chanona, Ehecatl Antonio", "affiliation": "Imperial College London" }, { "name": "Oluleye, Gbemi", "affiliation": "The Grantham Institute for Climate Change, Imperial College London" } ], "keywords": [ "Sustainable and circular supply chain and production", "Supply chain and logistics engineering, simulation and optimization", "Sustainable and circular manufacturing systems" ], "abstract": "Carbon Capture and Utilization (CCU) is a key pathway to decarbonize the chemical industry, but its economic viability strongly depends on policy and market conditions. This paper presents the optimal design of a CCU supply chain converting biogenic CO2-to-alcohols-to-olefins, aiming to minimize total cost of the supply chain, and integrating environmental performance. The optimal CCU system is evaluated by applying market-based mechanisms to quantify the impact of carbon pricing, hydrogen subsidies, product credits as demand pull instruments. Product Price Parity Index and Market Viability Index metrics are used to benchmark competitiveness against conventional fossil-based olefin production route. Results show that green olefins remain economically uncompetitive under current UK market conditions. However, a mix of mechanisms improve economic performance, enabling competitiveness with fossil-based route. This framework supports quantitative, policy-aware decision-making for CCU deployment.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA20.1", "code": "WeA20.1", "title": "Evaluating Operator Engagement with MPC Using Eye Tracking in Complex Process Environments (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA20", "sessionTitle": "JO-JPC: Advanced Process Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Chandra, Rubal", "affiliation": "Indian Institute of Technology, Madras" }, { "name": "Kaisare, Niket", "affiliation": "Indian Institute of Technology - Madras" }, { "name": "Srinivasan, Rajagopalan", "affiliation": "Indian Institute of Technology Madras" } ], "keywords": [ "Advanced process control", "Industrial applications of chemical process control" ], "abstract": "Chemical processes are overseen by operators through human-machine interfaces (HMIs). Advanced control schemes such as model predictive control (MPC) are increasingly used to manage these complex chemical processes. However, MPC is perceived as a complex technique. This makes it non-intuitive for the operators, prompting them to manually override the controller. This work aims to understand human interaction with these advanced systems, especially in the presence of abnormal situations. We use a reactor-separator system, equipped with HMI to mimic an industrial control system and an eye tracker to analyze human interactions with the control system. Human factors study is performed with student participants who play the role of plant operators in our study. A comparison is made with the conventional system that involves only PID control. Results show their propensity to manually turn off MPC when faced with abnormal situations. However, compared to conventional control, participants in our study show a proactive approach when dealing with MPC, an improved performance and a clear effect of learning with multiple repetitions. Finally, analysis of dwell duration provides new insights into their perception of advanced control systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA20.2", "code": "WeA20.2", "title": "Pseudo Predictor for Tracking Control of Fully Actuated Nonlinear Systems with a Constant Input Delay and Application to a Two-Stage Chemical Reactor (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA20", "sessionTitle": "JO-JPC: Advanced Process Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Zhang, Xujie", "affiliation": "Southern University of Science and Technology" }, { "name": "Duan, Guang-Ren", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Advanced process control", "Industrial applications of chemical process control", "Industrial applications of process control" ], "abstract": "This paper investigates the tracking control problem for fully actuated nonlinear systems with a constant input delay. We combine the fully actuated system (FAS) approach with a pseudo predictor strategy. This strategy predicts future tracking errors by integrating a user-defined linear stable error dynamics, thus avoiding the instability arising from the prediction based on the potentially unstable open-loop system. We then establish a two-layer stability analysis framework from an input-to-state stability (ISS) perspective. The outer-layer tracking error system is input-to-state stable with respect to the inner-layer prediction bias. Based on this property, the prediction bias dynamics can be separated from the tracking error dynamics, and the asymptotic stability of the tracking error follows from the prediction bias being stabilized by Lyapunov-Krasovskii (LK) theory. Furthermore, the effectiveness of the proposed method is verified through its application to a two-stage chemical reactor.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA20.3", "code": "WeA20.3", "title": "A Fully Actuated System Approach to Dynamic Event-Triggered Control of a Continuous Stirred Tank Reactor with Prescribed Performance (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA20", "sessionTitle": "JO-JPC: Advanced Process Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Wang, Tan", "affiliation": "Southern University of Science and Technology" }, { "name": "Fan, Jinpeng", "affiliation": "Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and T" }, { "name": "Chen, Qihua", "affiliation": "Southern University of Science and Technology" }, { "name": "Kong, He", "affiliation": "Southern University of Science and Technology" }, { "name": "Duan, Guang-Ren", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Advanced process control", "Industrial applications of process control", "Interaction between design and control in processes" ], "abstract": "This paper proposes a novel dynamic event-triggered control (ETC) scheme for the continuous stirred tank reactor (CSTR) system with prescribed performance based on the fully actuated system approach (FASA) framework. First, a new prescribed-time performance function (PTPF) is designed to encode transient bounds and convergence time. Subsequently, the high-order error FAS model under the PTPF is derived, which enables explicit cancellation of nonlinear terms and closed-loop eigenstructure assignment. Based on this, a FASA-based dynamic ETC strategy with prescribed performance is established, in which an enhanced error-dependent triggering mechanism is employed to improve communication efficiency without sacrificing control performance. Rigorous analysis shows that the tracking error is confined to a prescribed set within the prescribed time and that Zeno behavior is excluded.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA20.4", "code": "WeA20.4", "title": "RTO and Advanced Process Control for Electric Submersible Pumps Using Surrogate Models (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA20", "sessionTitle": "JO-JPC: Advanced Process Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Costa, Erbet Almeida", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Rebello, Carine", "affiliation": "NTNU: Norwegian University of Science and Technology" }, { "name": "Nogueira, Idelfonso", "affiliation": "NTNU" } ], "keywords": [ "Advanced process control", "Real-time optimization and control in chemical processes", "Machine learning and artificial intelligence in chemical process control" ], "abstract": "This paper proposes a real-time optimisation (RTO) framework that uses AI-based surrogate models integrated with the advanced regulatory control (ARC). The main contribution is a method that employs a vertically decomposed control architecture, in which the RTO is formulated using steady-state surrogate models for both objectives and constraints. The RTO layer is responsible for guiding advanced regulatory control (ARC) to the optimal point subject to constraints, and the ARC layer coordinates the PID controllers during dynamic behaviour. The case study is an Electric Submersible Pump (ESP) system controlled with an ARC scheme. Results show effective constraint handling, operation within feasible regions, and adaptability to shifts between production and efficiency maximisation—antagonistic goals in ESP operation. The method is computationally efficient, resulting in a 99% reduction in processing time compared to differential equation methods. The RTO also re-optimised operating conditions following unmeasured disturbances. From a practical standpoint, the results indicate that real-time implementation can be more efficient and requires less computational effort.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA20.5", "code": "WeA20.5", "title": "Fast and High-Precision Temperature Control for a Semiconductor Vertical Furnace Via Empirical Heat Capacity Modeling (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA20", "sessionTitle": "JO-JPC: Advanced Process Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Budiono, Christian Milleneuve", "affiliation": "The University of Tokyo" }, { "name": "Hirata, Akira", "affiliation": "Tokyo Electron Technology Solutions" }, { "name": "Yamaguchi, Tatsuya", "affiliation": "Tokyo Electron Technology Solutions" }, { "name": "Ohnishi, Wataru", "affiliation": "The University of Tokyo" } ], "keywords": [ "Industrial applications of chemical process control", "Process modeling, identification, and estimation techniques", "Industrial applications of process control" ], "abstract": "To continue to support technological advancements in integrated circuits, semiconductor vertical furnace, which is widely used for oxidation, layer deposition, and annealing process, needs fast and high-precision temperature control even more. Conventional model-based control methods use a predetermined reference trajectory, which limits its speed during a temperature rise/fall process. Trajectory generation methods based on linear-time-invariant (LTI) models find difficulty due to the temperature dependency of the furnace. Therefore, the aim of this paper is to propose a control framework to generate fast trajectory which can cope with temperature dependency. This can be achieved by using heat capacity as a model which can be calculated empirically. Experiments on a full-size semiconductor vertical furnace verified its performance, being able to track the average temperature from 300°C to 400°C with nearly 50% time reduction compared to the control with a predetermined reference trajectory.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA20.6", "code": "WeA20.6", "title": "A Robust Framework Based on Symbolic Regression to Develop Controllers for Crystallization Processes (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA20", "sessionTitle": "JO-JPC: Advanced Process Control I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Arrais Romero Dias, Lima, Fernando", "affiliation": "Federal University of Rio De Janeiro" }, { "name": "Guedes Fernandes de Moraes, Marcellus", "affiliation": "University of São Paulo" }, { "name": "Leblebici, M. Enis", "affiliation": "KU Leuven" }, { "name": "Secchi, Argimiro R.", "affiliation": "Peq - Coppe/ufrj" }, { "name": "Souza Jr., Maurício", "affiliation": "Federal University of Rio De Janeiro" }, { "name": "Nogueira, Idelfonso", "affiliation": "NTNU" } ], "keywords": [ "Machine learning and artificial intelligence in chemical process control", "Batch and semi-batch process control", "Advanced process control" ], "abstract": "This work introduces symbolic regression for controlling crystallization, presenting a robust methodology for controller development. Symbolic regression generated an equation to compute optimal control actions from temperature, supersaturation, and error measurements. The approach was tested in paracetamol batch crystallization in ethanol to regulate mass yield and crystal size by manipulating temperature. Its performance was compared to a nonlinear model predictive controller (NMPC) using a population balance model (PBM) as the internal model and a feedforward neural network trained with the same dataset. All approaches achieved successful control, but machine learning methods required a lower computational cost. Considering disturbance, plant model mismatches and noise, symbolic regression maintained variables near set-points with fewer temperature changes.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA21.1", "code": "WeA21.1", "title": "Voltage Control in Unbalanced Distribution Networks Using a Deep Deterministic Policy Gradient Reinforcement Learning Algorithm", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA21", "sessionTitle": "Resiliency of Power Systems with High Penetration of Renewables", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Bai, Wenlei", "affiliation": "ERCOT" }, { "name": "Meng, Fanlin", "affiliation": "University of Exeter" }, { "name": "Lee, Kwang Y.", "affiliation": "Baylor University" } ], "keywords": [ "Distributed optimization for smart grids", "Control and management of energy systems", "Energy management systems" ], "abstract": "As distributed energy resources (DERs) penetrate distribution networks increasingly, the system becomes more unbalanced and thus, voltage issues arise frequently. To maintain voltages within a valid range while tolerating slight violations for short periods, voltage-optimization controls have been implemented to address voltage issues. Rather than linearizing the AC power flow equations to control the voltage, a deep deterministic policy gradient (DDPG) reinforcement learning (RL) algorithm is proposed to output regulator setpoint controls continuously. Such an approach offers real-time control once the models are trained for precise regulation without linearization errors. An efficient three-phase unbalanced power flow solver ensures high-fidelity RL environment during training. The algorithm is validated on the IEEE 13 and 123 bus systems for a one-hour snapshot control and demonstrates the effectiveness of the RL learned policy in minimizing voltage violations under varying load condition, promising extended controls, such as capacitors in sequential operation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA21.2", "code": "WeA21.2", "title": "Dynamic Modular Reconstruction Deep Echo State Network for Photovoltaic Power Prediction", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA21", "sessionTitle": "Resiliency of Power Systems with High Penetration of Renewables", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Chen, Jianwei", "affiliation": "Beijing University of Chemical Technology" }, { "name": "Li, Dazi", "affiliation": "Beijing University of Chemical Technology" }, { "name": "Karimi, Hamid Reza", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Forecasting of power supply and demand", "Solar energy", "Energy management systems" ], "abstract": "Accurate prediction of photovoltaic power generation is essential for maintaining grid stability and optimizing energy management. However, high nonlinearity, uncertainty and complex timing dependence of its power output make accurate prediction challenging. To this end, this paper proposes a dynamic modular reconstruction deep echo state network (DMRDESN). Historical data is first mapped to a high-dimensional state space through the reservoir. Then, according to the dynamic state of the reservoir neurons, similar neurons are grouped by the layer clustering method to form a modular sub-reservoir. The central neuron is determined in each module and interacts with other sub-reservoirs. Then, according to the modular structure, the weight of the reservoir is systematically reconstructed to enhance its representation ability. Experimental results show that the proposed DMRDESN model is tested on Mackey-Glass system and photovoltaic power generation data sets, and its prediction accuracy and stability are better than other prediction models.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA21.3", "code": "WeA21.3", "title": "Koopman Operator Based Inter-Area Oscillation Damping Controller of Grid-Forming Converters (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA21", "sessionTitle": "Resiliency of Power Systems with High Penetration of Renewables", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Wang, Weiyu", "affiliation": "Changsha University of Science and Technology" }, { "name": "Yang, Zhilin", "affiliation": "Changsha University of Science & Technology" }, { "name": "Cao, Yijia", "affiliation": "Changsha University of Science and Technology" }, { "name": "Chen, Chun", "affiliation": "Changsha University of Science and Technology" }, { "name": "Li, Yong", "affiliation": "Hunan University" } ], "keywords": [ "Power systems stability" ], "abstract": "大规模可再生能源资源的整合，导致系统惯性和阻尼减少，且 导致严重低频风险增加 振荡。网格成形（GFM）转换器的出现包括 这是一种增强系统惯性的有前景解决方案。然而，GFM转换器也可能参与，并且有可能 放大电力系统固有的振荡模式。要处理 本期论文提出基于库普曼算子的方案 用于GFM转换器的阻尼控制器。非线性 功率系统的动力学用线性表示 通过动态模式分解（DMD）识别模型，库普曼算符的数值实现。基于 该模型被识别为线性二次调节器（LQR） 用于设计通过GFM增强系统阻尼 转换器。在区域间进行的模拟研究 电力系统测试基准测试验证 确定了模型，并展示了该模型的有效性 拟议的阻尼控制器。", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA21.4", "code": "WeA21.4", "title": "Small-Signal Stability Guaranteed Power Flow Calculation Based on Energy Function Convexity", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA21", "sessionTitle": "Resiliency of Power Systems with High Penetration of Renewables", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Sun, Boqiang", "affiliation": "Institute of Science Tokyo" }, { "name": "Yashiba, Hiroo", "affiliation": "Institute of Science Tokyo" }, { "name": "Ishizaki, Takayuki", "affiliation": "Tokyo Institute of Technology" } ], "keywords": [ "Power systems stability", "Control and management of energy systems", "Control and optimization for sustainability and energy systems" ], "abstract": "This paper proposes an optimization-based power flow calculation that guarantees small-signal stability for inverter-integrated power systems comprising synchronous generators, grid-forming (GFM), and grid-following (GFL) inverters. We demonstrate that the equilibrium of such a system can be determined using an equivalent system composed solely of GFM and GFL inverters. Based on this equivalence, we formulate the power flow calculation as an optimization problem by leveraging the convexity of the energy function. The solution is constrained within the convex domain of the energy function, thereby guaranteeing small-signal stability. Numerical simulations validate the effectiveness of the proposed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA21.5", "code": "WeA21.5", "title": "Energy Function-Based Small-Signal Stability Constrained Optimal Power Flow Calculation Using SOCP Relaxation", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA21", "sessionTitle": "Resiliency of Power Systems with High Penetration of Renewables", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Lee, Byeonghwa", "affiliation": "Institute of Science Tokyo" }, { "name": "Koizumi, Jigen", "affiliation": "Institute of Science Tokyo" }, { "name": "Terao, Kentaro", "affiliation": "Institute of Science Tokyo" }, { "name": "Nishino, Taku", "affiliation": "Tokyo Institute of Technology" }, { "name": "Iino, Yutaka", "affiliation": "WASEDA University" }, { "name": "Ishizaki, Takayuki", "affiliation": "Tokyo Institute of Technology" } ], "keywords": [ "Power systems stability", "Energy market", "Electrical distribution systems" ], "abstract": "This paper proposes an energy function-based small-signal stability constrained optimal power flow (EF-SSSC-OPF) calculation for a homogeneous lossy power system with a radial structure. The energy function is defined for a lossless power system, which is equivalent to a homogeneous lossy power system. The EF-SSSC is formulated as an inequality constraint requiring the minimum eigenvalue of the energy function’s Hessian to exceed a user-specified value. An alternative SOCP relaxation linearizes the EF-SSSC and convexifies the OPF, thereby formulating the EF-SSSC-OPF as a convex optimization problem. Numerical simulations demonstrate the effectiveness of the proposed method in ensuring small-signal stability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA21.6", "code": "WeA21.6", "title": "Geometric Decentralized Stability Certificate for Power Systems Based on Projecting DW Shells", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA21", "sessionTitle": "Resiliency of Power Systems with High Penetration of Renewables", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Huang, Linbin", "affiliation": "Zhejiang Univeristy" }, { "name": "Luo, Liangxiao", "affiliation": "Zhejiang University" }, { "name": "Leng, Ruohan", "affiliation": "Zhejiang University" }, { "name": "Xin, Huanhai", "affiliation": "Zhejiang University" }, { "name": "Wang, Dan", "affiliation": "Nanjing University" }, { "name": "Dorfler, Florian", "affiliation": "Swiss Federal Institute of Technology (ETH) Zurich" } ], "keywords": [ "Power systems stability", "Power electronics" ], "abstract": "The development of decentralized stability conditions has gained considerable attention due to the need to analyze multi-agent network systems, such as heterogeneous multi-converter power systems. This paper proposes a geometric decentralized stability condition based on Davis-Wielandt (DW) shell and its projections, which provides a geometric interpretation of the small-gain and small-phase theorems and enables decentralized stability analysis of power systems. It serves as a visualization method to understand the closed-loop interactions and assess the stability of large-scale network systems in a scalable and modular manner.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA22.1", "code": "WeA22.1", "title": "Modelica-Based Digital Twin for the Italian Natural Gas Network Infrastructure", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA22", "sessionTitle": "Control and Management of Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Milo, Sergio", "affiliation": "Politecnico Di Milano" }, { "name": "Petrovic, Stepa", "affiliation": "Politecnico Di Milano" }, { "name": "Croce, Michele", "affiliation": "Snam" }, { "name": "Casella, Francesco", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Control and management of energy systems" ], "abstract": "Transmission system operators in the natural gas sector, confronted with increasing challenges arising from the ongoing energy transition, require advanced optimization and simulation tools to support informed decision-making and efficient management of network infrastructure. This paper presents a methodology for developing a digital twin of a real gas transmission network. The proposed framework enables state estimation and data reconciliation to achieve a coherent and systematic representation of the physical system’s behavior. Preliminary validation has been conducted using synthetic data, with future work focusing on the application and validation of the approach using real operational data.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA22.2", "code": "WeA22.2", "title": "Broadband Impedance-Matching Control of Nonlinear Wave Energy Converters", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA22", "sessionTitle": "Control and Management of Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Bonfanti, Mauro", "affiliation": "Politecnico Di Torino" }, { "name": "Paduano, Bruno", "affiliation": "Politecnico Di Torino" }, { "name": "Niosi, Francesco", "affiliation": "Politecnico Di Torino" }, { "name": "Matiazzo, Giuliana", "affiliation": "Politecnico Di Torino" } ], "keywords": [ "Control and management of energy systems", "Energy communities" ], "abstract": "This paper presents a multi-frequency extension of the Impedance–Matching control framework for nonlinear Wave Energy Converters based on a spectral-domain technique. Building on previous single-frequency formulations, the proposed approach identifies the impedance-matching transfer function over a representative frequency range of the wave excitation spectrum, enabling improved energy absorption under irregular sea conditions. Two feedback controllers are compared: a conventional reactive controller and a broadband controller tuned via spectral-domain identification. Numerical experiments on a nonlinear point absorber model demonstrate enhanced absorbed power and improved phase alignment between excitation force and velocity, validating the effectiveness of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA22.3", "code": "WeA22.3", "title": "Safe Deep Reinforcement Learning for Building Heating Control and Demand-Side Flexibility", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA22", "sessionTitle": "Control and Management of Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Jüni, Colin", "affiliation": "Empa" }, { "name": "Montazeri`, Mina", "affiliation": "Empa" }, { "name": "Guo, Yi", "affiliation": "Beijing Institute of Technology" }, { "name": "Bellizio, Federica", "affiliation": "Empa" }, { "name": "Sansavini, Giovanni", "affiliation": "ETH" }, { "name": "Heer, Philipp", "affiliation": "Empa, Urban Energy Systems" } ], "keywords": [ "Control and management of energy systems", "Demand response", "Big data and machine learning applied to smart cities" ], "abstract": "Buildings account for approximately 40% of global energy consumption, and with the growing share of intermittent renewable energy sources, enabling demand-side flexibility, particularly in heating, ventilation and air conditioning systems, is essential for grid stability and energy efficiency. This paper presents a safe deep reinforcement learning-based control framework to optimize building space heating while enabling demand-side flexibility provision for power system operators. A deep deterministic policy gradient algorithm is used as the core deep reinforcement learning method, enabling the controller to learn an optimal heating strategy through interaction with the building thermal model while maintaining occupant comfort, minimizing energy cost, and providing flexibility. To address safety concerns with reinforcement learning, particularly regarding compliance with flexibility requests, we propose a real-time adaptive safety-filter to ensure that the system operates within predefined constraints during demand-side flexibility provision. The proposed real-time adaptive safety filter guarantees full compliance with flexibility requests from system operators and improves energy and cost efficiency — achieving up to 50% savings compared to a rule-based controller — while outperforming a standalone deep reinforcement learning-based controller in energy and cost metrics, with only a slight increase in comfort temperature violations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA22.4", "code": "WeA22.4", "title": "Optimisation of Multi-Zone District Heating Networks Via Dual Decomposition", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA22", "sessionTitle": "Control and Management of Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Jensen, Christian Møller", "affiliation": "Aalborg University" }, { "name": "Falsone, Alessandro", "affiliation": "Politecnico Di Milano" }, { "name": "Prandini, Maria", "affiliation": "Politecnico Di Milano" }, { "name": "Nielsen, Brian Kongsgaard", "affiliation": "Grundfos" }, { "name": "Bendtsen, Jan Dimon", "affiliation": "Aalborg Univ" }, { "name": "Kallesøe, Carsten Skovmose", "affiliation": "Grundfos" } ], "keywords": [ "Control and management of energy systems", "Distributed optimization and control for smart cities", "Thermal systems modelling" ], "abstract": "District heating networks are projected to play a major role in the decarbonisation of building energy use, but traditional architectures are subject to significant inefficiency due to heat losses. Splitting networks into multiple lower-temperature zones is a promising method of reducing heat losses, but introduces privacy and coordination concerns. We show that the problem of optimising a multi-zone district heating network can be cast as a multi-agent constraint-coupled optimisation problem with linear coupling constraints, and we propose a decentralised algorithm based on the dual decomposition technique for its solution. Numerical examples show that that our decentralised approach recovers the same solution as optimising centrally, and that the problem exhibits both the static and time-varying turnpike properties.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA22.5", "code": "WeA22.5", "title": "Stochastic Tube-Based Economic MPC for Microgrid Energy Management under Gaussian Uncertainty", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA22", "sessionTitle": "Control and Management of Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Dankir, Sara", "affiliation": "Institut De Robòtica I Informàtica Industrial (CSIC-UPC), Carrer Llorens Artigas, 4-6, 08028 Barcelona, Spain , TED: AEEP, FPL, Ab" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "Lasri, rAFIK", "affiliation": "TED: AEEP, FPL, Abdelmalek Essaadi University, Tetouan 93000" } ], "keywords": [ "Control and management of energy systems" ], "abstract": "This paper presents a Stochastic Tube-Based Economic Model Predictive Control (ST-EMPC) framework for microgrid energy management under Gaussian demand uncertainty. Existing tube-MPC methods rely on fixed or simplified uncertainty bounds and do not widely exploit forecasting-based uncertainty information. Therefore, we propose a controller that incorporates Gaussian disturbances through chance constraints driven by a forecast-dependent standard deviation, which enables adaptive constraint tightening and reduces robust MPC conservatism while maintaining feasibility. The framework is implemented in a grid-connected and multi-energy microgrid. Simulation results show full constraint feasibility, millisecond-level solve times, improved economic performance with only a 1.7% increase in cost compared to the oracle case, where the demand is fully known, and reliable operation under uncertainty, demonstrating the effectiveness and practical scalability of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA22.6", "code": "WeA22.6", "title": "Risk-Aware Control for Maximum Power Point Tracking in Grid-Connected Photovoltaic Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA22", "sessionTitle": "Control and Management of Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Petiafo, Alberta", "affiliation": "Ashesi University" }, { "name": "Enyam, Kobena B.", "affiliation": "Ashesi University" }, { "name": "Kwofie, Bridget Nana Berniah", "affiliation": "Ashesi University" }, { "name": "Nyako, Kwame Asiedu Owusu", "affiliation": "Youngstown State University" }, { "name": "Hammond, Desmond", "affiliation": "Ecole Centrale De Nantes" } ], "keywords": [ "Control and management of energy systems", "Solar energy" ], "abstract": "Modern Photovoltaic (PV) plants operate in highly dynamic and uncertain environments where irradiance, temperature, and load can change rapidly. Most maximum power point tracking (MPPT) algorithms are tuned for average conditions and provide little insight into risk or safety, which is increasingly critical for reliable grid-connected PV. In this paper, we formulate MPPT in dynamic environments as a risk aware control problem and propose a hybrid architecture that couples a Conditional Value-at-Risk (CVaR) model predictive layer with shielded reinforcement learning policy (RL) for fast and safe tracking. The predictive layer plans risk sensitive reference trajectories while the shielded RL policy executes these commands at converter timescales under hard voltage-current constraints. We validate the approach on a PV array and DC-DC converter model under diverse irradiance and partial shading scenarios. Results demonstrate superior performance against classical MPPT algorithms and learning based baselines.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA23.1", "code": "WeA23.1", "title": "Reinforcement Learning-Based Control Via Y-Wise Affine Neural Networks: Comparative Case Studies for Chemical Processes", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA23", "sessionTitle": "Reinforcement Learning and Decision-Making for Process Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Braniff, Austin", "affiliation": "West Virginia University" }, { "name": "Tian, Yuhe", "affiliation": "West Virginia University" } ], "keywords": [ "Machine learning and artificial intelligence in chemical process control", "Industrial applications of chemical process control", "Advanced process control" ], "abstract": "In this work we present an efficient and practically implementable approach for the application of reinforcement learning (RL)-based control in chemical process systems. This is an area that has yet to widely adopt RL-based control largely due to inherent challenges in trusting RL algorithms and the time-consuming process of training reliable agents. To address these challenges we leverage a class of RL algorithms termed Y-wise Affine Neural Network (YANN)- RL, which we have developed in our prior work (Braniff and Tian, 2025a). By strategically initializing actor and critic networks YANN-RL algorithms provide confident and interpretable starting points within control schemes. We apply this RL-based control approach to three different process engineering case studies publically available on the PC-Gym library (Bloor et al., 2026): (i) a continuous stirred tank reactor (CSTR), (ii) a four-tank system, and (iii) a multistage extraction column. Our approach is compared to several popular RL algorithms (PPO, SAC, DDPG, and TD3) and is benchmarked against nonlinear model predictive control (NMPC). These case studies demonstrate that YANN-RL can greatly reduce the training time and data needed, can be deployed with confidence for chemical process systems, and can approach the performance of NMPC without the knowledge of a full nonlinear model.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA23.2", "code": "WeA23.2", "title": "Deep Reinforcement Learning–Based Cycle-Time Control of a Vacuum Swing Adsorption Process for CO₂ Capture", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA23", "sessionTitle": "Reinforcement Learning and Decision-Making for Process Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Goulart, Douglas", "affiliation": "University of Campinas" }, { "name": "Dutra Pereira Filho, Renato", "affiliation": "University of Rio Grande" }, { "name": "Nogueira, Idelfonso", "affiliation": "NTNU" }, { "name": "Vasconcelos da Silva, Flávio", "affiliation": "Universidade Estadual De Campinas (UNICAMP)" } ], "keywords": [ "Machine learning and artificial intelligence in chemical process control", "Advanced process control" ], "abstract": "Achieving reliable and adaptive control of VSA processes for CO₂ capture remains challenging due to nonlinear cyclic transients, purity–recovery trade-offs, and sensitivity to variations in inlet composition. This work investigates a deep reinforcement learning controller that manipulates adsorption and evacuation durations to track recovery targets while enforcing product purity. Using a high-fidelity VSA simulator for training, the learned policy exhibits stable steady-state operation, effective tracking, and robustness to disturbances, demonstrating the potential of DRL for autonomous cycle-time control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA23.3", "code": "WeA23.3", "title": "Hierarchical Control Via MPC-RL for Multi-Timescale Battery Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA23", "sessionTitle": "Reinforcement Learning and Decision-Making for Process Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Pourjam, Rasa", "affiliation": "Imperial College London" }, { "name": "del Rio-Chanona, Ehecatl Antonio", "affiliation": "Imperial College London" }, { "name": "Quintanilla, Paulina", "affiliation": "University College London" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes", "Machine learning and artificial intelligence in chemical process control", "Real-time optimization and control in chemical processes" ], "abstract": "Multi-timescale systems present a fundamental challenge, where fast operational decisions must coexist with long-horizon sustainability targets. In this work, we proposed a new hierarchical control framework via Model Predictive Control (MPC) and Reinforcement Learning (RL) to separate decision-making on two distinct timescales. The high-level MPC optimizes long-horizon setpoints at the slow dynamic and on a fast timescale, a low-level pretrained RL agent tracks these setpoints in real time to maximize short-term objectives. RL is introduced to learn nonlinear control policies, without relying on model linearizations or requiring the heavy online computation from solving repeated optimal control problems. The framework is applied to a Battery Energy Storage System (BESS) operating in frequency regulation markets to balance fast profit opportunities (seconds) and slow battery degradation (weeks to months). The design employs a degradation-aware RL agent trained offline to generate safe long-horizon setpoints, and a degradation-unaware agent fine-tuned from it for fast runtime setpoint tracking. Compared to MPC baselines, the proposed approach successfully extends battery lifetime by 84% and increases operational profit by 34%.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA23.4", "code": "WeA23.4", "title": "Mixed-Integer Programming Formulations for Optimal Reconfiguration of Supply Chains", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA23", "sessionTitle": "Reinforcement Learning and Decision-Making for Process Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Ghilardi, Lavinia Marina Paola", "affiliation": "Politecnico Di Milano" }, { "name": "Walz, Olga", "affiliation": "RWTH Aachen UniversityOlga" }, { "name": "Klosterhalfen, Steffen", "affiliation": "BASF" }, { "name": "Tsay, Calvin", "affiliation": "Imperial College London" } ], "keywords": [ "Control and optimization of supply chains in chemical processes" ], "abstract": "Supply chains are interconnected networks of processes and operations producing and delivering high-value products. These chains are increasingly subjected to structural changes from the energy transition and other external factors. To address this, this work develops mixed-integer programming formulations to identify optimal reconfigurations that preserve industrial operations and profitability. We propose products and spatial neighborhoods to restrict the feasible search space and enable fast heuristic solutions. Furthermore, this restriction combines structural and product-based information, thus allowing to explore and define multiple reconfiguration scenarios. We demonstrate the approach using an agricultural waste case study, showing its ability to quickly produce good quality solutions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA23.5", "code": "WeA23.5", "title": "Learnable Decision Trees for Interpretable Energy Arbitrage Scheduling", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA23", "sessionTitle": "Reinforcement Learning and Decision-Making for Process Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Patron, Gabriel David", "affiliation": "Imperial College London" }, { "name": "Ganesan, Nandhini", "affiliation": "Bp" }, { "name": "Shah, Nilay", "affiliation": "Imperial College London" }, { "name": "Tsay, Calvin", "affiliation": "Imperial College London" } ], "keywords": [ "Control and optimization for sustainability and energy systems", "Machine learning and artificial intelligence in chemical process control", "Energy market" ], "abstract": "The scheduling of energy storage can enable grid operators to promote stability, and process operators to benefit from the variability of modern electricity markets. Decision trees provide a paradigm to learn parsimonious optimal control policies while maintaining interpretability. In this work, we present a decision tree strategy to learn battery energy storage electricity trading policies from the solution to offline open-loop optimal control problems. We explore supervised and direct economic training approaches, finding that the learned policies can provide effective energy trading strategies even when compared to the open-loop policy with perfect market foresight. We find that the resulting tree policies are intuitive in terms of directionality and magnitude of their trading decisions. Our work provides a promising initial step to balance performance with interpretability in electricity arbitrage.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA23.6", "code": "WeA23.6", "title": "Multi-Agent Systems for Process Diagnosis (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA23", "sessionTitle": "Reinforcement Learning and Decision-Making for Process Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Hu, Guangze", "affiliation": "The Hong Kong University of Science and Technology" }, { "name": "Lu, Jingyi", "affiliation": "East China University of Science and Technology" }, { "name": "Gao, Furong", "affiliation": "Hong Kong Univ of Sci & Tech" } ], "keywords": [ "Advanced process control" ], "abstract": "Industrial injection molding operates in a complex and dynamic environment. To provide interpretable and transferrable monitoring, we propose the Physics-to-Semantics Multi-Agent System (PIMAS). This framework transforms high-frequency sensor data and process parameters into semantic text for intelligent monitoring. PIMAS introduces a two-stage adaptive mechanism. First, a perception agent converts raw sensor streams into semantic descriptors. It employs a self-calibrating mechanism to ensure adaptability across different machines and materials. Second, the system filters industrial noise using a multi-level tolerance rule. A Retrieval-Augmented Generation (RAG) diagnostic agent then identifies root causes. It retrieves universal physical principles from specialized knowledge bases, which avoids reliance on specific dataset patterns. Experiments on real-world datasets demonstrate that PIMAS maintains high diagnostic accuracy, even when transferred between different process settings. The proposed solution is flexible, explainable, and deployment-ready.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA24.1", "code": "WeA24.1", "title": "Multi-Modal Methane Detection and Quantification Via Cross-Attention Fusion and Physics-Informed Fractional-Order Adversarial Learning", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA24", "sessionTitle": "Monitoring, Modeling and Control of Environmental Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Giri, Sachin", "affiliation": "MESA Lab, University of California, Merced" }, { "name": "Chen, YangQuan", "affiliation": "University of California, Merced" } ], "keywords": [ "AI and ML for environmental systems", "Air quality modeling and control", "Real time monitoring and control of environmental systems" ], "abstract": "We propose a novel multi-modal adversarial framework for methane detection and quantification, fusing Airborne Visible InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) with WorldView-3 satellite data. Our architecture utilizes parallel convolutional-transformer encoders linked by a bi-directional Cross-Attention Fusion module. To ensure physical consistency, we enforce radiative transfer constraints and a Fractional-Order Total Variation (FOTV) regularizer. The model generates methane enhancement maps, enabling flux estimation via Integrated Mass Enhancement (IME) and interpolated ERA5 wind fields. Qualitative Explainable AI (XAI) analysis confirms the model learns robust spectral-spatial features, overcoming single-sensor limitations. The proposed method is quantitatively compared with existing State of the art models for methane detection.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA24.2", "code": "WeA24.2", "title": "Distributed Multi-Agent Negotiation for Capacity Expansion Planning", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA24", "sessionTitle": "Monitoring, Modeling and Control of Environmental Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Amato, Valeria", "affiliation": "Politecnico Di Milano" }, { "name": "Amigoni, Francesco", "affiliation": "Politecn Milan" }, { "name": "Restelli, Marcello", "affiliation": "Politecnico Di MIlano" }, { "name": "Castelletti, Andrea", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Planning and management in environmental systems under deep uncertainty", "Control of large-scale environmental systems", "Participatory decision making in environmental systems" ], "abstract": "Traditional centralized energy models often ignore national strategic interests and data privacy, assuming unrealistic perfect cooperation. To address this limitation, we propose a distributed multi-agent framework for the Southern African Power Pool, modeling coordination as a Distributed Constraint Optimization Problem (DCOP). Since standard DCOP solvers fail due to the combinatorial complexity of large-scale networks, we introduce our negotiation algorithm. Agents iteratively exchange bids based on marginal costs, overcoming these computational limits and preserving privacy. Simulations show rapid convergence that closely matches the centralized cost-optimal benchmark, validating the framework for realistic, distributed energy planning.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA24.3", "code": "WeA24.3", "title": "On the Equity-Productivity Tradeoff in Agricultural Water Allocation", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA24", "sessionTitle": "Monitoring, Modeling and Control of Environmental Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Manzoor, Talha", "affiliation": "Lahore University of Management Sciences" }, { "name": "Hassan, Wasim", "affiliation": "Marquette University" } ], "keywords": [ "Water resource system modeling and control", "Modeling and estimation in agriculture", "Optimal control and operation of environment systems" ], "abstract": "Under the imperative to extract increasingly higher agricultural gains out of finite water resources, serious efforts are being put in to increase the productivity of water use in agricultural systems. In this paper, we develop a mathematical framework to investigate the tradeoff between productivity and equity in agricultural water allocation and present simulations inspired by a real-world irrigation system. We consider the effects of three different productivity enhancement initiatives: 1) Productivity enhancement through water allocation, 2) On-farm productivity enhancement, and 3) Enhancement in maximum crop yield. Our results indicate a clear tradeoff between equity and productivity in water allocation. However, on-farm productivity enhancements can increase overall productivity without any compromise on equity. Finally, while enhancement in maximum crop yield does enhance the total yield, it does not significantly affect either of the productivity or equity objectives. Thus, the potential of productivity to reconcile equity in agricultural water systems is different for different interventions and care must be exercised in promoting productivity gains as a single solution to uplift agricultural systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA24.4", "code": "WeA24.4", "title": "Optimal Nitrate Fertilisation", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA24", "sessionTitle": "Monitoring, Modeling and Control of Environmental Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Blanchini, Franco", "affiliation": "Univ. Degli Studi Di Udine" }, { "name": "Casagrande, Daniele", "affiliation": "University of Udine" }, { "name": "Salvato, Erica", "affiliation": "University of Trieste" }, { "name": "Tomasi, Nicola", "affiliation": "Department of Agrifood, Environmental and Animal Sciences, University of Udine, Italy" }, { "name": "Zanin, Laura", "affiliation": "Department of Agrifood, Environmental and Animal Sciences, University of Udine, Italy" } ], "keywords": [ "Optimal control and operation of environment systems", "Biological networks inference and modelling", "Modeling and estimation in agriculture" ], "abstract": "This paper addresses the optimization of nitrate fertilization by jointly considering the agronomic benefit of nitrate uptake and the economic and environmental costs associated with fertilizer use. Root absorption capacity of nitrate is known to evolve dynamically after fertilization, exhibiting a transient peak followed by a progressive decline, and fertilization strategies should account for this temporal behavior. We formulate the problem as an optimal control problem for a linear soil–plant model that incorporates nitrate dynamics in the soil, transporter activation, and feedback regulation exerted by amino acid accumulation. The model, obtained by linearizing a mechanistic nonlinear system, fits available experimental data and enables an explicit analytical solution of the optimal control law. Using a Pontryagin-based approach, the optimal strategy is determined in a single backward integration of the costate equation, and the fertilization interval is obtained directly from a threshold condition on the costate. Numerical simulations confirm that the optimal strategy consists of a single fertilization window and show that this strategy remains effective when applied to more detailed nonlinear models.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA24.5", "code": "WeA24.5", "title": "Spatiotemporal Water Quality Monitoring with Autonomous Surface Vehicle", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA24", "sessionTitle": "Monitoring, Modeling and Control of Environmental Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Alfa, Umar", "affiliation": "IMT Nord Europe" }, { "name": "Tijjani, Auwal Shehu", "affiliation": "IMT Nord Europe, Centres De Recherche" }, { "name": "Fabresse, Luc", "affiliation": "IMT Lille Douai" }, { "name": "Duviella, Eric", "affiliation": "IMT Lille Douai" }, { "name": "Bouchama, Abdellah", "affiliation": "IMT Nord Europe" }, { "name": "Houe Ngouna, Raymond", "affiliation": "Université Fédérale De Toulouse, ENIT-LGP" } ], "keywords": [ "Real time monitoring and control of environmental systems", "AI and ML for environmental systems", "Modeling and identification of environmental systems" ], "abstract": "This paper presents an integrated framework for autonomous water quality monitoring using an autonomous surface vehicle (ASV). We address the challenge of inefficient coverage in dynamic aquatic environments through a genetic algorithm (GA) path planning approach that achieves 48.40% reduction in path length compared to the conventional methods. The framework combines an optimized coverage planning with robust nonlinear proportional-integral-derivative (PID) control and a gaussian process (GP)-based water quality estimation, validated using ROS 2/Gazebo simulations parameterized with real measurements of water quality data from Heron lake (France). The results demonstrate improved path tracking with an index of 0.41m root mean square error (RMSE) and effective spatiotemporal monitoring of key water parameters including dissolved oxygen(DO), pH, and temperature.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA25.1", "code": "WeA25.1", "title": "A Parametric Robustness versus Dynamic Sensitivity Paradox in a Bistable Biomolecular Circuit", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA25", "sessionTitle": "Biosystems and Bioprocesses I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Chorasiya, Gunjan", "affiliation": "IIT Delhi" }, { "name": "Prakash, Rudra", "affiliation": "Indian Institute of Technology Delhi" }, { "name": "Sen, Shaunak", "affiliation": "Indian Institute of Technology Delhi" } ], "keywords": [ "Dynamics and control of biologically motivated nonlinear systems", "Biological networks inference and modelling", "Synthetic biology" ], "abstract": "Achieving robustness to multi-parametric perturbations where all parameters can change at the same time is challenging because the controller would also face the same disturbance as the plant. For nonlinear positive feedback, an important mechanism for cell fate determination in biomolecular contexts, quantitative aspects of robustness to such perturbations are generally unclear. Here we used mathematical methods of control and dynamical systems, interval analysis, and a benchmark model of a bistable biomolecular positive feedback circuit to address this. We confirmed that such perturbations can change the qualitative behaviour of the system extinguishing bistability. We obtained a quantitative relation between the relative variations in the stable and unstable steady-states in terms of the relative changes in parameters. We showed how the deviation in the trajectories near the unstable steady-state due to such perturbations could diverge almost exponentially after an initial transient, which could have a significant impact on the bistable switching dynamics. We found that the size of the eigenvalue for the unstable steady-state was greater than that for the stable steady-state, and proved this for certain parameters using a rigorous numerical construction. We identified a trade-off between enhancing the parameter space of bistability and increased sensitivity in the bistable dynamics due to parametric perturbations. We obtained rigorous bounds on the entire transient response for these perturbations. These results provide a quantitative insight into the robustness of a bistable biomolecular positive feedback circuit to multi-parametric perturbations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA25.2", "code": "WeA25.2", "title": "Scalable Barrier Function Synthesis for Cascaded Nonlinear Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA25", "sessionTitle": "Biosystems and Bioprocesses I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Keshtiarast Esfahani, Mahshad", "affiliation": "TU Delft" }, { "name": "Laurenti, Luca", "affiliation": "Delft University of Technology" }, { "name": "Mazo Jr, Manuel", "affiliation": "TU Delft" } ], "keywords": [ "Dynamics and control of biologically motivated nonlinear systems", "Synthetic biology" ], "abstract": "We present a compositional framework for verifying forward invariance of nonlinear dynamical systems with a cascaded structure, where variables are grouped into layers and each variable may be influenced by all preceding layers and the immediately following one. Our framework relies on barrier functions and exploits the cascade structure of the system to locally construct barrier functions for each layer, which are then combined into a non-smooth barrier function for the overall system. To show the efficacy of our framework, we develop an algorithmic framework to find barrier functions based on genetic algorithms and algebraic decompositions. On a set of experiments, including a non-linear 13-dimensional dynamical system, we demonstrate how our framework enables scalable and rigorous forward invariance analysis of dynamical systems with substantially improved performance compared to state-of-the-art methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA25.3", "code": "WeA25.3", "title": "Early Warning Signals in a Two-Gen Regulatory System Near Hopf Bifurcation", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA25", "sessionTitle": "Biosystems and Bioprocesses I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Arteaga, Angel Francisco", "affiliation": "CICESE" }, { "name": "Alvarez, Joaquin", "affiliation": "CICESE" }, { "name": "Domínguez-Hüttinger, Elisa", "affiliation": "Departamento De Biología Molecular Y Biotecnología Instituto De Investigaciones Biomédicas, Universidad Nacional Autónoma De Méx" }, { "name": "Pena Ramirez, Jonatan", "affiliation": "CICESE" } ], "keywords": [ "Dynamics and control of biologically motivated nonlinear systems", "Synthetic biology", "Biomedical system modeling, identification, and simulation" ], "abstract": "We investigate the onset of a Hopf bifurcation in a minimal biologically plausible model motif of two genes with three types of interconnections: activation, repression and self-activation. We show that the basal activation rate acts as a bifurcation parameter, and we provide the exact analytic value of this parameter, at which the Hopf bifurcation occurs. Moreover, due to the fact that near the bifurcation point the covariance matrix of the linearized system diverges, we show that the equilibrium point starts to show fluctuations even before the bifurcation occurs. These fluctuations can be considered as early warning signals announcing that a Hopf bifurcation is going to occur. The proposed mathematical framework was developed analytically and illustrated through numerical simulations. Ultimately, we consider that these results may be of interest in the context of pre-disease detection.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA25.4", "code": "WeA25.4", "title": "Optimizing Timing Precision in Gene Cascades with Graded Activation: An Analytical Approach", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA25", "sessionTitle": "Biosystems and Bioprocesses I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Hernandez Villamizar, Juan Sebastian", "affiliation": "Universidad De Los Andes" }, { "name": "Nieto, Cesar", "affiliation": "University of Delaware" }, { "name": "Rezaee, Sayeh", "affiliation": "University of Delaware" }, { "name": "Singh, Abhyudai", "affiliation": "University of Delaware" } ], "keywords": [ "Modelling, parameter identification and state estimation in biosystems", "Biological networks inference and modelling", "Dynamics and control of biologically motivated nonlinear systems" ], "abstract": "Gene activation cascades can function as molecular timers, although timing precision is limited by the inherent stochasticity of gene expression. Timing precision is typically quantified by the statistics of the First Passage Time (FPT), the time at which a downstream protein first reaches a functional threshold. While analytical results exist for single-gene systems and for multi-gene cascades with switch-like activation, the impact of graded activation on timing precision has been studied only numerically and lacks a complete analytical description. We address this gap by analyzing a two-gene cascade using a burst-dilution hybrid stochastic model. By defining a piecewise-linear activation function with a tunable activation threshold and slope, we derive exact solutions for the statistical moment dynamics of the FPT, avoiding the closure problems associated with nonlinear activation functions. For identical genes and a fixed maximum production rate, analytical approximations and simulations show that the optimal dose-response shifts from the linear-saturated limit at short timescales to an interior optimum with non-zero threshold and finite slope as the mean FPT increases. Furthermore, the optimal threshold increases monotonically with the mean FPT, while the optimal slope varies non-monotonically. These results establish an analytically tractable framework linking the shape of a graded activation function to timing precision in gene regulatory cascades.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA25.5", "code": "WeA25.5", "title": "Model Reduction of Multicellular Communication Systems Via Singular Perturbation: Sender–Receiver Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA25", "sessionTitle": "Biosystems and Bioprocesses I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Kotsuka, Taishi", "affiliation": "University of California, Santa Barbara" }, { "name": "Yeung, Enoch", "affiliation": "University of California, Santa Barbara" } ], "keywords": [ "Synthetic biology", "Modelling and control of microbial communities", "Dynamics and control of biologically motivated nonlinear systems" ], "abstract": "We investigate multicellular sender–receiver systems embedded in hydrogel beads, where diffusible signals mediate interactions among heterogeneous cells. Such systems are modeled by PDE–ODE couplings that combine three-dimensional diffusion with nonlinear intracellular dynamics, making analysis and simulation challenging. We show that the diffusion dynamics converges exponentially to a quasi-steady spatial profile and use singular perturbation theory to reduce the model to a finite-dimensional multi-agent network. A closed-form communication matrix derived from the spherical Green’s function captures the effective sender-receiver coupling. Numerical results show the reduced model closely matches the full dynamics while enabling scalable simulation of large cell populations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA25.6", "code": "WeA25.6", "title": "An Approach to Quantify Green-Pixel Regions under Canopy During Weeding Application in Vineyards", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA25", "sessionTitle": "Biosystems and Bioprocesses I", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Madni, Syed Shaham", "affiliation": "Hochschule Geisenheim University" }, { "name": "Tsoulias, Nikos", "affiliation": "Hochschule Geisenheim University" }, { "name": "Pamornnak, Burawich", "affiliation": "Hochschule Geisenheim University" }, { "name": "Sharipov, Galibjon", "affiliation": "Hochschule Geisenheim University" }, { "name": "Paraforos, Dimitrios S.", "affiliation": "Geisenheim University" } ], "keywords": [ "Agricultural robotics", "Computer vision in agriculture", "Sensing and perception in agriculture" ], "abstract": "This article presents a visual–inertial system integrated into a surveying robot to monitor weed density in vineyards. The system combines stereo camera, IMU, and GPS data to evaluate weed control by comparing green-pixel regions (GPR) before and after operations. A two-module architecture detects and tracks GPR within regions of interest using HSV features, texture patterns, and depth data, while geolocating frames through interpolation. Results show effective performance, producing real-world GPR maps. Statistical analysis confirms significant differences between pre- and post-operation data, with overall reduction of 55.6% in intra-row GPR, supporting the system’s applicability for automated viticulture weed management.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA26.1", "code": "WeA26.1", "title": "MASCOT: A Hybrid Multi-Agent Systems COntrol Testbed", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA26", "sessionTitle": "Autonomous Mobile Robots", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Bandaru, Aryan", "affiliation": "Indian Institute of Technology, Dharwad" }, { "name": "Pandit, Arvind", "affiliation": "Indrones Solutions Pvt Ltd" }, { "name": "Mulla, Ameer", "affiliation": "Indian Institute of Technology Dharwad" } ], "keywords": [ "Autonomous mobile robots", "Control architectures in automotive control", "Digital twins and IoT for aerospace systems control and monitoring" ], "abstract": "This paper presents a small-scale hybrid test-bed, MASCOT, developed for testing and validation of the control algorithms for multi-agent systems on multi-robot systems. Distributed control algorithms proposed in the literature use simplified models for agent dynamics like single or double integrator dynamics. MASCOT facilitates testing of such algorithms on real systems through simulations, physical experiments, and hybrid experiments with simplified user interface. MASCOT is developed using Robot Operating Systems 2 (ROS2) and Gazebo but provides user interface so that control engineers do not need the exposure to ROS2 or Gazebo. Currently, MASCOT uses Crazyflie 2.1 and Loco-positioning System for experimental part. Apart from distributed control algorithms, MASCOT also provides haptic interface for human-on-the-loop control strategies for multi-agent systems through bilateral teleoperation. The performance of the testbed is analyzed by implementing linear control laws such as leaderless consensus, leader-follower consensus, bearing based formation and non-linear control law for min-max time consensus. This work is published as an open-source ROS package under MIT license at https://github.com/IITDhHANS/mascotV2.git", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA26.2", "code": "WeA26.2", "title": "Density-Guided Control Barrier Functions for Deadlock-Free Swarm Navigation", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA26", "sessionTitle": "Autonomous Mobile Robots", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Dong, Zhaoqi", "affiliation": "Beijing Institute of Technology" }, { "name": "Chen, Lei", "affiliation": "Beijing Institude of Technology" }, { "name": "Zhu, Chunli", "affiliation": "Beijing Institute of Technology" } ], "keywords": [ "Autonomous mobile robots", "Intelligent transportation systems", "Automatic control, optimization, real-time operations in transportation" ], "abstract": "High local density brings large swarms to a standstill by shrinking safety-feasible motion to near zero, so decentralized planners stall and form persistent deadlocks. We presents a density-regulated control framework that integrates macroscopic traffic regulation with agent-level safety control. A density-guided reference velocity respects regional capacity limits and modulates inflow so agents slow down or reroute before saturation. A high-order control barrier function controller tracks this reference at the acceleration level, enforces second-order dynamics, and guarantees collision avoidance. Experiments show deadlock removal and real--time performance at scale.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA26.3", "code": "WeA26.3", "title": "Active Control of a Railway Pantograph System", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA26", "sessionTitle": "Autonomous Mobile Robots", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Meepaen, Kamonphop", "affiliation": "University of Birmingham" }, { "name": "Dixon, Roger", "affiliation": "University of Birmingham" }, { "name": "Stewart, Edd", "affiliation": "The University of Birmingham" } ], "keywords": [ "Autonomous mobile robots" ], "abstract": "Stable current collection in high-speed rail is critically dependent on the pantograph’s ability to accurately follow the overhead catenary wire. At high velocities, the wire exhibits rapid vertical displacement that the whole mechanism of the passive pantograph systems has difficulty responding to, leading to contact force fluctuation. To address this issue, this paper presents an active control framework for adjusting the pantograph head displacement to reduce the fluctuation of contact force. A multi-body dynamic model of the pantograph head is developed to simulate the interaction dynamics. Subsequently, a PI-Lead controller is designed to actively regulate the pantograph head’s position with a fast-dynamic response. Simulation results validate the proposed controller, showing it effectively minimizes the contact force fluctuation by compensating for rapid wire movement, thereby maintaining a stable contact.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA26.4", "code": "WeA26.4", "title": "Explainable Formulation of Autonomous System Operation on Multiple Levels: A Case Study for Mobile Robots", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA26", "sessionTitle": "Autonomous Mobile Robots", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Lelko, Attila", "affiliation": "SZTAKI Institute for Computer Science and Control" }, { "name": "Nemeth, Balazs", "affiliation": "SZTAKI" }, { "name": "Gaspar, Peter", "affiliation": "HUN-REN SZTAKI, Institute for Computer Science and Control, Hungarian Research Network" } ], "keywords": [ "Autonomous mobile robots", "Automatic control, optimization, real-time operations in transportation", "Intelligent transportation systems" ], "abstract": "The need for explainability to improve resilience and trustworthiness is a hot topic in various fields supported by autonomous decision-making systems. This motivates the development of systematic methods that are able to explain decisions to users in real-time, especially if the complex system is interacts with human beings. Improving the efficiency of the explainable formulation requires a rigorous definition of the target audience, because explainability level differs for everyday users and experts. This difference leads to the challenges related to defining explainability levels and selecting appropriate approximation structures. This paper proposes a methodology by which the explainability formulation at multiple levels can be effectively carried out. The methodology is presented based on a case study in which autonomous mobile robots perform packaging mission in a warehouse logistic system. The goal of the formulation is to explain healthy or faulty operation of the robots for professional, operator, and general users.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA26.5", "code": "WeA26.5", "title": "Heterogeneous Multi-Drone Formation Optimization for Load Transport", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA26", "sessionTitle": "Autonomous Mobile Robots", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Rizaldi, Ardian", "affiliation": "Gyeongsang National University" }, { "name": "Kim, Yoonsoo", "affiliation": "Gyeongsang National University" } ], "keywords": [ "Automatic control, optimization, real-time operations in transportation", "Aerospace mission control and operations", "Multi-vehicle systems" ], "abstract": "Transporting loads using multiple drones offers many advantages, including improved delivery performance and higher system resilience, yet achieving coordinated and energy-efficient control remains challenging. This study proposes a formation transition strategy for cooperative multi-drone payload transport that integrates trajectory control, collision avoidance, and dynamic formation optimization. A generalized dynamic model and a sliding-mode controller ensure robust tracking, while a virtual leader coordinates drones during transitions. Energy efficiency is enhanced using a Late Acceptance Hill Climbing algorithm. Simulations show reduced energy use and more balanced thrust distribution, achieving a 45.6% improvement.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA26.6", "code": "WeA26.6", "title": "Resilient Trust--Aware Distributed Observer Design for Connected Vehicle Platoons", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA26", "sessionTitle": "Autonomous Mobile Robots", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Nguyen, Quang Huy", "affiliation": "University Lorraine" }, { "name": "Meng, Shengya", "affiliation": "Universite De Lorraine" }, { "name": "Haddad, Madjid", "affiliation": "SEGULA Technologies" }, { "name": "Rafaralahy, Hugues", "affiliation": "Université De Lorraine" }, { "name": "Zemouche, Ali", "affiliation": "CRAN UMR CNRS 7039, University of Lorraine" } ], "keywords": [ "Intelligent transportation systems", "Cooperative navigation", "Autonomous mobile robots" ], "abstract": "This paper proposes a trust-aware distributed observer for vehicle platoons that maintains resilient state estimation under cyberattacks. A behavioral divergence metric evaluates the reliability of shared data, forming a dynamic neighbor set used to adapt observer's weighting gains. Stability conditions are derived via Lyapunov analysis. Simulations under bogus, replay, and DoS attacks demonstrate robust performance and stable platoon behavior.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA27.1", "code": "WeA27.1", "title": "Sea State Estimation from In-Service Motions of a DP Drilling Vessel Using Neural Networks", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA27", "sessionTitle": "AI, Data-Driven Methods and Control for Marine Surface Vessels", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Bisinotto, Gustavo", "affiliation": "Universidade De São Paulo" }, { "name": "Zago, Lariuss", "affiliation": "University of São Paulo" }, { "name": "Huang, Alex Saratani", "affiliation": "University of São Paulo" }, { "name": "Queiroz Filho, Asdrubal do Nascimento", "affiliation": "Fundação De Apoio à Universidade De São Paulo CNPJ68314830/0001-27" }, { "name": "Barbosa Medeiros, André", "affiliation": "Constellation" }, { "name": "Rosa, Douglas José", "affiliation": "Constellation Oil & Gas" }, { "name": "Brigido, José Ricardo", "affiliation": "Petrobras" }, { "name": "Tannuri, Eduardo Aoun", "affiliation": "University of Sao Paulo USP" } ], "keywords": [ "AI and embodied-AI in marine systems", "Modelling, identification and control in marine systems", "Simulation and digital-twin in marine systems" ], "abstract": "This paper explores a data-driven motion-based wave estimation system from the measured response of a dynamically positioned drilling vessel. Sea state estimation models using neural networks were trained exclusively with simulated data coming from the vessel’s dynamic model subjected to metocean conditions collected from reanalysis datasets. Inference performance from in-service motion records was assessed through direct comparison with reanalysis data and via an indirect evaluation scheme by analyzing the response of the real drilling vessel and the corresponding simulation driven by the estimated sea states. Results indicate the potential of the approach to provide reliable wave estimates to DP systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA27.2", "code": "WeA27.2", "title": "Fleet-Wide Hybrid Model for Ship Shaft Power Prediction", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA27", "sessionTitle": "AI, Data-Driven Methods and Control for Marine Surface Vessels", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Chouikri, Khalil", "affiliation": "Aix Marseille University" }, { "name": "Graton, Guillaume", "affiliation": "Ecole Centrale De Marseille" }, { "name": "Noura, Hassan", "affiliation": "Islamic University of Lebanon" } ], "keywords": [ "AI and embodied-AI in marine systems", "Decision and support in marine systems", "Modelling, identification and control in marine systems" ], "abstract": "Reducing the environmental footprint of maritime transport requires precise energy management, since fuel consumption is directly proportional to shaft power, accurate power prediction is a critical lever for optimizing energy efficiency and minimizing greenhouse gas emissions. However, traditional predictive models often struggle to balance physical interpretability with the flexibility needed to generalize across diverse operating conditions. This work proposes a Fleet-Wide Hybrid Semi-Parametric Model that combines domain knowledge from Computational Fluid Dynamics (CFD) and hydrodynamic resistance theory with a high-capacity Transformer-based learning architecture. By integrating vessel-specific identifiers and physics-informed features trained on high-frequency onboard data, the framework effectively captures both shared fleet-level dynamics and individual ship behaviors. Validation results demonstrate that the proposed hybrid approach significantly outperforms conventional models in accuracy and robustness, providing a scalable tool for real-time operational monitoring and decision-support to drive sustainable fleet management.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA27.3", "code": "WeA27.3", "title": "Prompt-Engineered Large Language Model Framework for Optimal Hybrid Battery Sizing in Marine Vessels", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA27", "sessionTitle": "AI, Data-Driven Methods and Control for Marine Surface Vessels", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Safari, Ashkan", "affiliation": "University of Windsor" }, { "name": "Sorouri, Hoda", "affiliation": "Aalborg University" }, { "name": "Oshnoei, Arman", "affiliation": "Aalborg University" }, { "name": "Davari, Pooya", "affiliation": "Aalborg University" } ], "keywords": [ "AI and embodied-AI in marine systems", "Maritime transport operation and automation", "Power and propulsion in marine systems" ], "abstract": "Optimal battery sizing in marine electrification is key to boosting energy efficiency, ensuring reliability, and reducing CO2 emissions while meeting maritime vessels’ power needs. The most recent sizing strategies utilize single-technology battery packs, which have limited energy density, slower charging capabilities, or shorter lifespans. Hybrid battery packs, combining multiple technologies, have better performance, efficiency, and durability by using each technology’s strengths. To this end, a novel prompt engineering-based Large Language Model (LLM) is developed in this work to provide a multi-objective strategy for optimal hybrid battery sizing of a small vessel. This strategy takes the vessel load profile, CO2 emissions, and battery characteristics, including Lithium Titanate Oxide (LTO), as the High-Power (HP), and Nickel Manganese Cobalt (NMC), as the High-Energy (HE). Considering these inputs, the objective of the strategy is to meet the load demand while reducing the CO2 emissions of the vessel. After applying the proposed strategy, the optimal sizing results showed that the LTOs should be in 261 series cells, and 12 parallel strings. For the NMC, the results are 165 in-series cells and 23 parallel strings. As a result, the LTO packs have a total mass of 2296.8 [kg], while the NMC packs have a mass of 10056.75 [kg]. Additionally, the total amount of 1135.35 [kg] CO2 is displaced by the proposed strategy.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA27.4", "code": "WeA27.4", "title": "Zone Barrier Lyapunov Adaptive Control for Marine Surface Vessels with Uncertain Input Gains and State Constraints", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA27", "sessionTitle": "AI, Data-Driven Methods and Control for Marine Surface Vessels", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Liang, Xiaoling", "affiliation": "National University of Singapore" }, { "name": "Chen, Xuanlin", "affiliation": "Zhejiang University" }, { "name": "Bao, Dan", "affiliation": "Nanjing Tech University" }, { "name": "Ge, Shuzhi Sam", "affiliation": "National University of Singapore" } ], "keywords": [ "Marine system guidance, navigation and control", "Autonomous marine systems and vehicles", "Marine robotics" ], "abstract": "This paper addresses safety motion control for autonomous surface vessels subject to state constraints and uncertain control gain functions, including unknown gain directions caused by actuator degradation and hydrodynamic variations. Zone-barrier Lyapunov function is employed to guarantee forward-invariant error bounds and state constraints satisfaction. An adaptive control-gain estimation mechanism is developed to identify and compensate unknown input effectiveness online, without prior knowledge of its sign. Rigorous Lyapunov analysis proves bounded closed-loop signals and asymptotic tracking. Simulation results demonstrate safe and reliable constrained vessel control under actuator uncertainties.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA27.5", "code": "WeA27.5", "title": "Model Predictive Control for Cooperative Docking between Autonomous Surface Vehicles with Disturbance Rejection", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA27", "sessionTitle": "AI, Data-Driven Methods and Control for Marine Surface Vessels", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Battocletti, Gianpietro", "affiliation": "Delft University of Technology" }, { "name": "Boskos, Dimitris", "affiliation": "Delft University of Technology" }, { "name": "De Schutter, Bart", "affiliation": "Delft University of Technology" } ], "keywords": [ "Autonomous marine systems and vehicles", "Modelling, identification and control in marine systems", "Marine system guidance, navigation and control" ], "abstract": "Uncrewed Surface Vehicles (USVs) are a popular and efficient type of marine craft that find application in a large number of water-based tasks. When multiple USVs operate in the same area, they may be required to dock to each other to perform a shared task. Existing approaches for the docking between autonomous USVs generally consider one USV as a stationary target, while the second one is tasked to reach the required docking pose. In this work, we propose a cooperative approach based on a centralized Model Predictive Control (MPC) controller for USV-USV docking, where two USVs work together to dock at an agreed location. Owing to its model-based nature, this approach allows the rejection of measured disturbances, inclusive of exogenous inputs, by anticipating their effect on the USVs through the MPC prediction model. This is particularly effective in case of almost-stationary disturbances such as water currents. In simulations, we demonstrate how the proposed approach allows for a faster and more efficient docking with respect to existing approaches.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA31.1", "code": "WeA31.1", "title": "Modeling and Optimal Control of Social Media Topic Dynamics: A BERTopic‑Enhanced Evolutionary Approach", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA31", "sessionTitle": "Social Networks and Opinion Dynamics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Gubar, Elena", "affiliation": "St. Petersburg State University" }, { "name": "Blekanov, Ivan", "affiliation": "St. Petersburg State University" }, { "name": "Taynitskiy, Vladislav", "affiliation": "Saint Petersburg State University" }, { "name": "Proskurnikov, Anton V.", "affiliation": "Politecnico Di Torino" } ], "keywords": [ "Social networks and opinion dynamics", "Game theories", "Social computing" ], "abstract": "Given the growing influence of social networks, this research overcomes traditional topic modeling limitations with a novel framework integrating BERTopic, evolutionary game theory, and multi-agent simulation in NetLogo. The goal is to model the dissemination and evolution of topics on social media. Methodologically, semantically meaningful topics are first extracted from extensive Weibo data. A composite user influence metric is then formulated to create payoff matrices for analyzing topic dynamics. Within NetLogo, we implement an evolutionary mechanism that captures the bounded rationality and stochastic decisions of real social media users. The study extends this model by introducing a controlled framework where target topics are promoted through artificially enhanced engagement metrics (likes, reposts). We formulate an optimal control problem with explicit constraints on intervention intensity, establishing and validating optimal strategies through numerical experiments. Empirical validation on independent Weibo data confirms the model accurately replicates real-world topic distributions. This work thus makes a dual contribution: enhancing the theoretical understanding of behavioral evolution in networks and providing practical, implementable conditions for optimal intervention strategies applicable from marketing to public promotions campaignes.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA31.2", "code": "WeA31.2", "title": "Robust Stability Analysis of Multilayered Opinion Dynamics with Time Delay", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA31", "sessionTitle": "Social Networks and Opinion Dynamics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Srivastava, Aditi", "affiliation": "Rajiv Gandhi National Aviation University" }, { "name": "Patel, Abhilash", "affiliation": "Indian Institute of Technology Kanpur" }, { "name": "Sahoo, Soumya Ranjan", "affiliation": "Indian Institute of Technology Kanpur" } ], "keywords": [ "Social networks and opinion dynamics", "Cyber-physical and human systems (CPHS)", "System dynamics and control in CPHS" ], "abstract": "The simultaneous occurrence of time delays and model uncertainties in multilayered social systems is a complex and challenging issue in real-world scenarios. Either factor may degrade system performance and potentially lead to instability. In this paper, we investigate the problem of robust stability analysis for achieving consensus in a delayed multilayered interaction network. New sufficient delay-dependent stability criteria are constructed using Lyapunov–Krasovskii functionals. These stability conditions are framed as linear matrix inequalities (LMIs), which is computationally efficient using convex optimization algorithms. Furthermore, the maximum allowable delay bound for the multilayered system is also derived. The proposed approach can be applied to a broad range of multilayered social dynamical systems to analyze the effects of time delays and norm-bounded uncertainties. Two numerical examples illustrate the effectiveness of the proposed methodology.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA31.3", "code": "WeA31.3", "title": "Replicator–Kuramoto Dynamics: Strategic Synchronization in Complex Networks", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA31", "sessionTitle": "Social Networks and Opinion Dynamics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Anzo, Andrés", "affiliation": "Benemérita Universidad Autónoma De Puebla" }, { "name": "Mojica-Nava, Eduardo", "affiliation": "Universidad Nacional De Colombia" } ], "keywords": [ "Social networks and opinion dynamics", "Cyber physical social systems (CPSS)", "Game theories" ], "abstract": "This paper introduces a population-game formulation of the Kuramoto Dilemma in which the binary strategies traditionally employed in evolutionary Kuramoto games are replaced by continuous cooperation levels governed by replicator dynamics. We propose a coevolutionary model where each oscillator is allowed to modulate its contribution to network synchronization through a continuous strategy, which directly scales the local coupling strength in the Kuramoto model. The evolutionary dynamics follow a two-strategy replicator equation driven by payoff differences computed from the local order-based benefit and angular-acceleration cost of the evolutionary Kuramoto game, and an expected weak Prisoner’s Dilemma payoff. We derive the full coupled system and analyze the linear stability of the incoherent equilibrium using a mean-field reduction, showing that the effective coupling and the payoff sensitivity to local order jointly determine the onset of synchronization and cooperative behavior. Numerical experiments confirm that replicator feedback introduces qualitatively new transitions not captured by imitation dynamics, including cooperation-induced shifts of the synchronization boundary. The proposed framework provides a mathematically grounded approach to studying coevolving dynamical and strategic processes in oscillator networks.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA31.4", "code": "WeA31.4", "title": "Topology-Based Conditions for Multiconsensus under the Signed Friedkin-Johnsen Model", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA31", "sessionTitle": "Social Networks and Opinion Dynamics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Shrinate, Aashi", "affiliation": "IIT Kanpur" }, { "name": "Siddharth, Tanmay", "affiliation": "IIT Kanpur" }, { "name": "Tripathy, Twinkle", "affiliation": "Indian Institute of Technology Kanpur" } ], "keywords": [ "Social networks and opinion dynamics" ], "abstract": "In this paper, we address the multiconsensus problem in networked systems, where agents are partitioned into disjoint subgroups and the states of agents within a subgroup are driven to consensus. Our objective is to present a distributed control law that leads to multiconsensus in signed digraphs. To this end, we examine the convergence of opinions under the opposing rule-based signed Friedkin-Johnsen (SFJ) model and present conditions that lead to multiconsensus under this model. Interestingly, the proposed conditions depend only on graph topology and signed interactions and not on the edge weights of the network. Consequently, the proposed SFJ-based control law relaxes the in-degree balance and homogeneity of trust-distrust, frequently assumed in the literature. Finally, we add simulation results to demonstrate the proposed conditions for multiconsensus.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA31.5", "code": "WeA31.5", "title": "Demographic Dependence of Vaccine Adoption under Opinion Persuasion", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA31", "sessionTitle": "Social Networks and Opinion Dynamics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Casu, Alessandro", "affiliation": "TU Eindhoven" }, { "name": "Quaresmini, Camilla", "affiliation": "Politecnico Di Milano" }, { "name": "Delabays, Robin", "affiliation": "University of Applied Sciences and Arts of Western Switzerland / HES-SO" }, { "name": "Mitchell, Lewis", "affiliation": "Adelaide University" }, { "name": "Pare, Philip", "affiliation": "Purdue University" } ], "keywords": [ "Social networks and opinion dynamics" ], "abstract": "Inspired by contagion models of social belief formation, we develop an epistemically-informed modeling framework, SIS-Vo, in which vaccine-related information propagates on a signed opinion network. Our model allows for heterogeneous treatment effects of policy messages across subpopulations through demographic-specific responses. We derive fixed-point characterizations of the healthy (disease-free) and endemic equilibria of this model, and obtain conditions for local stability of the healthy state in terms of the contact network and opinion-dependent vaccination capacities. Using numerical simulations, we illustrate how suitably targeted policy interventions, acting through opinion dynamics, can stabilize the epidemic process by moving the system towards the healthy regime. The SIS-Vo framework thus provides a natural basis for control-theoretic analysis of vaccination policies that remain robust even when misinformation targets specific subgroups.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA31.6", "code": "WeA31.6", "title": "A Human-AI Driven Multi-Agent Co-Evolutionary System for Opinion Dynamics in the Adaptive Social Network", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA31", "sessionTitle": "Social Networks and Opinion Dynamics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Xiao, Kun", "affiliation": "Macao Polytechnic University" }, { "name": "Zhang, Hongfeng", "affiliation": "Macao Polytechnic University" }, { "name": "Ding, Zedong", "affiliation": "The Hong Kong Polytechnic University" } ], "keywords": [ "Social networks and opinion dynamics", "Cyber-physical and human systems (CPHS)", "Cyber physical social systems (CPSS)" ], "abstract": "This study constructs a human-AI multi-agent system to simulate the co-evolution of public opinion between human users and social bots in the adaptive social network. Based on a discrete-time opinion update model and the similarity of states and dynamic connection mechanisms, this system constructs the co-evolutionary process between heterogeneous agents and networks. Simulation results show that when the target values of bots are randomly assigned, the information entropy of the final opinion distribution increases with both the bot ratio and the global sensitivity parameter. When bot target values are fixed, different target values generate distinct entropy growth patterns. In addition, the distribution of local standard deviation reveals that a high bot ratio reshapes the internal structure of local opinion uncertainty by creating more heterogeneous information environments. Furthermore, this study shows that the human subsystem can rapidly reach internal consensus, while the bot subsystem maintains a bounded deviation from its target value due to adaptive interactions with the surrounding network. And the whole system tends to form a moderate multi-cluster structure rather than a single global consensus. Overall, this study reveals the key mechanisms by which social bot intervention drives public opinion polarization and network structural differentiation, providing a new computational modeling perspective and theoretical reference for understanding polarization phenomena on real-world social media platforms.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA32.1", "code": "WeA32.1", "title": "Development of Nanopositioner with Modular Hybrid Reluctance Actuators", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA32", "sessionTitle": "Mechatronics High Performance Motion Control Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Yamamoto, Shintaro", "affiliation": "University of Fukui" }, { "name": "Fukuyama, Takamaro", "affiliation": "University of Fukui" }, { "name": "Takahashi, Kazuki", "affiliation": "University of Fukui" }, { "name": "Ito, Shingo", "affiliation": "University of Fukui" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "Mechatronic system integration", "High-performance motion control systems" ], "abstract": "This paper presents a nanopositioner developed with modular hybrid reluctance actuators (HRAs), which are for high flexibility to adjust the number of the motion axes dependent on applications. For the vertical motion of the mover to evaluate achievable performances such as positioning resolution as a feasibility study, a pair of modular HRAs are integrated. Analytical models indicate that the paired configuration improves the linearity between the coil current and the actuation force, which is desirable for motion control, in comparison with a single modular HRA. This linearity improvement is further confirmed by finite element analysis. To reject disturbances for high precision motion, a feedback controller is designed with a control bandwidth of 260Hz. In experiments, a point-to-point motion of 500um is successfully carried out, and steps of 20nm can be clearly resolved, demonstrating high-precision positioning of the nanopositioner.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA32.2", "code": "WeA32.2", "title": "Increasing Scan Speed in Atomic Force Microscopy Using Bimodal Excitation and Nonlinear Control", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA32", "sessionTitle": "Mechatronics High Performance Motion Control Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Hendrikx, Jonas", "affiliation": "Eindhoven University of Technology" }, { "name": "Steur, Erik", "affiliation": "Eindhoven University of Technology" }, { "name": "Voorhoeve, Robbert", "affiliation": "Eindhoven University of Technology" }, { "name": "Nijmeijer, Hendrik", "affiliation": "Eindhoven Univ of Technology" }, { "name": "van de Wouw, Nathan", "affiliation": "Eindhoven Univ of Technology" } ], "keywords": [ "Micro and nano mechatronic systems" ], "abstract": "This work presents a bimodal AFM control strategy that enhances topography tracking for advanced semiconductor metrology. By simultaneously exciting the first flexural and torsional resonance modes, and integrating both signals into a unified feedback framework, the method overcomes speed limitations imposed by the slow flexural response. An integral-gain scheduling scheme leverages the faster torsional dynamics to reduce overshoot and improve accuracy when imaging steep steps in the surface profile. A comprehensive simulation model incorporating tip–sample interaction, lock-in dynamics, and feedback control demonstrates substantial improvements in tracking performance compared to conventional single-mode AFM.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA32.3", "code": "WeA32.3", "title": "Large-Range Scanning by CLSM and Sidewall Region Segmentation for AFM Re-Characterization", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA32", "sessionTitle": "Mechatronics High Performance Motion Control Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Chen, Huang-Chih", "affiliation": "National Taiwan University" }, { "name": "Chou, Ting-An", "affiliation": "National Taiwan University" }, { "name": "Tan, Yong-Jia", "affiliation": "National Taiwan University" }, { "name": "Ho, Chia-Hsiang", "affiliation": "National Taiwan University" }, { "name": "Fu, Li-Chen", "affiliation": "National Taiwan Univ" } ], "keywords": [ "Micro and nano mechatronic systems", "Application of mechatronic principles", "High-performance motion control systems" ], "abstract": "Confocal laser scanning microscopy (CLSM) enables rapid, wide-area 3D imaging but remains limited by distortion due to light diffraction, preventing accurate measurement of fine or steep surface features. To address this, we develop a CLSM scanning system that identifies sidewalls on samples, preparing them for combination CLSM with atomic force microscopy (AFM), and to prepare for cooperative scanning strategies to achieve efficient, large-area measurement with improved sidewall characterization. The system enables high-speed mapping over hundreds of micrometers while selectively enhancing resolution where CLSM alone is insufficient, providing a practical solution for large-area topography acquisition.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA32.4", "code": "WeA32.4", "title": "A Frequency-Domain Approach for Identification and Compensation of Cable Hysteresis in a Stage Control System", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA32", "sessionTitle": "Mechatronics High Performance Motion Control Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Alferink, Dirk", "affiliation": "Eindhoven University of Technology" }, { "name": "Stolwijk, Levi", "affiliation": "Technische Universiteit Eindhoven" }, { "name": "van Keulen, Thijs Adriaan Cornelis", "affiliation": "Technische Universiteit Eindhoven" }, { "name": "Fey, Rob H.B.", "affiliation": "PO Box 513, Eindhoven University of Technology" }, { "name": "van de Wouw, Nathan", "affiliation": "Eindhoven Univ of Technology" }, { "name": "Heertjes, Marcel", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "High-performance motion control systems", "Mechatronic system estimation, identification, control", "Mechatronic system modeling, design, optimization" ], "abstract": "Physical connections in motion stages of lithography machines, such as cables, deteriorate tracking performance. These stages are high-precision positioning devices that follow aggressive reference trajectories. Although stages behave like free-floating masses, dynamic coupling with the environment (e.g., by cables) introduce hysteretic disturbance forces that degrades tracking performance. To mitigate this effect, this paper develops a frequency-domain framework for the identification of the hysteresis-related force laws under sinusoidal excitation in closed-loop stages. By employing higher-order describing functions, a nonlinear time-domain parametric hysteresis model is identified, enabling feedforward compensation of disturbance forces. Experimental results validate the framework’s effectiveness in both identification and compensation, highlighting its strong potential for reducing hysteresis-induced disturbances in stages coupled by cables.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA32.5", "code": "WeA32.5", "title": "Decentralized Motion and Resonant Damping Control for High-Bandwidth and Cross-Coupling Reduction in MIMO Nanopositioners", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA32", "sessionTitle": "Mechatronics High Performance Motion Control Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Natu, Aditya", "affiliation": "Delft University of Technology" }, { "name": "HosseinNia, S Hassan", "affiliation": "Delft University of Technology" } ], "keywords": [ "High-performance motion control systems", "Mechatronic system estimation, identification, control", "Application of mechatronic principles" ], "abstract": "Piezoelectric nanopositioning systems are widely used in precision applications that require nanometer accuracy and high-speed motion; however, lightly damped resonances and pronounced cross-axis coupling severely limit bandwidth and disturbance rejection. This paper presents a decentralized dual-loop control strategy for a two-axis nanopositioner, combining an inner non-minimum-phase resonant damping controller with an outer motion controller on each axis. The dominant diagonal resonance is actively damped to enable closed-loop bandwidths beyond the first structural mode, while a parallel band-pass damping path is specifically tuned to a higher-order resonance that predominantly affects the cross-coupling channels. Experimental results demonstrate that this targeted band-pass damping substantially reduces cross-axis coupling and enhances disturbance rejection, without compromising tracking accuracy.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA32.6", "code": "WeA32.6", "title": "Simple Data-Driven Robust Feedforward Control Method for Torque-Constant Variations in Galvano Scanners", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA32", "sessionTitle": "Mechatronics High Performance Motion Control Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Maeda, Yoshihiro", "affiliation": "Nagoya Institute of Technology" }, { "name": "Teramoto, Shota", "affiliation": "Nagoya Institute of Technology" }, { "name": "Yamaguchi, Daigo", "affiliation": "Nagoya Institute of Technology" } ], "keywords": [ "High-performance motion control systems", "Mechatronic system estimation, identification, control" ], "abstract": "In high-speed positioning of galvano scanners, suppressing coupled resonant vibrations in both the motor and mirror is essential to achieving high accuracy, whereas robustness to torque-constant variations is also required to maintain consistent performance. Data-driven feedforward (FF) control methods such as vibration-suppression learning control (VSLC) can suppress coupled vibrations using a single learning dataset; however, their robustness to torque-constant variations has not been examined. This paper presents a simple Robust-VSLC scheme that predicts plant responses under torque-constant variations from a single learning dataset by exploiting the linear action of the torque constant on the plant impulse response. The resulting prediction-oriented formulation enables robust FF design without additional experiments under plant variation conditions. Experiments on a galvano scanner demonstrate that Robust-VSLC improves positioning accuracy under torque-constant variations while preserving the vibration-suppression performance of the standard VSLC.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA33.1", "code": "WeA33.1", "title": "Stable and Reactive Imitation Learning with Trajectory-Guided Mean Flows", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA33", "sessionTitle": "Robotic Learning and Adaptation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Vaaler, Aksel", "affiliation": "NTNU" }, { "name": "Holden, Christian", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Egeland, Olav", "affiliation": "Norwegian Univ. of Sci. & Tech" } ], "keywords": [ "AI-powered robotics", "Robotic learning and adaptation", "Robotic grasping and manipulation" ], "abstract": "Robotic imitation learning often underperforms in dynamic environments due to slow inference and unstable action predictions. This work introduces Trajectory-Guided Mean Flow Policy (TG-MFP), a visuomotor learning framework that enables fast, consistent, and fully closed-loop robot control. TG-MFP generates action sequences efficiently while incorporating prior predictions to maintain coherent behavior during rapid environmental changes. We evaluate TG-MFP on several dynamic manipulation tasks and the widely used Robomimic benchmark suite, demonstrating substantially improved performance over state-of-the-art policies while preserving real-time control capability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA33.2", "code": "WeA33.2", "title": "Interactive Trajectory Planning with Learning-Based Distributionally Robust Model Predictive Control and Markov Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA33", "sessionTitle": "Robotic Learning and Adaptation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Börve, Erik", "affiliation": "Chalmers University of Technology" }, { "name": "Murgovski, Nikolce", "affiliation": "Chalmers University of Technology" }, { "name": "Haghir Chehreghani, Morteza", "affiliation": "Chalmers University of Technology and University of Gothenburg" }, { "name": "Laine, Leo", "affiliation": "Chalmers" } ], "keywords": [ "Autonomous navigation", "Human-robot interaction", "Robotic learning and adaptation" ], "abstract": "We investigate interactive trajectory planning subject to uncertainty in the decisions of surrounding agents. To control the ego-agent, we aim to first learn the decision distribution and solve a Stochastic Model Predictive Control (SMPC) problem. To account for errors in the learned distribution, we show that it is possible to utilize Probably Approximately Correct (PAC) learning in combination with Distributionally Robust (DR) optimization to obtain a solution which accounts for the errors induced by the learning model. The results indicate that our PAC learning-based DR-MPC framework provides a method to interpolate between a robust MPC and an omnipotent SMPC, based on the available number of samples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA33.3", "code": "WeA33.3", "title": "Causal DiffuseLLM: Text-Driven Causal Representation Learning for Counterfactual Image Generation", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA33", "sessionTitle": "Robotic Learning and Adaptation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Ye, Zhaoan", "affiliation": "University of Glasgow" }, { "name": "Zhao, Dezong", "affiliation": "University of Glasgow" }, { "name": "Zhao, Wenjing", "affiliation": "Hong Kong Polytechnic University" }, { "name": "Xue, Shibei", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Flynn, David", "affiliation": "University of Glasgow" } ], "keywords": [ "Robot perception and sensing", "Robotic learning and adaptation" ], "abstract": "Reliable robotic perception requires models that can reason about scene structure rather than rely on correlations. A reliable robotic perception system is especially important in environments with ambiguity, occlusion, and visually confounding factors. Furthermore, conventional causal generation models require manually tuning latent factors and thus cannot support natural-language-driven, end-to-end control. To address these challenges, a causally grounded vision-language diffusion framework with multimodal fusion, Causal Diffusion Models based on the Large Language Model (Causal DiffuseLLM), is presented to enable controllable and interpretable image generation. Semantic–causal alignment between textual prompts and visual latents is established through the integration of a LoRA-tuned LLaVA model with a Q-Former encoder. The framework is coupled with a diffusion backbone to enable causal interventions and high-fidelity synthesis, effectively reducing hallucination. Improved counterfactual image generation accuracy and enhanced cross-modal consistency are demonstrated on both synthetic shadow datasets and real-world image datasets.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA33.4", "code": "WeA33.4", "title": "Two-Player Adversarial Game Policy Based on Self-Play Reinforcement Learning", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA33", "sessionTitle": "Robotic Learning and Adaptation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Cui, Baoyi", "affiliation": "Harbin Institute of Technology, ShenZhen" }, { "name": "Lu, Weiyan", "affiliation": "Harbin Institution of Technology, Shenzhen" }, { "name": "Gong, Youmin", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Yuan, Qiufan", "affiliation": "Shanghai Institute of Aerospace System Engineering" }, { "name": "Ma, Guangfu", "affiliation": "Harbin Institute of Technology" }, { "name": "Shao, Zhen", "affiliation": "Harbin Institute of Technology" }, { "name": "Mei, Jie", "affiliation": "Harbin Institute of Technology, Shenzhen" } ], "keywords": [ "Robotic learning and adaptation", "Aerial, field, and marine robotics", "Task and motion planning" ], "abstract": "We investigate an adversarial game problem involving two homogeneous agents subject to unicycle kinematic constraints. We design a top-level role selection policy and two bottom-level meta-task policies to enable the agents to balance the opposing objectives of attacking the opponent and avoiding being attacked, which have conflicting reward functions. Both meta-task and role selection training are conducted using PPO. Self-play training is employed for training role selection, where the agents learn the optimal strategy by competing with its copy. Simulation experiments show that our method exhibits better convergence speed than commonly used MADDPG and PPO without meta-task training, achieving higher rewards under various initial relative states in Monte Carlo simulations. The learned policy is successfully transferred to real-world demonstrations involving two autonomous quadrotors in self-play and interactions between the trained policy and human operators.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA33.5", "code": "WeA33.5", "title": "Learning Spatiotemporal Tubes for Temporal Reach-Avoid-Stay Tasks Using Physics-Informed Neural Networks", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA33", "sessionTitle": "Robotic Learning and Adaptation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Basu, Ahan", "affiliation": "Indian Institute of Science" }, { "name": "Das, Ratnangshu", "affiliation": "Indian Institute of Science, Bangalore" }, { "name": "Jagtap, Pushpak", "affiliation": "Indian Institute of Science" } ], "keywords": [ "Task and motion planning", "Autonomous navigation", "Robotic learning and adaptation" ], "abstract": "This paper presents a Spatiotemporal Tube (STT)-based control framework for general control-affine MIMO nonlinear pure-feedback systems with unknown dynamics to satisfy prescribed time reach-avoid-stay tasks under external disturbances. The STT is defined as a time-varying ball, whose center and radius are jointly approximated by a Physics-Informed Neural Network (PINN). The constraints governing the STT are first formulated as loss functions of the PINN, and a training algorithm is proposed to minimize the overall violation. The PINN being trained on certain collocation points, we propose a Lipschitz-based validity condition to formally verify that the learned PINN satisfies the conditions over the continuous time horizon. Building on the learned STT representation, an approximation-free closed-form controller is defined to guarantee satisfaction of the T-RAS specification. Finally, the effectiveness and scalability of the framework are validated through two case studies involving a mobile robot and an aerial vehicle navigating through cluttered environments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA33.6", "code": "WeA33.6", "title": "Multi-Scale Frontier-Aware Coverage Path Planning Using DRL in Unknown Environments", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA33", "sessionTitle": "Robotic Learning and Adaptation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Li, Xuzhao", "affiliation": "Beijing Institute of Technology" }, { "name": "Zhou, Xuan", "affiliation": "Beijing Institute of Technology" }, { "name": "Yao, Jiyu", "affiliation": "Beijing Institute of Technology" }, { "name": "Zhang, Siying", "affiliation": "Beijing Institute of Technology" }, { "name": "Shi, Xiang", "affiliation": "Beijing Institute of Technology" }, { "name": "Deng, Fang", "affiliation": "Beijing Institute of Technology" } ], "keywords": [ "Task and motion planning", "Robotic learning and adaptation", "Robot perception and sensing" ], "abstract": "Deep Reinforcement Learning (DRL) exhibits superior performance in Coverage Path Planning (CPP) in unknown environments, yet faces challenges such as coverage holes and poor generalization. This paper proposes Multi-scale Frontier-aware Coverage Path Planning (MFACPP), a novel DRL-based algorithm to substantially reduce coverage duration in unknown environments. It incorporates multi-scale attention mechanisms and an adaptive frontier-aware reward to mitigate coverage holes while balancing exploration and coverage. Additionally, progressive balanced review curriculum learning is employed to enhance generalization through reconciliation of ongoing and prior experiences. Our method significantly outperforms baselines, increasing coverage rate by 10% and reducing average time by 23.8%, thus enhancing efficiency and generalization.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA35.1", "code": "WeA35.1", "title": "Industry-Integrated Motor Systems Education: Bridging Classical Control, AI, and Real-World Applications (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA35", "sessionTitle": "AI in Electric Motor Systems: Design, Estimation, Control, and Industry-Focused Education", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Hur, Pilwon", "affiliation": "Gwangju Institute of Science and Technology" }, { "name": "Ahn, Hyo-Sung", "affiliation": "Gwangju Institute of Science and Technology (GIST)" }, { "name": "Kim, Uehwan", "affiliation": "GIST" }, { "name": "Choi, Kyunghwan", "affiliation": "Korea Advanced Institute of Science and Technology" }, { "name": "Lee, Junpyo", "affiliation": "Samsung Electronics" } ], "keywords": [ "AI-powered robotics", "Mechatronic system integration", "Mechatronic system modeling, design, optimization" ], "abstract": "Educating engineers who bridge motor-systems theory with real-world deployment remains challenging. This paper presents a Samsung Electronics--GIST framework producing industry-ready specialists in intelligent motor systems. The program integrates classical control, signal processing, mechanical design, and AI within a project-based master's curriculum: foundational bootcamps, specialized coursework (field-oriented control, topology optimization, reinforcement learning, parameter estimation), and industry-mentored capstone projects on manufacturing challenges. Three cohorts produced 14 projects spanning sensorless control, AI-based fault detection, multi-domain optimization, and robot manipulation. Assessment shows significant learning gains---particularly in reinforcement learning---with graduates securing motor and mechatronics roles. The model shows how integrating academic rigor, AI, and authentic industrial context addresses the mechatronics workforce gap while advancing control-education pedagogy.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA35.2", "code": "WeA35.2", "title": "Online Physics-Informed Learning-Based Flux Linkage Estimation: Application to Adaptive MTPA Control of Synchronous Machines (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA35", "sessionTitle": "AI in Electric Motor Systems: Design, Estimation, Control, and Industry-Focused Education", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Jang, Seunghun", "affiliation": "Korean Adavanced Institute of Science and Technology" }, { "name": "Choi, Kyunghwan", "affiliation": "Korea Advanced Institute of Science and Technology" } ], "keywords": [ "Mechatronic system estimation, identification, control" ], "abstract": "Machine parameters such as flux linkages and inductances play a key role in achieving optimal torque control of synchronous machines (SMs). However, it is challenging to identify these parameters online based on the limited SM model and their complex nonlinear characteristics. A fully connected feedforward neural network (NN) is a promising candidate for modeling these parameters owing to its capability to approximate complex nonlinear functions. Therefore, this study proposes an online physics-informed learning framework for identifying the parameters of SMs using an NN model. The proposed method enables the NN-modeled flux linkages and the corresponding differential inductances to be learned in compliance with the governing physical laws of SMs. Consequently, the NN can effectively capture the nonlinear characteristics of SM parameters while maintaining physical consistency. The NN model learned online is used as an estimator for the flux linkages and differential inductances required for MTPA control. The effectiveness of the proposed method is validated through simulations conducted on a 35-kW interior permanent magnet synchronous machine (IPMSM) drive.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA35.3", "code": "WeA35.3", "title": "Compressor Motor Control Strategies for Thermal Management in AI Computing Environments: A Comprehensive Review (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA35", "sessionTitle": "AI in Electric Motor Systems: Design, Estimation, Control, and Industry-Focused Education", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Baek, Hyunjun", "affiliation": "Samsung Electronics" }, { "name": "Lee, Wonhee", "affiliation": "Samsung Electronics" }, { "name": "Jung, Bumun", "affiliation": "Samsung Electronics" }, { "name": "Lee, Hakjun", "affiliation": "Samsung Electronics" }, { "name": "Lee, Junpyo", "affiliation": "Samsung Electronics" }, { "name": "Hur, Pilwon", "affiliation": "Gwangju Institute of Science and Technology" } ], "keywords": [ "AI-powered robotics", "Mechatronic system integration", "Mechatronic system modeling, design, optimization" ], "abstract": "The exponential growth of AI computing has driven unprecedented power densities in data centers, with rack loads exceeding 50-100 kW and global annual data-center electricity consumption projected to reach 620-1,050 TWh by 2026. Unlike traditional cloud services, AI workloads exhibit highly stochastic and bursty thermal profiles that challenge conventional cooling control systems. This survey reviews compressor motor control strategies for vapor-compression refrigeration cycles in AI data-center thermal management over 2010-2025. We systematically analyze the evolution from classical field-oriented control (FOC) with PI regulators through advanced model predictive control (MPC) to emerging deep reinforcement learning (DRL). A review of 80 peer-reviewed papers shows that while PI-FOC remains industry standard, its bandwidth limitations render it inadequate for dynamic AI loads; MPC offers 10-20% energy savings with explicit constraint handling, while DRL demonstrates 15-25% potential improvements in simulation but remains at TRL 3-4. We identify critical gaps in sim-to-real transfer, safety guarantees, and formal verification, and point to virtual commissioning via digital twins, grid-interactive demand response, and waste-heat recovery as the most promising directions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA35.4", "code": "WeA35.4", "title": "Real-Time Prediction of Electric Motor Dynamics Via Operator Learning (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA35", "sessionTitle": "AI in Electric Motor Systems: Design, Estimation, Control, and Industry-Focused Education", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Myeong, Jeonguk", "affiliation": "Gwangju Institute of Science and Technology" }, { "name": "Hong, Seokwoo", "affiliation": "Gwangju Institute of Science and Technology" }, { "name": "Kim, Dongjin", "affiliation": "Korea Atomic Energy Research Institute" }, { "name": "Lee, Jaewook", "affiliation": "GIST" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "Mechatronic system estimation, identification, control" ], "abstract": "Accurate performance prediction is essential for the precision control and optimal design of electrical machines. While the flux linkage map is critical for controller performance, traditional Finite Element Analysis (FEA) is too computationally expensive for real-time applications or design optimization. This paper proposes an operator learning-based model for real-time flux linkage map prediction. By learning the non-linear mapping from operating conditions and design variables to flux linkage, the model significantly enhances inference speed. Integrating these maps with controllers allows real-time verification of design impacts on dynamic response, ultimately enabling a design-control co-optimization paradigm.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA35.5", "code": "WeA35.5", "title": "A Meta-Learning Approach for Speed Estimation of Brushed DC Motors", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA35", "sessionTitle": "AI in Electric Motor Systems: Design, Estimation, Control, and Industry-Focused Education", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Hächler, Cyrill", "affiliation": "HSLU" }, { "name": "Busetto, Riccardo", "affiliation": "IDSIA USI-SUPSI" }, { "name": "Forgione, Marco", "affiliation": "SUPSI-USI" }, { "name": "Rizzoli, Andrea Emilio", "affiliation": "SUPSI" }, { "name": "Prud'homme, Thierry", "affiliation": "Lucerne University of Applied Sciences and Arts" }, { "name": "Piga, Dario", "affiliation": "SUPSI-USI" } ], "keywords": [ "Mechatronic system estimation, identification, control" ], "abstract": "In brushed DC motors (BDCMs), commutation-induced current ripple encodes rotor speed, enabling sensorless speed estimation from current alone. However, existing methods for speed estimation often rely on motor parameters, voltage sensing, or hand-tuned signal processing, which limits robustness and scalability across heterogeneous motors. This work proposes a meta-learning framework for sensorless speed estimation trained on synthetic current trajectories that capture the spectral structure and variability of the BDCM current ripple. Using only armature current, a convolutional-recurrent meta-model predicts speed and associated uncertainty without real-world current-speed training data or motor-specific calibration. Experiments on real systems show robust generalization to unseen motors and operating regimes within the considered motor class, providing a scalable alternative to conventional ripple-based methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA35.6", "code": "WeA35.6", "title": "Higher-Order Filtering Sliding Mode Observer and Quasi-Continuous Control Design for a Thermoelectric System", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA35", "sessionTitle": "AI in Electric Motor Systems: Design, Estimation, Control, and Industry-Focused Education", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "van Rossum, Felix", "affiliation": "Leuphana University of Lueneburg" }, { "name": "Mercorelli, Paolo", "affiliation": "Leuphana University of Lueneburg" }, { "name": "Aschemann, Harald", "affiliation": "University of Rostock" } ], "keywords": [ "Mechatronics for advanced manufacturing and energy systems", "Mechatronic system estimation, identification, control" ], "abstract": "This paper presents a robust control and estimation framework for the cold-side temperature as controlled output in thermoelectric cooling systems. First, a second-order quasi-continuous sliding-mode controller (QCSMC) is proposed that provides a control action with significantly reduced chattering and guarantees finite-time convergence of the sliding variable. To enable an accurate state and disturbance reconstruction despite a nonlinear dynamics and additional measurement noise, a disturbance-extended Levant filtering differentiator is designed for the finite-time estimation of the hot-side temperature, its time derivative, and an unknown cold-side disturbance. These elements are integrated into a unified observer–controller architecture that ensures closed-loop compensation of model uncertainties and heat-flux disturbances respecting the physical current limits of the Peltier module. Simulation results confirm the proposed approach, demonstrating enhanced robustness, reduced chattering, and superior disturbance rejection compared with classical sliding-mode controllers and conventional observer schemes commonly used in thermoelectric cooling applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA36.1", "code": "WeA36.1", "title": "Hierarchical Learning of Battery Degradation under EV Charging Behavior", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA36", "sessionTitle": "Artificial Intelligence in Transportation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Yu, Yongjiang", "affiliation": "Southwest Jiaotong University" }, { "name": "Ge, Zijian", "affiliation": "Southwest Jiaotong University" }, { "name": "Meng, Yuanxiang", "affiliation": "Southwest Jiaotong University" }, { "name": "Wang, Lirun", "affiliation": "Southwest Jiaotong University" }, { "name": "Yang, Shunfeng", "affiliation": "Southwest Jiaotong University" } ], "keywords": [ "Artificial intelligence in transportation", "AI for aircraft and spacecraft navigation, guidance and control", "AI and embodied-AI in marine systems" ], "abstract": "Accurate early-life prediction of battery lifetime is vital for electric vehicles, where charging behavior strongly influences degradation and long-term reliability. This paper proposes a hierarchical, physics-informed framework that learns directly from early-stage charging data. Each cycle is represented through shared encoders that capture constantcurrent profiles, intermediate-resistance pulses, and constant-voltage decay, combined with the charging protocol into a compact feature representation. A lightweight recurrent–attention module models gradual degradation across cycles, while a physics-informed head estimates interpretable parameters such as ohmic resistance and polarization time constants under monotonic and smooth evolution constraints. A lifetime head integrates these features to produce point and interval predictions. Trained in two stages with cycle-dropout, the framework achieves improved accuracy and interpretability over purely data-driven baselines. The results demonstrate strong potential for accelerating electric vehicle battery evaluation, optimizing charging strategies, and enabling reliable lifetime assessment using only early charging data.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA36.2", "code": "WeA36.2", "title": "MaskAE: Masked Autoencoder-Based Intrusion Detection System for Data Poisoning in UAVs", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA36", "sessionTitle": "Artificial Intelligence in Transportation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Aboelmagd, Salma", "affiliation": "Florida State University" }, { "name": "Burnside, Alex", "affiliation": "Florida State University" }, { "name": "Ismail, Muhammad", "affiliation": "Tennessee Technological University" }, { "name": "Takiddin, Abdulrahman", "affiliation": "Florida State University" } ], "keywords": [ "Artificial intelligence in transportation", "AI for aircraft and spacecraft navigation, guidance and control", "Learning and adaptation in autonomous vehicles" ], "abstract": "Unmanned aerial vehicles (UAVs) increasingly sustain sensing and communications, demanding robust intrusion detection systems (IDSs). We investigate training-time data poisoning in UAV IDSs and propose a masked autoencoder (MaskAE) that detects attacks while remaining robust to corrupted supervision. Using fused cyber-physical telemetry, we inject data poisoning into the training set such that poisoned samples constitute 10%, 20%, and 30% of the data, while the test set includes correctly labeled conventional attacks, reflecting realistic deployment. Across poisoning levels, benchmark IDSs suffer detection rate degradations of 5 −18%, whereas MaskAE degrades by only 2−5%, demonstrating resilience for UAV IDS.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA36.3", "code": "WeA36.3", "title": "KINEMATIC-GATED: A Physically-Verified Data Pipeline to Expose and Correct Temporal Selection Bias in Autonomous Vehicle Safety Benchmarks", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA36", "sessionTitle": "Artificial Intelligence in Transportation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Granados, David", "affiliation": "UPC" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "García Martínez, Mario", "affiliation": "UPC/SEAT" } ], "keywords": [ "Artificial intelligence in transportation", "Intelligent transportation systems", "Autonomous vehicles" ], "abstract": "Real-world collision data are ethically and legally inaccessible for autonomous vehicle (AV) safety research, forcing reliance on synthetic simulation. However, publicly available simulation-based datasets suffer from three critical methodological deficiencies that invalidate rigorous safety validation: (1) unreliable ground truth from faulty physics engines (with observed total labeling error rates of approximately 25%), (2) logical data leakage permitting models to algebraically calculate targets rather than predicting them, and (3) severe selection bias where models are trained exclusively on emergency braking events. This paper introduces KINEMATIC-GATED, an end-to-end auditing pipeline that replaces simulator ground truth with a dual-gated detector based on first-principles (jerk thresholds > 200 m/s3 and geometric contact verification < 0.25 m). Furthermore, we introduce “Peace-Time Padding,” a temporal augmentation technique synthesizing safe driving context to mitigate selection bias. Experimental validation across five model architectures (SVM, XGBoost, LSTM, TCN, CNN-Transformer) demonstrates that bias rectification reverses performance rankings found in literature. While static models dominate biased benchmarks due to leakage, sequential models prove superior in physically consistent environments. Our results show the LSTM architecture achieving an AUPRC of 0.8792, significantly outperforming the static XGBoost baseline (0.8467), confirming that collision prediction is fundamentally a temporal dynamics task dependent on regime transitions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA36.4", "code": "WeA36.4", "title": "Agentic Quantum Planning Via Foundation Models for Truck-Drone Delivery Optimization", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA36", "sessionTitle": "Artificial Intelligence in Transportation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Liu, Xinyu", "affiliation": "North China Electric Power University" }, { "name": "Li, Chengxiang", "affiliation": "University of Sanya" }, { "name": "Song, Zihan", "affiliation": "Hunan Universiry" }, { "name": "Li, Bai", "affiliation": "Hunan University" }, { "name": "Lin, Fei", "affiliation": "Macau University of Science and Technology" }, { "name": "Wang, Jing", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Tian, Yulin", "affiliation": "Zhoukou Nomal University" }, { "name": "Yin, Xukun", "affiliation": "Institute of Applied Mathematics, Hebei Academy of Sciences" }, { "name": "Lu, Zhanhui", "affiliation": "North China Electric Power University" }, { "name": "Tian, Yong-Lin", "affiliation": "State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijin" } ], "keywords": [ "Artificial intelligence in transportation", "Transportation logistics" ], "abstract": "The truck-drone hybrid paradigm offers a promising solution for last-mile logistics,but large-scale deployment leads to a challenging combinatorial optimization problem. We propose Agentic Quantum Planning (AQP), a zero-shot multi-agent framework that decomposes truck-drone delivery into demand modeling, transfer-station selection, truck macro-route planning, and drone-route optimization. AQP converts customer distributions into value maps for VLM-based station placement, uses an LLM to sequence the truck route, and applies a quantum-classical drone-routing pipeline based on graph coarsening, GM-QAOA, and 2-opt refinement. Experiments on 100 synthetic instances show that value-map-guided station placement reduces the mean truck travel distance by 10.3% over the text-only baseline. On a 30-node drone-routing benchmark, the proposed pipeline achieves a best-case gap of 1.15% from the exact optimum.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA36.5", "code": "WeA36.5", "title": "Periodic Green Vehicle-Drone Cold-Chain Routing: Learning-Aided Multi-Objective Optimization Method", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA36", "sessionTitle": "Artificial Intelligence in Transportation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Yang, Mingyuan", "affiliation": "Harbin Engineering University" }, { "name": "Wang, Wei", "affiliation": "Harbin Engineering University" }, { "name": "Dong, Fuwang", "affiliation": "Harbin Engineering University" } ], "keywords": [ "Intelligent transportation systems", "Transportation logistics", "Artificial intelligence in transportation" ], "abstract": "This study investigates the periodic green vehicle-drone routing problem (PG-VDRP) in cold-chain transportation and formulates it as a bi-objective model to minimize the operation cost and carbon emission. To enhance solving efficiency, we propose an enhanced learning-based multi-objective approach (ELMOA) by integrating hybrid-strategy population initialization method and deep Q-Network-based adaptive operator selection mechanism. Notably, a Long Short-Term Memory-based policy optimization method is introduced to increase the agent decision ability by improving sample efficiency. Experiments demonstrate the ELMOA outperforms the CPLEX and several advanced algorithms in minimizing the proposed model on benchmark tests and a real urban case.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA36.6", "code": "WeA36.6", "title": "Distributionally Robust Multi-Agent Reinforcement Learning for Intelligent Traffic Control", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA36", "sessionTitle": "Artificial Intelligence in Transportation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Pei, Shuwei", "affiliation": "University of Groningen" }, { "name": "Borger, Joran", "affiliation": "University of Groningen" }, { "name": "Koşay, Arda", "affiliation": "Bilkent University" }, { "name": "Sayin, Muhammed Omer", "affiliation": "Bilkent University" }, { "name": "Ahmed, Saeed", "affiliation": "Faculty of Science and Engineering, University of Groningen" } ], "keywords": [ "Intelligent transportation systems", "Artificial intelligence in transportation", "Planning, management and security in transportation" ], "abstract": "Learning-based traffic signal control optimized for average performance often degrades under atypical conditions. To address this, we propose a distributionally robust multi-agent reinforcement learning (DR-MARL) framework, evaluated on a 3×3 Athens grid calibrated with pNEUMA trajectory data (Barmpounakis and Geroliminis, 2020). Our approach first trains a baseline MARL controller using proximal policy optimization. To capture demand uncertainty, we define eight heterogeneous origin-destination scenarios and train a contextual-bandit worst-case estimator to dynamically identify adversarial demand mixtures. Fine-tuning the baseline agents under these worst-case conditions yields our DR-MARL controller. Across all scenarios and an unseen Sioux Falls validation network, DR-MARL consistently improves upon the baseline, achieving up to 51% shorter queues and 38% higher speeds on the worst-performing scenarios.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA37.1", "code": "WeA37.1", "title": "Deep Learning-Based Distributed Event-Triggered Control for ESSs in MGs (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "09:50-10:10", "sessionCode": "WeA37", "sessionTitle": "Intelligent Control and Optimization for Renewable Power Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Tang, Qi", "affiliation": "Nanjing University of Posts and Telccommunications" }, { "name": "Fan, Sha", "affiliation": "Nanjing University of Posts and Telecommunications" }, { "name": "Deng, Chao", "affiliation": "Nanjing University of Posts and Telecommunications" } ], "keywords": [ "Social transportation and social energy", "Knowledge automation", "Cyber physical social systems (CPSS)" ], "abstract": "This paper aims to solve the problem of distributed control for energy storage systems (ESSs) in island MGs with limited communication resources. To deal with the problem, a novel data-driven event-trigger control framework is proposed. Specifically, based on a deep neural network (DNN), a state estimator is first designed to estimate the neighbor communication signals according to the measured local state signals. Then, based on the offline learning DNN estimator, a novel distributed event-trigger mechanism is designed to determine the communication intervals online for each ESS. Finally, the DNN-based distributed event-triggered control is proposed to realize the objectives of frequency restoration, proportional active power sharing, and state-of-charge balancing of ESSs with limited communication resources. As an advantage, the proposed observer eliminates the requirement of a precise system model and enhances the accuracy of estimation compared with the existing open-loop estimate strategy. Simulations on MGs with 4 ESSs are conducted to verify the effectiveness of the proposed control method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA37.2", "code": "WeA37.2", "title": "Fast and Robust Control Parameter Tuning for Transient Stability of Grid-Connected PV Systems Via Ordinal Optimization (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:10-10:30", "sessionCode": "WeA37", "sessionTitle": "Intelligent Control and Optimization for Renewable Power Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Wang, Jiazhou", "affiliation": "Tsinghua University" }, { "name": "Wang, Shuobin", "affiliation": "Tsinghua University" }, { "name": "Yan, Xinhua", "affiliation": "Xi'an Jiaotong University" }, { "name": "Zhu, Yuhang", "affiliation": "Tsinghua University" }, { "name": "He, Ziteng", "affiliation": "Tsinghua University" }, { "name": "Jia, Qing-Shan", "affiliation": "Tsinghua University" }, { "name": "Guan, Xiaohong", "affiliation": "Xi'an Jiaotong University" } ], "keywords": [ "Industrial and service applications of AI and intelligent automation", "AI for smart cities", "Smart city control and optimization" ], "abstract": "Appropriate converter control parameters are critical for ensuring the transient stability of grid-connected photovoltaic (PV) systems. However, conventional manual tuning and direct high-fidelity simulation-based optimization are computationally expensive. To address this issue, this paper proposes a surrogate-driven ordinal optimization (OO) framework for fast and robust parameter tuning. First, a surrogate model is trained to approximate transient stability performance and to efficiently evaluate a large candidate set and rank them. Next, OO is applied to select the top-s candidates for real-time digital simulator (RTDS) simulations, ensuring a high probability of obtaining truly good enough parameters with reduced simulation time. Case studies show that the proposed approach reliably obtains true top-1% control parameters with negligible optimization time and significantly reduced RTDS simulations, while outperforming widely used metaheuristic algorithms in both generalization capability and computational efficiency.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA37.3", "code": "WeA37.3", "title": "A Multi-Agent LLM-RL Synergistic Decision Framework for Virtual Power Plant Coalition Optimization in Dynamic Electricity Markets (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "10:30-10:50", "sessionCode": "WeA37", "sessionTitle": "Intelligent Control and Optimization for Renewable Power Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Sun, Yichen", "affiliation": "Shanghai University of Electric Power" } ], "keywords": [ "Urban energy distribution systems", "Agent & AI technology for business and economy", "Game theories" ], "abstract": "电力市场中协调多虚拟电厂（VPP）与共享储能（SES）的挑战，提出了一个将大型语言模型（LLMs）战略推理与强化学习（RL）自适应控制相结合的框架。Stackelberg 游戏通过两层优化生成内部价格信号，使个人目标和系统目标保持一致。协调问题被表述为受限马尔可夫决策过程（CMDP），旨在在严格的物理和经济约束优化条件下长期效益。 为解决这个问题，开发了三层混合架构：用于高层引导的大型语言模型战略层，用于精确控制的强化学习战术层，以及用于动态集成的融合层。实验结果表明，所提框架有效平衡了权力，稳定了市场价格，并确保了利润的公平分配，为复杂市场环境中的协作VPP优化提供了稳健的解决方案。", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA37.4", "code": "WeA37.4", "title": "Robust Power System Scheduling for Resilience Enhancement with Decision-Dependent Demand Response Uncertainty", "day": "Wednesday", "date": "August 26, 2026", "time": "10:50-11:10", "sessionCode": "WeA37", "sessionTitle": "Intelligent Control and Optimization for Renewable Power Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Qiu, Luru", "affiliation": "Xi'an Jiaotong University" }, { "name": "Ma, Donglai", "affiliation": "Xi'an Jiaotong University" }, { "name": "Cao, Xiaoyu", "affiliation": "Xi'an Jiaotong University" }, { "name": "Dong, Yuchen", "affiliation": "Xi'an Jiaotong University" }, { "name": "Chen, Mengxiao", "affiliation": "Xi'an Jiaotong University" }, { "name": "Yang, Lun", "affiliation": "Xi'an Jiaotong University" } ], "keywords": [ "Smart city security and resilience", "Decision making under uncertainty" ], "abstract": "More frequent extreme events challenge secure operation of power systems, underscoring the importance of adaptive response capabilities for enhancing system resilience. As a fast and controllable distributed demand-side resource, demand response (DR) can offer this adaptive capability via rapid load adjustments, particularly when conventional supply-side resources become constrained during disruptive events. However, the actual DR response has its own uncertainty and depends on the DR decisions, a feature that existing scheduling approaches have not yet taken into account. To address this challenge, we develop a two-stage robust scheduling framework that simultaneously accounts for decision-dependent DR uncertainty arising from DR load behavior and decision-independent disturbances, including load prediction deviations and N–k transmission contingencies. In this framework, the first stage determines the scheduled DR loads and pre-contingency generation schedules, while the second stage performs recourse dispatch to minimize the worst-case power imbalance under the combined uncertainty set. The resulting robust model is solved using a parametric column-and-constraint generation (C&CG) algorithm. Numerical tests on the IEEE RST-96 system demonstrate that the proposed method can substantially enhance power system resilience through DR scheduling while maintaining economic feasibility.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA37.5", "code": "WeA37.5", "title": "Delay-Dependent Robust Frequency Control for Microgrid with Coordinated Virtual Inertia", "day": "Wednesday", "date": "August 26, 2026", "time": "11:10-11:30", "sessionCode": "WeA37", "sessionTitle": "Intelligent Control and Optimization for Renewable Power Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Wei, Chen-Guang", "affiliation": "School of Artificial Intelligence and Automation, China University of Geosciences, Wuhan" }, { "name": "Shangguan, Xing-Chen", "affiliation": "China University of Geosciences" }, { "name": "Wu, Tong-Yu", "affiliation": "School of Automation, China University of Geosciences, Wuhan" }, { "name": "He, Yong", "affiliation": "China University of Geosciences" }, { "name": "Zhang, Chuan-Ke", "affiliation": "China University of Geosciences" }, { "name": "Wang, Hong-Zhang", "affiliation": "China University of Geosciences, Wuhan" } ], "keywords": [ "Urban energy distribution systems", "Cyber-physical urban systems", "Building automation" ], "abstract": "能量来源，以及两者的随机波动 发电和需求侧，结合低惯性 特性，显著放大频率偏移 数量级。与此同时，常规控制 依赖低带宽通信的架构 网络在MG中引入了显著的通信延迟 中央控制器，进一步复杂化系统频率 稳定性维护。为了应对这些挑战，这 论文提出了一种虚拟惯性控制协调稳健 延迟依赖的MG频率控制策略 系统（FCS），在克服时提供惯性支持。 鲁棒性限制。首先，FCS借助能源 基于存储的虚拟惯性开发为 对通信时间变化延迟的考虑 网络。随后，满足充足条件以确保 系统稳定性和 H_{infty} 性能是 通过柳普诺夫理论确立。此外， 引入自由权重矩阵以构造项 与手柄增益相关，获得的控制器增益 通过求解线性矩阵不等式。最后， 全面的模拟与实Ƌ", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeA37.6", "code": "WeA37.6", "title": "Stochastic Optimal Scheduling Framework for Net-Zero Carbon Park in Joint Energy Spot and Reserve Markets", "day": "Wednesday", "date": "August 26, 2026", "time": "11:30-11:50", "sessionCode": "WeA37", "sessionTitle": "Intelligent Control and Optimization for Renewable Power Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Li, Miaomiao", "affiliation": "Xi’an Jiaotong University" }, { "name": "Dong, Yuchen", "affiliation": "Xi'an Jiaotong University" }, { "name": "Cao, Xiaoyu", "affiliation": "Xi'an Jiaotong University" }, { "name": "Chen, Mengxiao", "affiliation": "Xi'an Jiaotong University" }, { "name": "Tian, Zhaoming", "affiliation": "Xi'an Jiaotong University" }, { "name": "Wang, Wenxuan", "affiliation": "Xi’an Jiaotong University" } ], "keywords": [ "Urban energy distribution systems", "Decision making under uncertainty" ], "abstract": "The global transition toward carbon neutrality has positioned Net-Zero Carbon Energy Parks (NZCEPs) as a pivotal measure for realizing industrial-scale decarbonization. However, the high volatility of renewable energy (RE) and the uncertainty of reserve activation pose significant challenges to their economic and reliable operation. This paper proposes a stochastic optimal scheduling strategy for a NZCEP participating in joint day-ahead energy spot and reserve markets. A two-stage stochastic programming framework is developed to optimize the coordinating internal flexibility resources like Power-to-X technologies and energy storage hedging against correlated uncertainties in RE generation, flexible load availability, and reserve activation. Case studies conducted on a typical NZCEP demonstrate that the proposed joint market participation strategy enables the park to achieve net profit and zero-carbon emissions without RE curtailment. Comparative analysis confirms that participating in both markets significantly outperforms scenarios limited to the spot market, transforming the NZCEP from a passive cost center into a proactive provider of grid flexibility.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB01.1", "code": "WeB01.1", "title": "When Control Loops Leave the Lab: Reflections from Two Years into a Control Engineering Service Provider (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB01", "sessionTitle": "From Research to Practice: Entrepreneurship in Control", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Jacobs, Laurens", "affiliation": "Nikwist" } ], "keywords": [ "Cyber physical human machine systems" ], "abstract": "The international academic community continues to produce great amounts of novel research results in the broad field of automatic control, as proven by the many contributions at the IFAC World Congress this week. However, much of this work focuses on proofs, numerical simulations, and experimental validations in a controlled environment, with the hope that practitioners in industry will recognize these contributions as solutions relevant to their engineering problems. Exceptions prove the rule, but several years of working at the intersection of academia and industry has shown that this hope is often wishful thinking and that active dissemination and support is essential to make a technology industrially relevant. Bridging this gap became the foundation for the control engineering service provider I co-founded two years ago after leaving academia. In this presentation, I will share our experiences so far in applying academic control methods in various industrial settings by teaming up, as consulted control experts, with R&D and engineering teams from different companies. Also the challenges we have faced and the questions that still remain open will be addressed, with the aim of stimulating an interactive discussion during the session.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB01.2", "code": "WeB01.2", "title": "From Paper to Product: Lessons Learned Scaling Physical Intelligence from TRL 1 to TRL 9 (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB01", "sessionTitle": "From Research to Practice: Entrepreneurship in Control", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Astudillo, Alejandro", "affiliation": "T-Robotics" } ], "keywords": [ "Cyber physical human machine systems" ], "abstract": "", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB01.3", "code": "WeB01.3", "title": "The Control Engineering Challenge for Humanoid Profitability: Lessons from the Industrial Robotics (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB01", "sessionTitle": "From Research to Practice: Entrepreneurship in Control", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Oh, Sehoon", "affiliation": "DGIST" } ], "keywords": [ "Cyber physical human machine systems" ], "abstract": "", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB01.4", "code": "WeB01.4", "title": "Benchmark Problems for HDD Head-Positioning Servo Control to Support Industry-Academia Collaboration (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB01", "sessionTitle": "From Research to Practice: Entrepreneurship in Control", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Atsumi, Takenori", "affiliation": "Chiba Institute of Technology" } ], "keywords": [ "Cyber physical human machine systems" ], "abstract": "As HDD capacity continues to grow, head-positioning servo control becomes increasingly demanding. In practice, however, it is difficult for universities to work on state-of-the-art HDD servo problems because the latest hardware is rarely accessible outside companies, and only a small number of engineers can spend time on controller design while many other development tasks run in parallel. To bridge this gap between academia and industry, we developed benchmark problems that isolate the controller-design task while staying close to modern HDD conditions. The benchmark problems are built from measurement-based plant dynamics and disturbance characteristics, enabling researchers to evaluate advanced control algorithms under realistic constraints without requiring proprietary HDD hardware. At the same time, strong solutions developed using the benchmark problems can be transferred to product-level evaluation with minimal rework. This tutorial outlines how the benchmark problems were developed with industry needs in mind, how they have been used to facilitate collaboration between academia and industry, and what has helped-and what has not-in moving advanced control research toward deployment in HDD systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB01.5", "code": "WeB01.5", "title": "Robotic 3D Printing Toolkit: From Research Toward a Product (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB01", "sessionTitle": "From Research to Practice: Entrepreneurship in Control", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Balta, Efe C.", "affiliation": "Inspire AG" } ], "keywords": [ "Cyber physical human machine systems" ], "abstract": "3D printing or additive manufacturing has seen great interest in the research and technology due to the flexibility it provides in material selection and design geometry. Despite various shortcomings in process optimization and stability, 3D printing reaches wide audiences and democratizes manufacturing today. Nevertheless, the key promises of flexibility and lot-size-one manufacturing with 3D printing are far from reality. Our research agenda bridges the gap between theory and practice in 3D printing by tightly integrating hardware, control theory, and software to improve stability, reliability, and repeatability. This talk focuses on aspects of the research that are inspired by practical needs and industrial collaborations, which resulted in multiple federal fundings and a potential industrialization step. The talk will outline the research and technology gaps and describe the strategy over the years to continuously improve and guide the research direction to fill not only research needs but also industry needs, resulting in key technologies for the congested 3D printing market with high potential to have significant impact. The talk will also outline experiences on integrating research agenda with industrialization and scale-up plans and potential funding paths for entrepreneurial academics.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB01.6", "code": "WeB01.6", "title": "Making the Case - Beyond the Science (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB01", "sessionTitle": "From Research to Practice: Entrepreneurship in Control", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Rupenyan, Alisa", "affiliation": "ZHAW Zurich University for Applied Sciences" }, { "name": "Ingole, Deepak", "affiliation": "ZHAW Zurich University of Applied Sciences" } ], "keywords": [ "Cyber physical human machine systems" ], "abstract": "One of the important skills to develop when researchers decide to take the commercialization route, is to finance the development of their product. This requires long-term planning and considering some aspects that might appear foreign to the founder, especially when coming from an academic background. While research funding can help in the early phases, soon it is not sufficient. There are different options for the way afterwards, which require convincing presentation covering not only the technological or scientific value of the product, but some commercial potential. In this presentation we will address this aspect, drawing on experience in working in startups, and evaluating multiple startup funding applications, and will outline what makes a convincing presentation and application.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.1", "code": "WeB02.1", "title": "Robust Torque Control for Hip Exoskeleton with Series Elastic Actuator: Integration of System Identification, Kalman Filtering and Sliding Mode Control", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:15", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Terreros, Ricardo", "affiliation": "University of São Paulo" }, { "name": "Adamu Marafa, Nasiru", "affiliation": "University of São Paulo" }, { "name": "Moreira, Melkzedekue", "affiliation": "Departament of Mechanical Engineering" }, { "name": "Moreno, Yecid", "affiliation": "University of São Paulo" }, { "name": "Terra, Marco Henrique", "affiliation": "Depto. Engenharia Elétrica - Escola De Engenharia De São Carlos" }, { "name": "Siqueira, Adriano A G", "affiliation": "Univ. of Sao Paulo" } ], "keywords": [ "Nonlinear observers and filters", "Application of nonlinear analysis and design", "Saturation and discontinuity" ], "abstract": "This paper presents the design, implementation and experimental validation of a robust torque control system for hip rehabilitation exoskeleton with series elastic actuator. The proposed approach integrates three fundamental stages: parametric identification comparing friction models, state estimation through Kalman filter with sensor fusion, and sliding mode control for torque tracking. The identification stage systematically compares viscous, Coulomb and Stribeck friction models using genetic algorithms, selecting the Coulomb model that achieves RMSE of 1.53 rad/s while maintaining parsimony. The Kalman filter fuses encoder position and motor velocity measurements, providing noise reduction exceeding 65% with RMSE of 0.94 rad/s. The sliding mode controller implements equivalent control based on the identified model combined with switching term for robustness, achieving torque tracking with RMSE of 0.0213 Nm and steady-state error less than 2%. Experimental validation on physical platform demonstrates the synergistic integration of precise estimation and robust control for rehabilitation robotics applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.2", "code": "WeB02.2", "title": "An LMI Approach to Time-Synchronized Control for LTI Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "13:15-13:20", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Oyama, Keigo", "affiliation": "Chulalongkorn University" }, { "name": "Banjerdpongchai, David", "affiliation": "Chulalongkorn Univ" } ], "keywords": [ "Application of nonlinear analysis and design", "Optimal control theory", "Linear systems" ], "abstract": "Time-synchronized stability is analyzed for LTI systems using homogeneous control. This paper addresses a fundamental limitation of existing time-synchronized controllers, namely, the requirement that the number of inputs must match the number of synchronized states. Furthermore, our analysis shows that while the existing homogeneous controller satisfies the definition of time synchronization under a specific condition, it produces oscillatory behavior during transient response. Since such oscillations are undesirable for synchronization, we develop a novel LMI condition that explicitly avoids oscillatory behavior in the state trajectory. The effectiveness of the proposed design is demonstrated through numerical simulations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.3", "code": "WeB02.3", "title": "Observer-Based Event-Triggered Sliding Mode Control Using Quantization", "day": "Wednesday", "date": "August 26, 2026", "time": "13:20-13:25", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Shekhar, Sudhanshu", "affiliation": "Indian Institute of Science" }, { "name": "Kumari, Kiran", "affiliation": "Indian Institute of Science" } ], "keywords": [ "Quantized control and communication constraints", "Observer design", "Sliding mode control" ], "abstract": "This paper addresses the robust event-triggered control of a chain of integrators systems under quantization, where full state information is not available. A higher-order sliding mode observer is employed to observe the unmeasured states in finite time. Using these estimates, a time-varying threshold-based event-triggering mechanism is designed to reduce unnecessary communication of states. Subsequently, the state estimates are quantized, and an event-triggered sliding mode control is proposed employing the quantized observed states. A Lyapunov analysis is used to show that the state trajectories of the closed-loop system and sliding variable remain bounded for all time, which implies that the system does not escape in finite time. Furthermore, a lower bound on the time elapsed between two consecutive triggering instants is established to guarantee the avoidance of Zeno behavior. A numerical simulation of a 3rd-order chain of integrators is provided to validate the effectiveness of the theoretical results.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.4", "code": "WeB02.4", "title": "Data-Driven Gain Tuning for Sliding Mode Control with Time-Delay Estimation Applied to Robot Manipulators", "day": "Wednesday", "date": "August 26, 2026", "time": "13:25-13:30", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Lee, Jinwoong", "affiliation": "Sejong University" }, { "name": "Lee, Seok Young", "affiliation": "Sejong University" } ], "keywords": [ "Sliding mode control", "Data-driven robust control", "Adaptive control design" ], "abstract": "This paper proposes a data-driven gain tuning strategy for sliding mode control with time-delay estimation (TDE) applied to robot manipulators. To address TDE errors, the error dynamics are reformulated using a discrete-time partial-form dynamic linearization (PFDL) model. A tuning law is derived to adjust the gain online by minimizing a cost function based on the pseudo-partial derivative (PPD). Conventional adaptive schemes typically introduce a prescribed region to mitigate a chattering phenomenon, yet they merely increase the gain outside this region. In contrast, the proposed data-driven strategy dynamically regulates the gain based on PPD outside the region, while enforcing gain decay inside it. Simulations confirm improved tracking accuracy over existing method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.5", "code": "WeB02.5", "title": "Reinforcement Learning-Based Fixed-Time Compliant Tracking Control for Manipulators with Input Saturation", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:35", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Chang, Zejiang", "affiliation": "Dalian University of Technology" }, { "name": "Yao, Xiang-Yu", "affiliation": "China University of Geosciences" }, { "name": "Ren, Wei", "affiliation": "Dalian University of Technology" } ], "keywords": [ "Sliding mode control", "Robust controller synthesis", "Stability of nonlinear systems" ], "abstract": "This paper focuses on fixed-time compliant tracking control for manipulators under external disturbances, model uncertainties and input saturation. To address these challenges, a reinforcement learning-based fixed-time sliding mode (RL-FSM) impedance controller is proposed. A fixed-time non-singular fast terminal sliding mode (FNFTSM) surface is incorporated to guarantee robustness and accelerate convergence. Additionally, in the reinforcement learning (RL) framework, actor neural networks (ANNs) approximate the system uncertainties, and critic neural networks (CNNs) evaluate approximation performance by minimizing the proposed long-term cost. Finally, numerical experiments in a ROS–Gazebo environment are performed on an IIWA manipulator to illustrate the effectiveness and superiority of the proposed controller.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.6", "code": "WeB02.6", "title": "Nonsingular Fixed-Time Sliding Mode Control with C1-Continuous Sliding Surface for Application in the Attitude Control of Tilt Trirotor UAV", "day": "Wednesday", "date": "August 26, 2026", "time": "13:35-13:40", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Rao, Shuncai", "affiliation": "National University of Defense Technology" }, { "name": "Wang, Xiangke", "affiliation": "National University of Defense Technology" }, { "name": "Yu, Li", "affiliation": "National University of Defense Technology" }, { "name": "Yang, Yu", "affiliation": "National University of Defense Technology" }, { "name": "Bowen, Nie", "affiliation": "China Aerodynamics Research and Development Center" }, { "name": "Guang, He", "affiliation": "National University of Defense Technology" } ], "keywords": [ "Sliding mode control", "Stability of nonlinear systems", "Robust control applications" ], "abstract": "This article presents a nonsingular fixed-time sliding mode control with C1 continuous sliding surface for the attitude control of tilt trirotor unmanned aerial vehicles. First, a practical fixed-time sliding surface is designed to address the issue that C1 continuity is often ignored when applying fixed-time control to second-order systems. Subsequently, a nonsingular fixed-time sliding mode controller is constructed and the stability of the closed\u0002loop system is proven. Based on the optimized control structure, a systematic parameter tuning method is summarized to simplify the parameter tuning work, which is rarely analyzed in detail in the existing literature. Finally, simulation studies are conducted on the attitude control of tilt trirotor unmanned aerial vehicle. Compared with the other two controllers, the proposed controller has no jump discontinuity and demonstrates significant advantages in control accuracy and chattering suppression.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.7", "code": "WeB02.7", "title": "Robust Fixed-Time Nonsingular Terminal Sliding Mode Control", "day": "Wednesday", "date": "August 26, 2026", "time": "13:40-13:45", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Labbadi, Moussa", "affiliation": "Bretagne INP" }, { "name": "Moulay, Emmanuel", "affiliation": "Université De Poitiers" }, { "name": "Defoort, Michael", "affiliation": "University of Valenciennes" }, { "name": "Arteaga, Marco A.", "affiliation": "UNAM" } ], "keywords": [ "Sliding mode control", "Stability of nonlinear systems", "Robustness analysis" ], "abstract": "In this paper, it is proposed a fixed-time nonsingular terminal sliding mode control for a class of second-order nonlinear systems subject to perturbations. A novel continuous terminal sliding manifold is introduced to ensure robust fixed-time stabilization. It is shown that the proposed scheme guarantees fixed-time stability of the closed-loop system in spite of the presence of perturbations. The effectiveness of the proposed approach is validated through its application to attitude tracking control of a quadrotor.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.8", "code": "WeB02.8", "title": "Modified Global Finite-Time Quasi-Continuous Second-Order Robust Feedback Control", "day": "Wednesday", "date": "August 26, 2026", "time": "13:45-13:50", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Ruderman, Michael", "affiliation": "University of Agder" }, { "name": "Efimov, Denis", "affiliation": "Inria" } ], "keywords": [ "Stability of nonlinear systems", "Analytic design", "Sliding mode control" ], "abstract": "A non-overshooting quasi-continuous sliding mode control with sub-optimal damping was recently introduced in Ruderman and Efimov (2025) for perturbed second-order systems. The present work proposes an essential modification of the nonlinear control law which (i) allows for a parameterizable control amplitude limitation in a large subset of the initial values, (ii) admits an entire state-space R 2 (that was not given in Ruderman and Efimov (2025)) for the finite-time control, and finally (iii) enables for the found analytic solution of the state trajectories in the unperturbed case. The latter allows also for an exact estimation of the finite convergence time, and open an avenue for other potentially interesting analysis of the control properties in the future. For a perturbed case, the solution-based and Lyapunov function-based approaches are developed to show the uniform global asymptotic stability. The proposed robustness and convergence analysis are accompanied by several illustrative numerical examples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.9", "code": "WeB02.9", "title": "Finite-Time Control for Simultaneous Regulation and Tracking of Nonholonomic Mobile Robots", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-13:55", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Mera, Manuel", "affiliation": "ESIME, Instituto Politecnico Nacional" }, { "name": "Ríos, Héctor", "affiliation": "SECIHTI - Instituto Tecnológico De La Laguna" }, { "name": "Ushirobira, Rosane", "affiliation": "Inria" }, { "name": "Efimov, Denis", "affiliation": "Inria" } ], "keywords": [ "Application of nonlinear analysis and design", "Output feedback nonlinear control", "Stability of nonlinear systems" ], "abstract": "This article presents a controller design that ensures finite-time convergence of the position and orientation of a non-holonomic mobile robot to any point or to any feasible, possibly non-smooth, trajectory in the state space, starting from almost any initial condition. The control design is based on previous results regarding finite-time convergence of the Heisenberg system, also known as Brockett's integrator. The design is based on the unit vector control, a well-known technique in the sliding mode control field. However, designing a sliding surface is not required. The finite-time performance of the controller is validated through numerical simulations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.10", "code": "WeB02.10", "title": "A Comparison of Finite-Time Unicycle Mobile Robot Controllers Based on Different Changes of Coordinates", "day": "Wednesday", "date": "August 26, 2026", "time": "13:55-14:00", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Rodrigues de Lima, Danilo", "affiliation": "Inria Lille" }, { "name": "Ushirobira, Rosane", "affiliation": "Inria" }, { "name": "Mera, Manuel", "affiliation": "ESIME, Instituto Politecnico Nacional" }, { "name": "Ríos, Héctor", "affiliation": "SECIHTI - Instituto Tecnológico De La Laguna" }, { "name": "Efimov, Denis", "affiliation": "Inria" } ], "keywords": [ "Application of nonlinear analysis and design", "Output regulation and tracking", "Output feedback nonlinear control" ], "abstract": "In this paper, we compare the performance of three different control algorithms for the stabilization problem in unicycle mobile robots (UMRs). All three control algorithms successfully achieve stability and convergence to the origin within a finite time. These control strategies are based on transformations of the unicycle model into different canonical forms of non-holonomic integrators, specifically the Heisenberg system and the chained-form. Notably, two strategies utilize the symmetry of the transformed systems, while one design is purely Lyapunov-based and uses time separation. In addition, we discuss the effect of different coordinate transformations on the performance of these control algorithms.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.11", "code": "WeB02.11", "title": "Adaptive Filtering and Dual Compensation for Resilient Coverage Control against Coordinated Cyber Attacks", "day": "Wednesday", "date": "August 26, 2026", "time": "14:00-14:05", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Gao, Yun", "affiliation": "The Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Huang, Yanjing", "affiliation": "The Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Gao, Hao", "affiliation": "Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Wu, Kaishun", "affiliation": "HKUST(GZ)" }, { "name": "Ji, Yiding", "affiliation": "Hong Kong University of Science and Technology (Guangzhou)" } ], "keywords": [ "Cooperative nonlinear control", "Distributed nonlinear control", "Robust control applications" ], "abstract": "This paper studies resilient coverage control for multi-robot systems under coordinated cyber attacks (CCA). We propose an adaptive safety-belt mechanism that screens exchanged neighbor information for compromised updates using increment-based consistency constraints, together with a nonlinear attack observer that reconstructs adversarial perturbations from the residual between observed and predicted neighbor motions. Based on these estimates, we design a double-layer coverage controller for attack compensation, which corrects corrupted position vectors of the Voronoi computation at the state layer and mitigates residual attack-induced deviations at the control level. An input-to-state type practical stability bound is established for the coverage error of the closed-loop system, proving that the robots converge to a bounded neighborhood of the nominal centroidal configuration under persistent coordinated attacks. Extensive simulations further validate the resilience of the proposed framework compared to several baseline methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.12", "code": "WeB02.12", "title": "Switching Adaptive Feedforward Control for Uncertain Linear Multivariable Systems: Periodic Disturbance Rejection", "day": "Wednesday", "date": "August 26, 2026", "time": "14:05-14:10", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Gong, Yizhou", "affiliation": "ShanghaiTech University" }, { "name": "Zhao, Yuhang", "affiliation": "ShanghaiTech University" }, { "name": "Liu, Song", "affiliation": "ShanghaiTech University" }, { "name": "Yang, Guitao", "affiliation": "Loughborough University" }, { "name": "Wang, Yang", "affiliation": "Shanghaitech University" } ], "keywords": [ "Disturbance rejection and input-to-state stability", "Adaptive control design", "Linear systems" ], "abstract": "This paper proposes a switching‑based adaptive feedforward control (SW‑AFC) framework for uncertain linear square multivariable systems under a single‑harmonic disturbance of known frequency. The method is model‑free, requiring no explicit plant dynamics and assuming only internal stability with known bounds on the frequency‑response matrix elements. To address singularities in parameter matrix estimation, a distance‑based switching logic selects parameter candidates based on the performance of an auxiliary estimator. The MIMO extension uses a new certainty‑equivalent stabilizer and a compact parametric error model derived via the swapping lemma to ensure scalability. Global asymptotic convergence and uniform boundedness are established through Lyapunov analysis with validation by numerical simulations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.13", "code": "WeB02.13", "title": "Optimal Setpoint Selection for PMSMs with Current Ripple and Switching Frequency Constraints: A Controller-Aware Framework", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:15", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Tran, Trung", "affiliation": "The University of Michigan" }, { "name": "Do, Huu-Thinh", "affiliation": "University of Michigan" }, { "name": "Perks, Jordan", "affiliation": "University of Michigan" }, { "name": "Hofmann, Heath", "affiliation": "University of Michigan" }, { "name": "Sun, Jing", "affiliation": "Univ of Michigan" }, { "name": "Kolmanovsky, Ilya V.", "affiliation": "University of Michigan" } ], "keywords": [ "Nonlinear control of switched & hybrid systems", "Model predictive control", "Linear parameter-varying systems" ], "abstract": "Current setpoint selection for electric motors is often performed independently of the controller design, leading to suboptimal operation when controller-dependent metrics are taken into consideration. This work proposes a controller-aware setpoint selection framework that integrates controller performance into the setpoint computation process for a three-phase interior-mounted permanent magnet synchronous machine (IPMSM). To illustrate the framework, current ripple and switching frequency performance maps are obtained by evaluating a finite control set model predictive controller (FCS-MPC) at static reference currents sampled across the operating condition space. Using these closed-loop performance maps, setpoint selection is then formulated as a constrained optimization problem, minimizing the squared current magnitude subject to current and voltage limits, as well as allowable ripple and switching frequency constraints. Simulation results show notable improvements in current ripple and switching frequency compared to conventional maximum torque per ampere with field-weakening (MTPA-FW) strategy at low and low-to-medium speeds.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.14", "code": "WeB02.14", "title": "Signal Injection for Systems with Direct Feedthrough – Application to Water Content Estimation in Fuel Cells", "day": "Wednesday", "date": "August 26, 2026", "time": "14:15-14:20", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Fontaine, Anne-Flor", "affiliation": "IFP Energies Nouvelles" }, { "name": "Bresch-Pietri, Delphine", "affiliation": "Mines Paris -- PSL" }, { "name": "Lance, Gontran", "affiliation": "IFP Energies Nouvelles" }, { "name": "Cacciuttolo, Quentin", "affiliation": "IFP Energies Nouvelles" }, { "name": "Di Meglio, Florent", "affiliation": "Mines Paris PSL" }, { "name": "Martin, Philippe", "affiliation": "Mines ParisTech" } ], "keywords": [ "Nonlinear observers and filters" ], "abstract": "Proton exchange membrane fuel cells (PEMFCs) suffer from water-management issues that cause drying or flooding, reducing performance and durability. This paper extends signal-injection and demodulation techniques to nonlinear feedthrough systems, such as PEMFCs. By leveraging averaging theory, system decomposition into low and high frequency components, and demodulation techniques, otherwise inaccessible state and parameter information is extracted from system outputs. The approach is applied to a two-state PEMFC model to recover temperature, liquid water saturation in the cathode catalyst layer, and ohmic resistance. Numerical simulations confirm the accuracy of the proposed method and show that estimation precision improves with excitation frequency.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.15", "code": "WeB02.15", "title": "Synchronous Observer Design for Landmark-Inertial SLAM with Almost-Global Convergence", "day": "Wednesday", "date": "August 26, 2026", "time": "14:20-14:25", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Saha, Arkadeep", "affiliation": "Indian Institute of Technology Bombay" }, { "name": "van Goor, Pieter", "affiliation": "University of Sydney" }, { "name": "Franchi, Antonio", "affiliation": "University of Twente and Sapienza University of Rome" }, { "name": "Banavar, Ravi", "affiliation": "Indian Institute of Technology" } ], "keywords": [ "Nonlinear observers and filters", "Observer design" ], "abstract": "Landmark Inertial Simultaneous Localisation and Mapping (LI-SLAM) is the problem of estimating the locations of landmarks in the environment and the robot's pose relative to those landmarks using landmark position measurements and measurements from Inertial Measurement Unit (IMU). This paper proposes a nonlinear observer for LI-SLAM posed in continuous time and analyses the observer in a base space that encodes all the observable states of LI-SLAM. The local exponential stability and almost-global asymptotic stability of the error dynamics in base space is established in the proof section and validated using simulations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.16", "code": "WeB02.16", "title": "Haptic-Based Complementary Filter for Rigid Body Rotations", "day": "Wednesday", "date": "August 26, 2026", "time": "14:25-14:30", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Kumar, Amit", "affiliation": "Nanyang Technological University (NTU), Singapore" }, { "name": "Campolo, Domenico", "affiliation": "Nanyang Technological University (NTU) Singapore" }, { "name": "Banavar, Ravi", "affiliation": "Indian Institute of Technology" } ], "keywords": [ "Nonlinear observers and filters", "Observer design" ], "abstract": "The non-commutative nature of 3D rotations poses well-known challenges in generalizing planar problems to three-dimensional ones, even more so in contact-rich tasks where haptic information (i.e., forces/torques) is involved. In this sense, not all learning-based algorithms that are currently available generalize to 3D orientation estimation. Non-linear filters defined on the special orthogonal group, SO3, are widely used with inertial measurement sensors; however, none of them have been used with haptic measurements. This paper presents a unique complementary filtering framework that initially interprets the geometric shape of objects in the form of superquadrics, exploits the symmetry of SO3, and uses force and vision sensors as measurements to provide an estimate of orientation. The framework's robustness and almost global stability are substantiated by a set of numerical experiments on a dual-arm robotic setup.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.17", "code": "WeB02.17", "title": "Cascaded Tightly-Coupled Observer Design for Single-Range-Aided Inertial Navigation", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:35", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Sifour, Oussama", "affiliation": "University of Quebec in Outaouais" }, { "name": "Tayebi, Abdelhamid", "affiliation": "Lakehead University" }, { "name": "Berkane, Soulaimane", "affiliation": "Université Du Québec En Outaouais" } ], "keywords": [ "Nonlinear observers and filters", "Observer design" ], "abstract": "This work introduces a single-range-aided navigation observer that reconstructs the full state of a rigid body using only an Inertial Measurement Unit (IMU), a body-frame vector measurement (e.g., magnetometer), and a distance measurement from a fixed anchor point. The design first formulates an extended linear time-varying (LTV) system to estimate body-frame position, body-frame velocity, and the gravity direction. The recovered gravity direction, combined with the body-frame vector measurement, is then used to reconstruct the full orientation on SO(3), resulting in a cascaded observer architecture. Almost Global Asymptotic Stability (AGAS) of the cascaded design is established under a uniform observability condition, ensuring robustness to sensor noise and trajectory variations. Simulation studies on three-dimensional trajectories demonstrate accurate estimation of position, velocity, and orientation, highlighting single-range aiding as a lightweight and effective modality for autonomous navigation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.18", "code": "WeB02.18", "title": "Relative Pose-Velocity Estimation Using Dual IMU Measurements and Relative Position Sensing", "day": "Wednesday", "date": "August 26, 2026", "time": "14:35-14:40", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Melis, Alessandro", "affiliation": "CNRS Sophia Antipolis, Nice" }, { "name": "Bouazza, Tarek", "affiliation": "Laboratoire I3S UMR 7271 UCA-CNRS" }, { "name": "Berkane, Soulaimane", "affiliation": "Université Du Québec En Outaouais" }, { "name": "Hamel, Tarek", "affiliation": "Université Côte D'Azur" } ], "keywords": [ "Nonlinear observers and filters", "Observer design" ], "abstract": "This paper addresses the problem of estimating the relative pose (position and orientation) and velocity of a vehicle with respect to a moving target, where both are equipped with Inertial Measurement Units (IMUs), assuming the availability of relative position or bearing measurements. The body-target relative dynamics are formulated on SE2(3) and recast into a linear time-varying (LTV) model in the ambient space R15, on which a deterministic Riccati observer is designed. We analyze the uniform observability (UO) conditions required to guarantee global exponential convergence of the estimation error in the ambient space for both measurement cases. In the case of relative position measurements, UO requires only a persistence-of-excitation condition on the target acceleration, whereas for bearing measurements, additional conditions are required. Building on this, a nonlinear complementary filter on SO(3) is designed to provide a smooth estimate of the orientation component of the state with almost global asymptotic stability. Finally, simulation results are provided to validate the proposed solution.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.19", "code": "WeB02.19", "title": "A Nonlinear Observer for Air-Velocity and Attitude Estimation Using Pitot and Barometric Measurements", "day": "Wednesday", "date": "August 26, 2026", "time": "14:40-14:45", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Nyoba Tchonkeu, Melone", "affiliation": "University of Quebec in Outaouais" }, { "name": "Berkane, Soulaimane", "affiliation": "Université Du Québec En Outaouais" }, { "name": "Hamel, Tarek", "affiliation": "Université Côte D'Azur" } ], "keywords": [ "Nonlinear observers and filters", "Stability of nonlinear systems", "Observer design" ], "abstract": "This paper addresses the problem of estimating air velocity and full attitude for unmanned aerial vehicles (UAVs) in GNSS-denied environments using minimal onboard sensing—an interesting and practically relevant challenge for UAV navigation. The contribution of the paper is twofold: (i) an observability analysis establishing the conditions for uniform observability (UO), which are useful for trajectory planning and motion control of the UAV; and (ii) the design of a nonlinear observer on SO(3)⋉R3×R that incorporates pitot-tube, barometric altitude, and magnetometer measurements as outputs, with IMU data used as inputs, within a unified framework. Simulation results are presented to confirm the convergence and robustness of the proposed design, including under minimally excited trajectories.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.20", "code": "WeB02.20", "title": "Combining IDA-PBC and Backstepping for Regulation and Trajectory Tracking", "day": "Wednesday", "date": "August 26, 2026", "time": "14:45-14:50", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Zhang, Le", "affiliation": "Technical University of Munich" }, { "name": "Kotyczka, Paul", "affiliation": "Technical University of Munich" } ], "keywords": [ "Passivity-based control", "Interconnected nonlinear systems", "Analytic design" ], "abstract": "Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) has gained success due to its physical intuition, but the difficulty of solving the matching PDE hinders its applicability. In this contribution, we present a control design approach that combines IDA-PBC with backstepping to reduce the matching PDE to be solved. This approach hints on the physically consistent interconnection and damping structure for the original IDA-PBC problem, can be extended to trajectory tracking, and is applicable to a variety of interconnected systems. Experiments on the magnetic levitation example demonstrate these advantages.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.21", "code": "WeB02.21", "title": "Lossless Optimal Transient Control for Rigid Bodies in 3D Space", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-14:55", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Zanella, Riccardo", "affiliation": "University of Twente" }, { "name": "Califano, Federico", "affiliation": "University of Twente" }, { "name": "Franchi, Antonio", "affiliation": "University of Twente and Sapienza University of Rome" }, { "name": "Stramigioli, Stefano", "affiliation": "University of Twente" } ], "keywords": [ "Passivity-based control", "Stability of nonlinear systems", "Optimal control theory" ], "abstract": "In this work, we propose a control scheme for rigid bodies designed to optimise transient behaviors. The search space for the optimal control input is parameterized to yield a passive, specifically lossless, nonlinear feedback controller. As a result, it can be combined with other stabilizing controllers without compromising the stability of the closed-loop system. The controller commands torques generating fictitious gyroscopic effects characteristics of 3D rotational rigid body motions, and as such does not inject nor extract kinetic energy from the system. We validate the controller in simulation using a model predictive control (MPC) scheme, successfully combining stability and performance in a stabilization task with obstacle avoidance constraints.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB02.22", "code": "WeB02.22", "title": "Adaptive Fuzzy Echo State Network Control for Cyber-Physical Systems Subject to Replay Attacks", "day": "Wednesday", "date": "August 26, 2026", "time": "14:55-15:00", "sessionCode": "WeB02", "sessionTitle": "Shotgun: Nonlinear Control Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Dong, Hanlin", "affiliation": "Southeast University" }, { "name": "Cao, Yang", "affiliation": "Southeast University" }, { "name": "Wei, Yiheng", "affiliation": "Southeast University" }, { "name": "Wu, Tao", "affiliation": "Yunnan University" } ], "keywords": [ "Stability of nonlinear systems", "Lyapunov methods", "Adaptive control design" ], "abstract": "This paper investigates adaptive tracking control for a class of uncertain nonlinear cyber-physical systems under replay attacks. A fuzzy echo state network is employed as a approximator to estimate unknown nonlinear dynamics, while a smooth tanh-based robust term is embedded in a backstepping controller to compensate approximation residuals and mitigate the impact of attacks. By constructing an appropriate Lyapunov function that incorporates both virtual tracking errors and FESN parameter adaptation, an explicit upper bound on the duration of each replay attack is derived under which all closed-loop signals remain bounded and the plant output asymptotically tracks the desired trajectory. Simulation results on a three-link cylindrical manipulator demonstrate that the proposed scheme effectively rejects multiple replay attacks, accelerates post-attack error convergence, and achieves accurate trajectory tracking.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.1", "code": "WeB03.1", "title": "Model-Free Finite-Horizon H-Infinity Control Via Off-Policy Double Minimax Q-Learning (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:15", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Yudho, Eduardo", "affiliation": "Cinvestav-IPN" }, { "name": "Yu, Wen", "affiliation": "Northeastern University" }, { "name": "Li, Xiaoou", "affiliation": "CINVESTAV-IPN" } ], "keywords": [ "Consensus and reinforcement learning control", "Neural and fuzzy adaptive control", "Data-driven control theory" ], "abstract": "Finite-horizon H-infinity control is essential for robust design but challenging when system dynamics are unknown. This paper introduces a model-free solution using off-policy reinforcement learning. We propose the Neural Network-based Double Minimax Q-learning (NN-DMQ) algorithm to solve the minimax optimization problem, managing adversarial interactions while mitigating Q-value overestimation bias. Simulations on a nonlinear inverted pendulum show that NN-DMQ achieves performance comparable or superior to classical model-based H-infinity controllers, especially under parametric uncertainty. NN-DMQ thus offers a highly effective model-free framework for robust control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.2", "code": "WeB03.2", "title": "Inverse Reinforcement Learning for Mean-Field Social Control Problems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:15-13:20", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Cao, Ying", "affiliation": "Shandong University" }, { "name": "Wang, Bing-Chang", "affiliation": "Shandong University" } ], "keywords": [ "Data-driven control theory", "Distributed optimization", "Stochastic control" ], "abstract": "This paper presents an inverse reinforcement learning (RL) framework for linear quadratic mean-field social control problems with multiplicative noise. The objective is to find the equivalent social cost weights and imitate the social optimal control policies from expert demonstrations. We first propose a model-based inverse RL algorithm, and then develop a model-free inverse RL approach by eliminating the dependence on system dynamics. The iterative equations derived from integral RL are implemented using only measured trajectory data. Moreover, the model-based and model-free approaches are equivalent under the rank conditions. Finally, we demonstrate the effectiveness of the approach by simulation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.3", "code": "WeB03.3", "title": "Continuous-Time Reinforcement Learning for Exploratory Zero-Sum Games and Risk-Sensitive Control (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:20-13:25", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Guo, Liangyuan", "affiliation": "Shandong University" }, { "name": "Wang, Bing-Chang", "affiliation": "Shandong University" }, { "name": "Wang, Guangchen", "affiliation": "Shandong University" } ], "keywords": [ "Data-driven control theory", "Learning methods for control", "Stochastic control" ], "abstract": "We study the continuous-time zero-sum games and risk-sentitive control with entropy regularization. The saddle-point distribution is shown to be Gaussian, which balances exploitation and exploration. When the temperature parameters are opposite numbers, the exploratory cost becomes zero despite the presence of regularization. We prove a verification theorem that ensures the optimal control pair constitutes a saddle-point equilibrium in exploratory zero-sum games. A partial equivalence of the exploratory solutions is shown between zero-sum games and risk-sensitive control problems. Finally, a model-free dual-actor critic algorithm is designed.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.4", "code": "WeB03.4", "title": "Sample-Efficient Model-Free Policy Gradient Methods for Stochastic LQR Via Robust Linear Regression (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:25-13:30", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Song, Bowen", "affiliation": "University of Stuttgart" }, { "name": "Gros, Sebastien", "affiliation": "NTNU" }, { "name": "Iannelli, Andrea", "affiliation": "University of Stuttgart" } ], "keywords": [ "Data-driven control theory", "Statistical analysis" ], "abstract": "Policy gradient algorithms are widely used in reinforcement learning and belong to the class of approximate dynamic programming methods. This paper studies two key policy gradient algorithms, the Natural Policy Gradient and the Gauss–Newton Method, for solving the linear quadratic regulator problem for unknown systems using stochastic data. The main challenge is the inconsistency of estimating random quantities in the policy gradient update due to the resulting errors-in-variables setting. This issue is addressed by proposing a robust primal–dual estimation procedure. Using this improved policy gradient update estimation scheme, this paper delivers a consistent estimator with a convergence rate of order mathcal{O}(epsilon^{-1}). Theoretical results are further supported by numerical experiments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.5", "code": "WeB03.5", "title": "A Digital Twin Framework for LSTM-Based Fault Diagnosis in Discrete Event Manufacturing Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:35", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Fahs, Alain", "affiliation": "Université De Reims Champagne-Ardenne" }, { "name": "Wabo Teingua, Ange Patrick", "affiliation": "Université De Reims Champagne-Ardenne" }, { "name": "Saddem, Ramla", "affiliation": "Université De Reims Champagne-Ardenne" }, { "name": "Plenk, Valentin", "affiliation": "Hof University of Applied Sciences" } ], "keywords": [ "Diagnosis of discrete event and hybrid systems" ], "abstract": "Digital Twin (DT) technology is increasingly used in manufacturing to enable real-time monitoring, prediction and decision support. In this work, we propose a DT dedicated to fault diagnosis in manufacturing systems modeled as Discrete Event Systems. Building on our previous contribution, which introduced a data-driven diagnostic method based on Long Short-Term Memory (LSTM) neural networks, we present an improved version of this approach and deliver a turnkey solution suitable for both shop-floor operators and plant managers. The effectiveness of the proposed DT is demonstrated using the CellFlex plant, a training and research platform at the URCA. CellFlex plant consists of eight stations operating around a central conveyor, forming a flexible miniaturized bottling line connected through industrial-standard networks. The obtained results confirm the relevance and practical applicability of the proposed approach for online fault diagnosis in industrial environments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.6", "code": "WeB03.6", "title": "Lure-And-Reveal: An Exposure Framework for Stealthy Deception Attack in Multi-Sensor Uncertain Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "13:35-13:40", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Tian, Meiqi", "affiliation": "The Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Liu, Yihan", "affiliation": "The Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Zhong, Bingzhuo", "affiliation": "Hong Kong University of Science and Technology (Guangzhou)" } ], "keywords": [ "Diagnosis of discrete event and hybrid systems", "Supervisory control and automata", "Security for stochastic systems" ], "abstract": "Multi-sensor integration via error-state Kalman filter (ES-KF) is widely employed for precise state estimation in cyber-physical systems (CPSs). However, this integration exposes the system to stealthy deception attacks that render conventional detection mechanisms ineffective. We propose an exposure framework to actively reveal such stealthy attacks without modifying sensor interfaces. The framework introduces a suspect mode in which the defender injects random exposure shakes into the nominal control inputs, thus creating a discrepancy between the defender’s true state estimates and the attacker’s manipulated state estimates, preventing the attack from remaining stealthy. We further derive an explicit exposure condition that characterizes the minimum shake magnitude to guarantee the finite-time exposure and a compensability condition that ensures the shakes do not degrade closed-loop performance. Simulation results based on a GNSS/INS-integrated UAV system verify the effectiveness of the proposed framework.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.7", "code": "WeB03.7", "title": "Modelling and Analysis of Aircraft Maintenance Service Chains Using Timed-Arc Colored Petri Nets", "day": "Wednesday", "date": "August 26, 2026", "time": "13:40-13:45", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Gu, Chao", "affiliation": "Queen’s University Belfast" }, { "name": "Athanasopoulos, Nikolaos", "affiliation": "Queen's University Belfast" }, { "name": "McLoone, Seán Francis", "affiliation": "Queen's University Belfast" } ], "keywords": [ "Discrete event modeling and simulation", "Petri nets" ], "abstract": "We present a modeling and analysis framework for aircraft maintenance scheduling based on timed-arc colored Petri nets (TACPN). We develop a multi-aircraft, multi-task maintenance TACPN model that incorporates task-feasibility constraints, maximum service intervals, and resource constraints such as manpower and hangar capacities. To assess whether a maintenance plan is feasible, we formulate two verification problems: execution admissibility, which checks whether a given finite workflow is valid, and feasible-schedule existence, which examines whether there is a scheduling execution that avoids all task violations. We show that both problems can be addressed using the open-source tool TAPAAL, and we demonstrate the framework through an example.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.8", "code": "WeB03.8", "title": "Federated Distributional Reinforcement Learning under Heterogeneous Environments Via Quantile Regression (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:45-13:50", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Wang, Wanmin", "affiliation": "Southeast University" }, { "name": "Liu, Hongzhe", "affiliation": "School of Mathematics, Southeast University" }, { "name": "Xu, Wenying", "affiliation": "Southeast University" }, { "name": "Yu, Wenwu", "affiliation": "Southeast University" }, { "name": "Zheng, Wei Xing", "affiliation": "Western Sydney University" } ], "keywords": [ "Distributed reinforcement learning", "Markov decision process", "Multi-agent systems" ], "abstract": "Federated reinforcement learning (FedRL) enables distributed agents to collaboratively solve sequential decision-making tasks without exposing private trajectories or data. Existing FedRL methods, however, often suffer from instability in heterogeneous environments and fail to capture distributional uncertainty, thus limiting robust and stable aggregation across agents. To address these challenges, we propose Federated Quantile Regression Deep Q-Network (Fed-QRDQN), which is the first Federated Distributional RL framework that models full return distributions via quantile regression. By capturing richer uncertainty, Fed-QRDQN stabilizes local training and enhances global aggregation across diverse agents. The framework further introduces an anchor-guided alignment mechanism to ensure update comparability with minimal communication overhead, and a Wasserstein-based aggregation with distribution distillation to preserve cross-client variability. Experiments demonstrate that Fed-QRDQN achieves faster convergence, higher final performance, and greater training stability compared to standard FedRL approaches.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.9", "code": "WeB03.9", "title": "Generalized Lotka-Volterra Model with Species Turnover in a Variable-Basis State Space", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-13:55", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Doliveira, Arthur", "affiliation": "Lis Umr 7020 Cnrs / Amu / Utln" }, { "name": "Roman, Christophe", "affiliation": "Lis Umr 7020 Cnrs / Amu / Utln" }, { "name": "Graton, Guillaume", "affiliation": "Ecole Centrale De Marseille" }, { "name": "Ouladsine, Mustapha", "affiliation": "Professeur à Aix Marseille Université" } ], "keywords": [ "Hybrid and switched systems modeling" ], "abstract": "The state space is a fundamental concept for describing the trajectory of a dynamic system. Depending on its form, it can highlight certain changes over time while ignoring others. This is particularly the case for the spaces associated with theoretical ecology models, notably the generalized Lotka-Volterra (gLV) model, which allows the modeling of interacting populations. The fixed-dimension state space classically used in gLV models does not account for the effective renewal of species through addition, removal, or mutation. To address this limitation, we propose to use a variable-basis state space introduced in a previous study. This framework leads to a reformulation of the gLV model within the context of hybrid dynamical systems. To illustrate the approach, we apply the proposed model to the gut microbiota, particularly in the context of bacteriotherapy following antibiotic treatment.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.10", "code": "WeB03.10", "title": "Solving Markov Decision Processes with Future Information Via MPC (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:55-14:00", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Sawant, Shambhuraj", "affiliation": "NTNU Trondheim" }, { "name": "Anand, Akhil", "affiliation": "Norwegian University of Science and Technology (NTNU)" }, { "name": "Reinhardt, Dirk Peter", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Gros, Sebastien", "affiliation": "NTNU" } ], "keywords": [ "Markov decision process", "Learning methods for control" ], "abstract": "Model Predictive Control (MPC) is widely used in industrial and robotic systems for enforcing constraints and embedding domain knowledge through finite-horizon optimization-based planning. However, despite these strengths, an MPC scheme typically does not yield optimal policies for sequential decision-making problems formulated as Markov Decision Processes (MDPs). Recent combinations of MPC with Reinforcement Learning (RL) alleviate this issue by treating MPC as a parameterized model of the optimal policy of an MDP and adjusting its parameters using data. While these approaches typically consider classical MDPs, many real-world problems include future information—such as forecasts, prices, or reference trajectories—at decision time, which must be included in the MDP state for optimal decision-making. Current MPC-RL approaches do not directly account for this augmented-state structure, raising the question of how to incorporate future information into MPC to obtain an optimal policy. This work establishes the structural requirements under which a parameterized MPC can exactly represent the optimal value functions and policy of an MDP with future information. We further demonstrate that such a parameterized MPC can serve as a structured function approximator, with its parameters learned using RL. The approach is illustrated on a point-mass racing task with future reference information.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.11", "code": "WeB03.11", "title": "Online Constrained Reinforcement Learning for Optimal Tracking (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:00-14:05", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Lee, Hyochan", "affiliation": "Korea Advanced Institute of Science and Technology" }, { "name": "Choi, Kyunghwan", "affiliation": "Korea Advanced Institute of Science and Technology" } ], "keywords": [ "Neural and fuzzy adaptive control", "Nonlinear adaptive control", "Learning methods for control" ], "abstract": "This paper presents a constrained online reinforcement learning framework for the optimal tracking control of constrained nonlinear systems. While reinforcement learning provides powerful tools for optimal control, conventional implementations typically rely on unconstrained minimization strategies. Since this approach does not restrict the policy search space within the feasible region, it often drives the control policy toward unbounded actions, exacerbating the instability inherent in nonlinear function approximation. To address these issues, the proposed method reformulates the Bellman optimality equation as a constrained optimization problem where the control policy and value function are treated as joint decision variables. Crucially, this formulation allows for the explicit incorporation of system constraints directly into the learning process. A Lagrangian-based primal-dual scheme is then employed to find a Karush-Kuhn-Tucker solution, promoting constraint satisfaction in practice (within tolerance). Experimental validation on a differential-wheeled mobile robot demonstrates that the algorithm enforces hard constraints in practice within tolerance during complex maneuvers while maintaining stable convergence of the value function.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.12", "code": "WeB03.12", "title": "Designing a Novel Fractional PID Controller Based on Prabhakar Derivative for Time-Delay Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "14:05-14:10", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Jafarpour, Mahdi", "affiliation": "National Yunlin University of Science and Technology" }, { "name": "Mobayen, Saleh", "affiliation": "National Yunlin University of Science and Technology" }, { "name": "Fekih, Afef", "affiliation": "Univ of Louisiana at Lafayette" } ], "keywords": [ "Optimal control of discrete event and hybrid systems", "Control under communication constraints", "Control over networks" ], "abstract": "For the control of time-delay systems, a new Prabhakar fractional-order PID controller is introduced. The Prabhakar operator adds more degrees of freedom than traditional fractional controllers based on the Riemann–Liouville or Caputo derivatives by utilizing the three-parameter Mittag-Leffler function. This approach would capture more complex non-local dynamics and deeper memory properties. A thorough examination of existence, uniqueness, and closed-loop behavior is used to construct comprehensive stability requirements in both finite-time and practical stability frameworks. According to simulation tests, the suggested controller outperforms conventional fractional-order PID designs in pulse-tracking applications, resulting in appreciable advances in tracking accuracy, transient response, and resilience to time-delay fluctuations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.13", "code": "WeB03.13", "title": "Dual-Timed Petri Net Modeling and Deadlock-Free Scheduling of Collaborative Heterogeneous Multi-Agent Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:15", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Li, Boyu", "affiliation": "Zhejiang University" }, { "name": "Wu, Weimin", "affiliation": "Zhejiang Univ" }, { "name": "Li, Dacheng", "affiliation": "Zhejiang University" }, { "name": "Li, Zhengchen", "affiliation": "Zhejiang University" }, { "name": "Wang, Shuo", "affiliation": "HuaQiao University" } ], "keywords": [ "Petri nets", "Discrete event modeling and simulation", "Multi-agent systems" ], "abstract": "Collaborative heterogeneous multi-agent systems (CHMAS) are widely used in logistics and manufacturing, but their spatiotemporal synchronization requirements tightly couple agent schedules and may lead to deadlocks. This paper presents a Petri net-based framework for modeling, evaluating, and constructing deadlock-free schedules in CHMAS. A Dual-Timed Petri Net (DTPN) is used to represent the logical precedence and temporal dynamics of a given schedule, enabling schedule decoding and makespan evaluation. Based on the marked-graph structure of the constructed DTPN, a liveness-based feasibility criterion is derived to identify deadlock-free schedules. Furthermore, a Bi-directional Liveness Check (BLC) algorithm is developed to prevent deadlock-inducing insertions during schedule construction. Experimental results show that BLC effectively reduces infeasible evaluations and improves search efficiency and solution quality in highly coupled scenarios.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.14", "code": "WeB03.14", "title": "Deadlock-Free Execution of Multi-AGV Plans under Delays: A Prioritized Dual-Time Petri Net Approach", "day": "Wednesday", "date": "August 26, 2026", "time": "14:15-14:20", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Li, Dacheng", "affiliation": "Zhejiang University" }, { "name": "Wu, Weimin", "affiliation": "Zhejiang Univ" }, { "name": "Li, Boyu", "affiliation": "Zhejiang University" }, { "name": "Wang, Zixi", "affiliation": "Zhejiang University" }, { "name": "Zhou, Jiazhong", "affiliation": "Huaqiao University" } ], "keywords": [ "Petri nets", "Multi-agent systems", "Discrete event modeling and simulation" ], "abstract": "The robust execution of Multi-Agent Path Finding (MAPF) plans under temporal uncertainty poses a significant challenge in logistics automation. When Automated Guided Vehicles experience unexpected delays, strict adherence to the pre-computed nominal plan ensures safety but often leads to unnecessary waiting and efficiency degradation. Conversely, blindly deviating from the scheduled order to reduce idling carries the risk of inducing deadlocks. To reconcile execution flexibility with safety, this paper proposes a novel control framework based on Prioritized Dual-Time Petri Nets (PDTPN). A graph-theoretic dependency analysis is developed to rigorously distinguish between rigid precedence constraints and switchable dependencies that allow for local reordering without creating circular waits. Based on this analysis, a systematic synthesis procedure transforms the MAPF plan into a PDTPN controller. Theoretical results demonstrate that the proposed framework guarantees deadlock-free operation under arbitrary bounded delays. Furthermore, the system naturally realizes a dynamic policy similar to First-Come-First-Served, significantly reducing the total accumulated execution time compared to fixed-priority approaches.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.15", "code": "WeB03.15", "title": "Online Order Estimation for Binary-Valued FIR System with Colored Noise", "day": "Wednesday", "date": "August 26, 2026", "time": "14:20-14:25", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Wang, Wenbin", "affiliation": "Academy of Mathematics and Systems Science, Chinese Academy of Sciences" }, { "name": "Guo, Jian", "affiliation": "The Hong Kong Polytechnic University" }, { "name": "Zhao, Yanlong", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Quantized systems", "Time series modeling", "Linear system identification" ], "abstract": "This paper studies online order estimation for binary-valued finite impulse response (FIR) systems with unknown order driven by colored moving-average (MA) noise. For colored noise, the main difficulty is that temporal dependence creates long-range correlations in the binary output, which obscure the contribution of the FIR dynamics. The proposed method overcomes this by exploiting a structural property of FIR-MA models: the autocorrelation function of the underlying linear process has finite support, and this support length is preserved under binary quantization. We use this property to construct a discontinuous objective function in the candidate order, built from binary correlation statistics and designed to jump at the true support length. This objective can be evaluated recursively using only low-dimensional summary variables, without storing the full data history, and is therefore suitable for real-time implementation in the presence of colored noise. We prove that the resulting order estimator converges almost surely to the true order . In the Gaussian noise case, we further derive an explicit linear relation, which enables joint online estimation of the system order and the FIR coefficients. Numerical experiments under various noise distributions and input designs confirm the robustness and accuracy of the proposed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.16", "code": "WeB03.16", "title": "Dissipativity and L2 Stability of Large-Scale Networks with Changing Interconnections", "day": "Wednesday", "date": "August 26, 2026", "time": "14:25-14:30", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Jang, Ingyu", "affiliation": "Duke University" }, { "name": "Bridgeman, Leila", "affiliation": "Duke University" } ], "keywords": [ "Stability and stabilization of hybrid systems", "Control of networks", "Multi-agent systems" ], "abstract": "In this paper, the L2 stability of switched networks is studied based on the QSR-dissipativity of each agent. While the integration of dissipativity with switched systems has received considerable attention, most previous studies have focused on passivity, internal stability, or feedback networks involving only two agents. This work makes two contributions: first, the relationship between switched QSR-dissipativity and L2 stability is established based on the properties of dissipativity parameters of switched systems; and second, conditions for L2 stability of networks consisting of QSR-dissipative agents with switching interconnection topologies are derived. Crucially, this shows that a common storage function will exist across all modes, avoiding the need to find one, which becomes computationally taxing for large networks with many possible configurations. Numerical examples demonstrate how this can facilitate stability analysis for networked systems under arbitrary switching of swarm drones.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.17", "code": "WeB03.17", "title": "Stabilizing Linear Time-Invariant Systems with Recurrent Spiking Neural Networks", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:35", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Klip, Ward", "affiliation": "Eindhoven University of Technology" }, { "name": "Petri, Elena", "affiliation": "Eindhoven University of Technology" }, { "name": "Heemels, Maurice", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Stability and stabilization of hybrid systems", "Event-based control", "Hybrid and switched systems modeling" ], "abstract": "The field of neuromorphic engineering aims to bring the advantages of biological spiking neurons, such as energy efficiency, adaptability, and fast event-based responses, to engineered systems. Also in the context of control, brain-inspired technologies are of great potential. In this paper, we present a systematic design method for novel neuromorphic control strategies for the stabilization of linear time-invariant systems using input signals that consist of fixed-amplitude spikes. As the only design freedom for the controller is the determination of the spiking times, the controller must be both event-based and impulsive in nature. Our method is based on firing a spike when it reduces the value of an appropriately chosen Lyapunov function. Our control schemes are formulated both as static state-based firing rules and as recurrent spiking neural networks. It is proven that in both cases this gives global practical stability of the closed-loop system and excludes Zeno-like behavior in the sense that that an infinite amount of spikes cannot occur in a finite amount of time. The approaches are illustrated with numerical examples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.18", "code": "WeB03.18", "title": "Safety-Critical Tracking Control for Switched Nonlinear Systems Based on Contraction Theory", "day": "Wednesday", "date": "August 26, 2026", "time": "14:35-14:40", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Liu, Qian", "affiliation": "Beijing University of Technology" }, { "name": "Li, Xiaoli", "affiliation": "Beijing University of Technology" } ], "keywords": [ "Stability and stabilization of hybrid systems", "Hybrid and switched systems modeling", "Adaptive gain scheduling autotuning control and switching control" ], "abstract": "This paper studies the safety-critical trajectory tracking problem of switched nonlinear systems based on contraction theory, where contraction is not required to hold for all subsystems. By extending the contraction theory to the design of switching control, a safe tracking control framework for switched systems is established, which does not require the reference trajectory to satisfy safety performance. On this basis, sufficient conditions are derived to verify the safe tracking property under a state-dependent switching law, which is constructed based on the states of the differential subsystems of the switched system. Furthermore, these conditions are formulated as a convex feasibility problem, and the switching feedback controller as well as the corresponding control contraction metrics are constructed via a bilinear sum-of-squares methodology. Finally, the effectiveness of the proposed framework is validated through a continuous stirred tank reactor system.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.19", "code": "WeB03.19", "title": "Linear-Quadratic Stochastic Team Problem under General Partial Observations (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:40-14:45", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Moon, Jun", "affiliation": "Hanyang University" } ], "keywords": [ "Stochastic control", "Stochastic differential equations", "Synthesis of stochastic systems" ], "abstract": "This paper considers the two-player linear-quadratic team optimal control problem for stochastic differential equations (SDEs) with random coefficients. Given the complete observation mathbb{F}, Player 1 and 2 have access to partial observations mathbb{G}_1 subset mathbb{F} and mathbb{G}_2 subset mathbb{F}, respectively, where mathbb{G}_1 cap mathbb{G}_2 neq emptyset corresponds to the common observation. We obtain the open-loop type team-optimal solution by the stochastic maximum principle, represented by the first-order optimality conditions with the adjoint equation, captured by the backward SDE. Then by identifying the appropriate four-step scheme transformation, together with the coupled stochastic Riccati differential equations (CSRDEs), we obtain the feedback-type team-optimal solution, which requires to compute the filtering state processes with respect to (hat{mathbb{G}},mathbb{G}_1,mathbb{G}_2). Finally, we state the verification theorem of the team-optimal solution obtained by the maximum principle and the four-step scheme transformation. In our paper, unlike the exiting works, the CSRDEs have random coefficients, which can be viewed as coupled matrix-valued BSDEs.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.20", "code": "WeB03.20", "title": "Long Time Behaviors of Discrete-Time Linear-Quadratic Optimal Control for Markov Jump Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:45-14:50", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Lin, Yu", "affiliation": "Shandong University" }, { "name": "Liang, Yong", "affiliation": "Shandong Normal University" }, { "name": "Wang, Bing-Chang", "affiliation": "Shandong University" } ], "keywords": [ "Stochastic control", "Stochastic hybrid systems" ], "abstract": "This paper investigates the long-time behavior of the optimal trajectory for the discrete-time Markov jump linear quadratic optimal control problem. By modifying the Bellman equation, a cell problem is constructed for the Markov jump system (MJS) to deal with non-homogeneous dynamics and cost functions. Solving the modified Bellman equation yields the solution to the cell problem in terms of coupled algebraic Riccati equations. Based on this, the relationship between the cell problem and ergodic control is revealed. Specifically, the quadratic value function yields the optimal ergodic control, while the ergodic constant is determined by the limit of the expectation of the modified function. Finally, the turnpike property of the MJS is derived from the cell problem, which shows that the optimal trajectory is exponentially close to the steady state and the number of deviation points is bounded.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.21", "code": "WeB03.21", "title": "A Linear-Quadratic Leader-Follower Differential Game with Mixed Deterministic and Stochastic Controls (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-14:55", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Shi, Jingtao", "affiliation": "Shandong University" } ], "keywords": [ "Stochastic control", "Stochastic hybrid systems", "Stochastic differential equations" ], "abstract": "This paper is concerned with a linear-quadratic (LQ) leader-follower differential game with mixed deterministic and stochastic controls. In the game, the follower is a random controller which means that the follower can choose adapted stochastic processes, while the leader is a deterministic controller which means that the leader can choose only deterministic time functions. Such problem is motivated by a pension fund insurance problem, with government, supervisory or employer being a deterministic leader and individual producer or retail investor being a random follower. The state feedback representation of an open-loop Stackelberg equilibrium solution is obtained, with the help of a system consisting of two cross-coupled Riccati equations and a two-point boundary value problem of ordinary differential equations (ODEs), whose solvability is investigated.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB03.22", "code": "WeB03.22", "title": "Let Others Help You: Influential Planning for Multi-Agent Systems under Temporal Logic Tasks", "day": "Wednesday", "date": "August 26, 2026", "time": "14:55-15:00", "sessionCode": "WeB03", "sessionTitle": "Shotgun: Learning and Stochastic Control Systems", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Ye, Bowen", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Wang, Yingzhu", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Zhao, Jianing", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Yin, Xiang", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Supervisory control and automata", "Event-based control", "Discrete event modeling and simulation" ], "abstract": "In this paper, we investigate the motion planning problem for multi-agent systems under temporal logic constraints. Unlike most existing works, which assume agents are either cooperative or adversarial, we consider a new scenario called influential planning. Specifically, we assume there are two agents: a leader and a follower, each with their own objectives characterized by temporal logic formulas. Our objective is to design a plan for the leader such that, when the follower pursues its own objectives, the leader's objectives are also satisfied. In other words, although the leader cannot directly control the follower's behavior, it can influence the follower's actions by strategically synthesizing its own plan. We provide an efficient algorithm for solving this type of influential planning problem, where specifications are expressed using co-safe linear temporal logic (scLTL) formulae. Case studies are presented to illustrate the effectiveness of our framework, demonstrating how the leader's strategic planning can indirectly guide the follower's behavior to achieve desired outcomes.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.1", "code": "WeB04.1", "title": "Large Scale Complex Rotating Machinery System Compound Fault Diagnosis Method Based on Cross-Domain Feature Deep Reinforcement Learning", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:15", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Liu, Yan", "affiliation": "Zhejiang University" }, { "name": "Sha, Nuo", "affiliation": "Zhejiang University" }, { "name": "Shou, Yiyang", "affiliation": "Zhejiang University" }, { "name": "Xu, Zuhua", "affiliation": "Zhejiang University" }, { "name": "Zhao, Jun", "affiliation": "Zhejiang University" }, { "name": "Song, Chunyue", "affiliation": "Zhejiang University" } ], "keywords": [ "AI methods for FDI/FTC", "Data-driven methods for FDI/FTC", "Applications of FDI/FTC" ], "abstract": "Large scale complex rotating machinery system compound faults involve coupled multi-source signals in both temporal and frequency domains. However, the distribution gaps and the intrinsic correlations between these domains are rarely considered, causing suboptimal diagnostic performance. To cope with it, a cross-domain feature deep reinforcement learning-based compound fault diagnosis method is proposed for rotating machinery system, which aims to collaboratively learn the crucial fault-related information from the temporal and frequency domains. First, we develop two parallel domain-specific feature leaning networks and a cross-domain transfer network. Two domain-specific feature learning networks are utilized to excavate domain-specific feature from the temporal and frequency domains. The cross-domain transfer network uses the neighbor features to fuse and transfer domain-shared feature. Then, a multi-domain deep reinforcement learning-based training framework is designed, in which the cross-domain feature collaborative learning is formulated to an agent reward maximum problem, modeling as a Markov decision process. Finally, the compound fault diagnosis performance of the proposed method is demonstrated on two large scale complex rotating machinery system cases.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.2", "code": "WeB04.2", "title": "Dynamic Optimal-Transport Graph Neural Network for Industrial Process Fault Diagnosis", "day": "Wednesday", "date": "August 26, 2026", "time": "13:15-13:20", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Mao, Longying", "affiliation": "Huzhou Normal University" }, { "name": "Yang, Zeyu", "affiliation": "Huzhou Normal University" }, { "name": "Ye, Lingjian", "affiliation": "Huzhou Normal University" }, { "name": "Wang, Peiliang", "affiliation": "Huzhou Normal University" }, { "name": "Song, Zhihuan", "affiliation": "Zhejiang University" } ], "keywords": [ "AI methods for FDI/FTC", "Data-driven methods for FDI/FTC", "Fault detection and isolation methods" ], "abstract": "Fault diagnosis in industrial processes necessitates modeling the underlying physical propagation mechanisms, often conceptualized as a ``path-resistance\" dynamic. This paper proposes a Dynamic Optimal-Transport Graph Neural Network (DOTGNN) that explicitly models fault transportation. Our framework features three key innovations: a dynamic optimal-transport graph (DOTG) for inferring latent fault propagation paths; a Kolmogorov-Arnold network (KAN) for adaptive learning of complex process nonlinearities; and a feature transportation loss (FTL) that imposes metric constraints to enhance inter-class separability in the latent space. Extensive validation on the Tennessee Eastman process (TEP) demonstrates that DOTGNN achieves a superior fault diagnosis accuracy of 96.4%, significantly outperforming existing benchmarks. The proposed method offers a principled and interpretable solution for industrial process monitoring.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.3", "code": "WeB04.3", "title": "A Semi-Supervised Fault Diagnosis Method for Industrial Systems Based on Graph Feature Extraction and Triple Attention Mechanism", "day": "Wednesday", "date": "August 26, 2026", "time": "13:20-13:25", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Qi, Yu", "affiliation": "Chongqing University" }, { "name": "Chai, Yi", "affiliation": "Chongqing University" }, { "name": "Zhu, Zheren", "affiliation": "Hangzhou Normal University" }, { "name": "Yao, Le", "affiliation": "Hangzhou Normal University" }, { "name": "Shen, Bingbing", "affiliation": "Zhejiang University" }, { "name": "Song, Zhihuan", "affiliation": "Zhejiang University" } ], "keywords": [ "AI methods for FDI/FTC", "Data-driven methods for FDI/FTC", "Process performance monitoring/statistical process control" ], "abstract": "Industrial fault diagnosis is vital for production safety and operation efficiency. To address labeled data scarcity and inaccurate feature extraction, we propose a semi-supervised Triple-Attention Graph-Structured GRAND (TAGGD), which realizes unified modeling of continuous data from static equipment and temporal vibration signals from rotating equipment via a general graph structure, strengthens fault feature identification with time-spatial-feature three-dimensional attention, and mines unlabeled data value while suppressing noise. Experiments on revised Tennessee Eastman (TE) and Case Western Reserve University (CWRU) datasets show our TAGGD significantly outperforms traditional methods in diagnostic accuracy, cross-scenario adaptability, and low labeling rate robustness, with favorable potential for industrial scenarios.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.4", "code": "WeB04.4", "title": "Health-Aware Fast Charging Using Homogenized Model with Heterogeneous Internal State Reconstruction", "day": "Wednesday", "date": "August 26, 2026", "time": "13:25-13:30", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Lodge, Alessio Alberto", "affiliation": "TNO" }, { "name": "Lombardo Pontillo, Alessio", "affiliation": "Politecnico Di Torino" }, { "name": "Hoekstra, Fsj", "affiliation": "TNO" }, { "name": "Medina, Robinson", "affiliation": "TNO" }, { "name": "Wilkins, Steven", "affiliation": "TNO Powetrains, Powertrains Department, P.O. Box 756, 5700 AT, Helmond" }, { "name": "Battiato, Ilenia", "affiliation": "Stanford University" } ], "keywords": [ "Control and management of energy systems", "Electric vehicles and charging stations", "Health aware control in processes" ], "abstract": "Fast charging of lithium-ion batteries is limited by lithium plating, which occurs when the anode potential drops below 0 V vs Li/Li+. Model-based control aims to maximize charging current while maintaining anode potentials above this threshold. In this work, a plating-free fast charging strategy is demonstrated using a Homogenized Model (HM) coupled with a classical PID controller. The HM, derived from homogenization theory applied to the Poisson-Nernst-Planck equations, retains the physics of the Doyle-Fuller-Newman model while capturing electrode microstructural heterogeneity in a one-dimensional double-continua formulation. By reconstructing three-dimensional distributions of electrochemical variables from precomputed closure variables, the HM enables non-invasive estimation of heterogeneous anode potentials, acting as a virtual sensor. Through MATLAB–COMSOL co-simulation, a PID controller regulates current to maintain the full 3D anode potential distribution above the plating limit, achieving model-based fast charging at a fraction of the computational cost of high-fidelity models. The results demonstrate the potential of HM-based control for safe, degradation-aware, and efficient fast charging of lithium-ion batteries.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.5", "code": "WeB04.5", "title": "Modeling and Analysis of a Wave Glider Incorporating Reverse Osmosis", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:35", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Tamajong, Michael Nkeh", "affiliation": "University of Maryland" }, { "name": "Cachon Delgado, Alvaro", "affiliation": "UCL" }, { "name": "Tasnim, Sara", "affiliation": "University of Maryland, College Park" }, { "name": "McGuire, Carson", "affiliation": "North Carolina State University" }, { "name": "Liu, Limeng", "affiliation": "University of Michigan" }, { "name": "Alam, Minhazul", "affiliation": "University of Michigan Ann Arbor" }, { "name": "Willcox, J. Scott", "affiliation": "Liquid Robotics, Inc" }, { "name": "Bryant, Matthew", "affiliation": "North Carolina State University" }, { "name": "Vermillion, Christopher", "affiliation": "University of Michigan" }, { "name": "Fathy, Hosam K.", "affiliation": "University of Maryland" } ], "keywords": [ "Control and management of energy systems", "Hydropower" ], "abstract": "This paper models the dynamics of a wave glider equipped with a reverse osmosis subsystem. The paper is motivated by the ability of wave gliders to harvest ocean wave energy, plus the possibility of utilizing the harvested energy for water desalination. Such mobile, anchorless desalination can be valuable to coastal communities, particularly in the aftermath of natural disasters. Existing work in the literature provides a rich portfolio of dynamic models of wave gliders without desalination. We extend these efforts by modeling the coupled dynamics of a wave glider integrated with a reverse osmosis power take-off system. Moreover, we focus on building a model simple enough to facilitate sensitivity, optimization, and control design efforts. An initial sensitivity study, utilizing this model, highlights the importance of tuning the stiffnesses of two different return springs in the integrated overall system to optimize both desalination rate and forward surge/travel velocity.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.6", "code": "WeB04.6", "title": "Multi-Domain Graph-Based Modeling of Energy Systems with Applications to Lithium-Ion Batteries", "day": "Wednesday", "date": "August 26, 2026", "time": "13:35-13:40", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Hemmat, Mahsa", "affiliation": "University of Minnesota" }, { "name": "Alleyne, Andrew G.", "affiliation": "University of Minnesota" } ], "keywords": [ "Control and management of energy systems", "Thermal systems modelling", "Energy storage systems" ], "abstract": "Graph-based models have been shown to provide a structured representation for complex multi-domain energy systems but face limitations when edge power flows depend on non-adjacent states or when a single edge carries multiple power-flow types driven by different inputs. This paper proposes two general extensions to address these limitations: a recursive state-to-input feedback scheme that embeds non-adjacent state dependencies into edge inputs without altering the graph structure, and a parallel edge decomposition method that represents composite interactions using sets of single-input edges while preserving energy conservation at the vertices. The extended framework is demonstrated on a lithium-ion battery module consisting of 36 parallel cells, and the resulting model predicts module temperatures with errors below 1°C. Validation on this electro-thermal battery system demonstrates the effectiveness of the extended framework for multi-domain systems that cannot be represented by previously established graph-based formulations, and indicates its potential for broader application to complex energy systems in control and design studies.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.7", "code": "WeB04.7", "title": "Hierarchical Control for Flexible Part-Load Operation of a Solar Absorption Chiller", "day": "Wednesday", "date": "August 26, 2026", "time": "13:40-13:45", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Garrido Satue, Manuel", "affiliation": "University of Seville" }, { "name": "Vargas, Manuel", "affiliation": "University of Seville" }, { "name": "Rubio, Francisco R.", "affiliation": "Universidad De Sevilla" }, { "name": "Ortega, M. G.", "affiliation": "Universidad De Sevilla" } ], "keywords": [ "Control and management of energy systems", "Thermal systems modelling", "Energy storage systems" ], "abstract": "Solar absorption chillers require tight control for flexible operation under variable cooling demand. This paper models and controls a solar-powered absorption chiller using a thermal energy storage unit. The core contribution is a hierarchical control strategy using nested loops to simultaneously regulate delivered cooling power and evaporator outlet temperature. This approach achieves continuous capacity modulation by adjusting the generator inlet temperature reference, overcoming the limitations of binary (on-off) control. Simulation confirms effective part-load operation. Additionally, the saturation of actuation signals provides reliable indicators for detecting operational limits (over-demand due to insufficient solar irradiance).", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.8", "code": "WeB04.8", "title": "A Multi-Scale Mutual Information Decomposition Algorithm for Fault Root Cause Diagnosis", "day": "Wednesday", "date": "August 26, 2026", "time": "13:45-13:50", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Chen, Rui", "affiliation": "Tongji University" }, { "name": "Liang, Shu", "affiliation": "Tongji University, School of Electronics and Information Engineering" }, { "name": "Fan, Rui", "affiliation": "Tongji University" }, { "name": "Zhou, Yuanqiang", "affiliation": "Tongji University" }, { "name": "Gao, Furong", "affiliation": "Hong Kong Univ of Sci & Tech" }, { "name": "Chen, Hong", "affiliation": "Tongji University" } ], "keywords": [ "Data-driven methods for FDI/FTC", "Applications of FDI/FTC", "Reliability and safety in processes" ], "abstract": "The coexistence of redundant, synergistic, and unique (RSU) causalities among fault variables, combined with multi-scale fault propagation, poses significant challenges for accurate root cause inference. This paper proposes a root cause diagnosis method based on multi-scale mutual information (MI) decomposition, which extracts multi-scale dependencies and quantifies RSU causal contributions. Specifically, multivariate variational mode decomposition decomposes the original time series into multi-scale components. Multi-order specific MI is then computed using kernel density estimation and sorted in ascending order. Based on predefined rules, the specific MI is decomposed into RSU causal increments, with expectations evaluated across all target states. Finally, a surrogate-based significance test identifies significant RSU causal structures at multiple time scales. Experimental results from an injection molding process demonstrate that the proposed algorithm accurately identifies fault root cause and provides an interpretable approach for analyzing causal interactions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.9", "code": "WeB04.9", "title": "Securing SoC and SoH Estimation Blocks in BESS: A DRL-Based Framework for FDIA Generation and Detection", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-13:55", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Selim, Alaa", "affiliation": "School of Engineering and Information Technology, University of New South Wales" }, { "name": "Mo, Huadong", "affiliation": "University of New South Wales" }, { "name": "Pota, Hemanshu", "affiliation": "University of New South Wales" } ], "keywords": [ "Energy storage systems", "Cyberphysical security in processes" ], "abstract": "This paper presents a deep reinforcement learning (DRL) framework for systematically generating and analysing false data injection attacks (FDIAs) on state-of-charge (SoC) and state-of-health (SoH) estimation blocks in battery energy storage systems (BESS). An equivalent-circuit lithium-ion cell with a UKF-based SoC/SoH estimator is embedded in a reinforcement-learning environment, where a Proximal Policy Optimization (PPO) agent injects bounded perturbations into voltage and current measurements under realistic FDIA constraints. A constrained, reward-shaped formulation explicitly trades off SoC estimation error, SoH bias and attack energy, enabling the agent to learn structured, standards-compliant attack patterns rather than arbitrary noise. Numerical results in MATLAB/Simulink show that the learned FDIAs can induce large, persistent SoH deviations while keeping SoC trajectories and UKF residuals close to nominal behaviour, thereby remaining stealthy with respect to both moving-average residual monitors and Cumulative Sum (CUSUM) detectors tuned to standards-compliant noise levels. The proposed framework (i) identifies concrete regimes where conventional residual-based thresholds either miss DRL-crafted attacks or detect them only after substantial SoH drift, and (ii) provides a quantitative stress-test and a generator of realistic attack datasets to support the design and benchmarking of more robust data-driven cyber-attack detectors for BESS.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.10", "code": "WeB04.10", "title": "Short-Term Scheduling and Unit Commitment for a Pumped Storage Hydropower Plant with Many Variable-Speed Units", "day": "Wednesday", "date": "August 26, 2026", "time": "13:55-14:00", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Mena Rosell, Joan", "affiliation": "Politecnico Di Milano" }, { "name": "Casella, Francesco", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Energy storage systems", "Hydropower", "Control and management of energy systems" ], "abstract": "This work addresses the Short-Term Hydro Scheduling (STHS) and Hydro Unit Commitment (HUC) problems for a Pumped Storage Hydropower plant, exploiting the idea that many variable-speed generation units create a continuous region of operation where the overall efficiency of the plant is nearly constant and maximum. This allows to decompose the problem into a whole-plant STHS formulated as a NLP and a HUC formulated as a MILP. This strategy allows for explicit treatment of nonlinearities and other restrictions, including an innovative layer of operational decision-making by considering each unit's operating mode, without creating computationally intractable mixed-integer optimization problems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.11", "code": "WeB04.11", "title": "How Modelling Dynamics Improves Fault Detection and Isolation for Gaussian LTI Systems: A Geometric Explanation", "day": "Wednesday", "date": "August 26, 2026", "time": "14:00-14:05", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Hu, Anbang", "affiliation": "University Duisburg-Essen" }, { "name": "Zhang, Ping", "affiliation": "University of Kaiserslautern-Landau" }, { "name": "Gao, Xinrui", "affiliation": "Technical University of Ilmenau" } ], "keywords": [ "Fault detection and isolation methods" ], "abstract": "This paper analyses the impact of introducing dynamic information on the performance of fault detection and isolation (FDI) in Gaussian linear time-invariant (LTI) systems. First of all, the FDI problem is formulated as hypothesis testing, where fault-free and faulty conditions are considered to be corresponding hypotheses. Then, Kullback–Leibler (KL) divergence is naturally derived to quantify the dissimilarity between different distributions associated with the hypotheses, i.e., dissimilarity between fault-free and different faulty conditions. By introducing the new concept of deemed-fault regions, it is geometrically shown how dynamic information reduces the overlap between the regions, thereby improving the correct isolation rate (CIR). This paper provides a theoretical analysis of the role of dynamic information in FDI problems. The theoretical results are validated by a simulated three-tank system.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.12", "code": "WeB04.12", "title": "A General Framework for Design and Analysis of Optimal Fault Detection", "day": "Wednesday", "date": "August 26, 2026", "time": "14:05-14:10", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Gao, Xinrui", "affiliation": "Technical University of Ilmenau" }, { "name": "Shardt, Yuri A.W.", "affiliation": "Technical University of Ilmenau" }, { "name": "Gopaluni, Bhushan", "affiliation": "University of British Columbia" } ], "keywords": [ "Fault detection and isolation methods", "Advanced process control" ], "abstract": "Fault detection and isolation (FDI) have been extensively studied in control engineering and process monitoring, yet a unified theoretical framework connecting different approaches remains elusive. This paper presents a general framework for design and analysis of optimal fault detection (FD), which bridges paradigms that are traditionally separate. Starting from a measure-theoretic perspective, FD is formulated as a unified optimisation problem defined on general signal spaces that encompasses both stochastic and deterministic systems. The duality between two complementary formulations of the optimisation problem is analysed using Lagrangian relaxation to show the intrinsic connections and differences. Several cases of implementations of optimal FD design derived from the framework are also presented.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.13", "code": "WeB04.13", "title": "Towards Online Detection of Plasticity in Soft Robots", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:15", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Dileep, Agneyan", "affiliation": "University of Lille, CRIStAL, Inria" }, { "name": "Peyron, Quentin", "affiliation": "Inria Université De Lille" }, { "name": "Cocquempot, Vincent", "affiliation": "University of LILLE" } ], "keywords": [ "Fault detection and isolation methods", "Applications of FDI/FTC", "Health/condition monitoring in processes" ], "abstract": "Soft robots are made of deformable materials, allowing them to perform tasks that rigid robots cannot, such as handling delicate objects or operating in tight spaces. However, their flexibility makes them more vulnerable to material degradation and permanent deformations known as plasticity. Plasticity accelerates material fatigue, decreases system performance, can lead to structural failure, and makes control strategies less effective. This work proposes a methodology to detect plasticity in soft robots subject to known or unknown applied forces using measured marker positions along the robot structure. The approach relies on a finite element method (FEM) model and the open-source SOFA framework, and it is experimentally validated on a tendon-actuated soft robot with noisy measurements.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.14", "code": "WeB04.14", "title": "Spectral-Theoretic Compliance in Graph-Based Process Monitoring", "day": "Wednesday", "date": "August 26, 2026", "time": "14:15-14:20", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Wolmarans, Wikus", "affiliation": "North-West University" }, { "name": "van Schoor, George", "affiliation": "North-West University" }, { "name": "Uren, Kenneth Richard", "affiliation": "North-West University" } ], "keywords": [ "Fault detection and isolation methods", "Monitoring, performance assessment, and fault detection in chemical process control" ], "abstract": "With industrial processes becoming more complex, on-going improvement of sophisticated and reliable fault detection and diagnosis (FDD) methods is essential. To this end, this work introduces the notion of spectral-theoretic compliance, which is intended to encompass the benefits relating to matrix symmetry in graph-based process monitoring methods. This work further reveals and discusses spectral-theoretic benefits of matrix symmetry not yet recognised in the field of FDD, namely representability, interpretability and numerical noise immunity. Practical examples of these benefits are illustrated using the established energy graph-based visualisation (EGBV) method as applied to a pilot process. Two approaches are proposed for achieving spectral-theoretic compliance, namely sample self-comparison (SSC) and singular value decomposition (SVD). A comparison of these approaches with the original non-compliant version of the EGBV method reveals that the aforementioned benefits can be attained without compromising on FDD performance. The work is concluded with recommendations for continued study on the topic.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.15", "code": "WeB04.15", "title": "A Fixed Time Global NTSMC-Based Approach to Mitigate Dynamic Instabilities in DFIG-Based Wind Energy Conversion Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "14:20-14:25", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Musarrat, Md Nafiz", "affiliation": "University of Louisiana at Lafayette" }, { "name": "Fekih, Afef", "affiliation": "Univ of Louisiana at Lafayette" } ], "keywords": [ "Fault-tolerant control methods", "Power systems stability", "Wind power" ], "abstract": "This paper proposes a fixed time global non-singular terminal SMC (FT-GNTSMC)-based approach for the effective mitigation of fault-induced transients in doubly-fed-induction-generator (DFIG)-based wind energy systems. The proposed approach combines the mitigation capabilities of dynamic voltage restorers (DVRs) with the robustness and global fast fixed time convergence of FT-GNTSMC. The stability and non-singularity of the proposed controller is proven using the Lyapunov stability theory. The performance of the proposed approach is assessed using a wind energy-based test microgrid subject to grid faults and sudden load variations. Comparative analysis with a standard SMC-based approach is also carried out. The obtained results confirmed the fast response and superior performance of the proposed FT-GNTSMC in mitigating the dynamic instabilities induced by grid faults.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.16", "code": "WeB04.16", "title": "Reliable Detection of Abnormal Bearing States under Unknown Samples", "day": "Wednesday", "date": "August 26, 2026", "time": "14:25-14:30", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Wang, Jing", "affiliation": "North China University of Technology (NCUT)" }, { "name": "Li, Ning", "affiliation": "North China University of Technology" }, { "name": "Zhou, Meng", "affiliation": "North China University of Technology" }, { "name": "Su, Rong", "affiliation": "Nanyang Technological University" } ], "keywords": [ "Health/condition monitoring in processes", "Reliability and safety in processes", "AI methods for FDI/FTC" ], "abstract": "Bearings are critical components in motion control systems, and reliable detection of abnormal conditions is essential. Traditional supervised learning methods often misclassify unknown faults as normal. This paper proposes a reliable abnormality diagnosis framework combining a supervised model with a Gated Network. Trained only on known samples, the Gated Network effectively identifies unknown data while ensuring reliable detection in supervised learning models. Experiments on the CWRU bearing dataset demonstrate that the framework achieves high accuracy and improves the decision reliability of conventional supervised models under unknown samples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.17", "code": "WeB04.17", "title": "Machine Learning for Electrolyzer Energy Efficiency: Review and Outlook", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:35", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Ramde, Ismail", "affiliation": "INSA Lyon, Université Lumière Lyon 2, Université Claude Bernard Lyon 1, Université Jean Monnet Saint-Etienne, DISP UR4570" }, { "name": "Kombaya Touckia, Jesus Vital", "affiliation": "Université Claude Bernard Lyon 1, INSA Lyon, Université Lumière Lyon 2, Université Jean Monnet Saint-Etienne, DISP UR4570," }, { "name": "Henry, Sébastien", "affiliation": "DISP Laboratory, University of Lyon, University Lyon 1" }, { "name": "Ouzrout, Yacine", "affiliation": "DISP Laboratory, University of Lyon, University Lyon 2" } ], "keywords": [ "Hydrogen systems for energy generation and storage", "Control and management of energy systems", "Advanced process control" ], "abstract": "Hydrogen production through water electrolysis is essential for low-carbon energy systems, but its competitiveness depends on efficient and reliable operation. This paper reviews artificial intelligence approaches applied to electrolyzer energy performance. Unlike broader reviews on green hydrogen, it focuses on the link between learning methods, operational data, reported efficiency gains, and industrial control perspectives. Thirty studies published between 2010 and 2025 are analyzed using a systematic review methodology. The results show that supervised learning and hybrid simulation-based models dominate, while pressure, degradation, benchmark datasets, and large-scale validation remain insufficiently addressed.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.18", "code": "WeB04.18", "title": "The Evolving Model Approach: A Dynamic Real-Time Optimization Strategy", "day": "Wednesday", "date": "August 26, 2026", "time": "14:35-14:40", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Damiri, Hazem", "affiliation": "Graz University of Technology" }, { "name": "Steinberger, Martin", "affiliation": "Graz University of Technology" }, { "name": "Horn, Martin", "affiliation": "Graz University of Technology" } ], "keywords": [ "Real-time optimization and control in chemical processes", "Biological and pharmaceutical systems", "Industrial applications of chemical process control" ], "abstract": "In this paper, a novel real time optimization (RTO) approach is developed for plants with dynamics described by Hammerstein models. The framework relies on adding a dynamic system to the plant model. Then, the parameters of this added system are tuned to shape the optimal input of the plant model. If the plant deviates from the optimal performance because of an external disturbance, the proposed method modifies this added system to compute a new input that drives the plant back to the optimal behavior. Simulation results show a better performance by comparing the new approach with previous methods from literature.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.19", "code": "WeB04.19", "title": "Autonomous Model Updating in AI Real-Time Optimization under Plant Drift", "day": "Wednesday", "date": "August 26, 2026", "time": "14:40-14:45", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Costa, Erbet Almeida", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Rebello, Carine", "affiliation": "NTNU: Norwegian University of Science and Technology" }, { "name": "Nogueira, Idelfonso", "affiliation": "NTNU" } ], "keywords": [ "Real-time optimization and control in chemical processes", "Machine learning and artificial intelligence in chemical process control", "Advanced process control" ], "abstract": "The use of artificial intelligence (AI) models in engineering applications has increased significantly in recent years. A key concern accompanying this growth is determining when such models require updating, how to detect the need for retraining, and how to update them effectively. This article proposes a strategy for detecting inconsistencies in the surrogate models used within AI-powered real-time optimization (AI-RTO). The methodology relies on a supervisory module that (i) verifies whether the plant is operating near steady state through a moving-window analysis of the controlled variables, (ii) evaluates the persistent mismatch between the optimum predicted by the AI-RTO and the measured plant outputs, and (iii) triggers data acquisition and model retraining only when both conditions are simultaneously satisfied. The retraining procedure first updates the network weights and, if the performance criterion is not met, performs a hyperparameter search. The strategy is evaluated on an artificial-lift system actuated by an electric submersible pump (ESP), subject to dynamic operational constraints, including the pump operating envelope and a minimum intake pressure limit. Four operating scenarios, with combined disturbances in the productivity index, choke gain, and pump head curve, are used to emulate plant drift. The results show that the proposed mechanism keeps the plant close to the actual maximum-flow operating point and enforces the dynamic envelope constraints, whereas a static AI-RTO progressively loses both feasibility and optimality.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.20", "code": "WeB04.20", "title": "GMM-Based Pareto Optimal Alarm Design for Multivariate Process Monitoring", "day": "Wednesday", "date": "August 26, 2026", "time": "14:45-14:50", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Yang, Nachuan", "affiliation": "University of Alberta" }, { "name": "Tao, Yifei", "affiliation": "University of Alberta" }, { "name": "Jia, Fanlin", "affiliation": "University of Alberta" }, { "name": "Chen, Tongwen", "affiliation": "University of Alberta" } ], "keywords": [ "Reliability and safety in processes", "Monitoring, performance assessment, and fault detection in chemical process control", "Fault detection and isolation methods" ], "abstract": "Univariate alarm systems are usually inadequate for multivariate industrial processes, where strong process correlations often lead to alarm flooding and ineffective fault detection. In this paper, we investigate a multi-objective design of multivariate alarms, which remains an open research problem. Historical process data are first modeled using a Gaussian mixture model (GMM) to capture representative fault patterns. Based on these patterns, the alarm design is further formulated as a multi-objective optimization problem, which is then solved through quadratic programming and bisection methods. The proposed method jointly minimizes the false alarm rate, missing alarm rate, and cross false alarm rate, achieving a Pareto optimal solution among multiple alarm objectives. Compared with heuristic methods and manual tuning, the proposed method provides explicit rate characterization and theoretical guarantees, which are essential for safety-critical applications. The effectiveness of our proposed new method is demonstrated through case studies.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.21", "code": "WeB04.21", "title": "Modelling and Control of a Shrouded Wind Turbine with Integrated Structural Dynamics", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-14:55", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Zhu, Hongzhong", "affiliation": "Kyushu University" }, { "name": "Hu, Changhong", "affiliation": "Kyushu University" }, { "name": "Watanabe, Seiya", "affiliation": "Kyushu University" }, { "name": "Watanabe, Koichi", "affiliation": "Kyushu University" }, { "name": "Uchida, Takanori", "affiliation": "Kyushu University" } ], "keywords": [ "Wind power", "Control and management of energy systems", "Power systems stability" ], "abstract": "This study investigates the feasibility of scaling a shrouded wind turbine to medium-large capacities, addressing the long-standing limitation that existing shrouded turbines remain small due to structural complexity and dynamic-load amplification. A comprehensive multibody dynamic model of a 200-kW downwind shrouded wind turbine is developed using a multi-body formulation. The flexibility of the tower and shroud-support structures is considered, enabling accurate representation of bending, torsional, and axial deformation modes. Modal analysis of the complete assembly identifies critical vibration modes, including roll and yaw modes of the shroud that occur in the rotor 3P excitation region. These modal characteristics are explicitly incorporated into the controller design, where a region-dependent rotor-speed strategy and notch-filtered PI control are used to avoid resonance crossings and enhance operational robustness. Dynamic simulations are conducted under turbulent wind conditions to evaluate structural responses and closed-loop performance. The results highlight practical design constraints for large shrouded turbines. The findings provide quantitative guidance for drivetrain sizing and control-system specifications, offering insights into the viability of upscaling shrouded concepts for higher-power applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB04.22", "code": "WeB04.22", "title": "Benchmarking Sequential Feedback Optimization for Wind Farm Power Maximization", "day": "Wednesday", "date": "August 26, 2026", "time": "14:55-15:00", "sessionCode": "WeB04", "sessionTitle": "Shotgun: Process and Power Systems II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Huang, Shijie", "affiliation": "TUDelft" }, { "name": "Grammatico, Sergio", "affiliation": "Delft Univ. of Tech" } ], "keywords": [ "Wind power", "Power plant control", "Control and management of energy systems" ], "abstract": "This paper benchmarks sequential feedback optimization (SFO) for wind farm power maximization using a medium-fidelity dynamic flow model. We compare SFO with two well-established approaches, adjoint-based economic model predictive control (AMPC) and extremum seeking control (ESC), under a common nine-turbine layout and identical operating constraints. The comparison focuses on steady-state power production and computational efficiency, both relevant for real-time implementation. The simulation results illustrate that SFO achieves higher steady-state power while preserving real-time feasibility, AMPC provides a better transient performance at a higher online computational cost and without guarantees of convergence to the steady-state optimum, and ESC offers a computationally inexpensive model-free baseline that may converge to locally optimal solutions. These results provide a practical reference for selecting wind farm control strategies and for designing scalable, real-time optimization methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB05.1", "code": "WeB05.1", "title": "A Two-Stage Dynamic Programming-Based Routing Method for Space Debris Removal Planning", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:25", "sessionCode": "WeB05", "sessionTitle": "LB: Applications of Control Systems Theory", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Kravchenko, Vadim", "affiliation": "Moscow Aviation Institute" }, { "name": "Ivanyukhin, Alexey", "affiliation": "Research Institute of Applied Mechanics and Electrodynamics (Moscow Aviation Institute) / RUDN University" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Flight dynamics modelling and identification", "Aerospace mission control and operations" ], "abstract": "The problem of determining a routing trajectory for the removal of space debris objects is considered. The approach is based on two classical combinatorial optimization problems: the knapsack problem and the traveling salesman problem. The knapsack problem is used for the preliminary selection of the most hazardous objects. Subsequently, the traveling salesman problem is applied to construct an optimal transfer route between these selected objects. Several missions for the disposal of the most hazardous space debris objects are analyzed as case studies.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB05.2", "code": "WeB05.2", "title": "Stability Analysis and Backstepping Control of Orbit–Attitude Coupled Dynamics under the Rigid-Body Potential", "day": "Wednesday", "date": "August 26, 2026", "time": "13:25-13:40", "sessionCode": "WeB05", "sessionTitle": "LB: Applications of Control Systems Theory", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Lee, Jinah", "affiliation": "Yonse University" }, { "name": "McMahon, Jay", "affiliation": "University of Colorado Boulder" }, { "name": "Scheeres, Daniel", "affiliation": "The University of Colorado" }, { "name": "Park, Chandeok", "affiliation": "Yonsei University" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Space exploration and transportation", "Aerospace mission control and operations" ], "abstract": "This study aims to propose a backstepping attitude controller to stabilize orbit–attitude coupled motions of a spacecraft orbiting a small celestial body, based on the rigid-body potential. Here, the rigid-body potential is the unified potential covering the orbit–attitude coupled motion of the spacecraft, which is obtained by integration over its finite volume. Firstly, this study derives the equations of motion and equilibrium states accounting for the rigid-body potential, to evaluate the gravitational interaction between orbit and attitude motions. After that, we evaluate the stability for each equilibrium and reorganize the equations of motion into subsystems according to the coupled variables. Finally, the backstepping attitude controller is developed to stabilize the unstable subsystem, which is formulated by the radial variation, tangential variation, and angular variation about the spin axis of the spacecraft. The numerical simulations demonstrate that the proposed controller successfully stabilizes a specific subsystem using an angle variable.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB05.3", "code": "WeB05.3", "title": "A New Genuine-Hamiltonian Energy-Shaping Approach and Its Equivalence to the IDA-PBC Method for Underactuated Mechanical Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "13:40-13:55", "sessionCode": "WeB05", "sessionTitle": "LB: Applications of Control Systems Theory", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Cervantes-Pérez, Luis", "affiliation": "Instituto Tecnológico De La Laguna" }, { "name": "Sandoval, Jesus", "affiliation": "Instituto Tecnologico De La Paz" }, { "name": "Santibanez, Victor", "affiliation": "Instituto Tecnologico De La Laguna" } ], "keywords": [ "Lagrangian and Hamiltonian systems", "Passivity-based control", "Stability of nonlinear systems" ], "abstract": "This paper reviews a recently introduced energy-shaping methodology, distinguished from other energy-shaping approaches by its genuine Hamiltonian formulation. The method was originally developed for fully actuated mechanical systems and has subsequently been extended to a class of underactuated mechanical systems. It has been shown to achieve several control objectives beyond classical regulation, including trajectory tracking, disturbance rejection, energy regulation, periodic motion generation, and parameter estimation, among others. We revisit its theoretical foundations and establish equivalence conditions with the well-known IDA-PBC method for underactuated mechanical systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB05.4", "code": "WeB05.4", "title": "Priority-Driven Intrinsic Parameter Optimization for FMCW Radar Perception", "day": "Wednesday", "date": "August 26, 2026", "time": "13:55-14:10", "sessionCode": "WeB05", "sessionTitle": "LB: Applications of Control Systems Theory", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Huang, Jun", "affiliation": "Wuhan University of Science and Technology" }, { "name": "Lei, Zike", "affiliation": "Wuhan University of Science and Technology" }, { "name": "Chen, Xi", "affiliation": "Wuhan University of Science and Technology" }, { "name": "Chen, Xiang", "affiliation": "Univ of Windsor" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "Robot perception and sensing", "Mechatronics for robotic systems" ], "abstract": "This paper presents a priority-driven intrinsic parameter optimization approach for FMCW radar to improve perception in multi-target scenarios. Three priority factors of dynamic target are proposed to quantify sensing importance based on target distance, velocity, and timeto- approach (TTA). Guided by these factors, key radar intrinsic parameters—including start frequency, bandwidth, chirp period, and beamforming phase shift—are jointly optimized to enhance sensing quality for high-priority targets. Then, the optimization problem is optimized through Particle Swarm Optimization (PSO). Simulation and experiments validate that the proposed approach improves target performance for high-priority targets, enables efficient, taskoriented radar resource allocation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB05.5", "code": "WeB05.5", "title": "Robustness Benchmarking of Single-Foot IMU-Based Gait Phase Detection Algorithms under Practical Perturbations", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:25", "sessionCode": "WeB05", "sessionTitle": "LB: Applications of Control Systems Theory", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Park, Jihwan", "affiliation": "Sejong University" }, { "name": "Kang, Brian Byunghyun", "affiliation": "Sejong University" }, { "name": "Yang, Hyungseung", "affiliation": "Sejong University" } ], "keywords": [ "Robustness analysis", "Robust estimation", "Fault detection and isolation" ], "abstract": "This paper presents a systematic robustness benchmarking framework for single-foot IMU-based gait phase detection algorithms. Three representative algorithms—Threshold-Based (TB), Template Matching (TM), and Bandpass Filter-Based (BPF)—are evaluated under five practical perturbation scenarios: sensor noise, attachment misalignment, walking speed variation, signal dropout, and cross-subject generalization. Experiments are conducted with two healthy male subjects walking on a treadmill at four speeds (2–5 km/h) using a foot-mounted 9-DOF IMU at 100 Hz. Ground truth is established via 60 fps video labeling. The Degradation Ratio (DR) metric quantifies performance loss relative to baseline. Results show that all algorithms are highly robust to misalignment (DR < 1.05 at 30°), while BPF demonstrates superior robustness to noise (DR = 1.38 at σ = 100 deg/s) and dropout (DR = 1.08 at 30% loss). TB achieves the highest baseline F1 (0.971) but shows greater vulnerability to signal degradation. These findings provide preliminary deployment guidelines for selecting gait detection algorithms in assistive robotics, pending validation on larger and more diverse cohorts.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB05.6", "code": "WeB05.6", "title": "Thermo-Economic Optimization and Carbon Footprint Assessment of an Absorption Heat Pump Cascade Organic Rankine Cycle", "day": "Wednesday", "date": "August 26, 2026", "time": "14:25-14:40", "sessionCode": "WeB05", "sessionTitle": "LB: Applications of Control Systems Theory", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Wang, Zongrun", "affiliation": "Shanxi Research Institute of Huairou Laboratory" }, { "name": "Sun, Li", "affiliation": "Southeast University" } ], "keywords": [ "Thermal systems modelling", "Life cycle assessment for energy systems", "Control and management of energy systems" ], "abstract": "在煤气化作业中，黑水洗涤后仍保留较大潜热潜能，但当前废热回收系统效率不理想，导致低品位热能显著耗散。为克服这一限制，本研究开发了一种结合吸收热泵与有机朗肯循环的混合系统，通过涡轮集成和直接热交换机制增强，以优化热能提取。所提构型通过Aspen Plus模拟软件进行了严格建模，随后通过综合的电能、动力经济学、经济学和环境分析进行了系统性的性能评估。结果表明：系统总运动效率为51%，吸收器因热负荷显著而表现出最高的运动破坏力（36.85千瓦）。发电机占运动破坏成本的最大份额，占总成本的52.1%。经济分析显示，该系统在第7年实现正净现值。此外，生命周期评估显示15年运行期间总CO₂排放量为61,155.70公斤CO₂q，主要归因\u0014", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB05.7", "code": "WeB05.7", "title": "The Role of Visualization in Agile Participatory Design of Control Systems in Water Management", "day": "Wednesday", "date": "August 26, 2026", "time": "14:40-14:55", "sessionCode": "WeB05", "sessionTitle": "LB: Applications of Control Systems Theory", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "van Nooijen, Ronald Robert Paul", "affiliation": "Delft University of Technology" }, { "name": "Kolechkina, Alla G.", "affiliation": "Delft University of Technology" }, { "name": "Berends, Thomas", "affiliation": "Technical University of Delft" }, { "name": "Klingen, Louise", "affiliation": "Vinluo Limited" }, { "name": "van Leeuwen, P. E. R. M.", "affiliation": "Deltares" } ], "keywords": [ "Water resource system modeling and control", "Real time monitoring and control of environmental systems", "Participatory decision making in environmental systems" ], "abstract": "Stakeholders are more likely to accept new control methods if they recognize the added value of those methods. In practice, this means the stakeholders and the designers of the control methods should agree on what constitutes ``added value''. To arrive on this agreement it is necessary to show the stakeholders how the system behavior changes when new methods are applied. Central to most presentations will be visualizations of the system behavior. These visualizations themselves should be developed in cooperation with the intended audience in an agile process. Some visualizations of a polder-boezem system created in preparation for a workshop with stakeholders are presented.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB05.8", "code": "WeB05.8", "title": "Design Optimization of Floating Platforms for Airborne Wind Energy Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "14:55-15:10", "sessionCode": "WeB05", "sessionTitle": "LB: Applications of Control Systems Theory", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Bertozzi, Andrea", "affiliation": "Politecnico Di Milano" }, { "name": "Alborghetti, Mattia", "affiliation": "Politecnico Di Milano" }, { "name": "Fagiano, Lorenzo", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Wind power" ], "abstract": "This study presents a design optimization framework for cost-effective floating platforms tailored to fly-gen Airborne Wind Energy Systems (AWES). We propose a conceptual design based on a parameterized cylindrical spar platform and a spread taut mooring system. The design optimization problem, accounting for windplane loads, is solved via the NSGA-II genetic algorithm, simultaneously minimizing CAPEX and deck motions to ensure operational safety during take-off and landing. To the best of authors’ knowledge, this is the first integrated design optimization framework for floating offshore AWES. Results aim to provide a scalable methodology for enhancing the economic viability of deep offshore AWES.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB06.1", "code": "WeB06.1", "title": "Data-Driven Feedback Linearization in the Koopman Observable Manifold (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB06", "sessionTitle": "Data-Driven Control V", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Varadan, Vishnu", "affiliation": "EPFL" }, { "name": "Rupenyan, Alisa", "affiliation": "ZHAW Zurich University for Applied Sciences" }, { "name": "Karimi, Alireza", "affiliation": "Ecole Polytechnique Federale De Lausanne" } ], "keywords": [ "Data-driven control theory", "Nonlinear system identification" ], "abstract": "This paper proposes a novel data-driven approach for feedback linearization of nonlinear control-affine systems by leveraging the Koopman operator framework. We establish theoretical connections between feedback linearization on the original state manifold and the higher-dimensional Koopman observable manifold using concepts from system immersion. For systems with exact Koopman bilinear representations, we provide closed-form solutions to the feedback linearization problem without having to solve partial differential equations. The simulation results and numerical examples demonstrate the effectiveness of the approach and show that approximate solutions work in practice.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB06.2", "code": "WeB06.2", "title": "Data-Driven Control of Wind Turbines Using Control Barrier Function Atop Ultra-Local Model (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB06", "sessionTitle": "Data-Driven Control V", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Kehal, Isra", "affiliation": "National Polytechnic School of Constantine" }, { "name": "Fruchard, Matthieu", "affiliation": "University of Orléans" }, { "name": "Ramdani, Nacim", "affiliation": "University of Orleans" } ], "keywords": [ "Data-driven control theory" ], "abstract": "Wind turbine (WT) control under disturbances induced by wind gusts and wake effect of upstream WT presents significant challenges due to highly nonlinear aerodynamics, turbulent inflow, and modeling uncertainties. While Model-Free Control (MFC) via Ultra-Local Models (ULM) offers disturbance rejection without requiring explicit plant models, it lacks formal stability and safety guarantees. This paper presents the integration of MFC with Control Barrier Function-based Funnel Control (CBF-FC) to ensure prescribed performance bounds in WT power regulation. We demonstrate that standalone MFC, despite excellent nominal performance, violates safety constraints under wake-induced disturbances, whereas the proposed MFC-CBF-FC framework guarantees the safe set invariance. The controller is validated on a medium-fidelity WFSim. Implementation concerns are addressed and the results illustrate that safety-critical model-free control is achievable for WT through the synergy of ULM estimation and optimization-based CBF constraints.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB06.3", "code": "WeB06.3", "title": "Kernel-Based Learning for Almost Sure Reachability Certificates (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB06", "sessionTitle": "Data-Driven Control V", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Majumdar, Rupak", "affiliation": "Max Planck Institute for Software Systems and University of California at Los Angeles" }, { "name": "Rubab, Tamzid Morshed", "affiliation": "Max Planck Institute for Software Systems" }, { "name": "Soudjani, Sadegh", "affiliation": "Max Planck Institute for Software Systems" } ], "keywords": [ "Reachability analysis, verification and abstraction of hybrid systems", "Randomized algorithms in stochastic systems", "Synthesis of stochastic systems" ], "abstract": "The growing deployment of autonomous systems increases the need for automated verification of their temporal properties, particularly when the underlying stochastic model is unknown. We study the problem of almost sure reachability for continuous-state stochastic systems using only state measurements. Our approach is distributionally robust: we employ empirical conditional mean embeddings to learn the system's transition structure in a reproducing kernel Hilbert space (RKHS), and synthesize certificates via sum-of-squares optimization. The resulting constraints are bilinear and require a tailored numerical procedure, combined with Gaussian process regression, to compute the necessary RKHS elements. While prior RKHS-based verification methods have been restricted to finite-horizon properties, we introduce the first data-driven and distributionally robust framework for certifying infinite-horizon almost sure reachability. In particular, we construct variant certificates that decrease along system trajectories with positive probability, characterizing almost sure reachability in the infinite-horizon setting. Numerical case studies illustrate the effectiveness of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB06.4", "code": "WeB06.4", "title": "Data-Driven Safe Exploration to Enhance Robust Performance (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB06", "sessionTitle": "Data-Driven Control V", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Qin, Zhaoming", "affiliation": "EPFL" }, { "name": "Zhu, Yuzheng", "affiliation": "École Polytechnique Fédérale De Lausanne" }, { "name": "Karimi, Alireza", "affiliation": "Ecole Polytechnique Federale De Lausanne" } ], "keywords": [ "Data-driven control theory", "Active learning and experiment design", "Learning methods for control" ], "abstract": "We propose a novel optimization-based safe exploration strategy for improving the robust performance of linear time-invariant systems. At each time step, the exploratory control input is obtained by solving a data-based semidefinite program (SDP) that ensures robust satisfaction of safety constraints and recursive feasibility for all admissible disturbances and all system dynamics consistent with the available data. Furthermore, the SDP is designed to systematically guide exploration toward trajectories that are most informative for reducing the worst-case performance bound. A simulation example demonstrates that the proposed performance-oriented exploration strategy achieves significantly improved robust performance compared to a safe maximum-energy exploration baseline.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB06.5", "code": "WeB06.5", "title": "Learning Dynamics from Infrequent Output Measurements for Uncertainty-Aware Optimal Control (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB06", "sessionTitle": "Data-Driven Control V", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Lefringhausen, Robert", "affiliation": "Technical University of Munich" }, { "name": "Springer, Theodor", "affiliation": "Technical University of Munich (TUM)" }, { "name": "Hirche, Sandra", "affiliation": "Technical University of Munich" } ], "keywords": [ "Probabilistic and Bayesian methods for system identification", "Data-driven control theory", "Time series modeling" ], "abstract": "Reliable optimal control is challenging when the dynamics of a nonlinear system are unknown and only infrequent, noisy output measurements are available. This work addresses this setting of limited sensing by formulating a Bayesian prior over the continuous-time dynamics and latent state trajectory in state-space form and updating it through a targeted Metropolis–Hastings sampler equipped with a numerical ODE integrator. The resulting posterior samples are used to formulate a scenario-based optimal control problem that accounts for the uncertainty in the dynamics and latent state and is solved using standard nonlinear programming methods. The approach is validated in a numerical case study on glucose regulation using a Type 1 diabetes model.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB07.1", "code": "WeB07.1", "title": "Distributed Consensus Control of Primary Pumps for Chilled Water Production", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB07", "sessionTitle": "Distributed Control and Estimation of Networked Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Kallesøe, Carsten Skovmose", "affiliation": "Grundfos" }, { "name": "Bromose, Lasse", "affiliation": "Grundfos Holding A/S" }, { "name": "Pedersen, Mikkel Schjøtt", "affiliation": "Grundfos Holding A/S" }, { "name": "Leth, John", "affiliation": "Aalborg University" } ], "keywords": [ "Distributed control and estimation", "Consensus" ], "abstract": "A significant amount of energy is consumed in supplying buildings. Part of this energy is used to produce chilled water for Heating, Ventilation, and Air Conditioning (HVAC) systems or for industrial cooling. In large systems, chilled water is generated in central chiller plants and circulated throughout the building to supply cooling loads. This paper focuses on controlling the flow through the chillers at these plants. It presents a distributed control architecture designed to maintain a high temperature difference across the chillers, along with consensus algorithms that ensure the desired flow distribution among them. Maintaining a high temperature difference and achieving the desired flow distribution provide optimal operating conditions for the chillers, thereby improving efficiency. Stability arguments for the distributed control setup are derived, and the theoretical results are validated through numerical studies.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB07.2", "code": "WeB07.2", "title": "Distributed Hybrid Feedback for Global Pose Synchronization of Multiple Rigid Body Systems on SE(3)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB07", "sessionTitle": "Distributed Control and Estimation of Networked Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Lin, Fengyu", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Wang, Miaomiao", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Su, Housheng", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Tayebi, Abdelhamid", "affiliation": "Lakehead University" } ], "keywords": [ "Distributed control and estimation", "Control of networks", "Hybrid and switched systems modeling" ], "abstract": "This paper investigates the problem of pose synchronization for multiple rigid body systems evolving on the matrix Lie group SE(3). We propose a distributed hybrid feedback control scheme with global asymptotic stability guarantees using relative pose and group velocity measurements. The key idea consists of constructing a new potential function on SE(3) times mathbb{R} with a generalized non-diagonal weighting matrix, and a set of auxiliary scalar variables with continuous-discrete hybrid dynamics. Based on the new potential function and the auxiliary scalar variables, a distributed geometric hybrid feedback designed directly on SE(3) is proposed to achieve global pose synchronization over undirected, connected and acyclic interaction graphs. Numerical simulation results are presented to illustrate the performance of the proposed distributed hybrid control scheme.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB07.3", "code": "WeB07.3", "title": "Distributed State Estimation with Opacity Enforcement", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB07", "sessionTitle": "Distributed Control and Estimation of Networked Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Liang, Dingguo", "affiliation": "University of Duisburg-Essen" }, { "name": "Zhang, Ping", "affiliation": "University of Kaiserslautern-Landau" } ], "keywords": [ "Distributed control and estimation", "Cyber security networked control" ], "abstract": "This paper proposes an opacity-based framework for privacy-preserving distributed state estimation over sensor networks, where communication among neighbouring nodes may be exposed to potential intruders. By leveraging detectable subspace decomposition, a novel distributed state estimation framework is constructed, in which each node receives only partial components of its neighbours’ state estimates. This design guarantees asymptotic convergence of the state estimation while limits information leakage to intruders, thus facilitating the enforcement of opacity. It is further shown that the opacity of the distributed observers with respect to a node’s intruder is equivalent to the undetectability of the system state by that intruder. Finally, simulation results demonstrate the effectiveness of the proposed methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB07.4", "code": "WeB07.4", "title": "Distributed State Estimation for Discrete-Time Systems with Unknown Inputs: An Optimization Approach", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB07", "sessionTitle": "Distributed Control and Estimation of Networked Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Zhao, Ruixuan", "affiliation": "University College London" }, { "name": "Yang, Guitao", "affiliation": "Loughborough University" }, { "name": "Bastianello, Nicola", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Chen, Boli", "affiliation": "University College London" } ], "keywords": [ "Distributed control and estimation", "Distributed optimization" ], "abstract": "This paper proposes a novel Distributed Unknown Input Observer (DUIO) framework for state estimation in large-scale systems subject to local unknown inputs. We consider systems where outputs are measured by a network of spatially distributed sensors and inputs are introduced through multiple dispersed channels. In this framework, each local node utilizes only its local input and output measurements to estimate the locally reconstructible state. Subsequently, nodes collaboratively reconstruct the whole system state via a distributed optimization algorithm that fuses these partial state estimates. We provide a rigorous analysis showing that the estimation error is bounded, with the error bound explicitly dependent on the number of communication iterations per time step and strong convexity constant determined by the system parameters. Furthermore, to counteract curvature anisotropy induced by poorly conditioned system geometry, we embed a normalization step into the distributed optimization procedure. Simulation results demonstrate the effectiveness of the proposed framework and the performance improvements yielded by the normalization procedure.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB07.5", "code": "WeB07.5", "title": "Multi-Agent Coverage Control with Poly-Annulus Conformal Mapping in Poriferous Environments", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB07", "sessionTitle": "Distributed Control and Estimation of Networked Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Feng, Xun", "affiliation": "China University of Geosciences" }, { "name": "Zhai, Chao", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Distributed control and estimation", "Multi-agent systems", "Control of networks" ], "abstract": "This paper introduces a diffeomorphic coverage formulation for multi-agent system (MAS) in the two-dimensional poriferous region. A poly-annulus conformal mapping is constructed in a decentralized manner, w hich transforms a poriferous region into a topologically equivalent one suitable for sectorial partition. Then, we propose a partition algorithm to adapt to the mapped region, which ensures exponential convergence of workload balance. In addition, by introducing a length metric, a distributed control law is designed to drive MAS towards the optimal positions, which not only optimizes the global coverage cost but also avoids obstacles. Convergence analyses confirm the asymptotic stability of the MAS and the reachability of the optimization problem. Finally, simulations demonstrate the practicality of the proposed poriferous coverage algorithm.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB07.6", "code": "WeB07.6", "title": "Distributed Formation Control with Pinned Rotational Group Motion", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB07", "sessionTitle": "Distributed Control and Estimation of Networked Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "van Gool, Joris Pieter", "affiliation": "University of Groningen" }, { "name": "Rosa, Muhammad Ridho", "affiliation": "University of Groningen" }, { "name": "Jayawardhana, Bayu", "affiliation": "University of Groningen" } ], "keywords": [ "Distributed control and estimation", "Multi-agent systems", "Kalman filtering" ], "abstract": "This paper presents a distributed control framework for achieving pinned rotational group motion of multi-agent systems that maintain rigid formation shape. The approach enables agents to maintain rigid formation geometry during rotational motion without centralised coordination or global positioning. Predictive rotational control via Kalman filtering, a virtual-agent method for local formation control, and distributed motion-parameter design based on visual measurements are integrated, with simulations showing robust formation control under perturbations and sensing limitations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB08.1", "code": "WeB08.1", "title": "Power Scheduling Strategies for Passive Eavesdroppers Over Periodically Time-Varying Channels (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB08", "sessionTitle": "Secure and Resilient State Estimation for Stochastic Systems under Cyber-Attacks", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "An, Jieyao", "affiliation": "Jiangsu Ocean University" }, { "name": "Deng, Yahan", "affiliation": "Northeastern University" }, { "name": "Zhang, Heng", "affiliation": "Jiangsu Ocean University" }, { "name": "Li, Yuzhe", "affiliation": "Northeastern University" }, { "name": "Zheng, Wei Xing", "affiliation": "Western Sydney University" } ], "keywords": [ "Security for stochastic systems", "Markov decision process", "Cyber security networked control" ], "abstract": "This paper investigates the problem of optimal power allocation for an intelligent and energy-constrained eavesdropper operating over periodically time-varying wireless channels. Due to temporal variations in channel quality, the eavesdropper can opportunistically exploit favorable channel conditions to improve the probability of intercepting sensor transmissions, thereby posing significant confidentiality risks to Cyber-Physical Systems. To characterize this dynamic adversarial setting, a unified switching dynamical model is developed to describe the evolution of the eavesdropper’s estimation error covariance under a periodically switched system and a time-varying wireless channel. The interception decision process is formulated as a partially observable Markov decision process, which captures the stochastic channel evolution, the eavesdropper’s information uncertainty, and the energy consumption associated with power allocation. To address the challenges of continuous action spaces and dynamic channel conditions, a reinforcement learning framework based on proximal policy optimization (PPO) is proposed. The resulting PPO-based least mean-square error strategy achieves stable policy updates and effectively balances interception performance with energy expenditure. Numerical simulations demonstrate that the learned policy significantly outperforms actor-critic, deep Q-network, and random baseline strategies in terms of convergence speed, robustness, and overall long-term reward.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB08.2", "code": "WeB08.2", "title": "A Communication-Efficient Approach for Networked Systems Via Innovation-Based Quantization (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB08", "sessionTitle": "Secure and Resilient State Estimation for Stochastic Systems under Cyber-Attacks", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Yang, Wen", "affiliation": "East China University of Science and Techonology" }, { "name": "Wu, Han", "affiliation": "East China University of Science and Technology" }, { "name": "Ding, Wenjie", "affiliation": "East China University of Science and Technology" }, { "name": "Wang, Jie", "affiliation": "East China University of Science and Technology" } ], "keywords": [ "Estimation and filtering", "Kalman filtering", "Cyber security networked control" ], "abstract": "This paper investigates efficient data transmission for remote state estimation. Compared with raw measurements, innovations follow a stable and state-independent distribution, enabling effective data compression that reduces communication bandwidth while maintaining the desired accuracy. A novel innovation-based feedback quantization scheme is developed to ensure bounded and tunable errors while further lowering bandwidth consumption. In addition, an optimization-based bit-allocation strategy is formulated to balance estimation accuracy against communication cost. Numerical results verify the effectiveness and advantages of the proposed framework.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB08.3", "code": "WeB08.3", "title": "Verifiable Model-Free Safety Filters Via Reinforcement Learning (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB08", "sessionTitle": "Secure and Resilient State Estimation for Stochastic Systems under Cyber-Attacks", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Yin, Bihui", "affiliation": "Tsinghua University" }, { "name": "Lu, Yiwen", "affiliation": "Tsinghua University" }, { "name": "Jiang, Yuchen", "affiliation": "Harbin Institute of Technology" }, { "name": "Mo, Yilin", "affiliation": "Tsinghua University" } ], "keywords": [ "Learning methods for control", "Data-driven control theory", "Filtering and smoothing" ], "abstract": "This paper presents a reinforcement learning approach of a model-free safety filter, drawing inspiration from the framework of model-based Predictive Safety Filters (PSFs). Similar to conventional PSFs, our method adopts a Quadratic Programming (QP) formulation by representing the filter as an unrolled QP solver network. However, unlike existing PSFs that derive QP parameters explicitly from system models, we learn these parameters directly through Deep Reinforcement Learning (DRL), thereby eliminating the dependency on accurate system identification. Furthermore, compared to traditional neural network-based methods, this QP structure allows us to furnish a formal certificate for the persistent safety of the learned filter. Numerical results demonstrate that our method outperforms both conventional model-based PSFs and RL-trained Multi-Layer Perceptron (MLP) baselines in terms of safety guarantees, minimal intervention, and per-step computational load.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB08.4", "code": "WeB08.4", "title": "Leader-Following Consensus for Multi-Agent Systems under Multiple FDI Attacks (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB08", "sessionTitle": "Secure and Resilient State Estimation for Stochastic Systems under Cyber-Attacks", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Gu, Yan", "affiliation": "East China University of Science and Technology" }, { "name": "Zhao, Zhiyun", "affiliation": "East China University of Science and Technology" } ], "keywords": [ "Consensus", "Cyber security networked control", "Multi-agent systems" ], "abstract": "This paper investigates the leader-following consensus problem of multi-agent systems under multiple false data injection (FDI) attacks and disturbances. Specifically, the attacks are injected into the sensor-to-controller (S-C), controller-to-actuator (C-A) and leader to-follower (L-F) channels. To address these challenges, an observer is proposed to estimate both the states of followers and FDI attacks, such that the estimation errors remain bounded. Furthermore, an attack compensator is constructed for each follower to mitigate the L-F FDI attacks. Based on the observer and attack compensator, a resilient control law is proposed for each follower to ensure that the tracking errors of all followers are uniformly ultimately bounded (UUB). Simulation results demonstrate the effectiveness of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB08.5", "code": "WeB08.5", "title": "Adaptive Secure Distributed Kalman Filtering against Sensor Attacks (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB08", "sessionTitle": "Secure and Resilient State Estimation for Stochastic Systems under Cyber-Attacks", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Ding, Wenjie", "affiliation": "East China University of Science and Technology" }, { "name": "Yang, Wen", "affiliation": "East China University of Science and Techonology" }, { "name": "Wu, Han", "affiliation": "East China University of Science and Technology" } ], "keywords": [ "Estimation and filtering", "Kalman filtering", "Security for stochastic systems" ], "abstract": "Anomaly detection algorithms in state estimation are rarely integrated with fusion methods. In distributed settings, sensors with low accuracy emph{can be regarded as} effectively compromised, yet they remain difficult to filter out using conventional detectors. To overcome this, we propose an adaptive detection framework for distributed Kalman filtering. We formally define emph{secure distributed Kalman filtering} and provide a theoretical proof of its resilience. Numerical examples and a real-world experiment demonstrate the effectiveness of our approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB08.6", "code": "WeB08.6", "title": "Dynamic Feature Extraction Via Probabilistic Reduced-Dimensional Vector Autoregressive Modeling for Building Cooling Load Forecasting (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB08", "sessionTitle": "Secure and Resilient State Estimation for Stochastic Systems under Cyber-Attacks", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Zhu, Zhongxi", "affiliation": "Lingnan University" }, { "name": "Mo, Yanfang", "affiliation": "Lingnan University, Hong Kong" }, { "name": "Liu, Yiren", "affiliation": "Lingnan University" }, { "name": "Qin, S. Joe", "affiliation": "Lingnan University, Hong Kong" } ], "keywords": [ "Filtering and smoothing", "Machine and deep learning for system identification", "Time series modeling" ], "abstract": "Cooling systems are major building energy users, making accurate load forecasting important for efficient operation and comfort. Dense sensor networks provide rich operational data but increase dimensionality and exposure to noise, faults, and data-integrity issues. Static dimensionality reduction methods such as principal component analysis retain directions of large instantaneous variance, whereas cooling-load forecasting for heating, ventilation, and air-conditioning systems often depends on lagged correlations and slowly evolving thermal dynamics. To address this limitation, we apply the probabilistic reduced-dimensional vector autoregressive (PredVAR) framework (Mo and Qin, 2025, Automatica) to extract features according to temporal predictability. Using the IEA FLEXLAB dataset, we show that predictability-oriented features can improve compact cooling-load forecasting and provide a practical preprocessing layer for building energy analytics, compared to static alternatives.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB09.1", "code": "WeB09.1", "title": "Language-Aided State Estimation (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB09", "sessionTitle": "JO-JSC: Filtering and Smoothing", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Miyoshi, Yuki", "affiliation": "Keio University" }, { "name": "Inoue, Masaki", "affiliation": "Keio University" }, { "name": "Fujimoto, Yusuke", "affiliation": "The University of Osaka" } ], "keywords": [ "Filtering and smoothing" ], "abstract": "Natural language data, such as text and speech, have become readily available through social networking services and chat platforms. By leveraging human observations expressed in natural language, this paper addresses the problem of state estimation for physical systems, in which humans act as sensing agents. To this end, we propose a Language-Aided Particle Filter (LAPF), a particle filter framework that structures human observations via natural language processing and incorporates them into the update step of the state estimation. Finally, the LAPF is applied to the water level estimation problem in an irrigation canal and its effectiveness is demonstrated.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB09.2", "code": "WeB09.2", "title": "Manifold Projection Methods for the Koopman Kalman Filter (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB09", "sessionTitle": "JO-JSC: Filtering and Smoothing", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Van Heck, Cedric", "affiliation": "Ghent University" }, { "name": "Coene, Annelies", "affiliation": "Cancer Research Institute Ghent" }, { "name": "Crevecoeur, Guillaume", "affiliation": "Ghent University" } ], "keywords": [ "Filtering and smoothing", "Kalman filtering", "Machine and deep learning for system identification" ], "abstract": "Koopman-based Kalman filters lift nonlinear states to a higher-dimensional space, inherently imposing manifold constraints. However, existing implementations often ignore possible deviations from this manifold. In this paper, we leverage the manifold structure by introducing covariance projection methods that enforce consistency within the Koopman Kalman filter. Additionally, we propose a novel projection approach that focuses on the measured subset and propagates it to the full state using first-order uncertainty propagation. Compared to alternative methods, our approach significantly improves convergence and estimation accuracy.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB09.3", "code": "WeB09.3", "title": "Improved Robot Pose Estimation Using Relative Bearing Measurements to Unknown Landmarks (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB09", "sessionTitle": "JO-JSC: Filtering and Smoothing", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Zamani, Behzad", "affiliation": "University of Melbourne" }, { "name": "Trumpf, Jochen", "affiliation": "The Australian National University" }, { "name": "Manzie, Chris", "affiliation": "The University of Melbourne" } ], "keywords": [ "Filtering and smoothing", "Estimation and filtering", "Kalman filtering" ], "abstract": "In this paper, we propose a modular nonlinear least squares filtering approach for pose estimation of a moving robot that obtains relative bearing measurements to landmarks whose global position is unknown to the robot. Unlike in the well-studied simultaneous localization and mapping (SLAM) problem, we are not trying to localize these unknown landmarks as part of the problem specification; they merely serve to aid the robot pose estimation task. In particular, we are not tracking cross-covariance information between robot pose and landmarks in order to enable a modular filter design, where the landmark-aided module can be switched off and on at will. We integrate the Covariance Intersection (CI) algorithm as part of our solution in order to prevent double counting of information when filter modules share estimates with each other. An alternative derivation of the CI algorithm based on nonlinear least squares estimation makes this integration possible. In a randomized simulation study, we compare the proposed method against a standard INS-GPS error-state Extended Kalman Filter (EKF) for robot pose estimation, Sola (2017), and demonstrate that our proposed landmark-aided solution achieves the same positioning accuracy while drastically improving the 3D orientation estimate of the robot. Due to the modular design, this landmark-aided functionality can be retro-fitted to any existing robot pose filtering algorithm in the form of an additional measurement update step.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB09.4", "code": "WeB09.4", "title": "Joint Battery State of Charge and Parameter Estimation Using Gaussian Integral Based Kalman Filtering (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB09", "sessionTitle": "JO-JSC: Filtering and Smoothing", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Lone, Jaffar Ali", "affiliation": "Indian Institute of Technology Patna" }, { "name": "Singh, ROHIT kumar", "affiliation": "Pandit Deendayal Energy University, Gandhinagar" }, { "name": "Bhaumik, Shovan", "affiliation": "Please Select" } ], "keywords": [ "Filtering and smoothing", "Estimation and filtering", "Time/parameter varying system identification" ], "abstract": "Accurate estimation of battery state of charge (SOC) and model parameters is essential for ensuring the safety, reliability, and efficiency of modern battery management systems. Conventional filtering methods such as the extended Kalman filter rely on local approximations that can degrade in accuracy when nonlinearities are significant. This paper proposes a Gaussian integral–based filtering framework for joint SOC and parameter estimation of lithium-ion batteries modeled with an equivalent circuit representation. The key advantage lies in exploiting the polynomial structure of the SOC–OCV (open-circuit voltage) relation, which enables exact evaluation of Gaussian integrals for mean and covariance propagation. The model is validated against experimental data, and performance is assessed under an urban dynamometer driving schedule. The obtained results demonstrate that the proposed filter achieves more accurate SOC estimation and parameter tracking than the other conventional Kalman filter variants, confirming its effectiveness as a practical solution for real-time battery management under dynamic operating conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB09.5", "code": "WeB09.5", "title": "Lagrangian Grid-Based Estimation of Nonlinear Systems with Invertible Dynamics (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB09", "sessionTitle": "JO-JSC: Filtering and Smoothing", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Dunik, Jindrich", "affiliation": "University of West Bohemia" }, { "name": "Matousek, Jakub", "affiliation": "University of West Bohemia" }, { "name": "Brandner, Marek", "affiliation": "University of West Bohemia" }, { "name": "Krejčí, Jan", "affiliation": "University of West Bohemia" }, { "name": "Choe, Yeongkwon", "affiliation": "Kangwon National University" } ], "keywords": [ "Filtering and smoothing", "Estimation and filtering", "Kalman filtering" ], "abstract": "This paper deals with the state estimation of non-linear and non-Gaussian systems with an emphasis on the numerical solution to the Bayesian recursive relations. In particular, this paper builds upon the Lagrangian grid-based filter (GbF) recently-developed for linear systems and extends it for systems with nonlinear dynamics that are invertible. The proposed nonlinear Lagrangian GbF reduces the computational complexity of the standard GbFs from quadratic to log-linear, while preserving all the strengths of the original GbF such as robustness, accuracy, and deterministic behaviour. The proposed filter is compared with the particle filter in several numerical studies using the publicly available MATLAB® implementation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB09.6", "code": "WeB09.6", "title": "Joint State and Parameter Estimation in Quantum Systems Using Cubature Kalman Filtering (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB09", "sessionTitle": "JO-JSC: Filtering and Smoothing", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Taslima, Eram", "affiliation": "Indian Institute of Technology (BHU)" }, { "name": "Kamal, Shyam", "affiliation": "Indian Institute of Technology (BHU), Varanasi" }, { "name": "Saket, R.K.", "affiliation": "Indian Institute of Technology (Banaras Hindu University) Varanasi (Uttar Pradesh)" } ], "keywords": [ "Filtering and smoothing", "Nonlinear system identification", "Time/parameter varying system identification" ], "abstract": "This paper addresses the challenge of state estimation for two-level quantum systems governed by stochastic master equations, particularly when key Hamiltonian parameters are unknown. The critical parameters such as the qubit resonance frequency and the decay rate play a crucial role in determining system dynamics, hence their accurate estimation is essential for reliable state reconstruction. A robust framework based on the cubature Kalman filter (CKF) is developed that effectively handles both correlated and decorrelated noise processes inherent to quantum homodyne measurement. The proposed approach effectively mitigates performance degradation caused by parametric uncertainty, providing enhanced adaptability and robustness. Numerical simulations on a qubit in a cavity show that the CKF-based method achieves better estimation accuracy and faster convergence compared to the extended Kalman filter", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB10.1", "code": "WeB10.1", "title": "Bluetooth Phased-Array Aided Inertial Navigation Using Factor Graphs: Experimental Verification", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB10", "sessionTitle": "Kalman Filtering I", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Sørensen, Glen Hjelmerud Mørkbak", "affiliation": "Norwegian University of Science and Technology (NTNU)" }, { "name": "Bryne, Torleiv H.", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Gryte, Kristoffer", "affiliation": "NTNU" }, { "name": "Johansen, Tor Arne", "affiliation": "Norwegian University of Science and Technology" } ], "keywords": [ "Estimation and filtering", "Filtering and smoothing", "Kalman filtering" ], "abstract": "Phased-array Bluetooth systems have emerged as a low-cost alternative for performing aided inertial navigation in GNSS-denied use cases such as warehouse logistics, drone landings, and autonomous docking. Basing a navigation system off of commercial-off-the-shelf components may reduce the barrier of entry for phased-array radio navigation systems, albeit at the cost of significantly noisier measurements and relatively short feasible range. In this paper, we compare robust estimation strategies for a factor graph optimisation-based estimator using experimental data collected from multirotor drone flight. We evaluate performance in loss-of-GNSS scenarios when aided by Bluetooth angular measurements, as well as range or barometric pressure.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB10.2", "code": "WeB10.2", "title": "Bayesian and Factor Graph Approaches in Terrain-Aided Navigation: Consistency Assessment", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB10", "sessionTitle": "Kalman Filtering I", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Dohnal, Ondrej", "affiliation": "University of West Bohemia in Pilsen" }, { "name": "Siriste, Marek", "affiliation": "University of West Bohemia in Pilsen" }, { "name": "Matousek, Jakub", "affiliation": "University of West Bohemia" }, { "name": "Straka, Ondrej", "affiliation": "University of West Bohemia" }, { "name": "Dunik, Jindrich", "affiliation": "University of West Bohemia" } ], "keywords": [ "Estimation and filtering", "Filtering and smoothing", "Kalman filtering" ], "abstract": "This paper deals with the terrain-aided navigation (TAN), which is appealing option for positioning in GNSS-denied or cluttered environments. TAN correlates readings of on-board sensors with pre-recorded terrain map, typically using Bayesian algorithms for state estimation (BSE) of nonlinear stochastic dynamic models, such as particle or point-mass filters. Consequently, this navigation system provides robust, but computationally intensive solution. In this paper, we design the TAN using the recent computationally efficient factor graph optimisation (FGO) with the emphasis on the models with non-differentiable functions typically used in the area. We provide description of the FGO- and BSE-based TAN systems in a unified framework and their thorough performance evaluation. The performance is assessed using the publicly available MATLAB implementation with the stress not only on accuracy assessment but mainly on consistency of the navigation solution in different scenarios.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB10.3", "code": "WeB10.3", "title": "A Quantum Algorithm for the Diffusion Step of Grid-Based Filter", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB10", "sessionTitle": "Kalman Filtering I", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Choe, Yeongkwon", "affiliation": "Kangwon National University" }, { "name": "Park, Chan Gook", "affiliation": "Seoul National Univ" }, { "name": "Dunik, Jindrich", "affiliation": "University of West Bohemia" }, { "name": "Krejčí, Jan", "affiliation": "University of West Bohemia" }, { "name": "Matousek, Jakub", "affiliation": "University of West Bohemia" }, { "name": "Brandner, Marek", "affiliation": "University of West Bohemia" } ], "keywords": [ "Estimation and filtering", "Filtering and smoothing", "Kalman filtering" ], "abstract": "We propose a simple quantum algorithm for implementing the diffusion step of grid-based Bayesian filters. The method encodes the advected state density and the process noise density into quantum registers and realizes diffusion using a quantum Fourier transform-based adder. This avoids the explicit convolution required in classical implementations and the repeated coin-flip operations used in quantum random walk approaches. Numerical simulations using a gate-based quantum computing simulator confirm that the proposed approach reproduces the desired probability densities while requiring significantly fewer quantum gates and much shallower circuit depth.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB10.4", "code": "WeB10.4", "title": "Physics-Based Prognostics for PEM Fuel Cells: EKF-Driven RUL Prediction Using a Load-Dependent Degradation Model", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB10", "sessionTitle": "Kalman Filtering I", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Houjayrie, Mouhamad", "affiliation": "Grenoble Alpes University, CNRS, Grenoble INP, GIPSA-Lab" }, { "name": "Cadet, Catherine", "affiliation": "GIPSA-Lab, Automatic Department" }, { "name": "Berenguer, Christophe", "affiliation": "Univ. Grenoble Alpes, CNRS, Grenoble INP" } ], "keywords": [ "Estimation and filtering", "Kalman filtering" ], "abstract": "This paper proposes a physics-based Prognostics and Health Management framework for proton-exchange-membrane (PEM) fuel cells and its use for remaining useful life (RUL) prediction. A reduced electrochemically active surface area (ECSA) and membrane-ageing model (degradation layer) is embedded into a state-space degradation–performance model whose dynamics depend on operating conditions and load. This model is identified online with an augmented-state extended Kalman filter (EKF) from stack-voltage measurements and measured operating conditions, within a reduced-state formulation focused on the dominant slow degradation quantities. Synthetic and IEEE PHM 2014 ageing data show accurate long-horizon Health Index (HI) and RUL forecasts with quantified uncertainty, yielding decision-oriented PHM quantities suitable for health-aware management and maintenance planning.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB10.5", "code": "WeB10.5", "title": "Rao-Blackwellized Particle Filter for Agent’s Intention Inference", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB10", "sessionTitle": "Kalman Filtering I", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Wang, Yixuan", "affiliation": "University of Florida" }, { "name": "Guralnik, Dan", "affiliation": "OHIO University" }, { "name": "Dixon, Warren E.", "affiliation": "Univ of Florida" } ], "keywords": [ "Estimation and filtering", "Kalman filtering" ], "abstract": "Inferring the eventual goal of a mobile agent from noisy observations of its trajectory is a fundamental estimation problem. We initiate the study of such intent inference using a variant of a Rao–Blackwellized Particle Filter (RBPF), subject to the assumption that the agent’s intent manifests through closed-loop behavior with a state-of-the-art provable practical stability property. Leveraging the assumed closed-form agent dynamics, the RBPF analytically marginalizes the linear-Gaussian substructure and updates particle weights only, improving sample efficiency over a standard particle filter. Two difference estimators are introduced: a Gaussian mixture model using the RBPF weights and a reduced version confining the mixture to the effective sample. We quantify how well the adversary can recover the agent’s intent using information-theoretic leakage metrics and provide computable lower bounds on the Kullback–Leibler (KL) divergence between the true intent distribution and RBPF estimates via Gaussian-mixture KL bounds. We also provide a bound on the difference in performance between the two estimators, highlighting the fact that the reduced estimator performs almost as well as the complete one. Experiments illustrate fast and accurate intent recovery for compliant agents, motivating future work on designing intent-obfuscating controllers.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB10.6", "code": "WeB10.6", "title": "The Hitchhiker's Guide to Particle Flow-Based Filters: A Survey", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB10", "sessionTitle": "Kalman Filtering I", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Kim, Sukkeun", "affiliation": "Karlsruhe Institute of Technology (KIT)" }, { "name": "Hanebeck, Uwe", "affiliation": "Karlsruhe Institute of Technology (KIT)" } ], "keywords": [ "Estimation and filtering", "Kalman filtering", "Filtering and smoothing" ], "abstract": "Particle flow-based filters migrate samples toward the posterior, instead of using importance weighting, to mitigate the particle degeneracy issue in particle filters. Although a large number of related studies exist, the absence of a survey makes it difficult to follow the current developments. This study aims to survey particle flow-based filters and provide a guide with numerical examples for those who are new to these filters. We survey three main branches of studies that employ the idea of particle flows in the measurement update step. The main algorithms from the three branches are compared with extensive analysis using numerical examples, highlighting their relative performance characteristics and practical considerations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB13.1", "code": "WeB13.1", "title": "Anderson Acceleration for Linearly Converging SQP-Type Methods", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB13", "sessionTitle": "Numerical Methods for Optimal Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Frey, Jonathan", "affiliation": "University of Freiburg" }, { "name": "Kiessling, David", "affiliation": "KU Leuven" }, { "name": "Baumgärtner, Katrin", "affiliation": "University of Freiburg" }, { "name": "Diehl, Moritz", "affiliation": "University of Freiburg" } ], "keywords": [ "Numerical methods for optimal control", "Real-time optimal control", "Model predictive control" ], "abstract": "Although Anderson acceleration (AA) is known to speed up fixed-point iterations, it is rarely applied in constrained optimization. We show that the local convergence behavior of a general family of (inexact) SQP-type methods can benefit from AA and introduce a simple heuristic to alleviate slower convergence farther from the solution. The method is implemented in the software framework acados. Numerical examples from optimal control illustrate consistent improvements in convergence.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB13.2", "code": "WeB13.2", "title": "A Condensed and Efficient Interior Point Solver for Reduced-Order Model Predictive Control", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB13", "sessionTitle": "Numerical Methods for Optimal Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Schurig, Roland", "affiliation": "TU Darmstadt" }, { "name": "Lenz, Eric", "affiliation": "Technische Universität Darmstadt" }, { "name": "Findeisen, Rolf", "affiliation": "TU Darmstadt" } ], "keywords": [ "Model predictive control", "Numerical methods for optimal control", "Real-time optimal control" ], "abstract": "To enable real-time optimal control of constrained systems, reduced-order model predictive control reduces the problem size by approximating the control sequence over the prediction horizon in a low-dimensional subspace while preserving stability and recursive feasibility. This paper introduces a primal-dual interior point solver specifically designed for reduced-order model predictive control in a condensed formulation. We leverage QR factorisation to eliminate equality constraints in a numerically robust manner, particularly beneficial for unstable systems, and incorporate efficient online updates to the factorisation. The solver employs Mehrotra's predictor-corrector algorithm to handle the resulting quadratic program, exploiting the problem's low dimensionality and multi-stage structure. Simulation results demonstrate fast performance with favourable comparisons to existing solvers, while keeping the stability properties of the original model predictive control formulation. Our C implementation is available online.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB13.3", "code": "WeB13.3", "title": "Parallel KKT Solver in PIQP for Multistage Optimization", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB13", "sessionTitle": "Numerical Methods for Optimal Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Song, Fenglong", "affiliation": "EPFL" }, { "name": "Schwan, Roland", "affiliation": "EPFL" }, { "name": "Chen, Yuwen", "affiliation": "Oxford" }, { "name": "Jones, Colin, N", "affiliation": "EPFL" } ], "keywords": [ "Numerical methods for optimal control, Real-time optimal control, Model predictive control" ], "abstract": "This paper presents an efficient parallel Cholesky factorization and triangular solve algorithm for the Karush–Kuhn–Tucker (KKT) systems arising in multistage optimization problems, with a focus on optimal control problems. The proposed approach directly parallelizes solving the KKT systems with block-tridiagonal–arrow KKT matrices on the linear algebra level arising in interior-point methods. The algorithm is implemented as a new backend of the PIQP solver and released as open source. Numerical experiments demonstrate substantial performance gains compared to other state-of-the-art solvers.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB13.4", "code": "WeB13.4", "title": "Time-Certified and Efficient NMPC Via Koopman Operator", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB13", "sessionTitle": "Numerical Methods for Optimal Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Wu, Liang", "affiliation": "Johns Hopkins University" }, { "name": "Che, Yunhong", "affiliation": "MIT" }, { "name": "Yang, Bo", "affiliation": "Tsinghua University" }, { "name": "Lin, Kangyu", "affiliation": "Kyoto University" }, { "name": "Drgona, Jan", "affiliation": "Pacific Northwest National Laboratory" } ], "keywords": [ "Model predictive control", "Numerical methods for optimal control", "Optimal control of PDE systems" ], "abstract": "Certifying and accelerating execution times of nonlinear model predictive control (NMPC) implementations are two core requirements. Execution-time certificate guarantees that the NMPC controller returns a solution before the next sampling time, and achieving faster worst-case and average execution times further enables its use in a wider set of applications. However, NMPC produces a nonlinear program (NLP) for which it is challenging to derive its execution time certificates. Our previous works, citep{wu2025direct,wu2025time} provide data-independent execution time certificates (certified number of iterations) for box-constrained quadratic programs (BoxQP). To apply the time-certified BoxQP algorithm citep{wu2025time} for state-input constrained NMPC, this paper i) learns a linear model via Koopman operator; ii) proposes a dynamic-relaxation construction approach yields a structured BoxQP rather than a general QP; iii) exploits the structure of BoxQP, where the dimension of the linear system solved in each iteration is reduced from 5N(n_u+n_x) to Nn_u (where n_u, n_x, N denote the number of inputs, states, and length of prediction horizon), yielding substantial speedups (when n_x gg n_u, as in PDE control).", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB13.5", "code": "WeB13.5", "title": "Harnessing Batched GPU Kernels for Solutions of Block Tridiagonal Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB13", "sessionTitle": "Numerical Methods for Optimal Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Jin, David", "affiliation": "Massachusetts Institute of Technology" }, { "name": "Montoison, Alexis", "affiliation": "Argonne National Laboratory" }, { "name": "Shin, Sungho", "affiliation": "Argonne National Laboratory" } ], "keywords": [ "Numerical methods for optimal control", "Structured linear systems", "Linear systems" ], "abstract": "Block-tridiagonal systems are prevalent in state estimation and optimal control and often dominate the computational cost. Improving the solvers directly impacts real-time performance. We present BlockDSS, a cross-platform (NVIDIA/AMD) GPU solver for block-tridiagonal symmetric positive semidefinite systems. Our method employs recursive Schur-complement reduction, transforming the original system into a hierarchy of subsystems solved in parallel using batched BLAS/LAPACK routines. BlockDSS demonstrates substantial speedups over state-of-the-art CPU solvers (e.g., CHOLMOD, HSL MA57) and remains competitive with NVIDIA cuDSS. For realistic problem sizes, performance is still limited by kernel-launch overhead.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB13.6", "code": "WeB13.6", "title": "Robust Online Constraint Removal for Linear MPC", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB13", "sessionTitle": "Numerical Methods for Optimal Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Lindner, Nora", "affiliation": "Ruhr University Bochum" }, { "name": "Monnigmann, Martin", "affiliation": "Ruhr-Universität Bochum" } ], "keywords": [ "Optimal control theory", "Numerical methods for optimal control", "Linear systems" ], "abstract": "We extend constraint removal to the case of model predictive control (MPC) for linear systems that are subject to a bounded additive disturbance. The variant of constraint removal treated here is based on the decrease of the optimal value function and the observation that every constraint can only become active for optimal cost function values larger than a constraint-specific bound. By exploiting a Lipschitz constant of the optimal value function, the worst-case effect of the disturbance on the optimal value function can be bounded, which allows the method to certify that removed constraints remain inactive for a guaranteed number of future time steps despite disturbances. The resulting disturbance-aware constraint removal method reduces the online computational burden of MPC without altering the optimal solution, and is illustrated with simulation examples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB14.1", "code": "WeB14.1", "title": "Distributed Optimization for Nonlinear Stochastic Multi-Agent Systems under Aperiodically Intermittent Communications (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB14", "sessionTitle": "Recent Advances in Nonlinear and Learning-Aided Control under Limited Information I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Jia, Wenwen", "affiliation": "Hohai University" }, { "name": "Wu, Jinhui", "affiliation": "Nanyang Technological University" }, { "name": "Wu, Yongbao", "affiliation": "Southeast University" }, { "name": "Yang, Fuwen", "affiliation": "Griffith University" } ], "keywords": [ "Cooperative nonlinear control" ], "abstract": "Recent advances in nonlinear and learning-aided control under limited information show that reliable coordination is achievable even with aperiodic, and noisy communications by combining with model-based feedback. Motivated by this perspective, this paper addresses the distributed optimization problem with an aperiodically intermittent communication network, where each agent is only able to exchange information with its neighbors in some disjoint time intervals. In order to effectively model the uncertainty and communication noise, a distributed optimization algorithm in the framework of stochastic differential dynamics is developed. Under some mild assumptions, the state solutions of the established optimization algorithm converge to an optimal solution of distributed optimization problem in the mean square sense. Finally, a simulation is characterized to demonstrate the effectiveness of the proposed distributed stochastic optimization algorithm.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB14.2", "code": "WeB14.2", "title": "A Two-Layer Self-Regulating Vehicle Mass and Road Grade Estimation Framework for Intelligent Vehicles (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB14", "sessionTitle": "Recent Advances in Nonlinear and Learning-Aided Control under Limited Information I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Chor, Wai Tong", "affiliation": "Monash University" }, { "name": "Tan, Chee Pin", "affiliation": "Monash University" }, { "name": "Bakibillah, A S M", "affiliation": "Tokyo Institute of Technology" }, { "name": "Pu, Ziyuan", "affiliation": "Southeast University" } ], "keywords": [ "Robust estimation", "System identification and adaptive control of distributed parameter systems", "Nonlinear observers and filters" ], "abstract": "Accurate estimation of vehicle mass and road grade is essential for reliable control, energy management, and state observation in intelligent vehicles. This paper presents a two-layer estimation framework that employs a Recursive Least M-Squares with Multiple Forgetting Factors (RLM-SMFF) algorithm with a Self-Regulating Adaptive Extended Kalman Filter (SRAEKF) to achieve simultaneous and robust estimation of mass-grade. The RLM-SMFF estimates the vehicle mass and a lumped disturbance term from standard onboard measurements, incorporating an M-estimator cost function to suppress impulsive outliers to enhance robustness. With these estimates, the SRAEKF infers the road grade using an innovation-based covariance adaptation that autonomously regulates the Kalman gain without manual tuning of the process and measurement noise covariances. The proposed architecture effectively eliminates the reliance on subjective parameter tuning and maintains stability under varying road slopes with the presence of measurement uncertainties. Simulation results demonstrate that the two-layer estimator outperforms benchmarks in the convergence speed, disturbance rejection, and estimation accuracy, validating its suitability for real-time vehicle implementation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB14.3", "code": "WeB14.3", "title": "Behavior-Informed Temporal Difference Model Predictive Control (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB14", "sessionTitle": "Recent Advances in Nonlinear and Learning-Aided Control under Limited Information I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Cho, Hyunseo", "affiliation": "Jeonbuk National University" }, { "name": "Lee, Kyeongdon", "affiliation": "KAIST" }, { "name": "Jin, Yongsik", "affiliation": "Daegu Gyeongbuk Institute of Science and Technology (DGIST)" }, { "name": "Han, Seungyong", "affiliation": "Jeonbuk National University" } ], "keywords": [ "Model predictive control", "Optimization-based estimation and control", "Robust learning systems" ], "abstract": "This paper proposes a Behavior-informed Temporal Difference Model Predictive Control (Bi-TDMPC) method based on Gated Recurrent Units (GRUs). In conventional TD learning for MPC, an optimized action policy is derived by predicting future latent states without directly utilizing physical state dynamics. Consequently, this latent-based approach facilitates sample-efficient learning by guiding the policy to maximize expected rewards. Extending the conventional method, the proposed Bi-TDMPC embeds GRU-based predictive references into the reward function to guide the policy toward the desired behavior. The GRU outputs are utilized as predictive references to formulate the loss function for training the reward network. The proposed method is validated on a two Degree-of-Freedom (DOF) robot manipulator. Simulation results demonstrate that Bi-TDMPC generates optimal actions exhibiting behaviors similar to the reference.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB14.4", "code": "WeB14.4", "title": "Co-Design of Event-Based Strategy and Controller for Unknown 2-D Roesser Systems with Noisy Data (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB14", "sessionTitle": "Recent Advances in Nonlinear and Learning-Aided Control under Limited Information I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Yang, Runmin", "affiliation": "Shandong University" }, { "name": "Yang, Rongni", "affiliation": "Shandong University" }, { "name": "Paszke, Wojciech", "affiliation": "University of Zielona Gora" }, { "name": "Zhang, Huiyan", "affiliation": "Chongqing Technology and Business University" } ], "keywords": [ "Linear systems", "Design methods for data-based control", "Data-driven robust control" ], "abstract": "This study addresses the data-driven stabilization problem of unknown two-dimensional (2-D) Roesser systems within an event-triggered framework. To cope with the absence of explicit model information, a novel analysis methodology is proposed that integrates the data-compatible set with performance conditions, enabling the synthesis of a state-feedback controller based solely on noisy input-state measurements. A relative-threshold event-triggering scheme (ETS) is first introduced, tailored to the bidirectional structure of the Roesser model, in order to improve the utilization efficiency of network resources. Subsequently, by employing the S-procedure in conjunction with slack variable techniques, tractable conditions are established to guarantee asymptotic stability with respect to all systems consistent with the acquired data. Furthermore, the controller gain and the event-triggered condition are jointly computed through the solution of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed data-driven design is validated through a simulation example.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB14.5", "code": "WeB14.5", "title": "A Novel Sampling-Based Dynamic Event-Triggered Control for Linear Interconnected Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB14", "sessionTitle": "Recent Advances in Nonlinear and Learning-Aided Control under Limited Information I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Sundararajan, Karpagavalli", "affiliation": "Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai 600 127" }, { "name": "Lee, Tae H.", "affiliation": "Jeonbuk National University" }, { "name": "Shanmugam, Lakshmanan", "affiliation": "School of Advanced Sciences, Vellore Institute of Technology, Chennai" } ], "keywords": [ "Adaptive control design", "Linear systems", "Lyapunov methods" ], "abstract": "This work designs a novel sampling-based dynamic event-triggering (SB-DET) control scheme for investigating the stability criteria of linear interconnected systems (LISs). To achieve this, a new adaptive law is constructed to flexibly adjust the threshold parameter and minimize unnecessary transmissions. The sufficient conditions for the stability criteria of LISs are derived in terms of linear matrix inequalities under both traditional sampling-based static event-triggering and proposed SB-DET control schemes by constructing an appropriate Lyapunov functional. Finally, the effectiveness of the proposed control and advantages of sampling-based triggering approaches are numerically validated through comparative studies on a network of eight inverted pendulums.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB15.1", "code": "WeB15.1", "title": "Design of Adaptive Observers for Frequency Synchronization of Oscillators", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB15", "sessionTitle": "Observer Design", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Emerton, Jesse", "affiliation": "University of Newcastle" }, { "name": "Chen, Zhiyong", "affiliation": "The University of Newcastle" }, { "name": "Donaire, Alejandro", "affiliation": "The University of Newcastle" } ], "keywords": [ "Adaptive control design", "Observer design", "Lyapunov methods" ], "abstract": "This paper investigates frequency synchronization in heterogeneous multi-agent systems where the internal frequencies are not communicated. Each agent is modeled as a linear oscillator whose system matrix encodes its frequency, making the problem one of synchronizing system dynamics rather than states. We consider a two-agent setting in which each agent measures only the other’s state and possesses a distinct, time-varying internal frequency. An adaptive observer is proposed to reconstruct the neighbour’s frequency and adjust the agent’s own dynamics, enabling convergence to a common nonzero oscillation frequency without collapsing the oscillations. We analyze the closed-loop error dynamics, establish frequency agreement, and demonstrate the approach and robustness through simulation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB15.2", "code": "WeB15.2", "title": "Filtering Homogeneous Observers with a Single First-Order Injection Filter", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB15", "sessionTitle": "Observer Design", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Patelski, Radosław", "affiliation": "Inria" }, { "name": "Ushirobira, Rosane", "affiliation": "Inria" }, { "name": "Efimov, Denis", "affiliation": "Inria" } ], "keywords": [ "Observer design", "Nonlinear observers and filters", "Lyapunov methods" ], "abstract": "In this paper, a new design of a filtering homogeneous observer for multi-input multi-output (MIMO) systems is presented. The result employs a single first-order homogeneous filter to simplify the design and analysis of the observer compared to recent solutions proposed in the literature, and enables the selection of a tuning parameter through the solution of linear matrix inequalities (LMIs). In the sequel, an extension of the proposed scheme with additional filtering properties and its application to linear time-varying (LTV) systems are discussed. The theoretical analysis is followed by a motivating application example, and the feasibility of the proposed approach is validated through a numerical simulation of an inverted pendulum and an outbreak spread model.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB15.3", "code": "WeB15.3", "title": "Extending KKL Observer Design to Systems with Non-Unique Backward Solutions", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB15", "sessionTitle": "Observer Design", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Alleaume, Valentin", "affiliation": "Mines Paris, Université PSL" }, { "name": "Bernard, Pauline", "affiliation": "Mines Paris, Université PSL" }, { "name": "Tanwani, Aneel", "affiliation": "LAAS -- CNRS, Université De Toulouse" }, { "name": "Di Meglio, Florent", "affiliation": "Mines Paris PSL" } ], "keywords": [ "Observer design", "Nonlinear observers and filters", "Nonlinear control of switched & hybrid systems" ], "abstract": "Kazantzis-Kravaris-Luenberger (KKL) observers consist in finding a smooth mapping T that transforms the system dynamics into a linear filter of the output in a space of larger dimension. Indeed, an observer is then obtained by running the filter and left-inverting the transformation to recover an estimate of the state, if the mapping is injective. In this paper, we are interested in adapting this framework to systems with non-unique backward solutions, a situation which can typically occur in nonsmooth systems. In this setting, the mapping T naturally becomes set-valued which is out of the scope of the current theory and calls for more general concepts of injectivity and regularity. We prove that upper semi continuity, local boundedness and set-valued injectivity of this map are sufficient conditions for designing a converging KKL observer. We show that the former two are satisfied for Carath´eodory ODE’s and Filippov differential inclusions. We also provide examples for which set-valued injectivity is satisfied and discuss its link with distinguishability. Finally, we illustrate the numerical implementation of this methodology on an harmonic oscillator subject to friction.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB15.4", "code": "WeB15.4", "title": "Observer Design for the Joint Estimation of State of Charge and Capacity of Lithium-Ion Batteries with Guaranteed Global Convergence", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB15", "sessionTitle": "Observer Design", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Hernandez, Hernando", "affiliation": "Université De Lorraine, CNRS, Alstom" }, { "name": "Postoyan, Romain", "affiliation": "CRAN, CNRS, Université De Lorraine" }, { "name": "Raël, Stéphane", "affiliation": "GREEN, Universite De Lorraine" }, { "name": "Blondel, Pierre", "affiliation": "Alstom" } ], "keywords": [ "Observer design", "Nonlinear observers and filters", "Stability of nonlinear systems" ], "abstract": "This paper addresses the joint estimation of the state of charge and the capacity of lithium-ion batteries. To this end, we present a nonlinear observer based on a dual-polarization equivalent circuit model that explicitly captures the distinct dynamics of the positive and negative electrodes. We provide design conditions that guarantee the uniform global exponential stability of the estimation error for currents that keep the same sign within a given range. The stability conditions are derived using Lyapunov theory and formulated as matrix inequalities that account for the nonlinearities of the open-circuit voltage and polarization functions. These matrix inequalities can then be used to construct the observer gains. The approach is illustrated through numerical simulations using experimental data from a lithium titanate oxide cell.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB15.5", "code": "WeB15.5", "title": "A past Measurement-Based Interval Observer for Linear Discrete-Time Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB15", "sessionTitle": "Observer Design", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Dinesh, Ajul", "affiliation": "Aalborg University" }, { "name": "Efimov, Denis", "affiliation": "Inria" } ], "keywords": [ "Observer design", "Positive linear systems", "Robust estimation" ], "abstract": "This paper presents the design of interval observers for linear discrete-time systems subject to unknown but bounded process disturbances and measurement noise. By exploiting sequences of current and past outputs and inputs, we develop past measurement-based interval observers (PMBIOs) that provide improved interval estimation accuracy. Compared to existing approaches, the proposed observer structure guarantees the existence of observer gains that provide stability and nonnegativity of the estimation error dynamics. The gain design conditions are formulated as linear matrix inequalities (LMIs), offering both optimality and computational tractability. The stability and robustness properties of the proposed PMBIOs are analyzed through input-to-state stability (ISS) conditions. Numerical simulation examples demonstrate the applicability and performance improvement of the proposed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB15.6", "code": "WeB15.6", "title": "Asynchronous Nonlinear Observer Design for Lipschitz Switched Systems Via LMI-Based Average Dwell-Time Condition", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB15", "sessionTitle": "Observer Design", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Hatem, Yacine", "affiliation": "Aix Marseille Université" }, { "name": "Zerrougui, Mohamed", "affiliation": "Aix Marseille University" }, { "name": "Ammour, Rabah", "affiliation": "Aix-Marseille University" } ], "keywords": [ "Observer design", "Switching stability and control" ], "abstract": "This paper investigates the observer design problem for a class of nonlinear Lipschitz switchedsystems in the presence of delayed observer switching. Owing to the switching delay, a temporary mismatch may occur between the active mode of the plant and that of the observer, which complicates the convergence analysis of the estimation error. To address this issue, a switched observer design is proposed under an admissible edge-dependent average dwell-time framework. The stability analysis is carried out by means of a multiple Lyapunov function approach, leading to sufficient conditions expressed in terms of linear matrix inequalities for both matched and mismatched switching phases. These conditions ensure the global uniform exponential stability of the estimation error dynamics while reducing the conservatism of the admissible edge-dependent dwell-time bound. A numerical example is provided to demonstrate the effectiveness of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB16.1", "code": "WeB16.1", "title": "Lyapunov Redesign for Perturbed Periodic LTV Systems with Experimental Validation (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB16", "sessionTitle": "Periodic Systems and Discrete Sliding Modes", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Sumenkov, Oleg", "affiliation": "Sirius University of Science and Technology" }, { "name": "Surov, Maksim", "affiliation": "Sirius University of Science and Technology" }, { "name": "Fridman, Leonid", "affiliation": "National Autonomous University of Mexico" }, { "name": "Gusev, Sergei V.", "affiliation": "St. Petersburg State Univ" }, { "name": "Tarabukin, Ivan", "affiliation": "Sirius University of Science and Technology" } ], "keywords": [ "Stability and stabilization of hybrid systems" ], "abstract": "This paper presents a Lyapunov redesign approach for stabilizing periodic linear time-varying (PLTV) systems subject to uncertainties in the state and control matrices, as well as bounded matched disturbances. The existence of a quadratic Lyapunov function for the nominal system with uncertainty in the state matrix is first established using a differential Riccati equation with periodic coefficients. It is then shown that any Lyapunov function constructed in this manner defines a suitable periodic LTV sliding manifold ensuring exponential stability of the system trajectories. A discontinuous control law is subsequently developed to guarantee finite-time convergence to this manifold, thereby preserving the Lyapunov function of the nominal system for the perturbed PLTV case. The proposed method is experimentally validated on the Butterfly robot benchmark, showing improved performance over the periodic LQR baseline in non-prehensile motion control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB16.2", "code": "WeB16.2", "title": "Sliding Mode Control and Subspace Stabilization Methodology for the Orbital Stabilization of Periodic Trajectories (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB16", "sessionTitle": "Periodic Systems and Discrete Sliding Modes", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Surov, Maksim", "affiliation": "Sirius University of Science and Technology" }, { "name": "Freidovich, Leonid", "affiliation": "Umeå Universitet" } ], "keywords": [ "Sliding mode control", "Stability of nonlinear systems", "Lagrangian and Hamiltonian systems" ], "abstract": "This paper presents a combined sliding-mode control and subspace stabilization methodology for orbital stabilization of periodic trajectories in underactuated mechanical systems with one degree of underactuation. The approach starts with partial feedback linearization and stabilization. Then, transverse linearization along the reference orbit is computed, resulting in a periodic linear time-varying system with a stable subspace. Sliding-mode control drives trajectories toward this subspace. The proposed design avoids solving computationally intensive periodic LQR problems and improves robustness to matched disturbances. The methodology is validated through experiments on the Butterfly robot.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB16.3", "code": "WeB16.3", "title": "An LMI Based Method of Sliding Variable Construction for LTV Systems with Periodic Coefficients (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB16", "sessionTitle": "Periodic Systems and Discrete Sliding Modes", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Tarabukin, Ivan", "affiliation": "Sirius University of Science and Technology" }, { "name": "Gusev, Sergei V.", "affiliation": "St. Petersburg State Univ" } ], "keywords": [ "Linear parameter-varying systems", "Robust linear matrix inequalities", "Robust control applications" ], "abstract": "The problem of stabilizing a LTV system with periodic coefficients and structured uncertainty in the system matrix is considered. A new approach to constructing a sliding variable in a robust control problem is proposed. It is based on the application of the Lyapunov inequality and a parameter-dependent S-procedure to obtain an infinite system of LMIs defining the sliding variable. An algorithm for constructing a solution to the resulting infinite LMIs system is proposed. As an example, the problem of periodic oscillations stabilization in the underactuated system ball-and-ellipse is considered. Simulation results show that the constructed sliding mode controller exhibits greater robustness to changes in system parameters than a linear controller calculated using the LQR method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB16.4", "code": "WeB16.4", "title": "Discrete-Time Multivariable Twisting Control with Digital Chattering Suppression and Improved Precision (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB16", "sessionTitle": "Periodic Systems and Discrete Sliding Modes", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Miranda-Villatoro, Félix Alfredo", "affiliation": "INRIA" }, { "name": "Castaños, Fernando", "affiliation": "Cinvestav" }, { "name": "Brogliato, Bernard", "affiliation": "Centre De L'université Grenoble-Alpes" } ], "keywords": [ "Sliding mode control", "Digital implementation", "Convex optimization" ], "abstract": "This note deals with the design of discrete-time multivariable twisting-type controllers. Zero-order-hold (ZOH) discretization of the plant is considered, with a backward Euler discrete-time implementation of the input, and an unknown matched perturbation. It is shown that in addition to digital chattering alleviation at both input and output, precision and energy effort can be improved (with respect to direct emulation schemes) by exploiting the monotone structure of the components of the controller. Numerical simulations demonstrate the theoretical findings.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB16.5", "code": "WeB16.5", "title": "Robust Sampled-Data Sliding Mode Control (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB16", "sessionTitle": "Periodic Systems and Discrete Sliding Modes", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Behera, Abhisek K.", "affiliation": "Indian Institute of Technology Roorkee" } ], "keywords": [ "Sliding mode control", "Sampled-data/digital control" ], "abstract": "In this paper, we propose a design methodology for the sliding mode control under the sampled data feedback. Unlike the traditional approach, here a dynamic hold unit is used based on the nominal plant model that emulates the analog control signal, and its state is reset to the fresh measurement at every sampling instant. Our controller is designed using the state of the (dynamic) hold unit, which essentially replicates the plant behavior at least in the nominal scenario. Here, the switching gain is designed by utilizing the inter-sampling error bound for any bounded sampling sequence. The ultimate boundedness of the plant trajectory is established under the proposed sampled data sliding mode control. A numerical example is taken to illustrate the design methodology.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB16.6", "code": "WeB16.6", "title": "Improved Barrier Function Framework for First-Order Sliding Mode Control with Time-Varying Accuracy (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB16", "sessionTitle": "Periodic Systems and Discrete Sliding Modes", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Aslmostafa Jarchelou, Ehsan", "affiliation": "École Centrale De Nantes" }, { "name": "Hamida, Mohamed Assaad", "affiliation": "Cnrs Umr 6004 Cd0962ls2n" }, { "name": "Laghrouche, Salah", "affiliation": "UTBM" }, { "name": "Plestan, Franck", "affiliation": "CNRS UMR 6004 Ecole Centrale De Nantes-LS2N" } ], "keywords": [ "Sliding mode control", "Lyapunov methods", "Adaptive control design" ], "abstract": "This work proposes an enhanced barrier function (BF) framework for first-order sliding mode control (SMC), improving both robustness and precision in the presence of bounded perturbations while maintaining a continuous control law. In contrast to the conventional BF-SMC approach with a constant target accuracy, the proposed method introduces a time-varying target accuracy that adapts according to system conditions, mitigating gain overestimation and preventing gain divergence in sampled implementations. A continuous finite-time entry strategy is also developed to guarantee stability from arbitrary initial conditions, overcoming a key limitation of existing BF-based designs. Lyapunov-based analysis establishes practical finite-time stability, and numerical examples demonstrate the superior robustness and precision of the proposed approach compared to classical BF-based SMC.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB17.1", "code": "WeB17.1", "title": "Delay-Dependent Invariance of Polyhedral Sets for Linear Discrete-Time Systems with Actuator Saturation", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Advanced Methods for Control and Reconstruction in Time Delay Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Barbosa, Otavio", "affiliation": "UFRN" }, { "name": "Dorea, Carlos E. T.", "affiliation": "Universidade Federal Do Rio Grande Do Norte" } ], "keywords": [ "Linear time-delay systems", "Controller constraints and structure", "Optimization-based estimation and control" ], "abstract": "In this paper we propose an optimization technique for the design of state-feedback controllers for discrete-time linear systems subject to state constraints, delayed input, and saturating actuator. We use the set-invariance approach to tackle the satisfaction of linear state constraints through the computation of polyhedral invariant sets. We borrow from the literature a polytopic model to represent saturation and a transformed model that enables a delay-dependent analysis of set invariance. We then derive conditions for positive invariance of a given polyhedron with respect to the transformed model and define conditions under which constraints enforcement can be achieved in the original model. An optimization approach is proposed to compute the controller gains and an associated invariant polyhedron. The proposal is illustrated through numerical experiments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB17.2", "code": "WeB17.2", "title": "Delay Margins for Second Order Multi-Agent Systems with Delayed Proportional-Derivative Consensus Protocols on a Class of Directed Multiplex Networks (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Advanced Methods for Control and Reconstruction in Time Delay Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Butcher, Eric", "affiliation": "University of Arizona" }, { "name": "Stilson, Neo", "affiliation": "University of Arizona" }, { "name": "Olson, Ethan Zachary", "affiliation": "University of Arizona" }, { "name": "Maadani, Mohammad", "affiliation": "University of Arizona" } ], "keywords": [ "Linear time-delay systems", "Infinite-dimensional multi-agent systems and networks", "Decentralized control" ], "abstract": "Delay margins are obtained for linear second order multi-agent systems with delayed proportional-derivative consensus protocols on a class of directed multiplex networks with layers corresponding to heterogeneous position and velocity coupling topologies which contain spanning trees and satisfy the L-assumption. In particular, three cases of homogeneous communication delay in the relative position and/or velocity feedback are considered. The delay margins are demonstrated with examples, while stability bounds for the cases of homogeneous coupling topologies, non-delayed consensus protocols, and for undirected graphs are obtained as special cases.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB17.3", "code": "WeB17.3", "title": "Adaptive Output-Based Predictive Control Integrated with a Parallel Feedforward Compensator for Input-Delay Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Advanced Methods for Control and Reconstruction in Time Delay Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Yamauchi, Ryosuke", "affiliation": "Kumamoto University" }, { "name": "Jinnouchi, Yoshitaka", "affiliation": "Kumamoto University" }, { "name": "Mizumoto, Ikuro", "affiliation": "Kumamoto Univ" } ], "keywords": [ "Linear time-delay systems", "Model predictive control", "Adaptive control design" ], "abstract": "This work presents an output predictive control (OPC) strategy for systems with input time delays, leveraging the almost strictly positive real (ASPR) property of the system. By incorporating a parallel feedforward compensator (PFC), the system acquires ASPR properties, enabling the design of a stable and straightforward predictive controller with enhanced performance. A detailed analysis of the stability of the obtained control system is conducted. Subsequently, we derive a guiding principle for setting controller parameters to design a stable control system using the proposed OPC system. The control performance of the proposed method, with parameters determined according to the guiding principle, will be validated through numerical simulations for an unstable and uncertain system with an input time delay.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB17.4", "code": "WeB17.4", "title": "Stable Predictor for Linear Systems with a Long Input Delay (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Advanced Methods for Control and Reconstruction in Time Delay Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Pyrkin, Anton", "affiliation": "ITMO University" } ], "keywords": [ "Linear time-delay systems", "Observer design", "Switching linear systems" ], "abstract": "The paper presents a new control algorithm for unstable linear systems with the long input delay. Unlike known analogues, the control law has been designed, which is the simplest to implement without requiring complex integration methods. At the same time, the problem of stabilization of a closed system is effectively solved, ensuring the boundedness of all state variables and the exponential stability of the equilibrium position.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB17.5", "code": "WeB17.5", "title": "State Reconstruction Via Output Delays for Linear Time-Invariant Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Advanced Methods for Control and Reconstruction in Time Delay Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Gomez, Marco Antonio", "affiliation": "Cinvestav" } ], "keywords": [ "Linear time-delay systems", "Observer design" ], "abstract": "In this note, we revisit the idea of reconstructing the state space from a finite number of output delayed samples in the context of linear time-invariant systems. We show that the number of delays required to recover the observable states matches the dimension of the observable subspace. We then discuss how these results motivate the construction of output–delay-based controllers.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB17.6", "code": "WeB17.6", "title": "Data-Driven Two-Sided Moment Matching for Linear Time-Delay Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Advanced Methods for Control and Reconstruction in Time Delay Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Chen, Yihan", "affiliation": "Imperial College London" }, { "name": "Zhang, Hanqing", "affiliation": "Imperial College London" }, { "name": "Scarciotti, Giordano", "affiliation": "Imperial College London" } ], "keywords": [ "Linear time-delay systems", "Control of complex systems", "Linear systems" ], "abstract": "This article proposes a two-sided moment matching framework for linear time-delay systems. We first introduce a so-called swapped moment matching approach based on the relation between moments and a dual Sylvester-like equation. This relation enables us to obtain a family of reduced-order models that has the same moments at user-defined interpolation points. Then, by combining the direct moment matching approach from the literature, and the swapped moment matching approach that we introduced, a two-sided moment matching framework is established and a family of reduced-order models that achieve moment matching at double the number of interpolation points, compared to the direct/swapped configurations, is constructed. Furthermore, a one-to-one relationship between swapped moments and the steady-state response of the swapped interconnection is investigated. Based on this, a data-driven approach to construct the reduced-order model without solving Sylvester-like equations is proposed. Finally, a numerical example on a vehicle platoon illustrates the proposed approaches.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB18.1", "code": "WeB18.1", "title": "An Evaluation Framework for Agentic AI in Manufacturing Standard Operating and Maintenance Procedures (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB18", "sessionTitle": "The Future of Operations in Industrial Plants through the Advances of Smart Manufacturing II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Koukas, Anastasios", "affiliation": "University of Groningen" }, { "name": "Raza, Shaina", "affiliation": "Vector Institute" }, { "name": "Maruster, Laura", "affiliation": "University of Groningen" }, { "name": "Emmanouilidis, Christos", "affiliation": "Univeristy of Groningen" } ], "keywords": [ "Industrial artificial intelligence", "Human-technology integration in manufacturing", "Intelligent manufacturing systems" ], "abstract": "Agentic AI systems are increasingly deployed in manufacturing and maintenance environments governed by Standard Operating and Maintenance Procedures (SOPs/SMPs). As these systems begin to support operational decision-making, evaluating them to ensure that they behave safely, transparently, and in alignment with procedural logic becomes essential. We propose an evaluation approach that aligns with the agentic AI workflow—encompassing a perception, planning, action, and reflection cycle—and maps each stage to operational and technical evaluation criteria relevant to industrial processes. The framework models realistic manufacturing workflows, where errors, uncertainty, and hierarchical decision structures are integral to everyday operations, and embeds evaluation directly within these settings. This work establishes the structure, logic, and metrics needed to systematically evaluate AI-driven agents in industrial contexts, providing a foundation for their reliable integration into manufacturing and maintenance systems and paving the way for future experimental validation and industry adoption.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB18.2", "code": "WeB18.2", "title": "Energy Digital Twin-Based Green Scheduling: Methodology and Practical Implementation (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB18", "sessionTitle": "The Future of Operations in Industrial Plants through the Advances of Smart Manufacturing II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Ragazzini, Lorenzo", "affiliation": "Politecnico Di Milano" }, { "name": "Negri, Elisa", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Cyber-physical production systems", "Manufacturing plant simulation, control and optimization", "Industrial artificial intelligence" ], "abstract": "Energy consumption optimization in manufacturing represents both an economic and environmental priority, yet existing Digital Twin applications lack structured methodologies that integrate energy modeling with production scheduling decisions. This paper proposes a four-phase methodology for developing Energy Digital Twins capable of improving production schedules through multi-objective optimization. The approach combines data-driven energy consumption modeling, discrete-event simulation, and NSGA-II optimization to generate Pareto-optimal solutions balancing energy costs and production deadlines. Validation using real industrial data demonstrates that Energy Digital Twin-based scheduling significantly outperforms traditional heuristic methods, achieving substantial cost reductions while maintaining deadline adherence. The structured methodology provides practitioners with a replicable framework for Energy Digital Twin implementation using existing manufacturing infrastructure.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB18.3", "code": "WeB18.3", "title": "Procedural Knowledge Extraction from Industrial Troubleshooting Guides Using Vision Language Models (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB18", "sessionTitle": "The Future of Operations in Industrial Plants through the Advances of Smart Manufacturing II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Gil de Avalle, Guillermo", "affiliation": "University of Groningen" }, { "name": "Maruster, Laura", "affiliation": "University of Groningen" }, { "name": "Emmanouilidis, Christos", "affiliation": "Univeristy of Groningen" } ], "keywords": [ "Industrial artificial intelligence", "Human-technology integration in manufacturing", "Maintenance engineering, management and services" ], "abstract": "ndustrial troubleshooting guides encode diagnostic procedures in flowchart-like diagrams where spatial layout and technical language jointly convey meaning. To integrate this knowledge into operator support systems, which assist shop-floor personnel in diagnosing and resolving equipment issues, the information must first be extracted and structured for machine interpretation. However, when performed manually, this extraction is costly and difficult to scale. Vision Language Models offer potential to automate this process by jointly interpreting visual and textual meaning, yet their performance on such guides remains underexplored. This paper evaluates two VLMs on extracting structured knowledge, comparing two prompting strategies: standard instruction-guided versus an augmented approach that cues troubleshooting layout patterns. Results reveal model-specific trade-offs between layout sensitivity and semantic robustness, informing preliminary deployment insights for similar industrial settings.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB18.4", "code": "WeB18.4", "title": "Towards a Maturity Model for the Application of Worker Data in Production Systems Management (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB18", "sessionTitle": "The Future of Operations in Industrial Plants through the Advances of Smart Manufacturing II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Figueiredo Pereira, Ana Marta", "affiliation": "Politecnico Di Milano" }, { "name": "Negri, Elisa", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Human-technology integration in manufacturing", "Intelligent manufacturing systems", "Manufacturing engineering and management" ], "abstract": "The current evolution from Industry 4.0 to Industry 5.0 presents many opportunities for the integration of digital technologies with human-centricity, sustainability and industrial resilience. One of these opportunities consists in integrating worker data into the production management process, in order to contribute to the well-being of these employees. However, there seems to be no integrated framework for manufacturing companies to assess their maturity in the application of these initiatives, nor to guide them in advancing their capabilities in this topic. As such, the goal of this paper is to propose a maturity model that allows manufacturers to understand how developed their integration of worker data into their activities is and how it can be improved. Based on existing literature, the topics of human-centricity in manufacturing, worker data utilization and maturity model development are merged. This allowed for the extraction of the dimensions and sub-dimensions in which companies must be evaluated, in order to be placed in one of the levels of the model. This paper presents this maturity model as a conceptual framework that progresses the academic conversation on human-centricity in manufacturing, as well as provide guidance to industrial practitioners to be more inclusive and human-centric.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB18.5", "code": "WeB18.5", "title": "Industrial Multi-Agent Systems in the Era of Generative Artificial Intelligence (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB18", "sessionTitle": "The Future of Operations in Industrial Plants through the Advances of Smart Manufacturing II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Piardi, Luis", "affiliation": "Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico De Bragança," }, { "name": "Sakurada, Lucas", "affiliation": "Instituto Politécnico De Bragança (IPB), Research Centre in Digitalization and Intelligent Robotics (CeDRI)" }, { "name": "Funchal, Gustavo", "affiliation": "Instituto Politecnico De Braganca" }, { "name": "Leitão, Paulo", "affiliation": "Polytechnic Institute of Bragança" } ], "keywords": [ "Industrial artificial intelligence", "Intelligent manufacturing systems", "AI-based enterprise systems" ], "abstract": "Multi-agent systems (MAS) is being recognized as a robust paradigm that provides modularity, scalability, robustness, flexibility, and distributed decision-making by decentralizing control across autonomous and cooperative entities. Despite its strong potential, its adoption in industrial environments, like manufacturing, energy, and healthcare, is far from what is expected. Currently, the rapid emergence of Generative Artificial Intelligence (GenAI) has reshaped the way society interacts with intelligent systems. Beyond its ability to understand and generate natural language content and support creative tasks, GenAI holds the potential to transform industrial processes by enabling new forms of autonomy and collaboration between humans and machines, and its potential is only beginning to be understood. The integration of MAS and GenAI stands out as a promising path to address complex, distributed, and dynamic environments, and its synergy transcends the capabilities of intelligence and autonomy, while significantly improving the agent's ability to interact with humans. In this context, this paper explores how GenAI can strengthen the MAS technology, paving the way for its broader industrial adoption. Presented as a position paper, this work aims to articulate a forward-looking perspective on the integration of MAS and GenAI and to outline the key research challenges that must be addressed for their industrial adoption. Specifically, the discussion emphasizes the complementarities between Agentic AI, an emerging paradigm inspired by the notion of agency, and traditional MAS approaches, as well as research challenges associated with combining MAS and GenAI. Overall, this work contributes to understanding the transformative potential of combining MAS with GenAI, while clarifying the challenges and opportunities for industrial applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB19.1", "code": "WeB19.1", "title": "State Dependent Inventory Dispatch Planning Using a Clustering and Reinforcement Learning Environment (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB19", "sessionTitle": "Data-Driven and AI-Based Modelling of Reliable, Resilient, and Sustainable Manufacturing-Distribution Systems", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Venkatadri, Uday", "affiliation": "Dalhousie University" }, { "name": "Lanka, Basava Sri Krishna Vamsy", "affiliation": "Dalhousie University" }, { "name": "Chadha, Simranjeet Singh", "affiliation": "Dalhousie University" }, { "name": "Diallo, Claver", "affiliation": "Dalhousie University" }, { "name": "Khatab, Abdelhakim", "affiliation": "Lorraine University/ National School of Engineering" } ], "keywords": [ "Data-driven and AI-based modelling of production and logistics", "Supply chain and logistics engineering, simulation and optimization", "Logistics and warehouse management" ], "abstract": "Managing inventory levels for Stock Keeping Units (SKUs) in warehouses involves balancing overage and underage costs, a challenge further compounded by dispatch constraints arising from truck container packing. The joint delivery or tailored aggregation methods known in the inventory literature are useful policies but are suitable for static steady-state analysis. Under dynamic inventory conditions, inventory dispatch to warehouses depends not only on inventory counts and targets but also on how shipments can be packed. In this paper, we propose a three-level decision hierarchical environment for packing shipments. In this methodology, SKUs are clustered by demand, value, and variability, the three main determinants of inventory costs. The question then becomes how to assign pallets to SKU clusters based on truck capacity. Once this is determined, the lower level problem is to assign pallet space to individual SKUs within the clusters. This paper focuses on clustering and the higher-level problem of assigning pallet space to clusters using a Q-Learning agent and Deep Q-Networks to guide dispatch planning using state-dependent policies. A realistic case study is explored based on discussions with a company that contracts a third party logistics supplier to move SKUs from its plants to its warehouses.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB19.2", "code": "WeB19.2", "title": "Framework for Inventory Management Based on Demand and Seasonality Classification: An Application of Unsupervised Learning and Large Language Models (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB19", "sessionTitle": "Data-Driven and AI-Based Modelling of Reliable, Resilient, and Sustainable Manufacturing-Distribution Systems", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Lanka, Basava Sri Krishna Vamsy", "affiliation": "Dalhousie University" }, { "name": "Venkatadri, Uday", "affiliation": "Dalhousie University" } ], "keywords": [ "Data-driven and AI-based modelling of production and logistics", "Supply chain and logistics engineering, simulation and optimization", "Logistics and warehouse management" ], "abstract": "Managing warehouse stock-keeping-units (SKUs) is challenging due to variable demand patterns influenced by factors such as randomness, seasonality, and trend. Tracking each SKU individually is often impractical, but grouping them into meaningful clusters enables custom inventory strategies. This paper presents a framework for SKU classification using unsupervised learning with k-means clustering, supported by feature engineering through STL decomposition and LLM-based cluster labeling. For classification, we focus on seasonality strength, mean demand, and coefficient of variation (CV). Cluster outputs from K-means clustering are further interpreted and annotated with large language models (LLMs), providing qualitative insights and safety-stock recommendations tailored to each cluster. Results show that LLMs can provide useful decision-oriented narratives, but also that different models may recommend different demand bases for safety-stock calculations. The methodology is described through a case study that was implemented recently for the internal supply chain of a producer of packaged consumer nutritional supplements.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB19.3", "code": "WeB19.3", "title": "Digital Transformation of Construction Logistics: Industry 4.0 for Logistics Planning and Coordination", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB19", "sessionTitle": "Data-Driven and AI-Based Modelling of Reliable, Resilient, and Sustainable Manufacturing-Distribution Systems", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Ngo, Uyen", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Sgarbossa, Fabio", "affiliation": "Norwegian University of Science and Technology - NTNU" }, { "name": "Andersen, Bjorn Sorskot", "affiliation": "Norwegian University of Science and Technology" } ], "keywords": [ "Industry X.0 for production and logistics", "Digital supply chain and production" ], "abstract": "Effective logistics planning and coordination are vital for improving construction project performance by integrating logistics activities from off-site supply to on-site installation. As projects grow more complex, digital transformation - driven by Industry 4.0 - offers opportunities to enhance construction logistics. This paper reviews technological enablers of Industry 4.0 in construction logistics planning and coordination, investigates digital transformation barriers and conditions, and proposes a Technology-Layer-Outcome mapping matrix for its digital transformation, aiming to enhance logistics outcomes.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB19.4", "code": "WeB19.4", "title": "Dynamic Analysis of an Integrated VMI-Push Supply Chain", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB19", "sessionTitle": "Data-Driven and AI-Based Modelling of Reliable, Resilient, and Sustainable Manufacturing-Distribution Systems", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Manqiong, Ma", "affiliation": "Donghua University" }, { "name": "Disney, Stephen", "affiliation": "University of Exeter" } ], "keywords": [ "Supply chain management in manufacturing", "Supply network dynamics and control", "Production and operations management" ], "abstract": "We study an integrated push supply chain in which a manufacturer observes the distributor's customer demand and is authorised to manage the distributor's inventory. The manufacturer does this by forecasting end-customer demand over the whole supply chain lead time and review period. The manufacturer releases an order to the production system that arrives after the production lead time. As soon as production is completed, it is immediately pushed to the distributor, arriving after the transportation lead time. We quantify the dynamics of this system via order and inventory variances and conduct an economic analysis.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB19.5", "code": "WeB19.5", "title": "Smart Sensor Design for Cost-Effective and Efficient Inventory Management in Perishable Supply Chains (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB19", "sessionTitle": "Data-Driven and AI-Based Modelling of Reliable, Resilient, and Sustainable Manufacturing-Distribution Systems", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Bonci, Andrea", "affiliation": "Università Politecnica Delle Marche" }, { "name": "Di Biase, Alessandro", "affiliation": "Politecnico Di Bari" }, { "name": "Orsini, Valentina", "affiliation": "Università Politecnica Delle Marche" } ], "keywords": [ "Supply chain management in manufacturing", "Supply network dynamics and control", "Supply chain and logistics engineering, simulation and optimization" ], "abstract": "Accurate inventory data is indispensable for the effective management of perishable supply chains, as even minor inaccuracies can lead to resource inefficiencies and negative environmental consequences. To address this enduring challenge, this paper presents a cost efficient and sustainable solution: a hybrid sensing architecture that combines a low-cost physical sensor with a tailored robust estimator and an outlier detection algorithm. The estimator enhances noisy measurements to deliver rapid and precise inventory assessments, thereby facilitating the timely identification of unexpected inventory losses. In the proposed case study, inventory inaccuracy is reduced by approximately 96% with respect to raw sensor data. The proposed methodology is validated within a Model Predictive Control (MPC) framework, demonstrating substantial gains in both operational efficiency and sustainability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB19.6", "code": "WeB19.6", "title": "Robustness and Resilience in Platform-Based Manufacturing Networks (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB19", "sessionTitle": "Data-Driven and AI-Based Modelling of Reliable, Resilient, and Sustainable Manufacturing-Distribution Systems", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Szaller, Ádám", "affiliation": "HUN-REN Institue for Computer Science and Control" }, { "name": "Zahoran, Laszlo", "affiliation": "HUN-REN Institute for Computer Science and Control" }, { "name": "Váncza, József", "affiliation": "Institute for Computer Science and Control (SZTAKI)" } ], "keywords": [ "Supply network dynamics and control", "Supply chain and logistics engineering, simulation and optimization", "Supply chain management in manufacturing" ], "abstract": "Platform-based manufacturing (PBM) networks coordinate production via digital platforms that match buyers with distributed suppliers. Despite their growing economic relevance, the robustness and resilience of PBM networks have not yet been analyzed in detail. This paper develops a metric framework and agent-based simulation design for assessing PBM robustness and resilience under demand, supply and platform-level shocks. Classical supply chain indicators such as service level, backlog, capacity utilization and time-to-recovery are adapted to the PBM context, and two platform-specific metrics are introduced: the Demand Smoothing Index, which quantifies resistence against volatility, and the Shock Absorption Ratio, which measures the robustness benefit of PBM relative to a traditional supply base. Results from agent-based simulation experiments illustrate how platform scale, different dispatch strategies and shocks in the supply and demand side influence shock the performance of the platform.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB20.1", "code": "WeB20.1", "title": "Sensitivity-Coordinated Distributed Economic Model Predictive Control for Optimal Flexible Operation of Chemical Processes (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB20", "sessionTitle": "JO-JPC: Advanced Process Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Klippel, Vincent", "affiliation": "Process Systems Engineering (AVT.SVT), RWTH Aachen University" }, { "name": "El Wajeh, Mohammad", "affiliation": "BASF SE, RWTH Aachen University" }, { "name": "Mhamdi, Adel", "affiliation": "RWTH Aachen University" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes" ], "abstract": "Flexibilization of chemical processes experiences a continued high interest mainly driven by highly volatile electricity prices. With the use of nonlinear, high-order, computationally challenging process models, the need for efficient solution schemes for economic nonlinear model predictive control (eNMPC) problems arises. Sensitivity-based distributed model predictive control (S-DMPC) is one such scheme family, which offers to account for interactions between subsystems without excessive overhead in subsystems predicting their neighbors’ responses. We investigate the original scheme of S-DMPC (Scheu et al. 2010) and several variants arising from assuming different sensitivity computation schemes. Subsequently, we apply these schemes to the flexibilization of a nonconvex electrified chemical process in offline optimization and closed-loop eNMPC, and draw comparisons between these methods, as well as to common reference schemes. The originally proposed sensitivity computation method turns out to be the most robustly performant under both scenarios and fulfills its expectations of higher cost savings than a noncooperative scheme, as well as lower computation time than a cooperative scheme.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB20.2", "code": "WeB20.2", "title": "Neural-Network Assisted MPC for Flow Reactors Including Reactions (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB20", "sessionTitle": "JO-JPC: Advanced Process Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Knoll, Sebastian", "affiliation": "Graz University of Technology" }, { "name": "Silber, Klara", "affiliation": "Research Center Pharmaceutical Engineering" }, { "name": "Hone, Christopher Andrew", "affiliation": "Research Center Pharmaceutical Engineering GmbH" }, { "name": "Kappe, C. Oliver", "affiliation": "University of Graz" }, { "name": "Steinberger, Martin", "affiliation": "Graz University of Technology" }, { "name": "Horn, Martin", "affiliation": "Graz University of Technology" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes", "Machine learning and artificial intelligence in chemical process control", "Real-time optimization and control in chemical processes" ], "abstract": "This paper presents a Neural-Network Assisted Model Predictive Control (NN-A-MPC) framework for robust control of complex systems such as flow reactors. The approach combines an iterative neural-network-based design-space exploitation (DSE) with physics-based model (PBM) optimization. The DSE provides a near-optimal warm start and reduces the search space for the PBM optimization in which the promising input regions are refined to ensure accuracy and physical feasibility. An integral compensation state enhances robustness against model mismatch and disturbances. Experimental validation on a Paal-Knorr flow reactor demonstrates accurate tracking while satisfying real-time constraints.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB20.3", "code": "WeB20.3", "title": "An Uncertainty Calibration Framework for Process Monitoring with Autoencoders (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB20", "sessionTitle": "JO-JPC: Advanced Process Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Yu, Jiaxin", "affiliation": "Zhejiang University" }, { "name": "Mercangöz, Mehmet", "affiliation": "Imperial College London" }, { "name": "Qin, S. Joe", "affiliation": "Lingnan University, Hong Kong" } ], "keywords": [ "Monitoring, performance assessment, and fault detection in chemical process control", "Fault detection and isolation methods", "Machine learning and artificial intelligence in chemical process control" ], "abstract": "Uncertainty is prevalent in process system engineering, but quantification of uncertainty is less studied in the field of process monitoring. This paper presents a novel uncertainty-calibrated process monitoring (UCPM) framework that augments encoder-decoder residuals with sample specific confidence for the process monitoring index. An auto-encoder is trained to learn a low dimensional manifold of normal process behavior. Then, its reconstruction residuals are compressed into a scalar out-of-distribution indicator using weighting metrics. Building on this indicator, a monotone bin-wise calibration is conducted to map this indicator to the empirical variance of the monitoring index. In this way, monotonicity can be enforced during the calibration by an isotonic projection, and per-sample standard deviation can be quantified to form a soft monitoring index. Case studies on the enhanced Tennessee Eastman process and a real multi-phase flow facility demonstrate the feasibility and effectiveness of the proposed uncertainty-aware process monitoring framework. This methodology requires the encoding-decoding structure only, calibrates uncertainty without parametric assumptions, and is applicable to existing auto-encoder monitoring methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB20.4", "code": "WeB20.4", "title": "A Hybrid Causal-Inference and Neuro-Fuzzy Framework for Advanced Process Monitoring (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB20", "sessionTitle": "JO-JPC: Advanced Process Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Ali, Husnain", "affiliation": "Hong Kong University of Science and Technology" }, { "name": "Liu, Jinfeng", "affiliation": "University of Alberta" }, { "name": "Gao, Furong", "affiliation": "Hong Kong Univ of Sci & Tech" } ], "keywords": [ "Monitoring, performance assessment, and fault detection in chemical process control", "Process modeling, identification, and estimation techniques", "Machine learning and artificial intelligence in chemical process control" ], "abstract": "Abstract: The emergence of Industry 5.0, automation, and advanced sensor networks has made modern industrial processes increasingly intricate and dynamic. Traditional monitoring methods often fall short in addressing the demands of real-time anomaly detection, key variable identification, and root cause analysis in chemical and industrial processes. To overcome these challenges, this work introduces a hybrid causal-inference and neuro-fuzzy framework that integrates dynamic inner global–local preserving projection (DiGLPP), an adaptive neuro-fuzzy inference system (ANFIS), and a causal lineage graph (CLG). The framework is designed to detect faults and trace the propagation paths of causal faults across process units. Its performance is benchmarked against established models, such as Wavelet-Principal Component Analysis (WT-PCA) and Dual-Attention Long Short-Term Memory Autoencoder (DALSTM-AE), with the Tennessee Eastman Process (TEP) serving as the primary benchmark. Results demonstrate that the proposed methodology not only improves the detection of challenging fault scenarios in the TEP process but also enables robust anomaly detection and diagnosis of critical process variables. These findings highlight its potential to meet the practical monitoring needs of complex real-world industrial systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB20.5", "code": "WeB20.5", "title": "Review on Trustworthy Process Monitoring and Fault Diagnosis with Epistemic Uncertainty (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB20", "sessionTitle": "JO-JPC: Advanced Process Control II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Liu, Tingting", "affiliation": "North China University of Technology" }, { "name": "Wang, Jing", "affiliation": "North China University of Technology (NCUT)" }, { "name": "Luo, Hao", "affiliation": "Harbin Institute of Technology" }, { "name": "Zhou, Meng", "affiliation": "North China University of Technology" }, { "name": "Zhang, Yanzhu", "affiliation": "Shenyang Ligong University" }, { "name": "Su, Rong", "affiliation": "Nanyang Technological University" }, { "name": "Wang, Zhenhua", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Monitoring, performance assessment, and fault detection in chemical process control", "Reliability and safety in processes", "Process modeling, identification, and estimation techniques" ], "abstract": "Significant epistemic uncertainty is common due to inadequate fault samples, environmental changes, and other factors, which affects the reliability of results. Although related research has increased about fault diagnosis and uncertainty quantification, most existing work has focused on specific methodological designs. There remains little systematic review for epistemic uncertainty. To address this issue, this paper reviews research on epistemic uncertainty of data-driven trustworthy process monitoring and fault diagnosis. Firstly, the primary sources and root causes are analyzed for epistemic uncertainty based on uniform fault diagnosis model. Secondly, this paper provides a systematic overview of existing uncertainties quantification methods. The principles, advantages, limitations, and the specific applications of these methods are compared from the perspectives of probability, belief, likelihood, and uncertainty measures. Finally, it summarizes the main challenges and future directions of current studies. This paper aims to provide a comprehensive reference for trustworthy process monitoring and fault diagnosis methods under epistemic uncertainty.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB21.1", "code": "WeB21.1", "title": "Distributed Covert Attack Detection for Interconnected Cyber-Physical Systems Based on Parity Space Method", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB21", "sessionTitle": "Cyberphysical Security in Processes", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Li, Jiahuan", "affiliation": "Beihang University" }, { "name": "Parisini, Thomas", "affiliation": "Imperial C., Aalborg U. & Univ. of Trieste" }, { "name": "Zhao, Dong", "affiliation": "Beihang University" } ], "keywords": [ "Cyberphysical security in processes" ], "abstract": "This paper investigates distributed covert attack detection in interconnected cyber-physical systems. Based on the quantification of the propagation effects of covert attacks among subsystems, a detection method using the parity space approach is proposed, which enables attack detection by multiple neighboring subsystems. To address the attenuation of attack effects caused by parity vectors, an improved method based on observer-based reconstruction is developed. The performance of the proposed approach is evaluated on a benchmark power grid system.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB21.2", "code": "WeB21.2", "title": "Observer-Based Input Reconstruction Resilient MPC against False Data Injection Attacks", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB21", "sessionTitle": "Cyberphysical Security in Processes", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Zhang, Yinli", "affiliation": "Beihang University" }, { "name": "Zhao, Dong", "affiliation": "Beihang University" }, { "name": "Chen, Zhiwen", "affiliation": "School of Automation, Central South University" } ], "keywords": [ "Cyberphysical security in processes" ], "abstract": "We present an observer-based input reconstruction resilient model predictive control (MPC) approach to attenuate adverse effects of false data injection (FDI) attacks for disturbed nonlinear cyber-physical systems. Initially, we introduce a disturbance observer to estimate the system states. Subsequently, we propose an input reconstruction method to mitigate the control input attack effect. Both the observer estimation error and the input reconstruction error are quantified for reconstruction parameter design. The input reconstruction mechanism is integrated into the self-triggered MPC framework to defend against attacks while reducing network resource consumption. In addition, we provide the recursive feasibility and stability analysis of the developed strategy in the presence of FDI attacks. In the end, the effectiveness of the developed strategy is verified by an unmanned aerial vehicle simulation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB21.3", "code": "WeB21.3", "title": "Condition-Based Software Rejuvenation Framework for Cyber-Physical Systems Using a Dual-Monitor Architecture", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB21", "sessionTitle": "Cyberphysical Security in Processes", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Siyyal, Shafqat Ali", "affiliation": "Università Politecnica Delle Marche" }, { "name": "Freddi, Alessandro", "affiliation": "Universita' Politecnica Delle Marche" }, { "name": "Maestre, Jose M.", "affiliation": "University of Seville" }, { "name": "Romagnoli, Raffaele", "affiliation": "Duquesne University" }, { "name": "Baldini, Alessandro", "affiliation": "Università Politecnica Delle Marche" }, { "name": "Longhi, Sauro", "affiliation": "Università Politecnica Delle Marche" } ], "keywords": [ "Cyberphysical security in processes", "Reliability and safety in processes", "Fault detection and isolation methods" ], "abstract": "Software Rejuvenation (SWR) is a defense mechanism for increasing the safety of Cyber-Physical Systems (CPSs) against run-time cyber-attacks. However, conventional time-based or periodic SWR strategies, while ensuring safety, suffer from operational limitations, including reduced system availability and mission interruptions caused by frequent restarts. To address these limitations, this work proposes a condition-based SWR framework that rejuvenates the system according to its current state, rather than a predetermined schedule. The proposed framework integrates a dual-monitor system: an observer-based monitor that generates residual signals to detect deviations from nominal behavior, and a predictive monitor that estimates the Time-to-Violation T * of safety constraints. A decision module fuses the outputs from both monitors to trigger rejuvenation only when an anomaly is identified, thus reducing unnecessary interruptions. The framework is validated through simulations on a non-linear quadrotor model, demonstrating a practical alternative to periodic SWR.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB21.4", "code": "WeB21.4", "title": "Detection and Isolation of Local and Neighboring Covert Cyberattacks through Overlapping Decompositions", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB21", "sessionTitle": "Cyberphysical Security in Processes", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Esmat, Muhammad", "affiliation": "University of British Columbia" }, { "name": "Al-Dabbagh, Ahmad", "affiliation": "University of British Columbia" } ], "keywords": [ "Fault detection and isolation methods", "Cyberphysical security in processes", "Distributed/networked FDI/FTC" ], "abstract": "This article addresses the problem of covert cyberattacks in an interconnected system. A detection and isolation appraoch is proposed, such that each subsystem can detect the presence of covert cyberattacks, whether locally or in neighboring subsystems. The proposed approach is based on overlapping decompositions of pairwise subsystems as well as the design of unknown input observers to estimate system states. Moreover, delectability analysis of local and neighbouring covert cyberattacks are provided, where having multiple simultaneous covert cyberattacks are also considered. The effectiveness of the proposed approach is validated through a simulation-based case study.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB21.5", "code": "WeB21.5", "title": "CP-IDS: A Cross-Plane Cooperative Intrusion Detection System Using Programmable Switches", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB21", "sessionTitle": "Cyberphysical Security in Processes", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Zhou, Xun", "affiliation": "Zhejiang University" }, { "name": "Chen, Xiang", "affiliation": "Zhejiang University" }, { "name": "Wang, Zirui", "affiliation": "Zhejiang University" } ], "keywords": [ "Fault detection and isolation methods", "Cyberphysical security in processes", "Health/condition monitoring in processes" ], "abstract": "The Internet integration of industrial control systems (ICSs), while enabling advanced industrial applications, exposes ICSs to severe threats (e.g., distributed denial-of-service (DDoS) and man-in-the-middle (MitM) attacks). Nevertheless, existing intrusion detection systems (IDSs) face limitations in detection comprehensiveness and inference timeliness. This paper presents CP-IDS, a cross-plane cooperative IDS framework leveraging programmable switches to overcome these limitations. Specifically, CP-IDS deploys a rule-based model in the switch data plane for line speed DDoS detection and a lightweight isolation forest model with payload signature matching in the switch control plane for timely MitM detection. The two models communicate via the switch's internal secure channel. Implemented on an Intel Tofino switch, CP-IDS demonstrates a 4.14% accuracy improvement and a 9x efficiency gain over state-of-the-art approaches, achieving comprehensive and timely intrusion detection for ICS networks.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB22.1", "code": "WeB22.1", "title": "Model Predictive Control and Robust Real-Time Optimization for Flexible Operation of District Heating Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB22", "sessionTitle": "MPC for Energy and Utility Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Poulsen, Magnus Hamann", "affiliation": "Technical University of Denmark" }, { "name": "Rønlev-Knudsen, Tobias", "affiliation": "Technical University of Denmark" }, { "name": "Kloppenborg Møller, Jan", "affiliation": "Technical University of Denmark" }, { "name": "Madsen, Henrik", "affiliation": "Tech. Univ. of Denmark" }, { "name": "Ritschel, Tobias K. S.", "affiliation": "Technical University of Denmark" } ], "keywords": [ "Control and optimization for sustainability and energy systems", "Model-predictive and optimization-based control in chemical processes", "Thermal systems modelling" ], "abstract": "District heating grids can be operated flexibly, e.g., to mitigate problems arising in the power grid due to intermittent renewable energy production. However, the temperatures required by the consumers are time-varying and the involved time delays are significant. Furthermore, advanced control strategies such as nonlinear model predictive control (MPC) involve large-scale optimization problems that must be solved numerically in real time, which may not always converge and can be computationally demanding. Therefore, we propose a bilevel approach where an offset-free linear-quadratic MPC algorithm determines the supply temperature in order to track temperature setpoints for the consumers, which are determined by an economic and robust real-time optimization algorithm that also provides optimal flow velocities. We demonstrate the efficacy of the proposed approach with a numerical example involving the AROMA network.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB22.2", "code": "WeB22.2", "title": "Modeling and Periodic Model Predictive Control of Micro-CHP Systems with Fuel Cell", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB22", "sessionTitle": "MPC for Energy and Utility Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Lu, Liang", "affiliation": "Ningbo Liangcon Information Technology Co., Ltd" }, { "name": "Cheng, Zhewu", "affiliation": "Ningbo Liangcon Information Technology Co., Ltd" }, { "name": "Herrero Durá, Juan Manuel", "affiliation": "Polytechnic Univ of Valencia" }, { "name": "Blasco, Xavier", "affiliation": "Polytechnic Univ of Valencia" } ], "keywords": [ "Control and optimization for sustainability and energy systems", "Energy management systems", "Model-predictive and optimization-based control in chemical processes" ], "abstract": "Fuel cell stacks can simultaneously produce electricity and heat which can be utilized in micro combined heat and power (micro-CHP) systems efficiently for home users to reduce heat energy loss and CO2 emissions. The proper management of energy flows in a micro-CHP system with electrical and thermal storage is crucial for maximizing the economic performance of the installation. In this paper, we propose a framework for modeling and a hierarchical model predictive control (MPC) based energy management scheme for micro-CHP system, which combines with a low level control (LLC) for significantly reducing computation. To demonstrate effectiveness of the approach, an example is presented for a domestic installation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB22.3", "code": "WeB22.3", "title": "Economic Dispatch of Combined Heat and Power System: A Multistage MPC Approach with Structure-Exploiting Solvers", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB22", "sessionTitle": "MPC for Energy and Utility Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Adegbege, Ambrose Adebayo", "affiliation": "The College of New Jersey" }, { "name": "Jubril, Abimbola Muhideen", "affiliation": "Obafemi Awolowo University" }, { "name": "Aransiola, Aaron O.", "affiliation": "Obafemi Awolowo University" }, { "name": "Oluleti, Victor Pelumi", "affiliation": "Obafemi Awolowo University" }, { "name": "Martins, Oluwapelumi Hephzibah", "affiliation": "Obafemi Awolowo University" } ], "keywords": [ "Control and optimization for sustainability and energy systems", "Energy management systems", "Control and management of energy systems" ], "abstract": "This paper presents a computationally efficient Model Predictive Control (MPC) framework for real-time economic dispatch of Combined Heat and Power (CHP) systems by exploiting the inherent multistage structure of the optimization problem. The proposed approach transforms the traditional static CHP economic dispatch into a dynamic multi-stage optimization problem over a receding horizon, enabling predictive capability and systematic constraint handling. Non-convex Feasible Operating Regions (FORs) are convexified through semi-algebraic lifting with auxiliary variables, while the problem is formulated as a tractable Quadratic Program (QP). Two efficient solution strategies are developed and compared: an Alternating Direction Method of Multipliers (ADMM) solver with slack variables to manage inequality constraints, and a structure-exploiting implementation using the PIQP solver with multistage backend that leverages specialized block-tri-diagonal-arrow factorization. Numerical validation on a standard 4-unit benchmark system demonstrates that both approaches achieve optimal dispatch matching the GAMS benchmark solution. The ADMM solver achieves solution times of SI{10.8}{millisecond} with 506 iterations (61.1× speedup over GAMS), while the PIQP multistage backend solves in SI{0.78}{millisecond} with only 9 iterations (845.5× speedup over GAMS). Both methods maintain optimality within 0.001% of the GAMS benchmark while significantly reducing computational overhead. The PIQP solver demonstrates superior convergence properties and numerical stability, making it particularly suitable for embedded control applications in modern energy systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB22.4", "code": "WeB22.4", "title": "Model Predictive Control for Proton Exchange Membrane Water Electrolysis Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB22", "sessionTitle": "MPC for Energy and Utility Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Fredriksen, Marius", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Jäschke, Johannes", "affiliation": "Norwegian University of Science & Technology" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes", "Hydrogen systems for energy generation and storage", "Reliability and safety in processes" ], "abstract": "Due to their high efficiency and fast dynamic responses, Proton Exchange Membrane (PEM) water electrolyzers are a promising technology for integrating green hydrogen production with renewable energy systems. However, the variable power output of renewable energy sources, such as wind and solar, requires control structures capable of managing power fluctuations to maintain safe and efficient plant operation. In this work, we investigate the application of Model Predictive Control (MPC) to regulate stack temperature and separator pressures while ensuring that the Hydrogen-to-Oxygen (HTO) ratio in the oxygen separator remains below the safety limit of 2 vol%. We perform a closed-loop simulation by connecting the MPC algorithm to the continuous plant model, evaluate the controller performance for a step reduction in stack power from 56 kW to 30 kW, and compare it with a simple control structure based on Proportional–Integral (PI) feedback controllers. Overall, the MPC performs well, effectively exploiting the power forecast to take proactive actions to mitigate the effects of the power drop while successfully enforcing the HTO constraint, also in the presence of some mismatch between the plant and control models. The MPC's predictive capabilities and ability to coordinate multiple inputs enable a more effective response than the PI-based alternative, which requires quite aggressive (worst-case) tuning that results in suboptimal performance under nominal operating conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB22.5", "code": "WeB22.5", "title": "NMPC Strategies for Optimal Operation of Carbon Dioxide Pipeline-Injection Networks", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB22", "sessionTitle": "MPC for Energy and Utility Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Kumaraswamy, Archana", "affiliation": "Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU)" }, { "name": "Jäschke, Johannes", "affiliation": "Norwegian University of Science & Technology" }, { "name": "Faanes, Audun", "affiliation": "NTNU" } ], "keywords": [ "Control and optimization for sustainability and energy systems", "Model-predictive and optimization-based control in chemical processes", "Transportation networks" ], "abstract": "Carbon dioxide transport and injection is a crucial technology in the decarbonisation roadmap. While research has focused mainly on steady-state design and optimisation of the transport and injection network, little attention has been paid to developing control strategies for operating this network. Given the complexity and multi-input-multi-output nature of this process, non-linear model predictive control (NMPC) has been applied to control the process in this study. The performance of 4 different NMPC formulations are compared for an offshore carbon dioxide pipeline-injection network. The control objectives considered include wellhead flow tracking, throughput maximisation, and pump energy minimisation. It is shown that control strategies neglecting a pump energy minimisation term in the objective may result in cost-ineffective operation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB23.1", "code": "WeB23.1", "title": "Interpretable Reinforcement Learning for Multi-Loop Adaptive PID Control (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB23", "sessionTitle": "Learning Interpretable and Safe Control Policies: Interface between Model-Free Learning and Model-Based Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Zhao, Tianwei", "affiliation": "The University of British Columbia" }, { "name": "Ren, Jiayang", "affiliation": "The University of British Columbia" }, { "name": "Zou, Chenxuanyin", "affiliation": "Northeastern University" }, { "name": "Yang, Ying", "affiliation": "The University of British Columbia" }, { "name": "Cao, Yankai", "affiliation": "University of British Columbia" }, { "name": "Gopaluni, Bhushan", "affiliation": "University of British Columbia" } ], "keywords": [ "Advanced process control", "Machine learning and artificial intelligence in chemical process control", "Real-time optimization and control in chemical processes" ], "abstract": "Static PID controllers struggle with nonlinear and time-varying systems, while neural-network tuning lacks the interpretability required in safety-sensitive applications. We present a model-free, interpretable framework using decoupled Adaptive Neuro-Fuzzy Inference System (ANFIS) policy networks for adaptive multi-loop PID control. Each ANFIS uses human-readable fuzzy rules, optimized by Proximal Policy Optimization (PPO) via environmental interaction without a system model. Applied to a nonlinear Continuous Stirred-Tank Reactor (CSTR) benchmark, the ANFIS-PPO controller matches a pre-tuned static PID on integral tracking error while reducing peak overshoot by 4--16times and settling time by nearly half, and outperforms a capacity-matched neural-network RL agent on both precision and seed-to-seed reproducibility, with rule-level transparency and high parametric efficiency.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB23.2", "code": "WeB23.2", "title": "Learning to Solve Parametric Mixed-Integer Optimal Control Problems Via Differentiable Predictive Control (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB23", "sessionTitle": "Learning Interpretable and Safe Control Policies: Interface between Model-Free Learning and Model-Based Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Boldocky, Jan", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Dadras Javan, Shahriar", "affiliation": "Ruhr University of Bochum, Chair of Automatic Control and System Theory" }, { "name": "Gulan, Martin", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Monnigmann, Martin", "affiliation": "Ruhr-Universität Bochum" }, { "name": "Drgona, Jan", "affiliation": "Pacific Northwest National Laboratory" } ], "keywords": [ "Advanced process control", "Machine learning and artificial intelligence in chemical process control", "Control and optimization for sustainability and energy systems" ], "abstract": "We propose a novel approach to solving input- and state-constrained parametric mixed-integer optimal control problems using Differentiable Predictive Control (DPC). Our approach follows the differentiable programming paradigm by learning an explicit neural policy that maps control parameters to integer- and continuous-valued decision variables. This policy is optimized via stochastic gradient descent by differentiating the model predictive control objective through the closed-loop finite-horizon response of the system dynamics. To handle integrality constraints, we incorporate three differentiable rounding strategies. The approach is evaluated on a conceptual thermal energy system, comparing its performance with the optimal solution for different lengths of the prediction horizon. The simulation results indicate that our self-supervised learning approach can yield high-quality performance while alleviating training scalability limitations prevalent in imitation learning and significantly reducing inference time by avoiding online optimization.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB23.3", "code": "WeB23.3", "title": "Fast, High-Performance, and Interpretable Quadcopter Control Using Decision Trees (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB23", "sessionTitle": "Learning Interpretable and Safe Control Policies: Interface between Model-Free Learning and Model-Based Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Ren, Jiayang", "affiliation": "The University of British Columbia" }, { "name": "Ming-Han, Juang", "affiliation": "National Cheng Kung University" }, { "name": "Zhao, Tianwei", "affiliation": "The University of British Columbia" }, { "name": "Cao, Yankai", "affiliation": "University of British Columbia" } ], "keywords": [ "Advanced process control" ], "abstract": "Real-time quadcopter control demands fast, interpretable methods on resource-limited hardware. Model Predictive Control (MPC) handles constrained multi-variable dynamics effectively, but its high online computational cost hinders onboard implementation. Alternatively, Explicit MPC pre-computes the control law offline, but struggles with scalability in high-dimensional state spaces. Neural network approximations offer efficiency but lack interpretability. To address these challenges, we apply an established data-driven control framework based on Oblique Decision Trees with Linear Prediction (ODT-LP) to quadcopter control. This ODT-LP-based quadcopter controller combines an offline-trained ODT-LP outer loop for position control with a standard inner loop for attitude control. Experiments in OpenAI Gym demonstrate near-MPC tracking performance with orders-of-magnitude lower online computing time, while preserving an interpretable tree structure.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB23.4", "code": "WeB23.4", "title": "Why Goal-Conditioned Reinforcement Learning Works: Relation to Dual Control (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB23", "sessionTitle": "Learning Interpretable and Safe Control Policies: Interface between Model-Free Learning and Model-Based Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Lawrence, Nathan P.", "affiliation": "University of British Columbia" }, { "name": "Mesbah, Ali", "affiliation": "University of California, Berkeley" } ], "keywords": [ "Machine learning and artificial intelligence in chemical process control" ], "abstract": "Goal-conditioned reinforcement learning (RL) concerns the problem of training an agent to maximize the probability of reaching target goal states. This paper presents an analysis of the goal-conditioned setting based on optimal control. In particular, we derive an optimality gap between more classical, often quadratic, objectives and the goal-conditioned reward, elucidating the success of goal-conditioned RL and why classical ``dense'' rewards can falter. We then consider the partially observed Markov decision setting and connect state estimation to our probabilistic reward, making the goal-conditioned reward well suited to dual control problems. The advantages of goal-conditioned policies are validated on nonlinear and uncertain environments using both RL and predictive control techniques.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB23.5", "code": "WeB23.5", "title": "Application of Sliding Mode Control with Disjoint Switching Manifolds and Distributed Digital Twin to a Steam Tracing System", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB23", "sessionTitle": "Learning Interpretable and Safe Control Policies: Interface between Model-Free Learning and Model-Based Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "D'Amico, Jay", "affiliation": "Louisiana Steam Equipment Co" }, { "name": "Chintalapati, Siddarth", "affiliation": "Louisiana Steam Equipment Company" }, { "name": "Fox, Kyle", "affiliation": "Louisiana Steam Equipment Company" }, { "name": "Kinzie, Ryan", "affiliation": "Louisiana Steam Equipment LLC" }, { "name": "OBrien, Scarlett", "affiliation": "Steam Solutions" }, { "name": "Shafiyee, Alif Muhammad", "affiliation": "Steam Solutions" }, { "name": "Drakunov, Sergey V.", "affiliation": "Embry-Riddle Aeronautical University" } ], "keywords": [ "Industrial applications of process control", "Advanced process control", "Thermal systems modelling" ], "abstract": "This paper presents a novel sliding mode control with disjoint switching manifolds applied to a large-scale industrial steam-tracing system. The proposed control algorithm incorporates a digital twin based on a set of coupled partial differential equations that model heat transfer across multiple layers of piping infrastructure. The digital twin enables real-time data fusion from heterogeneous sensor sources, including thermocouples, fiber-optic temperature sensors, and pressure transducers. This information is used to maintain the product temperature within the desired range. The sliding mode controller governs an array of fast-switching steam valves, ensuring high precision and robustness in the face of ambient temperature fluctuations, adverse weather conditions, insulation degradation, valve malfunctions, and sensor faults.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB23.6", "code": "WeB23.6", "title": "Supervised Learning of Lyapunov Functions for a Class of Homogeneous Finite-Time Convergent Discontinuous Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB23", "sessionTitle": "Learning Interpretable and Safe Control Policies: Interface between Model-Free Learning and Model-Based Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Mendoza Avila, Jesus", "affiliation": "Brandenburg University of Technology Cottbus-Senftenberg" }, { "name": "Schiffer, Johannes", "affiliation": "Brandenburg University of Technology Cottbus-Senftenberg" } ], "keywords": [ "Lyapunov methods", "Sliding mode control", "Robust learning systems" ], "abstract": "In this paper, we present an algorithm for supervised learning of Lyapunov functions for a class of homogeneous discontinuous systems. First, a formally established Lyapunov function based on the integral of a homogeneous norm of the system's solutions is used to generate training data. Then, a template function is taken from the family of generalized homogeneous polynomials, and its coefficients are trained by means of a novel gradient descent algorithm. Finally, it is proven that if the approximation error is sufficiently small, then the trained generalized homogeneous polynomial is truly a Lyapunov function for the system under study. The proposed methodology is illustrated by the design of a polynomial Lyapunov function for a second-order quasi-continuous sliding mode algorithm.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB24.1", "code": "WeB24.1", "title": "Climate Adaptation with Model-Free Multi-Objective Reinforcement Learning: The Case of Coastal Flood Adaptation in New York (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB24", "sessionTitle": "Energy Systems, Natural Resources and Environmental Management", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Longo, Emiliano", "affiliation": "Politecnico Di Milano" }, { "name": "Ficchi', Andrea", "affiliation": "Politecnico Di Milano" }, { "name": "Castelletti, Andrea", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Climate change mitigation and adaptation modeling", "AI and ML for environmental systems", "Optimal control and operation of environment systems" ], "abstract": "Sea level rise is increasing coastal flood risk worldwide and creating an urgent need for adaptive responses. Reinforcement Learning (RL) offers a powerful framework for efficiently timing investments under uncertain and evolving risks, by generating dynamic policies that respond to changing system conditions. Here we develop a general RL framework for dynamic adaptation, which accommodates multi-objective problems, produces a spectrum of optimal policies, and relies on a model-free batch algorithm without requiring a system transition model. Applied to New York, the framework yields adaptive policies that adjust to evolving flood risk over time. We analyse trade-offs among optimal policies across ensemble projections and examine how different scenarios yield distinct adaptation sequences.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB24.2", "code": "WeB24.2", "title": "Comparing Regional and Cooperative Air Quality Strategies Using Multi-Objective Optimization (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB24", "sessionTitle": "Energy Systems, Natural Resources and Environmental Management", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Arrighini, Michele", "affiliation": "University of Brescia" }, { "name": "Zecchi, Laura", "affiliation": "Università Di Brescia" }, { "name": "Marchesi, Claudio", "affiliation": "University of Brescia" }, { "name": "Guariso, Giorgio", "affiliation": "Politecnico Di Milano" }, { "name": "Volta, Marialuisa", "affiliation": "University of Brescia" } ], "keywords": [ "Air quality modeling and control", "Integrated assessment modeling", "Optimal control and operation of environment systems" ], "abstract": "Air pollution, particularly fine particulate matter (PM2.5), remains a critical environmental and public health issue in Italy's Po Valley due to numerous emission sources and meteorological conditions. Effective control requires coordinated policies across regional boundaries to address transboundary pollution. Here, we employ an Integrated Assessment Model (IAM) combined with multi-objective optimization to compare independent regional air quality plans with a cooperative strategy encompassing four Northern Italian regions. Our analysis reveals that interregional cooperation can enhance the efficiency of air pollution management in these areas. This approach supports integrated policy design that better accounts for pollutant transport and source interactions, informing future air quality governance in Italy and similar contexts.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB24.3", "code": "WeB24.3", "title": "Integrated Assessment of Climate and Economic Outcomes under Alternative Temperature Targets and Time Horizons (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB24", "sessionTitle": "Energy Systems, Natural Resources and Environmental Management", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Marchesi, Claudio", "affiliation": "University of Brescia" }, { "name": "Arrighini, Michele", "affiliation": "University of Brescia" }, { "name": "Zecchi, Laura", "affiliation": "Università Di Brescia" }, { "name": "Volta, Marialuisa", "affiliation": "University of Brescia" } ], "keywords": [ "Climate change mitigation and adaptation modeling", "Integrated assessment modeling", "Optimal control and operation of environment systems" ], "abstract": "This work presents a cost-effectiveness optimization framework coupling the Finite Amplitude Impulse Response (FaIR) climate model with a Dynamic Integrated model of Climate and Economy (DICE 2023) economic system to determine optimal greenhouse gas mitigation trajectories. The objective is to minimize the cumulative discounted abatement and damage costs under temperature targets of 1.5 °C and 2 °C above pre-industrial levels, considering alternative optimization horizons (2100 and 2200). Monte Carlo ensembles are used to quantify climate uncertainty. The novelty of this study lies in explicitly assessing how the choice of the optimization horizon affects the structure, cost, and long-term consistency of mitigation pathways. Results show that limiting the optimization to 2100 may lead to transient compliance with temperature targets while allowing continued warming beyond the optimization horizon, due to residual emissions and climate system inertia. In contrast, extending the horizon to 2200 produces smoother emission trajectories, ensures long-term temperature stabilization, and reduces total discounted costs. These findings highlight the importance of selecting appropriate planning horizons in integrated climate–economic assessments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB24.4", "code": "WeB24.4", "title": "Cloud Model-Based FITradeoff Approach for FPV Site Selection (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB24", "sessionTitle": "Energy Systems, Natural Resources and Environmental Management", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Zhao, Qian", "affiliation": "University of Modena and Reggio Emilia" }, { "name": "Balugani, Elia", "affiliation": "University of Modena and Reggio Emilia" }, { "name": "Lolli, Francesco", "affiliation": "University of Modena and Reggio Emilia" }, { "name": "Gamberini, Rita", "affiliation": "University of Modena and Reggio Emilia" } ], "keywords": [ "Climate change mitigation and adaptation modeling", "AI and ML for environmental systems", "Participatory decision making in environmental systems" ], "abstract": "This study proposes a cloud model-based Flexible and Interactive Tradeoff (FITradeoff) framework for robust floating photovoltaic (FPV) site selection under uncertainty. Uncertain criterion performances are represented by normal cloud models using expectation (Ex), entropy (En), and hyper-entropy (He), and Monte Carlo simulation is employed to generate stochastic decision matrices. In each simulation run, FITradeoff identifies potentially optimal alternatives under incomplete preference information, while Criteria Importance Through Intercriteria Correlation (CRITIC)-based benchmark weights guide the refinement of the feasible weight space. Robustness is measured by the frequency with which each alternative appears in the potentially optimal set across feasible runs. A case study in Sicily, Italy, identifies San Giovanni as the most robust site.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB24.5", "code": "WeB24.5", "title": "Data-Driven Interval Observer to Estimate the State of a Biohydrogen Production Process (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB24", "sessionTitle": "Energy Systems, Natural Resources and Environmental Management", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Gil-Fernández, Algemiro J.", "affiliation": "Universidad De Guanajuato" }, { "name": "Avilés, Jesús David", "affiliation": "Facultad De Ingeniería Y Negocios, UABC" }, { "name": "Lopez-Caamal, Fernando", "affiliation": "Universidad De Guanajuato" }, { "name": "Torres, Ixbalank", "affiliation": "Universidad De Guanajuato" } ], "keywords": [ "Modelling, parameter identification and state estimation in biosystems", "AI and ML for environmental systems", "Wastewater treatment processes" ], "abstract": "In this work, a data-driven interval observer is proposed to estimate the state of a dark fermentation bioreactor for biohydrogen production. The nonlinear dynamics of the process are described by a polytopic model identified from artificial data at different operating points. A robust Hinf-based interval observer is then designed to estimate the state of the biohydrogen production process by measuring the output hydrogen flow rate, despite the unknown inlet glucose concentration. Numerical simulations show that the proposed observer correctly estimates the unmeasured states and provides reliable interval bounds for biomass, substrate, and volatile fatty acids. This approach represents a promising strategy for real-time monitoring of dark fermentation processes.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB25.1", "code": "WeB25.1", "title": "Lyapunov Stability Analysis of a Class of Compartmental Infection Models", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB25", "sessionTitle": "Biosystems and Bioprocesses II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Yang, Wonjun", "affiliation": "Seoul National University" }, { "name": "Kim, Taekeun", "affiliation": "Institute of Basic Science" }, { "name": "Rao, Shodhan", "affiliation": "Ghent University Global Campus" } ], "keywords": [ "Dynamics and control of biologically motivated nonlinear systems" ], "abstract": "The global stability analysis of endemic equilibria of models of epidemic diseases is crucial to predict the potential of secondary waves of infection. Traditional methods have constructed Lyapunov functions based on trial-and-error, and Shuai and van den Driessche proposed a systematic framework to tackle this issue based on graph theory and matrix tree theorem. In this manuscript, we extend their work to improve its clarity by proposing an algebraic proof, and illustrate it by application to an SIR model with multiple parallel infectious stages and a malaria SEIR model to prove the uniqueness and global asymptotic stability of the endemic equilibria of these models.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB25.2", "code": "WeB25.2", "title": "An Evolutionary Game Model for the Stability of Biologics", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB25", "sessionTitle": "Biosystems and Bioprocesses II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Oblyschuk, David", "affiliation": "University of Birmingham" }, { "name": "Zhang, Tuo", "affiliation": "University of Birmingham" }, { "name": "Stella, Leonardo", "affiliation": "University of Birmingham" } ], "keywords": [ "Dynamics and control of biologically motivated nonlinear systems", "Modelling, parameter identification and state estimation in biosystems", "Pharmaceutical processes, food engineering and industrial biotechnology" ], "abstract": "Biopharmaceutical products, also called biologics, undergo a strict approval process for the assessment of their stability before commercialisation. This ensures safety and efficacy of these products. Kinetic models provide an estimate of stability behaviour and, in conjunction with machine learning algorithms, have recently been used for the prediction of biologics stability and their complex degradation pathways, especially aggregation and fragmentation. Motivated by the application of evolutionary game theory in the development of treatment strategies for cancer, we model the stability of biologics through evolutionary games and validate this model using a data-driven approach. The contribution of this paper is threefold. First, we develop a novel evolutionary game framework to model the stability of biologics. Second, we study the existence of equilibria and their asymptotic stability, linking them to the original game and its Nash equilibria. Finally, we extensively validate the proposed framework and its ability to capture aggregation and fragmentation in degradation pathways.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB25.3", "code": "WeB25.3", "title": "Mathematical Modeling of Early Detection and Optimal Control Strategies in Cancer Treatment", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB25", "sessionTitle": "Biosystems and Bioprocesses II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Riyawati, Ida", "affiliation": "Universitas Airlangga" }, { "name": "Fatmawati, Fatmawati", "affiliation": "Universitas Airlangga" }, { "name": "Alfiniyah, Cicik", "affiliation": "Universitas Airlangga" } ], "keywords": [ "Dynamics and control of gene expression and metabolic pathways", "Modelling, parameter identification and state estimation in biosystems", "Dynamics and control of biologically motivated nonlinear systems" ], "abstract": "Cancer is the second leading cause of death worldwide. Cancer can be suppressed by regulating lymphocyte cells, which are immune cells that play an important role in the body's defense. In this study we developed a system of nonlinear differential equations that describe the dynamics of interactions between cancer cells and lymphocyte cells, with cell growth following a logistic growth model. The optimal control problem is formulated to determine the most effective treatment strategy in minimizing cancer cells growth. Numerical simulation results show that the regular and efficient application of treatment control can significantly suppress cancer cell growth. The developed model is expected to serve as a mathematical basis for designing more effective, optimal, and cost-efficient cancer treatment strategies, thereby reducing the mortality rate from cancer.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB25.4", "code": "WeB25.4", "title": "Modeling and Simulation of Conductivity and pH in Ionic Equilibrium Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB25", "sessionTitle": "Biosystems and Bioprocesses II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Carstensen, Peter Emil", "affiliation": "Technical University of Denmark" }, { "name": "Groves, Teddy", "affiliation": "DTU Biosustain" }, { "name": "Nielsen, Lars", "affiliation": "The University of Queensland" }, { "name": "Krühne, Ulrich", "affiliation": "Technical University of Denmark" }, { "name": "Gernaey, Krist", "affiliation": "Technical University of Denmark" }, { "name": "Jorgensen, John Bagterp", "affiliation": "Technical University of Denmark" } ], "keywords": [ "Kinetic modelling, analysis and optimization of metabolism", "Pharmaceutical processes, food engineering and industrial biotechnology", "Dynamics and control of biologically motivated nonlinear systems" ], "abstract": "Modeling and simulation of ionic equilibria can be used for computing conductivity and pH in aqueous solutions. Conductivity and pH can easily be measured in many industrial biotechnology production processes and are related to the state of the bioprocess. Therefore, conductivity and pH are indicators of bioprocesses that can be used for monitoring and control. A model for conductivity and pH is needed to enable simulation as well as model-based estimation and control. Traditionally, conductivity and pH are computed by computing the ionic equilibria using formulations that are based on equilibrium constants, atom or group mass balances, and charge balances to ensure electro-neutrality. Such models become increasingly complex as additional ionic species and or polyprotic acids are added. Instead, we present a constrained optimization approach based on minimization of Gibbs energy subject to stoichiometric reaction constraints for computation of the ionic equilibria. This method is systematic and easy to implement with complex ionic mixtures as occur in industrial biotechnology. The equilibrium formulation based on minimizing Gibbs energy is embedded in fed-batch and continuous stirred tank reactor (CSTR) models. The resulting models consist of a system of index-1 differential algebraic equations, where the algebraic equations are the optimality conditions of the Gibbs-energy minimization problem. Case studies illustrate the simulation of conductivity and pH in fed-batch reactors and CSTRs for dilute, complex, multi-component acid-base systems relevant to industrial biotechnology.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB25.5", "code": "WeB25.5", "title": "Life in the Loop: Hybrid Bio-Digital Co-Evolutionary Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB25", "sessionTitle": "Biosystems and Bioprocesses II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Nadales, J.M.", "affiliation": "Universidad De Sevilla" }, { "name": "Kojima, Hiroki", "affiliation": "University of Tokyo" }, { "name": "Longo, Liam M.", "affiliation": "Institute of Science Tokyo" } ], "keywords": [ "Systems biology for biotechnology", "Modelling and control of microbial communities", "Dynamics and control of biologically motivated nonlinear systems" ], "abstract": "Researchers in computational and synthetic biology increasingly aim to create artificial entities that reproduce key properties of living systems. However, most approaches still struggle to capture biological complexity, limiting their potential and applicability. Hybrid systems that combine artificial and biological agents could open new ways to probe life’s underlying complexity, using digital agents to guide, modulate, or coordinate the behavior of living counterparts and thus enable new technological pathways. To support this, we present a framework for hybrid bio-digital systems and implement a platform in which microbial populations and evolving digital agents are coupled through real-time feedback, forming a shared eco-evolutionary laboratory where both domains reshape each other’s adaptive landscapes. As a proof of concept, we carry out an experiment in which virtual agents and a community of E. coli cells co-evolve through predator–prey like interactions, showing how information exchange drives co-regulation and the emergence of higher-order dynamics characteristic of evolving systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB25.6", "code": "WeB25.6", "title": "Comparative Analysis of Control Strategies for Biomass Tracking in Continuous Fermentation", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB25", "sessionTitle": "Biosystems and Bioprocesses II", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Carmel, Lipe", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Sartori, Giacomo", "affiliation": "University of Padova, NTNU Trondheim" }, { "name": "Bar, Nadav S.", "affiliation": "Norwegian Univ of Science and Technology" } ], "keywords": [ "Modelling and control of microbial communities" ], "abstract": "As the field of industrial biotechnology grows and gains importance, new challenges in control of real-time microbial fermentation processes arise, including a biomass set-point control strategy under low substrate feeding in order to maintain steady bioreactor operations. We developed an expression for a theoretical upper bound on biomass growth performance that can be used as a benchmark for control. We compared three controllers for Corynebacterium glutamicum: a cascaded PID with manipulated-variable switching, an LQR, and an NMPC. All controllers reached the biomass setpoint within 4% of the benchmark. The LQR gave good biomass tracking but poor volume regulation, the PID gave near-theoretical tracking with manipulated-variable chatter, and the NMPC reduced manipulated-variable variation by about 60% relative to PID. The NMPC was validated experimentally, resulting in near-theoretical performance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB26.1", "code": "WeB26.1", "title": "Online Model Reference-Gaussian Process Regression for Real-Time Lateral Control of Autonomous Vehicles", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB26", "sessionTitle": "AI and Learning-Based Control for Automotive Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Moon, Heemin", "affiliation": "Seoul National University" }, { "name": "Park, Heein", "affiliation": "Seoul National University" }, { "name": "Jang, Yeongjun", "affiliation": "Seoul National University" }, { "name": "Do, Yong Joo", "affiliation": "Seoul National University" }, { "name": "Chang, Hamin", "affiliation": "Purdue University" }, { "name": "Shim, Hyungbo", "affiliation": "Seoul National University" } ], "keywords": [ "AI and learning-based control for automotive systems", "Adaptive and robust control of automotive systems", "Automotive system identification and modelling" ], "abstract": "Model reference-Gaussian process regression (MR-GPR) is a data-driven control framework that synthesizes a reference tracking controller by identifying inverse dynamics of the system from measurement data collected offline. Although it minimizes the need for structural assumptions on the system and provides performance guarantees, it lacks adaptability to varying conditions as it relies solely on offline data. In this paper, we propose an online MR-GPR controller that utilizes streaming data to constantly update the identified inverse dynamics. Through simulations on the lateral control of autonomous vehicles, we demonstrate that the proposed controller effectively adapts to changing road conditions and driving scenarios, and outperforms both the conventional MR-GPR controller and a linear MPC in yaw-rate tracking performance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB26.2", "code": "WeB26.2", "title": "Decentralized Learning-Based Distributed Model Predictive Control of Heterogeneous Vehicle Platoons", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB26", "sessionTitle": "AI and Learning-Based Control for Automotive Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Xu, Zeyuan", "affiliation": "University of Pavia" }, { "name": "Li, Duo", "affiliation": "Newcastle University" }, { "name": "Li, Zixuan", "affiliation": "Harbin Engineering University" }, { "name": "Chen, Hao", "affiliation": "University of Shanghai for Science and Technology" }, { "name": "Hu, Zhijian", "affiliation": "LAAS-CNRS" }, { "name": "Ferrara, Antonella", "affiliation": "University of Pavia" } ], "keywords": [ "AI and learning-based control for automotive systems", "Autonomous vehicles", "Multi-vehicle systems" ], "abstract": "This paper presents a decentralized learning-based distributed model predictive control (DL-DMPC) method for privacy-preserving control of heterogeneous vehicle platoons. With a known communication topology, locally trained submodels are aggregated into a global model through virtual connections, which reduces privacy exposure while preserving vehicle heterogeneity. A generalization error bound is derived, and the learned model is embedded in a DMPC scheme with explicit closed-loop stability criteria. Simulations on heterogeneous platoons validate the proposed DL modeling and DL-DMPC control scheme.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB26.3", "code": "WeB26.3", "title": "Structured Reward Shaping for Vision-Based PPO Control in Autonomous Driving", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB26", "sessionTitle": "AI and Learning-Based Control for Automotive Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Tang, Zhenyu", "affiliation": "University of Tsukuba" }, { "name": "Nguyen-Van, Triet", "affiliation": "University of Tsukuba" }, { "name": "Kawai, Shin", "affiliation": "University of Tsukuba" } ], "keywords": [ "AI and learning-based control for automotive systems", "Autonomous vehicles", "Trajectory tracking and path following for AVs" ], "abstract": "This study presented an end-to-end reinforcement learning method for vehicle control using only visual observations. The proposed approach integrates geometric and dynamic quantities—such as road curvature, lateral error, heading error, and speed–curvature consistency—into a structured reward design, improving both the stability of PPO policy updates and the physical coherence of the learned behavior. Experiments conducted in CARLA demonstrated the effectiveness of each reward component through an ablation study and showed that the trained policy maintains high tracking performance under different weather conditions without retraining. Furthermore, the proposed method maintained stable lateral tracking performance under sparse or partially missing waypoint conditions, showing lower sensitivity to waypoint discretization than conventional waypoint-dependent controllers. Overall, the results indicate that combining visual representations with structured rewards enables robust and scalable reinforcement learning for vehicle control in complex environments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB26.4", "code": "WeB26.4", "title": "Data-Enabled Tube-Based Predictive Control for Human-Machine Cooperative Driving in Stochastic Low-Adhesion Surfaces", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB26", "sessionTitle": "AI and Learning-Based Control for Automotive Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Shi, Wanqing", "affiliation": "Jilin University" }, { "name": "Guo, Hongyan", "affiliation": "Jilin University" }, { "name": "Liu, Jun", "affiliation": "Jilin University" }, { "name": "Lv, Ying", "affiliation": "China FAW Corporation Limited" }, { "name": "Chen, Hong", "affiliation": "Tongji University" } ], "keywords": [ "AI and learning-based control for automotive systems", "Learning and adaptation in autonomous vehicles", "Adaptive and robust control of automotive systems" ], "abstract": "人机合作驾驶提升车辆安全 以及利用互补优势带来的适应性 人类直觉和自动化系统。然而， 随机低附着力路面——如覆盖的路面 在冰雪中——引入不可预测的变化 轮胎与路面摩擦，这对双方都构成了重大挑战 车辆稳定性与驾驶员-自动化协调。此 论文提出了一种基于数据的管状预测控制 （De-TubePC）为稳健的共享引导设计框架 在此类条件下进行控制。所提出的方法模型 摩擦变化作为随机扰动和应用 行为系统理论用于识别车辆动力学和 驾驶员特性直接来自历史数据， 消除了对模型显式识别的需求。 A 基于管的机构保证了弹道安全， 约束满足，而数据驱动的成本函数 同时优化路径追踪和横向稳定性， 以及人机协作。硬件在环 实验结果表明，De-TubePC 框架ऩ", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB26.5", "code": "WeB26.5", "title": "A Learning-Enhanced Path Tracking and Stability Controller", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB26", "sessionTitle": "AI and Learning-Based Control for Automotive Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Xie, Zhihao", "affiliation": "Tongji University" }, { "name": "Liu, Ming", "affiliation": "Tongji University" }, { "name": "Hu, Jincheng", "affiliation": "Loughborough University" }, { "name": "Zhang, Yuanjian", "affiliation": "Tongji University" }, { "name": "Huang, Yanjun", "affiliation": "Tongji University" }, { "name": "Chen, Hong", "affiliation": "Tongji University" } ], "keywords": [ "AI and learning-based control for automotive systems", "Nonlinear and optimal automotive control", "Vehicle dynamic systems" ], "abstract": "Accurate path tracking and adequate yaw stability constitute two fundamental and inherently coupled requirements in autonomous vehicle motion control. Their simultaneous satisfaction is challenging due to the nonlinear, time-varying, and partially unknown nature of vehicle dynamics. To address this issue, this paper develops a learning-enhanced predictive control framework in which a Mixture-of-Experts (MoE) model is employed to construct a data-driven representation of the underlying dynamics and to formulate a global force constraint that ensures the feasibility of the control inputs computed by the controller. The MoE predictor is integrated with a nominal model to form an augmented prediction structure that captures both known dynamics and unmodelled effects. This augmented model is embedded into a coordinated model predictive control formulation that jointly regulates path-tracking performance and yaw stability while maintaining consistency with dynamic constraints. Theoretical evaluations supported by simulations and experimental validation show that the proposed method improves prediction fidelity, enhances closed-loop tracking performance, and ensures stable vehicle motion across a broad range of operating conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB26.6", "code": "WeB26.6", "title": "Predictive Disturbance Compensation for Autonomous Vehicle Path Tracking: An RL-ARESO-MPC Framework", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB26", "sessionTitle": "AI and Learning-Based Control for Automotive Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Liu, Guochen", "affiliation": "Tianjin University" }, { "name": "Yin, Qian-Bao", "affiliation": "Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences" }, { "name": "Liu, Liguo", "affiliation": "Tianjin University" }, { "name": "Song, Kang", "affiliation": "Tianjin University" }, { "name": "Xue, Wenchao", "affiliation": "Chinese Academy of Sciences, Beijing 100190," }, { "name": "Xie, Hui", "affiliation": "Tianjin University" } ], "keywords": [ "AI and learning-based control for automotive systems", "Trajectory tracking and path following for AVs", "Adaptive and robust control of automotive systems" ], "abstract": "提出了一个复合控制框架 将强化学习（RL）与预测性结合 在动态下自动驾驶车辆路径跟踪控制 骚动。其核心是增强的简化阶 扩展国家观察员（ARESO）估计了这两种扰动 以及一阶导数，构造预测函数 扰动序列。该序列被纳入 模型预测控制器（MPC），实现主动 薪酬并显著增强了稳健性。前往 进一步提升适应性，这是一种近端策略 优化（PPO）代理经过训练，能够动态调整 ARESO的带宽在线，增强了对噪声的适应能力 以及非线性。实验结果显示 优点：该方法相比，误差减少了超过54.6 验证其实用性 有效性。", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB27.1", "code": "WeB27.1", "title": "A Novel Reinforcement Learning Framework with Enhanced Interpretability for Wave Energy Converters (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB27", "sessionTitle": "Dynamics and Control of Ocean Renewable Energy Systems I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Li, Bingqian", "affiliation": "School of Engineering, Trinity College Dublin, the University of Dublin" }, { "name": "Ringwood, John", "affiliation": "Maynooth University" }, { "name": "Zhan, Siyuan", "affiliation": "Trinity College Dublin" } ], "keywords": [ "Marine renewable energy systems", "Modelling, identification and control in marine systems" ], "abstract": "This paper presents a novel Reinforcement Learning framework with enhanced Interpretability (IRL) for the energy maximisation problem of Wave Energy Converters (WECs), subject to the availability of wave prediction and partial state estimation. Using a control-theoretic approach, the proposed method directly synthesises Linear Noncausal Optimal Control (LNOC) for energy-maximising control in a continuous action space, incorporating optimal control theory, model-free observer design, and data-driven parameter estimation. Therefore, this method provides several key contributions. First, the control-theoretic foundation not only reduces computational complexity and training data requirements, but also enhances the interpretability of the resulting policy. Second, the IRL introduces a filter-based equivalent representation of the unmeasurable state using filtered input and output signals, enabling the development of model-free LNOC to accommodate non-directly measured states. Third, the novel IRL method enables noncausal formulation that incorporates wave prediction to improve energy conversion efficiency. Finally, demonstrative numerical examples are provided based on a benchmark point-absorber WEC to verify the efficacy of the approach and ensure reproducibility for readers.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB27.2", "code": "WeB27.2", "title": "Simple Controllers for Wave Energy Revisited - Constrained Close-To-Optimal Control Is Possible (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB27", "sessionTitle": "Dynamics and Control of Ocean Renewable Energy Systems I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Fornaro, Pedro", "affiliation": "Centre for Ocean Energy Research - Maynooth University" }, { "name": "Gonzalez-Esculpi, Alejandro", "affiliation": "Maynooth University" }, { "name": "Gelos, Eugenio", "affiliation": "Maynooth University" }, { "name": "Ringwood, John", "affiliation": "Maynooth University" } ], "keywords": [ "Marine renewable energy systems", "Modelling, identification and control in marine systems" ], "abstract": "In wave energy systems, maximising useful energy output while satisfying physical constraints in the presence of model uncertainty is a challenging task. To solve this problem, optimisation-based (OB) controllers may be used. However, the power performance of OB controllers is critically dependent on the model precision. A novel and promising approach combines a Gaussian modulating envelope (GME) with suboptimal controllers. This method has the potential to maximise energy output, while complying with position and velocity constraints, even in the presence of model uncertainty or external disturbances. In this context, this paper evaluates the performance of three suboptimal controllers combined with the GME. Simulation results across varying sea states show that, while inherently suboptimal, model-independent, and with negligible computational time, GME-oriented approaches provide power performance comparable to traditional model predictive control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB27.3", "code": "WeB27.3", "title": "Power-Smoothing MPC for Wave Energy Converters (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB27", "sessionTitle": "Dynamics and Control of Ocean Renewable Energy Systems I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Veurink, Madelyn", "affiliation": "University of Michigan" }, { "name": "Shell, Jonathan", "affiliation": "University of Michigan" }, { "name": "Scruggs, Jeff", "affiliation": "University of Michigan" } ], "keywords": [ "Marine renewable energy systems", "Modelling, identification and control in marine systems" ], "abstract": "Maximizing and smoothing the power delivered to the grid by an ocean wave energy converter (WEC) requires real-time control based on feedback of the system’s dynamic response. Maximization of the average energy generation results in extremely large fluctuation of power through the WEC power train. This paper considers the use of localized energy storage to smooth the power output from a stochastically-excited WEC, prior to delivery to a utility grid. Even when the WEC dynamic model is linear, the presence of constraints on the energy storage capacity render the resulting optimal control problem nonlinear and nonconvex. Model Predictive control (MPC) is used to maximize the power generated by the WEC while satisfying these constraints. Since MPC requires solving the optimal control problem in real-time, the nonconvex constraints are replaced with conservative convex approximations to ensure computational feasibility. Results show that MPC with energy storage achieves 85% of unconstrained generation while dramatically reducing power variability and enabling significantly lower hardware power ratings compared to an unconstrained LQG baseline.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB27.4", "code": "WeB27.4", "title": "Physics Informed Neural Networks Based on Model Predictive Control with Koopman State Observer for Wave Energy Converters Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB27", "sessionTitle": "Dynamics and Control of Ocean Renewable Energy Systems I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Wijaya, Vincentius", "affiliation": "University of Southampton" }, { "name": "Gao, Teng", "affiliation": "University of Southampton" }, { "name": "Zhang, Yao", "affiliation": "Univesity College London; University of Southampton" }, { "name": "Zeng, Tianyi", "affiliation": "University of Nottingham" } ], "keywords": [ "Marine renewable energy systems", "Autonomous marine systems and vehicles" ], "abstract": "This paper presents a control framework for wave energy converters (WEC) that integrates physics-informed neural networks (PINN), a Koopman state observer, and model predictive control (MPC). PINN provide a physics-consistent surrogate model for prediction inside MPC, while the Koopman observer reconstructs unmeasured states from limited sensors. The resulting scheme can handle input and motion constraints. Numerical simulations with different measurement noise levels show that the scheme remains stable, with only negligible energy loss for low noise. This demonstrates its robustness and practicality for WEC operation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB27.5", "code": "WeB27.5", "title": "Real-Time Single-Iteration Model Predictive Control for Wave Energy Converters (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB27", "sessionTitle": "Dynamics and Control of Ocean Renewable Energy Systems I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Pirrera, Simone", "affiliation": "Politecnico Di Torino - DAUIN" }, { "name": "Faedo, Nicolás", "affiliation": "Politecnico Di Torino" }, { "name": "Fosson, Sophie M.", "affiliation": "Politecnico Di Torino" }, { "name": "Regruto, Diego", "affiliation": "Politecnico Di Torino" } ], "keywords": [ "Marine renewable energy systems", "Modelling, identification and control in marine systems", "Marine system guidance, navigation and control" ], "abstract": "This paper proposes a novel real-time algorithm for controlling wave energy converters (WECs). We begin by formulating the economic model predictive control (MPC) problem and apply a novel first-order optimization algorithm to define the controller dynamics using the single-iteration MPC approach. We theoretically analyze the convergence of the employed algorithm and the computational complexity of the obtained controller. Results from simulations with a benchmark WEC system indicate that the proposed approach significantly outperforms standard MPC, thanks to its ability to handle higher sampling rates.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB27.6", "code": "WeB27.6", "title": "Deep Reinforcement Learning Based Damping Control of Wave Energy Converter (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB27", "sessionTitle": "Dynamics and Control of Ocean Renewable Energy Systems I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Ji, Haochen", "affiliation": "The University of Hong Kong" }, { "name": "Li, Xiaofan", "affiliation": "The University of Hong Kong" }, { "name": "Yang, Lisheng", "affiliation": "University of Michigan" } ], "keywords": [ "Marine renewable energy systems", "AI and embodied-AI in marine systems", "Modelling, identification and control in marine systems" ], "abstract": "This paper proposes a damping control method for Wave Energy Converters (WECs) based on the Deep Deterministic Policy Gradient (DDPG) algorithm, aiming to maximize energy capture. By adjusting the equivalent resistance of the external load on a permanent magnet DC generator, continuous regulation of the equivalent damping of the Power-Take-Off (PTO) system is achieved, forming a semi-active control strategy that requires no energy feedback. A \"wave-to-wire\" simulation environment encompassing hydrodynamics, transmission systems, and generator models was established. An analytical solution for the optimal external resistance under regular wave conditions was derived to serve as a benchmark for control performance. Simulation results indicate that, compared to the optimal passive damping control based on frequency-domain analysis, the DDPG controller significantly enhances the system's energy capture efficiency while satisfying constraints on buoy displacement and generator current—achieving approximately a 100% increase in electrical energy output over a 50-second simulation period. This confirms the potential of deep reinforcement learning for achieving end-to-end optimal control in complex wave energy systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB31.1", "code": "WeB31.1", "title": "Mapping the Capability Frontier of Large Language Models: Insights from Generalized Work Activities and Patent Similarity", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB31", "sessionTitle": "LLM and Agents for Social and Economic Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Zhang, Gening", "affiliation": "National University of Defense Technology" }, { "name": "Wang, Tao", "affiliation": "National University of Defense Technology" }, { "name": "Zhang, Zhongshan", "affiliation": "National University of Defense Technology" }, { "name": "Lei, Shifeng", "affiliation": "HuNan Minmetals Hi-Tech Private Equity Funds" }, { "name": "Shen, Dayong", "affiliation": "National University of Defense Technology" }, { "name": "Yao, Feng", "affiliation": "National University of Defense Technology" } ], "keywords": [ "Social computing" ], "abstract": "The rapid advancement of large language model (LLM) technology is exerting profound impacts on human occupations. To investigate the heterogeneous relationships between different types of work activities and technological exposure levels, this study systematically assesses the potential influence of LLMs on various work activities by analyzing semantic similarity between occupational tasks and patent texts. The analytical approach first employed Elastic Net regression to screen 37 generalized work activity features, followed by quantile regression to examine marginal effects at three percentiles (10th, 50th, and 90th). Results indicate that LLMs demonstrate strong capability in handling routine information transfer, automated process monitoring, and multimodal data integration, while providing support for standardized decision-making. However, they remain unable to manage complex social interactions requiring deep contextual understanding or supplant human judgment in unstructured environments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB31.2", "code": "WeB31.2", "title": "LITD: An LLM-Integrated Training and Dispatching Strategy for Urban Taxi System", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB31", "sessionTitle": "LLM and Agents for Social and Economic Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Wang, Xuheng", "affiliation": "Beijing Jiaotong University" }, { "name": "Zhang, Hui", "affiliation": "Beijing Jiaotong University" }, { "name": "Li, Yidong", "affiliation": "Beijing Jiaotong University" } ], "keywords": [ "Agent & AI technology for business and economy", "Cyber physical social systems (CPSS)", "Knowledge automation" ], "abstract": "Ride-hailing dispatch systems are becoming an essential component of urban transportation. However, traditional methods often optimize vehicle matching and dispatching separately, which often results in failed matches or inefficient dispatch decisions. To overcome these limitations, we propose a Large Language Model (LLM) Integrated Training and Dispatching strategy called LITD. LITD is built upon two key modules. First, the LLM Based Feature Extension Module enriches reinforcement learning (RL) state representations by generating semantic features from regional supply–demand patterns, temporal dynamics, and vehicle states. Second, the LLM-Based Dispatch Strategy Module incorporates LLM reasoning into real-time operations. Experiments conducted on the New York Yellow Taxi dataset demonstrate the effectiveness of LITD in improving dispatch efficiency and users experience.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB31.3", "code": "WeB31.3", "title": "MedChainLLM: A Blockchain-Integrated Architecture for Secure, Scalable, and Privacy-Adaptive Medical Large Language Models", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB31", "sessionTitle": "LLM and Agents for Social and Economic Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Wang, Jing", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Zhang, Mengmeng", "affiliation": "Institute of Automation，Chinese Academy of Sciences" }, { "name": "Liang, Xiaolong", "affiliation": "Chinese Academy of Sciences" }, { "name": "Lv, Yisheng", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Wang, Fei-Yue", "affiliation": "Institute of Automation, Chinese Academy of Sciences" } ], "keywords": [ "Parallel intelligence", "Blockchain intelligence", "Knowledge automation" ], "abstract": "Medical large language models (LLMs) are promising for clinical question answering and decision-support workflows, but their real-world deployment is limited by privacy, access accountability, model-update traceability, and auditability. This paper proposes MedChainLLM, a blockchain-integrated architecture for secure and scalable medical LLM workflows. MedChainLLM separates off-chain medical computation from on-chain governance: raw medical data, prompts, full outputs, model weights, and LLM inference remain off-chain, while hashes, metadata, access events, inference-output hashes, and model-update hashes are recorded on-chain. The architecture combines smart-contract-based access control, tamper-evident audit logging, dynamic sharding, and privacy-aware federated model-update logging. Experiments using Qwen2.5-1.5B/3B/7B on a fixed 200-question MedQA subset show that medical QA accuracy is backbone-dependent, while blockchain logging preserves predictions and provides complete audit coverage with measurable overhead. Prototype and simulation results further support access control, tamper evidence, scalability, and privacy-governance analysis. MedChainLLM provides a workflow-level trust layer for accountable medical LLM deployment rather than a clinically validated diagnostic model.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB31.4", "code": "WeB31.4", "title": "Forest Carbon Sink Estimation System Based on Large Language Models", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB31", "sessionTitle": "LLM and Agents for Social and Economic Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Yang, Jian", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Zhao, Weikang", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Xu, Menghua", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Hua, Jing", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Wang, Haoyu", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Wang, Xiujuan", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Kang, Mengzhen", "affiliation": "CASIA" } ], "keywords": [ "Agent & AI technology for business and economy", "Social computing", "Knowledge automation" ], "abstract": "Amid global climate change, forests play a decisive role in carbon neutrality, but traditional carbon sink estimation suffers from high technical thresholds and poor regional adaptability. This study proposes an intelligent forest carbon sink estimation system based on large language models (LLMs), integrating natural language interaction, Retrieval-Augmented Generation (RAG), and tool calling. It parses user input via LLMs, complements data through a local knowledge base and web search, and calculates carbon storage using the IPCC-recommended biomass equation method. Experimental verification shows the system effectively reduces the systematic error of traditional methods, lowers operational barriers, and improves efficiency. While facing limitations like insufficient rare tree species data, it provides a new technical path for carbon sink calculation. Future research will focus on multi-model comparison, knowledge base expansion, and extension to blue carbon ecosystems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB31.6", "code": "WeB31.6", "title": "From Solvers to Framers: Modeling an LLM-Driven Dialogic Sandbox for Competition-Based Education", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB31", "sessionTitle": "LLM and Agents for Social and Economic Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Chen, Ruihua", "affiliation": "Peking University International S&T Innovation Center at Lin-Gang Special Area, China (Shanghai) Pilot Free Trade Zone" }, { "name": "Li, Bai", "affiliation": "Hunan University" }, { "name": "Hare, Ryan", "affiliation": "Department of Electrical and Computer Engineering, Rowan University, Glassboro" }, { "name": "Tang, Ying", "affiliation": "Rowan University" }, { "name": "Xie, Na", "affiliation": "Central University of Finance and Economics" }, { "name": "Wang, Fei-Yue", "affiliation": "Institute of Automation, Chinese Academy of Sciences" } ], "keywords": [ "Cyber physical social systems (CPSS)", "Agent & AI technology for business and economy", "Social computing" ], "abstract": "To shift educational focus from solving to problem framing, this paper proposes the Dialogic Sandbox Challenge, a novel simulation framework designed to assess students’ inquiry strategies. We present a three-layer technical architecture comprising: (1) a high-fidelity, deterministic simulation layer grounded in Econometric models, (2) a Social computing interface leveraging Agent & AI technology to facilitate parallel intelligence, and (3) a Computational analytics layer for verifiable process evaluation. This system utilizes knowledge automation protocols to constrain AI agents as passive consultants. A conceptual study focused on the low-altitude economy demonstrates how the architecture offers a scalable engineering blueprint for assessing human-agent collaboration.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB31.8", "code": "WeB31.8", "title": "Large-Small Model Collaboration for Time-Series Stock Prediction", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB31", "sessionTitle": "LLM and Agents for Social and Economic Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Wang, Jingcheng", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Dai, Xingyuan", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Lv, Yisheng", "affiliation": "Institute of Automation, Chinese Academy of Sciences" } ], "keywords": [ "Agent & AI technology for business and economy" ], "abstract": "Reliable stock forecasting requires capturing temporal dependencies while integrating diverse market signals under uncertainty. However, existing approaches rely on either small specialized models or large generic models, and thus struggle to jointly model multi-scale temporal structures and perform the reasoning needed for complex financial decisions. This paper introduces a large-small model collaborative framework integrating (i) a hypergraph-based predictor for structural spatiotemporal patterns, (ii) a fine-tuned large time-series model for generalized market dynamics, and (iii) a reasoning-capable LLM as the final forecaster. The framework fuses predictions from both models with historical signals via the LLM to produce the final forecast and a confidence analysis. Experiments on two major indices show consistent improvements over state-of-the-art baselines in accuracy.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB32.1", "code": "WeB32.1", "title": "Bandwidth: What Is Needed to Track a Double-Integrator-Based System?", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB32", "sessionTitle": "Mechatronic System Estimation, Identification and Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Heertjes, Marcel", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "High-performance motion control systems", "Mechatronic system integration", "Mechatronic system modeling, design, optimization" ], "abstract": "This paper concerns the tracking control of high-precision motion systems like wafer scanners, component mounters, and wire bonders such as used in the semiconductor industry. For tracking a third-order motion profile with a mass feedforward controller that has a biased mass estimate, the paper answers the question of what control bandwidth is needed to meet control specifications on both tracking error rejection and transient response.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB32.2", "code": "WeB32.2", "title": "Multistage Control of Switched Flat Systems: An Active Disturbance Rejection Control Approach", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB32", "sessionTitle": "Mechatronic System Estimation, Identification and Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Sira-Ramirez, Hebertt J.", "affiliation": "CINVESTAV-IPN" }, { "name": "Hernandez Barrera, Ana Belen", "affiliation": "Centro De Investigación Y De Estudios Avanzados Del Instituto Politécnico Nacional" }, { "name": "Medina Covarrubias, Adan", "affiliation": "Centro De Investigación Y De Estudios Avanzados Del Instituto Politécnico Nacional" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "Mechatronic system estimation, identification, control" ], "abstract": "A multistage ADRC controller design is proposed for SISO differentially flat systems, with measurable internal variables, which can be decomposed in a cascade of subsystems on physically-based grounds. The approach is based on the enhanced robustness properties of elementary classical controllers, acting on each subsystem of the original system. The elementary controllers are also realizable as switched controllers for systems that may undergo sliding regimes. The control of two coupled oscillators, by means of a switched double bridge, is used as an illustrative example of the extension of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB32.3", "code": "WeB32.3", "title": "Elementary Controllers: A Flatness-Based ADRC Approach", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB32", "sessionTitle": "Mechatronic System Estimation, Identification and Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Sira-Ramirez, Hebertt J.", "affiliation": "CINVESTAV-IPN" }, { "name": "Aguilar-Orduña, Mario Andrés", "affiliation": "ITAM" }, { "name": "Gomez Leon, Brian", "affiliation": "CINVESTAV" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Mechatronic system integration" ], "abstract": "This paper discusses the theoretical relationship between classical control schemes and Active Disturbance Rejection Control (ADRC). It presents a unified framework showing that classical P, PD, and PI controllers can be reformulated as ADRC schemes by incorporating disturbance compensation, while integral control achieves ADRC equivalence through lead compensation. Numerical simulations of a multistage buck-converter-driven magnetic levitation system confirm enhanced robustness, accurate tracking, and strong disturbance rejection.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB32.4", "code": "WeB32.4", "title": "Feature-Extraction-Based Estimation and Control for Cyclostationary Disturbances", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB32", "sessionTitle": "Mechatronic System Estimation, Identification and Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Yu, Pan", "affiliation": "Beijing University of Technology" }, { "name": "Xu, Chen", "affiliation": "Beijing University of Technology" }, { "name": "Liu, Qian", "affiliation": "Northeastern University" }, { "name": "Li, Xiaoli", "affiliation": "Beijing University of Technology" }, { "name": "Wang, Kang", "affiliation": "Beijing University of Technology" } ], "keywords": [ "High-performance motion control systems", "Adaptive and adaptable automation", "Mechatronic system estimation, identification, control" ], "abstract": "Vibration in mechatronic systems with rotating components exhibits cyclostationarity, as statistical characteristics vary periodically with rotation. However, real-time mitigation of adverse effects on control performance remains challenging. This paper develops a feature-extraction method for cyclostationary disturbances that integrates Hilbert and Fast Fourier Transforms to extract disturbance features using only system output. Unlike conventional observers, it updates weights via gradient descent based on a new performance index. Closed-loop stability and performance are rigorously analyzed with a systematic design procedure. Finally, the effectiveness and advantages of the proposed method are demonstrated through a case study and comparisons with existing representative approaches.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB32.5", "code": "WeB32.5", "title": "Loop-Element Sequence Selection for Reset Control Systems under Multi-Frequency Reset Input", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB32", "sessionTitle": "Mechatronic System Estimation, Identification and Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Hosseini, Ali", "affiliation": "TU Delft" }, { "name": "Sivakumar, Sanjay", "affiliation": "ASMPT" }, { "name": "van Eijk, Luke Franciscus", "affiliation": "Delft University of Technology" }, { "name": "Kostic, Dragan", "affiliation": "Eindhoven University of Technology" }, { "name": "HosseinNia, S Hassan", "affiliation": "Delft University of Technology" } ], "keywords": [ "High-performance motion control systems", "Mechatronic system estimation, identification, control", "Micro and nano mechatronic systems" ], "abstract": "This study develops a frequency-domain design framework for sequencing reset and linear elements in feedback loops subject to multi-frequency inputs. While Higher-Order Sinusoidal-Input Describing Functions (HOSIDFs) in existing reset-control studies assume single-frequency inputs and pure sinusoidal excitation, the proposed framework explicitly accounts for the reset element's input signal shaped by multiple sources of inputs. The method provides practical sequencing rules to reduce HOSIDFs while preserving the desired first-harmonic behavior. Design guidelines are formulated to minimize the closed-loop error while preserving the benefits of reset control over linear time-invariant designs. The effectiveness and practicality of the approach are demonstrated on an industrial wire bonding machine.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB32.6", "code": "WeB32.6", "title": "Online Identification in Closed-Loop for Mechatronic Systems: A CLOE Approach", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB32", "sessionTitle": "Mechatronic System Estimation, Identification and Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Bonal, Louis", "affiliation": "CentraleSupelec" }, { "name": "Laraba, Mohammed Tahar", "affiliation": "Safran Electronics & Defense, Massy, France" }, { "name": "Lhachemi, Hugo", "affiliation": "CentraleSupelec" }, { "name": "Olaru, Sorin", "affiliation": "CentraleSupelec" }, { "name": "Landau, Ioan Dore", "affiliation": "GIPSA-LAB," } ], "keywords": [ "Mechatronic system estimation, identification, control", "Mechatronic system fault detection, diagnostics, hardware-in-the-loop simulation" ], "abstract": "This paper addresses the challenge of online parametric identification in embedded mechatronic systems. Building on the Closed-Loop Output Error (CLOE) method, we propose an extension to cascade feedback architectures, where persistent excitation and identifiability conditions are more difficult to meet due to feedback interconnections and embedded control constraints. Simulations are carried out on a representative benchmark model, including a robust MISO controller and time-varying dynamics, highlighting the ability of the proposed scheme to track gradual changes in system dynamics and maintain robustness against noise and limited excitation. The results confirm the relevance of the extended CLOE framework for industrial applications requiring online monitoring and adaptive control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB33.1", "code": "WeB33.1", "title": "Collision Avoidance for Personal Mobility by Monte Carlo MPC Adapting to Multi-Modal Distribution of Pedestrian Crowds", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB33", "sessionTitle": "Robot Perception and Sensing", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Narita, Ryota", "affiliation": "Tokyo City University" }, { "name": "Sekiguchi, Kazuma", "affiliation": "Tokyo City University" }, { "name": "Nonaka, Kenichiro", "affiliation": "Tokyo City University" } ], "keywords": [ "Aerial, field, and marine robotics", "Robot perception and sensing", "Task and motion planning" ], "abstract": "Autonomous driving of personal mobility vehicles in urban areas has been actively researched; however, in congested urban environments, collision avoidance is challenging due to the occlusion of on-board cameras and LiDAR sensors. This paper presents a collision avoidance method for high-density environments that exploits the particle filter with LiDAR point clouds directly as observations, represents crowds as a multimodal probability distribution, and employs Monte Carlo model predictive control to handle multimodal optimization problems. Experiments on a self-driving wheelchair confirmed that the proposed method successfully avoided collisions with oncoming partially occluded pedestrians.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB33.2", "code": "WeB33.2", "title": "Self-Motion Estimation of a Robotic Fish Via a Hydrodynamic-Informed Neural Network with Pressure Sensing", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB33", "sessionTitle": "Robot Perception and Sensing", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Zhang, Jingran", "affiliation": "Zhejiang University" }, { "name": "Wu, Shuangpeng", "affiliation": "Zhejiang University" }, { "name": "Jiang, Daiyang", "affiliation": "ZheJiangUniversity" }, { "name": "Lu, Shuda", "affiliation": "Zhejiang University" }, { "name": "Liu, Xinrui", "affiliation": "Zhejiang University" }, { "name": "Xiong, Rong", "affiliation": "Zhejiang University" }, { "name": "Zheng, Xingwen", "affiliation": "Zhejiang University" } ], "keywords": [ "Robot perception and sensing", "Aerial, field, and marine robotics", "Biomedical and biomimetic mechatronic systems" ], "abstract": "Robotic fish have emerged as promising platforms for underwater exploration and environmental monitoring. Existing methods leverage sensory systems inspired by the fish lateral line, typically implemented as arrays of pressure sensors that measure flow-field variations around the robot to enable self-motion estimation. This paper proposes a hydrodynamic-informed neural network constrained by Lighthill’s hydrodynamic pressure formulation. To further enhance yaw consistency, the angular branch additionally incorporates a differentiable temporal integrator. Experiments on 2-D turning and 3-D spiral swimming demonstrate that the proposed approach attains low errors in self-motion estimation, while the learned dynamic-pressure trends remain compatible with Lighthill’s theoretical relation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB33.3", "code": "WeB33.3", "title": "Improving 6-DoF Object Tracking for Industrial Applications", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB33", "sessionTitle": "Robot Perception and Sensing", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Renaud, Charles", "affiliation": "Université Laval" }, { "name": "Beaulieu, Charles", "affiliation": "Laval University" }, { "name": "Lessard, Michel", "affiliation": "Technologies NeurobotIA Inc" }, { "name": "Garon, Mathieu", "affiliation": "Laval University" }, { "name": "Lalonde, Jean-Francois", "affiliation": "Université Laval" } ], "keywords": [ "Robot perception and sensing", "AI-powered robotics", "Robotic grasping and manipulation" ], "abstract": "In order to perform the 3D tracking of a known object in an industrial workpiece assembly, we build upon the render-and-compare approach of Garon et al. (2018) by making several improvements that lead to a more precise and robust tracking method, especially in the depth-only scenario. Our main contributions include a realistic background composition process for synthetic training data and a new normalization strategy for depth values. In the depth-only scenario, we obtain improvements of 73.77% and 68.50% in mean translation and rotation errors respectively compared to the baseline, while outperforming the state-of-the-art generic tracker of Wen et al. (2024) on the most challenging sequences of the Laval 6-DoF dataset (Garon et al. (2018)).", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB33.4", "code": "WeB33.4", "title": "SaliSLAM: Enhancing Visual SLAM with Contour-Based Saliency Information", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB33", "sessionTitle": "Robot Perception and Sensing", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Jin, Sheng", "affiliation": "Suzhou City University" }, { "name": "Chen, Jiayi", "affiliation": "Suzhou City University" }, { "name": "Chen, Liang", "affiliation": "Soochow University" }, { "name": "Zhuang, Hong", "affiliation": "Soochow University" } ], "keywords": [ "Robot perception and sensing", "AI-powered robotics", "Robotic learning and adaptation" ], "abstract": "Visual simultaneous localization and mapping (SLAM) systems often treat all feature points equally, resulting in the underutilization of important regions. We propose SaliSLAM, a saliency-driven visual SLAM system that weights feature contributions using saliency maps predicted from contour- and semantic-aware training data. A hybrid saliency computation method combines contour density, contour closure, and semantic information to construct indoor and outdoor datasets for saliency prediction. The predicted saliency is then incorporated into keyframe selection and bundle adjustment. Experiments on KITTI, TUM, and real robot sequences show that SaliSLAM improves localization accuracy and robustness over representative visual SLAM baselines.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB33.5", "code": "WeB33.5", "title": "Equivariant Filter for High Performance Image Tracking Using an Event Camera", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB33", "sessionTitle": "Robot Perception and Sensing", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Apps, Angus", "affiliation": "Australian National University" }, { "name": "Ge, Yixiao", "affiliation": "Australian National University" }, { "name": "Molloy, Timothy L.", "affiliation": "Monash University" }, { "name": "Mahony, Robert", "affiliation": "Australian National University" } ], "keywords": [ "Robot perception and sensing", "Mechatronic system estimation, identification, control" ], "abstract": "Image tracking is the problem of estimating the transformation that relates a moving image of a scene to an original reference image. The problem is important in control of autonomous vehicles or robots, where the image encodes information about the motion of the camera or environment, as well as in pure computer vision applications. In this paper, we present an equivariant filter design for high performance tracking of planar image transformations using an event camera. The design exploits the Asynchronous Event Blob (AEB) tracker (Wang et al., 2024) to extract feature-position measurements from the raw event stream, and an equivariant filter to compute an affine image translation and rotation using the special Euclidean group symmetry. The equivariant filter incorporates an equivalent-measurement update step that de-correlates the (highly temporally correlated) feature-position measurements provided by the AEB tracker. We evaluate the design experimentally using two datasets involving general and fast rotational motion. We benchmark results against direct optimisation (estimating the relative transformation from the raw blob tracks), and a covariance intersection approach for overcoming data correlation. Our design provides smooth image tracking for features moving up to 7000 pixels per second on the image plane.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB33.6", "code": "WeB33.6", "title": "A Co-Evolutionary Framework for Lifecycle-Aware and Sustainable Multi-Robot System", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB33", "sessionTitle": "Robot Perception and Sensing", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Wang, Shuo", "affiliation": "HuaQiao University" }, { "name": "Zhang, Tingqi", "affiliation": "ZHEJIANG UNIVERSITY" }, { "name": "Li, Boyu", "affiliation": "Zhejiang University" }, { "name": "Luo, Jiliang", "affiliation": "Huaqiao University" }, { "name": "Zhou, Jiazhong", "affiliation": "Huaqiao University" } ], "keywords": [ "Robot perception and sensing", "Robotic learning and adaptation", "Task and motion planning" ], "abstract": "The performance of multi-robot systems degrades over prolonged operation, leading to persistent discrepancies between planned trajectories and actual execution, thereby progressively undermining system sustainability. To address this issue, this paper develops a lifecycle-aware and co-evolutionary scheduling framework, in which performance degradation is encoded as time-varying edge traversal costs via a dynamic potential field. A lifecycle-adaptive heuristic algorithm is further developed to optimize routing decisions in response to evolving robot performance, thereby enabling adaptive traffic redistribution according to real-time system states. Simulation results demonstrate that the proposed approach enhances both system stability and operational efficiency, particularly under degradation conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB34.1", "code": "WeB34.1", "title": "Semi-Automated Evaluation and Recommendation Framework for Industrial Retrieval Augmented Generation Pipelines (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB34", "sessionTitle": "Robustness and Explainability in Artificial Intelligence for Automated Industrial Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Tamascelli, Nicola", "affiliation": "ABB AG Corporate Research" }, { "name": "Nakas, Georgios", "affiliation": "ABB" }, { "name": "Schoch, Nicolai", "affiliation": "ABB AG Corporate Research" }, { "name": "Hussein, Rana", "affiliation": "ABB Corporate Research Center Germany" }, { "name": "Borrison, Reuben", "affiliation": "ABB Corporate Research Center" }, { "name": "Tan, Ruomu", "affiliation": "ABB Corporate Research Center Germany" } ], "keywords": [ "AI tools in automation engineering and operation", "AI-driven modeling and control" ], "abstract": "Generative Artificial Intelligence (Gen-AI) technologies, particularly Large Language Models (LLMs) combined with Retrieval‑Augmented Generation (RAG), are increasingly adopted in industrial domains for tasks such as engineering assistance, operator support, and data analytics, yet systematic evaluation of these pipelines remains a major challenge due to their complexity and the interdependencies among components like document loaders, embedding models, retrievers, and LLMs. Traditional Natural Language Processing (NLP) metrics are often inadequate in industrial contexts, where latency, contextual accuracy, robustness, and compliance with operational KPIs are critical. To streamline evaluation and pipeline selection in such settings, this work introduces a semi‑automated, modular framework that supports pluggable components and systematic experimentation across configurations, enabling both high‑level comparison and fine‑grained component analysis. Applied to an industrial question‑answering use case in measurement instrumentation, the framework demonstrates its ability to identify optimal configurations and provide actionable insights into component interplay, thereby reducing reliance on trial‑and‑error and advancing the trustworthy deployment of gen-AI in safety‑critical environments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB34.2", "code": "WeB34.2", "title": "On Estimating Data Efficiency for Industrial Fault Classification (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB34", "sessionTitle": "Robustness and Explainability in Artificial Intelligence for Automated Industrial Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Borrison, Reuben", "affiliation": "ABB Corporate Research Center" }, { "name": "Sharma, Divyasheel", "affiliation": "ABB Corporate Research Center" } ], "keywords": [ "AI tools in automation engineering and operation", "Expert systems and cognitive-based control", "AI-driven modeling and control" ], "abstract": "Industrial fault classification is constrained by label scarcity, yet determining how much data is sufficient remains unclear. We propose a model-agnostic data-efficiency framework based on retention curves that estimate classifier performance as a function of training data, with uncertainty quantified via Moving Block Bootstrap and BCa intervals. Experiments on the Tennessee Eastman Process dataset reveal strong model- and class-dependent data requirements. The framework enables quantitative data planning by estimating data requirements and the marginal value of additional labeled samples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB34.3", "code": "WeB34.3", "title": "Autoencoder-Based Robustness Analysis for Alarm Flood Classification under Label Noise (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB34", "sessionTitle": "Robustness and Explainability in Artificial Intelligence for Automated Industrial Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Najafi, Amirhossein", "affiliation": "University of Alberta" }, { "name": "Manca, Gianluca", "affiliation": "Ruhr University Bochum" }, { "name": "Tamascelli, Nicola", "affiliation": "ABB AG Corporate Research" }, { "name": "Kunze, Franz Christopher", "affiliation": "Ruhr University Bochum" }, { "name": "Dix, Marcel", "affiliation": "ABB Corporate Research Center" }, { "name": "Hollender, Martin", "affiliation": "ABB Corporate Research" }, { "name": "Fay, Alexander", "affiliation": "Ruhr University Bochum" }, { "name": "Chen, Tongwen", "affiliation": "University of Alberta" } ], "keywords": [ "AI tools in automation engineering and operation", "Machine learning for modeling and prediction" ], "abstract": "Modern process plants are highly interconnected, and effective alarm management is essential for safe and reliable operation. Alarm flood classification aims to identify recurring alarm patterns and thus reduce the alarm load on operators. However, supervised classifiers are vulnerable to label noise caused by imperfect expert annotations. This paper proposes a novel autoencoder-based robustness analysis, which uses latent representations to generate structured label perturbations that mimic realistic mislabeling. The method is evaluated on two public alarm flood datasets and five relevant classification methods from the literature. Results show that label-noise robustness varies substantially across classifiers, even when their baseline performance on clean data is comparable, highlighting that robustness should be considered as an additional criterion when selecting alarm flood classification methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB34.4", "code": "WeB34.4", "title": "Real-Time Line-Based Room Segmentation and Continuous Euclidean Distance Fields (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB34", "sessionTitle": "Robustness and Explainability in Artificial Intelligence for Automated Industrial Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Warberg, Erik", "affiliation": "Saab" }, { "name": "Miksits, Adam", "affiliation": "Ericsson" }, { "name": "Barbosa, Fernando S.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Machine learning for modeling and prediction", "AI tools in automation engineering and operation" ], "abstract": "Continuous map representations, as opposed to discrete ones, such as grid maps, have been gaining traction in the research community. However, current approaches still incur high computational costs, preventing their use in large environments without sacrificing precision. In this paper, we propose a scalable method based on Gaussian Process-based Euclidean Distance Fields (GP-EDFs). By leveraging the structure inherent in indoor environments, namely walls and rooms, we achieve an accurate continuous map representation that is fast enough to update and use in real time. This is possible thanks to a novel line-based room segmentation algorithm, enabling the creation of smaller local GP-EDFs for each room. These local GP-EDFs also use line segments as shape priors, enabling a more efficient map representation using fewer data points. We evaluate this method in simulation experiments, and make the code available open-source.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB34.5", "code": "WeB34.5", "title": "Industrial AI Robustness Card for Time Series Models (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB34", "sessionTitle": "Robustness and Explainability in Artificial Intelligence for Automated Industrial Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Windmann, Alexander", "affiliation": "Helmut Schmidt University" }, { "name": "Stratmann, Benedikt", "affiliation": "Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB)" }, { "name": "Lyashenko, Mariya", "affiliation": "Siemens AG" }, { "name": "Niggemann, Oliver", "affiliation": "Helmut-Schmidt-Universität / Universität Der Bundeswehr Hamburg" } ], "keywords": [ "Machine learning for modeling and prediction", "AI tools in automation engineering and operation", "Knowledge-based and data-driven control" ], "abstract": "Industrial AI practitioners face vague robustness requirements in emerging regulations and standards but lack concrete, implementation-ready protocols. This paper introduces the Industrial AI Robustness Card for Time Series (IARC-TS), a lightweight protocol for documenting and evaluating industrial time series models. IARC-TS specifies required fields and an empirical measurement and reporting protocol that combines drift and operational domain monitoring, uncertainty quantification, and stress tests, and maps these to selected EU AI Act documentation, testing, and monitoring obligations. A biopharmaceutical soft sensor case study illustrates how IARC-TS supports reproducible robustness evidence and defines monitoring triggers.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB35.1", "code": "WeB35.1", "title": "Decentralized Pricing Mechanism for Resource Allocation in Metaverse Crowdsourcing: A Walrasian Equilibrium Approach (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB35", "sessionTitle": "Social Simulation and Social Intelligence for CPSS", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Guan, Sangtian", "affiliation": "Macau University of Science and Technology" }, { "name": "Li, Juanjuan", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Qin, Rui", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Zhang, Tengchao", "affiliation": "Macau University of Science and Technology" }, { "name": "Liang, Xiaolong", "affiliation": "Chinese Academy of Sciences" }, { "name": "Lin, Fei", "affiliation": "Macau University of Science and Technology" } ], "keywords": [ "Decentralized economics/ecosystems (DeEco)", "Computational economics", "Social computing" ], "abstract": "In this study, we propose a blockchain-enabled decentralized pricing mechanism driven by Walrasian equilibrium to address the resource allocation challenges in multi-scenario Metaverse crowdsourcing systems, in which contain heterogeneous application domains with distinct demand structures. We first formulate a social welfare maximization problem that explicitly incorporates suppliers’ privacy costs and intrinsic incentives derived from network effects. To solve this problem, we design a Decentralized Price Iteration (DPI) algorithm to compute the Walrasian equilibrium price via the smart contract. Theoretical analysis and empirical results demonstrate that the proposed algorithm robustly converges to a stable equilibrium under initially excess supply or demand. This work can serve as a foundational pricing protocol for the emerging markets of the decentralized economy (DeEco).", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB35.2", "code": "WeB35.2", "title": "Mechanism Design for Investment Regulation under Herding", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB35", "sessionTitle": "Social Simulation and Social Intelligence for CPSS", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Wang, Huisheng", "affiliation": "Tsinghua University" }, { "name": "Zhao, H. Vicky", "affiliation": "Tsinghua University" } ], "keywords": [ "Financial systems", "Game theories", "Computational economics" ], "abstract": "Herding, where investors imitate others' decisions rather than relying on their own analysis, is a prevalent phenomenon in financial markets. Excessive herding distorts rational decisions, amplifies volatility, and can be exploited by manipulators to harm the market. Traditional regulatory tools, such as information disclosure and transaction restrictions, are often imprecise and lack theoretical guarantees for effectiveness. This calls for a quantitative approach to regulating herding. We propose a regulator-leader-follower trilateral game framework based on optimal control theory to study the complex dynamics among them. The leader makes rational decisions, the follower maximizes utility while aligning with the leader's decisions, whereas the regulator designs a mechanism to maximize social welfare and minimize regulatory cost. We derive the follower's decisions and the regulator's mechanisms, theoretically analyze the impact of regulation on decisions, and investigate effective mechanisms to improve social welfare.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB35.3", "code": "WeB35.3", "title": "Strategic Gaussian Signaling under Linear Sensitivity Mismatch", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB35", "sessionTitle": "Social Simulation and Social Intelligence for CPSS", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Munif, Hassan", "affiliation": "Université De Lorraine, CNRS, CRAN" }, { "name": "Satheeskumar Varma, Vineeth", "affiliation": "CRAN - Université De Lauraine" }, { "name": "Lasaulce, Samson", "affiliation": "CNRS - Centrale Supelec - Universite Paris Sud" } ], "keywords": [ "Game theories" ], "abstract": "We analyze Stackelberg Gaussian signaling games where the encoder and decoder have a linear sensitivity mismatch. Unlike the standard additive-bias model, a sensitivity mismatch means the encoder prefers the decoder to track a linear transformation of the state rather than a shifted one. We derive the equilibrium structure for both noiseless (cheap-talk) and noisy signaling channels. In the noiseless case, the equilibrium admits a spectral characterization: the encoder transmits information only along eigenspaces associated with the negative eigenvalues of a mismatch matrix. In the noisy regime, we derive analytical thresholds for informative signaling, showing that communication collapses if the sensitivity mismatch or transmission cost exceeds a channel-dependent threshold.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB35.4", "code": "WeB35.4", "title": "Timely Information for Strategic Persuasion", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB35", "sessionTitle": "Social Simulation and Social Intelligence for CPSS", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Gundogan, Ahmet Bugra", "affiliation": "Bilkent University" }, { "name": "Bastopcu, Melih", "affiliation": "Bilkent University" } ], "keywords": [ "Game theories", "Cyber-physical and human systems (CPHS)", "Social networks and opinion dynamics" ], "abstract": "This work investigates a dynamic variant of a persuasion problem, in which a strategic sender seeks to influence a receiver's belief over time through controlling the timing of the information disclosure, under resource constraints. We consider a binary information source (i.e., taking values 0 or 1), where the source's state evolves according to a continuous-time Markov chain (CTMC). In this setting, the receiver aims to estimate the source's state as accurately as possible. In contrast, the sender seeks to persuade the receiver to estimate the state to be 1, regardless of whether this estimate reflects the true state. This misalignment between their objectives naturally leads to a Stackelberg game formulation where the sender, acting as the leader, chooses an information-revelation policy, and the receiver, as the follower, decides whether to follow the sender’s messages. As a result, the sender's objective is to maximize the long-term average time that the receiver's estimate equals 1, subject to a total sampling constraint and a constraint for the receiver to follow the sender's messages called incentive-compatibility (IC) constraint. We first consider the single-source problem and show that the sender’s optimal policy is to allocate a minimal sampling rate to the undesired state 0 (just enough to satisfy the IC constraint) and assign the remaining sampling rate to the desired state 1. Next, we extend the analysis to the multi-source case, where each source has a different minimal sampling rate. Our results show that the sender can leverage the timeliness of the revealed information to influence the receiver, thereby achieving a higher utility.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB35.5", "code": "WeB35.5", "title": "Q-Framework: CTMC As a Unified Formula for Fine-Grained Spreading Dynamics", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB35", "sessionTitle": "Social Simulation and Social Intelligence for CPSS", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Liu, Yang", "affiliation": "National University of Defense Technology" }, { "name": "He, Renjie", "affiliation": "National University of Defense Technology" }, { "name": "Wang, Tao", "affiliation": "National University of Defense Technology" }, { "name": "Xu, Shiyi", "affiliation": "National University of Defense Technology" }, { "name": "Pan, Lin", "affiliation": "National University of Defense Technology" }, { "name": "Cheng, Li", "affiliation": "National University of Defense Technology" }, { "name": "Yao, Feng", "affiliation": "National University of Defense Technology" } ], "keywords": [ "Social computing", "Social networks and opinion dynamics" ], "abstract": "Spreading phenomena in complex systems are a common and important dynamical process, often involving complex mechanisms and amount of heterogeneities. Traditional models for spreading process mostly adopt a macroscopic perspective, which may either oversimplify details or become analytically intractable. Moreover, These models are usually not universal across different scenarios. To address the dilemma, this paper tries to propose a reduced modeling approach based on Continuous-Time Markov Chains(CTMC), called as Q-framework. It simplifies the description of heterogeneous spreading dynamics by abstracting node interactions into a spreading matrix Q, and decoupling the analysis from modeling thus ensuring universality. With the help of the CTMC analysis framework, it can not only simulate macro long-term trends just like most traditional models do, but also describe the local transient changes. Based on the Q-framework, we simulated the spreading process on a small social network through experiments. The results show that the Q-framework is compatible with classical spreading models and further demonstrates its potential ability in describing fine-grained dynamics.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB35.6", "code": "WeB35.6", "title": "A Nonlinear Machine Learning Approach to Model Climate-Induced Migration", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB35", "sessionTitle": "Social Simulation and Social Intelligence for CPSS", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "De Nardi, Sabrina", "affiliation": "Università Degli Studi Di Brescia" }, { "name": "Carnevale, Claudio", "affiliation": "University of Brescia" }, { "name": "Piccoli, Gabriele", "affiliation": "University of Brescia" }, { "name": "Raccagni, Sara", "affiliation": "Università Degli Studi Di Brescia" } ], "keywords": [ "Control and automation to improve social and political stability", "Models & simulation for international stability" ], "abstract": "This study explores climate-induced migration in vulnerable regions by comparing statistical models with machine learning, focusing on Random Forests. Unlike linear approaches, our framework captures complex, nonlinear interactions among environmental and socioeconomic drivers: temperature anomalies, HDI, water stress, and agricultural GDP share. Applied to North Africa, Sub-Saharan Africa, and Southeast Asia, Random Forest greatly outperforms simpler models (correlation=0.81, MAE=0.95), accurately representing migration dynamics. The methodology provides a scalable, replicable tool to support adaptation strategies, migration management, and evidence-based climate policy in data-scarse, climate-sensitive contexts.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB36.1", "code": "WeB36.1", "title": "Experimental Comparison of Control Strategies for Scaled Autonomous Racing Cars", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB36", "sessionTitle": "Motion Control for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Juarez-Moreno, Luis C.", "affiliation": "Tecnologico De Monterrey" }, { "name": "Tudon-Martinez, Juan Carlos", "affiliation": "Tecnologico De Monterrey" }, { "name": "Sotelo, Carlos", "affiliation": "Tecnologico De Monterrey" }, { "name": "Sotelo, David", "affiliation": "Tecnologico De Monterrey" }, { "name": "Lozoya-Santos, Jorge De-J.", "affiliation": "Tecnologico De Monterrey" } ], "keywords": [ "Autonomous vehicles", "Motion control for AVs", "Control architectures in automotive control" ], "abstract": "The increasing interest in Autonomous Vehicles (AV) has brought forth the development of autonomous racing cars where RoboRacer stands out as a scaled, but realistic and affordable research platform. This paper presents a comparative evaluation of four state-of-the-art controllers in the RoboRacer platform: Pure Pursuit controller (PP), Model- and Acceleration-based Pursuit controller (MAP), Kinematic Model Predictive Controller (KMPC) and Single-Track Model Predictive Controller (STMPC). The study adopts the well-known kinematic and dynamic bicycle representations in world and Frenet coordinates. The contribution is twofold. First, a detailed controller performance comparison across different speeds. Second, energy efficiency is introduced as a key performance metric alongside lap time and lateral RMSE. Energy efficiency plays a central role because racing requires balancing speed/power use with battery or fuel longevity. Experimental results show that the STMPC achieves the fastest laps, lowest tracking error, and highest energy efficiency. MAP and STMPC stand out at higher speeds due to their treatment of nonlinear dynamics.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB36.2", "code": "WeB36.2", "title": "Predictive Safety Filtering for LPV-Based Lateral Vehicle Control with Enhanced Constraint Satisfaction", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB36", "sessionTitle": "Motion Control for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Pauca, Georgiana-Sinziana", "affiliation": "Gheorghe Asachi Technical University of Iasi" }, { "name": "Caruntu, Constantin - Florin", "affiliation": "Technical University \"Gheorghe Asachi\" of Iasi" } ], "keywords": [ "Motion control for AVs", "Vehicle dynamic systems", "Trajectory and path planning for AVs" ], "abstract": "As automation grows, especially in automotive and autonomous driving, the concept of safety has become a central requirement in industry. Supervised safety-control architectures offer a practical solution by monitoring input signals in real-time and modifying them only when necessary to meet safety constraints. A leading method is the Model Predictive Safety Filter (MPSF), which keeps future states safe through minimal corrections to the vehicle’s control input. Building on these principles, the present work proposes a safety-supervised system for lane keeping, modeling vehicle dynamics with error-based variables. As the velocity changes, the system becomes linear parameter-varying (LPV), allowing it to better capture high-dynamic behavior and approximate real vehicle behavior. The safety filter uses an invariant terminal set based on LMI-derived stability to promote constraint satisfaction and recursive feasibility. To preserve feasibility, a slack variable is introduced that softens state constraints. Simulation results demonstrate the effectiveness of the proposed filter in maintaining safety and robustness, even in unpredictable driving situations where disturbances and noise were introduced.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB36.3", "code": "WeB36.3", "title": "Two-Layer Formation Control of a Fleet of UAVs in a Cluttered Environment Via Ellipsoidal Structure", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB36", "sessionTitle": "Motion Control for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Bouin, Salomé, Esther, Anna", "affiliation": "MBDA France" }, { "name": "Piet-Lahanier, Helene", "affiliation": "ONERA" }, { "name": "Jouanneau, Laurent", "affiliation": "MBDA" }, { "name": "Formoso, Mathias", "affiliation": "MBDA" } ], "keywords": [ "Multi-vehicle systems", "Motion control for AVs", "Trajectory tracking and path following for AVs" ], "abstract": "This paper presents a two-layer control strategy for the formation flight of a fleet of Unmanned Autonomous Vehicles (UAVs) that follows an a priori defined trajectory over some delimited area. The design of the controls aims to constrain the fleet within a predefined virtual geometrical shape, which is selected here as an ellipsoid and to move the virtual structure towards the objective. The guidance of the virtual structure drives its centre to an aimed trajectory and adapts its shape and orientation to avoid collisions with unpredicted obstacles. Each UAV computes a low-level distributed control guidance to remain within the structure and avoid side-collisions. A analytical distribution of the UAVs inside the ellipsoid is determined so that they are evenly distributed within and their relative distances remain within known boundaries. This repartition also enables distributed control computation and simplifies reallocation in the structure when the number of vehicles in the fleet evolves. Simulations are provided to illustrate the performances of the resulting strategy.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB36.4", "code": "WeB36.4", "title": "WR-MPC: Safety-Oriented Wasserstein Repulsive NMPC for Autonomous Intersection Clearance", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB36", "sessionTitle": "Motion Control for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Gong, Zhengqing", "affiliation": "CentraleSupélec" }, { "name": "Font, Stephane", "affiliation": "CentraleSupélec" }, { "name": "Gao, Jiuchun", "affiliation": "IMT-Atlantique" }, { "name": "Patel, Raj Haresh", "affiliation": "Ampere Software Technology" }, { "name": "Sandou, Guillaume", "affiliation": "SUPELEC" } ], "keywords": [ "Trajectory and path planning for AVs", "Motion control for AVs" ], "abstract": "Autonomous driving in urban scenarios requires proactive planning that ensures safety and efficiency under uncertain predictions. To specifically address the challenges posed by such uncertainty, this paper proposes a nonlinear model predictive control framework that explicitly incorporates distributional safety objectives through the Wasserstein distance. By modeling ego and obstacle future states as probabilistic distributions, a Wasserstein repulsion term that penalizes proximity in the probability space is introduced. Simulation studies in tested intersection scenarios show smoother, safer, and more robust trajectories than a deterministic baseline, while the proposed variants mainly highlight the potential of this approach and open paths toward better managing the trade off between performance and computational efficiency.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB36.5", "code": "WeB36.5", "title": "A State-Embedded Hamiltonian Fast Marching Approach for Four-Wheel Steering Forklifts Path Planning in Constrained Spaces", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB36", "sessionTitle": "Motion Control for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Pascal, Julien", "affiliation": "Université Clermont Auvergne" }, { "name": "Mirebeau, Jean-Marie", "affiliation": "Centre Borelli, ENS Paris-Saclay, UMR 9010 CNRS, University Paris-Saclay" }, { "name": "Thuilot, Benoit", "affiliation": "Université Clermont Auvergne" }, { "name": "Checchin, Paul", "affiliation": "Université Clermont Auvergne," } ], "keywords": [ "Trajectory and path planning for AVs", "Motion control for AVs", "Intelligent transportation systems" ], "abstract": "This work introduces a deterministic global motion-planning strategy tailored to four-wheel-steering (4WS) forklifts navigating narrow and cluttered spaces. Rather than relying on sampling-based exploration, the approach builds on the Hamiltonian Fast Marching (HFM) framework and adapts it to the specific kinematic behavior of 4WS vehicles through a hybrid-state representation that captures their non-holonomic motion capabilities. The planner is evaluated through qualitative and quantitative experiments across several scenarios and benchmarked against widely used methods, including RRT, RRT*, Informed-RRT*, and SST. Results show more reliable trajectories, with improved obstacle clearance and path regularity.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB36.6", "code": "WeB36.6", "title": "HD-RRT*former: Sampling-Based Motion Planning for High-Dimensional Systems Using Transformer", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB36", "sessionTitle": "Motion Control for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Feng, Mingyang", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Zhao, Jianing", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Liu, Ruijia", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Li, Shaoyuan", "affiliation": "Shanghai Jiao Tong Univ" }, { "name": "Yin, Xiang", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Discrete event modeling and simulation", "Hybrid and switched systems modeling", "Optimal control of discrete event and hybrid systems" ], "abstract": "In this paper, we investigate the sampling-based motion planning for high-dimensional robotic systems in complex environments. Most existing sampling-based approaches make limited use of environmental information or previous sampling states, even though such information essentially provides valuable heuristics for guiding subsequent samples. To this end, we present HD-RRT*former, an efficient sampling-based motion planning algorithm which integrates the standard RRT* algorithm with a Transformer architecture, enabling autoregressive guidance of the entire sampling process. To be specific, we first introduce a physically-informed kinematic graph attention network (PI-KGAN) to approximate the environmental representations in the high-dimensional space, based on which, a two-stage training method is developed to achieve fast convergence in spite of the large sampling space and numerous invalid samples. We conducted extensive simulation experiments on both 6-DOF UR3 and 7-DOF Franka manipulators to validate the algorithm's performance, and finally deployed our algorithm on a real manipulator. Experiments show that our algorithm outperforms the existing variants of RRT algorithms in multiple metrics, including sampling efficiency and path quality. The relevant code can be found at https://github.com/fengmingyang666/HD-RRT-former.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB37.1", "code": "WeB37.1", "title": "Compressed Implicit Dual Gradient Tracking for Distributed Resource Allocation (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:10-13:30", "sessionCode": "WeB37", "sessionTitle": "Control and Optimization of Distributed Power Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Li, Yun-Long", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Qian, Kun", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Liu, Xiao-Kang", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Wang, Yan-Wu", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Siano, Pierluigi", "affiliation": "University of Salerno" } ], "keywords": [ "Smart city control and optimization", "Decision making under uncertainty", "Decentralized economics/ecosystems (DeEco)" ], "abstract": "This paper studies distributed resource allocation over communication-limited networks. The challenge lies in achieving fast convergence without incurring the heavy communication overhead associated with exchanging auxiliary variables for global gradient estimation. To tackle such issues, we propose a Compressed Implicit Dual Gradient Tracking (CIDGT) algorithm that combines a novel momentum mechanism with communication compression to implicitly and efficiently realize gradient tracking. This design reduces both communication and storage overheads. Significantly, we establish convergence guarantees for CIDGT under a broad class of general compressors. Numerical simulations validate its effectiveness.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB37.2", "code": "WeB37.2", "title": "Distributed Resilient Secondary Control for Multi-Bus DC Microgrids under FDI Attacks (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:30-13:50", "sessionCode": "WeB37", "sessionTitle": "Control and Optimization of Distributed Power Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Wen, Yongzhen", "affiliation": "Shandong University" }, { "name": "Liu, Zhiyong", "affiliation": "Shandong University" }, { "name": "Xing, Lantao", "affiliation": "Shandong University, Jinan" }, { "name": "Ding, Wenlong", "affiliation": "Shandong Universitu" } ], "keywords": [ "Security and privacy in CPHS", "System dynamics and control in CPHS", "Safety-critical and resilient systems" ], "abstract": "DC microgrids are increasingly valued for their high efficiency and strong capability to integrate renewable energy sources. In multi-bus systems, distributed secondary control is essential for achieving accurate current sharing and maintaining proper bus voltage regulation. However, its reliance on communication networks makes the system vulnerable to false data injection (FDI) attacks. To enhance the security and reliability of DC microgrids, this paper proposes a resilient distributed secondary control strategy capable of mitigating bounded FDI attacks. The approach introduces an auxiliary variable that effectively reduces the impact of attacks on communication channels. With appropriate controller parameter design, the proposed strategy ensures satisfactory current sharing and voltage regulation performance even under attack conditions. Simulation results demonstrate the effectiveness and robustness of the proposed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB37.3", "code": "WeB37.3", "title": "Online Distributed Optimization Based on Dynamic Probabilistic Quantization (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "13:50-14:10", "sessionCode": "WeB37", "sessionTitle": "Control and Optimization of Distributed Power Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Yao, Songquan", "affiliation": "Academy of Mathematics and Systems Science, Chinese Academy of Sciences" }, { "name": "Li, Tao", "affiliation": "Academy of Mathematics and Systems Science，Chinese Academy of Sciences" } ], "keywords": [ "Decision making under uncertainty" ], "abstract": "We propose an online distributed optimization algorithm based on online probabilistic quantization and unbalanced graphs. We design a time-varying probabilistic quantizer with adaptive quantization interval lengths, which adjusts quantizer parameters according to the state of each agent at each time step, thereby formulating an encoding-decoding strategy. Finally, we establish a sublinear upper bound on the dynamic regret and validate the effectiveness of the proposed algorithm through a numerical simulation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB37.4", "code": "WeB37.4", "title": "Distributed Specified-Time Secondary Control for Islanded Microgrids Over Asynchronous Communication Networks (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:10-14:30", "sessionCode": "WeB37", "sessionTitle": "Control and Optimization of Distributed Power Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Liu, Enbo", "affiliation": "Northwestern Polytechnical University" }, { "name": "Xian, Chengxin", "affiliation": "City University of Hong Kong" }, { "name": "Liu, Yongfang", "affiliation": "Northwestern Polytechnical University" }, { "name": "Wang, Huimin", "affiliation": "Northeastern University" }, { "name": "Zhao, Yu", "affiliation": "Northwestern Polytechnical University" } ], "keywords": [ "Smart city control and optimization" ], "abstract": "This paper investigates the distributed specified-time secondary control problem for islanded microgrids over asynchronous directed communication networks. For each distributed generator (DG), an asynchronous coordination mechanism is designed, based on which a specified-time secondary controller is developed to achieve frequency restoration and active power sharing at a user-specified time. Compared to existing finite-time and fixed-time secondary control schemes, the convergence time of the proposed controller can be explicitly specified in advance, independent of the system initial states and parameters. Furthermore, the strict assumption of synchronous and continuous communication among DGs is removed, allowing each DG to exchange information based on its independent local clock, which significantly enhances practical engineering applicability. Finally, simulation examples are provided to verify the effectiveness of the proposed controller.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB37.5", "code": "WeB37.5", "title": "Dynamic Average Consensus Observer-Based Distributed Secondary Control for Multi-Bus DC Microgrids (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:30-14:50", "sessionCode": "WeB37", "sessionTitle": "Control and Optimization of Distributed Power Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Wu, Jinhui", "affiliation": "Nanyang Technological University" }, { "name": "Yan, Yamin", "affiliation": "Nanyang Technological University" }, { "name": "Guo, Fanghong", "affiliation": "Zhejiang University of Technology" } ], "keywords": [ "Smart city control and optimization" ], "abstract": "Multi-bus DC microgrids (MGs), owing to their flexible architectures and strong compatibility with renewable energy sources, are emerging as a key component of future power systems. However, in a multi-bus configuration, the control objective shifts to regulating all bus voltages toward a common average value. In practice, directly obtaining voltage measurements from all buses in a distributed multi-bus MG is impractical due to privacy, communication, and scalability constraints. To address this challenge, this paper proposes a distributed control strategy based on dynamic average consensus observers to achieve both average voltage restoration and accurate current sharing. Furthermore, the stability of the closed-loop system is analyzed and proven under suitable assumptions. Simulation studies are carried out in the MATLAB/Simulink environment to demonstrate the effectiveness of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeB37.6", "code": "WeB37.6", "title": "Transient Stability Enhancement of Grid-Connected Converters under Hybrid Control (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "14:50-15:10", "sessionCode": "WeB37", "sessionTitle": "Control and Optimization of Distributed Power Systems", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Zhang, Xinmeng", "affiliation": "Shandong University" }, { "name": "Xing, Lantao", "affiliation": "Shandong University, Jinan" }, { "name": "Ding, Wenlong", "affiliation": "Shandong Universitu" } ], "keywords": [ "System dynamics and control in CPHS", "Cyber-physical and human systems (CPHS)" ], "abstract": "Modern power grids are increasingly dominated by converter-based resources, making transient stability under grid faults a critical concern. Conventional grid-forming (GFM) and grid-following (GFL) strategies often fail to maintain synchronization and current regulation under grid fault conditions. To address this issue, this paper investigates the transient stability of a hybrid control strategy that linearly combines GFM and GFL modulation signals. Within this framework, the GFM branch provides voltage support and frequency synchronization, whereas the GFL branch ensures current regulation and suppresses overcurrent. Furthermore, a droop-based control is incorporated to prevent power–angle divergence and enhance stabilization during voltage dips. Simulation results demonstrate that the proposed hybrid control strategy significantly improves fault ride-through (FRT) capability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC01.1", "code": "WeC01.1", "title": "Distributed State Estimation with Event-Triggered Measurement Sampling (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC01", "sessionTitle": "JO-NAHS: Distributed Optimization and Estimation", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Perez-Salesa, Irene", "affiliation": "University of Zaragoza" }, { "name": "Aldana-López, Rodrigo", "affiliation": "Universidad De Zaragoza" }, { "name": "Sagues, Carlos", "affiliation": "Universidad De Zaragoza" } ], "keywords": [ "Distributed control and estimation", "Kalman filtering", "Control under communication constraints" ], "abstract": "In this work, we focus on distributed state estimation under event-triggered measurement sampling and estimator-to-estimator communication. We design a distributed Kalman-like filter, with fully asynchronous transmissions of measurements and estimates. The estimator nodes leverage the implicit information in not receiving new sensor measurements between events, resulting in stable estimates for any transmission sequence. Moreover, we show that the performance of the centralized Kalman-Bucy filter with full measurement data can be approximated arbitrarily well with our event-triggered solution, by tuning the event thresholds and the consensus gain in the filter, while reducing communication.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC01.2", "code": "WeC01.2", "title": "Limit Cycle Pattern for Agent Networks Formation Using a 2D Spatially Distributed Van Der Pol Equation (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC01", "sessionTitle": "JO-NAHS: Distributed Optimization and Estimation", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Aguilar, Luis T.", "affiliation": "Instituto Politecnico Nacional" }, { "name": "Orlov, Yury", "affiliation": "CICESE" } ], "keywords": [ "Distributed control and estimation", "Multi-agent systems", "Consensus" ], "abstract": "The synchronization of a large-scale multi-agent system has motivated continuum approaches, where PDEs provide an accepted framework to describe the coupled spatial and temporal dynamics of collective behavior. A challenging problem consists of generating and maintaining prescribed spatial patterns in the sky, which a network of autonomous agents must follow. In this paper, we address the consensus problem in two-dimensional deployment formation by proposing a spatially distributed modified Van der Pol equation. The resulting PDE model provides an oscillatory consensus dynamics that drives agents toward prescribed patterns on the plane, thus extending an ODE-based consensus approach to spatially distributed systems. The stability of the self-generated wave in 2D space is studied in the framework of Lyapunov functionals. Theoretical developments are supported by numerical simulations, which illustrate stable limit cycle formation and validate the analytical results.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC01.3", "code": "WeC01.3", "title": "Finite-Time Distributed Nash and Generalized Nash Equilibrium Seeking Via Sign-Based Dynamics Over Time-Varying Networks (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC01", "sessionTitle": "JO-NAHS: Distributed Optimization and Estimation", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Shi, Xinli", "affiliation": "Southeast University" } ], "keywords": [ "Distributed optimization", "Consensus", "Multi-agent systems" ], "abstract": "This paper deals with distributed algorithms for finding Nash equilibrium (NE) and Generalized NE (GNE) of noncooperative games in finite time over a time-varying network. We will first provide a finite-time (FT) convergent NE-seeking dynamics based on global agents’ states. Then, two types of FT convergent distributed NE-seeking dynamics are proposed by estimating the global states, through an edge- and node-based discontinuous consensus protocol with disturbance rejection, respectively. Furthermore, an FT convergent primal-dual dynamics is proposed for obtaining GNE for noncooperative games with constraints, which is further applied for designing two FT distributed GNE seeking algorithms with global constraints and coupling constraints, respectively. Finally, the performance of the proposed algorithms is testified by several numerical examples over time-varying networks.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC01.4", "code": "WeC01.4", "title": "Distributed Dynamic Event-Triggered Generalized Nash Equilibrium Seeking (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC01", "sessionTitle": "JO-NAHS: Distributed Optimization and Estimation", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Deng, Dongdong", "affiliation": "Southeast University" }, { "name": "Zhang, Yiying", "affiliation": "Southeast University" }, { "name": "Shi, Xinli", "affiliation": "Southeast University" }, { "name": "Xu, Xiangping", "affiliation": "Hohai University" }, { "name": "Wan, Ying", "affiliation": "Southeast University" } ], "keywords": [ "Distributed optimization", "Control under communication constraints", "Multi-agent systems" ], "abstract": "This paper investigates the generalized Nash equilibrium (GNE) seeking problem in noncooperative games (NGs) with coupled linear equality constraints, where the players' dynamics are modeled as high-order integrator systems subject to bounded disturbances. By employing the primal-dual method and high-pass filter technology, we propose a novel distributed robust GNE seeking algorithm. To mitigate the impact of disturbances on system performance, a finite-time compensation mechanism based on sliding mode control is designed within the algorithm. Furthermore, to reduce network communication load, a dynamic event-triggered mechanism (DETM) is introduced, allowing players to exchange their state information only when specific event-triggering conditions are met, thereby reducing both communication frequency and energy consumption. Analyses based on Lyapunov theory establish the exponential convergence of players' strategies to the GNE and ensure the exclusion of Zeno behavior. Finally, the proposed algorithm is applied to solve an NG problem in the electricity market, validating its effectiveness.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC01.5", "code": "WeC01.5", "title": "Graph-Based Distributed Nash Equilibrium Seeking for Potential Games (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC01", "sessionTitle": "JO-NAHS: Distributed Optimization and Estimation", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Santejudean, Tudor", "affiliation": "Technical University of Cluj-Napoca" }, { "name": "Satheeskumar Varma, Vineeth", "affiliation": "CRAN - Université De Lauraine" }, { "name": "Morarescu, Irinel Constantin", "affiliation": "Universite De Lorraine" }, { "name": "Busoniu, Lucian", "affiliation": "Technical University of Cluj-Napoca" } ], "keywords": [ "Distributed optimization", "Multi-agent systems" ], "abstract": "We introduce Distributed Asynchronous Potential function Decrease (DAPD), a discrete-time, graph-based algorithm for finding pure Nash equilibria in ordinal potential games. Two different settings are studied: one in which cost functions are twice-differentiable and convex, with Lipschitz first derivatives, and another when costs are only Lipschitz-continuous and may be non-convex. A novel graph-based update scheduler is proposed, which accelerates DAPD convergence by allowing parallel, decentralized updates of non-neighboring players. The scheduler chooses the next player to update in each neighborhood as the one with the largest decrease in cost function at the previous update. The graph topology is fixed, connected and undirected, and the update of each player depends only on its neighbors. We prove that when run with the proposed scheduler, DAPD converges to a pure Nash equilibrium in the differentiable setting, and to an epsilon-Nash equilibrium in the Lipschitz setting. In numerical experiments, DAPD with the new scheduler converges faster than with a round-robin scheduler, while better balancing the number of computations and communications than several synchronous and asynchronous baselines.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC01.6", "code": "WeC01.6", "title": "Distributed Optimization with Total Constraints Over Multi-Agent Networks without Aggregator (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC01", "sessionTitle": "JO-NAHS: Distributed Optimization and Estimation", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Adachi, Ryosuke", "affiliation": "Yamaguchi University" }, { "name": "Wakasa, Yuji", "affiliation": "Yamaguchi Univ" } ], "keywords": [ "Distributed optimization", "Multi-agent systems", "Consensus" ], "abstract": "This paper addresses distributed algorithms based on the alternating direction method of multipliers (ADMM) to solve optimization problems with total constraints. The total constraints are incorporated into the distributed resources management problem, such as in smart grid applications. This study proposes a novel representation of the total constraints inspired by a data-aggregation protocol over tree networks. Utilizing the proposed representation, distributed algorithms without the aggregator and consensus achievement are derived from the framework based on the ADMM. Because the proposed algorithms do not require the aggregator, they exhibit enhanced scalability, flexibility, and security.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC02.1", "code": "WeC02.1", "title": "A New Tire-Road Interaction Friction Model for Vehicle Dynamics Applications (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC02", "sessionTitle": "JO-CEP: Vehicle Dynamics and Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Martinez Molina, John J.", "affiliation": "CNRS GIPSA-Lab" }, { "name": "Boulanger, Maxime", "affiliation": "Manufacture Française Des Pneumatiques Michelin, CNRS GIPSA-Lab" }, { "name": "Sename, Olivier", "affiliation": "Universite Grenoble Alpes / Grenoble INP" }, { "name": "Dairay, Thibault", "affiliation": "Manufacture Française Des Pneumatiques Michelin" }, { "name": "Vayssettes, Jérémy", "affiliation": "ISAE" } ], "keywords": [ "Vehicle dynamic systems", "Automotive system identification and modelling", "Adaptive and robust control of automotive systems" ], "abstract": "This paper presents a new tire-road interaction friction model. It is intended for use in vehicle dynamics studies, estimation and control design. The model is based on physical insights and captures the behavior of tire frictional forces with respect to combined (longitudinal and lateral) slip velocities. Compared with existent models, this model is described by a small number of parameters. Motivated by the nature of induced forces of friction, the behavior of the tire-road interaction forces is based on a Steinmetz equivalent circuit with particular energy dissipation terms. In this paper we illustrate the effectiveness of the model by using experimental data of a flat-track tire test machine.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC02.2", "code": "WeC02.2", "title": "A Domain-Transformed Planning Framework for Fuel-Efficient HEV Acceleration (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC02", "sessionTitle": "JO-CEP: Vehicle Dynamics and Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Gim, Juhui", "affiliation": "Changwon National University" }, { "name": "Park, Junkyu", "affiliation": "Changwon National University" }, { "name": "Ahn, Changsun", "affiliation": "Pusan National University" } ], "keywords": [ "Hybrid, electric and alternative drive vehicles", "Automatic control, optimization, real-time operations in transportation", "Autonomous vehicles" ], "abstract": "This paper proposes a fuel-efficient acceleration planning framework for autonomous hybrid electric vehicles (HEVs) with reduced computational complexity. The proposed approach simultaneously determines the vehicle velocity trajectory and battery-energy usage during acceleration while satisfying prescribed terminal conditions. To improve computational efficiency, the acceleration-planning problem is reformulated in the velocity domain, removing explicit time dependence from the original optimal control problem. The transformed formulation allows the application of necessary optimality conditions to identify stationary candidate solutions and admissible switching points, significantly reducing the feasible solution space. The proposed framework is validated through comparison with dynamic programming under representative acceleration scenarios. The results show that the proposed method achieves comparable fuel efficiency with substantially lower computational effort. In addition, the framework enables systematic analysis of the interaction between acceleration distance, battery usage, and fuel consumption, providing useful insight into fuel-efficient acceleration behavior. The planned velocity and SOC trajectories can also serve as reference trajectories for supervisory energy-management and launch-control applications in autonomous HEVs.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC02.3", "code": "WeC02.3", "title": "Experimental Validation of Predictive Optimal Control Based Eco-Driving (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC02", "sessionTitle": "JO-CEP: Vehicle Dynamics and Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Lakshmanan, Vinith Kumar", "affiliation": "IFP Energies Nouvelles" }, { "name": "Sciarretta, Antonio", "affiliation": "IFP" } ], "keywords": [ "Nonlinear and optimal automotive control" ], "abstract": "The main goal of Eco-Driving (ED) is to maximize energy efficiency. This study evaluates a optimal control based ED system, obtained using Pontryagin's Minimum Principle (PMP) for an electric vehicle, in real-world traffic. A Visual Advisory System (VAS) on a tablet advises the driver to follow a target eco-speed in real-time. Tests were performed with two Renault Zoe vehicles in real world traffic conditions, one equipped with the ED system and one standard, over three routes in Rueil-Malmaison, France. The ED vehicle achieved average energy savings of 4.6~% on the regional route, 13.5~% on the urban route, and approximately 4.0~% on the highway. An analysis, using Dynamic programming (DP) as benchmark, is performed to quantify the impacts of modeling assumptions and driver behavior. Segment-level comparisons revealed additional energy consumption resulting from model simplifications and driver tracking ability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC02.4", "code": "WeC02.4", "title": "Modeling Driver Behavior across Varying Levels of Shared Autonomy with an Autonomous Controller (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC02", "sessionTitle": "JO-CEP: Vehicle Dynamics and Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Mai, Rene", "affiliation": "Rensselaer Polytechnic Institute" }, { "name": "Sears, Katherine", "affiliation": "Rensselaer Polytechnic Institute" }, { "name": "Julius, Agung", "affiliation": "Rensselaer Polytechnic Institute" }, { "name": "Mishra, Sandipan", "affiliation": "Rensselaer Polytechnic Institute" } ], "keywords": [ "Control architectures in automotive control", "Autonomous vehicles", "Vehicle dynamic systems" ], "abstract": "Driver behavior varies across driving scenario, autonomy level, and even mood. Prior work introduced the generalized two-point model for human steering, a linear auto-regressive (multiple) exogenous input (ARX) model relating near- and far-point angles and history to human steering input. This paper examines how the generalized model varies across drivers, scenarios, and levels of autonomy in a shared human-machine autonomy setting. Eleven drivers participated in the study, each completing 40 runs with different levels of autonomous input; the resulting data was used to identify driver-specific steering models. We examine the frequency response from the near- and far-point angles to human steering input, characterizing how steering behavior changes with level of autonomous input. We find the generalized models used to predict steering input in different drivers are closer to each other when autonomy levels are matched, indicating driver behavior changes significantly with change in the level of autonomous input. To compare generalized models, we reformulate the difference between generalized models as changes in feedforward and feedback transfer functions, corresponding to changes in driver action and changes in driver perception or proprioception, respectively.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC02.5", "code": "WeC02.5", "title": "Optimal Energy Management under Spatio-Temporal Constraints: An Application to Solar-Powered Vehicles (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC02", "sessionTitle": "JO-CEP: Vehicle Dynamics and Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Montalto, Lorenzo", "affiliation": "Chalmers University of Technology" }, { "name": "Murgovski, Nikolce", "affiliation": "Chalmers University of Technology" }, { "name": "Jarebrant, Timothy", "affiliation": "Chalmers University of Technology" } ], "keywords": [ "Nonlinear and optimal automotive control", "Electric and solar vehicles", "Intelligent transportation systems" ], "abstract": "This paper addresses a nonlinear optimal control problem for mission planning of long-range solar-powered electric vehicles to optimize trip time and energy management while subject to spatio-temporal constraints. The problem is formulated using first-principles modeling and solved through direct multiple shooting. The output is a driving profile that minimizes trip time while guaranteeing journey completion, constraint compliance and differentiability. The method is applied to a vehicle competing in the Bridgestone World Solar Challenge, a 3022 km race across Australia, where the spatio-temporal constraints arise from the competition's rules. Simulation results are compared with telemetric data collected during the competition.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC02.6", "code": "WeC02.6", "title": "Reachability-Based Benchmarking of Physics-Based and Data-Driven Models for Automated Driving (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC02", "sessionTitle": "JO-CEP: Vehicle Dynamics and Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Conejo, Carlos", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "Morcego, Bernardo", "affiliation": "Universitat Politecnica De Catalunya" } ], "keywords": [ "Automotive system identification and modelling", "Learning and adaptation in autonomous vehicles", "Modeling, supervision, control and diagnosis of automotive systems" ], "abstract": "Accurate vehicle models are essential for prediction and control in automated driving. Physics-based approaches provide interpretability and accuracy when system parameters are well captured, while data-driven models adapt to unmodeled dynamics at the cost of interpretability. This paper presents a systematic reachability-based benchmark comparing both approaches on a real autonomous platform. Models are evaluated under nominal, predefined safe state, and unexpected malfunction scenarios using accuracy, computational, and safety metrics. Results show that physics-based models excel in foreseen conditions, whereas data-driven models remain robust under unexpected disturbances. The study provides practical guidelines for model selection in safety-critical applications and motivates future hybrid strategies that combine accuracy with adaptability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC03.1", "code": "WeC03.1", "title": "Event-Triggered Control for Dual-Rotor Helicopter with Asymmetric Constraints Using Fully Actuated System Approach", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC03", "sessionTitle": "Event-Triggered and Adaptive Control Based on the FAS Theory", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Duan, Yulin", "affiliation": "Southern University of Science and Technology" }, { "name": "Zhang, Jia'ming", "affiliation": "Beihang University" }, { "name": "Ren, Weijie", "affiliation": "Southern University of Science and Technology" }, { "name": "Duan, Guang-Ren", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Control using FAS approach" ], "abstract": "This paper presents an event-triggered tracking control scheme for a dual-rotor helicopter using a Fully Actuated System (FAS) approach. A novel composite event-triggered mechanism is introduced, where the triggering condition depends on both the control input and the instantaneous tracking error. This mechanism flexibly adjusts the triggering threshold, significantly reducing the number of controller updates. The designed controller guarantees that the tracking error remains within asymmetric user-defined constraints. The simulation results verify the effectiveness of the proposed scheme.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC03.2", "code": "WeC03.2", "title": "Adaptive Event-Triggered Control for High-Order Nonlinear Impulsive Systems Subjects to Parametric Uncertainty and External Disturbance", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC03", "sessionTitle": "Event-Triggered and Adaptive Control Based on the FAS Theory", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Li, Yuanen", "affiliation": "Sun Yat-Sen University" }, { "name": "Li, Xuefang", "affiliation": "Sun Yat-Sen University" }, { "name": "Liu, Wanquan", "affiliation": "Sun Yat-Sen University" } ], "keywords": [ "Control using FAS approach" ], "abstract": "This paper investigates adaptive event-triggered control for high-order nonlinear impulsive systems (NISs) with parametric uncertainty and external disturbance. Existing stabilization methods for NISs are typically developed using first-order state-space models, which become impractical for high-order NISs due to the increased system dimension and the resulting complexity in controller design. To address this issue, a high-order fully actuated system (HOFAS) approach-based adaptive control strategy is developed, enabling direct stabilization without order reduction. An event-triggered mechanism (ETM) is jointly designed to reduce unnecessary control updates and communication load. The proposed method guarantees stability and boundedness of all closed-loop signals under impulsive effects. Simulation results validate the effectiveness of the approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC03.3", "code": "WeC03.3", "title": "A Fully Actuated System Approach for Distributed Event-Triggered Secondary Control of Multi-Bus DC Microgrids with Quantized Communication", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC03", "sessionTitle": "Event-Triggered and Adaptive Control Based on the FAS Theory", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Zuo, Zhiqiang", "affiliation": "Tianjin University" }, { "name": "Zhang, Sijie", "affiliation": "Tianjin University" }, { "name": "Li, Peng", "affiliation": "Tianjin University" }, { "name": "Wang, Yijing", "affiliation": "Tianjin Univ" } ], "keywords": [ "Control using FAS approach", "Fully-actuated systems in industry" ], "abstract": "This paper proposes a distributed secondary control strategy that integrates event-triggered updates and quantized communication using a fully actuated system (FAS) approach for a multi-bus DC microgrid. First, an underactuated large-signal model of the DC microgrid is transformed into its equivalent FAS representation to enable direct controller synthesis. Based on the resulting FAS model, a distributed secondary control strategy combining an adaptive dynamic event-triggered mechanism (ETM) and a dynamic encoder-decoder mechanism is developed to achieve accurate voltage regulation and proportional current sharing. Communication among distributed generators is restricted to quantized data packets triggered by the ETM, which significantly reduces transmission frequency and data volume per update. Moreover, the closed-loop stability is rigorously proven, and the Zeno phenomenon is excluded. Finally, comprehensive simulations under various scenarios validate the effectiveness and superiority of the proposed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC03.4", "code": "WeC03.4", "title": "Adaptive Decentralized Control for Fully Actuated Switched Interconnected Systems Via Improved Dynamic Surface Control", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC03", "sessionTitle": "Event-Triggered and Adaptive Control Based on the FAS Theory", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Yu, Mei", "affiliation": "BeiHang University" }, { "name": "Duan, Haibin", "affiliation": "Beihang University(formerly Beijing University of Aeronautics and Astronautics)" }, { "name": "Wei, Chen", "affiliation": "Beijing University of Aeronautics and Astronautics" } ], "keywords": [ "High-order backstepping", "High-order strict feedback systems" ], "abstract": "An improved adaptive decentralized dynamic surface control (DSC) method is proposed to address the fixed-time fuzzy tracking control problem of the fully actuated switched interconnected systems (FASISs) in this paper. By adopting a novel first-order filter with time-varying gain and integrating it with the backstepping design, a new set of backstepping control algorithms with low complexity is developed, even for FASISs. Then, due to the existence of uncertain nonlinearities, an improverd fixed-time stability lemma with DSC is constructed to guarantee that the equilibrium point achieves practical fixed-time stability, and that all signals in the closed-loop system stay bounded within a fixed time interval. Finally, a numerical example is provided to verify the effectiveness of the proposed strategy.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC03.5", "code": "WeC03.5", "title": "Performance-Barrier-Based Event-Triggered Control for High-Order Fully Actuated Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC03", "sessionTitle": "Event-Triggered and Adaptive Control Based on the FAS Theory", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Liu, Zifan", "affiliation": "Shandong Universtity" }, { "name": "Li, Chengxiang", "affiliation": "Shandong University" }, { "name": "Xing, Lantao", "affiliation": "Shandong University, Jinan" } ], "keywords": [ "High-order strict feedback systems", "High-order backstepping" ], "abstract": "This paper proposes a novel event-triggered control (ETC) strategy for a class of high-order strict-feedback nonlin\u0002ear systems, which integrates the High-Order Fully Actuated (HOFA) system theory with the performance barrier concept. Unlike conventional ETC schemes that enforce a monotonically decreasing Lyapunov function, the proposed performance-barrier ETC (PETC) allows the Lyapunov functions to increase within a defined performance barrier. This relaxation effectively mitigates conservatism and reduces the triggering frequency. Rigorous analysis guarantees the uniform boundedness of all closed-loop signals and eliminates Zeno behavior. Simulation results further demonstrate the proposed method’s effectiveness.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC03.6", "code": "WeC03.6", "title": "Tracking Control for Inertia Wheel Inverted Pendulum Based on Fully Actuated System Theory", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC03", "sessionTitle": "Event-Triggered and Adaptive Control Based on the FAS Theory", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Liu, Haowen", "affiliation": "Southern University of Science and Technology" }, { "name": "Chen, Shengjia", "affiliation": "Southern University of Science and Technology" }, { "name": "Li, Ping", "affiliation": "Southern University of Science and Technology" }, { "name": "Duan, Guang-Ren", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Sub-fully actuated systems", "Control using FAS approach" ], "abstract": "This paper presents a tracking control strategy of an inertia wheel inverted pendulum (IWIP) based on a fully actuated system (FAS) approach. The dynamics of the IWIP system are simplified as a high-order FAS model, with which the control law can be synthesized for the desired tracking error dynamics in a very simple and straightforward way. To improve tracking performance, a feasible trajectory is derived from system dynamics constraint for the FAS model. However, the effectiveness of this system dynamics constraint is highly susceptible to mismatched disturbances. To mitigate this, a disturbance observer is incorporated to provide real-time estimates for active compensation within the constraint. Simulations and experiments demonstrate the effectiveness of the proposed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC04.1", "code": "WeC04.1", "title": "Ancilla-Assisted Stabilization of a Qubit", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC04", "sessionTitle": "Quantum Control III", "sessionType": "Regular Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Chittaro, Francesca Carlotta", "affiliation": "Università Di Trento" } ], "keywords": [ "Quantum control", "Quantum systems", "Coherent quantum control" ], "abstract": "This paper investigates ancilla-assisted stabilization of a qubit coupled to a noisy, coherently-controlled ancilla qubit. The goal is to engineer the system-ancilla coupling in order to robustly drive the system to a target pure state. Necessary conditions are established on the coupling to ensure the target state is the unique and globally asymptotically stable equilibrium, both in the case of amplitude damping channel and for a generic noise channel.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC04.2", "code": "WeC04.2", "title": "Adaptive Measurements for Time-Optimal Quantum State Transfer", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC04", "sessionTitle": "Quantum Control III", "sessionType": "Regular Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Fujimoto, Aoi", "affiliation": "Meiji University" }, { "name": "Ichihara, Hiroyuki", "affiliation": "Meiji University" }, { "name": "Kanamoto, Rina", "affiliation": "Meiji University" } ], "keywords": [ "Quantum optimal control", "Quantum control" ], "abstract": "We investigate how adaptive projective measurements affect the expected arrival time in quantum state transfer on the Bloch sphere, accounting for finite measurement cost and uncertainty growth. The problem is formulated as a quasi-variational inequality (QVI), and the optimal policy is computed by dynamic programming. Numerical results reveal three regimes depending on the measurement cost, showing that adaptive measurements can shorten the expected arrival time in both noise-free and noisy cases.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC04.3", "code": "WeC04.3", "title": "SLH-Based Quantum Stochastic Master Equation for Quantum Systems with Imperfect Measurements", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC04", "sessionTitle": "Quantum Control III", "sessionType": "Regular Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Thien, Rebbecca TY", "affiliation": "Universite Paris Saclay" }, { "name": "Liang, Weichao", "affiliation": "Xi'an Jiaotong University" }, { "name": "Petersen, Ian R", "affiliation": "The Australian National University" } ], "keywords": [ "Quantum systems", "Quantum filtering", "Quantum control" ], "abstract": "Stochastic Master Equations(SMEs) are crucial for quantum state estimation, feedback design, and state stabilisation. The existing theoretical SLH formalism for open quantum networks, the Hudson–Parthasarathy quantum stochastic calculus, and the standard single-channel SMEs, all provide powerful tools for modelling quantum systems, but they do not offer an integration of these with explicit treatment of imperfect, multi-channel measurements. In this work, we propose a systematic and physically motivated method for modelling measurement imperfections directly within the SLH network formalism, by introducing a virtual beam splitter into the network rather than introducing them by hand..This structure leads naturally to a modified quantum SME that incorporates the imperfection arising from non-ideal detection. Building on this, we extend the derivation from the single-input single-output(SISO) setting to general multi-input multi-output(MIMO) quantum networks, yielding explicit multi-channel diffusive and jump SMEs that consistently reflect the underlying network structure. The resulting framework provides a scalable method to go from SLH models to physically realistic SMEs for large-scale quantum technologies, enabling rigorous quantum filtering and feedback control in the presence of detection limitations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC04.4", "code": "WeC04.4", "title": "Open Qubit Parameter Identification with Bounded Pulses", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC04", "sessionTitle": "Quantum Control III", "sessionType": "Regular Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Aloui, Ghaieth", "affiliation": "Centre Inria d'Université Côte D'Azur" }, { "name": "Beschastnyi, Ivan", "affiliation": "Inria Centre d'Université Côte D'Azur" }, { "name": "Sacchelli, Ludovic", "affiliation": "Inria" } ], "keywords": [ "Quantum control", "Quantum systems", "Quantum observers" ], "abstract": "We address the problem of parameter identification for a single open qubit subjected to relaxation and dephasing. Our approach is based on selecting a minimal set of carefully chosen qubit configurations that can be reliably prepared and measured in order to provide an interpretable methodology of parameter identification while potentially minimizing experimental overhead. The protocol relies on saturating control pulses to generate these configurations. In an idealized regime of infinite-amplitude pulses, we demonstrate that the parameters can be reconstructed analytically from the measured observables. We then consider large but finite pulses as a perturbation of this ideal regime and provide bounds on the estimation error introduced by the practical implementation. This framework allows us to separate the sources of uncertainty in the estimation procedure, distinguishing between statistical fluctuations arising from repeated measurements and modeling errors due to deviations from the ideal pulse regime.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC05.1", "code": "WeC05.1", "title": "Adaptive Coverage Path Planning for Autonomous Tillage Tractors in Non-Convex Agricultural Fields", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:45", "sessionCode": "WeC05", "sessionTitle": "LB: Autonomous Vehicles and Navigation", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Kim, Yong-Hyun", "affiliation": "Seoul National University" }, { "name": "Kim, Hak-Jin", "affiliation": "Seoul National University" }, { "name": "Lee, Jumyeong", "affiliation": "Seoul National University" }, { "name": "Lee, Chankeun", "affiliation": "Seoul National University" }, { "name": "Ahn, Seokhyun", "affiliation": "Seoul National University" }, { "name": "Jeon, Chan-Woo", "affiliation": "Seoul National University" } ], "keywords": [ "Agricultural robotics", "Control in precision agriculture", "Positioning and navigation in agriculture and forestry" ], "abstract": "This study proposed an adaptive coverage path planning algorithm for autonomous tractors in non-convex agricultural fields. The proposed algorithm generates complete coverage path by integrating inner working paths and headland coverage paths under vehicle-implement maneuvering constraints. Inner-track endpoints were adjusted adaptively to reduce uncovered areas, and turning connections were generated using a geometric–heuristic strategy combining Reeds–Shepp or Dubins curves with Hybrid A* when geometric paths intersected field borders or previously covered areas. The algorithm was evaluated in simulation using four field borders obtained from Google Earth Pro and validated through field tests with an autonomous tractor platform. The simulation results showed coverage rates greater than 99% in all target fields, and the field test achieved a coverage rate of 98.65%, confirming the practical applicability of the proposed method under actual field conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC05.2", "code": "WeC05.2", "title": "Multimodal SLAM for Robust Localization across Challenging Environmental Conditions with LiDAR-Vision-Thermal Fusion (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:45-16:00", "sessionCode": "WeC05", "sessionTitle": "LB: Autonomous Vehicles and Navigation", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Jeon, Munsu", "affiliation": "Hanyang University" }, { "name": "Lee, Taehee", "affiliation": "Hyundai Motor Company" }, { "name": "Sung, Dae-Un", "affiliation": "Hyundai Motor Company" }, { "name": "Oh, Ki-Yong", "affiliation": "Hanyang University" } ], "keywords": [ "Autonomous mobile robots", "Autonomous vehicles", "Robotic vision for AVs" ], "abstract": "This study proposes a multimodal simultaneous localization and mapping (SLAM) framework for robust localization across challenging environmental conditions using vision, thermal, and light detection and ranging (LiDAR) sensing. The proposed framework is designed to maintain consistent state estimation under severe illumination changes and environmental variations by adaptively leveraging information of heterogeneous sensors. The proposed framework features three key characteristics. First, a systematic calibration method is established to ensure geometric consistency across multimodal sensors. The intrinsic parameters of optical and thermal cameras are estimated to correct lens distortions and projection errors, while the extrinsic parameters between the 3D LiDAR and both cameras are precisely calibrated. This unified calibration enables accurate spatial alignment of multimodal measurements within a common reference frame. Second, a perception quality quantification method is proposed to evaluate the reliability of each sensor at every frame. Specifically, the density and distribution of informative features are assessed from point cloud data and images. These features are transformed into quantitative quality metrics that reflect the sensing capability under varying environmental conditions such as low illumination or structural sparsity. Third, an adaptive state estimation strategy is developed to update the mobility’s position and orientation based on the quantified perception quality. The proposed framework dynamically adjusts sensor contributions during the SLAM update process, assigning higher weights to more reliable modalities. This adaptive fusion mechanism enhances robustness against degraded sensing conditions and prevents performance deterioration caused by unreliable measurements. Systematic field experiments conducted under daytime, nighttime, and diverse environmental conditions demonstrate the effectiveness of the proposed framework. The experimental results confirm that the proposed multimodal SLAM achieves stable and robust localization performance across challenging real-world scenarios.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC05.3", "code": "WeC05.3", "title": "A Policy-Support Level of Service for Mixed Robot-Pedestrian Environments", "day": "Wednesday", "date": "August 26, 2026", "time": "16:00-16:15", "sessionCode": "WeC05", "sessionTitle": "LB: Autonomous Vehicles and Navigation", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Akkeh, Jowel", "affiliation": "York University" }, { "name": "Mohammadi, Ahmad", "affiliation": "York University" }, { "name": "Park, Peter", "affiliation": "York University" }, { "name": "Sohn, Gunho", "affiliation": "York University" } ], "keywords": [ "Autonomous mobile robots", "Modeling and simulation of transportation systems", "Planning, management and security in transportation" ], "abstract": "Sidewalk delivery robots are emerging as a last‑mile delivery option, yet several municipalities have imposed bans due to the lack of regulatory frameworks. Traditional pedestrian Level of Service (LOS) methods are built around density alone and cannot reflect how policy decisions (e.g., speed limits, yielding rules) affect interaction quality. This study introduces a policy decision-support LOS framework that links policy and site‑condition measures to LOS outcomes. We use real‑world observations and simulation to generate a novel robot-pedestrian LOS framework. We develop an interactive GIS platform that allows planners to change policy scenarios and visualize sidewalk performance across the network.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC05.4", "code": "WeC05.4", "title": "YOLOv8-MPPI Drone Avoidance with Virtual Experiments", "day": "Wednesday", "date": "August 26, 2026", "time": "16:15-16:30", "sessionCode": "WeC05", "sessionTitle": "LB: Autonomous Vehicles and Navigation", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Nagano, Riku", "affiliation": "Hiroshima University" }, { "name": "Nagahara, Masaaki", "affiliation": "Hiroshima University" } ], "keywords": [ "Autonomous navigation", "Aerial, field, and marine robotics", "Robot perception and sensing" ], "abstract": "This paper presents a real-time obstacle avoidance framework for small aerial robots by integrating YOLOv8-based visual perception with Model Predictive Path Integral (MPPI) control. Safe navigation in cluttered environments remains a key challenge for practical drone deployment, especially when relying solely on onboard cameras. We address this by embedding real-time object detection results into a stochastic optimal control framework suitable for nonlinear dynamics. The proposed system detects obstacles from camera images using YOLOv8 and converts detections into distance estimates incorporated into the MPPI stage cost. MPPI samples multiple control trajectories, evaluates their costs, and updates the control input via importance-weighted averaging, enabling efficient optimization under nonconvex conditions. The cost function combines goal tracking, control regularization, and a safety-margin-based obstacle penalty while maintaining constant forward motion. A central contribution is a practical virtual experimentation workflow built on the CoDrone Simulator and its Python SDK. Instead of relying on risky real-flight tuning, the simulator enables repeatable multi-obstacle stress tests, controlled sensing uncertainty injection, parameter sweeps for safety margins and horizon length, and latency evaluation. These experiments provide systematic guidelines for selecting conservative and robust parameters before deployment. For sim-to-real transfer, the MPPI core remains unchanged, and only perception and command interfaces are replaced. The framework is validated on a DJI Tello drone, demonstrating stable obstacle avoidance and consistent behavior under camera-based uncertainty. The proposed workflow offers a safe and practical pathway for deploying vision-based stochastic control methods to real aerial robots.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC05.5", "code": "WeC05.5", "title": "LC-Hybrid A*: LLM-Driven Semantic Costmap and Planner Weight Tuning for Context-Aware Delivery Navigation", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:45", "sessionCode": "WeC05", "sessionTitle": "LB: Autonomous Vehicles and Navigation", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Lee, Taekyun", "affiliation": "Gwangju Institute of Science and Technology (GIST)" }, { "name": "Jeong, Chanyeong", "affiliation": "Gwangju Institute of Science and Technology (GIST)" }, { "name": "Kim, Hyunwoo", "affiliation": "Gwangju Institute of Science and Technology (GIST)" }, { "name": "Ahn, Hyo-Sung", "affiliation": "Gwangju Institute of Science and Technology (GIST)" } ], "keywords": [ "Autonomous navigation", "Mechatronic system integration", "Task and motion planning" ], "abstract": "Classical global planners such as A* and Hybrid A* typically rely on hand-tuned costmaps and weight parameters, an approach that becomes fragile when traversability depends on mission context (e.g., fragile goods vs. express delivery). We present a lightweight framework that maps a top-down scene image to a semantic terrain costmap while jointly adapting planner weights using a local LLM. The perception front-end combines Segment Anything Model (SAM) to generate instance/region masks and CLIP to assign semantic labels, yielding structured terrain segments. Conditioned on these segments and a user-specified delivery context, a prompt-engineered LLM produces terrain-specific risk costs and planner weights (𝛼,𝛽,𝛾) that balance distance, terrain risk, and steering smoothness. Building on these outputs, we introduce LC(LLM Cost)-Hybrid A*, an extension of Hybrid A* that incorporates LLM-conditioned terrain costs and adaptive weighting, while preserving nonholonomic feasibility via a simplified kinematic bicycle model. In 2D simulations across multiple delivery contexts, the method exhibits consistent parameter trends and generates context-dependent routes (e.g., avoiding grass for fragile payloads while allowing grass traversal for express missions).", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC05.6", "code": "WeC05.6", "title": "Gradient-Free Safety Filter Using Multiple Backup Policies", "day": "Wednesday", "date": "August 26, 2026", "time": "16:45-17:00", "sessionCode": "WeC05", "sessionTitle": "LB: Autonomous Vehicles and Navigation", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Hwang, Sunwoo", "affiliation": "Seoul National University" }, { "name": "Cho, Sihyun", "affiliation": "Seoul National University" }, { "name": "Kim, H. Jin", "affiliation": "Seoul National Univ" } ], "keywords": [ "Autonomous navigation", "Task and motion planning" ], "abstract": "This paper proposes a gradient-free safety filter for nonlinear systems using multiple backup policies. Conventional control barrier function (CBF)-based safety filters rely on gradients of barrier functions and online quadratic programming, which induce computational burden and possible input discontinuities. The proposed method constructs an implicit safe set using forward-invariant backup sets and generates safe control inputs via convex blending without solving optimization problems. The approach guarantees safety invariance while preserving input continuity. Simulation results on a planar multirotor demonstrate effective obstacle avoidance and smooth control behavior.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC05.7", "code": "WeC05.7", "title": "Comparative Study of PPO-KAN for Discrete Autonomous Parking Control", "day": "Wednesday", "date": "August 26, 2026", "time": "17:00-17:15", "sessionCode": "WeC05", "sessionTitle": "LB: Autonomous Vehicles and Navigation", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Chung, Seungwan", "affiliation": "Sejong University" }, { "name": "Kang, Chang Ho", "affiliation": "Sejong Univ" }, { "name": "Choi, Ji Hun", "affiliation": "Sejong University" }, { "name": "Kim, Sun Young", "affiliation": "Kunsan National University" } ], "keywords": [ "Autonomous vehicles", "AI and learning-based control for automotive systems", "Motion control for AVs" ], "abstract": "This paper proposes proximal policy optimization (PPO)-Kolmogorov-Arnold network (KAN), which replaces the actor and critic networks of PPO with KAN, and applies it to discrete autonomous parking control. The proposed method was compared with PPO-multi-layer perceptron (MLP), double deep Q-network (DDQN), and discrete soft actor-critic (SAC) in a 2D parking simulator, and its performance was evaluated across five parking tasks with different initial position conditions. Experimental results show that it maintains competitive parking performance while using fewer parameters, and exhibits relatively stable performance under changes in initial position. These findings suggest that KAN can be a practical alternative for discrete autonomous forward parking control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC05.8", "code": "WeC05.8", "title": "Autoencoder Based Real-Time Thrust Anomaly Detection for Multicopter-Type UAV", "day": "Wednesday", "date": "August 26, 2026", "time": "17:15-17:30", "sessionCode": "WeC05", "sessionTitle": "LB: Autonomous Vehicles and Navigation", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Lee, Seungshin", "affiliation": "Chungnam National University" }, { "name": "Seo, Donghoon", "affiliation": "Chungnam National University" }, { "name": "Seo, Young", "affiliation": "Chungnam National University" }, { "name": "Mo, Yeonghyeon", "affiliation": "Chungnam National University" }, { "name": "Kim, Seungkeun", "affiliation": "Chungnam National University" }, { "name": "Suk, Jinyoung", "affiliation": "Chungnam National Univ" } ], "keywords": [ "Condition monitoring and maintenance of aerospace systems", "AI for aircraft and spacecraft navigation, guidance and control", "Guidance, navigation and control of aircraft and spacecraft" ], "abstract": "This study proposes an autoencoder-based real-time thrust anomaly detection method for multicopter UAVs. The proposed semi-supervised learning approach detects thrust anomalies without requiring fault data, which are often difficult to obtain in practice. The trained autoencoder model was implemented on an onboard mission computer and validated through real flight tests using a hexacopter platform. Experimental results show that the system successfully detected motor failure within 0.14~s, achieving an F1 score of 0.996. These findings demonstrate the feasibility and effectiveness of the proposed method for real-time anomaly detection in UAV propulsion systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC06.1", "code": "WeC06.1", "title": "Robust Data-Enabled Predictive Control for Disturbance Rejection (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC06", "sessionTitle": "Data-Driven Control VI", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Kong, Taejune", "affiliation": "DGIST" }, { "name": "Dinkla, Rogier", "affiliation": "Delft University of Technology" }, { "name": "van Wingerden, Jan-Willem", "affiliation": "Delft University of Technology" }, { "name": "Oomen, Tom", "affiliation": "Eindhoven University of Technology" }, { "name": "Oh, Sehoon", "affiliation": "DGIST" } ], "keywords": [ "Data-driven control theory" ], "abstract": "This paper proposes a Robust Data-enabled Predictive Control (Robust DeePC) framework to address the limitation of conventional DeePC under unknown constant input disturbance. Motivated by the Internal Model Principle, the proposed method augments the system with a disturbance state and introduces an auxiliary disturbance to reformulate the input Hankel matrix. This leads to a modified Willems’ Fundamental Lemma that enables prediction and optimization while accounting for disturbance effects. As a result, the controller behaves with integral-like action, effectively rejecting steady-state errors caused by disturbance. The proposed method requires no additional system identification or disturbance modeling, maintaining the data-driven nature of DeePC. Simulation results demonstrate that Robust DeePC significantly improves tracking performance over conventional DeePC by effectively rejecting unknown disturbance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC06.2", "code": "WeC06.2", "title": "Data-Driven Controlled Invariance Via Monotone Embeddings (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC06", "sessionTitle": "Data-Driven Control VI", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Makdesi, Anas", "affiliation": "Ludwig Maximilian University of Munich" }, { "name": "Zamani, Majid", "affiliation": "University of Colorado Boulder" }, { "name": "Jafarpour, Saber", "affiliation": "University of Colorado Boulder" } ], "keywords": [ "Data-driven control theory", "Learning methods for control" ], "abstract": "We present a novel embedding method for general nonlinear systems that facilitates data-driven invariance analysis. By leveraging bounds on the system's Jacobian derivatives, we construct a higher-dimensional embedding system that alternatingly simulates the original, possibly unknown, dynamics. This simulation relation allows us to compute controlled invariant sets for the embedding that are guaranteed to be valid for the original system. A crucial property of our embedding is that it can be transformed into a monotone system, enabling the use of efficient algorithms for invariant set computation. We demonstrate the efficacy of our data-driven embedding for safety verification on several nonlinear examples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC06.4", "code": "WeC06.4", "title": "Probabilistic Reduced-Dimensional Nonlinear Dynamics Modeling with Oblique Projections and LSTM (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC06", "sessionTitle": "Data-Driven Control VI", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Jia, Zhenzhen", "affiliation": "Lingnan University" }, { "name": "Mo, Yanfang", "affiliation": "Lingnan University, Hong Kong" }, { "name": "Qin, S. Joe", "affiliation": "Lingnan University, Hong Kong" } ], "keywords": [ "Machine and deep learning for system identification", "Probabilistic and Bayesian methods for system identification", "Data-driven control theory" ], "abstract": "This study presents Pred-LSTM that extends the probabilistic reduced-dimensional vector autoregressive (PredVAR) approach introduced in~[Mo and Qin, Automatica, 2025]. It replaces the linear VAR structure with a long short-term memory (LSTM) network to capture latent nonlinear temporal dependencies, while preserving interpretability through an oblique-projection-based dynamic–static decomposition. The proposed hybrid learning strategy alternates between expectation–maximization (EM)-based dimensionality reduction and gradient-based neural network optimization. A synthetic case study on nonlinear latent dynamics demonstrates the superiority of Pred-LSTM over linear baselines, highlighting its promise for control-oriented applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC06.5", "code": "WeC06.5", "title": "Iterative System Identification for Sim-To-Real Transfer on the Labyrinth Game (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC06", "sessionTitle": "Data-Driven Control VI", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Bi, Thomas", "affiliation": "ETH Zurich" }, { "name": "D'Andrea, Raffaello", "affiliation": "ETH Zurich" } ], "keywords": [ "Consensus and reinforcement learning control", "Machine and deep learning for system identification", "Learning methods for control" ], "abstract": "Sim-to-real reinforcement learning for contact-rich robotic tasks remains challenging due to modeling errors in both actuation and environment physics. In this work, we propose an iterative, task-informed system identification procedure for sim-to-real transfer and demonstrate it on a robotic system based on the labyrinth marble game. The method separates the identification of the actuation and the environment. First, we excite the real system with generic signals and identify actuation parameters such as gear ratios, delays, friction, and damping by matching open-loop trajectories between the real system and the simulator. Using this calibrated actuation model, we train a control policy in simulation. Second, we deploy this policy on the real system and collect task-relevant trajectories of the board and marble. By replaying the measured board motion in simulation, we identify environment parameters such as marble restitution and friction that best reproduce the real marble behavior. We then retrain the policy in the updated simulation and deploy it again on the real system. Experiments show that the proposed iterative identification procedure significantly improves sim-to-real transfer compared to single-stage identification, and that task-informed identification of environment parameters is crucial for reliable performance in contact-rich settings.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC06.6", "code": "WeC06.6", "title": "Distributed Data-Driven LQR Control from Fragmented Data (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC06", "sessionTitle": "Data-Driven Control VI", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Malladi, Surya", "affiliation": "University of Groningen" }, { "name": "Monshizadeh, Nima", "affiliation": "Universiy of Groningen" } ], "keywords": [ "Data-driven control theory", "Control over networks" ], "abstract": "This paper addresses the problem of computing the optimal Linear-Quadratic Regulator (LQR) for a linear time-invariant system when the data required for controller synthesis are distributed across multiple computing agents. Unlike most existing data-driven control methods which implicitly assume centralized storage and access to all input-state data, we consider a setting where each agent possesses only a single data sample and raw data cannot be shared. We develop distributed dynamical algorithms that allow all agents, through local communication only, to collectively compute the unique stabilizing solution of the algebraic Riccati equation and hence the optimal LQR gain. The first algorithm guarantees practical convergence, while an augmented PI-type scheme ensures exact convergence to the optimal solution. The effectiveness of the proposed method is demonstrated through a distributed LQR design for helicopter hover dynamics.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC07.1", "code": "WeC07.1", "title": "Asymptotic Convergence of a Continuous-Time Decentralized Online Estimation Algorithm with Additive Noises (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC07", "sessionTitle": "Recent Advances in Stochastic Multi-Agent Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Fu, Xiaozheng", "affiliation": "Ningbo University" }, { "name": "Chen, Yan", "affiliation": "Academy of Mathematics and Systems Science, Chinese Academy of Sciences" }, { "name": "Li, Tao", "affiliation": "Academy of Mathematics and Systems Science，Chinese Academy of Sciences" } ], "keywords": [ "Estimation and filtering", "Distributed control and estimation", "Multi-agent systems" ], "abstract": "This work is concerned with the asymptotic convergence of a continuous-time decentralized online estimation algorithm with additive noises. Each node has a linear measurement of an unknown parameter with random measurement matrices. The stochastic asymptotic stability lemmas by numerical approximation theory are developed for non-autonomous linear stochastic differential equations with random time-varying coefficients. Based on the stability results, sufficient conditions are obtained for the algorithm to ensure mean square convergence under fixed topologies. Furthermore, a special case where the measurement matrices contain a Markov chain is investigated.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC07.2", "code": "WeC07.2", "title": "Modeling and Topology Estimation of Low Rank Dynamical Networks (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC07", "sessionTitle": "Recent Advances in Stochastic Multi-Agent Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Cao, Wenqi", "affiliation": "Peking University" }, { "name": "Li, Aming", "affiliation": "Peking University" } ], "keywords": [ "Linear system identification", "Estimation and filtering", "Time series modeling" ], "abstract": "Conventional topology learning methods for dynamical networks become inapplicable to processes exhibiting low-rank characteristics. To address this, we propose the low rank dynamical network model which ensures identifiability. By employing causal Wiener filtering, we establish a necessary and sufficient condition that links the sparsity pattern of the filter to conditional Granger causality. Building on this theoretical result, we develop a consistent method for estimating all network edges. Simulation results demonstrate the parsimony of the proposed framework and consistency of the topology estimation approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC07.3", "code": "WeC07.3", "title": "Distributed Stochastic Source Seeking for Multi-Agent Systems with Different Constraint Sets and Binary-Valued Measurements (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC07", "sessionTitle": "Recent Advances in Stochastic Multi-Agent Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Zhang, Yuan", "affiliation": "Sichuan University" }, { "name": "Su, Yi", "affiliation": "Sichuan University" }, { "name": "Liu, Shu-Jun", "affiliation": "Sichuan University" } ], "keywords": [ "Extremum seeking and model free adaptive control", "Stochastic control", "Multi-agent systems" ], "abstract": "This paper presents a distributed stochastic source seeking control law for navigating multiple velocity-actuated agents with different position constraint sets toward a signal source. In this framework, agents receive relative position information from their neighbors over an undirected communication graph and obtain binary-valued measurements of the signal strength after perturbing their positions. These binary-valued measurements indicate whether the signal strength is below a fixed threshold. It is further demonstrated that the proposed control law enables agents to reach average consensus and collectively converge to the source location. Numerical simulations are presented to illustrate the effectiveness of the proposed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC07.4", "code": "WeC07.4", "title": "Asymptotic Properties for the Distributed Stochastic Gradient Descent Algorithm Over a Graphon (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC07", "sessionTitle": "Recent Advances in Stochastic Multi-Agent Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Hou, Xuping", "affiliation": "China University of Geosciences" }, { "name": "Zhang, Yuanyuan", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Zong, Xiaofeng", "affiliation": "China University of Geosciences" }, { "name": "Li, Tao", "affiliation": "Academy of Mathematics and Systems Science，Chinese Academy of Sciences" } ], "keywords": [ "Distributed optimization", "Stochastic differential equations" ], "abstract": "Achieving distributed optimization objectives for extremely large-scale complex networks is fundamentally intractable using standard methods. In this work, based on graphon theory, we develop a distributed stochastic gradient descent algorithm over a graphon for such systems. Firstly, by using the tools of graphon theory and stochastic analysis, we establish a rigorous framework for analyzing the asymptotic properties of the proposed distributed algorithm and derive precise estimations of the convergence rates for both consensus and optimization errors, respectively. Furthermore, the relationship between the time-varying algorithm gains and the asymptotic convergence rates in mean square sense is clarified. It is illustrated that for a connected graphon with appropriately designed algorithm gains, the consensus and optimization errors converge uniformly to zero in mean square at explicitly quantified rates we estimated.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC07.5", "code": "WeC07.5", "title": "Consensus on Stochastic Higher-Order Interaction Multi-Agent Networks (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC07", "sessionTitle": "Recent Advances in Stochastic Multi-Agent Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Chen, Yuhao", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Lv, Hang", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Pan, Lulu", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Luo, Peng", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Wang, Peng", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Shao, Haibin", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Consensus", "Control of networks", "Multi-agent systems" ], "abstract": "This paper examines the consensus problem of stochastic higher-order interaction multi-agent networks where each agent holds a vector-valued state and the inter-agent interactions are characterized by matrix-valued stochastic processes. Depending on whether the inter-agent interactions are independent or not, homogeneously weighted and heterogeneously weighted higher-order interaction multi-agent networks are considered, respectively. For each case, the stochastic differential equation models with state-dependent diffusion terms are constructed, which are reformulated in the sense of text{It}hat{text{o}} calculus under a unified framework. Subsequently, necessary and/or sufficient conditions for stochastic higher-order interaction multi-agent networks to achieve asymptotically unbiased mean average consensus and asymptotically unbiased mean square average consensus are derived. Numerical simulations are conducted to validate the theoretical findings and demonstrate the effectiveness of the proposed conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC07.6", "code": "WeC07.6", "title": "Digitization Effect on Consensus of Multiagent Systems with Distributed Sliding Mode Control (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC07", "sessionTitle": "Recent Advances in Stochastic Multi-Agent Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Liu, Zhaohui", "affiliation": "RMIT University" }, { "name": "Yu, Xinghuo", "affiliation": "RMIT University" }, { "name": "Chen, Zhiyi", "affiliation": "RMIT University" }, { "name": "Cao, Zhenwei", "affiliation": "Swinburne University" } ], "keywords": [ "Sliding mode control", "Digital implementation", "Switching stability and control" ], "abstract": "In this paper, we investigate the digitization effect on the consensus of multi-agent systems (MASs) with second-order linear dynamics under a distributed sliding mode control (SMC).We derive the sufficient conditions under which the MASs with the distributed SMC digitized by zero-order-hold (ZOH) are asymptotically stable while achieving consensus. Further, we obtain the upper bounds of steady state consensus and show how sampling period influences the consensus. Moreover, we present a necessary and sufficient condition for the existence of periodic oscillations under ZOH discretization. Simulations are done to show the effectiveness of the results and also several typical digitization behaviors.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC08.1", "code": "WeC08.1", "title": "Quantum Encrypted Control Via Entangled Sensing and Actuation (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC08", "sessionTitle": "Resilient Cyber Physical-Human Systems", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Ren, Zihao", "affiliation": "The University of Sydney" }, { "name": "Quevedo, Daniel", "affiliation": "Queensland University of Technology (QUT)" }, { "name": "Sukkarieh, Salah", "affiliation": "The Univ of Sydney" }, { "name": "Shi, Guodong", "affiliation": "The University of Sydney" } ], "keywords": [ "Cyber security networked control", "Control over networks", "Control of networks" ], "abstract": "控制已被广泛研究，以确保网络控制系统系统状态和控制输入的机密性。本文提出了一种计算效率高的加密控制框架，用于由量子通信实现的网络系统。传感器与执行器之间的量子通道用于生成相同的密钥，并通过量子密钥分发进一步增强其安全性。这些密钥实现了轻量级加密和解密，同时保持机密性和控制准确性。我们开发了一种基于量子密钥的线性系统状态反馈控制的新型加密-解密架构，并表征量子态错误对闭环稳定性的影响。特别地，我们确立了内在量子噪声存在一个临界阈值，低于此阈值保证稳定性。与经典加密控制方案不同，后者可能在单个密钥-位错误下崩溃，而所提议的量子加密控制对密钥缺陷表现出强烈的鲁棒性。", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC08.2", "code": "WeC08.2", "title": "Secure Estimator Design for Lur'e-Type Systems with Nonuniformly and Synchronously Sampled Measurements under Attacks (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC08", "sessionTitle": "Resilient Cyber Physical-Human Systems", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Gootzen, Julian", "affiliation": "Eindhoven University of Technology" }, { "name": "Chong, Michelle", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Cyber security networked control", "Control under communication constraints" ], "abstract": "Motivated by the need for real-time health monitoring of power distribution grids, we propose a secure state estimator design for continuous time Lur’e type systems with non-uniformly and synchronously sampled outputs which have potentially been maliciously corrupted. The secure state estimator provides state estimates with accuracy independent of the sensor attack, when less than half of the sensors are under attack and when all inter-sample times are upper bounded. We show convergence of the state estimation error under an impulsive system framework and provide an upper bound on the estimation error that is independent of the attack signals. The stability conditions are formulated as linear matrix inequalities, which can be used to design the observer parameters. We demonstrate the capabilities of the proposed secure state estimator on a low-voltage power distribution grid.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC08.3", "code": "WeC08.3", "title": "Persistent Zero-Dynamics Attacks Via a Switching-Based Scheme (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC08", "sessionTitle": "Resilient Cyber Physical-Human Systems", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Kanellopoulos, Aris", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Ishii, Hideaki", "affiliation": "University of Tokyo" }, { "name": "Sandberg, Henrik", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Cyber security networked control", "Resilient networked control systems" ], "abstract": "We investigate the problem of designing false-data injection attacks on cyber-physical systems that continuously affect the system state but remain stealthy in the sensor measurements. Towards this, we employ zero-dynamics attacks over limited time intervals since such attacks can be detected in practice when the states grow unbounded. Hence, we develop a switching scheme between zero-dynamics attacks and optimal state transfers that guarantees the containment of the internal states of the system within a given, inconspicuous, subset. Specifically, this multi-phase approach comprises alternating injections of destabilizing zero-dynamics attacks and optimal inputs that drive the system between output-nulling manifolds. The effect on the measured output and the states of each phase of the attack is theoretically analyzed while their synthesis is shown to fulfill the requirements of a persistent attack. Simulation results showcase the efficacy of our approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC08.4", "code": "WeC08.4", "title": "Confidentiality of Linear Control Systems with Quadratic Output under Sensor Attacks (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC08", "sessionTitle": "Resilient Cyber Physical-Human Systems", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Manaa, Zeyad", "affiliation": "Eindhoven University of Technology" }, { "name": "van de Wouw, Nathan", "affiliation": "Eindhoven Univ of Technology" }, { "name": "Chong, Michelle", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Cyber security networked control" ], "abstract": "We study the state estimation problem for linear control systems with quadratic outputs which are locally unobservable at the equilibrium. We show that, despite this inherent lack of observability, an adversary with sensor read and write capability can induce observability by injecting an appropriate signal into the measurement channel. Taking the role of an adversary, we characterize when an injected signal can or cannot induce observability and, in the successful case, construct an observer that achieves local exponential convergence of state estimates to the true states of the system. A simulation study demonstrates our results.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC08.5", "code": "WeC08.5", "title": "Ergodicity Analysis Approach towards Resilient Consensus under Mobile Attacks (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC08", "sessionTitle": "Resilient Cyber Physical-Human Systems", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Zheng, Zhongmin", "affiliation": "The University of Tokyo" }, { "name": "Ishii, Hideaki", "affiliation": "University of Tokyo" } ], "keywords": [ "Consensus", "Resilient networked control systems", "Multi-agent systems" ], "abstract": "This paper presents a general framework for analyzing resilient consensus under time-varying fault-recovery patterns. Such patterns arise in multi-agent systems subject to transient faults or mobile attacks, where agents may become faulty at arbitrary times and later recover. The resulting status-varying and inherently combinatorial behavior makes conventional system-theoretic analysis difficult to apply directly. To address this issue, we represent the system dynamics through a sequence of status-dependent matrices, called upper stochastic matrices, obtained after a suitable permutation of the state vector. This representation allows us to connect resilient consensus with weak ergodicity of matrix products and to derive a necessary and sufficient condition for agreement. The proposed analysis extends existing ergodicity-based approaches for consensus over time-varying models to resilient systems with mobile attacks. We further derive explicit graph-theoretic sufficient conditions for known algorithms. The framework provides an effective tool for studying resilient consensus in systems with time-varying agent statuses.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC08.6", "code": "WeC08.6", "title": "Self-Healing Hybrid Control As a Proxy for Detection and Mitigation of Sensor Attacks in Cooperative Driving (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC08", "sessionTitle": "Resilient Cyber Physical-Human Systems", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Huisman, Mischa", "affiliation": "Eindhoven University of Technology" }, { "name": "Murguia, Carlos", "affiliation": "Eindhoven University of Technology" }, { "name": "Lefeber, Erjen", "affiliation": "Eindhoven Univ of Technology" }, { "name": "van de Wouw, Nathan", "affiliation": "Eindhoven Univ of Technology" } ], "keywords": [ "Supervisory control and automata", "Resilient networked control systems", "Multi-agent systems" ], "abstract": "We propose a real-time hybrid controller scheme to detect and mitigate False-Data Injection (FDI) attacks on Cooperative Adaptive Cruise Control (CACC). Our method uses sensor redundancy to create equivalent controller realizations, each driven by distinct sensor subsets but producing identical control inputs when no attack occurs. By comparing control signals and measurements via majority voting, the scheme identifies compromised sensors in real-time and switches to a healthy controller, even under unconstrained attacker switching. The hybrid controller utilizes attack-dependent flow and jump sets, and resets the states of compromised controllers, resulting in a self-healing architecture. Simulation results demonstrate the effectiveness of this approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC09.1", "code": "WeC09.1", "title": "Trajectory-Level Self-Supervision for Simulation Driven Estimators (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC09", "sessionTitle": "JO-JSC: System Identification", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Lakshminarayanan, Braghadeesh", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Rojas, Cristian R.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Machine and deep learning for system identification", "Estimation and filtering", "Learning methods for control" ], "abstract": "Recent advancements in modeling have led to the construction of high-fidelity simulators (digital twins) to represent physical systems. However, the parameters of these high fidelity-simulators must be calibrated to match a given physical system. This motivated the construction of simulation-driven parameter estimators, built by generating synthetic observations for sampled parameter values and learning a supervised mapping from observations to parameters. However, when the parameters of the physical system lie outside the sampled range, predictions suffer from an out-of-distribution (OOD) error. This paper introduces a fine-tuning approach based on trajectory-level self-supervision for the Two-Stage approach, a simulation-driven estimator, that mitigates OOD effects and improves its accuracy. The effectiveness of the proposed method is verified through numerical simulations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC09.2", "code": "WeC09.2", "title": "Distributed Modeling and Sensitivity-Based Identification of Water Loads in Textile Facades with Spacer Fabric (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC09", "sessionTitle": "JO-JSC: System Identification", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Gschweng, Melanie", "affiliation": "University of Stuttgart" }, { "name": "Wehmeier, Marc", "affiliation": "University of Stuttgart" }, { "name": "Sawodny, Oliver", "affiliation": "Univ of Stuttgart" } ], "keywords": [ "Modeling and identification of environmental systems", "Climate change mitigation and adaptation modeling" ], "abstract": "Urban overheating calls for building materials with climate adaptation impacts. The textile-based HydroSKIN facade with spacer fabric can, among other things, evaporate water on hot days to achieve surface temperature reductions of up to 20 K. As groundwork for the development of operational and control strategies for such systems, this study presents a coupled transport and storage model describing water flow within the element. Sensitivity analysis and experimental validation demonstrate the models ability to quantitatively capture and represent the dominant mechanisms with deviations in range of 50 grams during irrigation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC09.3", "code": "WeC09.3", "title": "Quantization-Aware Statistical Guarantees for Dynamical Models (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC09", "sessionTitle": "JO-JSC: System Identification", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Metakalard, Abdelkader", "affiliation": "Université De Lorraine, CNRS, CRAN, LORIA, F-54000 Nancy, France" }, { "name": "Lauer, Fabien", "affiliation": "Université De Lorraine" }, { "name": "Colin, Kévin", "affiliation": "CRAN, Université De Lorraine, UMR CNRS 7039" }, { "name": "Gilson, Marion", "affiliation": "University of Lorraine" } ], "keywords": [ "Nonlinear system identification", "Statistical analysis", "Hybrid and switched systems modeling" ], "abstract": "This paper provides statistical guarantees on the accuracy of dynamical models learned from dependent data sequences. Specifically, we develop uniform error bounds that apply to quantized models and imperfect optimization algorithms commonly used in practical contexts for system identification. Two families of bounds are obtained: slow-rate bounds via a block decomposition and fast-rate, variance-adaptive, bounds via a novel spaced-point strategy. The bounds scale with the number of bits required to encode the model and thus translate hardware constraints into interpretable statistical complexities.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC09.4", "code": "WeC09.4", "title": "State Elimination in Polynomial Models: A Linear Algebraic Approach (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC09", "sessionTitle": "JO-JSC: System Identification", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "De, Sarthak", "affiliation": "Center for Dynamical Systems, Signal Processing, and Data Analytics (STADIUS), Dept. of Electrical Engineering (ESAT), KU Leuven" }, { "name": "De Moor, Bart", "affiliation": "K.U.Leuven" } ], "keywords": [ "Nonlinear system identification", "Realization theory", "Data-driven control theory" ], "abstract": "We derive a method to obtain the difference equation for discrete-time, autonomous, time-invariant, single-output polynomial state-space models, by proposing a linear algebraic framework for state elimination based on the novel ``model matrix”. For strongly locally observable models we show that the left null space of the model matrix is spanned by the rows of the Macaulay matrix associated with the corresponding output difference polynomial. We propose a state elimination algorithm that uses the singular value decomposition to compute a basis of the left null space and directly recovers the output difference polynomial. The method naturally accommodates floating-point coefficients. Numerical examples demonstrate the effectiveness of the proposed framework.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC09.5", "code": "WeC09.5", "title": "On Continuous-Time Sparse Identification of Nonlinear Polynomial Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC09", "sessionTitle": "JO-JSC: System Identification", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Alamir, Mazen", "affiliation": "Gipsa-Lab (CNRS-University of Grenoble)" } ], "keywords": [ "Nonlinear system identification", "Data-driven control theory", "Time series modeling" ], "abstract": "This paper leverages recent advances in high derivatives reconstruction from noisy-time series and sparse multivariate polynomial identification in order to improve the process of parsimoniously identifying, from a small amount of data, unknown Single-Input/Single-Output nonlinear dynamics of relative degree up to 4. The methodology is illustrated on the Electronic Throttle Controlled automotive system.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC09.6", "code": "WeC09.6", "title": "Recursive Identification for FIR Systems with Interval Binary Observations (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC09", "sessionTitle": "JO-JSC: System Identification", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Liu, Xingrui", "affiliation": "Chinese Academy of Sciences" }, { "name": "Li, Xin", "affiliation": "Chinese Academy of Sciences" }, { "name": "Shao, Mingjie", "affiliation": "Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences" }, { "name": "Zhao, Yanlong", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Nonlinear system identification", "Probabilistic and Bayesian methods for system identification" ], "abstract": "This paper investigates the identification of finite impulse response systems based on interval binary observations, where only the information on whether the outputs lie within a prefixed interval is available. Unlike existing works that typically consider single-threshold binary observations, the distribution function of interval binary observations no longer exhibits monotonicity. This makes it difficult for most existing identification algorithms to ensure a unique solution at each recursive step. To overcome this challenge, a novel three-step recursive identification algorithm named the partition–convergence–decision algorithm is developed. First, to ensure that the distribution function remains monotonic, the parameter space is partitioned into multiple partition regions. Secondly, a recursive projection identification algorithm is executed in parallel across all partition regions. Finally, a recursive decision criterion based on the prediction errors of the interval binary observations is constructed to determine the partition region containing the true system parameter. Both the almost sure and the mean square convergence of the proposed recursive projection identification algorithm within the correct region are proved, and the effectiveness of the recursive decision criterion in the probabilistic sense is established. A simulation example is presented to validate the theoretical results.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC10.1", "code": "WeC10.1", "title": "Constraint-Informed Neural Network–Enhanced EKF for Bearings-Only Target Motion Analysis", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC10", "sessionTitle": "Kalman Filtering II", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Cauchepin, Yann", "affiliation": "Naval Group, Univ. Grenoble Alpes, Inria, GIPSA-Lab" }, { "name": "Kibangou, Alain", "affiliation": "GIPSA-Lab, Univ. Grenoble Alpes, CNRS" }, { "name": "Fourati, Hassen", "affiliation": "GIPSA-LAB, CNRS" } ], "keywords": [ "Estimation and filtering", "Physics informed and grey box model identification", "Kalman filtering" ], "abstract": "This paper addresses the problem of Bearings-Only Target Motion Analysis and investigates the enhancement of Extended Kalman Filter (EKF) estimation through Artificial Intelligence (AI). Precisely, to improve tracking performance, we propose an AI-aided estimation framework based on a Constraint-Informed Neural Network (CINN), which incorporates soft physical constraints through a customized loss function optimized via Bayesian search. The CINN is trained using a tailored set of input features, mainly derived from the EKF, to capture and complement its estimation behavior. Monte Carlo simulations conducted on a simulated database generated according to a generic protocol demonstrate the effectiveness of the proposed EKF-CINN approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC10.2", "code": "WeC10.2", "title": "Extending Gaussian Process Submodel Online Learning (GPSOL) to State-Dependent and Time-Varying Hidden Functions", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC10", "sessionTitle": "Kalman Filtering II", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Husmann, Ricus", "affiliation": "University of Rostock" }, { "name": "Weishaupt, Sven", "affiliation": "University of Rostock" }, { "name": "Aschemann, Harald", "affiliation": "University of Rostock" } ], "keywords": [ "Gaussian process", "Learning methods for control", "Kalman filtering" ], "abstract": "This paper presents several extensions to the previously presented GPSOL algorithm for the online learning of submodels. This algorithm was built by seamlessly integrating the Recursive Gaussian Process Regression (RGP), as a way to learn the submodel (or hidden function), into an Extended Kalman Filter. The first extension of the algorithm aims at the consideration of uncertain inputs of the RGP. The proposed linearization-based approach takes advantage of the RGP structure to allow for an efficient and online-capable calculation of the RGP gradients. This enables the consideration of Kalman Filter states as RGP inputs, which greatly enhances the applicability of the algorithm to a much larger class of systems. As a second extension, an unlearning law is introduced into the RGP to handle time-varying submodels. Here, special care is taken to preserve the essential properties of the covariance matrix. Furthermore, we propose an adaptation strategy for the unlearning rate based on the Mahalanobis distance. The benefits of all extensions are demonstrated in a statistical evaluation for a nonlinear simulation model. Furthermore, the general benefits of the proposed method for the state estimation quality are shown.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC10.3", "code": "WeC10.3", "title": "NN-Based and Handcrafted Stochastic Dynamic Event-Triggering Mechanisms for Event-Based Estimation", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC10", "sessionTitle": "Kalman Filtering II", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Schmitt, Eva Julia", "affiliation": "Otto Von Guericke University" }, { "name": "Perez-Salesa, Irene", "affiliation": "University of Zaragoza" }, { "name": "Noack, Benjamin", "affiliation": "Otto Von Guericke University (OVGU)" }, { "name": "Sagues, Carlos", "affiliation": "Universidad De Zaragoza" } ], "keywords": [ "Kalman filtering", "Discrete event modeling and simulation", "Estimation and filtering" ], "abstract": "Event-based transmissions allow to efficiently reduce the communications overhead in wireless sensor networks. In the past, several event-based triggers and suitable remote estimators have been proposed. While the trigger parameters are usually chosen statically, in this paper, a dynamic approach is explored to design stochastic event-triggers using a handcrafted and a neural network (NN)-based approach. The benefit of the developed dynamic event-triggering mechanisms (DETMs) over static policies is the adaptability to varying system conditions which allows to maintain a specified transmission rate. Furthermore, the novel DETMs can be combined with existing estimators that use the implicit information conveyed in non-transmission instants. Opposed to other approaches, the reliability of the resulting estimates is maintained with the proposed DETMs by design.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC10.4", "code": "WeC10.4", "title": "Time Scale Generation by Kalman Smoother and Steady-State Kalman Filter for Atomic Clock Ensembles", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC10", "sessionTitle": "Kalman Filtering II", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Mochida, Shunsuke", "affiliation": "National Institute of Information and Communications Technology" }, { "name": "Kawaguchi, Takahiro", "affiliation": "Gunma University" }, { "name": "Yano, Yuichiro", "affiliation": "National Institute of Information and Communications Technology" }, { "name": "Hanado, Yuko", "affiliation": "National Institute of Information and Communications Technology" }, { "name": "Kurata, Yosuke", "affiliation": "Seiko Solutions Inc" }, { "name": "Koike, Masakazu", "affiliation": "Tokyo University of Marine Science and Technology" }, { "name": "Ishizaki, Takayuki", "affiliation": "Tokyo Institute of Technology" } ], "keywords": [ "Kalman filtering", "Filtering and smoothing", "Estimation and filtering" ], "abstract": "This paper investigates the performance of time scales generated by the Kalman filter and smoother for atomic clock ensembles. We mathematically prove that, under the linear Gaussian assumption, the time scale generated by the Kalman smoother represents the theoretical limit of the frequency stability, quantified by the Allan deviation, achievable by an ensemble of atomic clocks. In addition, an algorithm in the form of a steady-state Kalman filter is proposed to enhance the short-term stability of the generated time scale. The proposed method is obtained by partially modifying the Kalman gain of the steady-state Kalman filter. Numerical simulations with an ensemble of second-order clocks show that the proposed approach yields better short-term stability than the conventional steady-state Kalman filter and is close to the theoretical limit given by the Kalman smoother.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC10.5", "code": "WeC10.5", "title": "Denoising Diffusion Model-Enhanced Intelligent Particle Filter", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC10", "sessionTitle": "Kalman Filtering II", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Liu, Yifan", "affiliation": "China University of Petroleum (East China)" }, { "name": "Sheng, Li", "affiliation": "China University of Petroleum (East China)" }, { "name": "Gao, Ming", "affiliation": "China University of Petroleum (East China)" }, { "name": "Zhou, Donghua", "affiliation": "Shandong Univ. of Science and Technology" }, { "name": "Li, Chunyu", "affiliation": "China University of Petroleum (East China)" } ], "keywords": [ "Kalman filtering", "Machine and deep learning for system identification", "Estimation and filtering" ], "abstract": "Traditional state estimation methods exhibit inherent limitations in handling system nonlinearities. In this work, denoising diffusion probabilistic models (DDPMs) are introduced into the particle filtering framework for enhanced state estimation. To overcome the problem of particle degeneracy, a regression-based DDPM is trained to generate high-quality particles. The conventional sampling step is replaced by the reverse process of the DDPM, ensuring that the generated particles approximately follow the optimal proposal distribution. Then, particle weights are derived by leveraging the evidence lower bounds (ELBOs) to approximate the proposal density values. Finally, the effectiveness of the proposed intelligent particle filter is demonstrated by a numerical example.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC10.6", "code": "WeC10.6", "title": "Compressed Sensing under Unknown-But-Bounded Noises", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC10", "sessionTitle": "Kalman Filtering II", "sessionType": "Regular Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Erofeeva, Victoria", "affiliation": "Institute for Problems in Mechanical Engineering of the Russian Academy of Sciences" }, { "name": "Granichin, Oleg", "affiliation": "Sirius University of Science and Technology" }, { "name": "Len, Irina", "affiliation": "St. Petersburg State University" }, { "name": "Smetanina, Vlada", "affiliation": "Sirius University of Science and Technology" } ], "keywords": [ "Randomized algorithms in stochastic systems", "Stochastic adaptive control", "Estimation and filtering" ], "abstract": "Standard compressed sensing (CS) theory typically assumes noise bounded in l 2 -norm (e.g., Gaussian). However, in practice, noise can be unknown-but-bounded, as in low-light imaging or MRI artifacts. This paper presents an analysis of a proposed compressed sensing recovery algorithm that has been designed for parameter estimation under unknown-but-bounded noise. Experiments on images with various non-Gaussian noises demonstrate that proposed method outperforms classical l 2 -constrained recovery.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC13.1", "code": "WeC13.1", "title": "Vector-Space Optimization Framework Based on Projected Contraction Condition for Control Design with Input Saturation", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC13", "sessionTitle": "Optimization-Based Methods for Estimation and Control in Nonlinear Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Ryu, Myeongseok", "affiliation": "Korea Advanced Institute of Science and Technology (KAIST)" }, { "name": "Choi, Kyunghwan", "affiliation": "Korea Advanced Institute of Science and Technology" }, { "name": "You, Sesun", "affiliation": "Incheon National University" } ], "keywords": [ "Optimization-based estimation and control" ], "abstract": "Conventional feedback control design based on contraction theory typically requires matrix-valued contraction metrics, which can limit real-time applicability as the system dimension increases and make direct handling of input saturation nontrivial. To address these issues, we project the contraction condition onto the instantaneous trajectory-error direction and introduce the metric-weighted error vector as the optimization variable. This yields a lower-dimensional formulation that avoids direct optimization over the full matrix-valued metric and enables input saturation constraints to be incorporated directly. Additionally, an energy-based constraint is introduced to resolve the scale ambiguity of condition-number minimization and maintain sufficient control effort. The effectiveness of the proposed method is validated through numerical simulations using the Lorenz system.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC13.2", "code": "WeC13.2", "title": "Iterative Model Predictive Path Integral for Safe Reinforcement Learning Control (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC13", "sessionTitle": "Optimization-Based Methods for Estimation and Control in Nonlinear Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Poinsignon, Elliot", "affiliation": "Framatome, L2S" }, { "name": "Hill, Ashley", "affiliation": "Framatome" }, { "name": "Stoica, Cristina", "affiliation": "CentraleSupélec, Université Paris-Saclay" }, { "name": "Segond, Mathieu", "affiliation": "Framatome" } ], "keywords": [ "Optimization-based estimation and control", "Learning methods for optimal control" ], "abstract": "This paper proposes a new Model Predictive Path Integral (MPPI) safety filter for Reinforcement Learning-based control which can handle non-smooth/non-convex constraints (e.g., arising from flight in cluttered environment). Standard MPPI can suffer from poor sampling efficiency in such environments. Therefore to mitigate this issue, this paper proposes an iterative strategy that increases sample efficiency by guiding the generation of samples toward safer regions of the state-space. The proposed approaches are validated in a custom realistic simulator.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC13.3", "code": "WeC13.3", "title": "Tailoring the Microstructure of Steels During Quenching Using Optimal Control (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC13", "sessionTitle": "Optimization-Based Methods for Estimation and Control in Nonlinear Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Baumann, Henry", "affiliation": "Karlsruhe Institute of Technology (KIT)" }, { "name": "Ratke, Denis", "affiliation": "Karlsruhe Institute of Technology" }, { "name": "Martschin, Juri", "affiliation": "Technical University Dortmund, Institute of Forming Technology and Lightweight Components" }, { "name": "Tekkaya, Erman", "affiliation": "Uni Dortmund" }, { "name": "Meurer, Thomas", "affiliation": "Karlsruhe Institute of Technology (KIT)" } ], "keywords": [ "Applications of optimal control", "Optimal control of PDE systems", "Control of complex systems" ], "abstract": "Tailoring the phase transformation of steels is crucial during the production of hardened components, since the phase composition determines the mechanical properties of the product. Therein, the temperature history of the steel sample during quenching from an initial austenitic phase is a driving factor for the microstructure evolution. Employing diffusionless and diffusion-controlled phase transformation kinetics, an optimization-based scheme is proposed to control the phase composition of a steel sample. The approach first solves decoupled optimal phase-control problems sequentially to determine a desired temperature trajectory to achieve different phase compositions along one workpiece. These temperature trajectories represent the transient thermal references for the steel sample to reach the desired phase composition. For this purpose, a model order reduction technique is applied to the heat equation and a tracking type optimal control problem is formulated and solved. Two different desired phase compositions are prescribed for two subareas of a steel sample to evaluate the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC13.4", "code": "WeC13.4", "title": "Koopman-Based NMPC for Virtually Coupled Train Control System (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC13", "sessionTitle": "Optimization-Based Methods for Estimation and Control in Nonlinear Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Zhang, Yiwen", "affiliation": "Beijing Jiaotong University" }, { "name": "Calogero, Lorenzo", "affiliation": "Politecnico Di Torino" }, { "name": "Li, Shukai", "affiliation": "Beijing Jiaotong University" }, { "name": "Rizzo, Alessandro", "affiliation": "Politecnico Di Torino" }, { "name": "Proskurnikov, Anton V.", "affiliation": "Politecnico Di Torino" } ], "keywords": [ "Model predictive control", "Optimization-based estimation and control", "Interconnected nonlinear systems" ], "abstract": "This paper investigates an analytical Koopman-based nonlinear model predictive control (K-NMPC) approach for tracking control of virtually coupled train systems. A nonlinear train movement model incorporating train dynamics, speed and control input limits, passenger comfort constraints, and collision avoidance is systematically lifted into a finite-dimensional Koopman space through closed-form observable functions. After freezing the affine parameter-varying lifted predictor along the shifted predicted trajectory, the online optimal control problem is solved as a quadratic program that can be solved efficiently. The proposed K-NMPC is benchmarked against a time-discrete NMPC scheme, demonstrating comparable control performance with significantly reduced online computation time and strong potential for real-time implementation in practical virtually coupled train control systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC13.5", "code": "WeC13.5", "title": "Balancing a Flying Inverted Pendulum with an Unknown Length Using Model Predictive Control and a Genetic Algorithm Estimator", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC13", "sessionTitle": "Optimization-Based Methods for Estimation and Control in Nonlinear Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Paul, Esther", "affiliation": "The University of New South Wales" }, { "name": "Torok, Mitchell", "affiliation": "The University of New South Wales" }, { "name": "Deghat, Mohammad", "affiliation": "University of New South Wales" } ], "keywords": [ "Optimization-based estimation and control", "Model predictive control", "Real-time optimal control" ], "abstract": "This paper proposes an online Genetic Algorithm (GA) estimator and a Model Predictive Control (MPC) approach to solve the flying inverted pendulum problem in a practical experiment where the pendulum length is unknown. The performance of the MPC approach was demonstrated on a practical system through disturbance and trajectory tracking tests to assess controller robustness and tracking accuracy. The convergence speed and accuracy of the online GA estimator were validated on a practical system using different initial conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC13.6", "code": "WeC13.6", "title": "A Simple and General Framework for Robust Stability in Moving Horizon Estimation (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC13", "sessionTitle": "Optimization-Based Methods for Estimation and Control in Nonlinear Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Zemouche, Ali", "affiliation": "CRAN UMR CNRS 7039, University of Lorraine" }, { "name": "Guerra, Thierry Marie", "affiliation": "Polytechnic University Hauts-De-France Valenciennes" } ], "keywords": [ "Optimization-based estimation and control", "Robust estimation", "Stability of nonlinear systems" ], "abstract": "This paper addresses the robust stability analysis of Moving Horizon Estimation (MHE) using a general yet simple framework. Building on newly introduced mathematical lemmas and under suitable assumptions, we establish a novel qualitative stability criterion. We then examine a particular case based on the well-known filtering-prior prediction strategy, for which we derive quantitative stability conditions and provide a brief analytical comparison with existing results.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC14.1", "code": "WeC14.1", "title": "Neural ODE Predictive Control with Error Dynamics Learned from Demonstrations for Trajectory Tracking (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC14", "sessionTitle": "Recent Advances in Nonlinear and Learning-Aided Control under Limited Information II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Woo, Junhui", "affiliation": "Kyungpook National University" }, { "name": "Kim, Taehyeong", "affiliation": "Kyungpook National University" }, { "name": "Lee, Sangmoon", "affiliation": "Kyungpook National University" } ], "keywords": [ "Model predictive control", "Design methods for data-based control", "Learning methods for optimal control" ], "abstract": "This paper presents a data-driven optimal control framework that incorporates a Neural ODE–based error dynamics model, built with MLP blocks, into an MPC scheme. Traditional MPC depends on fixed analytical models, which reduces adaptability and causes uncertainties under changing conditions. The proposed approach learns the control input term and then the full control-affine error dynamics using an efficient MLP structure. The Neural ODE applies Euler integration to capture temporal error evolution and replaces the analytical prediction model. Using demonstration data from a PD controller, the method enables more accurate trajectory tracking under uncertainty.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC14.2", "code": "WeC14.2", "title": "Output Reachability Analysis of Wind Turbine Systems under Fuzzy Time-Dependent Sampled-Data Control (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC14", "sessionTitle": "Recent Advances in Nonlinear and Learning-Aided Control under Limited Information II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Balasubramani, Visakamoorthi", "affiliation": "Kyungpook National University" }, { "name": "Hur, Sung-ho", "affiliation": "Kyungpook National University" } ], "keywords": [ "Sampled-data/digital control", "Lyapunov methods", "Application of nonlinear analysis and design" ], "abstract": "This article presents a time-dependent sampled-data control strategy for synthesizing the output reachability of permanent magnet synchronous generator-based wind turbine systems using the fuzzy approach. First, the nonlinear wind turbine model is represented as a set of fuzzy linear subsystems with bounded disturbances. Unlike conventional sampled-data control, a sampling-time variable dependent sampled-data control that varies within each sampling period is designed using fuzzy rules, thereby forming a closed-loop system. Next, a sampling-variable-dependent discontinuous Lyapunov-Krasovskii functional combined with a fuzzy membership function-dependent H∞ technique is employed to derive sufficient reachability conditions. Finally, the applicability and superiority of the proposed control strategy are demonstrated through simulations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC14.3", "code": "WeC14.3", "title": "A New Fuzzy Memory Sampled-Data Control for Vehicle Active Suspension Systems under Time-Varying Loads and Cyber-Attacks (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC14", "sessionTitle": "Recent Advances in Nonlinear and Learning-Aided Control under Limited Information II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Moorthy, Janani", "affiliation": "The Gandhigram Rural Institute (Deemed to Be University), Gandhigram - 624302, Tamil Nadu, India" }, { "name": "Palanisamy, Muthukumar", "affiliation": "The Gandhigram Rural Institute (Deemed to Be University)" } ], "keywords": [ "Sampled-data/digital control", "Lyapunov methods", "Application of nonlinear analysis and design" ], "abstract": "This article proposes a novel time-constrained aperiodic sampled-data controller for vehicle active suspension systems (VASSs) subject to road disturbances, transmission delays, cyber-attacks, and varying vehicle loads. First, to capture the variations in sprung and unsprung masses, a Takagi–Sugeno fuzzy model is developed to represent the nonlinear suspension dynamics. By incorporating transmission delays and cyber-attacks, a new time-constrained fuzzy memory sampled-data control scheme is established for the system. In this framework, cyber-attacks are modeled using a Bernoulli-distributed stochastic variable. Furthermore, the sufficient asymptotic stability condition for the VASS is obtained using a Lyapunov functional and formulated in terms of linear matrix inequalities, ensuring the desired minimal H∞ performance. Ultimately, simulation studies are presented to demonstrate the applicability and superiority of the developed control strategy.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC14.4", "code": "WeC14.4", "title": "Enhancing Automotive Paint Micro Defect Detection Via Generative Video Augmentation (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC14", "sessionTitle": "Recent Advances in Nonlinear and Learning-Aided Control under Limited Information II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Nam, Changwoo", "affiliation": "Jeonbuk National University" }, { "name": "Lee, Sang Jun", "affiliation": "Jeonbuk National University" } ], "keywords": [ "Nonlinearity learning from data", "Fault detection and isolation", "Robust learning systems" ], "abstract": "Detecting micro-defects on automotive paint surfaces is a challenging task, as these defects exhibit extremely low contrast and remain nearly imperceptible under standard static lighting. To resolve this, utilizing specialized lighting to induce dynamic visual changes, such as shifting reflections and shadows, is essential for revealing defect features to deep learning models. Thus, we devised a specialized data acquisition system and an automated dataset construction pipeline to efficiently extract and label defect sequences from the captured images. However, acquiring sufficient data in industrial settings is difficult. To address this problem, we propose a data augmentation pipeline using WanVideo to synthesize realistic defect sequences. Experimental results demonstrate that our method significantly improves detection performance, confirming that generative video augmentation effectively overcomes data scarcity in industrial inspection.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC14.5", "code": "WeC14.5", "title": "Unsupervised Selective Multi-View Pixel Optimization Method for Omnidirectional Stereo Matching (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC14", "sessionTitle": "Recent Advances in Nonlinear and Learning-Aided Control under Limited Information II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Lee, Jae Myeong", "affiliation": "Jeonbuk National University" }, { "name": "Lee, Sang Jun", "affiliation": "Jeonbuk National University" } ], "keywords": [ "Nonlinearity learning from data", "Optimization-based estimation and control", "Learning methods for optimal control" ], "abstract": "Omnidirectional multi-view stereo matching allows for full-surround depth perception using only a small number of field-of-view (FOV) fisheye cameras. However, supervised methods, which need ground truth data, face the significant challenge of high labeling costs. While fisheye cameras offer a wide FOV, unsupervised learning based on photometric loss can lead to mismatching due to occlusion problems within the overlapping regions. To address this, we propose the selective multi-view pixel minimum reprojection loss which selects the most optimal pixel from the overlapping multi-view regions and incorporates it into the reprojection loss calculation. Through experiments, we achieved higher performance compared to existing studies and demonstrated performance comparable to supervised methods. The code will be publicly available at https://github.com/Jmyeong/SMP-loss.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC15.1", "code": "WeC15.1", "title": "An Extension of Multi-High-Gain Observer Approach to a Class of Triangular Nonlinear Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC15", "sessionTitle": "Nonlinear Observers", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Besancon, Gildas", "affiliation": "Grenoble INP - UGA" } ], "keywords": [ "Nonlinear observers and filters" ], "abstract": "Building upon a recent result on 'multi-high-gain' observer design for a class of systems which are not observable for any input, this paper proposes a new observer solution which can be applied to an extended class of triangular systems. This observer requires an adapted excitation condition, in a way which can be more easily satisfied as compared to previously addressed special cases, thanks to the multi high gain framework. It is also underlined how the proposed design turns out to look like a KKL approach. Simulation examples illustrate its application.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC15.2", "code": "WeC15.2", "title": "Vision-Aided Relative State Estimation for Approach and Landing on a Moving Platform with Inertial Measurements", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC15", "sessionTitle": "Nonlinear Observers", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Bouazza, Tarek", "affiliation": "Laboratoire I3S UMR 7271 UCA-CNRS" }, { "name": "Melis, Alessandro", "affiliation": "CNRS Sophia Antipolis, Nice" }, { "name": "Berkane, Soulaimane", "affiliation": "Université Du Québec En Outaouais" }, { "name": "Mahony, Robert", "affiliation": "Australian National University" }, { "name": "Hamel, Tarek", "affiliation": "Université Côte D'Azur" } ], "keywords": [ "Nonlinear observers and filters", "Observer design" ], "abstract": "This paper tackles the problem of estimating the relative position, orientation, and velocity between a UAV and a planar platform undergoing arbitrary 3D motion during approach and landing. The estimation relies on measurements from Inertial Measurement Units (IMUs) mounted on both systems, assuming there is a suitable communication channel to exchange data, together with visual information provided by an onboard monocular camera, from which the bearing (line-of-sight direction) to the platform’s center and the normal vector of its planar surface are extracted. We propose a cascade observer with a complementary filter on SO(3) to reconstruct the relative attitude, followed by a linear Riccati observer for relative position and velocity estimation. Convergence of both observers is established under persistently exciting conditions, and the cascade is shown to be almost globally asymptotically and locally exponentially stable. We further extend the design to the case where the platform’s rotation is restricted to its normal axis and show that its measured linear acceleration can be exploited to recover the remaining unobservable rotation angle. A sufficient condition to ensure local exponential convergence in this setting is provided. The performance of the proposed observers is validated through extensive simulations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC15.3", "code": "WeC15.3", "title": "A Nonlinear State Observer Using past Measurements with Its Application to Bearing-Only Target Motion Analysis", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC15", "sessionTitle": "Nonlinear Observers", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Dinesh, Ajul", "affiliation": "Aalborg University" }, { "name": "Efimov, Denis", "affiliation": "Inria" } ], "keywords": [ "Nonlinear observers and filters", "Robust estimation", "Stability of nonlinear systems" ], "abstract": "This paper presents the design of nonlinear observers that utilize both current and past measurements of outputs and inputs to estimate the states of discrete-time dynamical systems. Initially, for generic discrete-time systems with Lipschitz nonlinear dynamics, we design past measurement-dependent observers with time-varying gains to ensure the asymptotic stability of the estimation error dynamics. The proposed observer design is then applied for state estimation in a BOTMA scenario, where the source agent estimates the states of a maneuvering target in the presence of disturbances, relying only on a sequence of noisy bearing measurements and input values. The time-varying observer gains for BOTMA are obtained by solving a set of time-varying linear matrix inequalities, and input-to-state stability (ISS) of the error dynamics is established under perturbations. Compared to existing stochastic filtering-based approaches for BOTMA, the proposed method provides robustness and stability guarantees. Simulation comparisons further demonstrate the effectiveness of the proposed observers for state estimation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC15.4", "code": "WeC15.4", "title": "On the Behavior Assignment Problem", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC15", "sessionTitle": "Nonlinear Observers", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Mazzolani, Francesca", "affiliation": "University of Bologna" }, { "name": "Bin, Michelangelo", "affiliation": "University of Bologna" }, { "name": "Marconi, Lorenzo", "affiliation": "Univ. Di Bologna" } ], "keywords": [ "Nonlinear observers and filters", "Output feedback nonlinear control" ], "abstract": "This paper introduces the asymptotic behavior assignment problem for nonlinear systems. Given a controlled system and a reference system with an ``open'' input, the goal is to design a regulator such that, for every admissible input, the asymptotic input--output behavior of the closed-loop system reproduces that of the reference. This formulation captures, as special cases, classical model matching, disturbance rejection, and master--slave synchronization, but does not assume that an explicit tracking or regulation error is available for feedback. Motivated by nonlinear output regulation, we discuss how steady-state concepts for autonomous systems must be adapted when the closed-loop dynamics is not autonomous. In a SISO normal-form setting we devise sufficient conditions for the solution of the behavior assignment problem by introducing a synchrony-detection signal whose convergence to zero is equivalent to successful behavior assignment, thereby reducing the problem to a standard stabilization one. Two examples---a tunnel-diode circuit with multiple input-dependent equilibria, and a pendulum frequency-matching problem---illustrate how the proposed framework avoids artificially selecting a specific steady state.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC15.5", "code": "WeC15.5", "title": "Prescribed-Time Mean-Square Stabilization with Nonholonomic Constraints", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC15", "sessionTitle": "Nonlinear Observers", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Li, Wuquan", "affiliation": "Ludong University" }, { "name": "Wang, Hui", "affiliation": "Ludong University" }, { "name": "Krstic, Miroslav", "affiliation": "Univ. of California at San Diego" } ], "keywords": [ "Output feedback nonlinear control", "Lyapunov methods", "Observer design" ], "abstract": "We solve the prescribed-time mean-square stabilization problem, providing the first output-feedback designs for stochastic systems with nonholonomic constraints. In contrast to existing designs, which typically assume known growth rates and guarantee only asymptotic performance, our designs achieve convergence within a user-specified time, regardless of the initial conditions even when the growth rates are unknown. With the effect of stochastic noise and nonholonomic constraints, how to construct a new observer and a novel output-feedback control to achieve prescribed-time convergence, is a challenging problem. Our control scheme ensures that the system states, observers, and controllers converge to zero within the same prescribed time.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC15.6", "code": "WeC15.6", "title": "On Hyperexponential Observers for Linear Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC15", "sessionTitle": "Nonlinear Observers", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Zhdanov, Viktor", "affiliation": "ITMO University" }, { "name": "Zimenko, Konstantin", "affiliation": "ITMO University" }, { "name": "Efimov, Denis", "affiliation": "Inria" }, { "name": "Polyakov, Andrey", "affiliation": "INRIA Lille" } ], "keywords": [ "Nonlinear observers and filters", "Observer design", "Lyapunov methods" ], "abstract": "This paper addresses the problem of observer design for linear systems with emphasis on achieving hyperexponential convergence of the estimation error. Theorems on hyperexponential stability at origin are proposed for both explicitly and implicitly defined Lyapunov functions. Based on these results, a novel time-invariant hyperexponential observer, which is not finite-time, is proposed. Numerical simulations illustrate that, compared to a finite-time counterpart, the proposed hyperexponential observer ensures a comparable convergence rate in the vicinity of the origin, while offering reduced sensitivity to measurement noise.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC16.1", "code": "WeC16.1", "title": "Sliding-Mode Controllers Implementation for Direct Yaw Moment Control in Four-Wheel Steered Ground Vehicles (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC16", "sessionTitle": "Sliding Mode Applications in Robotics and Autonomous Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Grikenis, Tomas", "affiliation": "Vilnius Gediminas Technical University" }, { "name": "González, Andrés", "affiliation": "Universidad Nacional Autónoma De México" }, { "name": "Kojis, Paulius", "affiliation": "Vilnius Gediminas Technical University" }, { "name": "Fridman, Leonid", "affiliation": "National Autonomous University of Mexico" }, { "name": "Skrickij, Viktor", "affiliation": "Vilnius Tech" } ], "keywords": [ "Adaptive and robust control of automotive systems", "Control architectures in automotive control", "Vehicle dynamic systems" ], "abstract": "In this paper different classes of sliding mode controllers are applied for direct yaw moment control. Theoretical and practical advantages of four principal sliding mode controllers are obtained from their implementation in four-wheel steering, which has gained popularity for ground vehicles due to its potential to enhance handling and stability. While most commercial solutions rely on event-triggered, velocity-dependent strategies, robust controllers such as sliding mode controllers are required to significantly improve vehicle dynamic performance. Although robust control schemes are found in the literature for direct yaw moment control, such as the super-twisting algorithm and barrier function adaptation of relay sliding mode control, the saturation of the actuators or sample-and-hold issues are not considered, as they degrade their performance. Therefore, a modified barrier function adaptation is proposed, which guarantees the predefined performance of the state without the knowledge of the upper bound of the perturbations, in the case of actuator saturation and sample-and-hold implementations. Simulations of a relay sliding-mode controller, a super-twisting algorithm, a barrier function adaptation of relay sliding mode, and the proposal are conducted using an experimentally validated high-fidelity mathematical model. The results show that the proposed barrier function adaption methodology was the only one that could track yaw rate references without oscillations. After that, the modified barrier function adaptation is applied within hardware-in-the-loop. The results are benchmarked against an event-triggered control method performing both open-loop and closed-loop manoeuvres to demonstrate the robustness of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC16.2", "code": "WeC16.2", "title": "PID-Like Sliding Mode Controller for Helicopter Attitude Regulation (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC16", "sessionTitle": "Sliding Mode Applications in Robotics and Autonomous Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Iglesias Rios, Matias", "affiliation": "National Autonomous University of Mexico" }, { "name": "PérezVentura, Ulises", "affiliation": "Universidad Nacional Autónoma De México" }, { "name": "Fridman, Leonid", "affiliation": "National Autonomous University of Mexico" }, { "name": "Mujica-Ortega, Hoover", "affiliation": "Universidad Nacional Autónoma De México" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Mechatronic system modeling, design, optimization" ], "abstract": "This paper presents a gain-design methodology for a Proportional–Integral–Derivative (PID)-like sliding-mode controller applied to the elevation subsystem of a three-degree-of-freedom (3-DOF) helicopter prototype. The dominant elevation dynamics are modeled as a double integrator with static gain, while parasitic effects are represented by a transport delay. The proposed methodology comprises two steps. First, the Robust Feedback Self-Oscillation Test (RFSOT) is employed to identify the magnitude of the parasitic delay. Second, a systematic gain-tuning strategy is developed, based on the describing function approach, to minimize either the amplitude of the fundamental chattering harmonic and the root-mean-square (RMS) value of the control signal. The effectiveness of the proposed approach is validated through real-time experiments conducted on the elevation subsystem of a 3-DOF helicopter laboratory platform.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC16.3", "code": "WeC16.3", "title": "Integral Sliding Mode for Human Joint-Space Tracking in Upper-Limb Robotic Rehabilitation (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC16", "sessionTitle": "Sliding Mode Applications in Robotics and Autonomous Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Alessi, Chiara", "affiliation": "University of Pavia" }, { "name": "Sacchi, Nikolas", "affiliation": "University of Pavia" }, { "name": "Ferrara, Antonella", "affiliation": "University of Pavia" } ], "keywords": [ "Sliding mode control" ], "abstract": "End effector (EE) based robotic rehabilitation for the upper limb relies on patient–robot interaction through a handle attached to the EE of the robot. An emerging trend in this field is the adoption of collaborative industrial manipulators, which provide a flexible and cost-effective solution. However, a major limitation of EE systems compared to exoskeletons lies in their inability to directly control the patient's arm joint angles, since only the hand position is commanded. In this paper, we propose a control framework for a EE system composed by a collaborative manipulator that is based on Integral Sliding Mode Control (ISMC) to achieve joint-space trajectory tracking of the patient's arm. In particular, the ISMC component is employed to compensate for uncertainties in the dynamics of the human arm, ensuring robust tracking performance. The proposed approach aims to bridge the gap between EE devices and exoskeletons by enhancing the capability of patient joint-level guidance in rehabilitation therapy.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC16.4", "code": "WeC16.4", "title": "Integral Sliding Mode Control–Based Extremum Seeking Control (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC16", "sessionTitle": "Sliding Mode Applications in Robotics and Autonomous Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Kim, Hyuntae", "affiliation": "University of Oxford" } ], "keywords": [ "Sliding mode control", "Nonlinear control of switched & hybrid systems" ], "abstract": "We present a dither- and averaging-free extremum-seeking controller for an unknown static performance map with first-order actuation and band-limited sensing, combining super-twisting differentiation, fixed-magnitude search, a flow-interval ratio surrogate, left-limit relay logic, and bounded-rate amplitude projection to obtain local practical convergence under an eventually-unsaturated operating regime.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC16.5", "code": "WeC16.5", "title": "Robust Tracking of Curvature-Constrained Paths for Uncertain Dubins Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC16", "sessionTitle": "Sliding Mode Applications in Robotics and Autonomous Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Xue, Xingjian", "affiliation": "Northeastern University" }, { "name": "Yong, Sze Zheng", "affiliation": "Northeastern University" } ], "keywords": [ "Sliding mode control", "Output regulation and tracking", "Uncertain systems" ], "abstract": "This paper presents a robust tracking controller for tracking curvature-constrained paths by vehicles/robots with uncertain Dubins dynamics. Although Dubins paths have been widely used in vehicular and robotic applications, robust and convergent tracking under model uncertainties remains understudied. To address this, we propose path tracking controllers based on sliding mode control, formulated in the transverse coordinate frame, which guarantee invariance and convergence of both lateral and heading errors to zero in the presence of bounded disturbances. Simulation results show that the proposed method reliably tracks paths despite disturbances and significantly outperforms existing methods based on sliding mode controllers.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC16.6", "code": "WeC16.6", "title": "Practical Predefined-Time Adaptive Sliding Mode Control for Underactuated Surface Vehicles with Input Quantization (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC16", "sessionTitle": "Sliding Mode Applications in Robotics and Autonomous Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Jiang, Tao", "affiliation": "Dalian Maritime University" }, { "name": "Yan, Yan", "affiliation": "Dalian Maritime University" }, { "name": "Yu, Shuanghe", "affiliation": "Dalian Maritime University" } ], "keywords": [ "Sliding mode control", "Robust controller synthesis", "Analytic design" ], "abstract": "This work focuses on the predefined-time tracking control problem for underactuated surface vehicles (USVs) in the presence of system uncertainties, marine environmental disturbances, and input quantization. A coordinate transformation is first employed to tackle the underactuation of USVs and facilitate the synthesis of sliding mode control (SMC) algorithms. Then, by integrating time-varying function techniques, a uniform quantization mechanism, and adaptive gain dynamics, a practical predefined-time adaptive SMC algorithm is developed. With this algorithm, the tracking error of USVs is steered to a small neighborhood of the origin within a predefined time, even in the presence of lumped uncertainties. The validity of the proposed algorithm is further demonstrated via simulation experiments using the CyberShip II model.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC17.1", "code": "WeC17.1", "title": "A Novel Approach Based on H-Infinity Control Design for Hydropower Plant (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Application-Oriented Modeling and Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Yegin, Mustafa Oguz", "affiliation": "Czech Technical University in Prague" }, { "name": "Cepova, Klara", "affiliation": "Czech Technical University in Prague" }, { "name": "Fiser, Jaromir", "affiliation": "Czech Technical Univ in Prague" }, { "name": "Vyhlidal, Tomas", "affiliation": "Czech Technical Univ in Prague, Faculty of Mechanical Engineering" } ], "keywords": [ "Linear time-delay systems", "Robust control applications", "Uncertain systems" ], "abstract": "This paper introduces a novel method to controller design for a hydropower generation unit by using a neutral non-minimum phase LTI model extracted from experimental data. The proposed method ensures robust stabilization under parameter variations that may be caused by the system’s nonlinear characteristics and maintains high performance despite control input constraints. The design integrates an H-infinity based control, Smith-predictor structure, and an input shaper composed of an FIR filter for limiting the output undershoot/overshoot and a notch filter constructed by using the H-infinity-norm of a matrix derived from the closed-loop system. Simulation results demonstrate improved robustness and enhanced transient performance compared with existing methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC17.2", "code": "WeC17.2", "title": "DDE Modelling of Lasers with Fibre Bragg Grating Feedback (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Application-Oriented Modeling and Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Steele, Joe", "affiliation": "University of Auckland" }, { "name": "Krauskopf, Bernd", "affiliation": "University of Auckland" }, { "name": "Broderick, Neil", "affiliation": "University of Auckland" } ], "keywords": [ "Model reduction of distributed parameter systems", "Nonlinear time-delay systems", "Analytic design" ], "abstract": "Semiconductor lasers subject to external feedback exhibit rich delay-driven dynamics that can be shaped for control of laser output. Fibre Bragg gratings (FBGs) are important photonic elements that provide spectrally selective feedback, yet existing convolution-based models hinder analytical progress and limit control-oriented design. We propose a simplified representation of FBG feedback by via a set of discrete delays. The resulting delay differential equation (DDE) formulation preserves the essential physics while enabling the efficient study of stability and bifurcations from a dynamical systems perspective. The DDE model has been validated against convolution-based approaches and supports systematic exploration of control strategies for lasers influenced by frequency dependent delayed feedback.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC17.3", "code": "WeC17.3", "title": "From Resonance to Chaos in a DDE Climate Model (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Application-Oriented Modeling and Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Bolduc-St-Aubin, Samuel", "affiliation": "University of Auckland" }, { "name": "Krauskopf, Bernd", "affiliation": "University of Auckland" } ], "keywords": [ "Nonlinear time-delay systems" ], "abstract": "The El Niño Southern Oscillation (ENSO) is a major climate phenomenon characterized by sea surface temperature variations in the Equatorial Pacific Ocean. Conceptual models following the delayed-action oscillator (DAO) approach simplify its essential physics to tractable mathematical models in the form of delay differential equations (DDEs). We perform a detailed bifurcation analysis of a periodically forced ENSO DDE model, motivated by ENSO's tendency to phase-lock with the seasonal cycle. This conceptual system exhibits rich dynamics, including invariant tori and chaos. We demonstrate that chaos emerges via overlapping resonance tongues as one varies the external forcing frequency, which is equivalent to varying the strength of the nonlinearity.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC17.4", "code": "WeC17.4", "title": "Properties of Vehicular Platooning with Non-Constant Time Headway (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Application-Oriented Modeling and Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Zhao, Naixuan", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Baldi, Simone", "affiliation": "Southeast University" } ], "keywords": [ "Decentralized control", "Structural and geometric control", "Robust control applications" ], "abstract": "Spacing policies defined in terms of a constant time headway between adjacent vehicles are traditional in the vehicular platooning literature. This paper presents a new platooning framework where the constant time headway is generalized to a possibly non-constant time headway. We show analytic design conditions for making the proposed generalized time headway satisfy all the desirable properties reported in the literature for the constant time headway, such as disturbance decoupling, string stability and collision avoidance. Numerical simulations are conducted to further validate the newly proposed generalized spacing policy. The simulations illustrate the flexibility of non-constant time headway over a constant one, especially showing that the proposed time headway is capable of modulating itself during acceleration/deceleration phases so as to provide a smoother response.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC17.5", "code": "WeC17.5", "title": "Digital Design of Delay-Based Control for Hybrid Switch-Mode DC-DC Converters (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Application-Oriented Modeling and Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Duman Mammadov, Ayse", "affiliation": "Istanbul Technical University" }, { "name": "Moreno-Negrete, Erick", "affiliation": "Universidad Autónoma De San Luis Potosí" }, { "name": "Hernández-Gallardo, Julián-Alejandro", "affiliation": "Universidad Autónoma De San Luis Potosí" }, { "name": "Dincel, Emre", "affiliation": "Istanbul Technical University" }, { "name": "Mendez-Barrios, Cesar Fernando", "affiliation": "Universidad Autónoma De San Luis Potosí" }, { "name": "Söylemez, Mehmet Turan", "affiliation": "Istanbul Tecnical University" } ], "keywords": [ "Analytic design", "Digital implementation", "Sampled-data/digital control" ], "abstract": "Efficient voltage regulation in DC-DC converters is required for integrating intermittent renewable energy sources, yet achieving robust stability in high-gain hybrid topologies circuits remains a significant challenge for conventional control strategies. To address these limitations, this paper proposes a digital delay-based PI-Pdelta control scheme for an Active Switched-Inductor Step-Up 2-Cell (ASL-SU2C) converter. The proposed methodology utilizes a discrete-time design based on dominant pole placement, where specific performance criteria are met by assigning a target pole pair while constraining the remaining poles within a prescribed stable region via Nyquist analysis. A key advantage of the PI-Pdelta structure over prevailing PID and standard PI-delta strategies is its enhanced flexibility in zero positioning, which effectively eliminates undesirable transient effects. Processor-In-Loop (PIL) simulations demonstrate the efficacy and better robustness of the proposed controller under varying load conditions and supply disturbances.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC17.6", "code": "WeC17.6", "title": "Constrained Optimization of the Control Signal of Time-Periodic Axial Tailstock Excitation for Turning Slender Workpieces (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Application-Oriented Modeling and Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Martinovich, Kristof", "affiliation": "Budapest University of Technology and Economics" }, { "name": "Bachrathy, Daniel", "affiliation": "Budapest University of Technology and Economics" } ], "keywords": [ "Parametric optimization", "Linear parameter-varying systems", "Linear time-delay systems" ], "abstract": "A chatter suppression method is investigated for the turning operation of slender workpieces realized by axial time-periodic excitation. The model incorporates a piezo actuator built into the tailstock. The increase in stability is demonstrated for different control signals. A constrained optimization problem is formulated that accounts for the limiting factors and can be applied to any chosen operational parameter to achieve optimal chatter suppression.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC18.1", "code": "WeC18.1", "title": "Data-Driven Filament Width Prediction for Real-Time 3D Concrete Printing", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC18", "sessionTitle": "Advanced Manufacturing and Industrial Automation in Cyber-Physical Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Ali, Ame Saleh", "affiliation": "University of Lille, CRIStAL CNRS 9189" }, { "name": "Lakhal, Othman", "affiliation": "University Lille, CRIStAL, CNRS-UMR 9189," }, { "name": "Belarouci, Abdelkader", "affiliation": "Université De Lille, CRIStAL, CNRS-UMR 9189" }, { "name": "Merzouki, Rochdi", "affiliation": "University of Lille/CRIStAL CNRS 9189" } ], "keywords": [ "Industrial artificial intelligence", "Robotics in manufacturing systems", "Cyber-physical production systems" ], "abstract": "This study investigates filament width prediction in extrusion-based three-dimensional concrete printing using statistical and Machine Learning approaches. Multimodal data from process parameters and filament width were collected to analyze temporal and spatial dependencies. Correlation analysis and Analysis of Variance identified key factors, yet they could not capture the nonlinear dynamics of the process. To address this, we develop Mixture of Experts Long Short Term-Memory (MoE-LSTM) and Mixture of Recursions LSTM (MoR-LSTM) models for filament width prediction up to a 5-second horizon. The MoE-LSTM achieved a mean absolute error of 0.22–0.41% with inference latency of 0.15 milliseconds.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC18.2", "code": "WeC18.2", "title": "On the Importance of Having a Specification Methodology for the Adoption of the IEC 61499 Standard", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC18", "sessionTitle": "Advanced Manufacturing and Industrial Automation in Cyber-Physical Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Gambo, Arthur Oussounkiri Eliezer", "affiliation": "Université De Reims Champagne-Ardenne" }, { "name": "Lecasse, Stéphane", "affiliation": "Université De Reims Champagne-Ardenne" }, { "name": "Annebicque, David", "affiliation": "University of Reims - URCA - IUT De Troyes" }, { "name": "Emprin, Fabien", "affiliation": "Universite De Reims Champagne Ardenne" }, { "name": "Riera, Bernard", "affiliation": "Université De Reims Champagne Ardenne CReSTIC EA3804" } ], "keywords": [ "Advanced manufacturing and remanufacturing technologies", "Collaborative networked organizations principles", "Internet-of-things and sensing enterprise" ], "abstract": "The evolution of production systems toward Cyber-Physical Production Systems (CPPS) requires a transformation in the design and specification approaches of automation systems. While the IEC 61131-3 standard has dominated centralized system engineering for more than three decades, its limitations in terms of reusability, flexibility, and distribution are now evident. The IEC 61499 standard, on the other hand, introduces an event-driven, modular, and distributed approach that is better suited to Industry 4.0. However, it still suffers from a lack of specification methodologies comparable to those available for IEC 61131-3 (particularly Grafcet). This paper provides an in-depth comparative analysis of the two standards from a specification perspective and draws on feedback related to the pedagogical use of Grafcet.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC18.3", "code": "WeC18.3", "title": "Analysis of Frequency-Controlled and Power-Adjusted Ultrasonic Separators for Flow Regime Operation (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC18", "sessionTitle": "Advanced Manufacturing and Industrial Automation in Cyber-Physical Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Silva Jr, Agesinaldo M.", "affiliation": "EPUSP" }, { "name": "Giraldo Atehortua, Carlos Mario", "affiliation": "Universidade De São Paulo" }, { "name": "Ramírez González, Eduardo José", "affiliation": "Escola Politecnica Da USP" }, { "name": "Lopes, José Henrique", "affiliation": "Universidade Federal De Alagoas" }, { "name": "Buiochi, Flavio", "affiliation": "University of Sao Paulo" }, { "name": "Tsuzuki, Marcos de Sales Guerra", "affiliation": "University of Sao Paulo" } ], "keywords": [ "Manufacturing engineering and management" ], "abstract": "Ultrasonic standing waves provide a powerful, non-invasive mechanism for manipulating dispersed particles in liquid media, with growing relevance in separation, monitoring, and microfluidic applications. This work investigates the trapping and migration dynamics of oil droplets suspended in water under an ultrasonic standing wave field. A coupled theoretical–numerical framework was developed to characterize the forces acting on droplets in the Rayleigh regime. The acoustic field was computed using frequency-domain finite element simulations in COMSOL Multiphysics. The resulting pressure and velocity fields were used to drive a particle-tracing model that predicts droplet trajectories and equilibrium trapping positions. Numerical experiments conducted in a built ultrasonic chamber validated the numerical predictions. The results demonstrate that the proposed modeling framework provides a robust foundation for resonant tracked coupled with and predicting droplet behavior in acoustically driven separation systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC18.4", "code": "WeC18.4", "title": "Ultrasonic Interface Profiling for Pattern-Based Control of Bi-Phase Liquid Mixtures in Industrial Separators (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC18", "sessionTitle": "Advanced Manufacturing and Industrial Automation in Cyber-Physical Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Silva Jr, Agesinaldo M.", "affiliation": "EPUSP" }, { "name": "Duran, Guilherme C.", "affiliation": "EPUSP" }, { "name": "Tanabi, Naser", "affiliation": "EPUSP" }, { "name": "Giraldo Atehortua, Carlos Mario", "affiliation": "Universidade De São Paulo" }, { "name": "Lopes, José Henrique", "affiliation": "Universidade Federal De Alagoas" }, { "name": "Vieira Pereira, Luiz Octavio", "affiliation": "Petrobras" }, { "name": "Buiochi, Flavio", "affiliation": "University of Sao Paulo" }, { "name": "Tsuzuki, Marcos de Sales Guerra", "affiliation": "University of Sao Paulo" } ], "keywords": [ "Manufacturing engineering and management", "Industrial artificial intelligence" ], "abstract": "This paper presents a retrofittable, non-invasive ultrasonic interface-profiling framework for real-time supervision of oil--water separation in horizontal industrial separators. The method targets oil-and-gas production facilities, including subsea concepts, where continuous interface information is needed to reduce off-spec carryover and support tighter separator control using only wall-mounted transducers. Ultrasonic propagation is modeled in the frequency domain using the Helmholtz equation with interface continuity conditions, and transmission responses are computed using the method of fundamental solutions across a range of operating scenarios. Synthetic time-domain waveforms from a circumferential through-transmission array are compressed into compact 16times16 amplitude maps. A lightweight convolutional neural network maps each amplitude map to a one-dimensional vertical confidence profile, from which a continuous interface height is inferred by peak detection and parabolic interpolation. A hierarchical hyperparameter study identifies suitable values for the profile resolution H, the Gaussian target width sigma, and the learning rate. Using the selected configuration, the final model is trained on the combined training and validation sets and evaluated on a strictly held-out test subset. Numerical results show accurate single-interface localization with sub-wavelength mean absolute error and smooth, interpretable profiles suitable for digital-twin-based monitoring and closed-loop control. Because the regression output is a spatial confidence distribution rather than a single scalar estimate, the formulation also extends naturally to multi-interface cases.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC18.5", "code": "WeC18.5", "title": "AAS and DPP-Based Architecture for Phase-Oriented Normalization and Lifecycle Evaluation of Industrial Drive Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC18", "sessionTitle": "Advanced Manufacturing and Industrial Automation in Cyber-Physical Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Schöttke, Dirk", "affiliation": "HTW Berlin – University of Applied Sciences" }, { "name": "Nowshin, Israt", "affiliation": "HTW Berlin" }, { "name": "Schaefer, Stephan", "affiliation": "University of Applied Science HTW Berlin" }, { "name": "Buettner, Daniel", "affiliation": "HTW Berlin" }, { "name": "Tauber, Bernd", "affiliation": "EAW Relaistechnik GmbH" }, { "name": "Gordt, Alexander", "affiliation": "Objective Partner AG" } ], "keywords": [ "Cyber-physical production systems", "Enterprise interoperability", "Manufacturing prognostics and health management" ], "abstract": "The lifecycle-oriented assessment of industrial assets requires operational data to be linked to technical reference information and operating context. While the Asset Administration Shell (AAS) provides a standardized structure for interoperable asset-related information and the Digital Product Passport (DPP) establishes a lifecycle-oriented information perspective, the integration of validated operational time-series data for phase-oriented comparative assessment remains insufficiently addressed. This paper presents an AAS/DPP-based architecture for integrating, validating, normalizing, and structuring lifecycle-relevant operational data from industrial drive systems. The proposed workflow combines syntactic and semantic validation of AAS-related structures with phase-oriented normalization and the documentation of deviations in condition- and service-related information structures. The approach is demonstrated in an industrial proof of concept with ten real frequency-converter motor systems subjected to an identical phase-based test profile. The results show that reference-based normalization enables a traceable comparison of operational profiles within one asset class and makes abnormal operating behaviour visible under defined load conditions. The contribution of the paper is thus a semantically and methodologically grounded basis for comparable lifecycle-oriented assessment rather than a validated fault diagnosis or predictive maintenance model.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC18.6", "code": "WeC18.6", "title": "Towards a Cybernetic Model for Interactive Human-Centred Cultural Heritage Spaces and Control Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC18", "sessionTitle": "Advanced Manufacturing and Industrial Automation in Cyber-Physical Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Murphy, Cian", "affiliation": "South East Technological University Waterford" }, { "name": "Carew, Peter J.", "affiliation": "South East Technological University" }, { "name": "Stapleton, Larry", "affiliation": "Knewfutures Consulting" } ], "keywords": [ "Digital culture", "Human-centric automation/AI Systems, and human agency", "Diversity and inclusion in digital culture" ], "abstract": "Cultural Heritage spaces provide citizens with a profound opportunity to gain a unique insight into historical events that have had a regional or global impact. They can also support individuals to obtain a sense of identity and to learn more about the evolution of society over the years. Immersive technologies such as Augmented Reality (AR) and Virtual Reality (VR) have contributed to ensuring the user journey in Cultural Heritage spaces is now a more digitised and interactive experience. AR tour guides for instance are now frequently seen within Cultural Heritage sites to enhance engagement and education. VR is often used in the form of a dedicated headset that provides users with a fully immersive environment to interact with artefacts without needing to be physically present. The term Cybernetics was originally coined by Norbert Wiener in 1948 and focused on communication and automatic control systems within machines and living things. Cybernetics can impact the user experience through its focus on feedback which can be positive or negative, and it can help with understanding the aspects of the user journey that are operating satisfactorily. This research presents a Cybernetic Model for Interactive Human-Centred Cultural Heritage Spaces and Control Systems and references the use of interactive devices in these spaces.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC19.1", "code": "WeC19.1", "title": "HRI Technology Demonstrators with Sensorized High Payload Robots (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC19", "sessionTitle": "Advanced Robotics for the Manufacturing of the Future", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Borelli, Simone", "affiliation": "Università Di Genova" }, { "name": "Giovinazzo, Francesco", "affiliation": "University of Genoa, Department of Informatics, Bioengineering, Robotics and Systems Engineering" }, { "name": "Grella, Francesco", "affiliation": "Università Di Genova" }, { "name": "Figueroa Saire, Pedro Luis", "affiliation": "Università Di Genova" }, { "name": "Sabzevari, Danial", "affiliation": "Università Di Genova" }, { "name": "Bagherian, Vahid", "affiliation": "Università Di Genova" }, { "name": "Pour, Peyman Peyvandi", "affiliation": "Università Di Genova" }, { "name": "Khalid, Muhammad Usman", "affiliation": "Università Di Genova" }, { "name": "Zoppi, Matteo", "affiliation": "Università Di Genova" }, { "name": "Cannata, Giorgio", "affiliation": "Università Di Genova" } ], "keywords": [ "Industry X.0 for production and logistics", "Robotics in manufacturing systems", "Human-technology integration in manufacturing" ], "abstract": "In this paper, we present the technology demonstrators developed in recent research activities, showing that safe and intuitive cooperation or physical collaboration between humans and heavy-payload industrial robots can be effectively realized within the framework of Industry 5.0. While collaborative robots have transformed Human-Robot Interaction (HRI) in manufacturing light-duty tasks, extending such collaboration to large industrial manipulators remains challenging due to strict safety constraints. Our work, carried out within the European projects H2020 Collaborate and HE Sestosenso, investigates advanced sensing technologies and control strategies to address these challenges and enable human-centered cooperation in demanding industrial contexts.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC19.2", "code": "WeC19.2", "title": "Language Model Based Multi-Agent System for Human-In-The-Loop Control of Tending Robots Toward Industry 5.0 (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC19", "sessionTitle": "Advanced Robotics for the Manufacturing of the Future", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Park, Jongsu", "affiliation": "Kyung Hee University" }, { "name": "Murdivien, Shokhikha Amalana", "affiliation": "Kyung Hee University" }, { "name": "Um, Jumyung", "affiliation": "Kyung Hee University" } ], "keywords": [ "Human-technology integration in manufacturing", "Industry X.0 for production and logistics", "Industrial artificial intelligence" ], "abstract": "Industry 5.0 emphasizes human-centric production where operators collaborate seamlessly with intelligent robots and machines. This paper introduces a novel multi-agent system utilizing a large language model to facilitate flexible Human-in-the-Loop control for tending robots and associated machinery. The system leverages a standardized Asset Administration Shell for all data exchange, integrating real-time sensor monitoring and natural language processing agents. The system continuously evaluates sensor data, and the language model can proactively identify uncertainties or anomalies, requesting human guidance when needed. Human operators can then use natural voice commands, which are parsed by the language model and translated into immediate actions to interrupt, modify, or adapt ongoing robotic tasks. This approach provides an intuitive and flexible interface, enhancing the operator's role in complex manufacturing environments and addressing key challenges in human-robot collaboration and interoperability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC19.3", "code": "WeC19.3", "title": "Reinforcement Learning for Automated Aerostructure Sealing: Optimizing Throughput and Bead Uniformity with Few-Shot Data (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC19", "sessionTitle": "Advanced Robotics for the Manufacturing of the Future", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Narvato, Rey Christian", "affiliation": "University of Nottingham" }, { "name": "Kendall, Peter", "affiliation": "University of Nottingham" }, { "name": "Sanderson, David", "affiliation": "University of Nottingham" }, { "name": "Ratchev, Svetan", "affiliation": "University of Nottingham" }, { "name": "Arellano, Giovanna Martinez", "affiliation": "University of Nottingham" } ], "keywords": [ "Robotics in manufacturing systems", "Industrial artificial intelligence", "Manufacturing plant simulation, control and optimization" ], "abstract": "Sealing aerostructures is a critical assembly process in which sealant is deposited between joining parts to prevent material leakage. Currently, robotic sealing systems use open-loop control methods, in the form of rigid robot programs. However, since pneumatic pressure and robot velocity are manually selected, deposition uniformity is highly reliant on an operator’s domain knowledge. To overcome this reliance, adaptive control approaches are being developed, but a crucial aspect is missing, the integration of key performance indicators (KPIs) into controller behavior. Although integrating KPIs into an objective function is an accepted optimization approach, there is a lack of multi-objective control approaches applied to the sealant process. This is crucial for developing automated sealing systems capable of achieving desired manufacturing outcomes such as bead width uniformity and cycle time minimization. Reinforcement learning (RL) can align reward functions with manufacturing KPIs. However training is limited by data acquisition costs, which results in significant material waste. This is further compounded by sealant curing in which viscosity changes within hours. Therefore, lengthy data collection would render the trained policy obsolete before deployment. To address these limitations, a Q-learning based multi-variable control framework is developed. This is driven by a synthetic data generation (SDG) pipeline which consumes 13.5ml of material by leveraging the sealant’s fundamental characteristics. This data is used to train the Q-learning controller capable of dynamically adjusting pneumatic pressure and tool center point (TCP) velocity to achieve a target bead width. The resulting controller is validated first in simulation, achieving 99.8% success rate when tasked to converge a bead width to within a tolerance of ± 0.5mm. The policy is deployed onto a KUKA KR4 R600 robotic sealing cell, achieving 91.1% success rate. These results demonstrate development of a reliable, multi-variable controllers from minimal data that can optimize desired manufacturing KPIs.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC19.4", "code": "WeC19.4", "title": "Friction-Cone-Based Grasp State Estimation Using Arrayed Tactile Sensors for Shared Autonomy in Tele-Manipulation (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC19", "sessionTitle": "Advanced Robotics for the Manufacturing of the Future", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Lee, Wooryeol", "affiliation": "KIST" }, { "name": "Lee, YoungJun", "affiliation": "Korea Institute of Science and Technology" }, { "name": "Lee, Young Min", "affiliation": "Korea Institute of Science and Technology" }, { "name": "You, Bum-Jae", "affiliation": "Korea Institute of Science and Technology (KIST)" }, { "name": "Ihn, Yong Seok", "affiliation": "Korea Institute of Science and Technology" } ], "keywords": [ "Robotics in manufacturing systems" ], "abstract": "Tactile sensing is essential for stable robotic manipulation, especially in tele-manipulation tasks that involve shared autonomy between human operators and robots. While humans naturally use tactile feedback to regulate grip and prevent slip, robotic systems often rely on binary slip detection, which limits continuous and adaptive control. This paper presents a friction-cone-based method for estimating grasp state using an arrayed multi-axis tactile sensor. The proposed approach computes a continuous slip--stick ratio over the contact area by combining node-wise friction-cone tests with compensation for curvature-induced shear forces, enabling early detection of incipient slip and inference of slip direction and rotational tendencies. Experiments with planar and curved objects show that the estimator achieves 94.5% accuracy in distinguishing static and slipping states and provides force estimates consistent with an external force/torque sensor. These results indicate that tactile-based grasp state estimation can enhance the robustness and autonomy of tele-manipulation systems, allowing shared-control robots to adjust grip forces automatically in response to changing contact conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC19.5", "code": "WeC19.5", "title": "Semi-Autonomous Arm-Hand Teleoperation with Grasping Assistance", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC19", "sessionTitle": "Advanced Robotics for the Manufacturing of the Future", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Lei, Xiang", "affiliation": "Tsinghua University" }, { "name": "Yang, Xu", "affiliation": "Tsinghua University" }, { "name": "Lu, Yiwen", "affiliation": "Tsinghua University" }, { "name": "Mo, Yilin", "affiliation": "Tsinghua University" }, { "name": "You, Keyou", "affiliation": "Tsinghua University" } ], "keywords": [ "Teleoperation", "Human-robot interaction", "Shared control" ], "abstract": "Teleoperation enables a wide range of real-world robotic applications, yet controlling dexterous robotic hands for complex grasping tasks remains operationally challenging and time-intensive. To address these challenges, we present Semi-Autonomous arm-hand teleoperation with Grasping Assistance (SAGA), a novel two-stage framework for increased operational efficiency. The framework operates in two stages: 1) emph{pre-grasp positioning} through shared control that guides the dexterous robotic manipulator to appropriate pre-grasp poses, and 2) emph{grasping execution} leveraging reinforcement learning for autonomous object manipulation. Experiments demonstrate that the proposed SAGA framework realizes efficient and generalizable grasping across diverse objects while facilitating user-friendly intelligent teleoperation. The code is available at url{https://github.com/Lei00764/SAGA.git}.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC19.6", "code": "WeC19.6", "title": "Hierarchical Gradient-Guided Multi-Tree RRT and QP-CBF for Safe Teleoperation", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC19", "sessionTitle": "Advanced Robotics for the Manufacturing of the Future", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Tuerxun, Alapati", "affiliation": "Tsinghua University" }, { "name": "Yang, Xu", "affiliation": "Tsinghua University" }, { "name": "Mo, Yilin", "affiliation": "Tsinghua University" } ], "keywords": [ "Teleoperation", "Task and motion planning", "Shared control" ], "abstract": "Safe real-time manipulation in cluttered environments represents a fundamental challenge for redundant robotic arms, requiring the integration of strategic global planning with reactive safety-critical control. This paper presents a hierarchical framework that unifies Gradient-Guided Multi-Tree RRT (GGMT-RRT) with Quadratic Programming Control Barrier Function (QP-CBF), enabling both global path planning and formal safety guarantees for general redundant manipulators. Our approach introduces three synergistic innovations: (1) GGMT-RRT that leverages JAX automatic differentiation to compute collision gradients, steering sampling toward collision-free regions and generating multiple diverse path candidates with significantly faster convergence compared to standard RRT, (2) batch-optimized collision detection employing multi-sphere geometric models with GPU-accelerated vectorized operations that process hundreds of configurations simultaneously, enabling efficient scaling to high-DOF systems, and (3) hierarchical dual-layer architecture where strategic GGMT-RRT planning at high frequency provides global path options while QP-CBF reactive control ensures formal safety guarantees through barrier function constraints at the same frequency. Experimental validation on a 7-DOF manipulator demonstrates robust real-time performance in complex multi-obstacle scenarios, achieving high planning success rate with zero safety violations. The framework's modular design facilitates adaptation to various redundant manipulator configurations, providing a practical solution for safe teleoperation and autonomous manipulation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC20.1", "code": "WeC20.1", "title": "Expediting Global Optimization of Gas Transport Networks with Difference-Of-Convex Continuous Piecewise Linear Surrogates (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC20", "sessionTitle": "JO-JPC: Control and Optimization for Sustainability and Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Zhang, Zhiyu", "affiliation": "College of Control Science and Engineering, Zhejiang University" }, { "name": "Kazda, Kody", "affiliation": "Queen's University" }, { "name": "Li, Xiang", "affiliation": "Zhejiang University" }, { "name": "Shao, Zhijiang", "affiliation": "Zhejiang University" } ], "keywords": [ "Control and optimization for sustainability and energy systems" ], "abstract": "Natural gas plays a crucial role in the global energy landscape, and efficient operations of gas transport networks bring substantial economic and environmental benefits. Yet, the nonlinear, nonconvex physics of gas networks makes real-time global optimization challenging. To address this, a two-step global optimization framework is developed. The framework employs ϵ-precise continuous piecewise linear surrogate models via a novel difference-of-convex-based formulation; this structure leads an efficient mixed-integer quadratically constrained programming relaxation of the original problem. Solving the relaxation to global optimality supplies a valid lower bound and a high-quality warm start for a subsequent local refinement, delivering a feasible solution along with a certifiable optimality gap. Numerical experiments on a benchmark network demonstrate that the method reduces solve times by at least 90% compared to a state-of-the-art global NLP solver, while achieving solutions of comparable quality with far tighter optimality gaps.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC20.2", "code": "WeC20.2", "title": "Green Hydrogen Production: Uncertainty-Aware Predictive Energy Management (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC20", "sessionTitle": "JO-JPC: Control and Optimization for Sustainability and Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Hochedlinger, Sebastian", "affiliation": "TU Wien" }, { "name": "Ritzberger, Daniel", "affiliation": "Vienna University of Technology" }, { "name": "Jakubek, Stefan M.", "affiliation": "Technical Univ. of Vienna/Austria" }, { "name": "Hametner, Christoph", "affiliation": "TU Wien" } ], "keywords": [ "Control and optimization for sustainability and energy systems", "Energy management systems", "Advanced process control" ], "abstract": "Large-scale green hydrogen production via electrolysis is subject to highly variable external drivers, such as weather, electricity price, and demand. To ensure cost-effective operation, this paper proposes a two-stage predictive energy management strategy that leverages forecasts of these external drivers. The framework combines an offline dynamic programming optimization with an online control law. Forecast uncertainty is explicitly incorporated into the optimization to enhance robustness of the generated reference against forecast inaccuracies. Simulation studies using real historical data demonstrate that the proposed approach significantly reduces hydrogen production costs compared to deterministic methods, thereby improving the overall economic performance of the system.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC20.3", "code": "WeC20.3", "title": "Physics-Informed Neural Network-Based Multi-Horizon Model Predictive Control of Chemical Plants with Renewable Supply (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC20", "sessionTitle": "JO-JPC: Control and Optimization for Sustainability and Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Tousi, Javad", "affiliation": "RPTU" }, { "name": "Görges, Daniel", "affiliation": "University of Kaiserslautern" } ], "keywords": [ "Control and optimization for sustainability and energy systems", "Machine learning and artificial intelligence in chemical process control", "Model-predictive and optimization-based control in chemical processes" ], "abstract": "To reduce carbon footprint and production costs, the chemical industry is increasingly integrating renewable energy into plant operations. Since renewable generation depends on variable weather, incorporating power forecasts is essential for effective planning, and production schedules must be continuously updated. Model Predictive Control (MPC) can use such forecasts to optimize control inputs, but extending its prediction horizon to capture long-term variations often becomes computationally intractable. This paper proposes a novel multi-horizon MPC framework based on a Physics-Informed Neural Network (PINN) model that combines short-term control accuracy with long-term predictions while addressing uncertainty and maintaining computational efficiency. An average production goal is included to enhance flexibility and ensure that the final target is met under power constraints. Simulation results on a chemical plant demonstrate the performance of the proposed approach in both computational efficiency and energy costs reduction.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC20.4", "code": "WeC20.4", "title": "Reduced-Order MPC for Dynamic Fuel Cell Power Tracking under Spatially Distributed Safety Constraints (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC20", "sessionTitle": "JO-JPC: Control and Optimization for Sustainability and Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Fuchs, Benjamin", "affiliation": "TU Wien" }, { "name": "Kozek, Martin", "affiliation": "Vienna University of Technology" }, { "name": "Hametner, Christoph", "affiliation": "TU Wien" }, { "name": "Jakubek, Stefan M.", "affiliation": "Technical Univ. of Vienna/Austria" } ], "keywords": [ "Control and optimization for sustainability and energy systems", "Model-predictive and optimization-based control in chemical processes", "Hydrogen systems for energy generation and storage" ], "abstract": "In this work, a fuel cell control scheme is presented to track a dynamic power demand while taking spatially distributed adverse cell effects along the channels into account. The model predictive control scheme is based on a distributed-parameter fuel cell model and, to efficiently account for nonlinearities, employs a reduced-order local model network. The reduced-order controller is coupled to a reduced-order observer estimating the dominant system dynamics. Tested in a simulation scenario and compared to a baseline controller, the proposed control concept shows accurate dynamic power tracking while meeting spatially distributed constraints, thus avoiding operating in adverse conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC20.5", "code": "WeC20.5", "title": "Deep Reinforcement Learning Based Constrained Economic Model Predictive Control for Household Microgrids (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC20", "sessionTitle": "JO-JPC: Control and Optimization for Sustainability and Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Xu, Ce", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "Jha, Mayank Shekhar", "affiliation": "University of Lorraine" }, { "name": "Costa-Castelló, Ramon", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" } ], "keywords": [ "Machine learning and artificial intelligence in chemical process control" ], "abstract": "In this paper, we present a novel model free, forecast informed reinforcement learning framework for economic control of household microgrids within Deep Reinforcement Learning framework using Deep Deterministic Policy Gradient approach. The proposed approach augments electricity price and demand forecasts to the system states to steer long horizon decisions, while a reduced action parameterization enforces instantaneous power balance and input bounds by construction. System's safety is ensured through a reciprocal barrier function that regularizes the singular behavior of classical barrier terms and preserves forward invariance in discrete time. The controller learns the economic policy and value function without an explicit system model and maintains feasibility during training and deployment. Simulations on microgrids with storage and photovoltaic resources show constraint satisfaction, robustness to model mismatch and forecast errors, and operating costs comparable to classical Economic Model Predictive Control (EMPC). The approach unifies key principles of EMPC with data driven control and provides a scalable baseline for safe, economically efficient operation of distributed energy resources. The efficacy of the approach is assessed in simulation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC20.6", "code": "WeC20.6", "title": "Control Architecture Design Based on Primal Decomposition with Local Constraints - Applied to a Thermal Energy Network (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC20", "sessionTitle": "JO-JPC: Control and Optimization for Sustainability and Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Varadarajan, Hari Prasad", "affiliation": "DIFFER" }, { "name": "Krishnamoorthy, Dinesh", "affiliation": "Norwegian University of Science and Technology (NTNU)" } ], "keywords": [ "Real-time optimization and control in chemical processes", "Advanced process control", "Control and optimization for sustainability and energy systems" ], "abstract": "This paper considers the problem of distributed feedback optimizing control that achieves real-time coordination without requiring online optimization. Distributed feedback optimization approaches based on Lagrangian decomposition, relax the coupling constraints and enforce them on a slower timescale through updates of the associated Lagrange multipliers. In contrast, primal decomposition adopts a resource-directive approach that inherently preserves primal feasibility of the coupling constraints even during transients. Yet, shared resources are allocated without the explicit knowledge of local subsystem constraints, which can lead to inconsistencies. To address this limitation, this paper proposes a novel feedback-optimizing control architecture based on primal decomposition that simultaneously enforces feasibility of the local and shared constraints using advanced process control tools. The resulting control architecture asymptotically achieves optimal steady-state performance using simple control laws. We demonstrate this with a thermal energy network with a common heat source. This work bridges distributed optimization principles and process control elements, offering a scalable and computationally efficient solution for energy and process systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC21.1", "code": "WeC21.1", "title": "Tracking Stability of Multi-UAVs against Deterministic and Stochastic Disturbances (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC21", "sessionTitle": "Safe, Fault Resilient and Health-Aware Control Design and Learning", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Yang, Feng", "affiliation": "Nanjing Normal University" }, { "name": "Mao, Qi", "affiliation": "City University of Hong Kong" }, { "name": "Li, Shi", "affiliation": "Nanjing Normal University" }, { "name": "Cong, Lu", "affiliation": "Nanhang Jincheng College" }, { "name": "Zhang, Yifan", "affiliation": "NanJing Nomal University" }, { "name": "Chen, Jun", "affiliation": "Nanjing Normal University" } ], "keywords": [ "Distributed/networked FDI/FTC", "Reliability and safety in processes" ], "abstract": "In this paper, we investigate the tracking stability of phase-shifted circular reference trajectories in multi-UAV systems subject to deterministic disturbances, stochastic perturbations, and actuator saturation, whereby a trend-driven adaptive annealed-PID tracking strategy is then proposed. Within this framework, a low-pass filtered trend of the error energy for each vehicle regulates a slow outer-loop that adaptively modulates bounded PID gains. Meanwhile, the inner loop ensures continuous control via leaky integration and anti-windup compensation. Drawing upon the polytopic vertex--linear matrix inequality (LMI) framework, we establish input-to-state stability under deterministic disturbances and mean-square boundedness under stochastic perturbations. Numerical simulations demonstrate the effectiveness and robustness of the developed tracking control approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC21.2", "code": "WeC21.2", "title": "Resilient AFE Drive Control Using Neural Networks with Tracking Guarantees (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC21", "sessionTitle": "Safe, Fault Resilient and Health-Aware Control Design and Learning", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Kirsch, Nicolas", "affiliation": "Ecole Polytechnique Fédérale De Lausanne" }, { "name": "Arghir, Catalin", "affiliation": "Swiss Federal Institute of Technology (ETH) Zurich" }, { "name": "Mastellone, Silvia", "affiliation": "University of Applied Science Northwest Switzerland" }, { "name": "Ferrari-Trecate, Giancarlo", "affiliation": "Ecole Polytechnique Fédérale De Lausanne" } ], "keywords": [ "AI methods for FDI/FTC", "Power electronics" ], "abstract": "Industrial installations across several sectors have seen a dramatic increase in productivity, accuracy and efficiency over the last decade due to expanded utilization of medium voltage, variable speed power electronic converters to drive their processes. Specifically, active front-end (AFE) drives have become popular due to their ability to deliver power while maintaining safe electrical setpoints. However, under abnormal grid conditions such as phase loss, conventional AFE control may fail to enforce safety constraints, potentially leading to drive shutdown and significant financial losses. In this work, we propose using reference-tracking Performance Boosting (rPB) to improve the resilience of standard AFE control to faults. This neural-network control framework provides a principled way to optimize transient performance while preserving the steady-state tracking properties of AFE-based drives. By carefully shaping the input signals to the rPB controller, we ensure that it activates only during grid faults, leaving nominal operation unaffected. Simulation results show that the proposed approach successfully maintains the DC bus voltage and the grid current within safe limits during single-phase loss events.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC21.3", "code": "WeC21.3", "title": "Machine Learning Algorithms for Fault Identification in Fuel Cells (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC21", "sessionTitle": "Safe, Fault Resilient and Health-Aware Control Design and Learning", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Maione, Francesco", "affiliation": "Politecnico Di Bari" }, { "name": "Lino, Paolo", "affiliation": "Politecnico Di Bari" }, { "name": "Giannino, Giuseppe", "affiliation": "Isotta Fraschini Motori S.p.A" }, { "name": "Coates, Erlend M.", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Osen, Ottar Laurits", "affiliation": "Norwegian University of Science and Technology - NTNU" }, { "name": "Maione, Guido", "affiliation": "Politecnico Di Bari" } ], "keywords": [ "Health/condition monitoring in processes", "Fault detection and isolation methods", "Hydrogen systems for energy generation and storage" ], "abstract": "The digitalization of the maritime sector, together with related environmental regulations, is reshaping ways to maintain new and innovative marine propulsion systems. This work proposes a data-driven incremental learning strategy for fuel-cell-based systems, where labelled fault data are typically scarce. Starting from normal-operation measurements only, an outlier detector based on One-Class SVM identifies deviations from healthy behavior. As new data become available, three incremental OC-SVM schemes are tested and re-trained to select the most accurate. Detected anomalies are then classified using traditional Machine Learning models, selecting and deploying the best performer. Simulation results confirm the effectiveness and robustness of the proposed approach", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC21.4", "code": "WeC21.4", "title": "Safe Adaptive Feedback Control Via Barrier States (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC21", "sessionTitle": "Safe, Fault Resilient and Health-Aware Control Design and Learning", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Satharasi, Trivikram", "affiliation": "University of Florida" }, { "name": "Ogri, Tochukwu Elijah", "affiliation": "University of Florida" }, { "name": "Qureshi, Muzaffar", "affiliation": "University of Florida" }, { "name": "Volle, Kyle", "affiliation": "University of Florida" }, { "name": "Kamalapurkar, Rushikesh", "affiliation": "University of Florida" } ], "keywords": [ "Reliability and safety in processes", "Process modeling, identification, and estimation techniques", "Control and optimization for sustainability and energy systems" ], "abstract": "This paper presents a safe feedback control framework for nonlinear control-affine systems with parametric uncertainty by leveraging adaptive dynamic programming (ADP) with barrier-state augmentation. The developed ADP-based controller enforces control invariance by optimizing a value function that explicitly penalizes the barrier state, thereby embedding safety directly into the Bellman structure. The near-optimal control policy computed using model-based reinforcement learning is combined with a concurrent learning estimator to identify the unknown parameters and guarantee uniform convergence without requiring persistency of excitation. Using a barrier-state Lyapunov function, we establish boundedness of the barrier dynamics and prove closed-loop stability and safety. Numerical simulations on an optimal obstacle-avoidance problem validate the effectiveness of the developed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC21.5", "code": "WeC21.5", "title": "Beyond DNNs: Noise-Robust Occupation-Kernel Digital Twin (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC21", "sessionTitle": "Safe, Fault Resilient and Health-Aware Control Design and Learning", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Chen, Haowei", "affiliation": "University of Florida" }, { "name": "Kamalapurkar, Rushikesh", "affiliation": "University of Florida" }, { "name": "Rosenfeld, Joel", "affiliation": "University of South Florida" } ], "keywords": [ "Data-driven methods for FDI/FTC", "AI methods for FDI/FTC", "Reliability and safety in processes" ], "abstract": "We present an occupation-kernel digital twin (OKDT) that combines the Mori--Zwanzig projection with kernel ridge regression. We discretize the continuous occupation kernel using an exponential envelope, resulting in a training problem that is convex and admits a closed-form solution. The resulting model suppresses sensor noise with an O(N^{-1/2}) finite-sample bound, and propagates that bound to long prediction horizons. Benchmarks on cylinder-wake flow, the Van der Pol oscillator, and a noisy linear system show that our approach remains stable where vector kernels and deep neural networks diverge.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC22.1", "code": "WeC22.1", "title": "Practical Implementation of Dynamic Optimization and Modifier Adaptation for Economic Performance", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC22", "sessionTitle": "Real-Time Optimization and Bayesian Methods for Process Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Oliveira-Silva, Erika", "affiliation": "Universidad De Valladolid" }, { "name": "de Prada, Cesar", "affiliation": "University of Valladolid" }, { "name": "Navia, Daniel", "affiliation": "Universidad Técnica Federico Santa María" }, { "name": "Gutierrez, Gloria", "affiliation": "University of Valladolid ( VAT ESQ4718001C)" }, { "name": "Marmol, Sergio", "affiliation": "Petronor" }, { "name": "González, Rafael", "affiliation": "Petronor" } ], "keywords": [ "Real-time optimization and control in chemical processes", "Model-predictive and optimization-based control in chemical processes", "Advanced process control" ], "abstract": "Traditional real-time optimization (RTO) methods face limitations due to possible structural errors on rigorous nonlinearmodels and steady-state data, making them unsuitable for processes with slow dynamics or persistent disturbances. This paper presents a fast, practical economic optimization approach that integrates Modifier Adaptation (MA) with dynamic optimization using a linear dynamic model from the MPC control layer. The method minimizes model-development effort while accelerating industrial deployment. Results show that the dynamic optimization–MA framework (DOMA), driven by transient data, improves economic performance under dynamic conditions. The proposed approach offers a promising foundation for broader industrial applications of MA-based process optimization.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC22.2", "code": "WeC22.2", "title": "Model-Based Exploration of Feasible Operable Space under Experimental Budget", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC22", "sessionTitle": "Real-Time Optimization and Bayesian Methods for Process Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Saccardo, Alberto", "affiliation": "Imperial College London" }, { "name": "Sandrin, Marco", "affiliation": "Siemens, Imperial College London" }, { "name": "Chachuat, Benoit", "affiliation": "Imperial College London" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Biological and pharmaceutical systems" ], "abstract": "We address the problem of designing model-based experimental campaigns composed of multiple runs under a prescribed experimental budget, with the specific aim of maximally exploring the feasible operable space. Given a process model and input/output constraints, we formulate a maximin-style problem and introduce an inverse-distance criterion to select a finite set of input realizations whose model-based responses span the output space as widely as possible. To alleviate the resulting nonconvexity and combinatorial complexity, we propose a two-step decomposition strategy: a subset-selection subproblem on a discretized input domain generated using nested sampling, followed by a refinement subproblem using gradient-based search. The methodology is demonstrated on a CSTR cascade case study with six inputs and three outputs. Numerical results show that the approach yields boundary-seeking designs and is computationally tractable for campaigns with a few dozen experiments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC22.3", "code": "WeC22.3", "title": "Bayesian Optimization of a Multi-Product Chemical Reactor Using Composite Models and Partial Physics Knowledge", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC22", "sessionTitle": "Real-Time Optimization and Bayesian Methods for Process Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Dong, Liqiu", "affiliation": "Imperial College London" }, { "name": "Zagorowska, Marta", "affiliation": "TU Delft" }, { "name": "Mercangöz, Mehmet", "affiliation": "Imperial College London" } ], "keywords": [ "Machine learning and artificial intelligence in chemical process control", "Real-time optimization and control in chemical processes", "Industrial applications of chemical process control" ], "abstract": "We study data-driven real-time economic optimization of a multi-product chemical reactor when no reliable first-principles model is available beyond a steady-state energy balance. Instead of learning the economic objective directly as a black-box function, we use a composite formulation in which Gaussian process (GP) models predict physically meaningful outputs, including product concentrations and reactor temperature, while profit is computed analytically from these predictions together with raw-material, product, and utility prices. This preserves the structure of the economic objective, makes it parametric in changing prices without needing retraining, and allows candidate operating points to be checked against the available energy balance through a physics residual. The GPs also provide predictive uncertainty, which is exploited in a Bayesian optimization (BO) framework both for data-efficient exploration and for conservative enforcement of the reactor temperature constraint through an upper confidence bound. The acquisition function additionally penalizes large energy-balance mismatch obtained by substituting the GP-predicted outputs and candidate inputs into the available steady-state energy balance. The approach is demonstrated on a benchmark simulation of a non-isothermal multi-product reactor. Relative to a trust-region safe BO implementation, the proposed method achieves better simulated economic performance within the available iteration budget. Relative to a purely data-driven BO approach that does not use the available physics information, it avoids reactor temperature constraint violations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC22.4", "code": "WeC22.4", "title": "Adaptive Tuning of Online Feedback Optimization for Process Control Applications", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC22", "sessionTitle": "Real-Time Optimization and Bayesian Methods for Process Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Zagorowska, Marta", "affiliation": "TU Delft" }, { "name": "Ortmann, Lukas", "affiliation": "Eastern Switzerland University of Applied Sciences" }, { "name": "Belgioioso, Giuseppe", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Imsland, Lars", "affiliation": "Norwegian University of Science and Technology" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes", "Real-time optimization and control in chemical processes", "Advanced process control" ], "abstract": "Online Feedback Optimization leverages properties of optimization algorithms to develop controllers for systems with limited model availability, which is often the case in process control. The interplay between the parameters of the chosen optimization algorithm, as well as lack of direct connection to the characteristics of the underlying process make their tuning challenging. We propose a method for adaptive tuning of Online Feedback Optimization controllers based on scaled projected gradient descent by using sensitivity of the desired objective to the parameters of the algorithm. The proposed adaptive tuning method limits the operator-tunable parameters to scalar values that represent how much the control inputs and the objective can change between iterations without requiring either additional information about the controlled system or repeated experiments. Numerical studies on a gas lift and a continuously-stirred tank reactor processes confirm that our adaptive scheme improves closed-loop performance of Online Feedback optimization compared to manual tuning methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC22.5", "code": "WeC22.5", "title": "Bayesian Symbolic Regression for Missing Physics", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC22", "sessionTitle": "Real-Time Optimization and Bayesian Methods for Process Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Strouwen, Arno", "affiliation": "KULeuven" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Machine learning and artificial intelligence in chemical process control", "Model-predictive and optimization-based control in chemical processes" ], "abstract": "Model-based approaches for (bio)process systems often suffer from incomplete knowledge of the underlying physical, chemical, or biological laws. Universal differential equations, which embed neural networks within differential equations, have emerged as powerful tools to learn this missing physics from experimental data. However, neural networks are inherently opaque, motivating their post-processing via symbolic regression to obtain interpretable mathematical expressions. Genetic algorithm-based symbolic regression is a popular approach for this post-processing step, but provides only point estimates and cannot quantify the confidence we should place in a discovered equation. We address this limitation by applying Bayesian symbolic regression, which uses Reversible Jump Markov Chain Monte Carlo to sample from the posterior distribution over symbolic expression trees. This approach naturally quantifies uncertainty in the recovered model structure. We demonstrate the methodology on a Lotka-Volterra predator-prey system and then show how a well-designed experiment leads to lower uncertainty in a fed-batch bioreactor case study.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC23.1", "code": "WeC23.1", "title": "Learning-Based Hierarchical Volt/Var and Demand Flexibility Control in Active Distribution Networks (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC23", "sessionTitle": "Advanced Control and Machine Learning Strategies for Dependable Smart Energy Systems", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Hashemnezhad, Mohammad", "affiliation": "Cyprus University of Technology" }, { "name": "Aristidou, Petros", "affiliation": "Cyprus University of Technology" } ], "keywords": [ "Electrical distribution systems", "Power systems stability", "Real time simulators for energy systems" ], "abstract": "High PV penetration increases voltage variability in Active Distribution Networks~(ADNs). While inverter-based Volt/Var Control~(VVC) is the primary means of maintaining voltage limits, its effectiveness is constrained once reactive power capability is saturated. To address this, we propose a hierarchical voltage control scheme where Multi-Agent Reinforcement Learning~(MARL) coordinates inverter reactive power, and a single aggregator agent adjusts active power from fast flexible loads only when voltage violations persist and reactive headroom is insufficient. An activation gate ensures that flexibility is used sparingly. Case studies on a high-PV feeder show that VVC alone resolves moderate deviations, while conditional flexibility effectively mitigates severe over- and under-voltage with only 10–15% load adjustment.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC23.2", "code": "WeC23.2", "title": "Interpretable Data-Driven Fault Detection for Heat Pumps (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC23", "sessionTitle": "Advanced Control and Machine Learning Strategies for Dependable Smart Energy Systems", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Ahmadpour, Mehran", "affiliation": "Fraunhofer Research Institution for Energy Infrastructures and Geotechnologies IEG" }, { "name": "Jamali, Shahin", "affiliation": "Fraunhofer Research Institution for Energy Infrastructures and Geotechnologies IEG" }, { "name": "Kneiske, Tanja Manuela", "affiliation": "Technical University of Berlin" } ], "keywords": [ "AI methods for FDI/FTC", "Data-driven methods for FDI/FTC", "Thermal systems modelling" ], "abstract": "This study addresses the problems caused by low interpretability in machine-learning (ML) fault detection for heat pumps (HPs) by including explainability in the modelling process and performing a reproducible benchmark. A comparison is made between classical ML methods (Random Forest, gradient boosted trees) and deep neural network on an open HP dataset. Performance metrics, including accuracy, precision, recall, F1-score, and Matthews correlation coefficient (MCC), are used to evaluate the models, with deep neural networks achieving the best performance, albeit with lower explainability. Therefore, an explainability approach based on SHAP (Shapley Additive Explanations) is applied to create instance-specific attributions that clarify the roles of features such as compressor power, temperatures, and coefficient of performance in the model’s predictions. Visualization methods for these explainability values are introduced to provide operators with clear insights into the model’s decision-making process. These explanations enable operators to prioritize diagnostic actions effectively. The availability of the dataset enables benchmarking and ensures the evaluation is reproducible. In addition to providing real-time decision support, the explanations build operator trust in the model by clarifying the decision pathways, thereby supporting the industrial deployment of ML-based HP fault detection.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC23.3", "code": "WeC23.3", "title": "Robust Predictive Control for Maximum Power Point Tracking in Floating Offshore Wind Turbines (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC23", "sessionTitle": "Advanced Control and Machine Learning Strategies for Dependable Smart Energy Systems", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Mohammadi Shahir, Mohammad", "affiliation": "Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000, Nantes, France" }, { "name": "Mojallizadeh, Mohammad Rasool", "affiliation": "Arts Et Metiers Institute of Technology" }, { "name": "Hamida, Mohamed Assaad", "affiliation": "Cnrs Umr 6004 Cd0962ls2n" }, { "name": "Plestan, Franck", "affiliation": "CNRS UMR 6004 Ecole Centrale De Nantes-LS2N" } ], "keywords": [ "Wind power", "Control and management of energy systems" ], "abstract": "This study develops a robust control strategy for a floating offshore wind turbine (FOWT) that maximizes the power extraction in below-rated wind speeds (Region II). To this end, optimal predictive control (OPC) and second-order integral sliding mode control (ISMC) are integrated to mitigate their limitations and ensure optimal performance along with high robustness to environmental and system perturbations. The performance of the proposed controller is analyzed through simulations in MATLAB/Simulink and OpenFAST. Additionally, to evaluate the effectiveness of the proposed approach, the simulation results are compared with the baseline ROSCO.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC23.4", "code": "WeC23.4", "title": "Koopman Operator Approach to Nonlinear PLL Analysis and Robust Gain Retuning (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC23", "sessionTitle": "Advanced Control and Machine Learning Strategies for Dependable Smart Energy Systems", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Najarzadeh, Reza", "affiliation": "TU Ilmenau" }, { "name": "Reger, Johann", "affiliation": "TU Ilmenau" } ], "keywords": [ "Power electronics", "Control and management of energy systems" ], "abstract": "Phase-locked loops (PLLs) are fundamental components in grid-connected converters, where proportional–integral (PI) controllers typically serve as loop filters. However, fixed PI gains often fail to maintain robustness under varying grid conditions. This paper introduces a Koopman operator-based framework that derives a finite-dimensional linear surrogate of the nonlinear PLL dynamics. Using this lifted linear model, the nominal PI gains are first estimated using a Kalman filtering scheme and then retuned via an H-infinity optimization to minimize the induced gain from disturbances to the error states. Simulation results demonstrate that the proposed retuning strategy accelerates the convergence of the errors while ensuring robustness against grid disturbances. The study establishes Koopman-based modeling as a systematic and effective alternative to conventional PLL design, improving both model fidelity and robustness.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC23.5", "code": "WeC23.5", "title": "Model Predictive Control of Coupled Electrical and Thermal Networks with Pumped Thermal Energy Storage: A Real-Time Co-Simulation Study (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC23", "sessionTitle": "Advanced Control and Machine Learning Strategies for Dependable Smart Energy Systems", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Yehia, Sary", "affiliation": "The University of Manchester" }, { "name": "Xu, Yiqiao", "affiliation": "University of Manchester" }, { "name": "Parisio, Alessandra", "affiliation": "The University of Manchester" } ], "keywords": [ "Real time simulators for energy systems", "Energy management systems", "Multi-energy networks" ], "abstract": "This paper presents a real-time model predictive control (MPC) framework that coordinates heating, ventilation, and air conditioning (HVAC) flexibility, pumped thermal energy storage (PTES), and distributed generation while enforcing unbalanced three-phase AC network constraints. A convexified power-flow model enables tractable integration of detailed network physics within the MPC. The framework is validated through a high fidelity MATLAB–RTDS co-simulation using low-latency User Datagram Protocol (UDP) communication. Tests on a modified IEEE 13-bus feeder demonstrate price-responsive operation, thermal comfort, and network feasibility in real time.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC23.6", "code": "WeC23.6", "title": "Comparative Analysis of Distributed and Centralized Optimal Control for Demand Response in Large-Scale District Heating (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC23", "sessionTitle": "Advanced Control and Machine Learning Strategies for Dependable Smart Energy Systems", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Mohammadyari, Milad", "affiliation": "Vlaamse Instelling Voor Technologisch Onderzoek (VITO) NV, Boeretang 200, 2400, Mol, Belgium" }, { "name": "Vanhoudt, Dirk", "affiliation": "Vlaamse Instelling Voor Technologisch Onderzoek (VITO) NV, Boeretang 200, 2400, Mol, Belgium" }, { "name": "Parisio, Alessandra", "affiliation": "The University of Manchester" }, { "name": "Plestan, Franck", "affiliation": "CNRS UMR 6004 Ecole Centrale De Nantes-LS2N" }, { "name": "Van Oevelen, Tijs", "affiliation": "Vlaamse Instelling Voor Technologisch Onderzoek (VITO) NV" } ], "keywords": [ "Control and management of energy systems", "Demand response", "Thermal systems modelling" ], "abstract": "District heating (DH) systems are drivers for the integration of renewable sources into the energy system, which could be significantly enhanced through demand response using advanced optimal control strategies to manage energy flexibility. Designing an effective optimal controller for DH systems is challenging due to nonlinear and nonconvex dynamics, control authority, and privacy considerations. Furthermore, centralized approaches may fail to scale as the number of controllable buildings increases. Therefore, this paper presents a new distributed optimal control scheme for demand response in DH systems, formulated using symmetric alternating direction method of multipliers (S-ADMM). The controller is based on a nonlinear variable-flow and variable-temperature (VF-VT) thermal-hydraulic model of the substation heat exchanger and the heat supplier. The performance of the distributed scheme, in terms of optimality and computational scalability, is discussed and compared with that of the centralized approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC24.1", "code": "WeC24.1", "title": "Active Disturbance Rejection Control of the Water Level of Main Irrigation Canals Subjected to Unknown Withdrawals (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC24", "sessionTitle": "Water Resource System Modeling and Control; Control of Large-Scale Environmental Systems; Planning and Management in Environmental Systems under Deep Uncertainty", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Cerro-Sánchez, Alberto", "affiliation": "Universidad De Castilla-La Mancha" }, { "name": "Mehallel, Aissa", "affiliation": "La Universidad De Castilla-La Mancha" }, { "name": "Feliu-Batlle, Vicente", "affiliation": "Univ of Castilla-La Mancha. CIF: Q-1368009E" }, { "name": "Rivas-Perez, Raul", "affiliation": "Havana Technological University" } ], "keywords": [ "Real time monitoring and control of environmental systems" ], "abstract": "In this work, the impact of withdrawals of unknown origin are removed from irrigation canals by applying a recently introduced formulation of Active Disturbance Rejection Control (ADRC). An experimental platform is identified and used to validate this control system. The control design development is well described in the document, and simulation and experimental results are depicted and compared with other control systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC24.2", "code": "WeC24.2", "title": "Data-Driven Forecasting and Control of Multipurpose Water Reservoir Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC24", "sessionTitle": "Water Resource System Modeling and Control; Control of Large-Scale Environmental Systems; Planning and Management in Environmental Systems under Deep Uncertainty", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Spinelli, Davide", "affiliation": "Politecnico Di Milano" }, { "name": "Palcic, Giulio", "affiliation": "Polytechnic University of Milan" }, { "name": "Longo, Emiliano", "affiliation": "Politecnico Di Milano" }, { "name": "Giuliani, Matteo", "affiliation": "Politecnico Di Milano" }, { "name": "Castelletti, Andrea", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Water resource system modeling and control", "AI and ML for environmental systems", "Natural resources management" ], "abstract": "Subseasonal-to-seasonal forecasts are critical for water management, yet standard operational products often lack basin-scale skill. This paper introduces a novel forecast model integrating global teleconnections and local drivers via advanced feature extraction. We assess its operational value using an Evolutionary Multi-Objective Direct Policy Search framework to design control policies for the Lake Como system. Numerical experiments demonstrate that our forecasts are substantially more accurate than existing benchmarks and yield tangible operational benefits. Overall, these results show that AI-enhanced subseasonal-to-seasonal forecasts can significantly improve multi-objective reservoir control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC24.3", "code": "WeC24.3", "title": "Data-Driven Control-Oriented Modelling for MPC-Based Control of Urban Drainage Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC24", "sessionTitle": "Water Resource System Modeling and Control; Control of Large-Scale Environmental Systems; Planning and Management in Environmental Systems under Deep Uncertainty", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Romero Ben, Luis", "affiliation": "Universitat Politècnica De Catalunya" }, { "name": "Joseph-Duran, Bernat", "affiliation": "CETAQUA" }, { "name": "Sunyer Roqueta, David", "affiliation": "AQUATEC" }, { "name": "Cembrano, Gabriela", "affiliation": "CSIC-UPC" }, { "name": "Meseguer, Jordi", "affiliation": "CETAQUA" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "Carrasco Mínguez, Alejandro", "affiliation": "Canal De Isabel II SA M.P" } ], "keywords": [ "Water resource system modeling and control", "Modeling and identification of environmental systems", "Real time monitoring and control of environmental systems" ], "abstract": "This article presents a data-driven, control-oriented modelling methodology for urban drainage systems (UDS). The proposed framework requires three main key components: input-output data from the element to be modelled, expert knowledge to define the model structure, and data-fitting techniques to obtain optimal parameters. The methodology is evaluated using a realistic benchmark from an UDS in Madrid, Spain. The results show high model accuracy and improved performance within a MPC scheme, reducing discharge and increasing treatment facilities utilization.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC24.4", "code": "WeC24.4", "title": "From Heuristic Rules to Modern Control: Advancing Irrigation Management in the Ebro Delta (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC24", "sessionTitle": "Water Resource System Modeling and Control; Control of Large-Scale Environmental Systems; Planning and Management in Environmental Systems under Deep Uncertainty", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Kolton-Jusid, Alberto", "affiliation": "Universitat Politècnica De Catalunya" }, { "name": "Blesa, Joaquim", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "Ocampo-Martinez, Carlos", "affiliation": "Universitat Politecnica De Catalunya (UPC)" } ], "keywords": [ "Water resource system modeling and control", "Real time monitoring and control of environmental systems", "Participatory decision making in environmental systems" ], "abstract": "The Ebro Delta hosts a rich and diverse ecosystem of major environmental and agricultural importance, largely sustained by rice cultivation. As a result, effective water management is essential for ensuring the long-term sustainability of the region. However, to date, no studies have examined irrigation management practices in this area. This paper models part of the northeastern irrigation network using EPA SWMM and compares the existing heuristic-based operational strategy with a structured control strategy. This comparison highlights the potential for improving system management through a better understanding of network dynamics and the adoption of systematic decision-making approaches. Results show that the proposed control strategy can enhance irrigation performance, demonstrating the potential for more efficient and responsive water management in the Ebro Delta. Additionally, preliminary considerations related to the water–energy–food–environment nexus are discussed.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC24.5", "code": "WeC24.5", "title": "The Impact of Sensor Placement on Graph Neural Network Based Leakage Detection (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC24", "sessionTitle": "Water Resource System Modeling and Control; Control of Large-Scale Environmental Systems; Planning and Management in Environmental Systems under Deep Uncertainty", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "van Gemert, Jarne Jeannetta Huberta", "affiliation": "University of Technology Eindhoven" }, { "name": "Breschi, Valentina", "affiliation": "Eindhoven University of Technology" }, { "name": "Yntema, Doekle R.", "affiliation": "Wetsus" }, { "name": "Keesman, Karel", "affiliation": "Wageningen University" }, { "name": "Lazar, Mircea", "affiliation": "Eindhoven Univ. of Technology" } ], "keywords": [ "Water resource system modeling and control", "Modeling and identification of environmental systems", "Real time monitoring and control of environmental systems" ], "abstract": "Sensor placement for leakage detection in water distribution networks is an important and practical challenge for water utilities. Recent work has shown that graph neural networks can estimate and predict pressures and detect leaks, but their performance strongly depends on the available sensor measurements and configurations. In this paper, we propose a novel PageRank-Centrality-based sensor placement method and investigate how sensor placement influences the performance of pressure reconstruction, prediction, and GNN-based leakage detection on the EPANET Net1.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC24.6", "code": "WeC24.6", "title": "Inferring Community-Level Interaction Structures in Groundwater Consumption Using DMDc", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC24", "sessionTitle": "Water Resource System Modeling and Control; Control of Large-Scale Environmental Systems; Planning and Management in Environmental Systems under Deep Uncertainty", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Ahmed, Hassaan", "affiliation": "Lahore University of Management Sciences" }, { "name": "Muhammad, Abubakr", "affiliation": "LUMS School of Science & Engineering, Pakistan" } ], "keywords": [ "Modeling and identification of environmental systems", "Water resource system modeling and control", "Control of large-scale environmental systems" ], "abstract": "Groundwater-dependent regions face increasing pressure from prolonged extraction and declining recharge, making quantitative assessments of long-term sustainability essential. Such assessments must account not only for the physical dynamics of the resource but also for the community-level behavioral patterns that drive extraction. In this work, each Sub-Watershed Region (HUC12) in the High Plains Aquifer (U.S.A) with non-zero consumption is modeled as a community-level agent within a socio-ecological system. Dynamic Mode Decomposition with Control (DMDc) is then used as a system identification tool to infer the coupled groundwater–community dynamics. Applying dimensionality reduction to aggregated withdrawal data yields a reduced-order model that captures the dominant modes of community behavior and enables estimation of sociological parameters. Applied to real-world High Plains Aquifer data, the framework provides a data-driven basis for understanding community-scale extraction dynamics and informing sustainability assessments and policy analysis.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC26.1", "code": "WeC26.1", "title": "Funnel Cruise Control with Input Constraints", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC26", "sessionTitle": "Autonomous and Multi-Vehicle Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Zhang, Yanan", "affiliation": "Northwest A&F University" }, { "name": "Jiacheng, Song", "affiliation": "Northwest A&F University" } ], "keywords": [ "Autonomous vehicles" ], "abstract": "We investigate automatic control for intelligent vehicles exhibiting uncertain and nonlinear dynamics, while accounting for actuator limitations. The core goal is to guarantee that the deviation between actual and desired spacing remains confined to a prescribed transient boundary, thereby attaining satisfactory dynamic and asymptotic behavior of any smooth safety distance reference. To tackle this issue, a new funnel-based spacing regulator is developed that is equipped with an adjustable time-varying element that expands the prescribed performance envelope upon actuator saturation occurrence, thereby ensuring compliance with the input constraints. This controller can handle input saturation without the need for a model, possesses low complexity, and extends the traditional funnel cruise control methodology. Some simulations were conducted to compare the designed controller with traditional funnel cruise control, which demonstrated its effectiveness and improvement.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC26.2", "code": "WeC26.2", "title": "Event-Triggered Neural Network-Based Adaptive Cruise Control", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC26", "sessionTitle": "Autonomous and Multi-Vehicle Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Zhang, Yanan", "affiliation": "Northwest A&F University" }, { "name": "Jiacheng, Song", "affiliation": "Northwest A&F University" }, { "name": "Ju, Peilun", "affiliation": "Chang’an University" } ], "keywords": [ "Autonomous vehicles" ], "abstract": "This paper focuses on the nonlinear driving resistance of vehicles and designs an on-demand adaptive cruise control (ACC) algorithm without a precise model. While ensuring safety control, it reduces the execution frequency of the actuator. Firstly, Radial Basis Function Neural Networks (RBF NNs) are used to characterize uncertain driving resistance and reconstruct the nonlinear dynamic model of the vehicle. Secondly, in response to the safety distance target of vehicle adaptive cruise control, a virtual controller is designed using backstepping control technology to convert vehicle distance control into speed control, and a speed feedback controller is designed. Moreover, when designing the speed control term, an event-triggered mechanism is introduced to achieve on-demand control of the actuator, and system stability is analyzed according to Lyapunov theory. Finally, the feasibility and superiority of the designed algorithm were verified using the Economic Commission for Europe (ECE) operating conditions to test the speed curve.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC26.3", "code": "WeC26.3", "title": "Embodied Opinion Dynamics for Safety-Critical Motion Control in Dynamic Environments", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC26", "sessionTitle": "Autonomous and Multi-Vehicle Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Tang, Zhiqi", "affiliation": "University of Manchester" }, { "name": "Xing, Yu", "affiliation": "RWTH Aachen" } ], "keywords": [ "Autonomous vehicles", "Adaptive and robust control of automotive systems", "Intelligent transportation systems" ], "abstract": "This paper proposes a novel adaptive control framework that embeds nonlinear opinion dynamics within the dynamical sensorimotor layers of an automated vehicle governed by second-order nonholonomic bicycle kinematics. The framework enables an ego vehicle to perform adaptive decision-making and achieve safe motion control under interaction uncertainty with non-cooperative neighboring agents. We consider a representative case study in which an ego vehicle autonomously attempts to merge into a lane occupied by human-driven or automated vehicles whose intentions are unknown. Within the proposed framework, the ego vehicle adaptively selects and executes merging versus non-merging behaviors in response to changing environmental conditions. Formal safety guarantees, as well as equilibrium and stability analyses of the closed-loop system, are provided. Numerical simulations further demonstrate the effectiveness of the proposed approach. system.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC26.4", "code": "WeC26.4", "title": "A Verifiable LLM-Driven Semantic-To-Execution Framework for Adaptive UAV Swarm Coordination in Disaster-Response Missions", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC26", "sessionTitle": "Autonomous and Multi-Vehicle Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Luo, Peifen", "affiliation": "Guangdong University of Technology" }, { "name": "Meng, Wei", "affiliation": "Guangdong University of Technology" } ], "keywords": [ "Mission planning and decision making for AVs", "Multi-vehicle systems", "Autonomous vehicles" ], "abstract": "Coordinating heterogeneous Unmanned Aerial Vehicles (UAVs) in disaster-response missions requires fast, reliable, and adaptive decision-making under dynamic constraints and intermittent communication. Existing model-based and learning-based methods struggle to reconfigure plans online, while large language model (LLM)-based planners provide semantic reasoning but lack iterative optimization and verifiable execution. This paper proposes a unified LLM-driven semantic-to-execution framework that integrates an LLM-assisted adaptive scheduling module with a verifiable decision-making architecture. The LLM provides semantic pruning and contextual constraints that guide large-scale optimization, and the verification layer ensures feasibility, consistency, and robust reallocation under failures. High-fidelity Unity3D simulations demonstrate that our framework achieves greater success rates, with significantly higher path efficiency and robust adaptive recovery compared to state-of-the-art baselines, proving the necessity of verifiable execution for semantically-grounded multi-UAV autonomy.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC26.5", "code": "WeC26.5", "title": "Multi-Task Bayesian Optimization for Tuning Decentralized Trajectory Generation in Multi-UAV Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC26", "sessionTitle": "Autonomous and Multi-Vehicle Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Manzoni, Marta", "affiliation": "Politecnico Di Milano" }, { "name": "Nazzari, Alessandro", "affiliation": "Politecnico Di Milano" }, { "name": "Rubinacci, Roberto", "affiliation": "Politecnico Di Milano" }, { "name": "Lovera, Marco", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Multi-vehicle systems" ], "abstract": "This paper investigates the use of Multi-Task Bayesian Optimization for tuning decentralized trajectory generation algorithms in multi-drone systems. We treat each task as a trajectory generation scenario defined by a specific number of drone-to-drone interactions. To model relationships across scenarios, we employ Multi-Task Gaussian Processes, which capture shared structure across tasks and enable efficient information transfer during optimization. We compare two strategies: optimizing the average mission time across all tasks and optimizing each task individually. Through a comprehensive simulation campaign, we show that single-task optimization leads to progressively shorter mission times as swarm size grows, but requires significantly more optimization time than the average-task approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC26.6", "code": "WeC26.6", "title": "A Lower Bound of the Time Headway for String Stabilizing ACC/CACC Systems under Both Lag and Delay", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC26", "sessionTitle": "Autonomous and Multi-Vehicle Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Ma, Guoqi", "affiliation": "Harbin Institute of Technology" }, { "name": "Duan, Guang-Ren", "affiliation": "Harbin Institute of Technology" }, { "name": "Ge, Shuzhi Sam", "affiliation": "National University of Singapore" } ], "keywords": [ "Multi-vehicle systems", "Intelligent transportation systems", "Autonomous vehicles" ], "abstract": "Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) are two significant methodologies for achieving prescribed automatic vehicle following in connected and autonomous vehicles, where the inter-vehicular spacing is of the foremost concern for safety, mobility, etc. Under the Constant Time Headway Policy (CTHP) for spacing, selection of the time headway (h_w) is crucial for maintaining a tight spacing between vehicles while achieving desired platooning performance criteria such as string stability. Existing results have shown the effect of the vehicle parasitic dynamics (lag or delay) on the lower bound of the employable time headway for ensuring string stability. In particular, when the vehicle is modeled by a first-order inertia system, a lower bound of h_w was provided as frac{2 tau_0}{1 + k_a}, where tau_0 is the upper bound of the parasitic lag, and k_a in [0, 1) is the feedforward gain of the predecessor vehicle's acceleration; when a general delay model is adopted, a lower bound of h_w was provided as frac{2 ell_0}{1 + k_a}, where ell_0 is the upper bound of the parasitic delay. In this paper, by taking into account both parasitic lag and parasitic delay in the vehicle dynamic model, a lower bound of h_w is derived as frac{2 (tau_0 + ell_0)}{1 + k_a}. In addition, under a high-order vehicle dynamic model, a conjecture on a lower bound of h_w is also presented. An illustrative example and comparative simulation results are finally provided to validate the effectiveness of the achieved results.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC27.1", "code": "WeC27.1", "title": "Health-Aware Receding-Horizon Spectral Control for Wave Energy Converters Using a Reliability Metric (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC27", "sessionTitle": "Dynamics and Control of Ocean Renewable Energy Systems II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Ziaei, Amin", "affiliation": "Maynooth University" }, { "name": "Said, Hafiz Ahsan", "affiliation": "Maynooth University" }, { "name": "Ringwood, John", "affiliation": "Maynooth University" } ], "keywords": [ "Marine renewable energy systems", "Modelling, identification and control in marine systems", "Sensors and actuators in marine systems" ], "abstract": "Wave energy, harnessed through wave energy converters, holds strong potential to contribute to the renewable energy mix. To enhance the commercial viability of wave energy converters, effective control strategies for maximising energy production are essential. However, conventional energy-maximising controllers for wave energy converters often induce excessive device motion, which accelerates device wear, shortens device lifetime, and increases operational expenditure. This paper introduces a novel health-aware control framework for wave energy converters based on a receding-horizon spectral optimal control method. The proposed approach balances energy capture with power take-off lifetime, ultimately reducing the levelised cost of energy. The proposed health-aware spectral control formulation requires no extra terms to guarantee the convexity of the optimisation problem and stability. Simulation results confirm that the proposed controller can effectively adjust the trade-off between energy capture and power take-off lifetime.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC27.2", "code": "WeC27.2", "title": "Receding Horizon Optimal Control of Tidal Barrage Power Plants (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC27", "sessionTitle": "Dynamics and Control of Ocean Renewable Energy Systems II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Skiarski, Agustina", "affiliation": "Maynooth University" }, { "name": "Faedo, Nicolás", "affiliation": "Politecnico Di Torino" }, { "name": "Ringwood, John", "affiliation": "Maynooth University" } ], "keywords": [ "Marine renewable energy systems", "Modelling, identification and control in marine systems", "Power and propulsion in marine systems" ], "abstract": "This study presents a receding horizon optimal control framework for tidal barrages, a type of renewable power plant that generates energy from the tidal range resource. In a tidal barrage, a wall separates a basin from the open sea, with turbines and sluice gates that allow the passage of water. As the tidal elevation varies throughout the day, the basin can be filled and emptied through the turbines, thus generating mechanical, and electrical, power. The operation of tidal barrages can be optimised by computing the optimal trajectory of the flow through the barrage. In this study, moment-based control is used to discretise the nonlinear optimal control problem of tidal barrage operation, and a receding horizon algorithm is proposed to update the optimal trajectories through time. Receding horizon control enables real measurements of the tidal elevation at each time step to be included, thus accounting for weather driven tidal variations, which enhances the control solution by 2%, compared to only using the astronomic tidal variations. A closed-loop control is then added to track the reference state trajectory in real time, and comply with constraints.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC27.3", "code": "WeC27.3", "title": "Noncausal Optimal Control-Based Pumping Strategy to Suppress Pitch Motion of FOWTs (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC27", "sessionTitle": "Dynamics and Control of Ocean Renewable Energy Systems II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Wang, Peng", "affiliation": "Columbia University" }, { "name": "Bai, Haomeng", "affiliation": "University of Manchester" }, { "name": "Apsley, David", "affiliation": "The University of Manchester" }, { "name": "Stansby, Peter", "affiliation": "University of Manchester" }, { "name": "Li, Guang", "affiliation": "University of Manchester" } ], "keywords": [ "Marine renewable energy systems", "Marine system guidance, navigation and control", "Modelling, identification and control in marine systems" ], "abstract": "Semi-submersible floating offshore wind turbines (FOWTs) are susceptible to wave- and wind-induced pitch motion, which poses challenges to platform stability and structural fatigue damage. Existing control strategies mainly rely on passive damping, which becomes ineffective under varying sea states. To address this issue, this paper proposes an active pumping strategy for water ballast using multi-objective noncausal optimal controller to suppress pitch motion by pumping water three columns, thereby generating a counteracting moment against wave and wind excitations. Firstly, the dynamic model of the triple-column VolturnUS floating platform, coupled with hydrodynamic and aerodynamic forces, is developed using the Euler–Lagrange method. Based on this model, a multi-objective noncausal optimal controller is developed to balance pitch motion suppression and pumping power. When pitch minimization is prioritized, compared with the conventional passive damping control, the proposed method reduces the pitch motion by 85% but with high power demand. When power consumption is penalized, the controller can still reduce the pitch by 82% while lowering power usage by 95%, indicating a trade-off between motion suppression and pump energy consumption.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC27.4", "code": "WeC27.4", "title": "Tuning of an Optimal Controller for Tidal Barrage Operational Optimisation (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC27", "sessionTitle": "Dynamics and Control of Ocean Renewable Energy Systems II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Skiarski, Agustina", "affiliation": "Maynooth University" }, { "name": "Faedo, Nicolás", "affiliation": "Politecnico Di Torino" }, { "name": "Ringwood, John", "affiliation": "Maynooth University" } ], "keywords": [ "Marine renewable energy systems", "Modelling, identification and control in marine systems", "Power and propulsion in marine systems" ], "abstract": "Tidal barrages generate electricity by utilising the tidal range resource, i.e. the variations in sea water level throughout the day. Scheduling tidal barrage operation requires solving an associated nonlinear and nonconvex optimal control problem, which is difficult to address analytically. Hence, the solution must be approximated by numerical techniques, and the question arises as to how the optimal control problem can be best discretised and solved in a computationally feasible way, and if the solution computed is a global or local optimum. This study implements moment-based control to discretise and solve the tidal barrage optimal control problem, and tests a range of tuning parameters within the controller. The aim is to evaluate how the different tuning decisions affects the performance of the controller, in terms of energy generation (derived from the control solution), numerical convergence, and runtime. The methodology here presented enables to effectively select controller parameters, ensuring that convergence to an optimal solution is achieved, even in nonlinear and nonconvex programs.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC27.5", "code": "WeC27.5", "title": "Constraint-Aware Impedance-Matching Control for a Moored Pendulum Wave Energy Converter (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC27", "sessionTitle": "Dynamics and Control of Ocean Renewable Energy Systems II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Paduano, Bruno", "affiliation": "Politecnico Di Torino" }, { "name": "Niosi, Francesco", "affiliation": "Politecnico Di Torino" }, { "name": "Carapellese, Fabio", "affiliation": "Politecnico Di Torino" }, { "name": "Faedo, Nicolás", "affiliation": "Politecnico Di Torino" }, { "name": "Sirigu, Sergej Antonello", "affiliation": "Politecnico Di Torino" }, { "name": "Sannino, Gianmaria", "affiliation": "Enea Research Center" }, { "name": "Matiazzo, Giuliana", "affiliation": "Politecnico Di Torino" } ], "keywords": [ "Modelling, identification and control in marine systems" ], "abstract": "Impedance-matching (IM) control offers a simple framework for power maximisation in wave energy converters, but its unconstrained formulation may violate PTO limits in realistic conditions. This paper applies a recently introduced constraint-handling mechanism (CHM), which embeds soft constraints into IM synthesis through a frequency-domain added impedance. The method is demonstrated on the nonlinear, moored PeWEC device using a data-driven linear model derived from a high-fidelity textsc{OrcaFlex} simulation. Across directionally distributed irregular sea states, the CHM effectively limits PTO velocity while preserving representative power absorption, showing its practicality for constrained WEC control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC31.1", "code": "WeC31.1", "title": "Blockchain-Based Incentive Mechanism for Decentralized Data Labeling", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC31", "sessionTitle": "Decentralized Economic Models and Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Qin, Rui", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Liang, Xiaolong", "affiliation": "Chinese Academy of Sciences" }, { "name": "Li, Juanjuan", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Zhang, Tengchao", "affiliation": "Macau University of Science and Technology" } ], "keywords": [ "Blockchain intelligence", "Decentralized economics/ecosystems (DeEco)", "Agent & AI technology for business and economy" ], "abstract": "High-quality labeled data is essential for current artificial intelligence (AI) development. However, existing centralized labeling platforms suffer from a lack of transparency, inconsistent quality, and weak incentive alignment. These platforms typically rely on centralized oversight or simple aggregation rules that are easily manipulated in open environments. To address this issue, we propose a decentralized data labeling framework leveraging blockchain and smart contracts. We also design a incentive mechanism that couples reward distribution with reputation management to motivate sustained high-quality contributions from both labelers and validators. To validate our proposed method, we design some computaional experiments with 30% malicious labelers and 70% honest labelers. The experimental results show that our method achieves 94% labeling accuracy, which substantially outperforms the baseline method (70%). Therefore, our proposed method can maintain high data quality even in adversarial, trustless settings.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC31.2", "code": "WeC31.2", "title": "A Real-Time Autonomous Coordination Mechanism Driven by Enterprise Incentive Tokens for Multi-Agent Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC31", "sessionTitle": "Decentralized Economic Models and Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Li, Juanjuan", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Liang, Xiaolong", "affiliation": "Chinese Academy of Sciences" }, { "name": "Qin, Rui", "affiliation": "Institute of Automation, Chinese Academy of Sciences" }, { "name": "Hao, Jiayang", "affiliation": "Institute of Automation CAS" }, { "name": "Jiang, Tai", "affiliation": "Macau University of Science and Technology" }, { "name": "Wang, Fei-Yue", "affiliation": "Institute of Automation, Chinese Academy of Sciences" } ], "keywords": [ "Econometric models and methods", "Decentralized economics/ecosystems (DeEco)", "Blockchain intelligence" ], "abstract": "Modern enterprise systems are evolving into complex and distributed Multi-Agent Systems (MAS), where the coordination of self-interested agents poses a fundamental challenge. However, traditional centralized control architectures often fail to address the negative externalities caused by local optimization behaviors. To address this challenge, this paper proposes a real-time autonomous coordination mechanism driven by a novel incentive, namely enterprise incentive token (EIT). It integrates dynamic economic regulation with verifiable trust through a dual-token model. While non-fungible tokens (NFTs) anchor workflows to real-world assets (RWAs) and enable verifiable state tracking, fungible tokens (FTs) serve as dynamic incentive signals automatically executed by smart contracts. By translating global constraints into shadow prices, the EIT-based coordination mechanism induces agents to internalize coordination costs. Furthermore, simulation experimental results within a multi-team KPI management scenario demonstrate that the proposed mechanism effectively mitigates coordination failure and achieves near-optimal system efficiency. This work advocates for a paradigm shift from rigid centralized planning to real-time market-based autonomous coordination.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC31.3", "code": "WeC31.3", "title": "On Unified Adaptive Black-Litterman Mean-Variance Portfolio Management", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC31", "sessionTitle": "Decentralized Economic Models and Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Li, Chi-Lin", "affiliation": "Boston University" }, { "name": "Hsieh, Chung-Han", "affiliation": "National Tsing Hua University" } ], "keywords": [ "Business and financial analytics", "Econometric models and methods", "Financial systems" ], "abstract": "This paper proposes a unified adaptive portfolio-management framework that combines factor-based view generation, Black-Litterman (BL) posterior estimation, EWMA covariance estimation, and mean-variance optimization. The key mechanism is a dynamic sliding window that adjusts the estimation horizon according to realized portfolio volatility, thereby updating factor estimates, BL posterior expected returns, and portfolio weights over time. In a ten-year empirical study of the top 100 market-capitalization constituents of the S&P 500 with turnover transaction costs, the proposed method outperforms dynamic mean-variance optimization without BL views and provides stronger downside risk control, while its relative performance remains benchmark-dependent.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC31.4", "code": "WeC31.4", "title": "From Correctness to Success Anticipation: Dual-Timescale Prompt-Based Student Simulation (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC31", "sessionTitle": "Decentralized Economic Models and Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Xie, Yuanhan", "affiliation": "National University of Defense Technology" }, { "name": "Lei, Shifeng", "affiliation": "HuNan Minmetals Hi-Tech Private Equity Funds" }, { "name": "Cheng, Li", "affiliation": "National University of Defense Technology" }, { "name": "Shen, Dayong", "affiliation": "National University of Defense Technology" }, { "name": "Zhang, Zhongshan", "affiliation": "National University of Defense Technology" }, { "name": "Yao, Feng", "affiliation": "National University of Defense Technology" }, { "name": "Wang, Tao", "affiliation": "National University of Defense Technology" } ], "keywords": [ "Social computing", "Generative AI in control education", "Knowledge automation" ], "abstract": "Large language models (LLMs) are increasingly used to simulate learners, yet most student simulators reduce performance modelling to correctness and ignore how students anticipate success. We propose a prompt-based simulator that wraps a single LLM with dual-timescale state summaries: a concept-mastery summary from long-term interaction logs and a tutoring summary capturing short-term exposure. The simulator jointly predicts success anticipation and answer choice. On the DBE-KT22 dataset, mastery summaries markedly improve success-anticipation accuracy, while tutoring summaries mainly boost answer accuracy. These distinct roles position state-aware, prompt-based simulators as practical testbeds for metacognition-related signals and adaptive tutoring policies. The code, data, and appendices are publicly available in the https://github.com/XIE20000502/From-correctness-to-success-anticipation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC31.5", "code": "WeC31.5", "title": "LOCVF: A Layered On-Chain and Off-Chain Valuation Framework for Federated Learning", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC31", "sessionTitle": "Decentralized Economic Models and Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Li, Cheng", "affiliation": "Renmin University of China" }, { "name": "Liang, Xiaolong", "affiliation": "Chinese Academy of Sciences" }, { "name": "Yuan, Yong", "affiliation": "Renmin University of China" } ], "keywords": [ "Blockchain intelligence", "Agent & AI technology for business and economy", "Game theories" ], "abstract": "With the growing use of distributed intelligent systems, ensuring efficient model training and fair value exchange under strict privacy constraints has become a major challenge. This paper presents LOCVF, a layered on-chain and off-chain valuation framework for federated learning. The framework integrates Shapley-based initialization with deviation-aware dynamic weighting, enabling fair and adaptive contribution assessment. Intensive computations are per- formed off-chain, whereas smart contracts provide verifiable settlements on-chain. Experiments demonstrate that LOCVF improves robustness and stability in noisy, non-IID environments. It outperforms static valuation methods while reducing computational and on-chain overhead. The framework is expected to facilitate the deployment of scalable and trustworthy AI systems in resource-constrained environments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC32.1", "code": "WeC32.1", "title": "Recursive Learning of Feedforward and Compliance Compensation Parameters for Precision Motion Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC32", "sessionTitle": "Mechatronic Principles in Motion and Robotic Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Wind, Michiel", "affiliation": "Eindhoven University of Technology" }, { "name": "Pierssens, Jens", "affiliation": "Eindhoven University of Technology" }, { "name": "Beerens, Ruud", "affiliation": "ASML" }, { "name": "Dolk, Victor", "affiliation": "ASML" }, { "name": "van Keulen, Thijs Adriaan Cornelis", "affiliation": "Technische Universiteit Eindhoven" } ], "keywords": [ "High-performance motion control systems", "Mechatronic system estimation, identification, control" ], "abstract": "To meet the stringent requirements of future motion systems exhibiting time-varying and/or position-dependent behavior, online data must be leveraged to improve control performance. This paper presents a recursive algorithm for simultaneous learning of feedforward and compliance compensation parameters. A multivariate regression formulation is proposed that jointly estimates friction, mass, jerk, and compliance compensation parameters while mitigating parameter coupling. Experimental results on a high-tech semiconductor metrology and inspection system demonstrate an order-of-magnitude improvement in servo performance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC32.2", "code": "WeC32.2", "title": "Force Controller with User-Selected Position for Antagonistically Driven Pneumatic Artificial Muscles", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC32", "sessionTitle": "Mechatronic Principles in Motion and Robotic Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Wang, Genmeng", "affiliation": "INSA Lyon" }, { "name": "Grolleau, Pierre-Elouan", "affiliation": "INSA De Lyon" }, { "name": "Chalard, Rémi", "affiliation": "Université D'Evry" }, { "name": "Pham, Minh Tu", "affiliation": "INSA De Lyon" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Soft robotics", "Human mechatronics and human-machine interaction" ], "abstract": "A force controller based on a user selected position is presented in this paper for an antagonistically driven pneumatic artificial muscles system. A detailed model of the experimental prototype is provided, serving as the base for an input–output linearization approach. When subjected to user-applied external torque, the controller guides the system to reproduce the torque specified by a reference model. Experimental evaluations with both linear and nonlinear reference models demonstrate that the proposed controller delivers consistent and reliable tracking performance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC32.3", "code": "WeC32.3", "title": "The Soft-PVTOL: An Experimental Validation", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC32", "sessionTitle": "Mechatronic Principles in Motion and Robotic Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Verdín Monzón, Rodolfo Isaac", "affiliation": "Center for Research in Optics" }, { "name": "Moreno Jimenez, Hugo Alberto", "affiliation": "Centro De Investigaciones En óptica" }, { "name": "Spong, Mark W.", "affiliation": "Univ. of Texas at Dallas" }, { "name": "Flores, Gerardo", "affiliation": "Texas A&M International University" } ], "keywords": [ "Mechatronic system integration", "Soft robotics", "Mechatronics for robotic systems" ], "abstract": "This paper presents the first experimental realization of a Soft-PVTOL aerial vehicle, a compliant variant of the classical Planar Vertical Take-Off and Landing (PVTOL) system in which the vehicle’s motion is directly influenced by the curvature of its deformable arms. Unlike conventional multirotor and PVTOL platforms that rely exclusively on thrust modulation, the proposed design introduces a deformation-based degree of freedom that modifies the lateral aerodynamic response. The platform incorporates tendon-driven soft arms with controllable curvature and embedded IMUs for real-time deformation sensing, fully integrated with the PX4 autopilot firmware for onboard estimation, logging, and closed-loop operation. Experimental flight trials demonstrate a consistent and reproducible coupling between arm curvature and translational behavior, validating the feasibility of morphology-induced motion in underactuated aerial systems. The results establish the Soft-PVTOL as a new experimental benchmark for studying nonlinear control, soft aerial robotics, and morphing-enabled mobility.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC32.4", "code": "WeC32.4", "title": "Run-To-Run Indirect Trajectory Tracking Control of Electromechanical Systems Based on Identifiable and Flat Models", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC32", "sessionTitle": "Mechatronic Principles in Motion and Robotic Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Serrano-Seco, Eloy", "affiliation": "Universidad De Zaragoza" }, { "name": "Ramirez-Laboreo, Edgar", "affiliation": "Universidad De Zaragoza" }, { "name": "Moya-Lasheras, Eduardo", "affiliation": "Universidad De Zaragoza" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "Mechatronic system estimation, identification, control" ], "abstract": "Differentially flat models are frequently used to design feedforward controllers for electromechanical systems. However, control performance depends on model accuracy, which makes feedback imperative. This paper presents a control scheme for electromechanical systems in which measuring or estimating the output to be controlled---typically the position---is not feasible. It employs an identifiable-model-based controller and predictor, coupled with an iterative loop that updates model parameters using the error between a measurable output and its prediction. Simulations on electromechanical switching devices show effective tracking of the desired position trajectory using only coil current measurements.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC32.5", "code": "WeC32.5", "title": "A New Friction Model for Simulation, Estimation and Motion Control", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC32", "sessionTitle": "Mechatronic Principles in Motion and Robotic Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Martinez Molina, John J.", "affiliation": "CNRS GIPSA-Lab" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "Mechatronic system estimation, identification, control", "Application of mechatronic principles" ], "abstract": "This paper presents a new model of friction forces intended to be used for simulation, estimation and motion control. The model is based on physical insights and captures the behavior of friction forces with respect to the slip velocities. Compared with existing models, this model is very simple, requiring a small number of parameters to be tuned. Motivated by the nature of induced forces of friction, the behavior of friction forces has been modeled as a Steinmetz equivalent circuit, which includes particular dissipation terms related to Foucault's currents. Simulated examples illustrate the effectiveness of the model in capturing the behavior of friction forces observed in nature.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC32.6", "code": "WeC32.6", "title": "Data-Driven Vibration Suppression of Stacker Crane through Mast Top Position Tracking Control", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC32", "sessionTitle": "Mechatronic Principles in Motion and Robotic Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Hamanaka, Kiyotaka", "affiliation": "The University of Tokyo" }, { "name": "Ohnishi, Wataru", "affiliation": "The University of Tokyo" }, { "name": "Koseki, Takafumi", "affiliation": "The University of Tokyo" }, { "name": "Asai, Mitsuki", "affiliation": "Toyota Industries Corporation" }, { "name": "Ano, Shuta", "affiliation": "Toyota Industries Corporation" }, { "name": "Nawa, Masamichi", "affiliation": "Toyota Industries Corporation" }, { "name": "Kato, Norihiko", "affiliation": "Toyota Industries Corporation" } ], "keywords": [ "Smart structures and vibration control" ], "abstract": "High-speed stacker crane operation is essential for warehouse throughput improvement. However, high-speed operation that does not account for the dynamics of the stacker crane can excite the natural vibrations of the mast and reduce the overall throughput instead. Conventional stacker cranes lack sensors at the mast top and cannot directly measure vibrations, which limits vibration suppression performance. This study utilizes a sensor that can detect the mast top position and constructs a reference tracking control system for the mast top position. Additionally, iterative learning control (ILC) is employed to compensate for nonlinear dynamics and output disturbances. ILC can realize higher-precision position tracking performance, which leads to a drastic reduction of mast vibration. The superiority of the proposed method was demonstrated through experiments using a testbench machine.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC33.1", "code": "WeC33.1", "title": "MESII: Dataset and Parameters Identification of Multiple KUKA IIWA Collaborative Manipulators (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC33", "sessionTitle": "JO-MECH: Robot Perception and Sensing", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Ardiani, Fabio", "affiliation": "Nimble One" }, { "name": "Mujica, Martin", "affiliation": "LAAS-CNRS, University of Toulouse" }, { "name": "Benoussaad, Mourad", "affiliation": "INP-ENIT, University of Toulouse" }, { "name": "Cherif, Mehdi", "affiliation": "Bordeaux University" }, { "name": "Janot, Alexandre", "affiliation": "ONERA" }, { "name": "Fourquet, Jean-Yves", "affiliation": "LGP-ENIT" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Human-robot interaction" ], "abstract": "This paper presents the MESII Dataset (Manipulator Experimental System Identification and Interaction Dataset), a novel resource featuring numerous motion sequences of the 7-DoF KUKA iiwa manipulator for evaluating identification and estimation methods. The dataset includes several trajectories, from single-joint movements, to multi-joint coordinated motions, with and without payloads attached to the manipulator’s end-effector. It includes information obtained from the propioceptive sensors of position and torque, as well as an external force/torque sensor placed on the end-effector. Some of these trajectories are specifically optimized for the identification of dynamic parameters and signals to study their effect on the dynamic model. Tests were performed on three identical KUKA iiwa robots to analyze inter-robot variability. Since the robot is often used in physical Human-Robot Interaction (pHRI), the dataset also includes sequences involving human interaction, using the force/torque sensor as ground truth for external forces. Data is provided in ROS-compatible rosbags, supporting real-time evaluation. Applications of the dataset demonstrate its value in tackling state-of-the-art challenges without requiring access to physical robots or complex new experiments. All data and related tools are publicly available at https://github.com/mmujica93/MESII_Dataset/.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC33.2", "code": "WeC33.2", "title": "Low-Resolution Perception for Robotic Packing (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC33", "sessionTitle": "JO-MECH: Robot Perception and Sensing", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Preziosa, Giuseppe Fabio", "affiliation": "Politecnico Di Milano" }, { "name": "Vignoni, Federico", "affiliation": "Politecnico Di Milano" }, { "name": "Castellano, Chiara", "affiliation": "Politecnico Di Milano" }, { "name": "Faroni, Marco", "affiliation": "Politecnico Di Milano" }, { "name": "Zanchettin, Andrea Maria", "affiliation": "Politecnico Di Milano" }, { "name": "Rocco, Paolo", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Robot perception and sensing", "Robotic grasping and manipulation", "Mechatronics for robotic systems" ], "abstract": "This work tackles the problem of scalable perception for robotic packing with low-cost, low-resolution depth sensing. We propose a framework where reconstruction cues drive next-view selection and grasp evidence updates a per-object stability estimate, jointly deciding what to acquire next and when to grasp. During the reconstruction, a low-resolution Next Best View (NBV) strategy explicitly avoids redundant views while preserving task-relevant geometry. We validate the approach in two steps: (i) an ablation study of the utility function under very low resolution, and (ii) a full end-to-end evaluation across policies, showing how low-resolution perception is a practical, scalable option for robotic packing.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC33.3", "code": "WeC33.3", "title": "On Hybrid Inverse Dynamic Modeling for Industrial Robots (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC33", "sessionTitle": "JO-MECH: Robot Perception and Sensing", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Clavel, Sacha", "affiliation": "Stäubli" }, { "name": "Alamir, Mazen", "affiliation": "Gipsa-Lab (CNRS-University of Grenoble)" }, { "name": "Faure-Favre, Julien", "affiliation": "Stäubli" }, { "name": "Blanc, Stéphane", "affiliation": "Stäubli" } ], "keywords": [ "Robotic learning and adaptation" ], "abstract": "Inverse dynamic modeling is a key component of feedforward control in robotics. Hybrid approaches combining physics-based and data-driven models have emerged as an effective way to improve modeling accuracy. This paper presents an in-depth study of such an approach, based on Gated Recurrent Unit (GRU) neural networks for application on 4-axis and 6-axis Stäubli industrial robots. The proposed approach is evaluated against a black-box model and demonstrates improved accuracy and extrapolation capabilities. Furthermore, the influence of several hyperparameters on model performance is analyzed.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC33.4", "code": "WeC33.4", "title": "From Entities to Areas: A Semantically Driven Clustering Approach for Area Delimitation on 3D Scene Graphs (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC33", "sessionTitle": "JO-MECH: Robot Perception and Sensing", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Valdes Saucedo, Mario Alberto", "affiliation": "Lulea University of Technology" }, { "name": "Patel, Akash", "affiliation": "Luleå University of Technology" }, { "name": "Blounas, Taxiarchis-Foivos", "affiliation": "Luleå University of Technology" }, { "name": "Kanellakis, Christoforos", "affiliation": "Luleå University of Technology" }, { "name": "Nikolakopoulos, George", "affiliation": "Luleå University of Technology" } ], "keywords": [ "Robot perception and sensing", "Aerial, field, and marine robotics", "Autonomous navigation" ], "abstract": "3D scene graph (3DSG) generation is a rapidly evolving field that plays a significant role in robotic autonomy. Traditionally, the focus has been on indoor environments, where robots understand and navigate spaces by abstracting objects and geometric information in a structured graph format. Expanding upon this idea, this paper introduces a 3DSG construction architecture, which enables scene-agnostic abstraction of the environment, with the goal of facilitating the adoption of 3DSG for autonomous agents in both indoor and outdoor environments. We propose a novel approach for area delimitation in 3DSGs that leverages label propagation to cluster entities (i.e. objects of interest) into areas that are both semantically and topologically distinguishable within a scene. Towards this end, we establish label propagation for 3DSGs, by formulating a dynamic set of propagation factors that accommodate to the relevance of semantic information and their natural decay through the topological structure of the 3DSG. Additionally, to achieve scene-agnostic area delimitation, we introduce a single-step optimization process for the calculation of clutter-aware propagation factors based on the approximation of an optimal set of factors that maximize inter-area eccentricity while minimizing intra-area eccentricity.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC33.5", "code": "WeC33.5", "title": "6D Object Pose Estimation Enhanced with Normal Vector Images and Adaptive Multimodal Fusion (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC33", "sessionTitle": "JO-MECH: Robot Perception and Sensing", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Yao, Xifan", "affiliation": "Fuyao University of Science and Technology" }, { "name": "Jiang, Zhenhong", "affiliation": "South China University of Technology" }, { "name": "Xie, Tingbo", "affiliation": "Fuyao University of Science and Technology" }, { "name": "Meng, Junting", "affiliation": "South China University of Technology" } ], "keywords": [ "Robotic learning and adaptation", "Mechatronic system estimation, identification, control", "Robot perception and sensing" ], "abstract": "Accurate 6D object pose estimation is essential for robotic grasping, augmented reality, and autonomous driving. Existing methods often rely solely on RGB or depth data, limiting their ability to fully leverage available information. We propose an enhanced approach integrating RGB, point cloud, mask, and normal vector data through weighted multimodal feature fusion. By introducing normal vector images, our method captures richer geometric details from depth data. The Intra-modality and Inter-modality Feature Weighting Modules perform adaptive weighting and fusion of multimodal features, significantly boosting performance. Evaluations on LineMOD and YCB-Video datasets confirm our method outperforms state-of-the-art techniques, demonstrating strong potential for real-world applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC33.6", "code": "WeC33.6", "title": "Robust Adaptive Backstepping Impedance Control of Robots in Unknown Environments (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC33", "sessionTitle": "JO-MECH: Robot Perception and Sensing", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Nazmara, Reza", "affiliation": "University of Porto" }, { "name": "Kshirsagar, Alap", "affiliation": "Technische Universität Darmstadt" }, { "name": "Peters, Jan", "affiliation": "TU Darmstadt / DFKI" }, { "name": "Aguiar, A. Pedro", "affiliation": "Faculty of Engineering, University of Porto (FEUP)" } ], "keywords": [ "Robotic grasping and manipulation", "Robotic learning and adaptation" ], "abstract": "This paper presents a Robust Adaptive Backstepping Impedance Control (RABIC) strategy for robots operating in contact-rich and uncertain environments. The proposed approach considers fully coupled system dynamics and accounts for key uncertainties, including external disturbances and unmodeled dynamics, without requiring knowledge of exact robot dynamic parameters. A backstepping-based adaptive impedance controller is developed to track a reference impedance model in the inner loop. To address uncertainties, a Taylor series–based estimator is used for system dynamics, along with an adaptive estimator for the upper bound of external forces. Stability analysis establishes semi-global practical finite-time stability. Simulation results on a mobile manipulator and experiments on a Franka Emika Panda robot validate the approach, demonstrating improved safety over PD control while maintaining accurate trajectory tracking and force regulation. The RABIC framework provides a foundation for future research on adaptive and learning-based impedance control for coupled mobile and fixed-base manipulators.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC34.1", "code": "WeC34.1", "title": "Digital Twin Based Error Correction for Long-Term Accurate Navigation and Motion Control of Hybrid Robots (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC34", "sessionTitle": "Digital Twin and Telematics: Towards Intelligent and Sustainable Cyber-Physical Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Bao, Danyu", "affiliation": "Southwest Jiaotong University" }, { "name": "Ma, Lei", "affiliation": "Southwest Jiaotong University" }, { "name": "Zhao, Duo", "affiliation": "Southwest Jiaotong University" }, { "name": "Shen, Kai", "affiliation": "Southwest Jiaotong University" }, { "name": "Zhang, Muhua", "affiliation": "Southwest Jiaotong University" } ], "keywords": [ "Cloud control and robotics", "AI in networked control", "Cyber physical systems" ], "abstract": "Industrial mobile manipulators for automated inspection often fail to achieve complete visual coverage of target components, as small localization drifts accumulate across both the mobile base and the articulated arm, degrading the reliability of downstream tasks. Traditional methods lack the semantic understanding and safety assurances to correct these deviations in complex environments. A closed-loop correction framework driven by a Digital Twin (DT) that integrates high-level semantic reasoning with low-level safety-critical control is proposed in this article. Upon detecting an incomplete view, a Vision\u0002Language-Action (VLA) model infers a corrective action chunk, and the DT validates these actions for collision avoidance and kinematic feasibility before execution on the real robot. Experiments on a mobile platform with a CR5 arm show that our VLA-DT method raises detection completeness from 64% to over 98%, achieving safe, smooth, and collision-free corrections.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC34.2", "code": "WeC34.2", "title": "Automating Overhead Catenary System Bolt Assembly: A Dual-Arm Robot Integrated with Visual Perception and Digital Twin (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC34", "sessionTitle": "Digital Twin and Telematics: Towards Intelligent and Sustainable Cyber-Physical Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Qiu, Zhengquan", "affiliation": "Southwest Jiaotong University" }, { "name": "Ma, Lei", "affiliation": "Southwest Jiaotong University" }, { "name": "Xu, Jian", "affiliation": "Southwest Jiaotong University" }, { "name": "Wang, Dongrui", "affiliation": "Southwest Jiaotong University" }, { "name": "Lu, Wen Ru", "affiliation": "Southwest Jiaotong University" }, { "name": "Wang, Yutao", "affiliation": "Southwest Jiaotong University" } ], "keywords": [ "Cloud control and robotics", "AI in networked control", "Cyber physical systems" ], "abstract": "This paper addresses the challenge of automated precision bolt alignment in unstructured railway catenary environments by proposing a digital twin-enabled robotic system that integrates visual perception with dual-arm cooperation. The system adopts a coarse-to-fine alignment strategy: initially, YOLOV7-based visual servoing is employed for fast detection and coarse positioning, followed by the use of 3D point cloud data for high-precision 6-DoF pose estimation and fine alignment. Within a knowledge-based decision framework supported by the digital twin, the physical system is mirrored in a virtual environment to enable real-time synchronization, motion verification, and safety evaluation. A dual-arm cooperative control scheme is designed to ensure coordinated operation and dynamic interaction between the virtual and physical spaces. Experimental results demonstrate that the proposed digital twin-assisted approach achieves accurate, reliable, and adaptive bolt alignment, providing an effective solution for automated fastening in complex and unstructured scenarios.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC34.3", "code": "WeC34.3", "title": "A Digital Twin Communication Architecture for Heterogeneous Agricultural Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC34", "sessionTitle": "Digital Twin and Telematics: Towards Intelligent and Sustainable Cyber-Physical Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Morgan Pereira, Pedro Henrique", "affiliation": "SENAI Institute of Innovation in Integrated Solutions in Metal Mechanics" }, { "name": "Cainelli, Gustavo", "affiliation": "Institut Für Automation Und Kommunikation" }, { "name": "Pignaton de Freitas, Edison", "affiliation": "Federal University of Rio Grande Do Sul" }, { "name": "Pereira, Carlos Eduardo", "affiliation": "Federal Univ. of Rio Grande Do Sul - UFRGS" }, { "name": "Dussin Bampi, Matheus", "affiliation": "Federal University of Rio Grande Do Sul" } ], "keywords": [ "Cyber physical systems", "Remote data acquisition and fusion", "Remote control" ], "abstract": "Agricultural systems increasingly combine heterogeneous assets, such as tractors and UAVs, that rely on different protocols and data models. This paper presents a Digital Twin communication architecture in which each asset exposes an OPC UA information model structured according to Asset Administration Shell principles. The architecture is evaluated in a two-node testbed based on Raspberry Pi 4 and 5 platforms, involving ISOBUS and MAVLink data streams. Results demonstrate protocol decoupling and semantically structured data exchange; however, latencies in the 100–300 ms range limit the evaluated implementation to telemetry, supervision, diagnostics, and high-level coordination rather than critical control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC34.4", "code": "WeC34.4", "title": "Orchestrating Digital Twins: Joint Model and Sensing Scheduling for Frugal Multi-Fidelity Architectures (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC34", "sessionTitle": "Digital Twin and Telematics: Towards Intelligent and Sustainable Cyber-Physical Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Hamzaoui, Mohammed Adel", "affiliation": "LabSTICC - Southern Brittany University" }, { "name": "Julien, Nathalie", "affiliation": "University of Southern Brittany" } ], "keywords": [ "Digital twins for cyber physical systems", "Control software architecture", "Cyber physical systems" ], "abstract": "Digital twins are increasingly deployed under tight energy and computation constraints, yet most works treat model selection, sensing and information freshness in isolation. We advocate an orchestrator-centered view where these choices are made jointly and formalize the resulting Joint Model & Sensing Scheduling Problem (JMSSP) as a constrained Markov decision process with a composite cost capturing accuracy, Age-of-Information / Age-of-Digital-Twin, and digital sobriety. Building on this CMDP view, we derive a Lagrangian relaxation that interprets resource constraints as prices for computation and sensing, and we exploit weak submodularity to justify greedy, budget-aware sensing rules in linear–Gaussian settings. Together, these results provide a mathematical backbone for frugal, adaptive digital-twin architectures and open the door for industrial case studies and applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC34.5", "code": "WeC34.5", "title": "XR Interaction and Digital Twin-Driven Virtual Teaching System for Catenary Inspection Robots (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC34", "sessionTitle": "Digital Twin and Telematics: Towards Intelligent and Sustainable Cyber-Physical Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Zhao, Duo", "affiliation": "Southwest Jiaotong University" }, { "name": "Huang, Ganke", "affiliation": "Southwest Jiaotong University" }, { "name": "Liu, Minyu", "affiliation": "Southwest Jiaotong University" }, { "name": "Ren, Tai", "affiliation": "Southwest Jiaotong University" }, { "name": "Bao, Danyu", "affiliation": "Southwest Jiaotong University" }, { "name": "Zhu, Ziqing", "affiliation": "Southwest Jiaotong University" } ], "keywords": [ "Digital twins for cyber physical systems", "Intelligent human-machine interaction", "Bio-inspired algorithms and optimization-based control" ], "abstract": "Catenary inspection robots are widely recognized for mitigating the high risks, low precision, and inefficiency inherent in manual catenary maintenance for rail transit. However, significant bottlenecks persist in operational mechanism modeling, path planning, and teaching/debugging processes. To address these challenges, this study develops a virtual teaching system integrating a \"Geometry–Physics–Data–Rule\" digital twin framework, XR-based interaction (coupled with NVIDIA Isaac Sim and ROS), and a cross-entropy optimized RRT (CE-RRT) algorithm. Experimental validation demonstrates that the system can generate collision-free trajectories, achieve high-precision teaching, and enhance operational efficiency—thereby providing robust support for the intelligent upgrading of rail transit maintenance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC34.6", "code": "WeC34.6", "title": "A Memetic Algorithm-Driven Scheduling Mechanism for DAG Workloads in 5G-PLCs (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC34", "sessionTitle": "Digital Twin and Telematics: Towards Intelligent and Sustainable Cyber-Physical Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Cao, Qingyun", "affiliation": "Hangzhou Dianzi University" }, { "name": "Wang, Jiankai", "affiliation": "Hangzhou Dianzi University" }, { "name": "Ji, Fule", "affiliation": "Hangzhou Dianzi University" }, { "name": "Sun, Danfeng", "affiliation": "Hangzhou Dianzi University" }, { "name": "Wu, Huifeng", "affiliation": "Hangzhou Dianzi University" } ], "keywords": [ "Networking for internet of things", "Remote control" ], "abstract": "The rapid evolution of Industry 4.0 and the Industrial Internet of Things (IIoT) has led to a proliferation of diverse industrial tasks and intensified inter-machine communication. In this context, traditional wired infrastructures are becoming increasingly unmanageable and rigid due to the massive scale of connected devices, making the adoption of wireless technologies, particularly 5G, an imperative transition. As programmable logic controllers (PLCs) serve as the key computing nodes for industrial control, the system is evolving to interconnect these PLCs via 5G. However, considering the strict real-time constraints of industrial control, efficiently mapping heterogeneous tasks onto PLCs over 5G networks remains a formidable challenge. To address this problem, this paper proposes a novel scheduling mechanism driven by a memetic algorithm. Our evaluation relies on a hybrid experimental setup, utilizing empirical data from a real 5G deployment to model communication costs within a simulated system. Extensive experimental results demonstrate that the proposed mechanism exhibits superior adaptability and performance compared to baseline algorithms.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC35.1", "code": "WeC35.1", "title": "Zero-Shot Prediction for Household Electricity and Its Integration into Scenario-Based MPC for Economical Battery Management (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC35", "sessionTitle": "Control for Energy Efficient and Resilient Smart Cities", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Abe, Eiga", "affiliation": "Institute of Science Tokyo" }, { "name": "Tanaka, Taichi", "affiliation": "Institute of Science Tokyo" }, { "name": "Yamasaki, Hiroki", "affiliation": "Hiroshima University" }, { "name": "Kamikawa, Ryoma", "affiliation": "Hiroshima University" }, { "name": "Nagahara, Masaaki", "affiliation": "Hiroshima University" }, { "name": "Luque, Joaquin", "affiliation": "University of Seville" }, { "name": "Hatanaka, Takeshi", "affiliation": "Institute of Science Tokyo" } ], "keywords": [ "Cyber-physical and human systems (CPHS)", "Control approaches for reaching the United Nations SDGs", "Social transportation and social energy" ], "abstract": "Recent advances in Transformer-based models have enabled accurate time-series forecasting even with limited historical data. These models, called zero-shot models, are known to be available for unseen tasks without task-specific training. In this paper, we investigate the applicability of zero-shot models to household-level electricity forecasting and control. We first exemplify that the zero-shot models, Chronos and TabPFN, outperform conventional models in demand and photovoltaic generation forecasting. Moreover, we propose a household battery management method that incorporates quantiles provided by zero-shot models into scenario-based model predictive control. It is finally demonstrated that the proposed method reduces operational costs compared to the conventional framework that relies solely on point forecasts.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC35.2", "code": "WeC35.2", "title": "Control and Performance Analysis of Improved Coil Sequencing for Inductive Dynamic Wireless Power Transfer in Electric Vehicles (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC35", "sessionTitle": "Control for Energy Efficient and Resilient Smart Cities", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Prado, Edemar", "affiliation": "Federal University of Bahia" }, { "name": "Meira Gomes, Zariff", "affiliation": "Institut VEDECOM" }, { "name": "Le Gall, Yann", "affiliation": "Institut VEDECOM" }, { "name": "Sehimi, Yacine", "affiliation": "Institut VEDECOM" }, { "name": "Bolsi, Pedro Cerutti", "affiliation": "UFSM" }, { "name": "Sartori, Hamiltom Confortin", "affiliation": "UFSM" }, { "name": "Hassan, Hussein Al Haj", "affiliation": "Institut VEDECOM" }, { "name": "Damm, Gilney", "affiliation": "University Gustave Eiffel" }, { "name": "Ripoll, Christophe", "affiliation": "Renault SAS and Institut VEDECOM" }, { "name": "Pinheiro, José Renes", "affiliation": "Univali, UFSM, UFBA" } ], "keywords": [ "Control approaches for reaching the United Nations SDGs" ], "abstract": "In dynamic wireless power transfer (DWPT) systems, a critical aspect of the operation of successive transmitter coils is the on/off switching algorithm employed to achieve efficient wireless charging of electric vehicles (EVs). The sequencing between coils represents a challenge in DWPT, as it can lead to reductions in the transferred power as well as in the overall system efficiency. This paper proposes an improved coil‑sequencing algorithm for inductive DWPT in EVs, designed to mitigate performance degradation. To this end, coil sequencing and control techniques are introduced, focusing on the instantaneous operation of two transmitting coils. The control scheme employs a modified extremum‑seeking control algorithm either to track the resonant frequency of the system or to regulate the power delivered to the secondary side. Results demonstrate that the proposed approach enables the system to reliably deliver 30 kW while effectively eliminating power sags during coil‑to‑coil activation events.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC35.3", "code": "WeC35.3", "title": "Input-Constrained Human Assist Control Via Time-Varying Control Barrier Function for Viability Tube (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC35", "sessionTitle": "Control for Energy Efficient and Resilient Smart Cities", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Haiya, Yukiie", "affiliation": "Tokyo University of Science" }, { "name": "Aoki, Haruto", "affiliation": "Tokyo University of Science" }, { "name": "Nakamura, Hisakazu", "affiliation": "Tokyo University of Science" } ], "keywords": [ "Cyber-physical and human systems (CPHS)", "System dynamics and control in CPHS", "Safety-critical and resilient systems" ], "abstract": "Control Barrier Functions (CBFs) can theoretically guarantee the safety of systems such as robots and vehicles. To prevent side-impact collisions between automobiles, both input constraints and moving obstacles must be considered. This paper proposes a human assist control law based on a time-varying CBF (Tv-CBF) formulated for a viability tube. The proposed method ensures that these constraints are satisfied as long as the system starts within the viability tube, thereby providing theoretical safety guarantees. The practical effectiveness of the approach is demonstrated through computer simulations in a representative side-impact collision scenario.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC35.4", "code": "WeC35.4", "title": "Model Predictive Control of a Thermoelectric Microgrid for Enhanced Operational Flexibility (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC35", "sessionTitle": "Control for Energy Efficient and Resilient Smart Cities", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Achnib, Asma", "affiliation": "University Gustave Eiffel, IFSTTAR, COSYS" }, { "name": "Damm, Gilney", "affiliation": "University Gustave Eiffel" }, { "name": "Zaoui, Hadjer", "affiliation": "COSYS Laboratory, Univ. Gustave Eiffel, Marne-La-Vallée, France" } ], "keywords": [ "Control approaches for reaching the United Nations SDGs", "Smart city security and resilience", "Smart city control and optimization" ], "abstract": "The paper proposes a coupled electro-thermal model for a district heating network and a Model Predictive Control (MPC) strategy designed to satisfy thermal comfort while respecting dynamic electrical power limits. The model incorporates indoor temperature dynamics, radiator heat exchange, return-temperature behavior, and pump electricity consumption. The proposed MPC is assessed on a three-building case study using real weather data from Paris. Results show that the MPC reduces energy consumption and ensures constraint satisfaction more effectively than a PID controller, demonstrating its potential for demand coordination under electrical grid limitations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC35.5", "code": "WeC35.5", "title": "Cloud-Assisted Dual-Mode Excavation Detection Framework for Pipeline Inspection Using Quadruped Robot", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC35", "sessionTitle": "Control for Energy Efficient and Resilient Smart Cities", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Yu, Yicheng", "affiliation": "Zhejiang Sci-Tech University" }, { "name": "Wu, Ping", "affiliation": "Zhejiang Sci-Tech University" }, { "name": "Gao, JinFeng", "affiliation": "Zhejiang University; Zhejiang Sci-Tech University" }, { "name": "Fan, Xin", "affiliation": "Zhejiang Sci-Tech University" }, { "name": "He, Guojun", "affiliation": "National Pipeline Network Group Zhejiang" }, { "name": "Yan, Hongping", "affiliation": "National Pipeline Network Group Zhejiang" }, { "name": "Yi, Xin", "affiliation": "National Pipeline Network Group Zhejiang" } ], "keywords": [ "Smart buildings and building automation", "Big data and machine learning applied to smart cities" ], "abstract": "Unauthorized or unsafe excavation near underground gas pipelines poses significant risks to infrastructure integrity and public safety. To address the limitations of manual inspection and fixed surveillance systems, this paper presents a cloud-assisted dual-mode excavation detection framework for pipeline inspection using quadruped robots. In the proposed system, the robot performs autonomous mobility, visual data acquisition, and mode-aware inspection, while cloud-side computing conducts excavator detection and safety assessment. Specifically, an improved YOLO-based excavator detector, termed MBM-YOLO, is developed to support two operational modes: a patrol mode for identifying unreported excavation activities, and a supervision mode for determining whether authorized excavators intrude into restricted zones defined by electronic fences. To improve robustness in cluttered construction environments, the Mixed Aggregation Network with Faster Convolutional Gated Linear Unit(MANet-FCGLU) module is introduced to replace the C3k2 module in YOLO11 for enhanced semantic representation. In addition, a Bidirectional Multi-branch Auxiliary Feature Pyramid Network(BMAFPN) neck is designed by integrating Bidirectional Feature Pyramid Network (BiFPN) with multi-branch auxiliary fusion to strengthen cross-scale feature consistency and improve the perception of small and distant excavators. Field experiments demonstrate that the proposed framework achieves reliable excavator detection and effective safety assessment under varying illumination conditions, background complexity, and target scales, showing its potential for practical robotic pipeline inspection.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC35.6", "code": "WeC35.6", "title": "Parametrized Iterative Learning Control for Reference Control in Water Distribution Networks", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC35", "sessionTitle": "Control for Energy Efficient and Resilient Smart Cities", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Kallesøe, Carsten Skovmose", "affiliation": "Grundfos" }, { "name": "Deleuran, Joakim Børlum", "affiliation": "Grundfos" }, { "name": "Balla, Krisztian Mark", "affiliation": "Grundfos Holding A/S" }, { "name": "Wisniewski, Rafal", "affiliation": "Aalborg University" } ], "keywords": [ "Water distribution systems", "Distributed optimization and control for smart cities" ], "abstract": "Water scarcity is an increasing global challenge. Pressure management is widely regarded as an economically viable approach to reducing leakages in water distribution networks while simultaneously limiting pressure variations experienced by consumers. Resilience is crucial in water distribution systems. In the context of pressure management, resilience translates into having multiple supply points within each pressure zone. This paper proposes a distributed control approach for pressure management in networks with multiple supply points. The approach ensures the desired pressure at a critical point within the network while maintaining a predefined distribution of flows from the supply points, thereby avoiding overloading of the water resources supplying the network. The control strategy is purely data-driven and based on an iterative learning control approach. The usability of the proposed control method is demonstrated on a simulations of a realistic water supply network.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC36.1", "code": "WeC36.1", "title": "A Context-Aware Lateral Adaptive Model Predictive Control and Zeroing Control Barrier Function Framework for Comfort-Critical Autonomous Vehicles", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC36", "sessionTitle": "Robotic Vision for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "D'Souza, Joshua", "affiliation": "Aston University" }, { "name": "Kim, Jisun", "affiliation": "Aston University" }, { "name": "Wan, Jian", "affiliation": "Aston University" }, { "name": "Manso, Luis J.", "affiliation": "Aston University" } ], "keywords": [ "Autonomous vehicles", "Modeling, supervision, control and diagnosis of automotive systems", "Motion control for AVs" ], "abstract": "This paper proposes a framework for comfort-critical vehicle control. A context-aware comfort envelope defined by the lateral acceleration, lateral velocity, and yaw rate is introduced and dynamically adapted using fuzzy inference. The adaptive model predictive control (AMPC) regulates the lateral and yaw motion of the vehicle, while the zeroing control barrier functions (ZCBFs) ensure forward invariance of the comfort envelope. A MATLAB/Simulink implementation is evaluated using the ISO 3888-1 manoeuvre under varying driving conditions. The results show the consistency and effectiveness of the controller relative to the baseline AMPC in reducing the root mean square in lateral acceleration and jerk.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC36.2", "code": "WeC36.2", "title": "Real-Time GICP on GPU Using Label-Pruned KD-Trees for Semantic Registration", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC36", "sessionTitle": "Robotic Vision for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Gabrielli, Simone", "affiliation": "Politecnico Di Milano" }, { "name": "Corno, Matteo", "affiliation": "Politecnico Di Milano" }, { "name": "Savaresi, Sergio", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Robotic vision for AVs", "Autonomous vehicles" ], "abstract": "Generalized Iterative Closest Point (GICP) is widely used for LiDAR-based localization due to its accuracy and robustness, but its CPU implementation remains a computational bottleneck for high-rate point cloud registration. This paper presents a GPU-based implementation of GICP that can perform high accuracy alignments at frequencies higher than 50 Hz with raw point clouds. The proposed pipeline preserves Fast GICP’s objective and convergence behavior, enables real-time operation on embedded GPUs, and supports lightweight semantic integration without modifying the underlying cost function. To further reduce correspondence search time, we introduce a label-pruned KD-tree that efficiently restricts nearest-neighbor queries to geometric classes derived from per-point covariance structure. Experiments on KITTI demonstrate that the method matches the accuracy of CPU Fast GICP while significantly reducing runtime, and that semantic weighting improves robustness in geometrically degenerate scenes with negligible computational overhead.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC36.3", "code": "WeC36.3", "title": "Color-Based Vehicle Classification for an Autonomous Racing Context", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC36", "sessionTitle": "Robotic Vision for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Riva, Alessandro", "affiliation": "Politecnico Di Milano" }, { "name": "Corno, Matteo", "affiliation": "Politecnico Di Milano" }, { "name": "Savaresi, Sergio", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Robotic vision for AVs", "Autonomous vehicles", "Multi-vehicle systems" ], "abstract": "Autonomous racing has rapidly evolved from single-vehicle events to multi-vehicle interactive competitions. In such scenarios, reliable local scene understanding to detect opponent vehicles should be paired with opponent recognition to maintain global race awareness. This ability allows the Ego vehicle to track race rankings and adjust planning strategies according to the specific opponent. In this work, we address the problem of recognizing individual racecars based solely on their livery colors, since all vehicles share identical shapes. We compare a histogram-based encoding approach, paired with Support Vector Machine (SVM) and Random Forest (RF) classifiers, against well-established neural models for end-to-end classification, specifically ResNet, DenseNet, and EfficientNet. We evaluate these methods on a custom dataset collected by the PoliMOVE Autonomous Racing Team. The results demonstrate that the proposed deep-learning solutions effectively classify opponent vehicles based on livery colors, outperforming the evaluated classical machine learning techniques. Among these, EfficientNetB0 achieves the best trade-off between classification performance (with more than 85% accuracy) and model complexity.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC36.4", "code": "WeC36.4", "title": "Visual-Servoing Path-Following Control with Field-Of-View Constraints Using Barrier Lyapunov Functions", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC36", "sessionTitle": "Robotic Vision for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Ramadhan, Sami Fauzan", "affiliation": "Institut Teknologi Bandung" }, { "name": "Sofyan, Adri F", "affiliation": "Institut Teknologi Bandung" }, { "name": "Santosa, Muhammad Fahmi", "affiliation": "Institut Teknologi Bandung" }, { "name": "Widyotriatmo, Augie", "affiliation": "Bandung Institute of Technology" } ], "keywords": [ "Trajectory tracking and path following for AVs", "Autonomous vehicles", "Robotic vision for AVs" ], "abstract": "This paper presents a visual servoing path-following controller for autonomous vehicles that enforces field-of-view (FOV) constraints on a vision-based look-ahead point. The method reformulates path-following using car-like kinematics in polar coordinates and directly constrains the vision-derived look-ahead angle tied to the camera FOV. A composite Lyapunov function, combining a quadratic heading-error term and a logarithmic barrier term, keeps the look-ahead angle strictly within the camera half-angle limit during motion. Using LaSalle’s principle, we establish asymptotic convergence of both the heading error and the constrained look-ahead angle. Simulations show accurate tracking while maintaining continuous visibility.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC36.5", "code": "WeC36.5", "title": "Robust Vehicle Navigation Via EKF-Based IMU-GNSS-Pseudolite-Odometer Fusion", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC36", "sessionTitle": "Robotic Vision for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 325", "authors": [ { "name": "Chen, Chih-Chun", "affiliation": "RWTH Aachen University" }, { "name": "Noh, Yeon Jung", "affiliation": "RWTH Aachen University" }, { "name": "Vallery, Heike", "affiliation": "Delft University of Technology" } ], "keywords": [ "Guidance, navigation and control for AVs", "Autonomous mobile robots", "Autonomous vehicles" ], "abstract": "Precise position estimation is crucial in autonomous vehicles. However, in challenging environments, GNSS signals are often degraded or obstructed by multipath and blockage. To enhance robustness in GNSS-limited scenarios, this study explores the potential of pseudolites (PLs) as complementary ranging sources, providing improved signal availability. Building on this insight, this work investigates multi-sensor fusion of IMU, GNSS, PL, and Odometer (Odo) data using an Extended Kalman Filter (EKF) in both loosely coupled (LC) and tightly coupled (TC) methods. Simulations on 8-shape and serpentine trajectories under varying GNSS visibility demonstrate that the TC method achieves up to 35% and 40% reduction in 3D mean absolute error (MAE) compared with the Least Squares (LS) and LC approaches, respectively. Under full GNSS conditions, PL integration can reduce the 3D MAE by 8%, while odometer inclusion provides an average improvement of 3%. In GNSS-denied tunnel environments, the IMU-PL-Odo configuration achieves the best performance, improving the 3D MAE by up to 59% and 97% compared to the IMU-PL and IMU-Odo configurations, respectively.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC37.1", "code": "WeC37.1", "title": "Bounded Integral Control for Uncertain ISS Systems with Convex Input Constraints", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-15:50", "sessionCode": "WeC37", "sessionTitle": "Dissemination: Control Theory and Applications", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Konstantopoulos, George", "affiliation": "University of Patras" }, { "name": "Papageorgiou, Panos", "affiliation": "University of Patras" }, { "name": "Bechlioulis, Charalampos", "affiliation": "University of Patras" } ], "keywords": [ "Application of nonlinear analysis and design", "Stability of nonlinear systems", "Controller constraints and structure" ], "abstract": "In this paper, a new Bounded Integral Controller (BIC) is proposed to replace the conventional Integral Control (IC) for regulating uncertain Input-to-State Stable (ISS) nonlinear systems and additionally ensuring that the control input evolution remains within a prescribed compact convex set for all time. This is particularly important for controlling multi-input systems with uncertainties or unknown dynamics/parameters and handling input constraints that introduce couplings between the control input elements forming a specific compact and convex set. Given this set, the proposed BIC takes a suitable nonlinear dynamic form and employing ISS and invariant set theory, it is analytically proven that the trajectory of the entire control input vector will remain within the desired set independently of the plant dynamic structure or parameters. Contrary to the original BIC and its recent extensions, which either limit the control input elements independently or restrict them within a ball set (Euclidean norm bound), the proposed approach may constrain the input evolution within any given compact convex set, thus leading to a generalisation of the original BIC. In order to illustrate the theoretical analysis of the proposed BIC and compare its performance with respect to the conventional methods, one academic and two realistic examples from the area of robotics and power systems are investigated using a simulated underwater robot and a power converter in an experimental platform, respectively, each introducing different input constraints.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC37.2", "code": "WeC37.2", "title": "Heuristic Search for Linear Positive Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "15:50-16:10", "sessionCode": "WeC37", "sessionTitle": "Dissemination: Control Theory and Applications", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Ohlin, David", "affiliation": "Lund University" }, { "name": "Rantzer, Anders", "affiliation": "Lund Univ" }, { "name": "Tegling, Emma", "affiliation": "Lund University" } ], "keywords": [ "Control of networks", "Distributed optimization" ], "abstract": "This work considers infinite-horizon optimal control of positive linear systems applied to the case of network routing problems. We demonstrate the equivalence between Stochastic Shortest Path (SSP) problems and optimal control of a certain class of linear systems. This is used to construct a heuristic search framework for linear positive systems inspired by existing methods for SSP. We propose a heuristics-based algorithm for efficiently finding local solutions to the analyzed class of optimal control problems with a given initial state and positive linear dynamics. By leveraging the bound on optimality in each state provided by the heuristics, we also derive a novel distributed algorithm for calculating local controllers within a specified performance bound, with a distributed condition for termination. More fundamentally, the results allow for analysis of the conditions for explicit solutions to the Bellman equation utilized by heuristic search methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC37.3", "code": "WeC37.3", "title": "Feedback Stabilization of a Nanoparticle at the Intensity Minimum of an Optical Double-Well Potential", "day": "Wednesday", "date": "August 26, 2026", "time": "16:10-16:30", "sessionCode": "WeC37", "sessionTitle": "Dissemination: Control Theory and Applications", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Mlynar, Vojtech", "affiliation": "TU Wien" }, { "name": "Dago, Salambo", "affiliation": "University of Vienna" }, { "name": "Rieser, Jakob", "affiliation": "University of Vienna" }, { "name": "Ciampini, Mario Arnolfo", "affiliation": "University of Vienna" }, { "name": "Aspelmeyer, Markus", "affiliation": "University of Vienna" }, { "name": "Kiesel, Nikolai", "affiliation": "University of Vienna" }, { "name": "Kugi, Andreas", "affiliation": "TU Wien" }, { "name": "Deutschmann-Olek, Andreas", "affiliation": "TU Wien" } ], "keywords": [ "Applications of optimal control", "Real-time optimal control", "Adaptive control design" ], "abstract": "In this work, we develop and analyze adaptive feedback control strategies to stabilize and confine a nanoparticle at the unstable intensity minimum of an optical double-well potential. The resulting stochastic optimal control problem for a noise-driven mechanical particle in a nonlinear optical potential must account for unavoidable experimental imperfections such as measurement nonlinearities and slow drifts of the optical setup. To address these issues, we simplify the model in the vicinity of the unstable equilibrium and employ indirect adaptive control techniques to dynamically follow changes in the potential landscape. Our approach leads to a simple and efficient Linear Quadratic Gaussian (LQG) controller that can be implemented on fast and cost-effective FPGAs, ensuring accessibility and reproducibility. We demonstrate that this strategy successfully tracks the intensity minimum and significantly reduces the nanoparticle’s residual state variance, effectively lowering its center-of-mass temperature. While conventional optical traps rely on confining optical forces in the light field at the intensity maxima, trapping at intensity minima mitigates absorption heating, which is crucial for advanced quantum experiments. Since LQG control naturally extends into the quantum regime, our results provide a promising pathway for future experiments on quantum state preparation beyond the current absorption heating limitation, like matter-wave interference and tests of the quantum-gravity interface.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC37.4", "code": "WeC37.4", "title": "Closed-Loop Data-Enabled Predictive Control and Its Equivalence with Closed-Loop Subspace Predictive Control", "day": "Wednesday", "date": "August 26, 2026", "time": "16:30-16:50", "sessionCode": "WeC37", "sessionTitle": "Dissemination: Control Theory and Applications", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Dinkla, Rogier", "affiliation": "Delft University of Technology" }, { "name": "Oomen, Tom", "affiliation": "Eindhoven University of Technology" }, { "name": "Mulders, Sebastiaan Paul", "affiliation": "Delft University of Technology" }, { "name": "van Wingerden, Jan-Willem", "affiliation": "Delft University of Technology" } ], "keywords": [ "Data-driven control theory", "Linear system identification", "Learning methods for control" ], "abstract": "Factors like growing data availability and increasing system complexity have sparked interest in data driven predictive control (DDPC) methods like Data-enabled Predictive Control (DeePC). However, closed-loop identification bias arises in the presence of noise, which reduces the effectiveness of obtained control policies. In our Automatica paper we propose Closed-loop Data-enabled Predictive Control (CLDeePC), a framework that unifies different approaches to address this challenge. To this end, CL-DeePC incorporates instrumental variables (IVs) to synthesize and sequentially apply consistent single or multi-step-ahead predictors. Furthermore, a computationally efficient CL-DeePC implementation is developed that reveals an equivalence with Closed-loop Subspace Predictive Control (CL-SPC). Time marching simulations of DeePC and CL-DeePC are conducted using Hankel matrices of past data that are updated at every time step to induce potentially troublesome closed-loop correlations between inputs and noise. Compared to DeePC, CL-DeePC simulations demonstrate superior reference tracking, with a sensitivity study finding a 48% lower susceptibility to noise-induced reference tracking performance degradation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC37.5", "code": "WeC37.5", "title": "Local Stability of Congestion Control Protocols: A MIMO Gain and Phase Perspective", "day": "Wednesday", "date": "August 26, 2026", "time": "16:50-17:10", "sessionCode": "WeC37", "sessionTitle": "Dissemination: Control Theory and Applications", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Zhang, Ding", "affiliation": "Hong Kong University of Science and Technology" }, { "name": "Lestas, Ioannis", "affiliation": "University of Cambridge," }, { "name": "Qiu, Li", "affiliation": "Chinese University of Hong Kong, Shenzhen" } ], "keywords": [ "Decentralized control", "Linear systems", "Control of complex systems" ], "abstract": "This paper presents a systematic approach to analyzing the stability of linearized models of network congestion control protocols from a novel multi-input multi-output (MIMO) gain and phase perspective. We leverage a recently developed MIMO phase concept to revisit several classical stability results of congestion control protocols and enhance this analysis by entangling gain and phase information. This entanglement allows us to study protocols with significant phase lag or networks with delays. Particularly, the gain-phase entanglement is realized through two methods: (1) an explicit method based on frequency partitioning, which yields a set of easily verifiable, distributed stability conditions for typical TCP networks; and (2) an implicit method based on the Davis-Wielandt shell, which refines (1) but is more difficult to verify. Both methods exploit global phase bounds on network switches, resulting in partially decentralized conditions that emphasize phase characteristics. These conditions are advantageous in networks where switches exhibit similar phase responses. The effectiveness of the proposed conditions is demonstrated by a numerical example showcasing the stabilization of a Reno/RED network.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC37.6", "code": "WeC37.6", "title": "Adaptive Nonlinear Model Predictive Control of Monoclonal Antibody Glycosylation in CHO Cell Culture", "day": "Wednesday", "date": "August 26, 2026", "time": "17:10-17:30", "sessionCode": "WeC37", "sessionTitle": "Dissemination: Control Theory and Applications", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 326", "authors": [ { "name": "Ma, Yingjie", "affiliation": "Nanjing University" }, { "name": "Guo, Jing", "affiliation": "Polytechnique Montréal" }, { "name": "Dubs, Alexis", "affiliation": "Massachusetts Institute of Technology" }, { "name": "Ganko, Krystian", "affiliation": "Massachusetts Institute of Technology" }, { "name": "Braatz, Richard D.", "affiliation": "Massachusetts Institute of Technology" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes", "Biological and pharmaceutical systems", "Batch and semi-batch process control" ], "abstract": "N-glycosylation is a critical quality attribute of monoclonal antibodies (mAbs), the dominant class of biopharmaceuticals. Controlling glycosylation remains difficult due to intrinsic pathway complexity, limited online measurements, and a lack of tailored control strategies. This work applies an adaptive nonlinear model predictive control (ANMPC) framework to a fed-batch mAb production process, using a multiscale model that links extracellular conditions to intracellular Golgi reactions to predict glycan profiles. Model parameters are updated online as new measurements arrive, after which a shrinking-horizon optimization computes the control inputs; only the first control move is implemented each cycle. Case studies show that, with a minimal day-1 galactose excitation, ANMPC mitigates model–plant mismatch and achieves up to 130% and 96% higher performance than open-loop optimization and state NMPC, respectively. Under more realistic conditions (partial measurement availability and longer preparation time), ANMPC maintains comparable performance, indicating robustness to practical limitations. Overall, the results demonstrate that ANMPC can actively shape glycan distributions in silico and offers a viable path toward closed-loop control of mAb glycosylation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Model-Free Finite-Horizon H-Infinity Control Via Off-Policy Double Minimax Q-Learning (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Yudho, Eduardo", "affiliation": "Cinvestav-IPN" }, { "name": "Yu, Wen", "affiliation": "Northeastern University" }, { "name": "Li, Xiaoou", "affiliation": "CINVESTAV-IPN" } ], "keywords": [ "Consensus and reinforcement learning control", "Neural and fuzzy adaptive control", "Data-driven control theory" ], "abstract": "Finite-horizon H-infinity control is essential for robust design but challenging when system dynamics are unknown. This paper introduces a model-free solution using off-policy reinforcement learning. We propose the Neural Network-based Double Minimax Q-learning (NN-DMQ) algorithm to solve the minimax optimization problem, managing adversarial interactions while mitigating Q-value overestimation bias. Simulations on a nonlinear inverted pendulum show that NN-DMQ achieves performance comparable or superior to classical model-based H-infinity controllers, especially under parametric uncertainty. NN-DMQ thus offers a highly effective model-free framework for robust control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Inverse Reinforcement Learning for Mean-Field Social Control Problems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Cao, Ying", "affiliation": "Shandong University" }, { "name": "Wang, Bing-Chang", "affiliation": "Shandong University" } ], "keywords": [ "Data-driven control theory", "Distributed optimization", "Stochastic control" ], "abstract": "This paper presents an inverse reinforcement learning (RL) framework for linear quadratic mean-field social control problems with multiplicative noise. The objective is to find the equivalent social cost weights and imitate the social optimal control policies from expert demonstrations. We first propose a model-based inverse RL algorithm, and then develop a model-free inverse RL approach by eliminating the dependence on system dynamics. The iterative equations derived from integral RL are implemented using only measured trajectory data. Moreover, the model-based and model-free approaches are equivalent under the rank conditions. Finally, we demonstrate the effectiveness of the approach by simulation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Continuous-Time Reinforcement Learning for Exploratory Zero-Sum Games and Risk-Sensitive Control (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Guo, Liangyuan", "affiliation": "Shandong University" }, { "name": "Wang, Bing-Chang", "affiliation": "Shandong University" }, { "name": "Wang, Guangchen", "affiliation": "Shandong University" } ], "keywords": [ "Data-driven control theory", "Learning methods for control", "Stochastic control" ], "abstract": "We study the continuous-time zero-sum games and risk-sentitive control with entropy regularization. The saddle-point distribution is shown to be Gaussian, which balances exploitation and exploration. When the temperature parameters are opposite numbers, the exploratory cost becomes zero despite the presence of regularization. We prove a verification theorem that ensures the optimal control pair constitutes a saddle-point equilibrium in exploratory zero-sum games. A partial equivalence of the exploratory solutions is shown between zero-sum games and risk-sensitive control problems. Finally, a model-free dual-actor critic algorithm is designed.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Sample-Efficient Model-Free Policy Gradient Methods for Stochastic LQR Via Robust Linear Regression (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Song, Bowen", "affiliation": "University of Stuttgart" }, { "name": "Gros, Sebastien", "affiliation": "NTNU" }, { "name": "Iannelli, Andrea", "affiliation": "University of Stuttgart" } ], "keywords": [ "Data-driven control theory", "Statistical analysis" ], "abstract": "Policy gradient algorithms are widely used in reinforcement learning and belong to the class of approximate dynamic programming methods. This paper studies two key policy gradient algorithms, the Natural Policy Gradient and the Gauss–Newton Method, for solving the linear quadratic regulator problem for unknown systems using stochastic data. The main challenge is the inconsistency of estimating random quantities in the policy gradient update due to the resulting errors-in-variables setting. This issue is addressed by proposing a robust primal–dual estimation procedure. Using this improved policy gradient update estimation scheme, this paper delivers a consistent estimator with a convergence rate of order mathcal{O}(epsilon^{-1}). Theoretical results are further supported by numerical experiments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "A Digital Twin Framework for LSTM-Based Fault Diagnosis in Discrete Event Manufacturing Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Fahs, Alain", "affiliation": "Université De Reims Champagne-Ardenne" }, { "name": "Wabo Teingua, Ange Patrick", "affiliation": "Université De Reims Champagne-Ardenne" }, { "name": "Saddem, Ramla", "affiliation": "Université De Reims Champagne-Ardenne" }, { "name": "Plenk, Valentin", "affiliation": "Hof University of Applied Sciences" } ], "keywords": [ "Diagnosis of discrete event and hybrid systems" ], "abstract": "Digital Twin (DT) technology is increasingly used in manufacturing to enable real-time monitoring, prediction and decision support. In this work, we propose a DT dedicated to fault diagnosis in manufacturing systems modeled as Discrete Event Systems. Building on our previous contribution, which introduced a data-driven diagnostic method based on Long Short-Term Memory (LSTM) neural networks, we present an improved version of this approach and deliver a turnkey solution suitable for both shop-floor operators and plant managers. The effectiveness of the proposed DT is demonstrated using the CellFlex plant, a training and research platform at the URCA. CellFlex plant consists of eight stations operating around a central conveyor, forming a flexible miniaturized bottling line connected through industrial-standard networks. The obtained results confirm the relevance and practical applicability of the proposed approach for online fault diagnosis in industrial environments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Lure-And-Reveal: An Exposure Framework for Stealthy Deception Attack in Multi-Sensor Uncertain Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Tian, Meiqi", "affiliation": "The Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Liu, Yihan", "affiliation": "The Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Zhong, Bingzhuo", "affiliation": "Hong Kong University of Science and Technology (Guangzhou)" } ], "keywords": [ "Diagnosis of discrete event and hybrid systems", "Supervisory control and automata", "Security for stochastic systems" ], "abstract": "Multi-sensor integration via error-state Kalman filter (ES-KF) is widely employed for precise state estimation in cyber-physical systems (CPSs). However, this integration exposes the system to stealthy deception attacks that render conventional detection mechanisms ineffective. We propose an exposure framework to actively reveal such stealthy attacks without modifying sensor interfaces. The framework introduces a suspect mode in which the defender injects random exposure shakes into the nominal control inputs, thus creating a discrepancy between the defender’s true state estimates and the attacker’s manipulated state estimates, preventing the attack from remaining stealthy. We further derive an explicit exposure condition that characterizes the minimum shake magnitude to guarantee the finite-time exposure and a compensability condition that ensures the shakes do not degrade closed-loop performance. Simulation results based on a GNSS/INS-integrated UAV system verify the effectiveness of the proposed framework.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Modelling and Analysis of Aircraft Maintenance Service Chains Using Timed-Arc Colored Petri Nets", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Gu, Chao", "affiliation": "Queen’s University Belfast" }, { "name": "Athanasopoulos, Nikolaos", "affiliation": "Queen's University Belfast" }, { "name": "McLoone, Seán Francis", "affiliation": "Queen's University Belfast" } ], "keywords": [ "Discrete event modeling and simulation", "Petri nets" ], "abstract": "We present a modeling and analysis framework for aircraft maintenance scheduling based on timed-arc colored Petri nets (TACPN). We develop a multi-aircraft, multi-task maintenance TACPN model that incorporates task-feasibility constraints, maximum service intervals, and resource constraints such as manpower and hangar capacities. To assess whether a maintenance plan is feasible, we formulate two verification problems: execution admissibility, which checks whether a given finite workflow is valid, and feasible-schedule existence, which examines whether there is a scheduling execution that avoids all task violations. We show that both problems can be addressed using the open-source tool TAPAAL, and we demonstrate the framework through an example.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Federated Distributional Reinforcement Learning under Heterogeneous Environments Via Quantile Regression (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Wang, Wanmin", "affiliation": "Southeast University" }, { "name": "Liu, Hongzhe", "affiliation": "School of Mathematics, Southeast University" }, { "name": "Xu, Wenying", "affiliation": "Southeast University" }, { "name": "Yu, Wenwu", "affiliation": "Southeast University" }, { "name": "Zheng, Wei Xing", "affiliation": "Western Sydney University" } ], "keywords": [ "Distributed reinforcement learning", "Markov decision process", "Multi-agent systems" ], "abstract": "Federated reinforcement learning (FedRL) enables distributed agents to collaboratively solve sequential decision-making tasks without exposing private trajectories or data. Existing FedRL methods, however, often suffer from instability in heterogeneous environments and fail to capture distributional uncertainty, thus limiting robust and stable aggregation across agents. To address these challenges, we propose Federated Quantile Regression Deep Q-Network (Fed-QRDQN), which is the first Federated Distributional RL framework that models full return distributions via quantile regression. By capturing richer uncertainty, Fed-QRDQN stabilizes local training and enhances global aggregation across diverse agents. The framework further introduces an anchor-guided alignment mechanism to ensure update comparability with minimal communication overhead, and a Wasserstein-based aggregation with distribution distillation to preserve cross-client variability. Experiments demonstrate that Fed-QRDQN achieves faster convergence, higher final performance, and greater training stability compared to standard FedRL approaches.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Generalized Lotka-Volterra Model with Species Turnover in a Variable-Basis State Space", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Doliveira, Arthur", "affiliation": "Lis Umr 7020 Cnrs / Amu / Utln" }, { "name": "Roman, Christophe", "affiliation": "Lis Umr 7020 Cnrs / Amu / Utln" }, { "name": "Graton, Guillaume", "affiliation": "Ecole Centrale De Marseille" }, { "name": "Ouladsine, Mustapha", "affiliation": "Professeur à Aix Marseille Université" } ], "keywords": [ "Hybrid and switched systems modeling" ], "abstract": "The state space is a fundamental concept for describing the trajectory of a dynamic system. Depending on its form, it can highlight certain changes over time while ignoring others. This is particularly the case for the spaces associated with theoretical ecology models, notably the generalized Lotka-Volterra (gLV) model, which allows the modeling of interacting populations. The fixed-dimension state space classically used in gLV models does not account for the effective renewal of species through addition, removal, or mutation. To address this limitation, we propose to use a variable-basis state space introduced in a previous study. This framework leads to a reformulation of the gLV model within the context of hybrid dynamical systems. To illustrate the approach, we apply the proposed model to the gut microbiota, particularly in the context of bacteriotherapy following antibiotic treatment.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Solving Markov Decision Processes with Future Information Via MPC (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Sawant, Shambhuraj", "affiliation": "NTNU Trondheim" }, { "name": "Anand, Akhil", "affiliation": "Norwegian University of Science and Technology (NTNU)" }, { "name": "Reinhardt, Dirk Peter", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Gros, Sebastien", "affiliation": "NTNU" } ], "keywords": [ "Markov decision process", "Learning methods for control" ], "abstract": "Model Predictive Control (MPC) is widely used in industrial and robotic systems for enforcing constraints and embedding domain knowledge through finite-horizon optimization-based planning. However, despite these strengths, an MPC scheme typically does not yield optimal policies for sequential decision-making problems formulated as Markov Decision Processes (MDPs). Recent combinations of MPC with Reinforcement Learning (RL) alleviate this issue by treating MPC as a parameterized model of the optimal policy of an MDP and adjusting its parameters using data. While these approaches typically consider classical MDPs, many real-world problems include future information—such as forecasts, prices, or reference trajectories—at decision time, which must be included in the MDP state for optimal decision-making. Current MPC-RL approaches do not directly account for this augmented-state structure, raising the question of how to incorporate future information into MPC to obtain an optimal policy. This work establishes the structural requirements under which a parameterized MPC can exactly represent the optimal value functions and policy of an MDP with future information. We further demonstrate that such a parameterized MPC can serve as a structured function approximator, with its parameters learned using RL. The approach is illustrated on a point-mass racing task with future reference information.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Online Constrained Reinforcement Learning for Optimal Tracking (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Lee, Hyochan", "affiliation": "Korea Advanced Institute of Science and Technology" }, { "name": "Choi, Kyunghwan", "affiliation": "Korea Advanced Institute of Science and Technology" } ], "keywords": [ "Neural and fuzzy adaptive control", "Nonlinear adaptive control", "Learning methods for control" ], "abstract": "This paper presents a constrained online reinforcement learning framework for the optimal tracking control of constrained nonlinear systems. While reinforcement learning provides powerful tools for optimal control, conventional implementations typically rely on unconstrained minimization strategies. Since this approach does not restrict the policy search space within the feasible region, it often drives the control policy toward unbounded actions, exacerbating the instability inherent in nonlinear function approximation. To address these issues, the proposed method reformulates the Bellman optimality equation as a constrained optimization problem where the control policy and value function are treated as joint decision variables. Crucially, this formulation allows for the explicit incorporation of system constraints directly into the learning process. A Lagrangian-based primal-dual scheme is then employed to find a Karush-Kuhn-Tucker solution, promoting constraint satisfaction in practice (within tolerance). Experimental validation on a differential-wheeled mobile robot demonstrates that the algorithm enforces hard constraints in practice within tolerance during complex maneuvers while maintaining stable convergence of the value function.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Designing a Novel Fractional PID Controller Based on Prabhakar Derivative for Time-Delay Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Jafarpour, Mahdi", "affiliation": "National Yunlin University of Science and Technology" }, { "name": "Mobayen, Saleh", "affiliation": "National Yunlin University of Science and Technology" }, { "name": "Fekih, Afef", "affiliation": "Univ of Louisiana at Lafayette" } ], "keywords": [ "Optimal control of discrete event and hybrid systems", "Control under communication constraints", "Control over networks" ], "abstract": "For the control of time-delay systems, a new Prabhakar fractional-order PID controller is introduced. The Prabhakar operator adds more degrees of freedom than traditional fractional controllers based on the Riemann–Liouville or Caputo derivatives by utilizing the three-parameter Mittag-Leffler function. This approach would capture more complex non-local dynamics and deeper memory properties. A thorough examination of existence, uniqueness, and closed-loop behavior is used to construct comprehensive stability requirements in both finite-time and practical stability frameworks. According to simulation tests, the suggested controller outperforms conventional fractional-order PID designs in pulse-tracking applications, resulting in appreciable advances in tracking accuracy, transient response, and resilience to time-delay fluctuations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Dual-Timed Petri Net Modeling and Deadlock-Free Scheduling of Collaborative Heterogeneous Multi-Agent Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Li, Boyu", "affiliation": "Zhejiang University" }, { "name": "Wu, Weimin", "affiliation": "Zhejiang Univ" }, { "name": "Li, Dacheng", "affiliation": "Zhejiang University" }, { "name": "Li, Zhengchen", "affiliation": "Zhejiang University" }, { "name": "Wang, Shuo", "affiliation": "HuaQiao University" } ], "keywords": [ "Petri nets", "Discrete event modeling and simulation", "Multi-agent systems" ], "abstract": "Collaborative heterogeneous multi-agent systems (CHMAS) are widely used in logistics and manufacturing, but their spatiotemporal synchronization requirements tightly couple agent schedules and may lead to deadlocks. This paper presents a Petri net-based framework for modeling, evaluating, and constructing deadlock-free schedules in CHMAS. A Dual-Timed Petri Net (DTPN) is used to represent the logical precedence and temporal dynamics of a given schedule, enabling schedule decoding and makespan evaluation. Based on the marked-graph structure of the constructed DTPN, a liveness-based feasibility criterion is derived to identify deadlock-free schedules. Furthermore, a Bi-directional Liveness Check (BLC) algorithm is developed to prevent deadlock-inducing insertions during schedule construction. Experimental results show that BLC effectively reduces infeasible evaluations and improves search efficiency and solution quality in highly coupled scenarios.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Deadlock-Free Execution of Multi-AGV Plans under Delays: A Prioritized Dual-Time Petri Net Approach", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Li, Dacheng", "affiliation": "Zhejiang University" }, { "name": "Wu, Weimin", "affiliation": "Zhejiang Univ" }, { "name": "Li, Boyu", "affiliation": "Zhejiang University" }, { "name": "Wang, Zixi", "affiliation": "Zhejiang University" }, { "name": "Zhou, Jiazhong", "affiliation": "Huaqiao University" } ], "keywords": [ "Petri nets", "Multi-agent systems", "Discrete event modeling and simulation" ], "abstract": "The robust execution of Multi-Agent Path Finding (MAPF) plans under temporal uncertainty poses a significant challenge in logistics automation. When Automated Guided Vehicles experience unexpected delays, strict adherence to the pre-computed nominal plan ensures safety but often leads to unnecessary waiting and efficiency degradation. Conversely, blindly deviating from the scheduled order to reduce idling carries the risk of inducing deadlocks. To reconcile execution flexibility with safety, this paper proposes a novel control framework based on Prioritized Dual-Time Petri Nets (PDTPN). A graph-theoretic dependency analysis is developed to rigorously distinguish between rigid precedence constraints and switchable dependencies that allow for local reordering without creating circular waits. Based on this analysis, a systematic synthesis procedure transforms the MAPF plan into a PDTPN controller. Theoretical results demonstrate that the proposed framework guarantees deadlock-free operation under arbitrary bounded delays. Furthermore, the system naturally realizes a dynamic policy similar to First-Come-First-Served, significantly reducing the total accumulated execution time compared to fixed-priority approaches.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Online Order Estimation for Binary-Valued FIR System with Colored Noise", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Wang, Wenbin", "affiliation": "Academy of Mathematics and Systems Science, Chinese Academy of Sciences" }, { "name": "Guo, Jian", "affiliation": "The Hong Kong Polytechnic University" }, { "name": "Zhao, Yanlong", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Quantized systems", "Time series modeling", "Linear system identification" ], "abstract": "This paper studies online order estimation for binary-valued finite impulse response (FIR) systems with unknown order driven by colored moving-average (MA) noise. For colored noise, the main difficulty is that temporal dependence creates long-range correlations in the binary output, which obscure the contribution of the FIR dynamics. The proposed method overcomes this by exploiting a structural property of FIR-MA models: the autocorrelation function of the underlying linear process has finite support, and this support length is preserved under binary quantization. We use this property to construct a discontinuous objective function in the candidate order, built from binary correlation statistics and designed to jump at the true support length. This objective can be evaluated recursively using only low-dimensional summary variables, without storing the full data history, and is therefore suitable for real-time implementation in the presence of colored noise. We prove that the resulting order estimator converges almost surely to the true order . In the Gaussian noise case, we further derive an explicit linear relation, which enables joint online estimation of the system order and the FIR coefficients. Numerical experiments under various noise distributions and input designs confirm the robustness and accuracy of the proposed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Dissipativity and L2 Stability of Large-Scale Networks with Changing Interconnections", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Jang, Ingyu", "affiliation": "Duke University" }, { "name": "Bridgeman, Leila", "affiliation": "Duke University" } ], "keywords": [ "Stability and stabilization of hybrid systems", "Control of networks", "Multi-agent systems" ], "abstract": "In this paper, the L2 stability of switched networks is studied based on the QSR-dissipativity of each agent. While the integration of dissipativity with switched systems has received considerable attention, most previous studies have focused on passivity, internal stability, or feedback networks involving only two agents. This work makes two contributions: first, the relationship between switched QSR-dissipativity and L2 stability is established based on the properties of dissipativity parameters of switched systems; and second, conditions for L2 stability of networks consisting of QSR-dissipative agents with switching interconnection topologies are derived. Crucially, this shows that a common storage function will exist across all modes, avoiding the need to find one, which becomes computationally taxing for large networks with many possible configurations. Numerical examples demonstrate how this can facilitate stability analysis for networked systems under arbitrary switching of swarm drones.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Stabilizing Linear Time-Invariant Systems with Recurrent Spiking Neural Networks", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Klip, Ward", "affiliation": "Eindhoven University of Technology" }, { "name": "Petri, Elena", "affiliation": "Eindhoven University of Technology" }, { "name": "Heemels, Maurice", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Stability and stabilization of hybrid systems", "Event-based control", "Hybrid and switched systems modeling" ], "abstract": "The field of neuromorphic engineering aims to bring the advantages of biological spiking neurons, such as energy efficiency, adaptability, and fast event-based responses, to engineered systems. Also in the context of control, brain-inspired technologies are of great potential. In this paper, we present a systematic design method for novel neuromorphic control strategies for the stabilization of linear time-invariant systems using input signals that consist of fixed-amplitude spikes. As the only design freedom for the controller is the determination of the spiking times, the controller must be both event-based and impulsive in nature. Our method is based on firing a spike when it reduces the value of an appropriately chosen Lyapunov function. Our control schemes are formulated both as static state-based firing rules and as recurrent spiking neural networks. It is proven that in both cases this gives global practical stability of the closed-loop system and excludes Zeno-like behavior in the sense that that an infinite amount of spikes cannot occur in a finite amount of time. The approaches are illustrated with numerical examples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Safety-Critical Tracking Control for Switched Nonlinear Systems Based on Contraction Theory", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Liu, Qian", "affiliation": "Beijing University of Technology" }, { "name": "Li, Xiaoli", "affiliation": "Beijing University of Technology" } ], "keywords": [ "Stability and stabilization of hybrid systems", "Hybrid and switched systems modeling", "Adaptive gain scheduling autotuning control and switching control" ], "abstract": "This paper studies the safety-critical trajectory tracking problem of switched nonlinear systems based on contraction theory, where contraction is not required to hold for all subsystems. By extending the contraction theory to the design of switching control, a safe tracking control framework for switched systems is established, which does not require the reference trajectory to satisfy safety performance. On this basis, sufficient conditions are derived to verify the safe tracking property under a state-dependent switching law, which is constructed based on the states of the differential subsystems of the switched system. Furthermore, these conditions are formulated as a convex feasibility problem, and the switching feedback controller as well as the corresponding control contraction metrics are constructed via a bilinear sum-of-squares methodology. Finally, the effectiveness of the proposed framework is validated through a continuous stirred tank reactor system.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Linear-Quadratic Stochastic Team Problem under General Partial Observations (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Moon, Jun", "affiliation": "Hanyang University" } ], "keywords": [ "Stochastic control", "Stochastic differential equations", "Synthesis of stochastic systems" ], "abstract": "This paper considers the two-player linear-quadratic team optimal control problem for stochastic differential equations (SDEs) with random coefficients. Given the complete observation mathbb{F}, Player 1 and 2 have access to partial observations mathbb{G}_1 subset mathbb{F} and mathbb{G}_2 subset mathbb{F}, respectively, where mathbb{G}_1 cap mathbb{G}_2 neq emptyset corresponds to the common observation. We obtain the open-loop type team-optimal solution by the stochastic maximum principle, represented by the first-order optimality conditions with the adjoint equation, captured by the backward SDE. Then by identifying the appropriate four-step scheme transformation, together with the coupled stochastic Riccati differential equations (CSRDEs), we obtain the feedback-type team-optimal solution, which requires to compute the filtering state processes with respect to (hat{mathbb{G}},mathbb{G}_1,mathbb{G}_2). Finally, we state the verification theorem of the team-optimal solution obtained by the maximum principle and the four-step scheme transformation. In our paper, unlike the exiting works, the CSRDEs have random coefficients, which can be viewed as coupled matrix-valued BSDEs.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Long Time Behaviors of Discrete-Time Linear-Quadratic Optimal Control for Markov Jump Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Lin, Yu", "affiliation": "Shandong University" }, { "name": "Liang, Yong", "affiliation": "Shandong Normal University" }, { "name": "Wang, Bing-Chang", "affiliation": "Shandong University" } ], "keywords": [ "Stochastic control", "Stochastic hybrid systems" ], "abstract": "This paper investigates the long-time behavior of the optimal trajectory for the discrete-time Markov jump linear quadratic optimal control problem. By modifying the Bellman equation, a cell problem is constructed for the Markov jump system (MJS) to deal with non-homogeneous dynamics and cost functions. Solving the modified Bellman equation yields the solution to the cell problem in terms of coupled algebraic Riccati equations. Based on this, the relationship between the cell problem and ergodic control is revealed. Specifically, the quadratic value function yields the optimal ergodic control, while the ergodic constant is determined by the limit of the expectation of the modified function. Finally, the turnpike property of the MJS is derived from the cell problem, which shows that the optimal trajectory is exponentially close to the steady state and the number of deviation points is bounded.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "A Linear-Quadratic Leader-Follower Differential Game with Mixed Deterministic and Stochastic Controls (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Shi, Jingtao", "affiliation": "Shandong University" } ], "keywords": [ "Stochastic control", "Stochastic hybrid systems", "Stochastic differential equations" ], "abstract": "This paper is concerned with a linear-quadratic (LQ) leader-follower differential game with mixed deterministic and stochastic controls. In the game, the follower is a random controller which means that the follower can choose adapted stochastic processes, while the leader is a deterministic controller which means that the leader can choose only deterministic time functions. Such problem is motivated by a pension fund insurance problem, with government, supervisory or employer being a deterministic leader and individual producer or retail investor being a random follower. The state feedback representation of an open-loop Stackelberg equilibrium solution is obtained, with the help of a system consisting of two cross-coupled Riccati equations and a two-point boundary value problem of ordinary differential equations (ODEs), whose solvability is investigated.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Let Others Help You: Influential Planning for Multi-Agent Systems under Temporal Logic Tasks", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ye, Bowen", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Wang, Yingzhu", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Zhao, Jianing", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Yin, Xiang", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Supervisory control and automata", "Event-based control", "Discrete event modeling and simulation" ], "abstract": "In this paper, we investigate the motion planning problem for multi-agent systems under temporal logic constraints. Unlike most existing works, which assume agents are either cooperative or adversarial, we consider a new scenario called influential planning. Specifically, we assume there are two agents: a leader and a follower, each with their own objectives characterized by temporal logic formulas. Our objective is to design a plan for the leader such that, when the follower pursues its own objectives, the leader's objectives are also satisfied. In other words, although the leader cannot directly control the follower's behavior, it can influence the follower's actions by strategically synthesizing its own plan. We provide an efficient algorithm for solving this type of influential planning problem, where specifications are expressed using co-safe linear temporal logic (scLTL) formulae. Case studies are presented to illustrate the effectiveness of our framework, demonstrating how the leader's strategic planning can indirectly guide the follower's behavior to achieve desired outcomes.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Multi-Objective Control and Manipulability Maximization of Robot Manipulators", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Vargas, Lucas", "affiliation": "Norwegian Univ. of Life Sciences & Fed. Univ. of Rio De Janeiro" }, { "name": "Candea Leite, Antonio", "affiliation": "Norwegian University of Life Sciences" }, { "name": "Costa, Ramon R.", "affiliation": "Federal University of Rio De Janeiro" } ], "keywords": [ "Robotic grasping and manipulation", "Mechatronic system modeling, design, optimization", "Mechatronic system estimation, identification, control" ], "abstract": "In this work, we revisit the use of the filtered inverse algorithm to address multi-objective control of robot manipulators. The method employs the concept of dynamic inversion of the Jacobian matrix to handle kinematic singularities and augmented task-space problems, which may be ill-posed and involve conflicting goals. Herein, we evaluate different approaches for incorporating both trajectory tracking and the additional control objective of velocity manipulability maximization, as it correlates with the energetic efficiency of robotic operations. Finally, numerical simulations of a redundant planar arm demonstrate the behavior and performance of the proposed solution.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Towards Simulation-Based Motion Planning for Deformable Linear Objects", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Völz, Andreas", "affiliation": "Friedrich-Alexander-Universität Erlangen-Nürnberg" }, { "name": "Graichen, Knut", "affiliation": "Friedrich-Alexander-University Erlangen-Nuremberg" } ], "keywords": [ "Robotic grasping and manipulation", "Task and motion planning" ], "abstract": "This paper investigates the use of physics simulation for the motion planning of deformable linear objects (DLOs) like cables and ropes. Existing work has largely focused on the modeling of equilibrium configurations in such a way that standard sampling-based planners can be applied. However, these methods are difficult to extend to scenarios that require or allow contact between the DLO and the environment. Therefore, it seems attractive to directly use physics simulations like MuJoCo for the planning process instead of relying on equilibrium models. Concepts for lattice-based and tree-based planning are presented and compared to a state-of-the-art model for an intentionally simplified task to highlight advantages and challenges.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Spatial Event Based Adaptive Control for Rehabilitation Robotic Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhou, Shou-Han", "affiliation": "Cardiff University" }, { "name": "Mareels, Iven", "affiliation": "Federation University Australia" } ], "keywords": [ "Robotic learning and adaptation", "Adaptive and adaptable automation", "Medical and rehabilitation robotics" ], "abstract": "In fields such as rehabilitation and biomechanics, many robotic systems have been developed to interact directly with humans. However, the speed of human movement is not constant for a given task, as the time required to complete an action varies with individual decisions. To address this variability, we develop a spatially based event controller that adapts to unknown parameters while allowing for movements in multiple directions, addressing limitations of existing spatial controllers. We derive the conditions on the controller parameters and event design that ensure system stability, and then present simulation examples demonstrating the controller’s ability to track spatial paths without constraining the terminal time.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Map and Navigation in Unknown Environments with Brain-Inspired Learning Approach", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Shen, Xiangyuan", "affiliation": "Huazhong University of Scienceand Technology" }, { "name": "Hu, Bin", "affiliation": "South China University of Technology" }, { "name": "Guan, Zhi-Hong", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Chen, Long", "affiliation": "Wuhan Institute of Technology" }, { "name": "Li, Tao", "affiliation": "Hubei Normal University" } ], "keywords": [ "Robotic learning and adaptation", "Autonomous navigation", "Robot perception and sensing" ], "abstract": "Simultaneous localization and mapping (SLAM) and navigation are core capabilities for agents, yet traditional methods rely on high-precision sensors and perform poorly in rapidly changing large-scale environments. Inspired by mammals' spatial cognitive and navigation mechanisms in neuroscience, this paper proposes a novel brain-inspired computational network for learning cognitive map representations and navigation in unknown environments. The network model diverse spatial cells to integrate perception and motion information for environmental representation, establishes a dynamically growing place cell-based cognitive map, and updates synaptic strength between place cells via agent-environment interaction to restructure the map. Additionally, a place cell sequence planning algorithm is designed for navigation using the cognitive map as input. Simulation and physical-robot experiments show that the proposed method can dynamically construct and update cognitive maps during environmental interaction and can improve navigation efficiency in the tested dynamic scenarios. These results suggest a feasible brain-inspired alternative for map learning and navigation in unknown environments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Residual Reinforcement Learning for Robot Teleoperation under Stochastic Delays", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Deng, Kai-Ze", "affiliation": "Technische Universität München" }, { "name": "Yang, Zewen", "affiliation": "Technical University of Munich" } ], "keywords": [ "Robotic learning and adaptation", "Teleoperation", "AI-powered robotics" ], "abstract": "Stochastic communication delays in teleoperation introduce signal discontinuities that undermine control stability and degrade control performance. Consequently, the conventional reinforcement learning (RL) methods struggle with the delayed observations due to the delay-induced observations, leading to high-frequency chattering. To address this, we propose a hybrid control framework, delay-resilient RL, integrating a state estimator utilizing Long Short-Term Memory (LSTM) with a residual RL policy, which is resilient to stochastic delays. The LSTM reconstructs smooth, continuous state estimates from delayed observations, enabling the RL agent to learn a residual torque compensation policy that balances tracking accuracy with velocity smoothness. Experimental validation on Franka Panda robots demonstrates that our approach significantly outperforms the state-of-the-art baselines, ensuring robust and stable teleoperation even under high-variance stochastic delays.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Enhancing Attack Detection for Mobile Robots Via Parametric Final-State Distribution Modeling (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Horikoshi, Ken", "affiliation": "The University of Electro-Communications" }, { "name": "Watanabe, Yohei", "affiliation": "The University of Electro-Communications" }, { "name": "Iwamoto, Mitsugu", "affiliation": "The University of Electro-Communications" }, { "name": "Tanaka, Takashi", "affiliation": "Purdue University" }, { "name": "Sawada, Kenji", "affiliation": "The University of Osaka" } ], "keywords": [ "Security for stochastic systems" ], "abstract": "Stealthy attacks and defenses in mobile systems have been studied as zero-sum games, where an attacker covertly drives the system to an unsafe region and a defender detects attacks from noisy trajectories. This paper experimentally evaluates such a game-theoretic framework on a mobile robot. Although the framework predicts constant attacks and likelihood-ratio tests as equilibrium strategies, robot experiments show large errors in the predicted detection failure rate due to a mismatch in final-state variance. We model this effect using empirical Gaussian final-state distributions. Experiments and simulations reduce the prediction error to 5% and clarify the model's limits.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Adaptive Impedance Matching Control for Railway Broadband Vibration Energy Harvesting: A Machine Learning Surrogate Approach", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Mansattha, Muhammad", "affiliation": "University of Birmingham" }, { "name": "Dixon, Roger", "affiliation": "University of Birmingham" }, { "name": "Stewart, Edd", "affiliation": "The University of Birmingham" } ], "keywords": [ "Smart structures and vibration control", "Mechatronic system estimation, identification, control", "Mechatronics for advanced manufacturing and energy systems" ], "abstract": "Conventional Electromagnetic Vibration Energy Harvesters (EVEHs) can be inefficient when tuned to fixed impedances, particularly under the non-stationary, broadband conditions typical of railway environments. To overcome this limitation, this paper introduces an adaptive impedance matching controller driven by a Machine Learning (ML) surrogate model. By leveraging a Random Forest (RF) regression trained on statistical signal features, the proposed system predicts the optimal complex load impedance in real-time, enabling precise complex conjugate matching. Experimental validation confirms that the controller not only tracks the theoretical maximum power during sinusoidal sweeps but also significantly outperforms traditional fixed-tuning strategies in real-world benchmarks. Specifically, under non-stationary railway vibration profiles, with instantaneous power improvements exceeding 20% during off-resonance events, proving it is the most reliable power source for automated condition monitoring systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Varying Bundle Size Reactive Multi-Task Assignment Using Selective Cost Estimation for Multi-Agent Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Dahlquist, Niklas", "affiliation": "Luleå University of Technology" }, { "name": "Velhal, Shridhar", "affiliation": "Lulea Technical University" }, { "name": "Nikolakopoulos, George", "affiliation": "Luleå University of Technology" } ], "keywords": [ "Task and motion planning", "Aerial, field, and marine robotics" ], "abstract": "This paper presents a scalable framework for multi-robot task allocation in complex environments where estimating task execution costs is computationally expensive. While combinatorial auction-based approaches offer reliable solutions, the exponential complexity of bundle generation typically renders them intractable for real-time reactive applications, particularly when accurate path planning is required for cost validation. We address this through a distributed, two-stage multi-fidelity bundle generation approach. Agents utilize a local search tree guided by a low-fidelity heuristic (such as euclidean distance) to rapidly explore the bundle space, applying high-fidelity path planning only to the most promising candidates in a best-first manner. These refined bids are then submitted to a central coordinator that solves a set packing problem to ensure global feasibility and maximize the overall utility. Simulation results in multiple environments demonstrate that the framework is able to improve the performance of reactive auction-based task allocation. Overall, the presented framework is shown to enable reactive task allocation with dynamic bundle sizes in multiple settings without exposing the agents' state and internal cost estimation models.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Multi-Robot Allocation and Optimization in a Multi-Mission Framework", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Miloradovic, Branko", "affiliation": "Mälardalen University" }, { "name": "Frasheri, Mirgita", "affiliation": "Aarhus University" } ], "keywords": [ "Task and motion planning", "Autonomous navigation" ], "abstract": "This paper presents a framework for multi-mission multi-robot task allocation that integrates continuous-time routing with a lightweight bucketed reservation layer. Rather than collapsing all objectives into a single global mission, the framework keeps missions distinct and enables controlled sharing of robots across stakeholders with differing priorities and limited information exchange. The reservation layer overlays coarse time buckets on the planning horizon, allowing planners to specify time-phased mission quotas and enforce one-mission-per-robot commitments within each interval, all while preserving continuous task timing. This structure provides an operational control interface through which operators can adjust mission priorities over time without disclosing internal task details, enabling responsive, interpretable, and privacy-aware coordination. The results show that the proposed framework delivers feasible, continuous-time schedules that respect mission-level policies and achieve coordinated mission progress.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Lazy-pRRTC: Accelerating pRRTC with Coarse-To-Fine Collision Checking on GPU", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Lee, Ming-Hsiu", "affiliation": "Institute of Information Science, Academia Sinica" }, { "name": "Liu, Jing-Sin", "affiliation": "Academia Sinica" } ], "keywords": [ "Task and motion planning", "Autonomous navigation", "High-performance motion control systems" ], "abstract": "pRRTC is a GPU-accelerated RRT-Connect algorithm that uses parallelism for both sample expansion and collision detection. However, setting the sample size for discrete collision detection equal to the number of threads per block may not meet the finer collision resolution required by certain applications. In this paper, we leverage a lazy strategy to enhance the efficiency of pRRTC to mitigate the safety and discretization accuracy tradeoff set by default number of threads per block. Our approach reduces a significant number of fine collision detection by deferring fine full path collision check until after the initial path linking start and goal is generated by pRRTC with its default number of discretization. Simulations in environments with 35 randomly placed rectangular obstacles and walls with narrow passages show that in safety-aware fine discretization lazy-pRRTC achieves accurate tree extension with approximately 3× higher efficiency than its predecessor, pRRTC, and enables efficient waypoints generation for fast navigation in harder environments due to significantly fewer state collision checks.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Toward Certifiable Robotic Surgery Policy Via a Markov Decision Process Framework", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhong, Zhiyi", "affiliation": "The University of Hong Kong" }, { "name": "Lin, Lin", "affiliation": "Southern University of Science and Technology" }, { "name": "Dai, Jing", "affiliation": "The Chinese University of Hong Kong" }, { "name": "Lam, James", "affiliation": "Univ of Hong Kong" }, { "name": "Kwok, Ka Wai", "affiliation": "The Chinese University of Hong Kong" } ], "keywords": [ "Task and motion planning", "Autonomous navigation", "Robotic learning and adaptation" ], "abstract": "This paper introduces a certification framework that analyzes deep reinforcement learning policies used in autonomous surgical planning. Current learning-based controllers lack formal safety guarantees, and we address this by representing Deep Reinforcement Learning (DRL)-generated surgical plans as explicit Markov decision processes. First, the feasibility of a surgical plan is established by two conditions: absorption stability at the target state and finite-time reachability to it. After the feasibility assessment, a quantitative robustness index is derived from a reachability-layer decomposition. This index measures the resilience of the surgical plan when a single state transition is disrupted such as by tissue deformation. Finally, the theoretical approach has been implemented in an interactive visual interface for verification and evaluation. The effectiveness of this framework has been verified through an illustrative simulation on an ultrasound navigation task and identify the critical transitions required to reach the target position.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Teaching Learning Based GMPC Framework for Skid Steered Robot in Human Aware Environment", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Shekhar Sahasrabudhe, Kartik", "affiliation": "Robotics Innovation Lab, Department of Design and Manufacturing (DM), IISc" }, { "name": "Vijay Pawar, Aditya", "affiliation": "Robotics Innovation Lab, Department of Design and Manufacturing (DM), IISc" }, { "name": "K, Kalaivanan", "affiliation": "Indian Institute of Science (IISc)" }, { "name": "S, Sushmitha", "affiliation": "Robotics Innovation Lab, Department of Design and Manufacturing (DM), IISc" }, { "name": "Susri B S, Tharun", "affiliation": "Robotics Innovation Lab, Department of Design and Manufacturing (DM), IISc" }, { "name": "RoyChowdhury, Abhra", "affiliation": "Indian Institute of Science Bangalore" } ], "keywords": [ "Task and motion planning", "Autonomous navigation", "Robotic learning and adaptation" ], "abstract": "Bio inspired metaheuristic algorithms are optimization methods that mimic natural phenomena, biological evolution to solve complex problems. This paper proposes a hybrid navigation framework combining Teaching-Learning-Based optimization(TLBO) algorithm for Bézier curve path planning and Geometric Model Predictive Control (GMPC) for trajectory tracking in a dynamic environment, implemented on a skid-steered mobile robot. Experimental validation across 45 trials with varying obstacle configurations and human interaction scenarios demonstrates framework accuracy of 79.8%±2.1% in simulation and 70.7%±21.2% accuracy in real-time experiment with significant performance observed in dynamic human interaction scenarios.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "A Full-State Constrained Real-Time Trajectory Planning Framework for Underactuated Overhead Cranes", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Xinghai, Xing", "affiliation": "Nankai University" }, { "name": "Lu, Biao", "affiliation": "Nankai University, Tianjin, China" }, { "name": "Zhi, Jiayi", "affiliation": "Nankai University" }, { "name": "Fang, Yongchun", "affiliation": "Nankai Univ" }, { "name": "Yang, Yan", "affiliation": "Xuzhou Heavy Machinery Co., Ltd" }, { "name": "Ding, Weili", "affiliation": "Yanshan University" } ], "keywords": [ "Task and motion planning", "Mechatronic system modeling, design, optimization", "High-performance motion control systems" ], "abstract": "Underactuated overhead cranes present significant challenges in trajectory planning due to their complex nonlinear dynamics, coupling effects between actuated and unactuated states, and the necessity of real-time feasibility. To bridge the gap between theoretical research and industrial application, this paper proposes a full-state constrained trajectory planning framework that ensures dynamic feasibility while maintaining real-time computational performance. The proposed method explicitly incorporates system dynamics and full-state constraints into the optimization process, enabling simultaneous regulation of both actuated and unactuated variables. A partial model simplification strategy is introduced to accelerate computation without sacrificing dynamic consistency, allowing real-time online trajectory generation. The framework also demonstrates robustness against modeling uncertainties and effectively balances multiple objectives, including obstacle avoidance, motion smoothness, and time efficiency. Extensive simulations and experimental validations on overhead crane systems verify the framework’s effectiveness, achieving dynamically feasible and smooth trajectories with precise control of unactuated variables under complex operating conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Coverage-Aware Viewpoint Refinement for Robotic Visual Inspection", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Staderini, Vanessa", "affiliation": "AIT Austrian Institute of Technology GmbH" }, { "name": "Alibekov, Ulugbek", "affiliation": "AIT Austrian Institute of Technology GmbH" }, { "name": "Glück, Tobias", "affiliation": "Austrian Institute of Technology" }, { "name": "Kugi, Andreas", "affiliation": "TU Wien" } ], "keywords": [ "Task and motion planning", "Robot perception and sensing", "Mechatronic system modeling, design, optimization" ], "abstract": "Automatic visual quality inspection is a critical application in modern manufacturing, leveraging robotics and computer vision to improve efficiency and precision. Previous methodologies often address the inspection challenge from a singular perspective of robotics or computer vision, which constrains the performance and generalization of the inspection performance. This work presents a new framework focused on refining the inspection pose (viewpoints) candidates to improve the overall coverage. This process integrates the sensor model, environment constraints for collision avoidance, the kinematics of the robotic system, and the model of the inspected object. The final inspection plan is computed by adopting a path planner to derive a collision-free trajectory and visit the viewpoints in the order obtained by solving the Traveling Salesman Problem. Our framework is extensively evaluated in simulation and compared to the state of the art, demonstrating superior performance in achieving extensive coverage. Real-world experiments are conducted to prove the effectiveness of our methods. In both cases, results are presented for different objects and two robotic setups: (i) a robot with 6-dof and (ii) a robotic system with 7-dof.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "NMPC-Based Motion Planning with Adaptive Weighting for Dynamic Object Interception", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Cai, Chen", "affiliation": "University of Kaiserslautern" }, { "name": "Kohli, Saksham", "affiliation": "University of Kaiserslautern-Landau" }, { "name": "Liu, Steven", "affiliation": "University of Kaiserslautern Landau" } ], "keywords": [ "Task and motion planning", "Robotic grasping and manipulation" ], "abstract": "This paper presents a nonlinear Model Predictive Control (MPC) planner for dynamic object interception using cooperative manipulator systems under closed-chain constraints. We introduce an Adaptive-Terminal (AT) formulation that employs cost shaping to mitigate actuator power violations common in Primitive-Terminal (PT) approaches. Experimental validation on a physical platform demonstrates superior motion quality and robustness compared to the PT baseline. Crucially, the system exhibits excellent real-time performance, achieving an average computation time of 19ms -- less than half the 40 ms sampling interval. This establishes the framework's suitability for agile, safety-critical cooperative tasks.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Bridging Discrete Planning and Continuous Execution for Redundant Robot Manipulators", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Yan, Teng", "affiliation": "The Hong Kong University of Science and Technology" }, { "name": "Yu, Yue", "affiliation": "The Hong Kong University of Science and Technology(GUANGZHOU)" }, { "name": "Liu, Yihan", "affiliation": "The Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Zhong, Bingzhuo", "affiliation": "Hong Kong University of Science and Technology (Guangzhou)" } ], "keywords": [ "Task and motion planning", "Robotic grasping and manipulation", "AI-powered robotics" ], "abstract": "Voxel-grid reinforcement learning is commonly used for path planning in redundant manipulators due to its simplicity and reproducibility. However, direct execution through point-wise numerical inverse kinematics on 7-DoF arms often yields step-size jitter, abrupt joint transitions, and instability near singular configurations. This work proposes an offline bridging framework that enables smooth continuous execution without modifying the core discrete planning architecture. On the planning side, step-normalized 26-neighbour Cartesian actions with geometric tie-breaking reduce unnecessary turns and oscillations. On the execution side, a task-priority damped least-squares (TP-DLS) inverse kinematics (IK) solver ensures stable tracking through null-space posture regulation and joint centering under trust-region and velocity constraints. Experiments on a 7-DoF manipulator show that this bridge improves planning success in dense scenes from 0.58 to 1.00, shortens representative path length from 1.53 m to 1.10 m, and reduces peak joint accelerations by over an order of magnitude while maintaining sub-millimeter end-effector accuracy. These results demonstrate that discretely planned RL paths can be made reliably executable through principled integration with established IK techniques.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Parametric Identification of Linear Time-Periodic Systems in Observable Canonical Form", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Roshan Nahad, Aylar", "affiliation": "Middle East Technical University" }, { "name": "Ankarali, Mustafa Mert", "affiliation": "Middle East Technical University (METU)" } ], "keywords": [ "Time/parameter varying system identification", "Linear system identification" ], "abstract": "This paper introduces a non-iterative parametric identification algorithm for linear time-periodic (LTP) systems. The proposed method reduces the identification task to solving a set of linear equations and yields a state-space representation in the observable canonical form. This frequency-domain approach leverages periodic input test signals and enables model complexity reduction through truncation of the harmonic transfer functions. The resulting approach provides an efficient and structured framework for modeling LTP systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Recursive Identification of EIV-ARX Models for Time Varying SISO Processes", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Das, Deepanjhan", "affiliation": "Indian Institute of Technology Madras, India" }, { "name": "Narasimhan, Shankar", "affiliation": "Indian Institute of Technology, Madras, INDIA" } ], "keywords": [ "Time/parameter varying system identification", "Linear system identification" ], "abstract": "This paper proposes a recursive algorithm, rARX-DIPCA, for identifying errors-in-variables autoregressive models with exogenous input (EIV-ARX), for tracking time-varying SISO processes. Building on a recently developed recursive iterative PCA method, the proposed algorithm recursively updates model parameters and noise variances as new measurements arrive, without storing historical data beyond a specified lag window. The method enables real-time adaptation to sensor degradation, and changes in model coefficients. The algorithm simultaneously identifies process order, time delay, and noise variances while maintaining computational efficiency through online covariance updates. Simulation studies on benchmark systems demonstrate effective tracking performance and practical applicability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "A Unified Framework for Identifying Floquet-Equivalent Models of Linear Discrete-Time Periodic Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Yilmaz, Onurcan", "affiliation": "Hacettepe University" }, { "name": "Sarıtaş, Serkan", "affiliation": "Middle East Technical University" }, { "name": "Ankarali, Mustafa Mert", "affiliation": "Middle East Technical University (METU)" }, { "name": "Uyanik, Ismail", "affiliation": "Hacettepe University" } ], "keywords": [ "Time/parameter varying system identification", "Linear system identification", "Data-driven control theory" ], "abstract": "This paper presents a data-driven framework for identifying linear discrete-time periodic (LDTP) systems and extracting their Floquet-equivalent models. Identification of LDTP systems is challenging due to periodically varying state-transition matrices, while Floquet reduction requires numerically sensitive matrix-root computations of the monodromy matrix. The proposed approach integrates an optimization-based estimator with a numerically robust Schur–Pad´e procedure for computing the principal P-th matrix root of the monodromy. A Monte Carlo study on randomly generated stable systems examines how system order, period length, and monodromy conditioning affect both identification accuracy and Floquet feasibility. The resulting workflow provides a reliable and systematic route for recovering periodic dynamics and their Floquet structure using only input–output data", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Efficient Learning of Affine and Rational Dependency LPV Models with Linear Fractional Representation", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Drenth, Roel", "affiliation": "Eindhoven University of Technology" }, { "name": "Hoekstra, Jan H.", "affiliation": "Eindhoven University of Technology" }, { "name": "Schoukens, Maarten", "affiliation": "Eindhoven University of Technology" }, { "name": "Tóth, Roland", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Time/parameter varying system identification", "Nonlinear system identification", "Machine and deep learning for system identification" ], "abstract": "Identifying control-friendly models of nonlinear systems remains one of the major challenges at the intersection of system identification and control. The Linear Parameter-Varying (LPV) framework offers a promising solution, but existing identification methods often rely on model structures with affine scheduling dependency. Instead, this work proposes the use of LPV models with Linear Fractional Representation (LFR) admitting a rational scheduling-dependency, capable of modelling complex nonlinear systems with fewer scheduling variables compared to affine models. This work introduces a direct parameterization to ensure well-posedness of rational LPV-LFR models, which by joint-estimation of an LPV plant and scheduling map, using only input-output data, is capable of modelling complex nonlinear systems. Accuracy of the proposed approach is shown on two simulation examples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Design and Control of an Asymmetric-Torque Exoskeleton for Gait Rehabilitation in Hemiparetic Patients", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Cho, Kwonseung", "affiliation": "Gwangju Institute of Science and Technology" }, { "name": "Moon, Sunwoong", "affiliation": "GIST" }, { "name": "Cha, MyeongJu", "affiliation": "Gwangju Institute of Science and Technology" }, { "name": "Sung, Jiyoon", "affiliation": "Gwangju Institute of Science and Technology" }, { "name": "Kim, Kyunghwan", "affiliation": "NT Research Inc" }, { "name": "Hur, Pilwon", "affiliation": "Gwangju Institute of Science and Technology" } ], "keywords": [ "Wearable robotics", "Human-robot interaction", "Humanoid and legged robots" ], "abstract": "This work introduces RoboWear21, an asymmetric lower-limb exoskeleton developed to accommodate the differing mechanical demands of paretic and non-paretic limbs. The device integrates side-specific actuators, passive hip DOFs, and a hierarchical controller combining gravity compensation, disturbance observer, and gait-phase-dependent torque generation. Gait state is estimated through an IMU-based swing detection scheme and an adaptive oscillator that maps hip motion to a continuous phase variable. Bench and user tests with three healthy participants demonstrated joint tracking RMSE up to 2.177°, phase estimation with an overall RMSE of 1.191 ± 0.894% ( R 2 = 0.997 ± 0.002), and gravity-compensation deviations within 0.022°, suggesting the system's suitability for individualized assistance in hemiparetic gait rehabilitation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Thigh-Angle–Only Gait Phase Recognition Via LSTM for Normal and Asymmetric Walking", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Koo, Seonmin", "affiliation": "Sangmyung University" }, { "name": "Jo, Jung-Hee", "affiliation": "Sangmyung University" }, { "name": "Choi, Hyunjin", "affiliation": "Sangmyung University" } ], "keywords": [ "Wearable robotics", "Medical and rehabilitation robotics", "Human-robot interaction" ], "abstract": "Hip-assistive wearable robots are lightweight, portable, and easy to use, but they typically lack foot-mounted sensors, making accurate identification of gait events particularly challenging in asymmetric or pathological gait. Existing approaches have either relied on additional shoe sensors or have been validated only on healthy users, limiting their applicability in sensor-minimal configurations and abnormal walking conditions. This study proposes an LSTM–based stance and swing state recognition framework using only absolute thigh angle signals obtained from a hip-assistive wearable robot. In the implemented bilateral configuration, left and right thigh-angle sequences are processed by limb-specific LSTM encoders and fused to predict stance and swing states for both limbs. Experiments on normal walking and hemiplegic-like asymmetric gait achieved approximately 87% accuracy without using foot sensors as model inputs. The full estimation pipeline was further implemented in a pseudo-online and real-time setting, demonstrating its feasibility for embedded execution on wearable robots.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Tracking Control for Fixed-Wing AAVs under Multiple Constraints: A Differential Flatness-Based Approach", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zheng, Jiayi", "affiliation": "National University of Defense Technology" }, { "name": "Zhao, Shulong", "affiliation": "National University of Defense Technology" }, { "name": "Wang, Xiangke", "affiliation": "National University of Defense Technology" } ], "keywords": [ "Application of nonlinear analysis and design", "Control barrier functions and state space constraints", "Nonlinear model reduction" ], "abstract": "In this paper, we investigate the problem of differential flatness-based two-layer control strategy for fixed-wing autonomous aerial vehicles (AAVs). Firstly, the dynamics of fixed-wing AAVs is transformed through differential flatness, where all states and inputs are denoted as the functions of flat outputs and their derivatives. Based on this transformation, the multiple constraints existed in practical flights can be unified to the constraints on flat outputs. This ensures that the inherent connections among constraints are fully regarded, and the propagation of constraints occurred in dynamics of fixed-wing AAVs is resolved. Then, we design a two-layer control strategy, consisting of control commands (accelerations) and actual controllers (thrust and control surfaces). It balances the stability and practical feasibility for fixed-wing AAVs. Finally, a simulation is conducted to verify the effectiveness of the proposed method in an obstacle environment.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Perception-Limited Smooth Safety Filtering", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Smaili, Lyes", "affiliation": "Université Du Québec En Outaouais" }, { "name": "Berkane, Soulaimane", "affiliation": "Université Du Québec En Outaouais" } ], "keywords": [ "Application of nonlinear analysis and design", "Optimization-based estimation and control" ], "abstract": "This paper develops a smooth safety-filtering framework for nonlinear control-affine systems under limited perception. Classical Control Barrier Function (CBF) filters assume global availability of the safety function---its value and gradient must be known everywhere---an assumption incompatible with sensing-limited settings, and the resulting filters often exhibit nonsmooth switching when constraints activate. We propose two complementary perception-aware safety filters applicable to general control-invariant safety sets. The first introduces a smooth perception gate that modulates barrier constraints based on sensing range, yielding a closed-form Lipschitz-safe controller with forward-invariance guarantees. The second replaces the hard CBF constraint with a differentiable penalty term, leading to a smooth unconstrained optimization-based safety filter consistent with CBF principles. For both designs, we establish existence, uniqueness, and forward invariance of the closed-loop trajectories. Numerical results demonstrate that the proposed smooth filters enable the synthesis of higher-order tracking controllers for systems such as drones and second-order ground robots, offering substantially smoother and more robust safety-critical behaviors than classical CBF-based filters.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Set-Relaxed Disturbance-Resistant High-Order Control Barrier Functions with Reduced Conservativeness", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhang, Tianyu", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Xu, Jun", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Ma, Jie", "affiliation": "Harbin Institute of Technology" }, { "name": "Li, Jiangang", "affiliation": "Harbin Institute of Technology Shenzhen Graduate School" } ], "keywords": [ "Control barrier functions and state space constraints" ], "abstract": "This paper proposes a set-relaxed disturbance-resistant high-order control barrier function (SRDR-HOCBF) frameworks to address limitations in existing robust CBF methods under parameter uncertainties and external disturbances. The framework employs a recursive virtual constraint relaxation mechanism to systematically enlarge the forward invariant set, and theoretical proofs establish the forward invariance under bounded uncertainties and disturbances. Comparative simulations on a horizontal pendulum and a mobile navigation system validate its superiority in safety maintenance over traditional HOCBF. And it is particularly effective in reducing initial state requirements, outperforming other methods under broader initial conditions when integrated with control strategies.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Reciprocal-Compensated Control Barrier Function against Parametric Uncertainties (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Wang, Xinyang", "affiliation": "Harbin Institute of Technology" }, { "name": "Xiao, Wei", "affiliation": "MIT, Boston University" }, { "name": "Zhang, Hongwei", "affiliation": "Harbin Institute of Technology, Shenzhen" } ], "keywords": [ "Control barrier functions and state space constraints", "Adaptive control design" ], "abstract": "Control barrier functions (CBFs) have proven effective in guaranteeing the safety of control systems; however, accurate system model is usually required for CBF-based controller design, which is generally difficult to obtain in practice. While uncertainty estimation and compensation can enhance robustness of CBFs, existing methods typically need the bounds of uncertain term to reject residual estimation error. This paper considers a more complex scenario where the system is subject to completely unknown parametric uncertainties, including both measurement errors and parametric deviations. Such compound uncertainty poses significant challenge for existing CBF approaches, which require the bounds of both measurement error and the parameter deviation to guarantee safety. To overcome this limitation, we propose a novel class of CBFs, called the reciprocal-compensated uncertainty-aware CBF, to enforce robust safety against uncertainties without requiring any prior knowledge of these uncertainties. A simulation example demonstrates the effectiveness of our approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Aircraft Trajectory Management Based on Integral Control Barrier Functions: A Static Obstacle Avoidance Case Study", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Dan, Hayato", "affiliation": "Institute of Science Tokyo" }, { "name": "Kurabayashi, Daisuke", "affiliation": "Tokyo Institute of Technology" } ], "keywords": [ "Control barrier functions and state space constraints", "Application of nonlinear analysis and design" ], "abstract": "This paper proposes an integral control barrier function (I-CBF)-based safety augmentation method for aircraft trajectory management with static obstacle avoidance. We consider a point-mass model of a cruising aircraft in which thrust, bank angle, and flight-path angle are inputs, while a waypoint-based guidance law and low-level proportional controllers define input dynamics. To handle the position-based safety constraint within the I-CBF framework, we define a barrier function as the minimum safety margin to the obstacle over a short-horizon predicted trajectory. The required gradients with respect to the current state and input are computed by integrating sensitivity equations along the prediction. This yields a linear constraint on the auxiliary input and a small quadratic programming, which can be incorporated into the I-CBF framework. Simulation using a Boeing 787-8 model shows that the proposed safety augmentation keeps the aircraft away from the static obstacle with only small deviations from the nominal waypoint-tracking path.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Safety Critical Control for Nonlinear Affine Systems with Unknown Disturbances and Input Constraints", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Chai, Haoyu", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Chen, Yong", "affiliation": "Uestc" }, { "name": "Lotfy Haridy, Ahmed", "affiliation": "School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China And" }, { "name": "Ali, Tofik Seid", "affiliation": "University of Electronic Science and Technology of China" } ], "keywords": [ "Control barrier functions and state space constraints", "Application of nonlinear analysis and design", "Optimal control theory" ], "abstract": "For nonlinear affine systems affected by unknown disturbances and input constraints, this study develops a safety critical control method based on higher-order control barrier functions(HoCBF). Firstly, to suppress the persistent impact of unknown disturbances on the safety constraint performance, a disturbance observer-based tunable input-to-state-safe HoCBF is designed, further reducing the conservatism of the safety constraints. Secondly, a time-varying function is incorporated into the construction of the HoCBF to address input constraints in safety critical control. By designing an auxiliary dynamic system to dynamically adjust the safety set, the conflict between input saturation and safety constraints is mitigated, effectively preventing infeasible solutions in quadratic programming problems under multiple constraints. Finally, the effectiveness and superiority of the suggested framework are validated via experiments on unmanned ground vehicles cooperative obstacle avoidance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Analysis of Feasibility Margin As a Control Barrier Function under Input Constraints", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Xu, Shuo", "affiliation": "Peking University" }, { "name": "Gong, Zhengning", "affiliation": "Peking University" }, { "name": "Lin, Yicheng", "affiliation": "Peking University" }, { "name": "Sun, Zhiyong", "affiliation": "Peking University (PKU)" } ], "keywords": [ "Control barrier functions and state space constraints", "Controller constraints and structure", "Application of nonlinear analysis and design" ], "abstract": "Quadratic Programs (QP) subject to Control Barrier Function (CBF)-based constraints are widely employed to design safety-critical controllers. However, ensuring the feasibility of the QP under input constraints remains a significant challenge. In this work, we propose a feasibility-margin-based CBF as a proactive safety filter to guarantee the dynamic feasibility of CBF-QP with input constraints. We first characterize the feasibility margin using support functions defined by the geometry of input constraints. We then propose a novel safe control method that employs the feasibility margin as a Control Barrier Function (FMA-CBF) for safety-critical control systems subject to polytopic input constraints. Furthermore, we formulate a unified QP that enforces both the original safety constraints and the feasibility margin constraints to guarantee feasibility. The efficacy of the proposed method is validated through double-integrator systems and unicycle robots with obstacle avoidance tasks.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Obstacle Avoidance of a Unicycle Via First-Order Control Barrier Function and Adaptive Point Selection", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhao, Bangwei", "affiliation": "Xiamen University" }, { "name": "Guan, Jinting", "affiliation": "Xiamen University" }, { "name": "Qian, Yangyang", "affiliation": "Lingnan University" }, { "name": "Yu, Xiao", "affiliation": "Xiamen University" } ], "keywords": [ "Control barrier functions and state space constraints", "Controller constraints and structure", "Application of nonlinear analysis and design" ], "abstract": "This paper addresses the safe navigation problem for a unicycle-type mobile robot operating in obstacle-cluttered environments. Existing safe control approaches typically employ control barrier functions (CBFs) to formulate a quadratic programming (QP) problem that minimally modifies a given nominal control input to ensure safety. However, within this CBF-QP framework, the direct application of high-order or hybrid-order CBFs to unicycle-type robots remains limited in practicality. To overcome this limitation, we first analyze the relative position dynamics between the robot and obstacles and develop a novel safe control method using a first-order CBF. This formulation enables effective obstacle avoidance based directly on point cloud data from an onboard LiDAR. Furthermore, to alleviate the computational burden associated with processing dense point clouds, we propose an efficient point cloud filtering strategy that significantly reduces the number of CBF constraints in the QP without compromising safety. Finally, the efficacy of the proposed method is validated on the NVIDIA IsaacSim platform.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Control of Multi-Agent Systems with Input Constraints by Time-Varying Control Barrier Functions", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Chiang, Ming-Li", "affiliation": "National Taiwan University" }, { "name": "Chuang, Che-Jung", "affiliation": "National Taiwan University" } ], "keywords": [ "Control barrier functions and state space constraints", "Controller constraints and structure", "Optimization-based estimation and control" ], "abstract": "This paper considers trajectory tracking control and collision avoidance for linear multi-agent systems (MAS) with bounded input constraints based on the control barrier function (CBF) design. We identify the conflict between leader tracking performance and follower control freedom in input-constrained multi-agent systems. And then propose a uniformly time-varying CBF to cope with the state constraints. Moreover, the trade-off between the control freedom of the leader and follower agents is examined. Conservativeness about the satisfaction of the constraints is quantified as a condition on the selection of the function used for the controller design. Some simulations are provided to illustrate the effects of the virtual leader actuation on the swarm of the follower agents and to demonstrate the efficacy of our design.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Feasible-Set Reshaping for Constraint Qualification in Optimization-Based Control", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Wu, Si", "affiliation": "Northeastern University, China" }, { "name": "Liu, Tengfei", "affiliation": "Northeastern University" }, { "name": "Hong, Yiguang", "affiliation": "Chinese Academy of Sciences" }, { "name": "Jiang, Zhong-Ping", "affiliation": "Tandon School of Engineering, New York University" }, { "name": "Chai, Tianyou", "affiliation": "Northeastern Univ" } ], "keywords": [ "Control barrier functions and state space constraints", "Convex optimization" ], "abstract": "This paper presents a novel feasible-set reshaping technique to optimization-based control with ensured constraint qualification. In our problem setting, the feasible set of admissible control inputs depends on the real-time state of the plant, and the linear independence constraint qualification (LICQ) may not be satisfied in some regions of interest. By feasible-set reshaping, we project the constraints of the original feasible set onto an appropriately chosen constant matrix with its rows forming a positive span of the space of the optimization variable. It is proved that the reshaped feasible set is nonempty and satisfies LICQ, as long as the original feasible set is nonempty. The effectiveness of the proposed method is verified by constructing Lipschitz continuous quadratic-program-based controllers based on the reshaped feasible sets.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Computationally Efficient and Scalable Multi-Robot Collision Avoidance Via Control Barrier Proximal Dynamics", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ma, Ruijie", "affiliation": "Zhejiang University" }, { "name": "Zhao, Chengcheng", "affiliation": "Zhejiang University" } ], "keywords": [ "Control barrier functions and state space constraints", "Decentralized control" ], "abstract": "Control Barrier Function based Quadratic Programs (CBF-QPs) are widely used for collision avoidance in multi-robot systems, but their real-time implementation is limited by the computational cost of online optimization. Recently, Control Barrier Proximal Dynamics (CBPD) reformulates CBF-QPs as continuous-time dynamics and offers significant computational speedups. However, existing results are restricted to affine constraints and cannot handle the nonlinear quadratic constraints arising in collision avoidance. This paper proposes a Collision Avoidance-CBPD (CA-CBPD) framework. We establish strong contraction under a time-varying metric and prove that its tracking error with respect to the QP solution remains uniformly bounded. The maximum safety violation is explicitly quantified, enabling a robust compensation strategy with guaranteed safety. Numerical results show that CA-CBPD achieves over 200× speedup compared with CBF-QP while maintaining reliable collision avoidance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Robust Safety Design for Strict-Feedback Nonlinear Systems Via Observer-Based Linear Time Varying Feedback", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Imtiaz Ur, Rehman", "affiliation": "Laboratoire ImViA EA 7535, équipe VIBOT" }, { "name": "Labbadi, Moussa", "affiliation": "Bretagne INP" }, { "name": "Abadi, Amine", "affiliation": "Laboratoire ImViA EA 7535, équipe VIBOT" }, { "name": "Lew Yan Voon, Lew Fock Chong", "affiliation": "Laboratoire ImViA EA 7535, équipe VIBOT" } ], "keywords": [ "Control barrier functions and state space constraints", "Disturbance rejection and input-to-state stability", "Stability of nonlinear systems" ], "abstract": "This paper develops a robust safety-critical control method for nonlinear strictfeedback systems with mismatched disturbances. Using a state transformation and a linear time-varying disturbance observer, the system is converted into a form that enables safe control design. The approach ensures forward invariance of the safety set and also applies to disturbancefree systems. Safety is proven for all cases, and a numerical example illustrates the results.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Safe Model-Based Reinforcement Learning Via Model Predictive Control and Control Barrier Functions", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Dzhumageldyev, Kerim", "affiliation": "Delft University of Technology" }, { "name": "Airaldi, Filippo", "affiliation": "Delft University of Technology" }, { "name": "Dabiri, Azita", "affiliation": "Delft University of Technology" } ], "keywords": [ "Control barrier functions and state space constraints", "Model predictive control", "Optimal control theory" ], "abstract": "Optimal control strategies are often combined with safety certificates to ensure both performance and safety in safety-critical systems. A prominent example is combining Model Predictive Control (MPC) with Control Barrier Functions (CBF). Yet, efficient tuning of MPC parameters and choosing an appropriate class Kappa function in the CBF is challenging and problem dependent. This paper introduces a safe model-based Reinforcement Learning (RL) framework where a parametric MPC controller incorporates a CBF constraint with a parameterized class Kappa function and serves as a function approximator to learn improved safe control policies from data. Three variations of the framework are introduced, distinguished by the way the optimization problem is formulated and the class Kappa function is parameterized, including neural architectures. Numerical experiments on a discrete double-integrator with static and dynamic obstacles demonstrate that the proposed methods improve performance while ensuring safety.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Efficient Verification of Neural Control Barrier Functions with Smooth Nonlinear Activations (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhang, Jun", "affiliation": "Shanghai University" }, { "name": "Zhang, Haibo", "affiliation": "Beijing Institute of Control Engineering" }, { "name": "Liu, Chun", "affiliation": "Shanghai University" }, { "name": "Wang, Xiaofan", "affiliation": "Shanghai University" }, { "name": "Xu, Liang", "affiliation": "Shanghai University" } ], "keywords": [ "Control barrier functions and state space constraints", "Model validation", "Learning methods for optimal control" ], "abstract": "Formal verification of neural control barrier functions (NCBFs) remains challenging, especially for neural networks with nonlinear activations like tanh. Existing CROWN- based methods rely on conservative linear relaxations for Jacobian bounds, limiting scal- ability. We propose LightCROWN, which computes tighter Jacobian bounds by exploit- ing the analytical properties of activation functions. Experiments on nonlinear control sys- tems including the inverted pendulum, Dubins car, and planar quadrotor demonstrate that LightCROWN improves verification success rates up to 100%, while enhancing speed and scalability. Our approach provides a generalizable improvement for CROWN-based frameworks,enabling more efficient verification of complex NCBFs. The code can be found at github.com/ Autonomous-Systems-and-Control-Lab/verify-neural-CBF.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Safe Tracking Control of High Relative Degree Nonlinear Systems Using Gaussian Processes-Adapted High-Gain Observer and Control Lyapunov and Barrier Functions", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Xie, Mengxu", "affiliation": "Northeastern University" }, { "name": "Ma, Tong", "affiliation": "Northeastern University" } ], "keywords": [ "Control barrier functions and state space constraints", "Nonlinear observers and filters", "Output regulation and tracking" ], "abstract": "This paper presents an integrated safe-tracking control scheme for high-relative-degree nonlinear systems with uncertain dynamics and partial measurements. A Gaussian process (GPs) model and a high-gain observer jointly estimate the full state and learn unknown dynamics, with convergence of both estimation errors under suitable gain conditions. GP-based learning alleviates the need for large observer gains, mitigating peaking. Exponential control Lyapunov and barrier functions embedded in a one-step optimization-based controller with probabilistic guarantees enforce safety and tracking while prioritizing safety. Simulations show safe outputs, improved tracking, smoother inputs, and reduced observer gains versus GP-adapted with higher observer gains and observer-only approaches.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Disturbance Observer-Based Robust Control Barrier Functions", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Li, Jinlu", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Wang, Xinyang", "affiliation": "Harbin Institute of Technology" }, { "name": "Zhang, Hongwei", "affiliation": "Harbin Institute of Technology, Shenzhen" } ], "keywords": [ "Control barrier functions and state space constraints", "Observer design" ], "abstract": "Safety assurance for autonomous systems is challenged by unmatched disturbances, especially those with non-differentiable components like sensor noise. Existing methods are either incapable of dealing with such noise or are overly conservative. This paper proposes a novel disturbance observer-based disturbance rejection control barrier function framework for high-relative-degree safety constraints under composite disturbances. We integrate a disturbance observer with a robust disturbance rejection law to achieve less conservative performance while guaranteeing safety. Theoretical analysis and simulation study demonstrate that the proposed method guarantees safety under the unmatched composite disturbances, while outperforming a state-of-the-art robust approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Table-Based Iterative Synthesis of Control Barrier Functions Via Safety Capacity and Expected Safety Horizon Functions", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Duan, Yue", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Cao, Yuxiao", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Zeng, Xiangrui", "affiliation": "Huazhong University of Science and Technology" } ], "keywords": [ "Control barrier functions and state space constraints", "Optimization-based estimation and control", "Numerical methods for optimal control" ], "abstract": "Control barrier functions (CBFs) provide safety filters for constrained systems, but synthesizing a useful CBF can be difficult when the safe set is nonconvex or poorly represented by a prescribed function class. This paper develops a sampled-data, table-based CBF synthesis framework that uses finite-state prediction rather than a fixed analytic parametrization. The method evaluates each grid state through an instantaneous safety capacity, which measures the fraction of admissible inputs that are one-step safe, and an expected safety horizon, which accumulates this capacity along predicted sampled trajectories. The resulting update distinguishes states that are immediately feasible but have poor future recoverability from those with longer-term safety margins. Dubins car obstacle-avoidance simulations illustrate the construction of non-polynomial safe sets in cluttered environments and compare the result with a polynomial SOS-CBF baseline.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Neural Network-Based Co-Design of Output-Feedback Control Barrier Function and Observer with Input Constraints", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Jagabathula, Vaishnavi", "affiliation": "Indian Institute of Science, Bengaluru" }, { "name": "Basu, Ahan", "affiliation": "Indian Institute of Science" }, { "name": "Jagtap, Pushpak", "affiliation": "Indian Institute of Science" } ], "keywords": [ "Control barrier functions and state space constraints", "Output feedback nonlinear control" ], "abstract": "Control Barrier Functions (CBFs) provide a powerful framework for ensuring safety in dynamical systems. However, their application typically relies on full state information, which is often violated in real-world due to the availability of partial state information. In this work, we propose a neural network-based framework for the co design of a safety controller, observer, and CBF for partially observed continuous-time systems with input constraints. By formulating barrier conditions over an augmented state space, our approach ensures safety without requiring bounded estimation errors or handcrafted barrier functions. All components are jointly trained by formulating appropriate loss functions, and we introduce a validity condition to provide formal safety guarantees beyond the training data. Finally, we demonstrate the effectiveness of the proposed approach through several case studies.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Barrier Certificates for Uncertain Temporal Specifications", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Mamduhi, Mohammad H.", "affiliation": "University of Birmingham" }, { "name": "Soudjani, Sadegh", "affiliation": "Max Planck Institute for Software Systems" } ], "keywords": [ "Control barrier functions and state space constraints", "Uncertain systems", "Analytic design" ], "abstract": "This paper studies satisfying temporal logic specifications on stochastic dynamical systems, where the predicates evolve randomly over time. Such randomness may arise from uncertain environment models or external stochastic processes causing the sets associated with predicate satisfaction to vary in a non-deterministic manner. As a result, verifying whether a stochastic dynamical system satisfies a temporal specification depends also on the uncertainty in the predicates. We develop a certificate-based framework to bound the probability of satisfying temporal logic specifications with randomly evolving predicates. We first show that temporal logic specifications with stochastic predicates can be transformed to specifications with deterministic predicates on an augmented space which is extended to include the stochastic space of predicate’s uncertainty. We then utilize barrier certificates on an augmented space to provide tractable optimization-based conditions and to avoid the computational burden of dynamic programming. Focusing on linear dynamics and safety-type specifications, we derive analytical conditions under which barrier certificates guarantee bounds on the probability of violating the stochastic safety predicates. The approach is demonstrated on numerical case studies.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Approximation-Free Control Barrier Functions for Prescribed-Time Reach-Avoid of Unknown Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Sawarkar, Shubham", "affiliation": "Indian Institute of Science, Bengaluru" }, { "name": "Jagtap, Pushpak", "affiliation": "Indian Institute of Science" } ], "keywords": [ "Control barrier functions and state space constraints", "Uncertain systems", "Lyapunov methods" ], "abstract": "We study the prescribed-time reach-avoid (PT-RA) control problem for nonlinear systems with unknown dynamics operating in environments with moving obstacles. Unlike robust or learning-based Control Barrier Function (CBF) methods, the proposed framework re- quires neither online model learning nor uncertainty bound estimation. A CBF-based Quadratic Program (CBF-QP) is solved on a simple virtual system to generate a safe reference satisfying PT-RA conditions with respect to time-varying, tightened obstacle and goal sets. The true system is confined to a Virtual Confinement Zone (VCZ) around this reference using an approximation-free feedback law. This construction guarantees real-time safety and prescribed- time target reachability under unknown dynamics and dynamic constraints without explicit model identification or offline precomputation. Simulation results illustrate reliable dynamic obstacle avoidance and timely convergence to the target set.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Refined Barrier Conditions for Finite-Time Safety and Reach-Avoid Guarantees in Stochastic Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Xue, Bai", "affiliation": "Institute of Software" }, { "name": "Ong, Luke", "affiliation": "College of Computing and Data Science, Nanyang Technological University, Singapore" }, { "name": "Wagner, Dominik", "affiliation": "College of Computing and Data Science, Nanyang Technological University, Singapore" }, { "name": "Wang, Peixin", "affiliation": "Software Engineering Institute, East China Normal University, China" } ], "keywords": [ "Lyapunov methods" ], "abstract": "Providing finite-time probabilistic safety and reach-avoid guarantees is crucial for safety-critical stochastic systems. Existing barrier certificate methods often rely on a restrictive boundedness assumption for auxiliary functions, limiting their applicability. This paper presents refined barrier-like conditions that remove this assumption. Specifically, we establish conditions for deriving upper bounds on finite-time safety probabilities in discrete-time systems and lower bounds on finite-time reach-avoid probabilities in continuous-time systems. This key relaxation significantly expands the class of verifiable systems, especially those with unbounded state spaces, and facilitates the application of advanced optimization techniques, such as semi-definite programming with polynomial functions. The efficacy of our approach is validated through numerical examples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Forward-Invariant Control of Switched Systems (I)", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Long, Lijun", "affiliation": "State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang" }, { "name": "Huang, Chunxiao", "affiliation": "Northeastern University" }, { "name": "Chen, Zhiyong", "affiliation": "The University of Newcastle" } ], "keywords": [ "Nonlinear control of switched & hybrid systems" ], "abstract": "This paper investigates forward-invariant control of switched systems, allowing different subsystems to possess different safe sets. By analyzing the influence of subsystem dynamics and switching signals on the forward invariance of the safe set, a relaxed safety condition for individual subsystems is proposed. This condition requires the sub-tangential condition to hold only on a subregion of the safe set, rather than on the entire set. Consequently, individual subsystems may be unsafe, while overall system safety is achieved through switching control. Based on these relaxed safety conditions, an extended Nagumo’s theorem is established within a switched-systems framework. A clear and intuitive proof is provided for the practical safe sets commonly used in engineering, without relying on nontrivial tools from topology or functional analysis. In a special case, a necessary and sufficient condition is provided for the forward invariance of the safe set under arbitrary switchings. Finally, a compass-like biped walking robot example is used to demonstrate the effectiveness of the proposed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Prescription for Bounding Inputs in Krasovskii Passivity Based Control", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Kawano, Yu", "affiliation": "Hiroshima University" }, { "name": "Namba, Takumi", "affiliation": "Ritsumeikan University" }, { "name": "Cucuzzella, Michele", "affiliation": "University of Groningen" } ], "keywords": [ "Passivity-based control", "Controller constraints and structure" ], "abstract": "Krasovskii passivity is a passivity property defined by selecting the time derivative of the input as the input port variable. Because of this structure, Krasovskii passivity naturally yields integral controllers which are Krasovskii passive. In this paper, we show that such integral control schemes can easily be adapted to handle input bound constraints. Our approach consists of passing the inputs through activation-like functions and modifying the controllers so as to preserve their Krasovskii passivity. We apply the proposed tailoring method to stabilization, output consensus, and input consensus problems. For consensus controllers, we additionally demonstrate how slope constraints on the inputs can be enforced.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Interconnection and Damping Assignment Passivity-Based Control Using Sparse Neural ODEs", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Botteghi, Nicolo", "affiliation": "Politecnico Di Milano" }, { "name": "Brook, Owen", "affiliation": "Imperial College London" }, { "name": "Fasel, Urban", "affiliation": "Imperial College London" }, { "name": "Califano, Federico", "affiliation": "University of Twente" } ], "keywords": [ "Passivity-based control", "Learning methods for optimal control" ], "abstract": "Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) is a nonlinear control technique that assigns a port-Hamiltonian (pH) structure to a controlled system using a state-feedback law. While IDA-PBC has been extensively studied and applied to many systems, its practical implementation often remains confined to academic examples and, almost exclusively, to stabilization tasks. The main limitation of IDA-PBC stems from the complexity of analytically solving a set of partial differential equations (PDEs), referred to as the matching conditions, which enforce the pH structure of the closed-loop system. However, this is extremely challenging, especially for complex physical systems and tasks. In this work, we propose a novel numerical approach for designing IDA-PBC controllers without solving the matching PDEs exactly. We cast the IDA-PBC problem as the learning of a neural ordinary differential equation. In particular, we rely on sparse dictionary learning to parametrize the desired closed-loop system as a sparse linear combination of nonlinear state-dependent functions. Optimization of the controller parameters is achieved by solving a multi-objective optimization problem whose cost function is composed of a generic task-dependent cost and a matching condition-dependent cost. Our numerical results show that the proposed method enables (i) IDA-PBC to be applicable to complex tasks beyond stabilization, such as the discovery of periodic oscillatory behaviors, (ii) the derivation of closed-form expressions of the controlled system, including residual terms in case of approximate matching, and (iii) stability analysis of the learned controller.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Robust Torque Control for Hip Exoskeleton with Series Elastic Actuator: Integration of System Identification, Kalman Filtering and Sliding Mode Control", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Terreros, Ricardo", "affiliation": "University of São Paulo" }, { "name": "Adamu Marafa, Nasiru", "affiliation": "University of São Paulo" }, { "name": "Moreira, Melkzedekue", "affiliation": "Departament of Mechanical Engineering" }, { "name": "Moreno, Yecid", "affiliation": "University of São Paulo" }, { "name": "Terra, Marco Henrique", "affiliation": "Depto. Engenharia Elétrica - Escola De Engenharia De São Carlos" }, { "name": "Siqueira, Adriano A G", "affiliation": "Univ. of Sao Paulo" } ], "keywords": [ "Nonlinear observers and filters", "Application of nonlinear analysis and design", "Saturation and discontinuity" ], "abstract": "This paper presents the design, implementation and experimental validation of a robust torque control system for hip rehabilitation exoskeleton with series elastic actuator. The proposed approach integrates three fundamental stages: parametric identification comparing friction models, state estimation through Kalman filter with sensor fusion, and sliding mode control for torque tracking. The identification stage systematically compares viscous, Coulomb and Stribeck friction models using genetic algorithms, selecting the Coulomb model that achieves RMSE of 1.53 rad/s while maintaining parsimony. The Kalman filter fuses encoder position and motor velocity measurements, providing noise reduction exceeding 65% with RMSE of 0.94 rad/s. The sliding mode controller implements equivalent control based on the identified model combined with switching term for robustness, achieving torque tracking with RMSE of 0.0213 Nm and steady-state error less than 2%. Experimental validation on physical platform demonstrates the synergistic integration of precise estimation and robust control for rehabilitation robotics applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "An LMI Approach to Time-Synchronized Control for LTI Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Oyama, Keigo", "affiliation": "Chulalongkorn University" }, { "name": "Banjerdpongchai, David", "affiliation": "Chulalongkorn Univ" } ], "keywords": [ "Application of nonlinear analysis and design", "Optimal control theory", "Linear systems" ], "abstract": "Time-synchronized stability is analyzed for LTI systems using homogeneous control. This paper addresses a fundamental limitation of existing time-synchronized controllers, namely, the requirement that the number of inputs must match the number of synchronized states. Furthermore, our analysis shows that while the existing homogeneous controller satisfies the definition of time synchronization under a specific condition, it produces oscillatory behavior during transient response. Since such oscillations are undesirable for synchronization, we develop a novel LMI condition that explicitly avoids oscillatory behavior in the state trajectory. The effectiveness of the proposed design is demonstrated through numerical simulations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Observer-Based Event-Triggered Sliding Mode Control Using Quantization", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Shekhar, Sudhanshu", "affiliation": "Indian Institute of Science" }, { "name": "Kumari, Kiran", "affiliation": "Indian Institute of Science" } ], "keywords": [ "Quantized control and communication constraints", "Observer design", "Sliding mode control" ], "abstract": "This paper addresses the robust event-triggered control of a chain of integrators systems under quantization, where full state information is not available. A higher-order sliding mode observer is employed to observe the unmeasured states in finite time. Using these estimates, a time-varying threshold-based event-triggering mechanism is designed to reduce unnecessary communication of states. Subsequently, the state estimates are quantized, and an event-triggered sliding mode control is proposed employing the quantized observed states. A Lyapunov analysis is used to show that the state trajectories of the closed-loop system and sliding variable remain bounded for all time, which implies that the system does not escape in finite time. Furthermore, a lower bound on the time elapsed between two consecutive triggering instants is established to guarantee the avoidance of Zeno behavior. A numerical simulation of a 3rd-order chain of integrators is provided to validate the effectiveness of the theoretical results.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Data-Driven Gain Tuning for Sliding Mode Control with Time-Delay Estimation Applied to Robot Manipulators", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Lee, Jinwoong", "affiliation": "Sejong University" }, { "name": "Lee, Seok Young", "affiliation": "Sejong University" } ], "keywords": [ "Sliding mode control", "Data-driven robust control", "Adaptive control design" ], "abstract": "This paper proposes a data-driven gain tuning strategy for sliding mode control with time-delay estimation (TDE) applied to robot manipulators. To address TDE errors, the error dynamics are reformulated using a discrete-time partial-form dynamic linearization (PFDL) model. A tuning law is derived to adjust the gain online by minimizing a cost function based on the pseudo-partial derivative (PPD). Conventional adaptive schemes typically introduce a prescribed region to mitigate a chattering phenomenon, yet they merely increase the gain outside this region. In contrast, the proposed data-driven strategy dynamically regulates the gain based on PPD outside the region, while enforcing gain decay inside it. Simulations confirm improved tracking accuracy over existing method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Reinforcement Learning-Based Fixed-Time Compliant Tracking Control for Manipulators with Input Saturation", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Chang, Zejiang", "affiliation": "Dalian University of Technology" }, { "name": "Yao, Xiang-Yu", "affiliation": "China University of Geosciences" }, { "name": "Ren, Wei", "affiliation": "Dalian University of Technology" } ], "keywords": [ "Sliding mode control", "Robust controller synthesis", "Stability of nonlinear systems" ], "abstract": "This paper focuses on fixed-time compliant tracking control for manipulators under external disturbances, model uncertainties and input saturation. To address these challenges, a reinforcement learning-based fixed-time sliding mode (RL-FSM) impedance controller is proposed. A fixed-time non-singular fast terminal sliding mode (FNFTSM) surface is incorporated to guarantee robustness and accelerate convergence. Additionally, in the reinforcement learning (RL) framework, actor neural networks (ANNs) approximate the system uncertainties, and critic neural networks (CNNs) evaluate approximation performance by minimizing the proposed long-term cost. Finally, numerical experiments in a ROS–Gazebo environment are performed on an IIWA manipulator to illustrate the effectiveness and superiority of the proposed controller.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Nonsingular Fixed-Time Sliding Mode Control with C1-Continuous Sliding Surface for Application in the Attitude Control of Tilt Trirotor UAV", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Rao, Shuncai", "affiliation": "National University of Defense Technology" }, { "name": "Wang, Xiangke", "affiliation": "National University of Defense Technology" }, { "name": "Yu, Li", "affiliation": "National University of Defense Technology" }, { "name": "Yang, Yu", "affiliation": "National University of Defense Technology" }, { "name": "Bowen, Nie", "affiliation": "China Aerodynamics Research and Development Center" }, { "name": "Guang, He", "affiliation": "National University of Defense Technology" } ], "keywords": [ "Sliding mode control", "Stability of nonlinear systems", "Robust control applications" ], "abstract": "This article presents a nonsingular fixed-time sliding mode control with C1 continuous sliding surface for the attitude control of tilt trirotor unmanned aerial vehicles. First, a practical fixed-time sliding surface is designed to address the issue that C1 continuity is often ignored when applying fixed-time control to second-order systems. Subsequently, a nonsingular fixed-time sliding mode controller is constructed and the stability of the closed\u0002loop system is proven. Based on the optimized control structure, a systematic parameter tuning method is summarized to simplify the parameter tuning work, which is rarely analyzed in detail in the existing literature. Finally, simulation studies are conducted on the attitude control of tilt trirotor unmanned aerial vehicle. Compared with the other two controllers, the proposed controller has no jump discontinuity and demonstrates significant advantages in control accuracy and chattering suppression.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Robust Fixed-Time Nonsingular Terminal Sliding Mode Control", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Labbadi, Moussa", "affiliation": "Bretagne INP" }, { "name": "Moulay, Emmanuel", "affiliation": "Université De Poitiers" }, { "name": "Defoort, Michael", "affiliation": "University of Valenciennes" }, { "name": "Arteaga, Marco A.", "affiliation": "UNAM" } ], "keywords": [ "Sliding mode control", "Stability of nonlinear systems", "Robustness analysis" ], "abstract": "In this paper, it is proposed a fixed-time nonsingular terminal sliding mode control for a class of second-order nonlinear systems subject to perturbations. A novel continuous terminal sliding manifold is introduced to ensure robust fixed-time stabilization. It is shown that the proposed scheme guarantees fixed-time stability of the closed-loop system in spite of the presence of perturbations. The effectiveness of the proposed approach is validated through its application to attitude tracking control of a quadrotor.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Modified Global Finite-Time Quasi-Continuous Second-Order Robust Feedback Control", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ruderman, Michael", "affiliation": "University of Agder" }, { "name": "Efimov, Denis", "affiliation": "Inria" } ], "keywords": [ "Stability of nonlinear systems", "Analytic design", "Sliding mode control" ], "abstract": "A non-overshooting quasi-continuous sliding mode control with sub-optimal damping was recently introduced in Ruderman and Efimov (2025) for perturbed second-order systems. The present work proposes an essential modification of the nonlinear control law which (i) allows for a parameterizable control amplitude limitation in a large subset of the initial values, (ii) admits an entire state-space R 2 (that was not given in Ruderman and Efimov (2025)) for the finite-time control, and finally (iii) enables for the found analytic solution of the state trajectories in the unperturbed case. The latter allows also for an exact estimation of the finite convergence time, and open an avenue for other potentially interesting analysis of the control properties in the future. For a perturbed case, the solution-based and Lyapunov function-based approaches are developed to show the uniform global asymptotic stability. The proposed robustness and convergence analysis are accompanied by several illustrative numerical examples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Finite-Time Control for Simultaneous Regulation and Tracking of Nonholonomic Mobile Robots", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Mera, Manuel", "affiliation": "ESIME, Instituto Politecnico Nacional" }, { "name": "Ríos, Héctor", "affiliation": "SECIHTI - Instituto Tecnológico De La Laguna" }, { "name": "Ushirobira, Rosane", "affiliation": "Inria" }, { "name": "Efimov, Denis", "affiliation": "Inria" } ], "keywords": [ "Application of nonlinear analysis and design", "Output feedback nonlinear control", "Stability of nonlinear systems" ], "abstract": "This article presents a controller design that ensures finite-time convergence of the position and orientation of a non-holonomic mobile robot to any point or to any feasible, possibly non-smooth, trajectory in the state space, starting from almost any initial condition. The control design is based on previous results regarding finite-time convergence of the Heisenberg system, also known as Brockett's integrator. The design is based on the unit vector control, a well-known technique in the sliding mode control field. However, designing a sliding surface is not required. The finite-time performance of the controller is validated through numerical simulations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "A Comparison of Finite-Time Unicycle Mobile Robot Controllers Based on Different Changes of Coordinates", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Rodrigues de Lima, Danilo", "affiliation": "Inria Lille" }, { "name": "Ushirobira, Rosane", "affiliation": "Inria" }, { "name": "Mera, Manuel", "affiliation": "ESIME, Instituto Politecnico Nacional" }, { "name": "Ríos, Héctor", "affiliation": "SECIHTI - Instituto Tecnológico De La Laguna" }, { "name": "Efimov, Denis", "affiliation": "Inria" } ], "keywords": [ "Application of nonlinear analysis and design", "Output regulation and tracking", "Output feedback nonlinear control" ], "abstract": "In this paper, we compare the performance of three different control algorithms for the stabilization problem in unicycle mobile robots (UMRs). All three control algorithms successfully achieve stability and convergence to the origin within a finite time. These control strategies are based on transformations of the unicycle model into different canonical forms of non-holonomic integrators, specifically the Heisenberg system and the chained-form. Notably, two strategies utilize the symmetry of the transformed systems, while one design is purely Lyapunov-based and uses time separation. In addition, we discuss the effect of different coordinate transformations on the performance of these control algorithms.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Adaptive Filtering and Dual Compensation for Resilient Coverage Control against Coordinated Cyber Attacks", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Gao, Yun", "affiliation": "The Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Huang, Yanjing", "affiliation": "The Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Gao, Hao", "affiliation": "Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Wu, Kaishun", "affiliation": "HKUST(GZ)" }, { "name": "Ji, Yiding", "affiliation": "Hong Kong University of Science and Technology (Guangzhou)" } ], "keywords": [ "Cooperative nonlinear control", "Distributed nonlinear control", "Robust control applications" ], "abstract": "This paper studies resilient coverage control for multi-robot systems under coordinated cyber attacks (CCA). We propose an adaptive safety-belt mechanism that screens exchanged neighbor information for compromised updates using increment-based consistency constraints, together with a nonlinear attack observer that reconstructs adversarial perturbations from the residual between observed and predicted neighbor motions. Based on these estimates, we design a double-layer coverage controller for attack compensation, which corrects corrupted position vectors of the Voronoi computation at the state layer and mitigates residual attack-induced deviations at the control level. An input-to-state type practical stability bound is established for the coverage error of the closed-loop system, proving that the robots converge to a bounded neighborhood of the nominal centroidal configuration under persistent coordinated attacks. Extensive simulations further validate the resilience of the proposed framework compared to several baseline methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Switching Adaptive Feedforward Control for Uncertain Linear Multivariable Systems: Periodic Disturbance Rejection", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Gong, Yizhou", "affiliation": "ShanghaiTech University" }, { "name": "Zhao, Yuhang", "affiliation": "ShanghaiTech University" }, { "name": "Liu, Song", "affiliation": "ShanghaiTech University" }, { "name": "Yang, Guitao", "affiliation": "Loughborough University" }, { "name": "Wang, Yang", "affiliation": "Shanghaitech University" } ], "keywords": [ "Disturbance rejection and input-to-state stability", "Adaptive control design", "Linear systems" ], "abstract": "This paper proposes a switching‑based adaptive feedforward control (SW‑AFC) framework for uncertain linear square multivariable systems under a single‑harmonic disturbance of known frequency. The method is model‑free, requiring no explicit plant dynamics and assuming only internal stability with known bounds on the frequency‑response matrix elements. To address singularities in parameter matrix estimation, a distance‑based switching logic selects parameter candidates based on the performance of an auxiliary estimator. The MIMO extension uses a new certainty‑equivalent stabilizer and a compact parametric error model derived via the swapping lemma to ensure scalability. Global asymptotic convergence and uniform boundedness are established through Lyapunov analysis with validation by numerical simulations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Optimal Setpoint Selection for PMSMs with Current Ripple and Switching Frequency Constraints: A Controller-Aware Framework", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Tran, Trung", "affiliation": "The University of Michigan" }, { "name": "Do, Huu-Thinh", "affiliation": "University of Michigan" }, { "name": "Perks, Jordan", "affiliation": "University of Michigan" }, { "name": "Hofmann, Heath", "affiliation": "University of Michigan" }, { "name": "Sun, Jing", "affiliation": "Univ of Michigan" }, { "name": "Kolmanovsky, Ilya V.", "affiliation": "University of Michigan" } ], "keywords": [ "Nonlinear control of switched & hybrid systems", "Model predictive control", "Linear parameter-varying systems" ], "abstract": "Current setpoint selection for electric motors is often performed independently of the controller design, leading to suboptimal operation when controller-dependent metrics are taken into consideration. This work proposes a controller-aware setpoint selection framework that integrates controller performance into the setpoint computation process for a three-phase interior-mounted permanent magnet synchronous machine (IPMSM). To illustrate the framework, current ripple and switching frequency performance maps are obtained by evaluating a finite control set model predictive controller (FCS-MPC) at static reference currents sampled across the operating condition space. Using these closed-loop performance maps, setpoint selection is then formulated as a constrained optimization problem, minimizing the squared current magnitude subject to current and voltage limits, as well as allowable ripple and switching frequency constraints. Simulation results show notable improvements in current ripple and switching frequency compared to conventional maximum torque per ampere with field-weakening (MTPA-FW) strategy at low and low-to-medium speeds.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Signal Injection for Systems with Direct Feedthrough – Application to Water Content Estimation in Fuel Cells", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Fontaine, Anne-Flor", "affiliation": "IFP Energies Nouvelles" }, { "name": "Bresch-Pietri, Delphine", "affiliation": "Mines Paris -- PSL" }, { "name": "Lance, Gontran", "affiliation": "IFP Energies Nouvelles" }, { "name": "Cacciuttolo, Quentin", "affiliation": "IFP Energies Nouvelles" }, { "name": "Di Meglio, Florent", "affiliation": "Mines Paris PSL" }, { "name": "Martin, Philippe", "affiliation": "Mines ParisTech" } ], "keywords": [ "Nonlinear observers and filters" ], "abstract": "Proton exchange membrane fuel cells (PEMFCs) suffer from water-management issues that cause drying or flooding, reducing performance and durability. This paper extends signal-injection and demodulation techniques to nonlinear feedthrough systems, such as PEMFCs. By leveraging averaging theory, system decomposition into low and high frequency components, and demodulation techniques, otherwise inaccessible state and parameter information is extracted from system outputs. The approach is applied to a two-state PEMFC model to recover temperature, liquid water saturation in the cathode catalyst layer, and ohmic resistance. Numerical simulations confirm the accuracy of the proposed method and show that estimation precision improves with excitation frequency.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Synchronous Observer Design for Landmark-Inertial SLAM with Almost-Global Convergence", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Saha, Arkadeep", "affiliation": "Indian Institute of Technology Bombay" }, { "name": "van Goor, Pieter", "affiliation": "University of Sydney" }, { "name": "Franchi, Antonio", "affiliation": "University of Twente and Sapienza University of Rome" }, { "name": "Banavar, Ravi", "affiliation": "Indian Institute of Technology" } ], "keywords": [ "Nonlinear observers and filters", "Observer design" ], "abstract": "Landmark Inertial Simultaneous Localisation and Mapping (LI-SLAM) is the problem of estimating the locations of landmarks in the environment and the robot's pose relative to those landmarks using landmark position measurements and measurements from Inertial Measurement Unit (IMU). This paper proposes a nonlinear observer for LI-SLAM posed in continuous time and analyses the observer in a base space that encodes all the observable states of LI-SLAM. The local exponential stability and almost-global asymptotic stability of the error dynamics in base space is established in the proof section and validated using simulations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Haptic-Based Complementary Filter for Rigid Body Rotations", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Kumar, Amit", "affiliation": "Nanyang Technological University (NTU), Singapore" }, { "name": "Campolo, Domenico", "affiliation": "Nanyang Technological University (NTU) Singapore" }, { "name": "Banavar, Ravi", "affiliation": "Indian Institute of Technology" } ], "keywords": [ "Nonlinear observers and filters", "Observer design" ], "abstract": "The non-commutative nature of 3D rotations poses well-known challenges in generalizing planar problems to three-dimensional ones, even more so in contact-rich tasks where haptic information (i.e., forces/torques) is involved. In this sense, not all learning-based algorithms that are currently available generalize to 3D orientation estimation. Non-linear filters defined on the special orthogonal group, SO3, are widely used with inertial measurement sensors; however, none of them have been used with haptic measurements. This paper presents a unique complementary filtering framework that initially interprets the geometric shape of objects in the form of superquadrics, exploits the symmetry of SO3, and uses force and vision sensors as measurements to provide an estimate of orientation. The framework's robustness and almost global stability are substantiated by a set of numerical experiments on a dual-arm robotic setup.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Cascaded Tightly-Coupled Observer Design for Single-Range-Aided Inertial Navigation", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Sifour, Oussama", "affiliation": "University of Quebec in Outaouais" }, { "name": "Tayebi, Abdelhamid", "affiliation": "Lakehead University" }, { "name": "Berkane, Soulaimane", "affiliation": "Université Du Québec En Outaouais" } ], "keywords": [ "Nonlinear observers and filters", "Observer design" ], "abstract": "This work introduces a single-range-aided navigation observer that reconstructs the full state of a rigid body using only an Inertial Measurement Unit (IMU), a body-frame vector measurement (e.g., magnetometer), and a distance measurement from a fixed anchor point. The design first formulates an extended linear time-varying (LTV) system to estimate body-frame position, body-frame velocity, and the gravity direction. The recovered gravity direction, combined with the body-frame vector measurement, is then used to reconstruct the full orientation on SO(3), resulting in a cascaded observer architecture. Almost Global Asymptotic Stability (AGAS) of the cascaded design is established under a uniform observability condition, ensuring robustness to sensor noise and trajectory variations. Simulation studies on three-dimensional trajectories demonstrate accurate estimation of position, velocity, and orientation, highlighting single-range aiding as a lightweight and effective modality for autonomous navigation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Relative Pose-Velocity Estimation Using Dual IMU Measurements and Relative Position Sensing", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Melis, Alessandro", "affiliation": "CNRS Sophia Antipolis, Nice" }, { "name": "Bouazza, Tarek", "affiliation": "Laboratoire I3S UMR 7271 UCA-CNRS" }, { "name": "Berkane, Soulaimane", "affiliation": "Université Du Québec En Outaouais" }, { "name": "Hamel, Tarek", "affiliation": "Université Côte D'Azur" } ], "keywords": [ "Nonlinear observers and filters", "Observer design" ], "abstract": "This paper addresses the problem of estimating the relative pose (position and orientation) and velocity of a vehicle with respect to a moving target, where both are equipped with Inertial Measurement Units (IMUs), assuming the availability of relative position or bearing measurements. The body-target relative dynamics are formulated on SE2(3) and recast into a linear time-varying (LTV) model in the ambient space R15, on which a deterministic Riccati observer is designed. We analyze the uniform observability (UO) conditions required to guarantee global exponential convergence of the estimation error in the ambient space for both measurement cases. In the case of relative position measurements, UO requires only a persistence-of-excitation condition on the target acceleration, whereas for bearing measurements, additional conditions are required. Building on this, a nonlinear complementary filter on SO(3) is designed to provide a smooth estimate of the orientation component of the state with almost global asymptotic stability. Finally, simulation results are provided to validate the proposed solution.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "A Nonlinear Observer for Air-Velocity and Attitude Estimation Using Pitot and Barometric Measurements", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Nyoba Tchonkeu, Melone", "affiliation": "University of Quebec in Outaouais" }, { "name": "Berkane, Soulaimane", "affiliation": "Université Du Québec En Outaouais" }, { "name": "Hamel, Tarek", "affiliation": "Université Côte D'Azur" } ], "keywords": [ "Nonlinear observers and filters", "Stability of nonlinear systems", "Observer design" ], "abstract": "This paper addresses the problem of estimating air velocity and full attitude for unmanned aerial vehicles (UAVs) in GNSS-denied environments using minimal onboard sensing—an interesting and practically relevant challenge for UAV navigation. The contribution of the paper is twofold: (i) an observability analysis establishing the conditions for uniform observability (UO), which are useful for trajectory planning and motion control of the UAV; and (ii) the design of a nonlinear observer on SO(3)⋉R3×R that incorporates pitot-tube, barometric altitude, and magnetometer measurements as outputs, with IMU data used as inputs, within a unified framework. Simulation results are presented to confirm the convergence and robustness of the proposed design, including under minimally excited trajectories.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Combining IDA-PBC and Backstepping for Regulation and Trajectory Tracking", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhang, Le", "affiliation": "Technical University of Munich" }, { "name": "Kotyczka, Paul", "affiliation": "Technical University of Munich" } ], "keywords": [ "Passivity-based control", "Interconnected nonlinear systems", "Analytic design" ], "abstract": "Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) has gained success due to its physical intuition, but the difficulty of solving the matching PDE hinders its applicability. In this contribution, we present a control design approach that combines IDA-PBC with backstepping to reduce the matching PDE to be solved. This approach hints on the physically consistent interconnection and damping structure for the original IDA-PBC problem, can be extended to trajectory tracking, and is applicable to a variety of interconnected systems. Experiments on the magnetic levitation example demonstrate these advantages.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Lossless Optimal Transient Control for Rigid Bodies in 3D Space", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zanella, Riccardo", "affiliation": "University of Twente" }, { "name": "Califano, Federico", "affiliation": "University of Twente" }, { "name": "Franchi, Antonio", "affiliation": "University of Twente and Sapienza University of Rome" }, { "name": "Stramigioli, Stefano", "affiliation": "University of Twente" } ], "keywords": [ "Passivity-based control", "Stability of nonlinear systems", "Optimal control theory" ], "abstract": "In this work, we propose a control scheme for rigid bodies designed to optimise transient behaviors. The search space for the optimal control input is parameterized to yield a passive, specifically lossless, nonlinear feedback controller. As a result, it can be combined with other stabilizing controllers without compromising the stability of the closed-loop system. The controller commands torques generating fictitious gyroscopic effects characteristics of 3D rotational rigid body motions, and as such does not inject nor extract kinetic energy from the system. We validate the controller in simulation using a model predictive control (MPC) scheme, successfully combining stability and performance in a stabilization task with obstacle avoidance constraints.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Adaptive Fuzzy Echo State Network Control for Cyber-Physical Systems Subject to Replay Attacks", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Dong, Hanlin", "affiliation": "Southeast University" }, { "name": "Cao, Yang", "affiliation": "Southeast University" }, { "name": "Wei, Yiheng", "affiliation": "Southeast University" }, { "name": "Wu, Tao", "affiliation": "Yunnan University" } ], "keywords": [ "Stability of nonlinear systems", "Lyapunov methods", "Adaptive control design" ], "abstract": "This paper investigates adaptive tracking control for a class of uncertain nonlinear cyber-physical systems under replay attacks. A fuzzy echo state network is employed as a approximator to estimate unknown nonlinear dynamics, while a smooth tanh-based robust term is embedded in a backstepping controller to compensate approximation residuals and mitigate the impact of attacks. By constructing an appropriate Lyapunov function that incorporates both virtual tracking errors and FESN parameter adaptation, an explicit upper bound on the duration of each replay attack is derived under which all closed-loop signals remain bounded and the plant output asymptotically tracks the desired trajectory. Simulation results on a three-link cylindrical manipulator demonstrate that the proposed scheme effectively rejects multiple replay attacks, accelerates post-attack error convergence, and achieves accurate trajectory tracking.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Large Scale Complex Rotating Machinery System Compound Fault Diagnosis Method Based on Cross-Domain Feature Deep Reinforcement Learning", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Liu, Yan", "affiliation": "Zhejiang University" }, { "name": "Sha, Nuo", "affiliation": "Zhejiang University" }, { "name": "Shou, Yiyang", "affiliation": "Zhejiang University" }, { "name": "Xu, Zuhua", "affiliation": "Zhejiang University" }, { "name": "Zhao, Jun", "affiliation": "Zhejiang University" }, { "name": "Song, Chunyue", "affiliation": "Zhejiang University" } ], "keywords": [ "AI methods for FDI/FTC", "Data-driven methods for FDI/FTC", "Applications of FDI/FTC" ], "abstract": "Large scale complex rotating machinery system compound faults involve coupled multi-source signals in both temporal and frequency domains. However, the distribution gaps and the intrinsic correlations between these domains are rarely considered, causing suboptimal diagnostic performance. To cope with it, a cross-domain feature deep reinforcement learning-based compound fault diagnosis method is proposed for rotating machinery system, which aims to collaboratively learn the crucial fault-related information from the temporal and frequency domains. First, we develop two parallel domain-specific feature leaning networks and a cross-domain transfer network. Two domain-specific feature learning networks are utilized to excavate domain-specific feature from the temporal and frequency domains. The cross-domain transfer network uses the neighbor features to fuse and transfer domain-shared feature. Then, a multi-domain deep reinforcement learning-based training framework is designed, in which the cross-domain feature collaborative learning is formulated to an agent reward maximum problem, modeling as a Markov decision process. Finally, the compound fault diagnosis performance of the proposed method is demonstrated on two large scale complex rotating machinery system cases.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Dynamic Optimal-Transport Graph Neural Network for Industrial Process Fault Diagnosis", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Mao, Longying", "affiliation": "Huzhou Normal University" }, { "name": "Yang, Zeyu", "affiliation": "Huzhou Normal University" }, { "name": "Ye, Lingjian", "affiliation": "Huzhou Normal University" }, { "name": "Wang, Peiliang", "affiliation": "Huzhou Normal University" }, { "name": "Song, Zhihuan", "affiliation": "Zhejiang University" } ], "keywords": [ "AI methods for FDI/FTC", "Data-driven methods for FDI/FTC", "Fault detection and isolation methods" ], "abstract": "Fault diagnosis in industrial processes necessitates modeling the underlying physical propagation mechanisms, often conceptualized as a ``path-resistance\" dynamic. This paper proposes a Dynamic Optimal-Transport Graph Neural Network (DOTGNN) that explicitly models fault transportation. Our framework features three key innovations: a dynamic optimal-transport graph (DOTG) for inferring latent fault propagation paths; a Kolmogorov-Arnold network (KAN) for adaptive learning of complex process nonlinearities; and a feature transportation loss (FTL) that imposes metric constraints to enhance inter-class separability in the latent space. Extensive validation on the Tennessee Eastman process (TEP) demonstrates that DOTGNN achieves a superior fault diagnosis accuracy of 96.4%, significantly outperforming existing benchmarks. The proposed method offers a principled and interpretable solution for industrial process monitoring.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "A Semi-Supervised Fault Diagnosis Method for Industrial Systems Based on Graph Feature Extraction and Triple Attention Mechanism", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Qi, Yu", "affiliation": "Chongqing University" }, { "name": "Chai, Yi", "affiliation": "Chongqing University" }, { "name": "Zhu, Zheren", "affiliation": "Hangzhou Normal University" }, { "name": "Yao, Le", "affiliation": "Hangzhou Normal University" }, { "name": "Shen, Bingbing", "affiliation": "Zhejiang University" }, { "name": "Song, Zhihuan", "affiliation": "Zhejiang University" } ], "keywords": [ "AI methods for FDI/FTC", "Data-driven methods for FDI/FTC", "Process performance monitoring/statistical process control" ], "abstract": "Industrial fault diagnosis is vital for production safety and operation efficiency. To address labeled data scarcity and inaccurate feature extraction, we propose a semi-supervised Triple-Attention Graph-Structured GRAND (TAGGD), which realizes unified modeling of continuous data from static equipment and temporal vibration signals from rotating equipment via a general graph structure, strengthens fault feature identification with time-spatial-feature three-dimensional attention, and mines unlabeled data value while suppressing noise. Experiments on revised Tennessee Eastman (TE) and Case Western Reserve University (CWRU) datasets show our TAGGD significantly outperforms traditional methods in diagnostic accuracy, cross-scenario adaptability, and low labeling rate robustness, with favorable potential for industrial scenarios.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Health-Aware Fast Charging Using Homogenized Model with Heterogeneous Internal State Reconstruction", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Lodge, Alessio Alberto", "affiliation": "TNO" }, { "name": "Lombardo Pontillo, Alessio", "affiliation": "Politecnico Di Torino" }, { "name": "Hoekstra, Fsj", "affiliation": "TNO" }, { "name": "Medina, Robinson", "affiliation": "TNO" }, { "name": "Wilkins, Steven", "affiliation": "TNO Powetrains, Powertrains Department, P.O. Box 756, 5700 AT, Helmond" }, { "name": "Battiato, Ilenia", "affiliation": "Stanford University" } ], "keywords": [ "Control and management of energy systems", "Electric vehicles and charging stations", "Health aware control in processes" ], "abstract": "Fast charging of lithium-ion batteries is limited by lithium plating, which occurs when the anode potential drops below 0 V vs Li/Li+. Model-based control aims to maximize charging current while maintaining anode potentials above this threshold. In this work, a plating-free fast charging strategy is demonstrated using a Homogenized Model (HM) coupled with a classical PID controller. The HM, derived from homogenization theory applied to the Poisson-Nernst-Planck equations, retains the physics of the Doyle-Fuller-Newman model while capturing electrode microstructural heterogeneity in a one-dimensional double-continua formulation. By reconstructing three-dimensional distributions of electrochemical variables from precomputed closure variables, the HM enables non-invasive estimation of heterogeneous anode potentials, acting as a virtual sensor. Through MATLAB–COMSOL co-simulation, a PID controller regulates current to maintain the full 3D anode potential distribution above the plating limit, achieving model-based fast charging at a fraction of the computational cost of high-fidelity models. The results demonstrate the potential of HM-based control for safe, degradation-aware, and efficient fast charging of lithium-ion batteries.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Modeling and Analysis of a Wave Glider Incorporating Reverse Osmosis", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Tamajong, Michael Nkeh", "affiliation": "University of Maryland" }, { "name": "Cachon Delgado, Alvaro", "affiliation": "UCL" }, { "name": "Tasnim, Sara", "affiliation": "University of Maryland, College Park" }, { "name": "McGuire, Carson", "affiliation": "North Carolina State University" }, { "name": "Liu, Limeng", "affiliation": "University of Michigan" }, { "name": "Alam, Minhazul", "affiliation": "University of Michigan Ann Arbor" }, { "name": "Willcox, J. Scott", "affiliation": "Liquid Robotics, Inc" }, { "name": "Bryant, Matthew", "affiliation": "North Carolina State University" }, { "name": "Vermillion, Christopher", "affiliation": "University of Michigan" }, { "name": "Fathy, Hosam K.", "affiliation": "University of Maryland" } ], "keywords": [ "Control and management of energy systems", "Hydropower" ], "abstract": "This paper models the dynamics of a wave glider equipped with a reverse osmosis subsystem. The paper is motivated by the ability of wave gliders to harvest ocean wave energy, plus the possibility of utilizing the harvested energy for water desalination. Such mobile, anchorless desalination can be valuable to coastal communities, particularly in the aftermath of natural disasters. Existing work in the literature provides a rich portfolio of dynamic models of wave gliders without desalination. We extend these efforts by modeling the coupled dynamics of a wave glider integrated with a reverse osmosis power take-off system. Moreover, we focus on building a model simple enough to facilitate sensitivity, optimization, and control design efforts. An initial sensitivity study, utilizing this model, highlights the importance of tuning the stiffnesses of two different return springs in the integrated overall system to optimize both desalination rate and forward surge/travel velocity.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Multi-Domain Graph-Based Modeling of Energy Systems with Applications to Lithium-Ion Batteries", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Hemmat, Mahsa", "affiliation": "University of Minnesota" }, { "name": "Alleyne, Andrew G.", "affiliation": "University of Minnesota" } ], "keywords": [ "Control and management of energy systems", "Thermal systems modelling", "Energy storage systems" ], "abstract": "Graph-based models have been shown to provide a structured representation for complex multi-domain energy systems but face limitations when edge power flows depend on non-adjacent states or when a single edge carries multiple power-flow types driven by different inputs. This paper proposes two general extensions to address these limitations: a recursive state-to-input feedback scheme that embeds non-adjacent state dependencies into edge inputs without altering the graph structure, and a parallel edge decomposition method that represents composite interactions using sets of single-input edges while preserving energy conservation at the vertices. The extended framework is demonstrated on a lithium-ion battery module consisting of 36 parallel cells, and the resulting model predicts module temperatures with errors below 1°C. Validation on this electro-thermal battery system demonstrates the effectiveness of the extended framework for multi-domain systems that cannot be represented by previously established graph-based formulations, and indicates its potential for broader application to complex energy systems in control and design studies.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Hierarchical Control for Flexible Part-Load Operation of a Solar Absorption Chiller", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Garrido Satue, Manuel", "affiliation": "University of Seville" }, { "name": "Vargas, Manuel", "affiliation": "University of Seville" }, { "name": "Rubio, Francisco R.", "affiliation": "Universidad De Sevilla" }, { "name": "Ortega, M. G.", "affiliation": "Universidad De Sevilla" } ], "keywords": [ "Control and management of energy systems", "Thermal systems modelling", "Energy storage systems" ], "abstract": "Solar absorption chillers require tight control for flexible operation under variable cooling demand. This paper models and controls a solar-powered absorption chiller using a thermal energy storage unit. The core contribution is a hierarchical control strategy using nested loops to simultaneously regulate delivered cooling power and evaporator outlet temperature. This approach achieves continuous capacity modulation by adjusting the generator inlet temperature reference, overcoming the limitations of binary (on-off) control. Simulation confirms effective part-load operation. Additionally, the saturation of actuation signals provides reliable indicators for detecting operational limits (over-demand due to insufficient solar irradiance).", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "A Multi-Scale Mutual Information Decomposition Algorithm for Fault Root Cause Diagnosis", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Chen, Rui", "affiliation": "Tongji University" }, { "name": "Liang, Shu", "affiliation": "Tongji University, School of Electronics and Information Engineering" }, { "name": "Fan, Rui", "affiliation": "Tongji University" }, { "name": "Zhou, Yuanqiang", "affiliation": "Tongji University" }, { "name": "Gao, Furong", "affiliation": "Hong Kong Univ of Sci & Tech" }, { "name": "Chen, Hong", "affiliation": "Tongji University" } ], "keywords": [ "Data-driven methods for FDI/FTC", "Applications of FDI/FTC", "Reliability and safety in processes" ], "abstract": "The coexistence of redundant, synergistic, and unique (RSU) causalities among fault variables, combined with multi-scale fault propagation, poses significant challenges for accurate root cause inference. This paper proposes a root cause diagnosis method based on multi-scale mutual information (MI) decomposition, which extracts multi-scale dependencies and quantifies RSU causal contributions. Specifically, multivariate variational mode decomposition decomposes the original time series into multi-scale components. Multi-order specific MI is then computed using kernel density estimation and sorted in ascending order. Based on predefined rules, the specific MI is decomposed into RSU causal increments, with expectations evaluated across all target states. Finally, a surrogate-based significance test identifies significant RSU causal structures at multiple time scales. Experimental results from an injection molding process demonstrate that the proposed algorithm accurately identifies fault root cause and provides an interpretable approach for analyzing causal interactions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Securing SoC and SoH Estimation Blocks in BESS: A DRL-Based Framework for FDIA Generation and Detection", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Selim, Alaa", "affiliation": "School of Engineering and Information Technology, University of New South Wales" }, { "name": "Mo, Huadong", "affiliation": "University of New South Wales" }, { "name": "Pota, Hemanshu", "affiliation": "University of New South Wales" } ], "keywords": [ "Energy storage systems", "Cyberphysical security in processes" ], "abstract": "This paper presents a deep reinforcement learning (DRL) framework for systematically generating and analysing false data injection attacks (FDIAs) on state-of-charge (SoC) and state-of-health (SoH) estimation blocks in battery energy storage systems (BESS). An equivalent-circuit lithium-ion cell with a UKF-based SoC/SoH estimator is embedded in a reinforcement-learning environment, where a Proximal Policy Optimization (PPO) agent injects bounded perturbations into voltage and current measurements under realistic FDIA constraints. A constrained, reward-shaped formulation explicitly trades off SoC estimation error, SoH bias and attack energy, enabling the agent to learn structured, standards-compliant attack patterns rather than arbitrary noise. Numerical results in MATLAB/Simulink show that the learned FDIAs can induce large, persistent SoH deviations while keeping SoC trajectories and UKF residuals close to nominal behaviour, thereby remaining stealthy with respect to both moving-average residual monitors and Cumulative Sum (CUSUM) detectors tuned to standards-compliant noise levels. The proposed framework (i) identifies concrete regimes where conventional residual-based thresholds either miss DRL-crafted attacks or detect them only after substantial SoH drift, and (ii) provides a quantitative stress-test and a generator of realistic attack datasets to support the design and benchmarking of more robust data-driven cyber-attack detectors for BESS.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Short-Term Scheduling and Unit Commitment for a Pumped Storage Hydropower Plant with Many Variable-Speed Units", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Mena Rosell, Joan", "affiliation": "Politecnico Di Milano" }, { "name": "Casella, Francesco", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Energy storage systems", "Hydropower", "Control and management of energy systems" ], "abstract": "This work addresses the Short-Term Hydro Scheduling (STHS) and Hydro Unit Commitment (HUC) problems for a Pumped Storage Hydropower plant, exploiting the idea that many variable-speed generation units create a continuous region of operation where the overall efficiency of the plant is nearly constant and maximum. This allows to decompose the problem into a whole-plant STHS formulated as a NLP and a HUC formulated as a MILP. This strategy allows for explicit treatment of nonlinearities and other restrictions, including an innovative layer of operational decision-making by considering each unit's operating mode, without creating computationally intractable mixed-integer optimization problems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "How Modelling Dynamics Improves Fault Detection and Isolation for Gaussian LTI Systems: A Geometric Explanation", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Hu, Anbang", "affiliation": "University Duisburg-Essen" }, { "name": "Zhang, Ping", "affiliation": "University of Kaiserslautern-Landau" }, { "name": "Gao, Xinrui", "affiliation": "Technical University of Ilmenau" } ], "keywords": [ "Fault detection and isolation methods" ], "abstract": "This paper analyses the impact of introducing dynamic information on the performance of fault detection and isolation (FDI) in Gaussian linear time-invariant (LTI) systems. First of all, the FDI problem is formulated as hypothesis testing, where fault-free and faulty conditions are considered to be corresponding hypotheses. Then, Kullback–Leibler (KL) divergence is naturally derived to quantify the dissimilarity between different distributions associated with the hypotheses, i.e., dissimilarity between fault-free and different faulty conditions. By introducing the new concept of deemed-fault regions, it is geometrically shown how dynamic information reduces the overlap between the regions, thereby improving the correct isolation rate (CIR). This paper provides a theoretical analysis of the role of dynamic information in FDI problems. The theoretical results are validated by a simulated three-tank system.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "A General Framework for Design and Analysis of Optimal Fault Detection", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Gao, Xinrui", "affiliation": "Technical University of Ilmenau" }, { "name": "Shardt, Yuri A.W.", "affiliation": "Technical University of Ilmenau" }, { "name": "Gopaluni, Bhushan", "affiliation": "University of British Columbia" } ], "keywords": [ "Fault detection and isolation methods", "Advanced process control" ], "abstract": "Fault detection and isolation (FDI) have been extensively studied in control engineering and process monitoring, yet a unified theoretical framework connecting different approaches remains elusive. This paper presents a general framework for design and analysis of optimal fault detection (FD), which bridges paradigms that are traditionally separate. Starting from a measure-theoretic perspective, FD is formulated as a unified optimisation problem defined on general signal spaces that encompasses both stochastic and deterministic systems. The duality between two complementary formulations of the optimisation problem is analysed using Lagrangian relaxation to show the intrinsic connections and differences. Several cases of implementations of optimal FD design derived from the framework are also presented.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Towards Online Detection of Plasticity in Soft Robots", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Dileep, Agneyan", "affiliation": "University of Lille, CRIStAL, Inria" }, { "name": "Peyron, Quentin", "affiliation": "Inria Université De Lille" }, { "name": "Cocquempot, Vincent", "affiliation": "University of LILLE" } ], "keywords": [ "Fault detection and isolation methods", "Applications of FDI/FTC", "Health/condition monitoring in processes" ], "abstract": "Soft robots are made of deformable materials, allowing them to perform tasks that rigid robots cannot, such as handling delicate objects or operating in tight spaces. However, their flexibility makes them more vulnerable to material degradation and permanent deformations known as plasticity. Plasticity accelerates material fatigue, decreases system performance, can lead to structural failure, and makes control strategies less effective. This work proposes a methodology to detect plasticity in soft robots subject to known or unknown applied forces using measured marker positions along the robot structure. The approach relies on a finite element method (FEM) model and the open-source SOFA framework, and it is experimentally validated on a tendon-actuated soft robot with noisy measurements.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Spectral-Theoretic Compliance in Graph-Based Process Monitoring", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Wolmarans, Wikus", "affiliation": "North-West University" }, { "name": "van Schoor, George", "affiliation": "North-West University" }, { "name": "Uren, Kenneth Richard", "affiliation": "North-West University" } ], "keywords": [ "Fault detection and isolation methods", "Monitoring, performance assessment, and fault detection in chemical process control" ], "abstract": "With industrial processes becoming more complex, on-going improvement of sophisticated and reliable fault detection and diagnosis (FDD) methods is essential. To this end, this work introduces the notion of spectral-theoretic compliance, which is intended to encompass the benefits relating to matrix symmetry in graph-based process monitoring methods. This work further reveals and discusses spectral-theoretic benefits of matrix symmetry not yet recognised in the field of FDD, namely representability, interpretability and numerical noise immunity. Practical examples of these benefits are illustrated using the established energy graph-based visualisation (EGBV) method as applied to a pilot process. Two approaches are proposed for achieving spectral-theoretic compliance, namely sample self-comparison (SSC) and singular value decomposition (SVD). A comparison of these approaches with the original non-compliant version of the EGBV method reveals that the aforementioned benefits can be attained without compromising on FDD performance. The work is concluded with recommendations for continued study on the topic.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "A Fixed Time Global NTSMC-Based Approach to Mitigate Dynamic Instabilities in DFIG-Based Wind Energy Conversion Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Musarrat, Md Nafiz", "affiliation": "University of Louisiana at Lafayette" }, { "name": "Fekih, Afef", "affiliation": "Univ of Louisiana at Lafayette" } ], "keywords": [ "Fault-tolerant control methods", "Power systems stability", "Wind power" ], "abstract": "This paper proposes a fixed time global non-singular terminal SMC (FT-GNTSMC)-based approach for the effective mitigation of fault-induced transients in doubly-fed-induction-generator (DFIG)-based wind energy systems. The proposed approach combines the mitigation capabilities of dynamic voltage restorers (DVRs) with the robustness and global fast fixed time convergence of FT-GNTSMC. The stability and non-singularity of the proposed controller is proven using the Lyapunov stability theory. The performance of the proposed approach is assessed using a wind energy-based test microgrid subject to grid faults and sudden load variations. Comparative analysis with a standard SMC-based approach is also carried out. The obtained results confirmed the fast response and superior performance of the proposed FT-GNTSMC in mitigating the dynamic instabilities induced by grid faults.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Reliable Detection of Abnormal Bearing States under Unknown Samples", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Wang, Jing", "affiliation": "North China University of Technology (NCUT)" }, { "name": "Li, Ning", "affiliation": "North China University of Technology" }, { "name": "Zhou, Meng", "affiliation": "North China University of Technology" }, { "name": "Su, Rong", "affiliation": "Nanyang Technological University" } ], "keywords": [ "Health/condition monitoring in processes", "Reliability and safety in processes", "AI methods for FDI/FTC" ], "abstract": "Bearings are critical components in motion control systems, and reliable detection of abnormal conditions is essential. Traditional supervised learning methods often misclassify unknown faults as normal. This paper proposes a reliable abnormality diagnosis framework combining a supervised model with a Gated Network. Trained only on known samples, the Gated Network effectively identifies unknown data while ensuring reliable detection in supervised learning models. Experiments on the CWRU bearing dataset demonstrate that the framework achieves high accuracy and improves the decision reliability of conventional supervised models under unknown samples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Machine Learning for Electrolyzer Energy Efficiency: Review and Outlook", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Ramde, Ismail", "affiliation": "INSA Lyon, Université Lumière Lyon 2, Université Claude Bernard Lyon 1, Université Jean Monnet Saint-Etienne, DISP UR4570" }, { "name": "Kombaya Touckia, Jesus Vital", "affiliation": "Université Claude Bernard Lyon 1, INSA Lyon, Université Lumière Lyon 2, Université Jean Monnet Saint-Etienne, DISP UR4570," }, { "name": "Henry, Sébastien", "affiliation": "DISP Laboratory, University of Lyon, University Lyon 1" }, { "name": "Ouzrout, Yacine", "affiliation": "DISP Laboratory, University of Lyon, University Lyon 2" } ], "keywords": [ "Hydrogen systems for energy generation and storage", "Control and management of energy systems", "Advanced process control" ], "abstract": "Hydrogen production through water electrolysis is essential for low-carbon energy systems, but its competitiveness depends on efficient and reliable operation. This paper reviews artificial intelligence approaches applied to electrolyzer energy performance. Unlike broader reviews on green hydrogen, it focuses on the link between learning methods, operational data, reported efficiency gains, and industrial control perspectives. Thirty studies published between 2010 and 2025 are analyzed using a systematic review methodology. The results show that supervised learning and hybrid simulation-based models dominate, while pressure, degradation, benchmark datasets, and large-scale validation remain insufficiently addressed.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "The Evolving Model Approach: A Dynamic Real-Time Optimization Strategy", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Damiri, Hazem", "affiliation": "Graz University of Technology" }, { "name": "Steinberger, Martin", "affiliation": "Graz University of Technology" }, { "name": "Horn, Martin", "affiliation": "Graz University of Technology" } ], "keywords": [ "Real-time optimization and control in chemical processes", "Biological and pharmaceutical systems", "Industrial applications of chemical process control" ], "abstract": "In this paper, a novel real time optimization (RTO) approach is developed for plants with dynamics described by Hammerstein models. The framework relies on adding a dynamic system to the plant model. Then, the parameters of this added system are tuned to shape the optimal input of the plant model. If the plant deviates from the optimal performance because of an external disturbance, the proposed method modifies this added system to compute a new input that drives the plant back to the optimal behavior. Simulation results show a better performance by comparing the new approach with previous methods from literature.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Autonomous Model Updating in AI Real-Time Optimization under Plant Drift", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Costa, Erbet Almeida", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Rebello, Carine", "affiliation": "NTNU: Norwegian University of Science and Technology" }, { "name": "Nogueira, Idelfonso", "affiliation": "NTNU" } ], "keywords": [ "Real-time optimization and control in chemical processes", "Machine learning and artificial intelligence in chemical process control", "Advanced process control" ], "abstract": "The use of artificial intelligence (AI) models in engineering applications has increased significantly in recent years. A key concern accompanying this growth is determining when such models require updating, how to detect the need for retraining, and how to update them effectively. This article proposes a strategy for detecting inconsistencies in the surrogate models used within AI-powered real-time optimization (AI-RTO). The methodology relies on a supervisory module that (i) verifies whether the plant is operating near steady state through a moving-window analysis of the controlled variables, (ii) evaluates the persistent mismatch between the optimum predicted by the AI-RTO and the measured plant outputs, and (iii) triggers data acquisition and model retraining only when both conditions are simultaneously satisfied. The retraining procedure first updates the network weights and, if the performance criterion is not met, performs a hyperparameter search. The strategy is evaluated on an artificial-lift system actuated by an electric submersible pump (ESP), subject to dynamic operational constraints, including the pump operating envelope and a minimum intake pressure limit. Four operating scenarios, with combined disturbances in the productivity index, choke gain, and pump head curve, are used to emulate plant drift. The results show that the proposed mechanism keeps the plant close to the actual maximum-flow operating point and enforces the dynamic envelope constraints, whereas a static AI-RTO progressively loses both feasibility and optimality.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "GMM-Based Pareto Optimal Alarm Design for Multivariate Process Monitoring", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Yang, Nachuan", "affiliation": "University of Alberta" }, { "name": "Tao, Yifei", "affiliation": "University of Alberta" }, { "name": "Jia, Fanlin", "affiliation": "University of Alberta" }, { "name": "Chen, Tongwen", "affiliation": "University of Alberta" } ], "keywords": [ "Reliability and safety in processes", "Monitoring, performance assessment, and fault detection in chemical process control", "Fault detection and isolation methods" ], "abstract": "Univariate alarm systems are usually inadequate for multivariate industrial processes, where strong process correlations often lead to alarm flooding and ineffective fault detection. In this paper, we investigate a multi-objective design of multivariate alarms, which remains an open research problem. Historical process data are first modeled using a Gaussian mixture model (GMM) to capture representative fault patterns. Based on these patterns, the alarm design is further formulated as a multi-objective optimization problem, which is then solved through quadratic programming and bisection methods. The proposed method jointly minimizes the false alarm rate, missing alarm rate, and cross false alarm rate, achieving a Pareto optimal solution among multiple alarm objectives. Compared with heuristic methods and manual tuning, the proposed method provides explicit rate characterization and theoretical guarantees, which are essential for safety-critical applications. The effectiveness of our proposed new method is demonstrated through case studies.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Modelling and Control of a Shrouded Wind Turbine with Integrated Structural Dynamics", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhu, Hongzhong", "affiliation": "Kyushu University" }, { "name": "Hu, Changhong", "affiliation": "Kyushu University" }, { "name": "Watanabe, Seiya", "affiliation": "Kyushu University" }, { "name": "Watanabe, Koichi", "affiliation": "Kyushu University" }, { "name": "Uchida, Takanori", "affiliation": "Kyushu University" } ], "keywords": [ "Wind power", "Control and management of energy systems", "Power systems stability" ], "abstract": "This study investigates the feasibility of scaling a shrouded wind turbine to medium-large capacities, addressing the long-standing limitation that existing shrouded turbines remain small due to structural complexity and dynamic-load amplification. A comprehensive multibody dynamic model of a 200-kW downwind shrouded wind turbine is developed using a multi-body formulation. The flexibility of the tower and shroud-support structures is considered, enabling accurate representation of bending, torsional, and axial deformation modes. Modal analysis of the complete assembly identifies critical vibration modes, including roll and yaw modes of the shroud that occur in the rotor 3P excitation region. These modal characteristics are explicitly incorporated into the controller design, where a region-dependent rotor-speed strategy and notch-filtered PI control are used to avoid resonance crossings and enhance operational robustness. Dynamic simulations are conducted under turbulent wind conditions to evaluate structural responses and closed-loop performance. The results highlight practical design constraints for large shrouded turbines. The findings provide quantitative guidance for drivetrain sizing and control-system specifications, offering insights into the viability of upscaling shrouded concepts for higher-power applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Benchmarking Sequential Feedback Optimization for Wind Farm Power Maximization", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Huang, Shijie", "affiliation": "TUDelft" }, { "name": "Grammatico, Sergio", "affiliation": "Delft Univ. of Tech" } ], "keywords": [ "Wind power", "Power plant control", "Control and management of energy systems" ], "abstract": "This paper benchmarks sequential feedback optimization (SFO) for wind farm power maximization using a medium-fidelity dynamic flow model. We compare SFO with two well-established approaches, adjoint-based economic model predictive control (AMPC) and extremum seeking control (ESC), under a common nine-turbine layout and identical operating constraints. The comparison focuses on steady-state power production and computational efficiency, both relevant for real-time implementation. The simulation results illustrate that SFO achieves higher steady-state power while preserving real-time feasibility, AMPC provides a better transient performance at a higher online computational cost and without guarantees of convergence to the steady-state optimum, and ESC offers a computationally inexpensive model-free baseline that may converge to locally optimal solutions. These results provide a practical reference for selecting wind farm control strategies and for designing scalable, real-time optimization methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Hybrid-State MFG Approach to Decentralized Charging Strategy Design for Large Populations of EVs", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Guo, Wanying", "affiliation": "Dalian University of Technology" }, { "name": "Zhang, Yuexi", "affiliation": "Dalian University of Technology" }, { "name": "Shen, Tielong", "affiliation": "Dalian University of Technology" } ], "keywords": [ "Optimal control theory", "Applications of optimal control", "Differential or dynamic games" ], "abstract": "This paper investigates the energy management problem for large populations of electric vehicles (EVs) with finite-continuous hybrid states. First, a novel model is proposed that integrates continuous state of charge and discrete events triggered by on-off charging mode switches. Then, a hierarchical optimization framework is developed to cope with the hybrid system. In this framework, the upper level, managed by the grid operator, achieves macroscopic load balancing for large populations of EVs by optimizing the finite state transition rates; the lower level involves decentralized decision-making, where individual EVs adjust their charging power to optimize their respective objectives. Given the analytical challenges posed by large-scale EVs charging behaviors, this paper formulates the coordination problem of the EV population as a mean-field game (MFG), where its equilibrium solution is characterized by two coupled sets of Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck (FP) equations. Compared to conventional HJB-FP equations, these equations incorporate additional terms associated with the finite state transition behavior. Furthermore, theoretical analysis shows that the MFG provides an varepsilon-Nash equilibrium for a finite number of EVs. Finally, an efficient numerical solution is illustrated for the optimal control problem, and simulation results demonstrate the effectiveness of the proposed framework and methodology.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Control of a Nitrogen-Vacancy Center As a Two-Qubit System", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Da Silva Tinoco, David", "affiliation": "INRIA" }, { "name": "Babin, Charles", "affiliation": "Université Bourgogne Europe" }, { "name": "Beschastnyi, Ivan", "affiliation": "Inria Centre d'Université Côte D'Azur" }, { "name": "Caillau, Jean Baptiste", "affiliation": "Université Côte d'Azur, CNRS, Inria, LJAD" }, { "name": "Sugny, Dominique", "affiliation": "University of Bourgogne" } ], "keywords": [ "Optimal control theory", "Applications of optimal control", "Numerical methods for optimal control" ], "abstract": "Nitrogen-vacancy (NV) centers are promising experimental platforms for quantum information processing. In this paper, we investigate their controllability and fundamental quantum speed limit for two-qubit gates. Such a quantum system consists of two coupled spins, an electronic and a nuclear spin, where only the former can be controlled directly via microwave pulses. We discuss the various physical approximations that lead to the system model before studying its controllability. We use this control issue as an example to demonstrate how standard geometric control tools can be applied to spin networks. We complete this analysis with a computation of the quantum speed limit using known analytical techniques on Lie groups and their algebras. We finally demonstrate, thanks to preliminary optimal control numerical experiments, that this limit can be approached while keeping a reasonable energy of the control field.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Basis Pursuit -- a Systems Viewpoint", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Marmary, Maya Vered", "affiliation": "Technion" }, { "name": "Grussler, Christian", "affiliation": "Technion - Israel Institute of Technology" } ], "keywords": [ "Optimal control theory", "Linear systems" ], "abstract": "Discrete-time minimum ell_1-norm has often been suggested as a solution for sparse optimal control of linear time-invariant systems. Unlike the continuous-time case, where controllability is guaranteed to provide the sparsest solution, this is no longer true in discrete-time. We propose a deterministic understanding of failure cases, leveraging the framework of total positivity to derive conditions under which the sparsest solution cannot be recovered. Thus, providing insights into the a priori design of sparse optimal control problems, as well as some more general compressed sensing settings, explaining why such failure is to be predicted.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Indirect Methods in Optimal Control on Banach Spaces", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Chertovskih, Roman", "affiliation": "Porto University" }, { "name": "Pogodaev, Nikolay", "affiliation": "Matrosov Institute for System Dynamics and Control Theory of Siberian Branch of Russian Academy of Sciences" }, { "name": "Staritsyn, Maxim", "affiliation": "Faculdade De Engenharia, Universidade Do Porto, Porto, Portugal" }, { "name": "Aguiar, A. Pedro", "affiliation": "Faculty of Engineering, University of Porto (FEUP)" } ], "keywords": [ "Optimal control theory", "Optimal control of PDE systems", "Control of distributed parameter systems" ], "abstract": "This work focuses on indirect descent methods for optimal control problems governed by nonlinear ordinary differential equations in Banach spaces, viewed as abstract models of distributed dynamics. As a reference line, we revisit the classical schemes, rooted in Pontryagin’s maximum principle, and highlight their sensitivity to local convexity and line-search procedures. We then develop an alternative method based on exact cost-increment formulas and finite-difference probes of the terminal cost. Numerical results for an Amari-type neural field illustrate monotone decrease of the cost, obtained without solving the adjoint equation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Geometry of Extremals Emerging from a Local Stable Manifold with and without Conjugate Points", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Oki, Takafumi", "affiliation": "Tokyo Denki University" }, { "name": "Otsuka, Naohisa", "affiliation": "Tokyo Denki Univ" }, { "name": "Wada, Shigeo", "affiliation": "Graduate School of Engineering, Tokyo Denki University" } ], "keywords": [ "Optimal control theory", "Stability of nonlinear systems", "Lagrangian and Hamiltonian systems" ], "abstract": "This paper revisits the infinite-horizon optimal control (IOC) problem from the perspective of a family of extremals emanating from the local stable manifold of the associated Hamiltonian system. We analyze conditions under which these extremals—parameterized by their root points on the manifold—form a Lagrangian submanifold, thereby yielding a stabilizing solution to the Hamilton–Jacobi–Bellman equation (HJBE). We further investigate how the emergence of conjugate points—instances where the Riccati differential equation along an extremal blows up—destroys this geometric structure. Additionally, we explore the connection between conjugate points and the uniqueness of solutions to a two-point boundary value problem (BVP) that incorporates the local stable manifold as a terminal condition. This BVP facilitates the generation of neighboring extremals around a reference extremal. Numerical examples using a cart-inverted-pendulum system illustrate these geometric properties through families of extremals corresponding to swing-up maneuvers and extremals exhibiting conjugate points that break the embedded submanifold structure.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "An Error Bound for Aggregation in Approximate Dynamic Programming", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Li, Yuchao", "affiliation": "Arizona State University" }, { "name": "Bertsekas, Dimitri P.", "affiliation": "Massachusetts Inst. of Tech" } ], "keywords": [ "Optimal control theory", "Stochastic optimal control problems", "Numerical methods for optimal control" ], "abstract": "We consider a general aggregation framework for discounted finite-state infinite horizon dynamic programming (DP) problems. It defines an aggregate problem whose optimal cost function can be obtained off-line by exact DP and then used as a terminal cost approximation for an on-line reinforcement learning (RL) scheme. We derive a bound on the error between the optimal cost functions of the aggregate problem and the original problem. This bound was first derived by Tsitsiklis and van Roy [TvR96] for the special case of hard aggregation. Our bound is similar but applies far more broadly, including to soft aggregation and feature-based aggregation schemes.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Two-Point Random Gradient-Free Methods for Model-Free Feedback Optimization", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Mehrnoosh, Amir", "affiliation": "Universite Catholique De Louvain" }, { "name": "Bianchin, Gianluca", "affiliation": "Université Catholique De Louvain" } ], "keywords": [ "Optimization-based estimation and control", "Design methods for data-based control", "Real-time optimal control" ], "abstract": "Feedback optimization steers the steady-state operation of dynamical systems to optimal operating points. However, most existing methods still require exact knowledge of the plant dynamics, which is rarely available in practice. In this paper, we introduce a randomized two-point gradient-free feedback optimization method inspired by zeroth-order optimization. Our controller evaluates plant performance at two points to estimate gradients and update control inputs in real-time. For problems with smooth, nonconvex objectives, our method achieves convergence to an ε-stationary point with iteration complexity O(pε-1), where p denotes the dimension of the input vector, thereby recovering the best-known bounds for static two-point optimization. Numerical simulations support the theoretical results.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Command Governor for Switched Linear Systems with Arbitrary Switching", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Nguyen, Hoai-Nam", "affiliation": "Telecom SudParis" } ], "keywords": [ "Optimization-based estimation and control", "Nonlinear control of switched & hybrid systems", "Control of hybrid systems" ], "abstract": "This paper proposes a new command governor (CG) scheme for the tracking of discrete-time switched linear systems subject to input and state constraints. The approach leverages a novel class of admissible sets, termed switch-dependent semi-ellipsoidal admissible sets, which exploit available information on the switching signal. These sets enable the design of a recursively feasible CG that guarantees closed-loop constraint satisfaction. The proposed approach is demonstrated through a numerical example.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Optimal Sensor Placement for Output Estimation Using an Artificial Bee Colony Algorithm with Pre-Filter", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Goetz, Raphael", "affiliation": "Eindhoven University of Technology, the Netherlands" }, { "name": "Dwaraga, Yuvan", "affiliation": "Eindhoven University of Technology" }, { "name": "van de Wouw, Nathan", "affiliation": "Eindhoven Univ of Technology" }, { "name": "Oomen, Tom", "affiliation": "Eindhoven University of Technology" }, { "name": "van de Wal, Marc", "affiliation": "ASML" }, { "name": "Sharif, Bardia", "affiliation": "Eindhoven University of Technology" }, { "name": "Zwart, Hans", "affiliation": "University of Twente" } ], "keywords": [ "Optimization-based estimation and control", "Observer design", "Linear systems" ], "abstract": "Sensor placement for maximizing the estimation performance of the Kalman filter is an NP-hard optimization problem. Furthermore, its feasible set grows combinatorially with the candidate locations and the number of sensors. In this paper, we study this sensor placement problem for a 3D thermoelastic system modelled as a discrete-time linear stochastic model. We use the Novel Binary Artificial Bee Colony (NBABC) algorithm with a Gramian-based pre-filter to reduce the computational complexity. Our results show the efficiency and the fast convergence of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Learning to Accelerate Krasnosel'skii–Mann Fixed-Point Iterations with Guarantees", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Martin, Andrea", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Belgioioso, Giuseppe", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Parametric optimization", "Convex optimization", "Large-scale and networked optimization problems" ], "abstract": "We introduce a principled learning to optimize (L2O) framework for solving fixed-point problems involving general nonexpansive mappings. Our idea is to deliberately inject summable perturbations into a standard Krasnosel'skii–Mann iteration to improve its average-case performance over a specific distribution of problems while retaining its convergence guarantees. Under a metric sub-regularity assumption, we prove that the proposed parametrization includes only iterations that locally achieve linear convergence—up to a vanishing bias term—and that it encompasses all iterations that do so at a sufficiently fast rate. We then demonstrate how our framework can be used to augment several widely-used operator splitting methods to accelerate the solution of structured monotone inclusion problems, and validate our approach on a best approximation problem using an L2O-augmented Douglas–Rachford splitting algorithm.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Wave-BO: Waveform-Aware Bayesian Optimization for Sample-Efficient Trajectory Shaping", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Asaki, Kyosuke", "affiliation": "Mitsubishi Electric Corporation" }, { "name": "Ito, Rin", "affiliation": "Mitsubishi Electric Corporation" }, { "name": "Takano, Naoto", "affiliation": "Mitsubishi Electric Corporation" }, { "name": "Masui, Hideyuki", "affiliation": "Mitsubishi Electric Corporation" }, { "name": "Akaho, Shotaro", "affiliation": "National Institute of Advanced Industrial Science and Technology" }, { "name": "Hirayama, Junichiro", "affiliation": "National Institute of Advanced Industrial Science and Technology" }, { "name": "Kanemura, Atsunori", "affiliation": "National Institute of Advanced Industrial Science and Technology" }, { "name": "Asoh, Hideki", "affiliation": "National Institute of Advanced Industrial Science and Technology" } ], "keywords": [ "Parametric optimization", "Design methods for data-based control", "Optimization-based estimation and control" ], "abstract": "High-precision positioning in manufacturing equipment requires fast settling with minimal vibration. The asymmetric S-curve (AS-curve) is a jerk-limited trajectory that enables high speed and precision, but its many tuning parameters make adjustment difficult. Bayesian Optimization (BO) is a well-established sample-efficient optimization method, but its performance can be improved by exploiting information closely related to control performance. We propose waveform-aware BO for sample-efficient AS-curve shaping. A Gaussian process regression (GPR) incorporating a distance metric between command waveforms yields an accurate model with few evaluations and accelerates BO convergence. Experimental results on a real-world setup demonstrate equivalent tuning using only 15% of the trials required by conventional BO.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Parametric Model Reduction for H2 Design Optimization", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Boksebeld, Niek Herman Jan", "affiliation": "Eindhoven University of Technology" }, { "name": "Terzin, Bogoljub", "affiliation": "Eindhoven University of Technology" }, { "name": "Weiland, Siep", "affiliation": "Eindhoven Univ. of Tech" } ], "keywords": [ "Parametric optimization", "Model reduction of distributed parameter systems" ], "abstract": "This paper addresses the problem of model reduction for parameter dependent systems where the reduction criterion expresses a design objective for the parameter dependent system. Specifically, we develop a reduction method for systems that are required to meet an explicit guarantee on the H 2 approximation error with respect to a design objective. This guarantee is combined with efficiency improvements on the reduction scheme and an error estimation. The performance of the method is illustrated on a thermal design optimization problem. Results indicate superior computational efficiency compared to classical methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Distributed Online Estimation with Momentum and Randomized Perturbations under Heavy-Tailed Noise and Dynamic Functional Drift", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Akinfiev, Ivan", "affiliation": "Saint Petersburg State University" }, { "name": "Tarasova, Elizaveta", "affiliation": "Saint Petersburg State University" }, { "name": "Salishev, Sergey", "affiliation": "St. Petersburg State University" }, { "name": "Granichina, Olga", "affiliation": "St. Petersburg State University" } ], "keywords": [ "Randomized algorithms in robust control", "Distributed parameters port Hamiltonian systems", "Robust estimation" ], "abstract": "This work addresses the problem of distributed online estimation in a dynamic and potentially heavy-tailed environment. The proposed distributed stochastic approximation algorithm incorporates momentum and operates under H¨older smoothness, Lyapunov strong convexity, functional drift, and sparse structural shifts. Synthetic tests on a drifting multi- dimensional Rosenbrock function with heavy-tailed noise confirm bounded tracking error and rapid recovery following abrupt changes. Equity market experiments further validate the method, yielding stable estimates for portfolio risk management and intraday mean-reversion strategies.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Reactive Planning Based Control for Mobile Robots in Obstacle-Cluttered Environments", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Tan, Li", "affiliation": "University of Science and Technology of China" }, { "name": "Xiong, Junlin", "affiliation": "University of Science and Technology of China" }, { "name": "Wang, Yan", "affiliation": "Harbin Institute of Technology" }, { "name": "Ren, Wei", "affiliation": "Dalian University of Technology" } ], "keywords": [ "Real-time optimal control", "Control barrier functions and state space constraints", "Adaptive control design" ], "abstract": "This paper addresses the motion control problem for mobile robots in obstacle-cluttered environments. The mobile robot has partial environment information only, and aims to move from an initial position to a target position without collisions. For this purpose, a reactive planning based control strategy (RPCS) is proposed. First, the initial and target positions are connected as a reference trajectory. Then, a reactive planning strategy (RPS) is developed to ensure the collision avoidance by modifying the reference trajectory locally based on the partial environment information. Next, an adaptive tracking control strategy (ATCS) is proposed to track the reference trajectory with potentially local modifications via the discretization techniques. Finally, the RPS and ATCS are combined to establish the RPCS, whose efficacy and advantages are illustrated by numerical examples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Trajectory Optimization by Pseudospectral Successive Convexification on Riemannian Manifolds", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Narumi, Tatsuya", "affiliation": "Tokyo University" }, { "name": "Sakai, Shin-ichiro", "affiliation": "Japan Aerospace Exploration Agency" } ], "keywords": [ "Real-time optimal control", "Optimal control theory", "Convex optimization" ], "abstract": "This paper proposes an intrinsic pseudospectral convexification framework for optimal control problems with manifold constraints. While pseudospectral successive convexification combines spectral collocation with successive convexification, classical pseudospectral methods are not geometry-consistent on manifolds. This is because interpolation and differentiation are performed in Euclidean coordinates. We introduce a geometry-consistent transcription that enables pseudospectral collocation without imposing manifold constraints extrinsically. The resulting method solves nonconvex manifold-constrained problems through a sequence of convex subproblems. A six-degree-of-freedom landing guidance example with unit quaternions and unit direction vectors demonstrates the practicality of the approach. The proposed method preserves manifold feasibility to machine precision and achieves significant computational speedups.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Strongly Alpha-Stabilizing Plug-In Tracking Controller Synthesis with Application to Magnetic Levitation System", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Lin, Yu-Jen", "affiliation": "National Sun Yat-Sen University" }, { "name": "Kao, Chung-Yao", "affiliation": "National Sun Yat-Sen University" }, { "name": "Khong, Sei Zhen", "affiliation": "National Sun Yat-Sen University" }, { "name": "Hara, Shinji", "affiliation": "Tokyo Institute of Technology" } ], "keywords": [ "Robust controller synthesis", "Analytic design", "Linear systems" ], "abstract": "This paper presents a stable plug-in controller design that improves the closed-loop performance of pre-stabilized single-input-single-output (SISO) linear time-invariant (LTI) systems without sacrificing inherent robustness. To ensure both controller stability and desired pole placement, the problem is reformulated via an s-domain transformation psi(s) = s - alpha (alpha > 0). This shifts the stability boundary, rendering the original system virtually unstable and converting the design into a strong stabilization problem. By analytically solving the transformed system and applying an inverse shift, the proposed non-iterative approach yields low-order controllers. Experimental validation on a magnetic levitation system demonstrates significantly improved tracking and leftward-shifted poles compared to a standalone proportional-integral-derivative controller.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Second-Order Hybrid Integrator-Gain System", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Weise, Christoph", "affiliation": "TU Ilmenau" }, { "name": "Wulff, Kai", "affiliation": "TU Ilmenau" }, { "name": "Hosseini, Ali", "affiliation": "TU Delft" }, { "name": "HosseinNia, S Hassan", "affiliation": "Delft University of Technology" }, { "name": "Reger, Johann", "affiliation": "TU Ilmenau" } ], "keywords": [ "Robust controller synthesis", "Switching stability and control" ], "abstract": "We introduce a second-order version of the hybrid integrator-gain system (HIGS). In the proportional mode the second state is either reset to zero or tracks the input. We derive a method for computing the describing function and higher-order harmonics in terms of a matrix exponential. In comparison to the HIGS the new element shows the amplitude response of a second order system whereas the phase drops to approximately −52°. Using a sector transformation we can show that the second-order HIGS is passive, which allows for a conservative circle-criterion-like condition to test for closed-loop stability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "A Proportional-Integral Equivalent-Input-Disturbance Method for Enhanced Disturbance Rejection in Generalized Repetitive-Control Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhang, Manli", "affiliation": "Wuhan University of Science and Technology" }, { "name": "Lu, Shaowu", "affiliation": "Wuhan University of Science and Technology" }, { "name": "Xie, Mingyuan", "affiliation": "Huazhong University of Science and Technology" }, { "name": "She, Jinhua", "affiliation": "Tokyo Univ. of Tech" }, { "name": "Wu, Min", "affiliation": "China University of Geosciences" } ], "keywords": [ "Robust estimation", "Learning methods for optimal control", "Linear time-delay systems" ], "abstract": "This paper presents a generalized repetitive-control (GRC) framework that achieves both precise tracking of periodic signals and suppression of aperiodic disturbances. The relationship between the ideal periodic internal model and the GRC structure is analyzed. Based on this analysis, a second-order Butterworth filter and a time-delay parameter are designed to ensure accurate steady-state tracking. In addition, the inherent limitation of the conventional equivalent-input-disturbance (EID) estimator is identified. The conventional EID estimator behaves as an integrator and therefore responds slowly to disturbances. To overcome this problem, a proportional-integral EID (PI-EID) estimator is developed. The new PI-EID estimator provides fast disturbance compensation while maintaining high estimation accuracy. The stability of the control system is guaranteed. Simulation results demonstrate that the proposed method significantly improves steady-state tracking accuracy when compared with modified repetitive control and complex-coefficient-filter-based repetitive control. The proposed method also achieves superior transient and steady-state disturbance rejection when compared with the conventional EID method and the improved EID method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Robust High-Gain Consensus Control for Delayed Multi-Agent Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Panin, Aleksandr", "affiliation": "ITMO University" }, { "name": "Tomashevich, Stanislav", "affiliation": "IPME RAS; ITMO University" }, { "name": "Borisov, Oleg", "affiliation": "ITMO University" }, { "name": "Bobtsov, Alexey", "affiliation": "ITMO University" } ], "keywords": [ "Robust time-delay systems", "Decentralized control", "Analytic design" ], "abstract": "This paper addresses the consensus problem for linear multi-agent systems with heterogeneous time-varying communication delays. Existing delay-dependent approaches based on Lyapunov--Krasovskii functionals and LMIs often suffer from high computational complexity and limited analytical insight. To overcome these limitations, an explicit modal decomposition framework is developed that exploits the Laplacian eigenstructure to decouple the network dynamics into independent subsystems. For each mode, delay-dependent stability conditions are derived in closed algebraic form using Sylvester’s criterion, enabling direct characterization of admissible delays and controller gains without numerical optimization. For agents with arbitrary relative degree, a dynamic high-gain controller is introduced to ensure simultaneous stabilization of all nonzero Laplacian modes under slowly varying heterogeneous delays. The proposed approach provides scalable and analytically tractable stability conditions that explicitly reveal the influence of network topology on delay robustness. Numerical examples demonstrate convergence to consensus and bounded control effort under time-varying delays.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Linear Quadratic Problem for Systems with Unknown Random State Delay", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Odorico, Elizandra Karla", "affiliation": "University of São Paulo" }, { "name": "Terra, Marco Henrique", "affiliation": "Depto. Engenharia Elétrica - Escola De Engenharia De São Carlos" } ], "keywords": [ "Robust time-delay systems", "Robust controller synthesis", "Control of hybrid systems" ], "abstract": "This paper develops a recursive solution to the state-feedback control problem for linear discrete-time systems with unknown random state delays and norm-bounded parametric uncertainties. It is assumed that the rate of variation between consecutive delays is bounded, and an unobserved Markov chain is used to model stochastic delay behavior. By employing the lifting technique, the original state-delayed system is converted into an equivalent delay-free Markovian jump linear system formulation. Leveraging this framework, an optimization problem is formulated that accounts for the impact of delayed state while simultaneously accommodating worst-case uncertainties. The stabilizing gains are then obtained via recursive Riccati equations, which establish standard conditions for stability and convergence. The performance of the proposed robust regulator is illustrated using a model of an F-16 aircraft. We present a comparative study using robust H_{infty} state-feedback controllers to demonstrate the effectiveness of the developed recursive regulator.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Tube-Based Stability Analysis of Lyapunov Redesign Model-Following Control for Trajectory Tracking with Unbounded Perturbations", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Tietze, Niclas", "affiliation": "Technische Universität Ilmenau" }, { "name": "Wulff, Kai", "affiliation": "TU Ilmenau" }, { "name": "Reger, Johann", "affiliation": "TU Ilmenau" } ], "keywords": [ "Robustness analysis", "Controller constraints and structure", "Stability of nonlinear systems" ], "abstract": "For a nonlinear system in Byrnes-Isidori form, subject to unbounded perturbations, i.e. perturbationsthat satisfy a given bound only locally on a subset of the state space, we apply the continuous approximation of Lyapunov redesign within the feedback linearisation model-following control (MFC) scheme for trajectory tracking. We establish practical tracking by generalising a tube-based stability analysis proposed for single-loop control to MFC. Conceptually, we exploit that the Lyapunov function used for the Lyapunov redesign satisfies a differential inequality, thereby guaranteeing that the solution of the perturbed closed loop remains in a tube along the a-priori known solution of the model simulated in the model control loop.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Parametric Quadratic Stabilizability of Bimodal Piecewise Affine Systems", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Zhang, Mengxuan", "affiliation": "The University of Osaka" }, { "name": "Fujisaki, Yasumasa", "affiliation": "The University of Osaka" } ], "keywords": [ "Robustness analysis", "Robust controller synthesis", "Robust linear matrix inequalities" ], "abstract": "This paper develops a linear matrix inequality (LMI) condition for the parametric quadratic stabilizability of bimodal piecewise linear systems under affine state feedback. The affine reference input induces equilibrium migration across switching regions. The proposed condition guarantees the existence and uniqueness of the equilibrium point together with quadratic stability of the closed-loop system.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Efficient Robustness Analysis Along a Trajectory with Uncertain Initial Conditions", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Robens, Johannes", "affiliation": "German Aerospace Center DLR-RM" }, { "name": "Pfifer, Harald", "affiliation": "Technische Universität Dresden" } ], "keywords": [ "Robustness analysis", "Uncertain systems", "Linear systems" ], "abstract": "Robustness analysis of uncertain nonlinear systems is often dominated by computationally expensive Monte-Carlo simulations, motivating the development of alternative approaches, including deterministic methods for worst-case assessment. An efficient solution approach is developed for a finite-horizon robustness analysis method that is based on a linear time-varying model along a nominal trajectory with quadratic constraints capturing nonlinear effects. The method leverages a transformed Riccati differential equation formulation with analytically optimized time-varying parameters to reduce computational complexity. Local quadratic constraints are iteratively refined using sparse grids. Application to Huygens' atmospheric entry flight demonstrates accurate estimation of worst-case bounds with moderate conservatism.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeC38", "code": "WeC38", "title": "Convergence Rate Comparison of PI and VI Algorithms to Stochastic LQR Problems", "day": "Wednesday", "date": "August 26, 2026", "time": "15:30-17:30", "sessionCode": "WeC38", "sessionTitle": "Poster Session Wednesday", "sessionType": "Interactive Session", "room": "Convention Hall - Room 301", "authors": [ { "name": "Wang, Dong", "affiliation": "Shandong University of Science and Technology" }, { "name": "Li, Zonghan", "affiliation": "Shandong University of Science and Technology" }, { "name": "Xin, Yanyi", "affiliation": "Shandong University of Science and Technology" }, { "name": "Zhang, Weihai", "affiliation": "Shandong University of Science and Technology" }, { "name": "Wei, Wei", "affiliation": "Shandong University of Science and Technology" } ], "keywords": [ "Stochastic optimal control problems", "Learning methods for optimal control", "Optimal control theory" ], "abstract": "This paper investigates the static output feedback control problem for linear quadratic regulation (LQR) in discrete-time stochastic systems with state- and control\u0002dependent noises. To solve the stochastic LQR problem, policy iteration (PI) and value iteration (VI) algorithms are provided. Furthermore, via the provided intermediate matrix technique, a comparative analysis of the convergence rates for the given PI and VI algorithms is presented, along with a detailed proof. Finally, simulation examples of the F-16 aircraft model are conducted to verify the effectiveness of the proposed algorithms and the validity of the relevant theories.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "We-WeNSP1.1", "code": "WeNSP1.1", "title": "Coverage Control across Scales: Data-Driven Solutions, Dynamic Scenarios, and Optimal Transport", "day": "Wednesday", "date": "August 26, 2026", "time": "17:40-18:30", "sessionCode": "WeNSP1", "sessionTitle": "Coverage Control across Scales: Data-Driven Solutions, Dynamic Scenarios, and Optimal Transport", "sessionType": "Semi-Plenary Session", "room": "Auditorium", "authors": [ { "name": "Martinez, Sonia", "affiliation": "Univ of California at San Diego" } ], "keywords": [ "Linear system identification" ], "abstract": "Multi-agent coordination critically relies on the group's ability to break down tasks, and solve them individually toward a common goal. A class of problems that realizes this paradigm is coverage control, which allows a multi-robot system to optimally deploy to service an area. In this talk, I will present recent advances to address dynamic coverage, robustness to sparse data, and discuss problem connections with optimal transport and machine learning for the coordination of very large swarms.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_3.html" }, { "id": "Th-ThM00.1", "code": "ThM00.1", "title": "Learning and Adaptation in Uncertain Dynamical Systems: Theory, Algorithms, and Challenges", "day": "Thursday", "date": "August 27, 2026", "time": "08:30-09:30", "sessionCode": "ThM00", "sessionTitle": "Learning and Adaptation in Uncertain Dynamical Systems: Theory, Algorithms, and Challenges", "sessionType": "Plenary Session", "room": "Auditorium", "authors": [ { "name": "Guo, Lei", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Adaptive control design" ], "abstract": "Learning and adaptation are fundamental to the modeling and control of uncertain dynamical systems, and they are also cornerstones of intelligent systems. While statistical learning has underpinned much of the progress in machine learning, its theoretical foundations often rest on idealized assumptions about data, such as independent and identically distributed (i.i.d.) samples. These assumptions are typically not valid for dynamical systems, especially those under feedback control, where the input-output data exhibit strong temporal correlations and inherent nonstationarity. This motivates the development of a more general theoretical framework that can account for the complex data characteristics intrinsic to dynamical systems. This talk addresses these challenges by presenting some of our recent advances in learning and adaptation for stochastic dynamical systems. We introduce general and verifiable data conditions that can guarantee global stability and estimation performance for adaptive learning and filtering algorithms with either diminishing or non-diminishing adaptation gains. Topics covered include gradient-based and Newton-type adaptive methods, distributed learning and filtering in networked systems, integrated offline-online learning frameworks. We also consider practical challenges such as saturated or limited-value measurements, finite data size, input-state constraints, and time-varying system parameters. The lecture concludes with a discussion of emerging research at the nexus of control and AI.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA01.1", "code": "ThA01.1", "title": "Large Language Models in Process Systems Engineering: Opportunities, Architectures, and Industrial Deployment Challenges (I)", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA01", "sessionTitle": "Large Language Models for Process Control", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Gopaluni, Bhushan", "affiliation": "University of British Columbia" }, { "name": "Kotamraju, Vidya", "affiliation": "Syris AI Systems" }, { "name": "Bhushan, Syon", "affiliation": "St. George's High School" } ], "keywords": [ "Electrical protection and fault diagnosis" ], "abstract": "Large Language Models (LLMs) have rapidly emerged as tools of interest across engineering disciplines, and Process Systems Engineering (PSE) is no exception. This survey provides a systematic review of LLM applications in PSE, organizing the literature into seven categories: (1) process design and engineering, (2) molecular design and synthesis, (3) process modeling and simulation, (4) time-series forecasting, (5) optimization and scheduling, (6) process control, and (7) fault detection and diagnosis. For each category, we summarize the state of the art, identify common methodological approaches, and critically assess demonstrated capabilities versus aspirational claims. We find that LLMs show genuine promise for tasks involving natural language, including querying documentation, synthesizing unstructured knowledge, and enabling flexible human-machine interaction. However, applications requiring real-time execution, constraint satisfaction, or formal safety guarantees remain challenging. We conclude by identifying open problems and productive research directions for the PSE community.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA01.2", "code": "ThA01.2", "title": "S2S: LLM-Powered Times Series Understanding for a Novel Explainable Fault Diagnosis Framework (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA01", "sessionTitle": "Large Language Models for Process Control", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Zhao, Chunhui", "affiliation": "Zhejiang University" } ], "keywords": [ "Advanced process control" ], "abstract": "Fault diagnosis is a critical link in ensuring the safe operation of industrial systems. Traditional time-series data diagnosis models typically output abstract results, such as anomaly scores or fault categories, but they cannot answer key questions like “why the fault occurred” or “how to perform maintenance.” Although large language models (LLMs) show great potential for fault diagnosis, they face the challenge of a semantic gap when processing time-series industrial signals; that is, continuous temporal data are difficult to encode into discrete tokens that language models can effectively process. Differing from the traditional “signal-to-category” paradigm in fault diagnosis, we propose a novel explainable fault diagnosis framework, namely the “Signal-to-Semantics” (S2S) fault diagnosis framework. Our research replaces the original paradigm of mapping abstract time-series data to abstract diagnostic results, and instead outputs reasoning processes and diagnostic texts that are comprehensible and verifiable by human experts, establishing a new generation of intelligent diagnosis frameworks for industrial equipment.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA01.3", "code": "ThA01.3", "title": "Hybrid LLM-First-Principles MPC (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA01", "sessionTitle": "Large Language Models for Process Control", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Kwon, Joseph", "affiliation": "Texas A&M University" } ], "keywords": [ "Advanced process control" ], "abstract": "", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA01.4", "code": "ThA01.4", "title": "A Tutorial on Autonomous Fault-Tolerant Control Using Knowledge-Grounded LLM Agents (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA01", "sessionTitle": "Large Language Models for Process Control", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Vyas, Javal", "affiliation": "Imperial College London" }, { "name": "Gill, Milapji Singh", "affiliation": "Helmut Schmidt University" }, { "name": "Markaj, Artan", "affiliation": "Helmut Schmidt University Hamburg" }, { "name": "Gehlhoff, Felix", "affiliation": "Helmut Schmidt University" }, { "name": "Mercangöz, Mehmet", "affiliation": "Imperial College London" } ], "keywords": [ "Advanced process control" ], "abstract": "Fault recovery in process plants still relies heavily on plant operators, especially when faults fall outside predefined supervisory logic. Operators interpret alarms, procedures, P&IDs, interlocks, and process trends, then decide how to move the plant to a safe operating mode without triggering a shutdown. This paper examines how Large Language Model (LLM) agents can support such recovery decisions. The proposed framework treats the LLM as a constrained supervisory planner. It uses plant-specific knowledge to propose recovery actions, and every proposal is checked by an external validator, either symbolic or simulation-based, before actuation. The paper develops three design dimensions for applying the framework: the recovery patterns for which LLM agents are useful, the validation strategies that separate admissible from inadmissible proposals, and the deployment constraints imposed by latency, knowledge engineering, safety integration, and model lifecycle management. To make the framework directly usable, two openly available executable Python environments are provided. Both re-implement established case studies, a modular mixing module and a continuous stirred-tank reactor, extended with configurable faults and defined interfaces for custom recovery and validation methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA01.5", "code": "ThA01.5", "title": "Large Model-Driven Industrial Embodied Intelligence: A Review", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA01", "sessionTitle": "Large Language Models for Process Control", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Zhang, Kexin", "affiliation": "Zhejiang University" }, { "name": "Zhou, Yang", "affiliation": "China University of Geosciences" }, { "name": "Cai, RongYao", "affiliation": "Zhejiang University" }, { "name": "Wu, Gao", "affiliation": "Zhejiang University" }, { "name": "Liu, Yong", "affiliation": "Zhejiang University" } ], "keywords": [ "LLMs for modeling and control", "Development of assistant systems for manufacturing systems", "LLM-enhanced human-in-the-loop" ], "abstract": "Large model-driven robotic embodied intelligence systems have achieved breakthrough progress in various tasks, thanks to the powerful cross-modal information processing and semantic understanding capabilities of large models. However, in the more traditional process industry and discrete manufacturing systems, research and applications of large model-based technologies are just in their infancy, and industrial embodied intelligence has thus become a key development direction for the future. This paper first attempts to provide a generalized definition and key components of industrial embodied intelligence. On this basis, a generalized architecture of industrial embodied intelligence is proposed, and the existing research and technical progress are elaborated in detail from three aspects: general multi-modal large models and industrial large models, large model-driven industrial data perception and knowledge extraction, and large model-driven task decision-making and optimal control. Finally, the key technical challenges in realizing industrial embodied intelligence are presented, providing guidance and reference for theoretical research, technological breakthroughs, and practical applications in this field.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.1", "code": "ThA02.1", "title": "Mixed-Integer Optimal Control for Mobile Sensor Placement in Distributed-Parameter Systems", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-09:55", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Alsayed, Ahmad", "affiliation": "Université Grenoble Alpes, CEA Grenoble" }, { "name": "Leirens, Sylvain", "affiliation": "Université Grenoble Alpes, CEA Leti" }, { "name": "Georges, Didier", "affiliation": "Grenoble Institute of Engineering and Management - Univ. Grenoble Alpes" } ], "keywords": [ "Linear system identification", "Active learning and experiment design" ], "abstract": "We address optimal trajectory design for mobile sensors in distributed-parameter systems. The problem is formulated as an optimal control program that minimizes a Fisher-information–based criterion over sensor initial positions and controls, while enforcing motion, domain, and separation constraints. Non-convex constraints are handled via an exact mixed-integer reformulation, and gradients are computed from a linearized sensitivity–adjoint scheme. The proposed framework is illustrated using a two-dimensional advection–diffusion system characterized by a parametric initial condition and diffusivity field.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.2", "code": "ThA02.2", "title": "Sparse Identification of Stochastic Dynamical Systems with Infinite Parameters Based on L1−regularization", "day": "Thursday", "date": "August 27, 2026", "time": "09:55-10:00", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Ren, Yiran", "affiliation": "Chinese Academy of Sciences" }, { "name": "Gan, Die", "affiliation": "Nankai University" }, { "name": "Li, Yibei", "affiliation": "Chinese Academy of Sciences" }, { "name": "Liu, Zhixin", "affiliation": "Academy of Mathematics and Systems Sciences" }, { "name": "Li, Chanying", "affiliation": "Academy of Mathematic and System Science, CAS" } ], "keywords": [ "Linear system identification", "Estimation and filtering" ], "abstract": "This paper studies the sparse identification problem of stochastic dynamical systems with infinite parameters. We first use a least squares (LS) algorithm to obtain the parameter estimates, where the dimension of parameters gradually increases with time. Based on the estimate, we propose a loss function with L_1 regularization term, by minimizing which we obtain an algorithm to estimate the unknown sparse infinite parameters. We establish the almost sure convergence result of the sparse algorithm, and further give the finite-time convergence of the set of zero elements. Our theoretical results are obtained without requiring the regression vectors to be independent and identically distributed (i.i.d.) or to satisfy the persistent excitation (PE) condition. A simulation example is given to verify the effectiveness of the theoretical results.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.3", "code": "ThA02.3", "title": "Identification of a Kalman Filter: Consistency of Local Solutions", "day": "Thursday", "date": "August 27, 2026", "time": "10:00-10:05", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Simpson, Leo", "affiliation": "University of Freiburg" }, { "name": "Diehl, Moritz", "affiliation": "University of Freiburg" } ], "keywords": [ "Linear system identification", "Estimation and filtering", "Kalman filtering" ], "abstract": "Prediction error and maximum likelihood methods are powerful tools for identifying linear dynamical systems and, in particular, enable the joint estimation of model parameters and the Kalman filter used for state estimation. A key limitation, however, is that these methods require solving a generally non-convex optimization problem to global optimality. This paper analyzes the statistical behavior of local minimizers in the special case where only the Kalman gain is estimated. We prove that these local solutions are statistically consistent estimates of the true Kalman gain. This follows from asymptotic unimodality: as the dataset grows, the objective function converges to a limit with a unique local (and therefore global) minimizer. We further provide guidelines for designing the optimization problem for Kalman filter tuning and discuss extensions to the joint estimation of additional linear parameters and noise covariances. Finally, the theoretical results are illustrated using three examples of increasing complexity. The main practical takeaway of this paper is that difficulties caused by local minimizers in system identification are, at least, not attributable to the tuning of the Kalman gain.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.4", "code": "ThA02.4", "title": "A Bayesian Optimization Approach for Optimal Tuning of Continuous-Time Predictor-Based Subspace Identification Parameters", "day": "Thursday", "date": "August 27, 2026", "time": "10:05-10:10", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Barbiero, Enrico", "affiliation": "Politecnico Di Milano" }, { "name": "Bruschi, Pietro", "affiliation": "Politecnico Di Milano" }, { "name": "Lovera, Marco", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Linear system identification", "Gaussian process", "Probabilistic and Bayesian methods for system identification" ], "abstract": "This paper addresses the challenge of systematically determining the optimal parameters for Continuous-Time Predictor-Based Subspace Identification (CT-PBSID) to maximize the accuracy of the identified model while significantly reducing the computational time with respect to grid search. The median of the Root Mean Square Error (RMSE) of the outputs in cross-validation is used as the objective in a Bayesian optimization framework, which efficiently converges to its minimum, thereby yielding the most accurate identified model. Simulations in a test example demonstrate the effectiveness and robustness of the proposed algorithm. In addition, the advantage in terms of computational time with respect to grid search is shown, suggesting that the method is effectively transferable to future industrial applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.5", "code": "ThA02.5", "title": "Retrieval and Rejection of Time-Varying Harmonics in Linear Systems", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:15", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Gres, Szymon", "affiliation": "INRIA" }, { "name": "Knudsen, Torben", "affiliation": "Aalborg University" }, { "name": "Wisniewski, Rafal", "affiliation": "Aalborg University" } ], "keywords": [ "Linear system identification", "Kalman filtering", "Time/parameter varying system identification" ], "abstract": "Many dynamical systems operate under unknown periodic disturbances, which degrade the performance of fault diagnosis and control algorithms if left untreated. In this paper, we propose a simple recursive subspace method for estimation and rejection of time-varying harmonic components in outputs of a system generated by a stochastic linear time-invariant plant and a deterministic linear time-varying harmonic subsystem. The method is validated on a toy example of a mechanical system, illustrating its effectiveness.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.6", "code": "ThA02.6", "title": "Identification of Reaction-Diffusion Systems from Finitely Many Non-Local Noisy Measurements Via Exponential Fitting (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:15-10:20", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Katz, Rami", "affiliation": "Tel Aviv University" }, { "name": "Giordano, Giulia", "affiliation": "Università Degli Studi Di Trento" }, { "name": "Batenkov, Dmitry", "affiliation": "Basis Research Institute" } ], "keywords": [ "Linear system identification", "Learning methods for control" ], "abstract": "Given a reaction-diffusion equation with unknown right-hand side, we consider the nonlinear inverse problem of estimating the associated leading eigenvalues and initial condition Fourier coefficients from a finite number of non-local noisy measurements. We define a reconstruction (i.e., estimation) criterion and, for small enough noise, we prove existence and uniqueness of the desired estimates. We derive closed-form expressions for the first-order condition numbers and bounds for their asymptotic behavior in a regime when the number of measured samples is fixed and the inter-sampling interval length is arbitrarily large. When computing the sought estimates numerically, our simulations show that the exponential fitting algorithm ESPRIT is first-order optimal, since its first-order condition numbers have the same asymptotic behavior as the analytic condition numbers in the considered regime.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.7", "code": "ThA02.7", "title": "A New Composite Learning DREM-Based Adaptive Trajectory Tracking Controller for Robot Manipulators with Guaranteed Parameter Convergence", "day": "Thursday", "date": "August 27, 2026", "time": "10:20-10:25", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Cervantes-Pérez, Luis", "affiliation": "Instituto Tecnológico De La Laguna" }, { "name": "Santibanez, Victor", "affiliation": "Instituto Tecnologico De La Laguna" }, { "name": "Sandoval, Jesus", "affiliation": "Instituto Tecnologico De La Paz" } ], "keywords": [ "Linear system identification", "Nonlinear adaptive control", "Learning methods for control" ], "abstract": "This paper presents a new composite adaptive trajectory tracking controller for fully actuated torque-driven robotic manipulators. The proposed approach integrates two powerful parameter identification techniques—namely, the dynamic regressor extension and mixing (DREM) methodology and the learning-based methodology—and exploits their combined benefits. As a first stage, the system parametrization is obtained using the power balance equation parametrization (PBEP), which yields a simpler and less computationally demanding regressor, thereby reducing the total computational cost of the proposed algorithm compared with the classical parametrization for mechanical systems. Compared with classical adaptive controllers, the proposed methodology guarantees exponential convergence to zero of the position, velocity, and parameter estimation errors—that is, the difference between the true and estimated parameters—without requiring verification of the persistent excitation condition in the regressor. Moreover, compared with the original learning-based controllers, the proposal removes the stringent requirement of verifying the invertibility of the regressor matrix, which further reduces computational cost and enhances its feasibility for implementation. Finally, the performance of the proposed controller is validated through experiments on a two-degree-of-freedom robotic manipulator, which support the claims presented.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.8", "code": "ThA02.8", "title": "A Normalized Gradient Algorithm for Exponential Estimation of Unknown Multi-Tone Sinusoidal Signal", "day": "Thursday", "date": "August 27, 2026", "time": "10:25-10:30", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Liao, Juan", "affiliation": "Southern University of Science and Technology" }, { "name": "Xu, Xiang", "affiliation": "Southern University of Science and Technology" }, { "name": "Liu, Tao", "affiliation": "Southern University of Science and Technology" } ], "keywords": [ "Linear system identification", "Nonlinear system identification", "Adaptive observer design" ], "abstract": "Recently, an exponentially convergent estimator was proposed in Liu et al. (2024) for frequency estimation of unknown continuous-time multi-tone sinusoidal signals. However, since this estimator employs a standard gradient algorithm in its parameter adaptation law, the fixed adaptation gain limits its ability to handle the measured signal with varying amplitudes. To overcome this limitation, we propose a new parameter adaptation law based on a normalized gradient algorithm. The resulting estimator features a time-varying adaptation gain that dynamically adjusts according to the measured signal amplitudes. Comparative simulations demonstrate the superior performance of the proposed estimator over the existing one, achieving reduced fluctuation and faster convergence.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.9", "code": "ThA02.9", "title": "Revisiting the Asymptotic Theory of FIR Model Estimation under a Balanced Asymptotic Setup", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:35", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Zhang, Meng", "affiliation": "The Chinese University of Hong Kong" }, { "name": "Mu, Biqiang", "affiliation": "AMSS, CAS" }, { "name": "Ljung, Lennart", "affiliation": "Linköping University" }, { "name": "Chen, Tianshi", "affiliation": "The Chinese University of Hong Kong, Shenzhen, 518172, China" } ], "keywords": [ "Linear system identification", "Statistical analysis", "Machine and deep learning for system identification" ], "abstract": "Quantifying the estimation error of a model estimate is a key problem in system identification for a given data record with finite sample size N. There are mainly two routes to address this problem: the large sample asymptotic theory based method and the non-asymptotic theory based method. However, the existing results are not very effective for quantifying the estimation error when N is not large, the model order n is not small, and n/N is not too small (e.g., n/N=0.5). In this paper, we revisit the asymptotic theory of the FIR model estimation with white noise input and measurement noise by the least squares (LS) method but under a more realistic asymptotic setup: let both N, nrainfty with n/Nragammain(0,1). We first derive the asymptotic variance and then establish the Central Limit Theorem for the squared estimation error of the LS method. Based on the obtained theoretical results, we provide two types of quantification for the estimation error of the LS method. Monte Carlo simulation demonstrates that the provided two types of quantification are more accurate than the classic ones, especially when gamma is not too small.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.10", "code": "ThA02.10", "title": "A Spectral Distance-Based Errors-In-Variables Approach for Identifying Noisy AR Models", "day": "Thursday", "date": "August 27, 2026", "time": "10:35-10:40", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Lenzi, Alice", "affiliation": "University of Bologna" }, { "name": "Diversi, Roberto", "affiliation": "University of Bologna" } ], "keywords": [ "Linear system identification", "Time series modeling" ], "abstract": "This paper presents an errors-in-variables identification method for autoregressive (AR) models in the presence of additive noise. The approach exploits the properties of the dynamic Frisch scheme and employs a loss function based on the discrete spectral distance between the power spectral density (PSD) of the noisy measurements and that of the estimated noisy AR model. The performance of the proposed identification algorithm is evaluated through Monte Carlo simulations and compared with existing methods, focusing on robustness to observation noise and spectral estimation accuracy. Simulation results demonstrate that the method is effective for both narrowband and broadband processes and achieves superior spectral estimation performance for narrowband signals. This is a valuable feature for applications such as fault diagnosis, biomedical signal processing and speech analysis.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.11", "code": "ThA02.11", "title": "Augmented Neural Ordinary Differential Equations for Power System Identification", "day": "Thursday", "date": "August 27, 2026", "time": "10:40-10:45", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Wolf, Hannes Max Hermann", "affiliation": "University of Kassel" }, { "name": "Hans, Christian Andreas", "affiliation": "University of Kassel" } ], "keywords": [ "Machine and deep learning for system identification", "Nonlinear system identification" ], "abstract": "Due the complexity of modern power systems, modeling based on first-principles becomes increasingly difficult. As an alternative, dynamical models for simulation and control design can be obtained by black-box identification techniques. One such technique for the identification of continuous-time systems are neural ordinary differential equations. For training and inference, they require initial values of system states, such as phase angles and frequencies. While frequencies can typically be measured, phase angle measurements are usually not available. To tackle this problem, we propose a novel structure based on augmented neural ordinary differential equations, learning latent phase angle representations on historic observations with temporal convolutional networks. Our approach combines state-of-the art deep learning techniques, avoiding the necessity of phase angle information for the system identification. Results show, that our approach clearly outperforms simpler augmentation techniques.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.12", "code": "ThA02.12", "title": "Least Costly Space-Filling Experiment Design for the Identification of a Nonlinear System", "day": "Thursday", "date": "August 27, 2026", "time": "10:45-10:50", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Kiss, Máté", "affiliation": "Eindhoven University of Technology" }, { "name": "Schoukens, Maarten", "affiliation": "Eindhoven University of Technology" }, { "name": "Tóth, Roland", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Nonlinear system identification", "Active learning and experiment design" ], "abstract": "The quality of an estimated nonlinear model highly depends on the data quality that was used for the system identification. By using a Gaussian Process-based optimal input design approach, a so-called space-filling dataset can be generated in the feature space of the system model. The design method is applicable for a broad type of signals and models and also incorporates information measures through optimality criteria into the signal design. However, the resulting input design can be costly to apply to the real system. The goal of this paper is to propose a space-filling input design that can minimize the experimentation cost in terms of a user defined measure, while still guaranteeing a prescribed level of space-fillingness. Through a Monte Carlo simulation study we demonstrate that the proposed method can appropriately shape the excitation signal to significantly reduce the experimental cost while the identified model performance remains adequate.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.13", "code": "ThA02.13", "title": "Designing Adaptive Observers for Nonlinearly Parameterized Systems Via Embedding into a Descriptor Dynamics", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-10:55", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Efimov, Denis", "affiliation": "Inria" }, { "name": "Ushirobira, Rosane", "affiliation": "Inria" }, { "name": "Ortega, Romeo", "affiliation": "Insituto Tecnologico Autonomo De Mexico" }, { "name": "Wang, Jian", "affiliation": "Hangzhou Dianzi University" } ], "keywords": [ "Nonlinear system identification", "Adaptive observer design" ], "abstract": "Many technological process models contain nonlinear functions with parameters that cannot be isolated or appear in an affine form after representation. This paper proposes a method for adaptively estimating systems with these non-separable nonlinear parameterizations by transforming the problem into an observation of an augmented state of a linearly parameterized nonlinear descriptor system. We propose a new adaptive observer design within this framework. The effectiveness of the developed method is shown through academic examples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.14", "code": "ThA02.14", "title": "A Koopman-Based Design for Data-Driven Control of Nonlinear Systems with Delays", "day": "Thursday", "date": "August 27, 2026", "time": "10:55-11:00", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Roy, Rahul", "affiliation": "North Carolina State University" }, { "name": "Chakrabortty, Aranya", "affiliation": "NC State University" } ], "keywords": [ "Nonlinear system identification", "Data-driven control theory", "Control under communication constraints" ], "abstract": "This paper develops a data-driven method for designing state-feedback controllers for nonlinear discrete-time dynamic systems in the presence of time-varying feedback delays. We first develop a Koopman autoencoder that learns linear latent representations of the nonlinear model directly from state measurements. Thereafter, we design a state-feedback controller in the Koopman-lifted space that is robust to the worst-case feedback delay. The two designs are illustrated using a power system model with wind power integration that contributes towards the system nonlinearity. The simulation results verify that the delay-robust Koopman-based controller can improve the control performance over a wide range of delays, thereby outperforming conventional delay-agnostic data-driven control approaches, which are shown to fail under realistic delay conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.15", "code": "ThA02.15", "title": "On the Nonexistence of Continuous Immersions for Discrete-Time Systems (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:00-11:05", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Ristich, Eron", "affiliation": "University of Michigan" }, { "name": "Sontag, Eduardo", "affiliation": "Northeastern University" }, { "name": "Ozay, Necmiye", "affiliation": "University of Michigan" } ], "keywords": [ "Nonlinear system identification", "Data-driven control theory", "Realization theory" ], "abstract": "Understanding when linear immersions of nonlinear dynamical systems exist is important since such immersions allow us to leverage the rich tools of linear system theory to analyze nonlinear dynamics. Recently, Liu et al. 2023 showed that continuous-time dynamical systems that admit countably many but more than one omega-limit sets cannot be immersed into finite dimensional linear systems with a one-to-one and continuous mapping. In this paper, we extend these results to discrete-time dynamics and show that similar obstructions exist also in discrete time. We further consider a generalization involving alpha-limit sets. Several examples are provided to demonstrate the results.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.16", "code": "ThA02.16", "title": "Instrumental Variable Identification of Nonlinear Continuous-Time Systems from Delay-Commutative Filtering", "day": "Thursday", "date": "August 27, 2026", "time": "11:05-11:10", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Rutschke, Théo", "affiliation": "CRAN, Université De Lorraine" }, { "name": "Garnier, Hugues", "affiliation": "University of Lorraine" }, { "name": "Jha, Mayank Shekhar", "affiliation": "University of Lorraine" }, { "name": "Wang, Liuping", "affiliation": "RMIT University" } ], "keywords": [ "Nonlinear system identification", "Filtering and smoothing", "Physics informed and grey box model identification" ], "abstract": "A novel method is presented for the direct continuous-time model identification of nonlinear systems subject to output measurement noise. The approach combines delayed state-variable filters with an instrumental-variable (IV) estimation scheme to remove the dominant stochastic bias present in least-squares-based formulations. The analysis further reveals residual modeling errors arising from output interpolation and imperfect commutation between delayed filtering and nonlinear mappings. Although these deterministic contributions cannot be removed by IV estimation, the imperfect commutation error can be mitigated through appropriate dSVF design and cutoff-frequency tuning. Monte Carlo simulations demonstrate robustness to high output measurement noise levels and to variations in the filter cutoff frequency.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.17", "code": "ThA02.17", "title": "Polynomial Constructibility of Nonlinear Systems: Graph-Theoretic Conditions and Reductions", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:15", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Ko, Jehyung", "affiliation": "University of Illinois at Urbana-Champaign" }, { "name": "Belabbas, Mohamed Ali", "affiliation": "University of Illinois, Urbana-Champaign" } ], "keywords": [ "Nonlinear system identification", "Linear system identification", "Time/parameter varying system identification" ], "abstract": "Polynomial systems arise naturally in control theory and related areas, yet their nonlinear structure often prevents direct analysis. This paper investigates the notion of polynomial constructibility, where the solution of a nonlinear system can be recovered as a polynomial function of the solution of a linear system. Our main results provide sufficient conditions for polynomial constructibility, formulated in terms of skeleton graphs and depth decompositions. In particular, we show that a large class of super-linearizable systems are polynomially constructible.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.18", "code": "ThA02.18", "title": "COPNet: Compositional Orthogonal Polynomial Networks for Compact and Reliable Nonlinear Modeling", "day": "Thursday", "date": "August 27, 2026", "time": "11:15-11:20", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Jaber, Halah", "affiliation": "University of Texas at San Antonio" }, { "name": "Franco, Eulises", "affiliation": "University of Texas at San Antonio" }, { "name": "Frye, Michael", "affiliation": "University of the Incarnate Word" }, { "name": "Walton, Claire", "affiliation": "University of Texas at San Antonio" } ], "keywords": [ "Nonlinear system identification", "Machine and deep learning for system identification" ], "abstract": "Approximating nonlinear dynamics with sharp transitions remains challenging in many engineering and control modeling problems. We propose COPNet, a compositional orthogonal polynomial network that uses the structure of orthogonal polynomials without explicitly constructing high degree polynomial expansions. COPNet is built from a learned second order recurrence inspired by classical orthogonal polynomial relations. Through multiplicative feature coupling and a two back recursive connection, COPNet forms a fixed width architecture that remains compact while developing expressive features across depth. In physics informed learning, COPNet can be paired with different polynomial families according to the structure of the target problem: Chebyshev based models for bounded spatiotemporal problems with sharp transitions, Hermite based models for localized Gaussian like behavior on large domains, and Legendre based models for bounded elliptic problems on uniform domains. We evaluate COPNet on the Burgers, Allen--Cahn, harmonic oscillator heat, and two dimensional Poisson equations. Across these benchmarks, COPNet achieves accurate solutions with compact architectures. On the main comparison problems, COPNet attains lower relative L^2 errors than reported PINN baselines while using fewer interior collocation points and narrower networks. These results support COPNet as an effective recurrence based architecture for efficient physics informed nonlinear modeling.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.19", "code": "ThA02.19", "title": "Deep Learning for Continuous Time Irregularly Sampled Systems", "day": "Thursday", "date": "August 27, 2026", "time": "11:20-11:25", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Yidan, Zhu", "affiliation": "Eindhoven University of Technology" }, { "name": "Beintema, Gerben Izaak", "affiliation": "Eindhoven University of Technology" }, { "name": "Schoukens, Maarten", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Nonlinear system identification", "Machine and deep learning for system identification" ], "abstract": "The availability of equidistant sampled data is a starting assumption for most identification approaches. However, in some scenarios, only non-equidistant sampled data is available, e.g. due to sensor imperfections or event-triggered sampling. This paper introduces Irregular SUBNET, tailored to identify continuous-time nonlinear state-space models starting from data sampled at irregular intervals. This approach introduces two main changes compared to the previously introduced continuous-time SUBNET identification approach: a sample interval aware encoder function for the estimation of the initial state, and the use of a variable length ODE integration to propagate the state information forward in time. The effectiveness of the proposed approach is validated on a simulation example and on the EMPS benchmark.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.20", "code": "ThA02.20", "title": "End-To-End AI Estimation of the Largest Lyapunov Exponent from Chaotic Time Series", "day": "Thursday", "date": "August 27, 2026", "time": "11:25-11:30", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Do Valle Alvarenga, João Pedro", "affiliation": "Politecnico Di Milano" }, { "name": "Sangiorgio, Matteo", "affiliation": "Politecnico Di Milano" }, { "name": "Dercole, Fabio", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Nonlinear system identification", "Machine and deep learning for system identification", "Learning methods for control" ], "abstract": "We present an end-to-end neural network approach to estimate the largest Lyapunov exponent (LLE) directly from time series. We address the research gap regarding system-agnostic generalization by training a Long Short-Term Memory (LSTM) on two structurally different maps: the logistic and Hénon maps. Results show that a jointly-trained network matches the accuracy of system-specific models ( R 2 ≈ 0.984), suggesting the network internalizes the underlying estimation algorithm rather than memorizing system-specific features.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.21", "code": "ThA02.21", "title": "Online System Identification of a Flexible Two-Link Robot Arm", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:35", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Narr, Christopher", "affiliation": "Technical University of Munich" }, { "name": "Teufel, Louis", "affiliation": "Technical University of Munich (TUM)" }, { "name": "Buss, Martin", "affiliation": "Technische Universitaet Muenchen" } ], "keywords": [ "Nonlinear system identification", "Physics informed and grey box model identification" ], "abstract": "This work addresses the online parameter identification of planar flexible two-link manipulators modeled by an assumed modes formulation. We exploit the linear-in-parameters structure of this model to perform online estimation of physically meaningful parameters, such as the motor torque constants, viscous and Coulomb friction coefficients, and structural damping parameters, using a recursive least squares scheme with an adaptive forgetting factor. This is in contrast to previous approaches that either require offline identification of friction parameters or neglect unknown parameters such as structural damping and motor torque constants. To handle the nonlinear dependence of the flexible modes on the second joint angle, three regressor constructions are proposed and compared: a model linearized around a nominal second joint angle, the full nonlinear model, and a lookup table approximation based on offline solutions of a configuration-dependent eigenvalue problem. Simulation results show that the linearized scheme does not provide reliable convergence, whereas both nonlinear variants substantially reduce the parameter errors. Importantly, the lookup table-based scheme closely matches the accuracy of the full nonlinear estimator while requiring significantly less computation time per step, which makes it a promising candidate for real-time parameter identification.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.22", "code": "ThA02.22", "title": "An Expectation-Maximization Algorithm for a Class of Wiener System Using Gaussian Sum Indicator Approximation", "day": "Thursday", "date": "August 27, 2026", "time": "11:35-11:40", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Orellana, Rafael", "affiliation": "Universidad De Santiago De Chile" }, { "name": "Cedeño, Angel L.", "affiliation": "Universidad Técnica Federico Santa María" }, { "name": "Coronel Mendez, María de los Angeles", "affiliation": "Universidad Tecnologica Metropolitana" }, { "name": "Aguero, Juan C", "affiliation": "Universidad Santa Maria" } ], "keywords": [ "Nonlinear system identification", "Probabilistic and Bayesian methods for system identification" ], "abstract": "In this paper, a Maximum Likelihood estimation algorithm for a Wiener system with a piecewise linear approximation to model the output non-linearity is developed. We propose a methodology to construct the probability density function associated with the piecewise linear function by using a Gaussian mixture indicator approximation. An Expectation-Maximization algorithm is proposed to estimate both the linear system model and the piecewise linear function parameters, obtaining closed-form expressions for the parameter estimators. The benefits of our proposal are illustrated via numerical simulations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.23", "code": "ThA02.23", "title": "Guaranteed Stable VAR(1) Estimation and a 50 Year Old Puzzle", "day": "Thursday", "date": "August 27, 2026", "time": "11:40-11:45", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Solo, Victor", "affiliation": "Univ of New South Wales" } ], "keywords": [ "Time series modeling", "Linear system identification", "Estimation and filtering" ], "abstract": "In both control and signal processing, there has been a decades long interest in constructing vector auto-regression (VAR) estimators with guaranteed stability. We revisit some classic work from the late 1970s and find a fatal flaw - namely that initiating estimators in forward and backward recursions are not guaranteed to be stable. Then, focussing on the VAR(1) case, we develop a new approach which yields a closed from estimator with the properties claimed for the classic estimator.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA02.24", "code": "ThA02.24", "title": "Multivariate Spectral Estimation Using the W-Cepstral Coefficients", "day": "Thursday", "date": "August 27, 2026", "time": "11:45-11:50", "sessionCode": "ThA02", "sessionTitle": "Shotgun: Linear and Nonlinear System Identification", "sessionType": "Interactive Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Zhu, Bin", "affiliation": "Sun Yat-Sen University" }, { "name": "Zorzi, Mattia", "affiliation": "Università Degli Studi Di Padova" } ], "keywords": [ "Time series modeling", "Linear system identification", "Realization theory" ], "abstract": "We introduce a new spectrum approximation framework for multivariate stationary processes based on a transportation-entropy formulation. Classic rational covariance extension methods allow to impose covariance constraints on the spectrum and, in the scalar case, additional constraints of cepstral coefficients to control the spectral zeros. However, extending the cepstral coefficients to the multivariate setting has remained challenging, since the standard matrix logarithm does not yield a tractable dual problem. Building on recent developments in optimal transport for Gaussian processes, we define a new class of cepstral-type quantities, called W-cepstral coefficients, derived from a spectral factor and compatible with a transportation-entropy functional. This leads to a well-posed convex optimization problem whose dual formulation can be explicitly characterized. We show that the proposed approach successfully identifies the spectrum of a multivariate stationary process from a finite set of covariance lags and W-cepstral coefficients, and we validate the theory through numerical simulations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.1", "code": "ThA03.1", "title": "An Application of Model Reference Adaptive Control for Multi-Agent Synchronization in Drone Networks", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-09:55", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Arevalo-Castiblanco, Miguel Felipe", "affiliation": "Rice University" }, { "name": "Wi, Yejin", "affiliation": "University of Houston" }, { "name": "Cescon, Marzia", "affiliation": "University of Houston" }, { "name": "Uribe, Cesar", "affiliation": "Rice University" } ], "keywords": [ "Adaptive control of multi-agent systems", "Model reference adaptive control", "Multi-agent systems" ], "abstract": "This paper presents the application of a Distributed Model Reference Adaptive Control (DMRAC) strategy for robust multi-agent synchronization of a network of drones. The proposed approach enables the development of controllers that can accommodate differences in real-life model parameters among agents, thereby enhancing overall network performance. We compare the performance of adaptive control laws with that of classical PID controllers for the reference tracking task. Each follower drone has a model reference adaptive controller that continuously updates its parameters based on real-time feedback and reference model information. This adaptability ensures adequate performance, which, compared to conventional non-adaptive techniques, can reduce the amount of energy required and consequently increase the operating duration of the drones. The experimental results, particularly in vertical velocity control, underscore the effectiveness of the proposed approach in achieving synchronized behavior.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.2", "code": "ThA03.2", "title": "A Canonical Internal Model for Disturbance Rejection for a Class of Nonlinear Systems Subject to Matched Trigonometric-Polynomial Disturbances", "day": "Thursday", "date": "August 27, 2026", "time": "09:55-10:00", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "He, Changran", "affiliation": "South China University of Technology" }, { "name": "Huang, Jie", "affiliation": "The Chinese University of Hong Kong" } ], "keywords": [ "Adaptive observer design", "Nonlinear adaptive control" ], "abstract": "Trigonometric-polynomial disturbances are among the most commonly encountered disturbances in practice, as they can approximate nearly any periodic signal with an unknown period. The most effective method for asymptotically rejecting this class of disturbances is through a dynamic compensator known as an internal model, which transforms the disturbance rejection problem into a stabilization problem for an augmented system. However, existing internal model design approaches rely heavily on the properties of the solution to the regulator equations. An effective internal model can only be constructed when this solution exhibits specific characteristics, such as being polynomial in the exogenous signal. For complex nonlinear systems, especially nonautonomous ones, solving the disturbance rejection problem using traditional methods remains challenging. In this paper, we propose a novel framework for disturbance rejection in a class of nonautonomous nonlinear systems affected by matched trigonometric-polynomial disturbances. The core of our approach is the design of a canonical internal model that directly converts the disturbance rejection problem into an adaptive stabilization problem for an augmented system. Unlike conventional methods, this internal model is synthesized directly from the given nonlinear plant and the knowledge of the exosystem, without relying on the solution of the regulator equations. This makes the approach applicable to a significantly broader class of nonautonomous nonlinear systems. Furthermore, we develop an adaptive disturbance observer comprising the canonical nonlinear internal model, a Luenberger-type state observer, and a parameter adaptation law. This observer ensures global asymptotic convergence of the disturbance estimate to the true disturbance without requiring persistent excitation (PE). Under the PE condition, both the disturbance estimation error and the parameter estimation error converge exponentially. By incorporating the disturbance estimate as a feedforward compensation signal, we establish sufficient conditions for achieving global trajectory tracking and asymptotic disturbance rejection.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.3", "code": "ThA03.3", "title": "Predictive and Inertia-Aware Motion Planning for USV Navigation in Cluttered Harbors", "day": "Thursday", "date": "August 27, 2026", "time": "10:00-10:05", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Nguyen, Tien-Thanh", "affiliation": "Royal Military Academy" }, { "name": "Vochten, Maxim", "affiliation": "Royal Military Academy" }, { "name": "De Cubber, Geert", "affiliation": "Royal Military Academy, Department of Mechanical Engineering" }, { "name": "Janssens, Bart", "affiliation": "Royal Military Academy" }, { "name": "Bruyninckx, Herman", "affiliation": "Katholieke Universiteit Leuven" } ], "keywords": [ "Autonomous marine systems and vehicles", "Marine robotics", "Marine system guidance, navigation and control" ], "abstract": "Autonomous navigation for Unmanned Surface Vehicles (USVs) in cluttered harbors presents a dual challenge: the rigorous constraints of hydrodynamic inertia and the complexity of perceiving diverse surface and underwater hazards amidst wave clutter. While recent neuro-symbolic planners excel on ground robots, they often fail in maritime settings due to kinematic mismatches and sensitivity to environmental noise. This paper presents a Predictive and Inertia-Aware Neuro-Symbolic framework designed to bridge this gap. First, to address the heterogeneity of maritime perception, we replace raw sensor inputs with a high-level representation based on Potential Collision Areas (PCAs). By aggregating object detections from multiple modalities (e.g., LiDAR, Sonar) and predicting their future trajectories, we construct dynamic 2D bounding boxes in the navigation plane that encapsulate future collision risks. This object-level abstraction unifies diverse sensor data and effectively filters out transient wave clutter that confuses standard planners. Second, we propose a physics-informed domain adaptation strategy where the neural policy is trained via constrained imitation learning. By subjecting the expert demonstrator to strict acceleration limits, the network implicitly internalizes hydrodynamic inertia, learning to initiate avoidance maneuvers well in advance. Validation in high-fidelity simulations demonstrates that our method successfully compensates for drift, handles multi-modal obstacle data, and proactively avoids dynamic threats, achieving safe navigation where standard kinematic baselines fail.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.4", "code": "ThA03.4", "title": "DSOM-GA: A Dual-Layer Self-Organizing Map Framework for Fault-Tolerant Multi-USV Task Allocation in Flow-Perturbed Coastal Environments", "day": "Thursday", "date": "August 27, 2026", "time": "10:05-10:10", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Rao, Xinyao", "affiliation": "Zhejiang University" }, { "name": "Chai, Li", "affiliation": "Zhejiang University" }, { "name": "Wang, Jiaxuan", "affiliation": "Zhejiang University" } ], "keywords": [ "Autonomous marine systems and vehicles", "Marine system guidance, navigation and control" ], "abstract": "In large-scale marine monitoring and inspection, coordinated fleets of unmanned surface vehicles (USVs) must perform efficient task allocation and path planning in dynamic environments characterized by ocean currents, obstacles, and potential system faults. To address these challenges, we propose a hierarchical framework (DSOM-GA) that combines a dual-layer self-organizing map for task partitioning with genetic algorithm-based task sequencing. The framework incorporates a coarse-to-fine, flow-aware path-cost evaluation scheme and a bounded reallocation mechanism for fault recovery. Extensive simulation-based mission-level evaluations show that the proposed framework reduces the normalized mission cost and improves the robustness of post-fault task reallocation relative to representative heuristic baselines.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.5", "code": "ThA03.5", "title": "Practical Adaptive Single-Controller Depth Regulation for Torpedo-Style AUVs", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:15", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Parkes, James A.", "affiliation": "University of Southampton" }, { "name": "Turner, Matthew C.", "affiliation": "University of Southampton" }, { "name": "Fang, Xinpeng", "affiliation": "University of Southampton" } ], "keywords": [ "Autonomous marine systems and vehicles", "Marine system guidance, navigation and control", "Dependability in marine systems" ], "abstract": "This paper presents a practical adaptive PD depth controller for torpedo-style autonomous underwater vehicles (AUVs), integrating a modified model reference adaptive control (MRAC) update law into a conventional PD architecture. The controller is evaluated on a REMUS 100 6-DOF nonlinear AUV model across multiple payloads and surge speeds. Results demonstrate that the adaptive scheme maintains accurate depth tracking with minimal overshoot and consistent rise times, while reducing control effort compared to a baseline PID controller. The adaptive gains evolve smoothly with payload and setpoint variations, providing robustness to dynamic changes. The approach offers a simple, modular method to enhance AUV performance without the complexity of fully adaptive controllers.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.6", "code": "ThA03.6", "title": "Lyapunov Constrained Soft Actor-Critic for Dynamic Positioning of Unmanned Surface Vehicles", "day": "Thursday", "date": "August 27, 2026", "time": "10:15-10:20", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Zou, Hang", "affiliation": "DongHua University" }, { "name": "Qi, Jie", "affiliation": "Donghua University" }, { "name": "Wu, Nailong", "affiliation": "DongHua University" }, { "name": "Li, Yanjie", "affiliation": "DongHua University" } ], "keywords": [ "Autonomous marine systems and vehicles", "Marine system guidance, navigation and control", "Modelling, identification and control in marine systems" ], "abstract": "This paper proposes a Lyapunov-constrained Soft Actor-Critic (LC-SAC) controller for dynamic positioning (DP) of unmanned surface vehicles (USVs). Due to the persisting disturbances from waves, wind, and ocean currents, as well as the resulting complex hydrodynamics, it is difficult to obtain an accurate USV model. To address this issue, a lightweight random Fourier feature (RFF) learning method is used to learn a unified model of USV dynamics and environmental disturbances. Considering the stringent stability and steady-state accuracy specifications in DP control, a Lyapunov-based stability constraint is integrated into the SAC framework via a primal-dual optimization scheme, in which a Lagrange multiplier enforces the stability condition during policy learning. Simulation results show that the proposed LC-SAC achieves faster convergence, smaller steady-state error, and stronger disturbance rejection than adaptive PID, Krasovskii-constrained RL, and standard SAC controllers.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.7", "code": "ThA03.7", "title": "Simple yet Effective Anti-Windup Techniques for Amplitude and Rate Saturation: An Autonomous Underwater Vehicle Case Study", "day": "Thursday", "date": "August 27, 2026", "time": "10:20-10:25", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Sarhadi, Pouria", "affiliation": "University of Hertfordshire" } ], "keywords": [ "Autonomous marine systems and vehicles", "Marine system guidance, navigation and control", "Modelling, identification and control in marine systems" ], "abstract": "Actuator amplitude and rate saturation (A&RSat), together with the associated windup problem, have long been recognised as challenges in control systems. Anti-windup (AW) schemes have been developed over the past decades and can generally be categorised into two main groups: classical and modern anti-windup (CAW and MAW) approaches. Classical methods have provided simple and effective solutions, primarily addressing amplitude saturation. In contrast, modern approaches offer powerful and theoretically sound frameworks capable of handling both amplitude and rate saturation. However, the derivation of MAW schemes often imposes restrictive conditions and can be complex to apply in practical engineering problems. Nevertheless, the literature has paid limited attention, if not largely ignored, to the potential of CAW schemes that can operate in the presence of both A&RSat. This paper revisits this issue and proposes modifications to two well-known controllers: PID and LQI. The results obtained, benchmarked on the REMUS AUV yaw control problem and compared with constrained MPC, indicate that these classical techniques can still provide simple yet effective solutions with comparable performance, at least for SISO systems. These findings may stimulate further research into solutions that achieve comparable performance with only one (or a limited number of) additional tuning parameters and enable straightforward implementation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.8", "code": "ThA03.8", "title": "Gradient-Free Plume Tracking Using a Swarm of Autonomous Underwater Vehicles", "day": "Thursday", "date": "August 27, 2026", "time": "10:25-10:30", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Nandakumar, Sreeharsh", "affiliation": "NIT Calicut" }, { "name": "K P, Sunny", "affiliation": "National Institute of Technology Calicut" }, { "name": "T K, Muhamed Jishad", "affiliation": "National Institute of Technology Calicut" }, { "name": "Radhakrishnan, Rahul", "affiliation": "National Institute of Technology Calicut" }, { "name": "Warier, Rakesh R", "affiliation": "National Institute of Technology Calicut" } ], "keywords": [ "Autonomous marine systems and vehicles", "Multi-vehicle systems", "Simulation and digital-twin in marine systems" ], "abstract": "Sustainable deep-sea mining requires effective monitoring of sediment plumes to safeguard vulnerable marine ecosystems. This paper presents a collaborative, technique for tracking sediment plumes without the explicit calculation of gradients for a swarm of realistic six-degree-of-freedom nonlinear unmanned underwater vehicles (UUVs). This vehicle model takes into account all hydrodynamic effects including thrust allocation. The suggested hybrid control framework combines the proposed control architecture with realistic UUV dynamics. We use Lyapunov-based stability analysis for the parameter selection and for theoretical stability. Numerical simulations validate the method, showing that it can coordinate swarms well and accurately localize the plumes. These results show that deploying cooperative UUV swarms for autonomous deep sea plume monitoring is a feasible option that will make marine operations safer and more environmentally friendly.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.9", "code": "ThA03.9", "title": "Performance Analysis of Homomorphically Encrypted PI Control with Anti-Windup for Anaesthetic Drug Administration", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:35", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Palma, David", "affiliation": "University of Udine" }, { "name": "Casagrande, Daniele", "affiliation": "University of Udine" }, { "name": "Montessoro, Pier Luca", "affiliation": "Università Degli Studi Di Udine" } ], "keywords": [ "Cyber security networked control", "Control over networks" ], "abstract": "This paper introduces a cloud-assisted closed-loop anaesthesia control system that preserves patient data privacy through homomorphic encryption. Sensor measurements are encrypted using the homomorphic CKKS scheme, and controller computations are performed directly on ciphertexts, maintaining confidentiality. Since homomorphic encryption cannot perform non-linear operations such as saturations and clampings, the paper analyses how such limitations affect the performance of a controller with anti-windup mechanism. The study is carried out by means of numerical simulations concerning the practical scenario of a patient whose level of anaesthesia must be regulated.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.10", "code": "ThA03.10", "title": "Safety-Preserving Vector Current Control in Grid-Connected Inverter-Based Resources under Stealthy Cyberattacks", "day": "Thursday", "date": "August 27, 2026", "time": "10:35-10:40", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Escudero, Cédric", "affiliation": "Laboratoire Ampère CNRS, INSA Lyon, Université De Lyon" }, { "name": "Sadabadi, Mahdieh S.", "affiliation": "The University of Manchester" } ], "keywords": [ "Cyber security networked control", "Resilient networked control systems", "Fault detection and diagnosis" ], "abstract": "This paper addresses the problem of designing a resilient vector current control strategy for grid-connected Inverter-Based Resources (IBRs) under stealthy cyberattacks. We investigate scenarios where sophisticated adversaries aim to compromise the safety of the grid-connected IBR by injecting false data into control input channels, specifically designed to bypass existing bad data detection mechanisms in the system. The proposed approach introduces a safety-preserving control framework that can maintain the safe operation of the grid-connected IBR even when subjected to such stealthy attacks. The proposed controller is a solution to a convex optimization problem. Simulation results demonstrate the effectiveness of the proposed approach in ensuring continued stable operation under stealthy attacks, thereby enhancing the cybersecurity posture of critical IBR interfaces in modernized power systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.11", "code": "ThA03.11", "title": "Shared Situational Awareness Using Hybrid Zonotopes with Confidence Metric", "day": "Thursday", "date": "August 27, 2026", "time": "10:40-10:45", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Narri, Vandana", "affiliation": "KTH Royal Institute of Technology and Scania AB CV" }, { "name": "Glunt, Jonah", "affiliation": "The Pennsylvania State University" }, { "name": "Robbins, Joshua", "affiliation": "Pennsylvania State University" }, { "name": "Mårtensson, Jonas", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Pangborn, Herschel", "affiliation": "The Pennsylvania State University" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Distributed control and estimation" ], "abstract": "Situational awareness for connected and automated vehicles describes the ability to perceive and predict the behavior of other road-users in the near surroundings. However, pedestrians can become occluded by vehicles or infrastructure, creating significant safety risks due to limited visibility. Vehicle-to-everything communication enables the sharing of perception data between connected road-users, allowing for a more comprehensive awareness. The main challenge is how to fuse perception data when measurements are inconsistent with the true locations of pedestrians. Inconsistent measurements can occur due to sensor noise, false positives, or unmodeled disturbances. This paper employs set-based estimation with constrained zonotopes to compute a confidence metric for the measurement from each sensor. Estimated sets and their confidences are then fused using hybrid zonotopes. This method can account for inconsistent measurements, enabling reliable and robust fusion of the sensor data. The effectiveness of the proposed method is demonstrated in experiment.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.12", "code": "ThA03.12", "title": "Modelling and Optimal Control for Bi-Directional Hydraulic PTO-Based Onshore Wave Energy Converters", "day": "Thursday", "date": "August 27, 2026", "time": "10:45-10:50", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Yu, Shuang-Rui", "affiliation": "University of Manchester" }, { "name": "Bai, Haomeng", "affiliation": "University of Manchester" }, { "name": "Li, Guang", "affiliation": "University of Manchester" } ], "keywords": [ "Marine renewable energy systems", "Modelling, identification and control in marine systems", "Simulation and digital-twin in marine systems" ], "abstract": "The performance of wave energy converters (WECs) relies on both device design and control strategies. This paper presents the modelling and simulation of a non-causal optimal control strategy for an onshore hinged WEC developed by Eco Wave Power Ltd (EWP). A standalone linear non-causal optimal control (LNOC) algorithm is implemented to improve the energy capture efficiency of the WEC. A motor-driven hydraulic pump is used within the hydraulic power take-off (HPTO) system to deliver the control torque. We compare the WEC control performance based on two PTO designs respectively: an existing HPTO design employed by EWP only allowing uni-directional power flow tailored for passive damping control and a modified HPTO design supporting bi-directional power flow suitable for active controller implementation. The HPTO system and the WEC are controlled in a hierarchical control architecture. Simulation results demonstrate that with the new HPTO design the LNOC controller can improve the energy capture output by 19.65% over a well-tuned passive controller. The proposed control scheme is also robust against nonlinear PTO dynamics. The enhanced power absorption shows a 0.57% to 0.63% deviation between the linear and nonlinear PTO models.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.13", "code": "ThA03.13", "title": "Vessel Trajectory Prediction Using COLREGs-Aware Optimal Planning", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-10:55", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Kaikkonen, David", "affiliation": "NIBE" }, { "name": "Ljungberg, Fredrik", "affiliation": "ABB Corporate Research" }, { "name": "Frisk, Erik", "affiliation": "Linköping University" } ], "keywords": [ "Marine system guidance, navigation and control", "Autonomous marine systems and vehicles", "Decision and support in marine systems" ], "abstract": "This paper presents a trajectory prediction method for marine vessels based on optimal planning. Crude initial trajectories respecting static obstacles are first generated using A*-search to provide a feasible warm start. In the second step, a numerical optimizer is used to ensure COLREG compliance. The prediction problem is posed as sequential trajectory planning from the perspective of each surrounding vessel, requiring only their current positions, velocities, and intended destinations as input. As the latter is included in AIS messages, this enables faster predictions than learning-based methods that typically require longer data histories. The proposed method is validated using real-world scenarios constructed from AIS data.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.14", "code": "ThA03.14", "title": "Cascaded Sliding Mode Based Practical Predefined Time Control of Marine Surface Vessels", "day": "Thursday", "date": "August 27, 2026", "time": "10:55-11:00", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Sarkar, Antara", "affiliation": "Indian Institute of Technology Guwahati" }, { "name": "Deka, Ankur", "affiliation": "Indian Institute of Technology Guwahati" }, { "name": "Basireddy, Sandeep Reddy", "affiliation": "Indian Institute of Technology Guwahati" } ], "keywords": [ "Marine system guidance, navigation and control", "Autonomous marine systems and vehicles", "Marine robotics" ], "abstract": "Predefined time control (PdTC) schemes enable control designers to pre-specify the time in which system trajectories should converge to the origin of the state-space, thereby enabling user-defined control of the system. However, PdTC schemes are comparatively rarer when cascade control structures (CCSs) are used for controller design especially for trajectory tracking problems in marine surface vessels (MSVs). Even when used for CCSs especially in conjunction with sliding mode control (SMC) methods, the controller design is limited to each loop in the CCS. In this paper, a PdTC scheme for an MSV trajectory tracking problem is proposed wherein a predefined time unified sliding surface is designed across both loops of the CCS, which to the best of the author's knowledge, has never been presented before for trajectory tracking problems in MSVs. The resulting controller structure is simple in the relevant literature and needs few parameters to be tuned. The stability of the closed-loop system is shown via Lyapunov theory and numerical simulations are presented to support the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.15", "code": "ThA03.15", "title": "Bounded Backstepping Controller for Trajectory Tracking of an Unmanned Surface Vessel", "day": "Thursday", "date": "August 27, 2026", "time": "11:00-11:05", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Leveque, Paul", "affiliation": "Université De Caen Normandie" }, { "name": "Oudainia, Mohamed Radjeb", "affiliation": "University of Caen" }, { "name": "Reuter, Johannes", "affiliation": "University of Applied Sciences Konstanz" }, { "name": "Ménard, Tomas", "affiliation": "ENSICAEN" } ], "keywords": [ "Marine system guidance, navigation and control", "Nonlinear and optimal automotive control", "Autonomous marine systems and vehicles" ], "abstract": "This paper addresses the trajectory-tracking problem for Unmanned Surface Vessels (USVs) in the presence of actuator input limitations. To handle these constraints, we propose a bounded backstepping control strategy that ensures the control inputs remain within predefined limits while preserving the structural advantages of the classical backstepping design. The stability of the closed-loop system is analyzed using Lyapunov theory, allowing the derivation of a control law that guarantees convergence of the tracking errors to zero. The proposed controller is validated through numerical simulations on a fully actuated USV model incorporating nonlinear damping terms and is compared with a classical backstepping approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.16", "code": "ThA03.16", "title": "Parameters and Drifting Current Estimation of 3-Degree of Freedom Marine Vessel Using Physics Informed Neural Network", "day": "Thursday", "date": "August 27, 2026", "time": "11:05-11:10", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Roman, Christophe", "affiliation": "Lis Umr 7020 Cnrs / Amu / Utln" }, { "name": "Saab, Ahmad", "affiliation": "Aix-Marseille University" }, { "name": "Noura, Hassan", "affiliation": "Aix-Marseille University" }, { "name": "Ouladsine, Mustapha", "affiliation": "Professeur à Aix Marseille Université" } ], "keywords": [ "Modelling, identification and control in marine systems", "AI and embodied-AI in marine systems" ], "abstract": "The objective of this paper is to evaluate the capability of Physics-Informed Neural Networks (PINNs) for parameter estimation and current-induced disturbance identification in a 3-degree-of-freedom (3-DOF) marine vessel model. The proposed approach uses a neural network to approximate the system states, where training is performed by minimizing the residuals of the governing differential equations. The developed algorithm is validated using simulation data. This work first compares the proposed method with classical parameter estimation techniques for first-order models, both with and without delay. Then, the proposed method is applied to a 3-DOF marine vessel model with unknown parameters and drifting current disturbances. The obtained results demonstrate the performance of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.17", "code": "ThA03.17", "title": "Data-Driven Modeling of Surface Vehicle Dynamics Using Deep Koopman Networks", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:15", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Choi, Jiyong", "affiliation": "Korea Advanced Institute of Science and Technology" }, { "name": "Kim, Jinwhan", "affiliation": "KAIST" } ], "keywords": [ "Modelling, identification and control in marine systems", "Marine robotics", "Autonomous mobile robots" ], "abstract": "This paper introduces a data-driven method for modeling surface vehicle dynamics by integrating a deep learning framework with the Koopman Operator. Ship dynamics involves strong nonlinearities due to hydrodynamic forces and actuation effects, making conventional approaches less effective. In order to address these nonlinearities, the proposed method learns a finite-dimensional linear time-invariant predictor in an observable space, while using monotonic rational-quadratic splines to capture nonlinear input effects. It identifies an end-to-end dynamics model without requiring prior knowledge of the system. Validation on turning and zigzag maneuvers shows high prediction accuracy for surge, sway, and yaw velocities.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.18", "code": "ThA03.18", "title": "GNN-Based Real-Time Graph Learning Using Memory Regressor Extension", "day": "Thursday", "date": "August 27, 2026", "time": "11:15-11:20", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Fallin, Brandon", "affiliation": "University of Florida" }, { "name": "Nino, Cristian", "affiliation": "University of Florida" }, { "name": "Dixon, Warren E.", "affiliation": "Univ of Florida" } ], "keywords": [ "Multi-agent systems", "Control over networks", "Nonlinear adaptive control" ], "abstract": "This paper develops a real-time graph learning framework for nonlinear multi-agent systems (MASs) subject to unknown inter-agent interaction dynamics. Unlike prior methods that rely on linear approximations or batch processing, we approximate the unknown interaction dynamics online using a graph neural network (GNN) for a MAS performing trajectory tracking and formation control. To identify the network topology, we leverage memory regressor extension (MRE), which uses a history stack of data to relax standard persistence of excitation conditions. Structural properties of the graph adjacency matrix are enforced by embedding the adaptive update law into a projected dynamical system.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.19", "code": "ThA03.19", "title": "Value-Based Online Allocation for Line Target Defense in Nonlinear Systems", "day": "Thursday", "date": "August 27, 2026", "time": "11:20-11:25", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Tang, Rugang", "affiliation": "The Hong Kong Polytechnic University" }, { "name": "Luo, Chengfeng", "affiliation": "Northwestern Polytechnical University" }, { "name": "Tan, Zheng", "affiliation": "The Hong Kong Polytechnic University" }, { "name": "Ning, Xin", "affiliation": "Northwestern Polytechnical University" }, { "name": "Wen, Chih-Yung", "affiliation": "The Hong Kong Polytechnic University" } ], "keywords": [ "Multi-agent systems", "Control under communication constraints" ], "abstract": "Addressing the intractability of high-dimensional multi-attacker multi-defender (MAMD) reach-avoid differential graphical (RADG) games, this paper presents a hierarchical control framework for defending a line target against multiple attackers using nonlinear heterogeneous agents. We explicitly decouple the global game into tractable single-attacker multi-defender (SAMD) sub-problems to mitigate the curse of dimensionality. A key innovation is the value-based greedy coalition (VBGC) strategy, which supersedes traditional geometric heuristics by dynamically allocating defenders based on learned game-theoretic value functions, thereby capturing the true heterogeneous dynamics of the team. To ensure consistency between layers, we introduce the concept of admissible partitions, providing rigorous topological constraints that guarantee the existence of a solution for each sub-game. At the execution layer, a distributed solver utilizing approximate dynamic programming (ADP) is developed to generate control policies without requiring persistence of excitation. Numerical simulations demonstrate the framework’s superior performance in coordinating heterogeneous agents compared to distance-based baselines.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.20", "code": "ThA03.20", "title": "Stochastic Social Learning: Herding Behavior in Open Systems (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:25-11:30", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Satheeskumar Varma, Vineeth", "affiliation": "CRAN - Université De Lauraine" }, { "name": "Macault, Emilien", "affiliation": "University of Lorraine" }, { "name": "Morarescu, Irinel Constantin", "affiliation": "Universite De Lorraine" } ], "keywords": [ "Multi-agent systems", "Randomized algorithms in stochastic systems" ], "abstract": "In this work we consider a group of agents interacting randomly in order to learn the best action. At the beginning of the process, all the agents independently observe a realization of a random signal corresponding to some action, with the best action being the most probable. The initial observations lead to the assignment of initial actions for the agents. Next, the agents observe in discrete time the action of a random agent in the network, and they update their own action. We consider the cases when the set of agents is fixed (closed network) and when the set of agents is time-varying (open network). In both situations we are analyzing if a majority of agents is able to learn the most probable realization of the state of nature. In the closed network case (or when the entry/exit rate is very small), the herding behavior hampers any learning of changes in the state of nature. On the other hand, it is shown that, under suitable conditions, open networks are able to learn the most probable realization even when this realization changes in time. Numerical simulations illustrate our theoretical results.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.21", "code": "ThA03.21", "title": "Timing-Aware Two-Player Stochastic Games with Self-Triggered Control", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:35", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Pan, Yunian", "affiliation": "New York University" }, { "name": "Zhu, Quanyan", "affiliation": "New York University" } ], "keywords": [ "Multi-agent systems", "Stochastic control", "Control under communication constraints" ], "abstract": "We study self-triggered two-player stochastic games on Piecewise Deterministic Markov Processes (PDMPs), where each agent decides when to observe and which open-loop action to hold. Augmenting the state with clocks and committed controls yields flow regions (both hold) and trigger surfaces (at least one updates). The framework covers both blind simultaneous (Nash) timing and observable sequential (Stackelberg) commitments; the former leads to coupled, intractable QVIs, while the latter admits a nested Hamilton–Jacobi–Bellman quasi-variational inequality and a tractable dynamic-programming decomposition. We outline a computational scheme based on implicit differentiation of the follower’s fixed point. A pursuit–evasion example illustrates the strategic timing interaction.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.22", "code": "ThA03.22", "title": "BIHIC: Brain Cognition Inspired Interpretable High-Definition Image Classification Model", "day": "Thursday", "date": "August 27, 2026", "time": "11:35-11:40", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Zhang, Ke", "affiliation": "National University of Defense Technology" }, { "name": "Shao, Tianhao", "affiliation": "Army Engineering University of PLA" }, { "name": "Zhang, Xiaoxiong", "affiliation": "National University of Defense Technology" }, { "name": "Wang, Fangxiao", "affiliation": "National University of Defense Technology" }, { "name": "Zhou, Xiaolei", "affiliation": "Army Engineering University of PLA" }, { "name": "Yan, Hao", "affiliation": "National University of Defense Technology" }, { "name": "Fan, Qiang", "affiliation": "National University of Defense Technology" }, { "name": "Huang, Shan", "affiliation": "National University of Defense Technology" } ], "keywords": [ "Neural and fuzzy adaptive control" ], "abstract": "Fuzzy neural networks (FNNs) combine the interpretability of fuzzy systems with the self-learning ability of neural networks, yet they struggle with high-dimensional unstructured data, facing challenges of rule explosion and computational collapse. Inspired by cognitive neuroscience, this paper proposes BIHIC, a Brain cognition inspired Interpretable High-definition Image Classification model based on a pre-trained StyleGAN and an FNN. The model transforms StyleGAN's high-dimensional latent codes into low-dimensional disentangled features via a transformation network, mitigating fuzzy rule explosion. The improved FNN utilizes these low-dimensional features for interpretable classification, enhanced by fuzzy rule visualization and feature visualization methods. Experiments on the CelebA-HQ face dataset show that our method maintains high classification accuracy while providing human-intuitive explanations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA03.23", "code": "ThA03.23", "title": "Structural Unification and a Direct Continuous–-Discrete Design Transformation in Nonlinear Affine Adaptive Control", "day": "Thursday", "date": "August 27, 2026", "time": "11:40-11:45", "sessionCode": "ThA03", "sessionTitle": "Shotgun: Design and Mechatronics", "sessionType": "Interactive Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Wu, Xiang-Hong", "affiliation": "National Taiwan University" }, { "name": "Li, Kang", "affiliation": "National Taiwan University" } ], "keywords": [ "Nonlinear adaptive control", "Model reference adaptive control" ], "abstract": "Despite decades of progress in continuous--time adaptive control, deploying such controllers on digital processors frequently requires altering their adaptive structure, thereby risking the loss of core stability guarantees. To address this challenge, this paper introduces a unified Lyapunov--based framework that establishes a direct correspondence between continuous--time and discrete--time nonlinear affine adaptive control systems. A transformation is developed that maps a wide class of continuous--time adaptive laws to their discrete--time realizations without modifying their structural form. Numerical results demonstrate that the proposed approach achieves robust tracking even at computation rates as low as 10 Hz.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.1", "code": "ThA04.1", "title": "Nonparametric Regulation for Altitude-Guided Navigation of SuperPressure Balloons", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-09:55", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Azhdari, Maryam", "affiliation": "Queen's University" }, { "name": "Guay, Martin", "affiliation": "Queen's Univ" }, { "name": "Harry, Telema", "affiliation": "Queen's University" }, { "name": "Wang, Shimin", "affiliation": "Massachusetts Institute of Technology" } ], "keywords": [ "Aerial and space robotics", "Guidance, navigation and control of aircraft and spacecraft", "Guidance, navigation and control for AVs" ], "abstract": "This paper presents a robust nonparametric output regulation framework for altitude-guided navigation and station-keeping of super-pressure balloons. Unlike extremum seeking control, which relies on local gradient estimation, the proposed regulator ensures robust output tracking under uncertain wind dynamics. A bearing–distance objective function is employed to minimize drift and maintain the balloon within a target region. Simulation studies using real atmospheric and wind data demonstrate improved tracking accuracy, reduced sensitivity to disturbances, and an improvement of 5–39 percent in time spent within the station-keeping zone compared to extremum seeking control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.2", "code": "ThA04.2", "title": "Null-Space Reinforcement Learning for Trajectory Optimization of Free-Floating Space Manipulators under Inertia Changes", "day": "Thursday", "date": "August 27, 2026", "time": "09:55-10:00", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Kim, Junesuk", "affiliation": "Seoul National University" }, { "name": "Park, Hyeongjun", "affiliation": "Seoul National University" } ], "keywords": [ "Aerial and space robotics", "Learning and adaptation in autonomous vehicles", "Space exploration and transportation" ], "abstract": "This paper presents NS-TD3, a null-space reinforcement learning framework for trajectory planning of a 7-DOF free-floating space manipulator performing repetitive module transport under abrupt inertia changes. The one-dimensional kinematic redundancy is parameterized by a scalar alpha, and a TD3 agent learns the optimal alpha(t) policy that restores base attitude across full forward-and-homing cycles-a task beyond any instantaneous strategy when payload attachment shifts the inertia coupling matrix mid-cycle. Without explicit knowledge of the inertia-change event, NS-TD3 reduces terminal attitude error by 67% over the minimum-norm (MN) baseline and 42% over a receding-horizon QP (RH-QP) using the same inertia model with explicit mode switching, while satisfying joint constraints and maintaining end-effector accuracy at pseudoinverse-level computational cost.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.3", "code": "ThA04.3", "title": "Expert-Guided Reinforcement-Learning for Autonomous Cooperative UAV Formation Control", "day": "Thursday", "date": "August 27, 2026", "time": "10:00-10:05", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Li, Wei", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Chen, Xin", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Xu, Huan", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Sun, Jiushun", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Mingyang, Xie", "affiliation": "Nanjing University of Aeronautics and Astronautics, Nanjing, China" } ], "keywords": [ "AI for aircraft and spacecraft navigation, guidance and control", "Aerospace mission control and operations", "Aerial and space robotics" ], "abstract": "This paper presents an autonomous cooperative control framework based on Expert-Guided Proximal Policy Optimization (EG-PPO) for unmanned aerial vehicle (UAV) diamond formation maintenance in cluttered environments. In the proposed framework, an artificial potential field-proportional-integral-derivative (APF-PID) controller is first designed to generate high-quality expert demonstrations, which are used to guide the initial policy learning process. Then, a cooperative reward function that jointly considers formation maintenance and obstacle avoidance is constructed to support multi-objective optimization. To focus on high-level cooperative decision-making, the problem is formulated in a two-dimensional planar environment with constant flight altitude, where the learned policy outputs velocity commands for each UAV. Simulation results show that the proposed EG-PPO method achieves faster convergence, smaller formation-maintenance errors, and smoother trajectories than baseline PPO. The framework also demonstrates good scalability in formation assembly tasks with different swarm sizes.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.4", "code": "ThA04.4", "title": "Anomaly Detection with Fuzzy Adaptive Kalman Filter on 3DoF Helicopter Model", "day": "Thursday", "date": "August 27, 2026", "time": "10:05-10:10", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Arıcan, Ahmet Çağrı", "affiliation": "Gazi University" }, { "name": "Çopur, Engin Hasan", "affiliation": "Necmettin Erbakan University" }, { "name": "Candan, Fethi", "affiliation": "Ankara University" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft" ], "abstract": "This paper presents a Fuzzy Adaptive Kalman Filter (FAKF) for anomaly and spoofing detection on a 3-DoF helicopter platform. Unlike classical Kalman filters with fixed noise assumptions, the proposed method dynamically updates process and measurement covariances using fuzzy logic driven by innovation statistics and residual consistency measures. An LQI controller regulates the helicopter’s elevation, travel, and pitch, while additive and drift-type spoofing attacks are injected into the sensor channels to evaluate robustness. Simulation results show that the FAKF effectively suppresses corrupted measurements, enhances anomaly sensitivity, and maintains stable state estimation under non-stationary and adversarial conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.5", "code": "ThA04.5", "title": "Large-Angle Attitude Maneuver of Spacecraft Using a Combination of Reaction Control System and Reaction Wheel Based on Integral Sliding Mode Control", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:15", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Ikeda, Yuichi", "affiliation": "Shonan Institute of Technology" }, { "name": "Takaku, Yuichi", "affiliation": "Tokyo Univercity of Science" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft" ], "abstract": "Missions involving rapid and large-angle attitude maneuvers have been conceived for astronomical and Earth observation satellites in recent years. Since the rotational motion of a spacecraft in such missions is nonlinear, it will be required to design an attitude control system that takes into account nonlinear motion. In light of the necessity for an actuator capable of generating large torque, it is imperative to consider the characteristics of an actuator when designing a control system. Actuators capable of generating large torques include the reaction control system (RCS). RCS provides an on/off input by using the reaction force of fuel injection from the thrusters, it can generate a large torque. In addition, the control system of current application satellites normally uses both RCS and reaction wheel (RW) conventionally used for attitude control. For the above reasons, this paper considers large angle attitude maneuver of spacecraft by a combination of RCS and RW. First, characteristics of RCS and RW are defined, and a design model for controller design is derived based on the relative motion equation of the spacecraft. Next, we design a nonlinear tracking controller using the integral sliding mode control (ISMC) method to ensure that the switching function remains bounded. Then, we propose a method to appropriately change RCS injection threshold according to spacecraft attitude by solving an optimization problem.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.6", "code": "ThA04.6", "title": "Linear Parameter Varying Control for a Tail-Free Airship with Distributed-Propulsion", "day": "Thursday", "date": "August 27, 2026", "time": "10:15-10:20", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Noelle, David", "affiliation": "Technische Universitaet Dresden" }, { "name": "Biertümpfel, Felix", "affiliation": "Technische Universität Dresden" }, { "name": "Riboldi, Carlo E.D.", "affiliation": "Politecnico Di Milano" }, { "name": "Pfifer, Harald", "affiliation": "Technische Universität Dresden" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft" ], "abstract": "The paper presents a linear parameter varying (LPV) controller design in the induced L2-framework for a tail-free airship. The considered airship is a technology demonstrator developed in the European Innovation Council project IPROP, which ultimately aims to design a high-altitude airship propelled by a novel ionic thruster system. The demonstrator considered in the present paper still uses conventional propellers driven by electric motors, but it already lacks traditional aerodynamic control surfaces and an an empennage. The latter increases aerodynamic efficiency and simplifies the structure but leads to a loss of natural stability. As a novelty, the stabilization and attitude control of the airship is purely achieved by differential thrust allocation. The LPV controller is scheduled with the airship's airspeed which enables a significantly larger flight envelope compared to a linear time invariant controller. The performance and robustness of the controller are evaluated in a high-fidelity nonlinear simulator.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.7", "code": "ThA04.7", "title": "Uncertainty-Aware Robust Transition Trajectory Optimization for Tilt-Wing UAVs", "day": "Thursday", "date": "August 27, 2026", "time": "10:20-10:25", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Yang, Yunjie", "affiliation": "Tsinghua University" }, { "name": "Xu, Chenzhou", "affiliation": "Tsinghua University" }, { "name": "Du, Zhihui", "affiliation": "Tsinghua University" }, { "name": "Liao, Wenan", "affiliation": "Tsinghua University" }, { "name": "Zhu, Jihong", "affiliation": "Tsinghua University" }, { "name": "Liu, Kai", "affiliation": "Tsinghua University" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Aerial and space robotics", "Aerospace mission control and operations" ], "abstract": "Tilt-wing unmanned aerial vehicles (UAVs) combine the vertical takeoff and landing capability of multi-rotors with the high-speed cruise efficiency of fixed-wing aircraft, but their transition phase involves strong aerodynamic coupling and time-varying control effectiveness. To improve robustness under uncertainties, this paper proposes a robust optimal transition trajectory optimization method for tilt-wing UAVs. Unlike existing deterministic optimization approaches, the proposed method explicitly accounts for stochastic uncertainties in the initial state, propeller thrust coefficients, and aerodynamic parameters. Correlated uncertainties commonly observed in coupled flight dynamics are efficiently decoupled using the Gram–Schmidt transformation, avoiding the need to construct new orthogonal polynomial bases. Moreover, a sinusoidal transformation of control inputs and an extended penalty function are introduced to convert the constrained optimization into an unconstrained formulation, simplifying numerical computation while ensuring control feasibility. Simulation results demonstrate that the proposed method significantly enhances the robustness of the transition flights.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.8", "code": "ThA04.8", "title": "Optimal Satellite Jamming-Avoidance Maneuvers under Directed Interference", "day": "Thursday", "date": "August 27, 2026", "time": "10:25-10:30", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Kim, Minchae", "affiliation": "Korea Advanced Institute of Science and Technology" }, { "name": "Park, Jeongho", "affiliation": "KAIST (Korea Advanced Institute of Science and Technology)" }, { "name": "Kim, Sung Jun", "affiliation": "Korea Advanced Institute of Science and Technology" }, { "name": "Yoon, Hyosang", "affiliation": "KAIST" }, { "name": "Choi, Han-Lim", "affiliation": "Korea Advanced Institute of Science and Technology" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Aerospace mission control and operations" ], "abstract": "This paper proposes an optimal control framework for designing avoidance maneuvers to protect a target satellite from intentional jamming by a non-cooperative spacecraft. The framework integrates J2-perturbed orbital dynamics with a link-budget-based jamming-to-signal ratio model and employs a smoothed antenna gain representation suitable for direct collocation. The resulting optimal control problem simultaneously minimizes jamming exposure and maneuver cost while enforcing recovery of the primary orbital elements after the avoidance maneuver. In the numerical case study, the proposed method reduces the maximum J/S ratio from 21.39 dB in the no-maneuver case to 0.72 dB and decreases the duration above the jamming-risk threshold by approximately 85%. Compared with a reactive baseline, the proposed method reduces the total delta-V by approximately 95%, demonstrating its ability to avoid jamming while efficiently recovering orbital geometry.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.9", "code": "ThA04.9", "title": "Linear-Nonlinear Sliding Mode Control of Finite Time Trajectory Tracking for Multirotor UAVs Using the Logarithmic Map of SO(3)", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:35", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Hsieh, Yao-Wen", "affiliation": "Chung Yuan Christian University" }, { "name": "Yu, Jen-te", "affiliation": "Chung Yuan Christian University" }, { "name": "Chuang, Cheng-Che", "affiliation": "Chung Yuan Christian University" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Aerospace mission control and operations", "Aerial and space robotics" ], "abstract": "This work presents a control scheme for trajectory tracking of multirotor UAVs featuring finite-time convergence of both position and attitude. The attitude error is expressed in the Lie algebra via the logarithmic map of SO(3), which transforms geodesics on the rotation manifold into straight lines in the Lie algebra, thereby providing the most natural and effective representation of attitude error. Both the position and the attitude controllers are designed based on a unified framework of linear-nonlinear sliding-mode control where the coexistence of the linear and fractional-exponent terms induces an adaptive two-phase dynamic behavior: a smooth decay is governed by the linear component when the error is relatively large, while the nonlinear fractional term accelerates convergence as the state approaches the neighborhood of the origin. In both regions, the corresponding dominant term ensures that the tracking error continues to converge rapidly toward zero. Computer simulations were conducted to validate the approach, and the preliminary results demonstrated the effectiveness of the proposed method supporting its feasibility in the UAV applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.10", "code": "ThA04.10", "title": "Adaptive Sliding Mode Attitude and Momentum Control of VLEO Spacecraft without Additional Actuators", "day": "Thursday", "date": "August 27, 2026", "time": "10:35-10:40", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Park, Jeongho", "affiliation": "KAIST (Korea Advanced Institute of Science and Technology)" }, { "name": "Wi, Junsung", "affiliation": "KAIST (Korea Advanced Institute of Science and Technology)" }, { "name": "Yoon, Hyosang", "affiliation": "KAIST" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Flight dynamics modelling and identification", "Aerospace mission control and operations" ], "abstract": "This paper addresses simultaneous attitude stabilization and angular momentum management for spacecraft operating in Very Low Earth Orbits (VLEOs), where aerodynamic disturbance torque is significant. Conventional momentum unloading methods often rely on auxiliary aerodynamic surfaces or specialized mechanisms, increasing system mass and complexity. In contrast, this work exploits naturally available aerodynamic torque while using reaction wheel torques as the sole commanded actuators. A two-loop adaptive sliding mode controller is developed, where the adaptive switching-gains in both loops provide robustness to aerodynamic model uncertainty. The control law is developed based on a nominal aerodynamic torque model derived from spacecraft symmetry, whereas the aerodynamic torque in simulation is generated from high-fidelity Direct Simulation Monte Carlo (DSMC) data for a realistic spacecraft geometry, so that closed-loop robustness is assessed under deliberate model–plant mismatch. Numerical simulations at 300 km altitude demonstrate asymptotic convergence of the attitude error and angular momentum over multiple orbital periods without any additional actuators or dedicated aerodynamic surfaces.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.11", "code": "ThA04.11", "title": "EFTG: An Enhanced Finite-Time Convergent Spatiotemporal Constrained Guidance Law", "day": "Thursday", "date": "August 27, 2026", "time": "10:40-10:45", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Gao, Longjie", "affiliation": "Xinjiang University" }, { "name": "Shi, Heng", "affiliation": "Tsinghua University" }, { "name": "Tao, Xiaoming", "affiliation": "Tsinghua University" }, { "name": "Yang, Luhua", "affiliation": "Tsinghua University" }, { "name": "Kuang, Minchi", "affiliation": "Tsinghua University" }, { "name": "Zhu, Jihong", "affiliation": "Tsinghua University" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Guidance, navigation and control for AVs", "Trajectory and path planning for AVs" ], "abstract": "This paper presents a new Enhanced Finite-Time Convergent Spatiotemporal Constrained Guidance Law (EFTG), which is achieved by augmenting an exact spatiotemporal guidance baseline with faster time-error convergence and saturation-aware compensation mechanisms. Rather than redefining the precise underlying time-to-go predictor, this study retains the exact baseline angle/time coordination structure and introduces a nonlinear finite-time time-control term, an auxiliary anti-saturation compensator and an H_infty-inspired dynamic output-feedback fusion strategy. Simulation results show that the EFTG method maintains accurate impact-time and impact-angle coordination while enhancing robustness, command smoothness and terminal precision during aggressive manoeuvres.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.12", "code": "ThA04.12", "title": "Periapsis Altitude Control for Mars Aerobraking Using Nonlinear Model Predictive Control and Continuous Low-Thrust Propulsion", "day": "Thursday", "date": "August 27, 2026", "time": "10:45-10:50", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Ferry, Matthieu", "affiliation": "Beihang University" }, { "name": "Liang, Yuying", "affiliation": "Beihang University" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Space exploration and transportation" ], "abstract": "The exploration of a planet often requires a spacecraft to enter a low circular orbit for detailed scientific observation. Aerobraking is an orbital maneuver which aims to lower the apoapsis by passing multiple times through the planet’s atmosphere. Aerobraking enables propellant mass savings but requires that the altitude at periapsis is properly maintained within a safe corridor to ensure sufficient drag for orbit reduction while preventing destructive heating. This paper proposes a periapsis altitude control strategy for Mars aerobraking using nonlinear model predictive control (NMPC) and continuous low-thrust propulsion. The designed NMPC optimizes a finite-horizon trajectory by predicting the altitude at periapsis and computing optimal thrust profile to ensure that the spacecraft’s altitude at periapsis is maintained within the prescribed safe corridor with minimal use of low-thrust actuators. Quantization of thrust values computed by the NMPC is handled by a pulse-width modulation (PWM) scheme to generate on-off commands to low-thrust actuators. Simulations show that the spacecraft’s altitude at periapsis is successfully maintained within the safe corridor despite atmospheric variations and uncertainty, thereby demonstrating that the proposed control strategy enhances robustness and autonomous capabilities for aerobraking maneuvers with minimal propellant consumption and enables more science.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.13", "code": "ThA04.13", "title": "Spacecraft Attitude Control with Feedforward for the Sensor Plane Alignment During a Scan of a Non-Euclidean Surface", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-10:55", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Groette, Mariusz Eivind", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Gravdahl, Jan Tommy", "affiliation": "Norwegian University of Science and Technology (NTNU)" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Trajectory tracking and path following for AVs", "Space exploration and transportation" ], "abstract": "Scanning the Earth surface with space-based imagers often involves aligning the sensor axis towards a fixed orientation with respect to a local-horizontal-local-vertical frame. The direction along the sensor plane is typically constrained to align towards the spacecraft velocity vector to minimize smear and obtain consistent overlapping images along a path on the surface. Because the surface may be non-Euclidean, we show that a more precise approach during imaging is to constrain the sensor plane towards the tangent velocity vector at the point where the sensor line-of-sight intersects the surface. We discuss prerequisites for claiming a PD-like attitude tracking controller with feedforward is stabilizing. We present strategies and numerical results for handling this time-varying attitude control problem given a non-Euclidean surface and an orbit.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.14", "code": "ThA04.14", "title": "Attitude Severity and the Limits of Planar Guidance: 6--DoF Optimal Landing vs. 2D--Composed 3D", "day": "Thursday", "date": "August 27, 2026", "time": "10:55-11:00", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Tsai, Yu-Liang", "affiliation": "National Taiwan University" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Vehicle dynamic systems", "Flight dynamics modelling and identification" ], "abstract": "This paper examines when planar landing-guidance approximations become unreliable for reusable rocket landing under off-nominal initial conditions. A full six-degree-of-freedom (6--DoF) nonlinear optimal control problem is transcribed with Dymos and solved using the Interior Point OPTimizer (IPOPT), and its solutions are compared against a 2D--composed 3D baseline obtained by combining two decoupled planar optimizations. Rather than proposing a new optimization algorithm, the paper aims to quantify the regime in which planar guidance remains adequate and the regime in which full 6--DoF optimization becomes necessary. Numerical results show that the planar composition can provide an effective warm start, but it tends to underestimate fuel usage and exhibits larger dynamics defects in strongly off-nominal, non-planar cases.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.15", "code": "ThA04.15", "title": "Synergy-Aware Group Attention for UAV Swarm Threat Assessment", "day": "Thursday", "date": "August 27, 2026", "time": "11:00-11:05", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Liu, Chunhao", "affiliation": "Nanjing University of Science and Technology" }, { "name": "Wu, Panlong", "affiliation": "Nanjing University of Science and Technology" }, { "name": "He, Shan", "affiliation": "Nanjing University of Science and Technology" }, { "name": "Liu, Xinan", "affiliation": "Nanjing University of Science and Technology" } ], "keywords": [ "Information processing and decision support in transportation", "Artificial intelligence in transportation", "AI for aircraft and spacecraft navigation, guidance and control" ], "abstract": "Low-cost unmanned aerial vehicle (UAV) swarms create coupled threat and response-urgency assessment challenges for air defense systems. Many existing methods provide useful baselines, yet they rarely encode semantic feature groups, swarm-coordination synergy, and threat-urgency coupling in one model. This paper proposes the Hierarchical Group Threat Attention Network (HGTAN), which combines a group-wise feature encoder, a Synergy Attention Module, and a dual-task decoder. A 16-indicator UAV swarm benchmark is organized into individual, swarm, adversarial, and environmental groups. Experiments on controlled synthetic scenarios show that HGTAN achieves strong dual-task performance and interpretable group-level attention patterns, with 0.923 threat macro-F1 and 0.886 urgency macro-F1.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.16", "code": "ThA04.16", "title": "A Survey on V2X Applications Supporting Intelligent Diagnostics and Services Integration", "day": "Thursday", "date": "August 27, 2026", "time": "11:05-11:10", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Dos Santos Roque, Alexandre", "affiliation": "Halmstad University, Federal University of Rio Grande Do Sul - UFRGS" }, { "name": "Vinel, Alexey", "affiliation": "Karlsruhe Institute of Technology (KIT)" }, { "name": "Pignaton de Freitas, Edison", "affiliation": "Federal University of Rio Grande Do Sul" } ], "keywords": [ "Information processing and decision support in transportation", "Intelligent transportation systems", "Automatic control, optimization, real-time operations in transportation" ], "abstract": "This survey presents a comprehensive study of V2X-supported vehicle fault diagnostics, with a specific emphasis on its transformative applications in increasingly complex automotive networks. With the rapid evolution of connected and autonomous vehicles, conventional in-vehicle diagnostic systems face limitations in providing proactive and holistic fault detection. We systematically review recent research that explores how Vehicle-to-Everything (V2X) communication facilitates enhanced fault identification, predictive maintenance, and real-time anomaly resolution by enabling seamless integration with external services. Key areas of discussion include architectural paradigms for secure data exchange, distributed diagnostic processing leveraging cloud-based platforms, and the critical role of robust V2X connectivity for real-time vehicular electronic health monitoring. This work synthesizes emerging applications and identifies pivotal research challenges for practical deployment, underscoring the significant potential of these integrated approaches to elevate vehicle safety and operational efficiency.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.17", "code": "ThA04.17", "title": "Distributed Resilient Control for Heterogeneous Platoon Dynamics under Actuator Attack", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:15", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Pandey, Ashutosh Chandra", "affiliation": "IIIT Delhi" }, { "name": "Basu Roy, Sayan", "affiliation": "Indraprastha Institute of Information Technology Delhi" } ], "keywords": [ "Intelligent transportation systems", "Automatic control, optimization, real-time operations in transportation", "Control architectures in automotive control" ], "abstract": "This paper proposes a resilient control algorithm to enhance the security of Cooperative Adaptive Cruise Control in vehicular platoons with unidirectional communication and heterogeneous dynamics subject to actuator attacks. The proposed framework employs a model reference adaptive control scheme to drive the heterogeneous platoon toward a reference homogeneous platoon using a distributed structure. Stability and convergence are established through Lyapunov analysis using a virtual platoon concept, which is employed solely for analysis and does not interact with the actual system. Simulation results demonstrate the effectiveness of the proposed algorithm against actuator attacks.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.18", "code": "ThA04.18", "title": "A Hybrid Physics-Based and Reinforcement Learning Framework for Electric Vehicle Charging Time Prediction", "day": "Thursday", "date": "August 27, 2026", "time": "11:15-11:20", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Aryasomayajula, Lakshmi Surya Praharshitha", "affiliation": "Cornell University" }, { "name": "Bai, Ting", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Malikopoulos, Andreas", "affiliation": "Cornell University" } ], "keywords": [ "Intelligent transportation systems", "Modeling and simulation of transportation systems" ], "abstract": "In this paper, we develop a hybrid prediction framework for accurate electric vehicle (EV) charging time estimation, a capability that is critical for trip planning, user satisfaction, and efficient operation of charging infrastructure. We combine a physics-informed gradient boosting model with a reinforcement learning (RL) approach. The physics-informed component captures the nonlinear constant-current/constant-voltage (CC–CV) charging dynamics and explicitly models state-of-health (SoH)–dependent capacity and power fade, providing a reliable baseline when historical data are limited. Building on this foundation, we introduce an RL component that progressively refines charging-time predictions as operational data accumulate, enabling improved long-term adaptation. Both models incorporate SoH degradation to maintain predictive accuracy over the battery lifetime. We evaluate the framework using 5,000 simulated charging sessions calibrated to manufacturer specifications and publicly available EV charging datasets. Our results show that the physics-informed gradient boosting model achieves coefficient of determination R2 =98.5% and mean absolute percentage error MAPE=2.1%, while the RL model further improves performance to R2 =99.2% and MAPE=1.6%, corresponding to a 23% accuracy gain over the physics-informed model and 35% improved robustness to battery aging compared to a linear baseline.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.19", "code": "ThA04.19", "title": "Distributed Traffic Signal Control of Interconnected Intersections: A Two-Lane Traffic Network Model", "day": "Thursday", "date": "August 27, 2026", "time": "11:20-11:25", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Ru, Xinfeng", "affiliation": "East China University of Science and Technology" }, { "name": "Bai, Ting", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Xia, Weiguo", "affiliation": "Dalian University of Technology" }, { "name": "Qin, Dongdong", "affiliation": "East China University of Science and Technology" }, { "name": "Malikopoulos, Andreas", "affiliation": "Cornell University" } ], "keywords": [ "Intelligent transportation systems", "Modeling and simulation of transportation systems", "Automatic control, optimization, real-time operations in transportation" ], "abstract": "In this paper, we investigate traffic signal control in a network of interconnected intersections, aiming to balance lane-level vehicle densities through optimal green-time allocation. We develop a two-lane traffic flow model that explicitly captures lane-specific propagation dynamics, addressing key limitations of conventional road-level formulations. The proposed model offers a more granular and flexible representation of urban traffic, enabling controllers to react more accurately to lane-specific congestion patterns. Building on this model, we design a distributed model predictive control (MPC) framework and integrate it with the efficient alternating direction method of multipliers (ADMM) to enhance scalability and real-time performance. To accommodate time-varying traffic conditions, we further introduce a data-driven method for forecasting dynamic split ratios. Comprehensive VISSIM simulations on a six-intersection network in Dalian, China, demonstrate that the proposed approach outperforms existing signal control strategies in both traffic efficiency and computational speed, showing its promise for real-time deployment.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.20", "code": "ThA04.20", "title": "Optimal Platoon Formation and Stable Benefit Allocation in Mixed-Energy Truck Fleets under Size Limitations", "day": "Thursday", "date": "August 27, 2026", "time": "11:25-11:30", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Bai, Ting", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Ru, Xinfeng", "affiliation": "East China University of Science and Technology" }, { "name": "Li, Shaoyuan", "affiliation": "Shanghai Jiao Tong Univ" }, { "name": "Malikopoulos, Andreas", "affiliation": "Cornell University" } ], "keywords": [ "Intelligent transportation systems", "Transportation logistics", "Information processing and decision support in transportation" ], "abstract": "In this paper, we investigate cooperative platoon formation and benefit allocation in mixed-energy truck fleets composed of both electric and fuel-powered trucks. The central challenge arises from the platoon-size constraint, which limits the number of trucks permitted in each platoon and introduces combinatorial coupling into the search for optimal platoon formation structures. We formulate this problem as a coalitional game with bounded coalition sizes and derive a closed-form characterization of the optimal coalition structure that maximizes the fleet-wide platooning benefit. Building on this structure, we develop a type-based least-core payoff allocation scheme that guarantees stability within the coalition-structure core (CS-core). For cases in which the CS-core is empty, we compute the least-core radius to determine the minimal relaxation required to achieve approximate stability. Through numerical studies, we demonstrate that the proposed framework consistently achieves the highest total platooning benefit among all feasible formation configurations while providing stable benefit allocations that outperform existing baseline methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.21", "code": "ThA04.21", "title": "Battery Degradation-Aware Route Planning for Electric Vehicles Considering Elevation and Road-Induced Vibration", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:35", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Sofyan, Adri F", "affiliation": "Institut Teknologi Bandung" }, { "name": "Widyotriatmo, Augie", "affiliation": "Bandung Institute of Technology" }, { "name": "Li, Panshuo", "affiliation": "Guangdong University of Technology" } ], "keywords": [ "Planning, management and security in transportation", "Electric and solar vehicles", "Trajectory and path planning for AVs" ], "abstract": "This study proposes an integrated electric vehicle (EV) route planning framework that extends battery lifespan by jointly considering the effects of 3D road topography and road-induced vibration. By formulating a combined cost function that mathematically unifies elevation-based energy consumption with vibration-induced thermal stress, the research evaluates trade-offs between travel distance, energy efficiency, and long-term capacity decay on a complex road network. Minimizing elevation changes and vibration produces much smoother energy usage. Specifically, the minimum-elevation route strikes the optimal balance, achieving the slowest capacity fade and the best final State of Charge (SoC), highlighting the fact that incorporating both mechanical and electrical stressors into routing decisions is essential for enhancing long-term battery health.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.22", "code": "ThA04.22", "title": "A Motion Planning Method in Multi-Occlusion Scenarios Accounting for Visibility Cost", "day": "Thursday", "date": "August 27, 2026", "time": "11:35-11:40", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Zhu, Yanting", "affiliation": "Tongji University" }, { "name": "Zhang, Chaojie", "affiliation": "Tongji University" }, { "name": "Wang, Jun", "affiliation": "Tongji University" }, { "name": "Guo, Yafeng", "affiliation": "Tongji University" } ], "keywords": [ "Planning, management and security in transportation", "Trajectory and path planning for AVs", "Mission planning and decision making for AVs" ], "abstract": "This paper presents a visibility-aware motion planner for autonomous driving in multi-occlusion scenarios. Critical blind spots are identified, and a set-based estimation method is used to infer the states of hidden traffic participants. A visibility cost, formulated from the temporal evolution of these state sets, guides lateral offset planning to actively enlarge the field of view of the ego vehicle. The proposed hierarchical planner incorporates this visibility cost into a sampling-based lateral path generator, followed by Hybrid A ∗ -based longitudinal speed planning. Simulations in unsignalized intersections, pedestrian dart-out, and two-way overtaking scenarios demonstrate that the proposed planner improves driving efficiency and reduces the time required to expose blind spots, while maintaining safety feasibility and ride comfort.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.23", "code": "ThA04.23", "title": "Data-Driven Prediction of Heavy-Haul Train Arrival and Yard Operations", "day": "Thursday", "date": "August 27, 2026", "time": "11:40-11:45", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Shahzad, Naveed", "affiliation": "Ecole De Technologie Supérieure, 1100 Notre Dame Street West, Montreal, QC, H3C 1K3, CANADA" }, { "name": "de Paula Ferreira, William", "affiliation": "École De Technologie Supérieure (ÉTS)" }, { "name": "Selim, Bassant", "affiliation": "École De Technologie Supérieure - ÉTS Montréal" }, { "name": "Hassan, Mohamed Ossama", "affiliation": "SimWell Canada" } ], "keywords": [ "Rail transportation modelling and control systems", "Artificial intelligence in transportation", "Automatic control, optimization, real-time operations in transportation" ], "abstract": "Heavy-haul yards need minute-scale forecasts for arrival, unloading, and departure to manage queues and resources. In this context, the literature is largely passenger-oriented and does not provide multi-stage heavy-haul forecasts that use both operational and temporal information. This study addresses that gap by developing an interpretable, multi-stage forecasting pipeline and quantifying gains over operator-fixed targets. Using about one million industrial timestamps, we reconstruct event-level durations and train stage-wise gradient-boosted models with operational, temporal, and congestion features; evaluation uses an unseen-train split by train name. The models achieve mean absolute error of 65.3, 16.1, and 114.6 minutes for arrival, unloading, and departure, respectively, with a 51 to 80 percent reduction compared with fixed stage targets. Feature-importance analysis indicates operational signals dominate. The forecasts enable earlier crew calls and equipment staging and support a large-scale simulation toward a cognitive digital twin of yard operations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA04.24", "code": "ThA04.24", "title": "Hierarchical Predictive Control of Large-Scale Systems with Application to Railway Vehicles", "day": "Thursday", "date": "August 27, 2026", "time": "11:45-11:50", "sessionCode": "ThA04", "sessionTitle": "Shotgun: Transportation Systems and Control II", "sessionType": "Interactive Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Puchades-Ibáñez, Mar", "affiliation": "Politecnico Di Milano" }, { "name": "La Bella, Alessio", "affiliation": "Politecnico Di Milano" }, { "name": "Incremona, Gian Paolo", "affiliation": "Politecnico Di Milano" }, { "name": "Colaneri, Patrizio", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Rail transportation modelling and control systems", "Control architectures in automotive control", "Nonlinear and optimal automotive control" ], "abstract": "This paper presents a hierarchical MPC architecture for large-scale systems, motivated by railway control applications. The approach decomposes the problem into a high-level linearized MPC for global coordination and a low-level layer ensuring feasibility under the true nonlinear dynamics. Coupling constraints and costs are handled at the high level, which provides reference trajectories to the low level. The low level further corrects high-level model approximations online, improving feasibility and optimality. The resulting design bridges centralized and decentralized control, offering a scalable and close-to-optimal control strategy. Its effectiveness is validated through simulations of a multi-train scenario, showing improved coordination and energy-efficient operation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA05.1", "code": "ThA05.1", "title": "A Control Strategy for Power Smoothing in Airborne Wind Energy Parks under Variable Wind Conditions", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:05", "sessionCode": "ThA05", "sessionTitle": "LB: Control Systems Design I", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "da Costa, Rui", "affiliation": "University of Porto" }, { "name": "Fernandes, Manuel C. R .M.", "affiliation": "Universidade Do Porto" }, { "name": "Roque, Luís", "affiliation": "SYSTEC-ISR ARISE, DMA, ISEP, Politécnico Do Porto," }, { "name": "Fontes, Dalila B. M. M.", "affiliation": "Universidade Porto" }, { "name": "Fontes, Fernando A. C. C.", "affiliation": "Universidade Do Porto" } ], "keywords": [ "Control and optimization for sustainability and energy systems", "Wind power", "Control and management of energy systems" ], "abstract": "Airborne Wind Energy (AWE) farms exhibit intrinsic power oscillations due to the cyclic traction–retraction operation of individual units. Previous work has addressed layout optimization and energy storage sizing under constant wind assumptions; however, realistic operation involves time-varying wind speeds that introduce additional dynamic variability in aggregate power output. This paper proposes a Model Predictive Control (MPC) framework for real-time charge–discharge management of a battery energy storage system to ensure smooth power injection into the grid under variable wind conditions. A dynamic model of the AWE farm power output coupling wind-dependent farm power generation and battery state-of-charge evolution is considered, including operational and safety constraints. The MPC controller minimizes power tracking error while enforcing state-of-charge and power limits. Preliminary simulation results indicate that predictive energy management significantly attenuates wind-induced fluctuations. The work opens a discussion on hierarchical smoothing strategies and predictive control architectures for large-scale airborne wind energy integration.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA05.2", "code": "ThA05.2", "title": "System-Level Disturbance Control", "day": "Thursday", "date": "August 27, 2026", "time": "10:05-10:20", "sessionCode": "ThA05", "sessionTitle": "LB: Control Systems Design I", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Van Meerbeeck, Gijs", "affiliation": "Eindhoven University of Technology" }, { "name": "van der Hulst, Maarten", "affiliation": "Eindhoven University of Technology" }, { "name": "Dirkx, Nic", "affiliation": "ASML" }, { "name": "González, Rodrigo A.", "affiliation": "Eindhoven University of Technology" }, { "name": "Tiels, Koen", "affiliation": "Eindhoven University of Technology" }, { "name": "van de Wijdeven, Jeroen", "affiliation": "ASML" }, { "name": "Oomen, Tom", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Control of complex systems", "Analytic design" ], "abstract": "System-level disturbance suppression in mechatronic systems can be achieved using a feedthrough (FT) control framework that leverages interconnections between subsystems. This paper explicitly incorporates disturbance models to overcome fundamental performance limitations of conventional FT control methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA05.3", "code": "ThA05.3", "title": "Machine Learning-Driven Event-Trigger Control for Semi-Markov Jump Neural Networks Via Looped Functional Approach", "day": "Thursday", "date": "August 27, 2026", "time": "10:20-10:35", "sessionCode": "ThA05", "sessionTitle": "LB: Control Systems Design I", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Narayanan, Aravinth", "affiliation": "Bharathiar University" }, { "name": "Rathinasamy, Sakthivel", "affiliation": "Bharathiar University" }, { "name": "Kwon, Ohmin", "affiliation": "Chungbuk National University" } ], "keywords": [ "Control of hybrid systems", "Sampled-data/digital control", "Learning methods for optimal control" ], "abstract": "This study examines the two-sided looped functional-based synchronization problem for semi-Markov jump neural networks via an intelligent event-triggered control technique. Specifically, an intelligent event-triggered control is formulated to reduce the communication cost and mini-batch gradient-based machine learning algorithm is deployed to optimize the threshold parameter in event-trigger control. Further, a two-sided looped type Lyapunov functional is configured and significant advantage of the suggested method is its elimination of the traditional requirement for a standard positive definite matrix in Lyapunov formulation. From there, sufficient requirements for ensuring synchronization are delineated within the context of linear matrix inequalities. To substantiate the validity and utility of the proposed control law, numerical example along with the graphical illustrations are offered.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA05.4", "code": "ThA05.4", "title": "Optimal Control of a Multi-Satellite Constellation Based on the Assignment Problem with Constraints", "day": "Thursday", "date": "August 27, 2026", "time": "10:35-10:50", "sessionCode": "ThA05", "sessionTitle": "LB: Control Systems Design I", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Ivanyukhin, Alexey", "affiliation": "Research Institute of Applied Mechanics and Electrodynamics (Moscow Aviation Institute) / RUDN University" } ], "keywords": [ "Control of multi satellite systems", "Aerospace mission control and operations", "Guidance, navigation and control of aircraft and spacecraft" ], "abstract": "This paper addresses the problem of multi-satellite constellation control for allocating shared service areas among satellites, taking into account their orbital motion, illumination conditions, and battery charge levels. A corresponding modified assignment problem for task allocation is formulated. A general formulation of the unbalanced assignment problem with additional constraints is considered. A relaxation scheme to a linear programming problem through a series of auxiliary problems is proposed. A model problem of continuous-coverage satellite constellation control is considered as a case study.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA05.5", "code": "ThA05.5", "title": "Design of a Work Engagement Control System for Job Demands-Resources Model", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:05", "sessionCode": "ThA05", "sessionTitle": "LB: Control Systems Design I", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Mochizuki, Misato", "affiliation": "Hiroshima University" }, { "name": "Kinoshita, Takuya", "affiliation": "Hiroshima University" } ], "keywords": [ "Cyber-physical and human systems (CPHS)" ], "abstract": "Work engagement is the state where employees are energetic, enthusiastic, and absorbed in their work. The Job Demands-Resources Model (JD-R Model) examined the effects of work environment, worker psychology to work engagement. The purpose of this study is to control work engagement through designing a control system based on the JD-R Model. The system was designed using Matlab/Simulink and verified by simulations. By designing a feedback control system using a PI controller, it was possible to track work engagement to the target value. Future prospects are to verify the usefulness through experiments and to expanded the system from an individual to multiple persons.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA05.6", "code": "ThA05.6", "title": "Resilient Control for Bus Bar Isolation Attack in Smart Power-Grids", "day": "Thursday", "date": "August 27, 2026", "time": "11:05-11:20", "sessionCode": "ThA05", "sessionTitle": "LB: Control Systems Design I", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Singha Roy, Suman", "affiliation": "Indian Institute of Technology, Delhi" }, { "name": "Datta, Subashish", "affiliation": "Indian Institute of Technology Delhi (IIT Delhi)" }, { "name": "Senroy, Nilanjan", "affiliation": "Indian Institute of Technology Delhi" } ], "keywords": [ "Cybersecurity in smart grids", "Power systems stability", "Cyberphysical security in processes" ], "abstract": "In this work, we introduce a new class of attack in the power-grids, where an intruder suddenly isolate a single or multiple bus-bars from the grid during the normal operation. This type of attack is referred to as bus-bar isolation attack. The impact of such attack is studied here through a standard IEEE-5 bus power-grid. To bring resiliency against such type of attacks, we propose a novel feedback control architecture and its design procedure. For this, we use the descriptor form model (linearized model of original differential-algebraic equation model) of power-grid, and show that the traditional (transformed) ordinary state space model of power-grid does not provide sufficient insight to design such resilient feedback control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA05.7", "code": "ThA05.7", "title": "Controller Design for Symbolic Input-Output Systems Using Feedforward Neural Networks", "day": "Thursday", "date": "August 27, 2026", "time": "11:20-11:35", "sessionCode": "ThA05", "sessionTitle": "LB: Control Systems Design I", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Hibino, Kohtaroh", "affiliation": "Meijo University" }, { "name": "Konaka, Eiji", "affiliation": "Meijo Univ" } ], "keywords": [ "Data-driven control theory", "Learning methods for control" ], "abstract": "This study investigates discrete-time nonlinear systems with symbolic inputs and outputs and proposes a data-driven approach for controller design based solely on observed input-output sequences. Here, “Symbolic” means that ordinal or distance relationships among symbols are unknown a priori and must be implicitly reconstructed from time-series data. The control objective is symbolic output tracking to a specified reference symbol. The controller is trained through a supervised learning procedure using a feedforward neural network. The results of numerical simulations based on benchmark nonlinear systems demonstrate that the proposed approach is effective.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA05.8", "code": "ThA05.8", "title": "Torque-Aware Control for Uniform Gravity Vector Distribution in Two-Axis Clinostats", "day": "Thursday", "date": "August 27, 2026", "time": "11:35-11:50", "sessionCode": "ThA05", "sessionTitle": "LB: Control Systems Design I", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Park, Sungwoo", "affiliation": "Seoul National University Hospital" }, { "name": "Kim, Yoon Jae", "affiliation": "Seoul National University Hospital" }, { "name": "Kim, Young Gyun", "affiliation": "Seoul National University" }, { "name": "Kim, Byeong Soo", "affiliation": "Seoul National University Hospital" }, { "name": "Jeon, Byoungjun", "affiliation": "Seoul National University Hospital" }, { "name": "Kim, Sungwan", "affiliation": "Seoul National University, Seoul" } ], "keywords": [ "Disturbance rejection and input-to-state stability", "Lyapunov methods", "Saturation and discontinuity" ], "abstract": "Two-axis clinostats simulate microgravity through time-averaged simulated microgravity (taSMG) by continuously reorienting the gravity direction vector. Although the reciprocal sinusoidal angular velocity profile achieves near-ideal kinematic uniformity, hardware experiments reveal a persistent gravity offset exceeding the 10-3 G threshold, attributable to a position-dependent load torque asymmetry on the outer-axis motor. This paper formalizes the dynamic origin of this residual bias through perturbation analysis of the nonlinear rotational dynamics, and proposes a cascaded algorithmic framework comprising: (1) a perturbation-derived bias-compensated reciprocal profile; (2) a torque-aware adaptive angular acceleration limiter; and (3) an integral feedback drift cancellation loop with Lyapunov-certified asymptotic stability. The proposed extensions require only software modification and one-time offline calibration, without additional actuators or structural redesign, offering a hardware-agnostic upgrade pathway for high-precision long-duration simulated microgravity experiments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA06.1", "code": "ThA06.1", "title": "Graphical Interpretation of Spectral Factorization in Dynamic Network Models (I)", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA06", "sessionTitle": "Data-Driven Modeling and Learning in Dynamic Networks", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Biparva, Darya", "affiliation": "University of Minnesota" }, { "name": "Materassi, Donatello", "affiliation": "University of Minnesota" } ], "keywords": [ "Distributed control and estimation", "Filtering and smoothing", "Probabilistic and Bayesian methods for system identification" ], "abstract": "We study the problem of reproducing a given multivariate power spectral density via a distributed system, where each node generates a component and exchanges minimal information to match cross-correlations. We introduce the notion of graphical spectral factorization, which decomposes the process into independent innovations and a structured communication network among nodes. In the non-causal case, conditional uncorrelation defines a spectral graphoid, enabling an acyclic structure of the network. For real-time, causal implementations, each process is split into past and present components to preserve causality while reasoning about sparsity. This framework provides a systematic approach for designing distributed stochastic systems that realize a target power spectral density with structured, sparse communication.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA06.2", "code": "ThA06.2", "title": "An Input-Output Data-Driven Dissipativity Approach for Compositional Stability Certification of Interconnected LTI MIMO Systems (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA06", "sessionTitle": "Data-Driven Modeling and Learning in Dynamic Networks", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Sandoval Carranza, Maria Alejandra", "affiliation": "Brandenburg University of Technology Cottbus-Senftenberg" }, { "name": "Machado Martinez, Juan Eduardo", "affiliation": "Brandenburgische Technische Universität Cottbus-Senftenberg" }, { "name": "Schiffer, Johannes", "affiliation": "Brandenburg University of Technology Cottbus-Senftenberg" } ], "keywords": [ "Data-driven control theory", "Multi-agent systems" ], "abstract": "We propose an input-output data-driven framework for certifying the stability of interconnected multiple-input-multiple-output linear time-invariant discrete-time systems via QSR-dissipativity under a structured class of supply rates motivated by the considered interconnection structure. That is, by using measured noise-free input-output trajectories of each subsystem, we verify QSR-dissipativity and extract local channel-wise passivity indices. These passivity indices are then used to derive conditions under which the equilibrium of the interconnected system is stable. In particular, the framework identifies how the lack of passivity in some subsystems can be compensated by surpluses in others. The proposed approach enables a compositional stability analysis by combining subsystem-level conditions into a criterion valid for the overall interconnected system. We illustrate via a numerical case study, how to compute channel-wise passivity indices and infer stability guarantees directly from data with the proposed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA06.3", "code": "ThA06.3", "title": "Data Driven Paint Structure Quality Evaluation in Automotive Industry", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA06", "sessionTitle": "Data-Driven Modeling and Learning in Dynamic Networks", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Spendla, Lukas", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Kebisek, Michal", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Tanuska, Pavol", "affiliation": "Slovak University of Technology" } ], "keywords": [ "Industrial artificial intelligence", "Industry X.0 for production and logistics", "Intelligent manufacturing systems" ], "abstract": "Assessment of automotive paint quality in real production environments remains a multifaceted challenge influenced by numerous interacting factors. Even minor deviations in application techniques or material properties may propagate into measurable defects. Automatization of paint quality evaluation is the current trend in this area; however, not all paint quality control processes can be fully automated with the required accuracy and confidence. This issue is significantly more important when manufacturing premium car models. Therefore, it is necessary to align the automated and semi-manual evaluation of paint quality to minimise human influence. To address this issue, we have proposed a data driven evaluation platform that integrates evaluation data from multiple systems to discover new knowledge. The implemented platform utilises statistical analysis and machine learning approaches required for the complex evaluation of the quality of the paint structure. The platform was deployed in the real production environment and provides an improved approach to evaluating the quality of the paint structure and was integrated into the quality evaluation processes of the company.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA06.4", "code": "ThA06.4", "title": "Online Nonstochastic Networked Control under Adversarial Packet Losses (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA06", "sessionTitle": "Data-Driven Modeling and Learning in Dynamic Networks", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Watanabe, Sho", "affiliation": "The University of Tokyo" }, { "name": "Ito, Kaito", "affiliation": "The University of Tokyo" }, { "name": "Ishii, Hideaki", "affiliation": "University of Tokyo" } ], "keywords": [ "Resilient networked control systems", "Learning methods for control", "Control over networks" ], "abstract": "This paper studies optimal control over networks under packet losses that may be adversarial, such as denial-of-service attacks. Existing stochastic models of packet losses do not capture strategic attacks, while designing controllers for worst-case packet-loss patterns is often overly pessimistic. To address this issue, we formulate the networked optimal control as an online control problem that aims to minimize regret. Here, regret measures the performance loss relative to the best controller in hindsight. Then, we design an online controller that achieves an widetilde{O}(sqrt{T}) regret over a control horizon T . The guarantee holds for any packet-loss sequence satisfying certain assumptions on its frequency and success-to-failure ratio, and remains valid even in the presence of adversarial disturbances and unknown cost functions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA06.5", "code": "ThA06.5", "title": "Leveraging Deep Learning for Object and Position Recognition of Load Carriers for Autonomous Logistics Vehicles", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA06", "sessionTitle": "Data-Driven Modeling and Learning in Dynamic Networks", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Legat, Christoph", "affiliation": "Technical University of Applied Sciences Augsburg" }, { "name": "Miller, Tobias", "affiliation": "Grenzebach Maschinenbau GmbH" }, { "name": "Riess, Marco", "affiliation": "Grenzebach Maschinenbau GmbH" } ], "keywords": [ "Industrial artificial intelligence", "Intelligent manufacturing systems" ], "abstract": "This work explores the use of artificial intelligence in mobile robotics to achieve autonomous detection and pose estimation of load carriers for automated pickup. A deep neural network is designed to recognize predefined landmarks on the carrier from RGBD data; these landmarks are then used to compute the carrier’s pose. The network operates directly on RGBD images to estimate landmark positions, which form the basis for determining the carrier’s location. The approach is validated in extensive experiments and comprises both software and hardware implementations. A deep learning–based framework is presented to detect load carriers and estimate their pose for use with autonomous logistics vehicles. Our method uses a convolutional neural network to identify characteristic reference points on the carrier from RGBD input and computes its pose by combining these inferred landmarks with prior geometric knowledge. Experiments show that the resulting accuracy is sufficient for reliable load carrier detection in industrial environments, confirming the suitability of the method for autonomous intralogistics applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA06.6", "code": "ThA06.6", "title": "Optimal Excitation and Measurement Patterns for Networks with Tree Topology (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA06", "sessionTitle": "Data-Driven Modeling and Learning in Dynamic Networks", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Mapurunga, Eduardo", "affiliation": "Universidade Federal Do Rio Grande Do Sul" }, { "name": "Bazanella, Alexandre S.", "affiliation": "Univ. Federal Do Rio Grande Do Sul" } ], "keywords": [ "Multi-agent systems", "Linear system identification" ], "abstract": "In this work we evaluate the excitation and measurement patterns (EMP) for networks with tree topology. We investigate guidelines for the selection of the minimal EMPs, i.e. those with the least number of excited and measured nodes combined, for which the accuracy obtained, in terms of the trace of the asymptotic covariance matrix, is optimal. We introduce the concept of partial information matrix as a means to systematically obtain the information matrix for any dynamic network. For a specific tree class, called cross, we show that the accuracy of a particular module depends on the magnitude of the parameters to be estimated. Furthermore, when all factors are equal, it is best to excite. We extend a topological condition for branches under which the accuracy of a particular module of the network is independent of the other parameters from the tree. We provide a numerical analysis showing that our guidelines could be used as a selection tool for minimal EMPs for tree networks.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA07.1", "code": "ThA07.1", "title": "How Does the Control Parameter Influence the Traffic Characterization of Event-Triggered Control Systems?", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA07", "sessionTitle": "Event-Based and Networked Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Chen, Tao", "affiliation": "Central South University" }, { "name": "Mo, Yinglun", "affiliation": "Central South University" }, { "name": "Hu, Wenfeng", "affiliation": "Central South University" } ], "keywords": [ "Diagnosis of discrete event and hybrid systems", "Discrete event modeling and simulation", "Event-based control" ], "abstract": "This work investigates how the control parameter influences the triggering behaviors of event-triggered control (ETC) systems, focusing on its role in changing the feasibility of the inter-event time (IET) transitions. To this end, the feasibility condition is reformulated as an equivalent linear feasibility problem. This reformulation transforms the analysis to a geometric problem of determining when the null space intersects the fixed polyhedral cone. As the control parameter varies, the null space changes accordingly. A control parameter value is identified as a critical value if, at that value, the null space and the polyhedral cone switch between having an intersection and being disjoint. Consequently, any changes in the feasibility of the IET transition can occur only at such critical values. To capture all possible changes, we construct a candidate set that contains all possible critical values. This result enables the exact identification of all critical values, providing a complete and analytically tractable characterization of how the control parameter governs the triggering behaviors of ETC systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA07.2", "code": "ThA07.2", "title": "Event-Triggered Laser Tracking Control for Interferometry Formation Flying in Generalized Circular Orbits", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA07", "sessionTitle": "Event-Based and Networked Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Iwaki, Takuya", "affiliation": "Japan Aerospace Exploration Agency" } ], "keywords": [ "Event-based control", "Control over networks" ], "abstract": "By configuring a laser interferometer among three spacecraft in orbit, a large-scale gravitational wave telescope can be constructed, which enables observations of low-frequency gravitational waves. To establish and maintain such a space interferometer, the laser must be tracked to the counterpart spacecraft with high accuracy. This study focuses on the problem of laser tracking control under limited inter-spacecraft communication for three-spacecraft formation flying in generalized circular orbits. In particular, we propose an event-triggered control for regulating the laser emission angles so that the interferometry laser link is consistently maintained. A numerical example illustrates that the proposed controller effectively reduces the communication load while maintaining the laser link among spacecraft.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA07.3", "code": "ThA07.3", "title": "Bumpless Event-Based Cloud Control of a Three-Tank Pilot Via Nonlinear Model Predictive Control and an Unscented Kalman Filter", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA07", "sessionTitle": "Event-Based and Networked Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Kortela, Jukka", "affiliation": "Aalto University, School of Chemical Engineering" }, { "name": "Miikki, Kim", "affiliation": "Aalto University" } ], "keywords": [ "Event-based control", "Control over networks", "Kalman filtering" ], "abstract": "This paper addresses bumpless transfer in event-based cloud control of a three-tank pilot plant. The proposed architecture coordinates a cloud-hosted nonlinear model predictive controller (NMPC) and local PID loops running on ABB System 800xA and a Raspberry Pi+Arduino edge stack. The main methodological novelty is a state-of-the-art ring-buffer mechanism that provides precise clock synchronization among the three device controllers and the Unscented Kalman Filter, enabling more advanced bumpless switching under heterogeneous communication delays. Two practical mechanisms—(i) UKF-based state alignment and (ii) a short command blend with PID integrator pre-initialization—enable seamless switching between controllers under realistic round-trip delays (150 ´ms cloud, 50–60ms local). A physics-based orifice model is used within NMPC, while plant state is estimated with an Unscented Kalman Filter (UKF). On a comprehensive scenario with cloud outages and latency inflation, the supervisor enforces bumpless handovers, preserves tight level tracking, and maintains low integral absolute error (IAE), as evidenced by smooth actuator trajectories and output levels. Compared with single-controller operation, the event-based supervisory scheme exploits the cloud when available and safely falls back to local control otherwise, without inducing detrimental bumps. The study demonstrates a reproducible path to deploy NMPC-in-the-cloud for industrial Internet of Things (IIoT) systems using an OPC DA/UA bridge and ABB integration, and provides an implementation blueprint suitable for replication on pilot rigs. The results are analyzed and discussed.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA07.4", "code": "ThA07.4", "title": "Distributed Cascade Filtering Design for Multi-Agent Systems with Discontinuous Data Exchange", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA07", "sessionTitle": "Event-Based and Networked Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Shahvali, Milad", "affiliation": "University of Cyprus" }, { "name": "Polycarpou, Marios M.", "affiliation": "University of Cyprus" } ], "keywords": [ "Event-based control", "Distributed control and estimation", "Multi-agent systems" ], "abstract": "This paper investigates a neighbor-to-neighbor event-triggered consensus control problem for a class of strict-feedback multi-agent systems under a directed communication graph, aiming to address the conflict between the requirements of distributed feasible consensus control and the limitations of communication resources in networked control systems. First, a triggering module is developed for each follower, integrating a neighbor-to-neighbor event-triggering mechanism to ensure that only sampled states are transmitted among neighboring agents. Then, a control module composed of multiple filter banks and a controller component is designed. Each filter bank contains two first-order low-pass filters connected in cascade, which take the sampled neighboring states as inputs and generate continuous and differentiable outputs used in the corresponding distributed dynamic surface controller. Consequently, the proposed approach avoids the non-differentiability of virtual control signals and can be implemented without relying on global information of the communication topology. The closed-loop stability analysis of the proposed scheme is presented and the Zeno behavior is excluded. Finally, simulation results demonstrate the effectiveness and applicability of the proposed strategy.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA07.5", "code": "ThA07.5", "title": "Supervisory Control with Observations of Both Events and States", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA07", "sessionTitle": "Event-Based and Networked Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Wang, Weilin", "affiliation": "Monash University" }, { "name": "Liang, Jiayuan", "affiliation": "University of Shanghai for Science and Technology" }, { "name": "Zhang, Hanran", "affiliation": "University of Shanghai for Science and Technology" }, { "name": "Sun, Xing", "affiliation": "PowerChina]{PowerChina HuaDong Engineering Corporation Limited" }, { "name": "Luo, Dan", "affiliation": "PowerChina]{PowerChina HuaDong Engineering Corporation Limited" }, { "name": "Gong, Chaohui", "affiliation": "University of Shanghai for Science and Technology" } ], "keywords": [ "Event-based control", "Supervisory control and automata" ], "abstract": "We investigate the combined observation of events and states in supervisory control of a discrete-event system. We compute the set of all state-event pairs such that the appearance (as transitions) of any such pair in an uncontrolled system and the nonexistence of a supervisor, no matter which controllable event set it uses, are equivalent.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA07.6", "code": "ThA07.6", "title": "Communication-Aware Synthesis of Safety Controller for Networked Control Systems", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA07", "sessionTitle": "Event-Based and Networked Control", "sessionType": "Regular Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Liu, Yihan", "affiliation": "The Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Tian, Meiqi", "affiliation": "The Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Yan, Teng", "affiliation": "The Hong Kong University of Science and Technology" }, { "name": "Zhong, Bingzhuo", "affiliation": "Hong Kong University of Science and Technology (Guangzhou)" } ], "keywords": [ "Reachability analysis, verification and abstraction of hybrid systems", "Supervisory control and automata", "Control under communication constraints" ], "abstract": "Networked control systems (NCS) are widely used in safety-critical applications, but they are often analyzed under the assumption of ideal communication channels. This work focuses on the synthesis of safety controllers for discrete-time linear systems affected by unknown disturbances operating in imperfect communication channels. The proposed method guarantees safety by constructing ellipsoidal robust safety invariant (RSI) sets and verifying their invariance through linear matrix inequalities (LMI), which are formulated and solved as semi-definite programming (SDP). In particular, our framework simultaneously considers controller synthesis and communication errors without requiring explicit modeling of the communication channel. A case study on cruise control problem demonstrates that the proposed controller ensures safety in the presence of unexpected disturbances and multiple communication imperfections simultaneously.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA08.1", "code": "ThA08.1", "title": "Device-Agnostic Modality-Adaptive Perception Embedding and Universal Time-Frequency Aggregation Transformer for Unknown-Domain Fault Diagnosis", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA08", "sessionTitle": "Fault Detection and Diagnosis I", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Qitong, Chen", "affiliation": "Soochow University" }, { "name": "Qi, Li", "affiliation": "Tsinghua University" }, { "name": "Li, Xuan", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Zhuang, Hong", "affiliation": "Soochow University" }, { "name": "Zhang, Yueyuan", "affiliation": "Soochow University" }, { "name": "Jin, Sheng", "affiliation": "Suzhou City University" }, { "name": "Chen, Liang", "affiliation": "Soochow University" } ], "keywords": [ "Fault detection and diagnosis" ], "abstract": "To address the challenges of modality heterogeneity and cross-device generalization in fault diagnosis, this paper proposes a Device-Agnostic Modality-Adaptive Perception Embedding (MAPE) and a Universal Time-Frequency Aggregation Transformer (TFAformer) for unknown-domain fault diagnosis. MAPE adaptively extracts unified time, frequency, and time–frequency features from multi-modal signals, regardless of modality type or quantity. TFAformer aggregates global and local representations via cross-attention and self-attention, capturing both private and shared characteristics. Extensive experiments on 10 models show MAPE outperforms existing embeddings by 7.23%, and the combined framework achieves average 90.20% accuracy on industrial robot, wind turbine, and bearing datasets, demonstrating strong generalization across devices and domains. The proposed framework is open-sourced at https://github.com/qtchen0730/TFAformer.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA08.2", "code": "ThA08.2", "title": "Data-Driven Probabilistic Fault Detection and Identification Via Density Flow Matching", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA08", "sessionTitle": "Fault Detection and Diagnosis I", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Ibrahim, Joshua", "affiliation": "California Institute of Technology" }, { "name": "Taheri, Mahdi", "affiliation": "California Institute of Technology (Caltech)" }, { "name": "Chung, Soon-Jo", "affiliation": "Caltech" }, { "name": "Hadaegh, Fred Y.", "affiliation": "California Institute of Technology" } ], "keywords": [ "Fault detection and diagnosis", "Data-driven control theory", "Machine and deep learning for system identification" ], "abstract": "Fault detection and identification (FDI) is critical for maintaining the safety and reliability of systems subject to actuator and sensor faults. In this paper, the problem of FDI for nonlinear control-affine systems under simultaneous actuator and sensor faults is studied. We model fault signatures through the evolution of the probability density flow along the trajectory and characterize detectability using the 2-Wasserstein metric. In order to introduce quantifiable guarantees for fault detectability based on system parameters and fault magnitudes, we derive upper bounds on the distributional separation between nominal and faulty dynamics. The latter is achieved through a stochastic contraction analysis of probability distributions in the 2-Wasserstein metric. A data-driven FDI method is developed by means of a conditional flow-matching scheme that learns neural vector fields governing density propagation under different fault profiles. To generalize the data-driven FDI method across continuous fault magnitudes, Gaussian bridge interpolation and Feature-wise Linear Modulation (FiLM) conditioning are incorporated. The effectiveness of our proposed method is illustrated on a spacecraft attitude control system, and its performance is compared with an augmented Extended Kalman Filter (EKF) baseline. The results confirm that trajectory-based distributional analysis provides improved discrimination between fault scenarios and enables reliable data-driven FDI with a lower false alarm rate compared with the augmented EKF.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA08.3", "code": "ThA08.3", "title": "Model-Free Anomaly Detection for Dynamical Systems with Gaussian Processes", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA08", "sessionTitle": "Fault Detection and Diagnosis I", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Penacho Riveiros, Alejandro", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Bastianello, Nicola", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Barreau, Matthieu", "affiliation": "KTH" } ], "keywords": [ "Fault detection and diagnosis", "Gaussian process", "Nonlinear system identification" ], "abstract": "In this paper we address the problem of detecting differences or anomalies in a dynamical system, based on historical data of nominal operations. This problem encompasses quality control, where newly manufactured systems are tested against desired nominal operations, and the detection of changes in the dynamics due to degradation or repairs. We propose a model free approach based on Gaussian processes (GPs). The idea is to train offline a GP based on nominal data, which is then deployed online to detect whether measurements of the system's state are compatible with nominal operations or if they deviate. Detection is made more challenging by the presence of process and measurement noise, which might obfuscate deviations in the dynamics. The detection then is based on a threshold that ensures a specific false positive rate. We showcase the promising performance of the proposed method with two systems, and highlight several interesting future research questions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA08.4", "code": "ThA08.4", "title": "Geometric Fault Identification Via Mirror Descent Learning", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA08", "sessionTitle": "Fault Detection and Diagnosis I", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Taheri, Mahdi", "affiliation": "California Institute of Technology (Caltech)" }, { "name": "Han, Haeyoon", "affiliation": "California Institute of Technology" }, { "name": "Chung, Soon-Jo", "affiliation": "Caltech" }, { "name": "Hadaegh, Fred Y.", "affiliation": "California Inst. of Tech" } ], "keywords": [ "Fault detection and diagnosis", "Learning methods for control", "Adaptive observer design" ], "abstract": "This paper develops a fault detection and identification (FDI) method for nonlinear control-affine systems under simultaneous actuator and sensor faults. We adopt a geometric approach to study the isolability of faults in the sense of the principal angles between subspaces corresponding to each actuator and sensor fault. As for the fault identification, a hybrid estimator that consists of a Luenberger-like observer with contraction guarantees is developed. Moreover, neural networks are embedded in the mentioned observer to estimate actuator and sensor faults. Considering that the training dataset for neural networks cannot be representative of every fault scenario, the last layer of each network is adapted using mirror descent-based laws. The mirror descent-based adaptive laws impose isolabilty conditions for fault channels and do not assume a quadratic parameter estimation space to preserve the geometry of the fault subspaces. A Lyapunov-based analysis establishes that the state and parameter estimation errors are uniformly ultimately bounded. The effectiveness of our proposed FDI method is illustrated on 3-axis attitude control system of a spacecraft.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA08.5", "code": "ThA08.5", "title": "Manifold Learning with Autoencoders for Subspace-Based Anomaly Detection", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA08", "sessionTitle": "Fault Detection and Diagnosis I", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Kuskonmaz, Bulut", "affiliation": "Aalborg University" }, { "name": "Gres, Szymon", "affiliation": "INRIA" }, { "name": "Wisniewski, Rafal", "affiliation": "Aalborg University" } ], "keywords": [ "Fault detection and diagnosis", "Learning methods for control", "Nonlinear system identification" ], "abstract": "In this paper, we propose a method to learn a manifold representation of data covariance Hankel matrices using an autoencoder, and demonstrate its use for fault detection in dynamical systems. The autoencoder is trained in two stages to capture both a low dimensional latent manifold representation and a complementary representation of Hankel matrices constructed from nominal data. We illustrate that when a Hankel matrix built from test data does not lie on a manifold obtained on data collected from a nominal reference system, a fault occurs. To detect it, we propose a simple residual which is tested in a classical hypothesis testing framework. For linear systems, this residual is closely related to the classical subspace fault detection residual based on the null space of the Hankel matrix. The performance of the proposed detection scheme is validated on linear and weakly nonlinear mechanical system.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA08.6", "code": "ThA08.6", "title": "A Retrieval-Augmented Generation Framework for Analysis of Industrial Control Loop Oscillations (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA08", "sessionTitle": "Fault Detection and Diagnosis I", "sessionType": "Regular Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Huang, Biao", "affiliation": "Univ. of Alberta" }, { "name": "Singh, Abhijeet", "affiliation": "University of Alberta" }, { "name": "Modir Rousta, Mohammadhossein", "affiliation": "University of Alberta" } ], "keywords": [ "Advanced process control" ], "abstract": "Industrial control system oscillations incur substantial economic costs through energy waste, accelerated equipment wear, and compromised product quality. Traditional diagnostic methods, reliant on manual expert scrutiny of data from thousands of control loops, present a critical scalability bottleneck. This tutorial presents a novel, scalable framework that integrates Large Language Models (LLMs) with specialized oscillation detection toolboxes via a Retrieval-Augmented Generation (RAG) architecture. The system features a programmatic command-line interface, decoupling analysis from graphical user interfaces and enabling automation. Our domain-specific RAG pipeline dynamically contextualizes LLM responses by retrieving relevant real-time analytical outputs and structured technical knowledge. This allows plant personnel to conduct investigations using natural language queries. The framework further enhances diagnostic reliability by applying LLM reasoning to interpret the results of signature algorithms, such as triangle-like shape detection for valve stiction. Rigorous validation on industrial datasets—including refinery process data, the International Stiction Database, and the Tennessee Eastman Process benchmark—demonstrates the system's robust performance, achieving excellent classification accuracy and correlation values. This work effectively democratizes high-fidelity oscillation analysis, transitioning it from a specialist-centric task to a scalable, accessible operational resource.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA09.1", "code": "ThA09.1", "title": "Lipschitz-Based Robustness Certification for Recurrent Neural Networks Via Convex Relaxation", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA09", "sessionTitle": "Machine and Deep Learning for System Identification", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Hamelbeck, Paul", "affiliation": "BTU Cottbus-Senftenberg" }, { "name": "Schiffer, Johannes", "affiliation": "Brandenburg University of Technology Cottbus-Senftenberg" } ], "keywords": [ "Machine and deep learning for system identification", "Data-driven control theory", "Learning methods for control" ], "abstract": "Robustness certification for recurrent neural networks (RNNs) is important in safety-critical control settings. We propose a relaxation-based method that formulates RNN layer interactions as a semidefinite program (SDP) to compute certified upper bounds on the Lipschitz constant. Evaluations on a synthetic multi-tank system compare certified and empirical estimates, showing reasonably tight bounds even for long sequences, further improved by incorporating input constraints. The results also highlight the influence of initialization errors, relevant for frequently reinitialized models such as in model predictive control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA09.2", "code": "ThA09.2", "title": "Fast State of Available Power Estimation for Lithium-Ion Batteries Using Embedded Physics-Informed Machine Learning", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA09", "sessionTitle": "Machine and Deep Learning for System Identification", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Braun, Marek", "affiliation": "Univ. Grenoble Alpes, CEA, LITEN" }, { "name": "Hernandez-Torres, David", "affiliation": "CEA" }, { "name": "Fiette, Sébastien", "affiliation": "Univ. Grenoble Alpes, CEA, LITEN" } ], "keywords": [ "Machine and deep learning for system identification", "Data-driven control theory", "Physics informed and grey box model identification" ], "abstract": "Accurate prediction of the internal states of Lithium-Ion Batteries is critical for the safety and efficiency of Battery Management Systems (BMS), particularly for advanced indicators such as the State of Available Power (SOAP). While physics-based electrochemical models like the Doyle-Fuller-Newman (DFN) or Pseudo-Two-Dimensional (P2D) model offer superior fidelity compared to empirical Equivalent Circuit Models (ECMs), their high computational complexity typically precludes their use in real-time embedded applications, specially when high mesh resolution and multiple model calls within specialized algorithms are needed. This paper proposes a novel Physics-Informed Machine Learning (PIML) approach, specifically utilizing Physics-Informed Neural Networks (PINNs), to generate a Reduced Order Model (ROM) of the battery cell dynamics. By embedding physical equations directly into the neural network loss function, we derive a data-efficient ROM capable of capturing the electrochemical behavior defined by the DFN and Single Particle Model (SPM) framework. This ROM is subsequently integrated into a SOAP estimation algorithm. We demonstrate that the PINN-based approach outperforms classical numerical solvers in terms of execution speed while maintaining a high degree of physical consistency. The results suggest that physics-embedded machine learning provides a viable pathway for implementing next-generation, physics-based power prediction algorithms on standard BMS hardware.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA09.3", "code": "ThA09.3", "title": "Generative Adversarial Networks As Coupled Dynamics", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA09", "sessionTitle": "Machine and Deep Learning for System Identification", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Bauso, Dario", "affiliation": "University of Groningen" }, { "name": "Astolfi, Alessandro", "affiliation": "King Abdullah University of Science and Technology (KAUST)" } ], "keywords": [ "Machine and deep learning for system identification", "Iterative and repetitive learning control", "Learning methods for control" ], "abstract": "We derive the coupled dynamics between generator and discriminator in continuous-time and discretized data space. We show that under fast-discriminator, adversarial learning and gradient-play yield a replicator dynamics for the generator, enabling Lyapunov stability analysis of the generator probability distribution. We derive local stability conditions for a reduced aggregate model and validate them through simulation using Electronic Health Records to synthesize realistic patient records.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA09.4", "code": "ThA09.4", "title": "KIND: A Kalman-Inspired Adaptive Estimator for SRF Cavity Detuning", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA09", "sessionTitle": "Machine and Deep Learning for System Identification", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Maalberg, Andrei", "affiliation": "Helmholtz-Zentrum Berlin" }, { "name": "Neumann, Axel", "affiliation": "Helmholtz-Zentrum Berlin" }, { "name": "Echevarria Fernandez, Pablo", "affiliation": "Helmholtz-Zentrum Berlin" }, { "name": "Ushakov, Andriy", "affiliation": "Helmholtz Zentrum Berlin" }, { "name": "Knobloch, Jens", "affiliation": "Helmholtz-Zentrum Berlin" } ], "keywords": [ "Machine and deep learning for system identification", "Kalman filtering", "Time series modeling" ], "abstract": "Superconducting radio frequency cavities with a high quality factor enable energy-efficient accelerator operation but are very sensitive to mechanical disturbances that detune their resonance. Accurate detuning estimation is therefore essential for efficient resonance control and stable beam conditions. This paper introduces Kalman-Inspired Neural Decomposition (KIND), a data-driven estimator that fuses a Dynamic Mode Decomposition model for stationary modal behavior with a Transformer-based predictor for transient dynamics. KIND further outputs learned uncertainty signals that indicate regime changes, enabling anomaly detection. Using operational cavity data, we compare KIND with a classical Kalman filtering baseline and discuss its potential as a foundation for future uncertainty-aware, forecast-based control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA09.5", "code": "ThA09.5", "title": "Roughness-Informed Federated Generative Adversarial Learning", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA09", "sessionTitle": "Machine and Deep Learning for System Identification", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Partohaghighi, Mohammad", "affiliation": "MESA Lab, UC Merced" }, { "name": "Marcia, Roummel", "affiliation": "University of California Merced" }, { "name": "West, Bruce", "affiliation": "North Carolina State University" }, { "name": "Chen, YangQuan", "affiliation": "University of California, Merced" } ], "keywords": [ "Machine and deep learning for system identification", "Learning methods for control", "Statistical analysis" ], "abstract": "Federated learning (FL) enables collaborative training of machine learning models without centralized access to raw data, but suffers from optimization instability and client drift under heterogeneous (non-IID) data distributions. These challenges are exacerbated for Generative Adversarial Networks (GANs), whose adversarial training dynamics are notoriously fragile even in centralized settings. In this work, we propose Roughness-Informed Federated Generative Adversarial Learning (RI-FedGAN), a new framework that stabilizes federated GAN training by explicitly measuring and controlling the local loss landscape roughness on each client. We introduce the Roughness Index (RI) as a per-client diagnostic that captures the local variability of the adversarial loss along random one-dimensional projections. Building on this, we develop RI-FedGAN-Prox, a proximal variant of federated GAN training where both the generator and discriminator on each client are regularized toward the global model with a strength that is adaptively scaled by the client's RI. Clients exhibiting highly oscillatory or unstable adversarial dynamics are thus automatically constrained, while smoother clients retain more freedom to explore. We further define a Full RI-FedGAN variant that combines RI-scaled proximal updates with an RI-weighted aggregation scheme, down-weighting unstable clients in the global update. Empirically, on MNIST and CIFAR-10 under various IID and non-IID partitioning schemes, RI-FedGAN-Prox and Full RI-FedGAN consistently improve classification score, Frechet Inception Distance (FID), Earth Mover's Distance (EMD), and robustness to mode collapse compared to standard FedGAN and fixed-proximal baselines. Our results demonstrate that roughness-aware regularization and aggregation is a promising ingredient for stabilizing generative modeling in federated environments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA09.6", "code": "ThA09.6", "title": "Time-Varying Deep State Space Models for Sequences with Switching Dynamics", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA09", "sessionTitle": "Machine and Deep Learning for System Identification", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Karilanova, Sanja", "affiliation": "Uppsala University" }, { "name": "Dey, Subhrakanti", "affiliation": "Uppsala University" }, { "name": "Ozcelikkale, Ayca", "affiliation": "Uppsala University" } ], "keywords": [ "Machine and deep learning for system identification", "Time/parameter varying system identification" ], "abstract": "The identification and modeling of time-varying systems is a fundamental challenge in signal processing and system identification. To address this challenge, we propose a class of time-varying state-space model (SSM) based neural networks in which the neurons' states are governed by time-varying dynamics. The proposed model provides the learnable time-varying dynamics through a dictionary of basis functions, where each basis function evolves differently over time. We evaluate the proposed approach on both synthetic data from switching systems and a speech denoising task where real audio is corrupted with switching dynamics noise. The results show that the proposed time-varying model consistently outperforms its time-invariant counterparts while maintaining comparable computational complexity. Our investigations also reveal which aspects of the time-varying dynamics of the data most need to be captured by the proposed time-invariant models, how the additional freedom provided by time-varying basis functions should be allocated across model components, and to what extent larger models can compensate for time-invariant limitations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA10.1", "code": "ThA10.1", "title": "Dynamic Encoding-Decoding Event-Triggered Control for Nonlinear Systems with External Disturbances (I)", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA10", "sessionTitle": "Advances in Identification and Control for Large-Scale Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Ji, Ruihang", "affiliation": "National University of Singapore" }, { "name": "Ge, Shuzhi", "affiliation": "National Univeristy of Singapore" }, { "name": "Zhao, Kai", "affiliation": "National University of Singapore" } ], "keywords": [ "Event-based control", "Nonlinear adaptive control" ], "abstract": "This paper proposes a dynamic encoding-decoding event-triggered control for a class of strict-feedback nonlinear systems. A dynamic encoding-decoding event-triggered scheme is designed from the viewpoint of signal encoding-decoding process. It makes only an L-length codeword be transmitted for each communication between the control and actuator boxes, where L can be user-specified according to communication bandwidth. At the same time, our proposed event-triggered scheme allows the triggering threshold to be dynamically updated based on control input signal itself rather than relying on Lyapunov stability requirement. The resulted dynamic encoding-decoding event-triggered control can guarantee asymptotic tracking performance without Zeno behavior. Finally, the effectiveness of the proposed control scheme is illustrated by simulation results.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA10.2", "code": "ThA10.2", "title": "Correlation-Aware Distributed Joint Localization and Target Tracking (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA10", "sessionTitle": "Advances in Identification and Control for Large-Scale Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Hou, Yi", "affiliation": "Harbin Institute of Technology" }, { "name": "Hao, Ning", "affiliation": "Harbin Institute of Technology" }, { "name": "He, Fenghua", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Estimation and filtering", "Distributed control and estimation", "Kalman filtering" ], "abstract": "Joint localization and target tracking are fundamental capabilities for multi-robot systems. Existing distributed approaches either neglect the cross-covariances between robots and targets, which leads to degraded estimation accuracy and increase of computational cost, or require transmitting full covariances, which incurs prohibitive communication costs. To address these challenges, we propose a distributed joint localization and tracking framework in which each robot maintains the local cross-covariance between its own state and the target states, while neighboring robot pairs disseminates only the marginal mean-covariance pairs for the robot and each target separately, deliberately omitting the cross-covariances. This design fully exploits the inherent correlations, enabling locally optimal estimation and eliminating the computational overhead arising from optimizing unknown cross-covariances. Additionally, owning to the selective transmitting mechanism, it achieves an efficient balance between accuracy and communication efficiency. Moreover, a dimensionally adaptive Inverse Covariance Intersection (ICI)-based approach is developed to fuse non-common state estimates and nonlinear measurements, achieving consistent and less conservative results. Extensive simulations demonstrate that it outperforms existing distributed methods in terms of both accuracy and efficiency.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA10.3", "code": "ThA10.3", "title": "Distributed Formation Control Using Noisy Bearing Measurements (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA10", "sessionTitle": "Advances in Identification and Control for Large-Scale Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Rao, Xinpei", "affiliation": "Chinese Academy of Sciences" }, { "name": "Liu, Yujing", "affiliation": "Academy of Mathematics and Systems Science, Chinese Academy of Sciences" }, { "name": "Li, Yibei", "affiliation": "Chinese Academy of Sciences" }, { "name": "Liu, Zhixin", "affiliation": "Academy of Mathematics and Systems Sciences" }, { "name": "Li, Chanying", "affiliation": "Academy of Mathematic and System Science, CAS" } ], "keywords": [ "Adaptive control of multi-agent systems" ], "abstract": "This paper studies the leader-follower formation control problem where followers can only obtain noisy bearing measurements of their local neighbors. We first develop a distributed localization algorithm to estimate the relative position with the leader, where a modified stochastic gradient (SG) algorithm with a compensation term is used. Based on the estimates, we design an adaptive control law to drive all agents to the desired formation where a decaying excitation signal is introduced to solve the issue caused by the excitation conditions required by the localization algorithm and the stability of formation control. We show that under certain non-persistent excitation (PE) conditions, the convergence of the distributed localization algorithm can be guaranteed. Furthermore, by verifying that the control laws satisfy these excitation conditions, we prove that the proposed control algorithm enables all agents to achieve the desired formation. The effectiveness of the theoretical results is validated by numerical simulations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA10.4", "code": "ThA10.4", "title": "A Game-Theoretic Approach to ECMPE Analysis for Interbank Networks (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA10", "sessionTitle": "Advances in Identification and Control for Large-Scale Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Yao, Yuhua", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Djehiche, Boualem", "affiliation": "Royal Technical University of Stockholm" }, { "name": "Hu, Xiaoming", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Multi-agent systems", "Control over networks", "Markov decision process" ], "abstract": "In this work we study how heterogeneous banks strategically operate interbank lending networks as a dynamic game. We model how links emerge from liquidity needs and evolving trust among borrowers, lenders, and intermediaries. Our main contributions are to use entropy-regularized constrained Markov perfect equilibrium (ECMPE) to analyze the steady-state behavior of such network and to give sufficient conditions for its existence. As a necessary step, we first integrate network formation, liquidity dynamics, and trust evolution into a unified dynamic-game model. Finally, we develop an efficient iterative algorithm for computing the ECMPE numerically and demonstrate its convergence in simulation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA10.5", "code": "ThA10.5", "title": "A Modular Digital Twin Framework for Urban Traffic Monitoring and Simulation in a Mobility Living Lab", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA10", "sessionTitle": "Advances in Identification and Control for Large-Scale Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Li, Yuxian", "affiliation": "Tecnológico De Monterrey" }, { "name": "Quihuis-Hernandez, Ricardo", "affiliation": "Tecnológico De Monterrey" }, { "name": "Tudon-Martinez, Juan Carlos", "affiliation": "Tecnologico De Monterrey" }, { "name": "Felix-Herran, Luis C.", "affiliation": "Tecnologico De Monterrey" }, { "name": "Lozoya-Santos, Jorge De-J.", "affiliation": "Tecnologico De Monterrey" } ], "keywords": [ "Big data and machine learning applied to smart cities", "IoT for cities" ], "abstract": "Presently, information intensive cities are confronted with challenges related to traffic congestion, urban design, increased polluting emissions, and a lack of information systems that facilitate management to conventional traffic monitoring systems. To address this problem, this article proposes the implementation of a modular urban traffic digital twin (DT) focused on urban mobility environment monitoring and simulation. This work presents a structured methodology for digital twin platform development, constructed by perception layer, data management phase, simulation function, and tridimensional visualization. The proposed platform integrates real-time and real world data acquisition, enabling the synchronization of physical and virtual environment, thus providing a modern instrument for the analysis of urban traffic data, thereby facilitating decision-making and the implementation of strategies to enhance urban issues and develop traffic management solutions. The results demonstrate the applicability of the proposed development methodology by validating the platform under a real-time connectivity environment, by measuring data transmission latency and platform functional evaluation. The focus of a modular design establishes a scalable foundation for advanced urban traffic management and data-driven decision-making in smart city applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA10.6", "code": "ThA10.6", "title": "A Long-Duration Autonomy Approach to Connected and Automated Vehicles", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA10", "sessionTitle": "Advances in Identification and Control for Large-Scale Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Beaver, Logan", "affiliation": "Old Dominion University" } ], "keywords": [ "Distributed optimization and control for smart cities", "Transportation networks", "Interconnected city networks" ], "abstract": "In this article, we present a long-duration autonomy approach for the control of connected and automated vehicles (CAVs) operating in a transportation network. In particular, we focus on the performance of CAVs at traffic bottlenecks, including roundabouts, merging roadways, and intersections. We take a principled approach based on optimal control, and derive a reactive controller with guarantees on safety, performance, and energy efficiency. We guarantee safety through high order control barrier functions (HOCBFs), which we ``lift'' to first order CBFs using time-optimal motion primitives. This yields a set of first-order CBFs that are compatible with the control bounds. We demonstrate the performance of our approach in simulation and compare it to an optimal control-based approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA13.1", "code": "ThA13.1", "title": "Distributed Gradient-Free Algorithm for Nonconvex Nonsmooth Optimization with Non-Asymptotic Convergence (I)", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA13", "sessionTitle": "Distributed Online Optimization and Games and Their Applications", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Hou, Jie", "affiliation": "Beijing Institute of Technology" }, { "name": "Jiang, Xia", "affiliation": "Nanyang Technological University" }, { "name": "Zeng, Xianlin", "affiliation": "Beijing Institute of Technology" } ], "keywords": [ "Consensus", "Distributed optimization", "Multi-agent systems" ], "abstract": "This paper addresses the challenge of solving distributed nonconvex nonsmooth optimization (DNNO) problems. While most existing works focused on DNNO with composite structures, only a limited number of studies tackled general DNNO problems. However, these studies primarily provided asymptotic convergence analysis for their proposed algorithms. In this paper, we advance the field by presenting a non-asymptotic convergence analysis for solving general DNNO problems. To measure the convergence performance of algorithms in DNNO, we introduce the concept of Goldstein stationarity. Building on this, we propose a distributed zeroth-order algorithm tailored for DNNO and establish an mathcal{O}(d^frac{3}{8}T^{-frac{1}{4}}) convergence rate for achieving a Goldstein stationary point. Finally, we validate the efficacy of the proposed algorithm through numerical experiments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA13.2", "code": "ThA13.2", "title": "Projection-Free Variance Reduction Algorithm for Distributed Online Stochastic Nonconvex Optimization (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA13", "sessionTitle": "Distributed Online Optimization and Games and Their Applications", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Jiang, Xia", "affiliation": "Nanyang Technological University" }, { "name": "Liu, Lu", "affiliation": "City University of Hong Kong" }, { "name": "Feng, Gang", "affiliation": "City Univ. of Hong Kong" }, { "name": "Xie, Lihua", "affiliation": "Nanyang Technological University" } ], "keywords": [ "Distributed optimization", "Distributed control and estimation", "Multi-agent systems" ], "abstract": "This paper studies online stochastic nonconvex optimization in multi-agent networks under some constraint sets. In this setting, each agent makes decisions from a feasible set using only locally available stochastic gradients from previous steps and information exchanged with its neighbors. This paper develops a projection-free distributed optimization algorithm, which replaces costly projection steps with efficient conditional gradient updates. To further mitigate the effects of stochastic gradient noise, the algorithm integrates recursive variance-reduced gradient estimators into its update process. Theoretically, this paper establishes that the proposed algorithm achieves a high-probability sublinear regret bound for online stochastic nonconvex optimization problems. Numerical experiments are conducted to demonstrate the efficiency of the proposed algorithm.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA13.3", "code": "ThA13.3", "title": "Incentive Mechanism for Noncooperative Games with Unincentivizable Agents (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA13", "sessionTitle": "Distributed Online Optimization and Games and Their Applications", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Yan, Yuyue", "affiliation": "The University of Tokyo" }, { "name": "Ishii, Hideaki", "affiliation": "University of Tokyo" }, { "name": "Ye, Maojiao", "affiliation": "Nanjing University of Science and Technology" } ], "keywords": [ "Multi-agent systems", "Control of networks", "Distributed optimization" ], "abstract": "A budget-balancing incentive mechanism for pseudo-gradient-based noncooperative dynamical systems is developed for achieving a largest admissible social welfare with unincentivizable agents. In the proposed approach, the system manager transfers utilities only among incentivizable agents and tries to move the Nash equilibrium of the entire game to maximize the social welfare function as much as possible. With and without the explicit knowledge of Nash equilibrium mapping, we present a primal method and a dual method to update the incentive functions. Sufficient conditions are derived under which agents’ state converges to the optimal target state as a Nash equilibrium. A couple of numerical examples are presented to illustrate the efficacy of our results.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA13.4", "code": "ThA13.4", "title": "Feedback Optimization of Linear Systems with Additive Disturbances: A Model Predictive Control Method (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA13", "sessionTitle": "Distributed Online Optimization and Games and Their Applications", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Qin, Zhengyan", "affiliation": "The University of Hong Kong" }, { "name": "Luo, Qianyue", "affiliation": "University of Hong Kong" }, { "name": "Liu, Tao", "affiliation": "The University of Hong Kong" }, { "name": "Liu, Tengfei", "affiliation": "Northeastern University" }, { "name": "Chai, Tianyou", "affiliation": "Northeastern Univ" }, { "name": "Lam, James", "affiliation": "Univ of Hong Kong" } ], "keywords": [ "Model predictive control of hybrid systems" ], "abstract": "This paper investigates the feedback optimization of linear time-invariant systems with unknown additive disturbances and state/input constraints. We propose a controller that combines a feedback optimization algorithm with a tracking model predictive control (MPC) algorithm. The feedback optimization algorithm generates a reference input signal based on real-time gradient measurements of the economic objective at the plant’s real-time output and control input, eliminating the need for the analytical form of the gradient function. The tracking MPC algorithm drives the actual system input to follow this reference signal while enforcing state and input constraints. The control input is obtained by solving a quadratic programming (QP) problem. We establish, under mild assumptions, recursive feasibility of the MPC problem, satisfaction of all state and input constraints, and input-to-state stability with respect to disturbance changes. If the disturbance change converges to zero, the state and input converge to their optimal trajectories. An economic optimization case study is used to demonstrate the method’s effectiveness.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA13.5", "code": "ThA13.5", "title": "Optimization of Markovian Switching One-Way Car-Sharing Networks Via DC Programming", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA13", "sessionTitle": "Distributed Online Optimization and Games and Their Applications", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Zhao, Chengyan", "affiliation": "Kyushu Institute of Technology" }, { "name": "Sakurama, Kazunori", "affiliation": "The University of Osaka" }, { "name": "Ogura, Masaki", "affiliation": "Hiroshima University" } ], "keywords": [ "Modeling and simulation of transportation systems", "Automatic control, optimization, real-time operations in transportation", "Vehicle dynamic systems" ], "abstract": "This paper studies the optimization of stochastic one-way car-sharing networks, where the network topology and station roles are governed by a Markov process. To regulate the network flow, we introduce a pricing-based car-sharing model on a directed network. The model is formulated as a positive linear system, and system performance indices are used for mathematically rigorous congestion-oriented design. To address the nonconvexity and computational complexity of the resulting optimization problems, we develop a DC (difference-of-convex) programming framework. By exploiting the log--log convexity of posynomial functions, the design problems are reformulated as DC programs. Simulation results demonstrate the effectiveness of the proposed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA13.6", "code": "ThA13.6", "title": "Selection of Suitable Optimization Algorithm for Equivalent Circuit Model Paramter Identification of 100Ah Prismatic Lithium-Ion Battery", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA13", "sessionTitle": "Distributed Online Optimization and Games and Their Applications", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "N, Pappa", "affiliation": "MIT Campus, Anna University" }, { "name": "Arvind P, Vishnu", "affiliation": "University" }, { "name": "J, Reegan", "affiliation": "University" }, { "name": "Jose V, Nivin", "affiliation": "University" }, { "name": "P, Anbumalar", "affiliation": "University" }, { "name": "N, Shambavi", "affiliation": "University" }, { "name": "Sutha Subbian, Sutha", "affiliation": "Department of Instrumentation Engg, MIT Campus, Anna University" } ], "keywords": [ "Modeling and simulation of transportation systems", "Electric and solar vehicles", "Automatic control, optimization, real-time operations in transportation" ], "abstract": "Accurate estimation of the State of Charge (SoC) is essential for the reliable and safe operation of lithium-ion batteries in Electric Vehicles (EVs). Conventional SoC estimation methods such as open-circuit voltage method and Coulomb counting have limitations including the accumulation of arbitrary errors over time and susceptibility to dynamic operating conditions. Additionally, battery systems exhibit highly non-linear characteristics. To overcome these limitations and handle the non-linearity, model-based techniques have been increasingly adopted in recent years. Hence, identification of accurate battery model parameters is very essential. The main objective of this work is to select suitable optimization algorithm for identifying the equivalent circuit model(ECM) parameters of 100Ah lithium-ion battery. Initially, a Hybrid Pulse Power Characterization (HPPC) test is conducted to identify the initial parameters of a Two RC-network Equivalent Circuit Model (ECM) for the 100Ah prismatic cell under discharging condition. The experiments are carried out on a fully charged battery. For HPPC test, discharging from 100% SoC to 0% SoC with step changes of 10%, 0.5C constant discharging and dynamic discharging with variable steps while recording the battery voltage. The ECM parameters are identified for all the 10 regions of battery voltages obtained through HPPC. Further, the ECM parameters, are optimized using Simplex, Nonlinear Least Squares (NLS), and Pattern Search (PS) algorithms. Finally, the performances of the optimization algorithms are compared by estimating battery voltage with optimized parameters against 0.5C constant discharging and dynamic discharging conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA14.1", "code": "ThA14.1", "title": "Virtual Reference Feedback Tuning Adapted for Parallel Branches: A Virtual Power Plant Application", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA14", "sessionTitle": "Design Methods for Data-Based Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Albrecht Fitarelli, Felipe", "affiliation": "Universidade Federal Do Rio Grande Do Sul" }, { "name": "Campestrini, Luciola", "affiliation": "Univ of Rio Grande Do Sul" }, { "name": "Bazanella, Alexandre S.", "affiliation": "Univ. Federal Do Rio Grande Do Sul" }, { "name": "Resener, Mariana", "affiliation": "Simon Fraser University" } ], "keywords": [ "Design methods for data-based control", "Applications of optimal control" ], "abstract": "This article presents the adaptation of the Virtual Reference Feedback Tuning (VRFT) for a control system with multiple parallel branches, since this data-driven method was originally conceived for single-branch structures. We present two possible approaches - the joint matrix tuning and the sequential tuning - and apply them to tune the controllers of a virtual power plant (VPP) composed of four photovoltaic plants, focusing on the regulation of the system’s active power. Simulated VPP results show that the obtained responses closely reproduce the specified desired behavior.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA14.2", "code": "ThA14.2", "title": "Performance-Oriented Decoupling Learning Control for Disturbed Multi-Variable Systems with Stability Guarantee", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA14", "sessionTitle": "Design Methods for Data-Based Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Yu, Pan", "affiliation": "Beijing University of Technology" }, { "name": "Zhang, Bozhi", "affiliation": "Beijing University of Technology" }, { "name": "Yu, Xiaowei", "affiliation": "Beijing University of Technology" }, { "name": "Liu, Kang-Zhi", "affiliation": "Chiba Univ" } ], "keywords": [ "Design methods for data-based control", "Decentralized control", "Adaptive control design" ], "abstract": "A performance-oriented decoupling learning control method is developed for multi-variable systems subject to unknown disturbances, which only uses the system outputs. The key is to devise a tailored residual neural network (TRNN)-based equivalent-input-disturbance (EID) estimator to handle the overall influence of coupling and unknown disturbances and then to design the resultant system. First, for a performance-oriented learning, an intermediate index of decoupling control is adopted to guide the backpropagation training of the feedforward neural network of the TRNN. Then, multiple simple residual terms of the TRNN are designed for the sake of closed-loop stability. Further, the resultant decoupled system is designed. Last, a case study of a pilot distillation column validates the superior decoupling performance over other methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA14.3", "code": "ThA14.3", "title": "A Behavioral Approach for Data-Driven Static Output Feedback Stabilization", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA14", "sessionTitle": "Design Methods for Data-Based Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Jia, Fang", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Li, Xianwei", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Li, Shaoyuan", "affiliation": "Shanghai Jiao Tong Univ" } ], "keywords": [ "Design methods for data-based control", "Linear systems", "Controller constraints and structure" ], "abstract": "This paper investigates the static output feedback (SOF) stabilization problem for discrete-time linear time-invariant (DLTI) systems using finite noise-free input-output data only. Within the behavioral framework, systems are described by input-output representations. A necessary and sufficient Lyapunov-based condition for SOF stabilization is first established in the model-based setting. An exact identification method is then proposed to reconstruct a minimal lag input-output model via the solution of a single matrix equation. Building on these results, a cone complementarity linearization (CCL)-based linear matrix inequality (LMI) algorithm is developed for data-driven SOF stabilization. The proposed method naturally accommodates structural constraints on the SOF gain, and its effectiveness is illustrated by numerical examples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA14.4", "code": "ThA14.4", "title": "Direct Data-Driven Approximate Optimal Control of Nonlinear Input-Affine Systems", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA14", "sessionTitle": "Design Methods for Data-Based Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Nortmann, Benita", "affiliation": "Empa (Swiss Federal Laboratories for Materials Science and Technology)" }, { "name": "Mylvaganam, Thulasi", "affiliation": "Imperial College London" } ], "keywords": [ "Design methods for data-based control", "Optimal control theory", "Stability of nonlinear systems" ], "abstract": "In this paper, we combine a data-driven system representation with a framework to systematically construct (approximate) solutions to nonlinear optimal control problems. By immersing the unknown dynamics into an extended state space, solutions are characterised via purely data-dependent algebraic conditions. This allows us to design dynamic state-feedback controllers with local stability and performance guarantees for unknown nonlinear, input-affine systems directly using data, without explicitly identifying the dynamics.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA14.5", "code": "ThA14.5", "title": "Data Informativity for Stability and Stabilization of K-Positive Linear Systems", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA14", "sessionTitle": "Design Methods for Data-Based Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Iwata, Takumi", "affiliation": "Hiroshima University" }, { "name": "Kawano, Yu", "affiliation": "Hiroshima University" }, { "name": "Peaucelle, Dimitri", "affiliation": "LAAS-CNRS" }, { "name": "Ebihara, Yoshio", "affiliation": "Kyushu University" }, { "name": "Nagahara, Masaaki", "affiliation": "Hiroshima University" } ], "keywords": [ "Design methods for data-based control", "Positive linear systems" ], "abstract": "We formulate a data-driven stabilization problem for discrete-time linear systems in the framework of linear programming (LP). It is known that if a polyhedral cone is invariant, referred to as K-positivity, then stability analysis reduces to solving an LP. Building on this fact, we establish a necessary and sufficient condition for the existence of a state feedback gain imposing both closed-loop K-positivity and stability, given a proper polyhedral cone. We also propose a data-driven design method of such a state feedback gain.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA14.6", "code": "ThA14.6", "title": "Data-Driven Control of Periodic Piecewise Linear Systems with Unstable Subsystems", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA14", "sessionTitle": "Design Methods for Data-Based Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Zhang, Yan", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Tan, Zhuolin", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Xie, Xiaochen", "affiliation": "Harbin Institute of Technology, Shenzhen" }, { "name": "Lam, James", "affiliation": "Univ of Hong Kong" } ], "keywords": [ "Design methods for data-based control", "Switching linear systems", "Lyapunov methods" ], "abstract": "This paper proposes a data-driven control framework for discrete-time periodic piecewise linear systems with unstable subsystems. Unlike conventional approaches that strictly require the stabilization of every subsystem, our framework allows some subsystems to remain unstable under the action of the controller. An equivalent data representation is established for the closed-loop periodic piecewise linear systems, with a corresponding data collection strategy. Sufficient conditions ensuring exponential stability are developed through analysis using piecewise Lyapunov functions. To address the computational challenges caused by the resulting non-convex bilinear matrix inequalities and to enforce physical constraints on the controller gain, an iterative algorithm is developed to solve these conditions. These theoretical results are formulated as solvable linear matrix inequalities. The effectiveness and engineering applicability of the proposed approach are verified through numerical simulations and a practical case study involving a DC-DC Boost converter. The results validate that the proposed data-driven framework can achieve exponential stability for general periodic systems with unstable subsystems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA15.1", "code": "ThA15.1", "title": "Distributed High-Gain Observer Design of Interconnected Nonlinear Systems for Vehicle Platoon Application (I)", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA15", "sessionTitle": "Advances in Estimation and Observer Design: From Theory to Emerging Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Li, Qi", "affiliation": "Université De Lorraine" }, { "name": "Meng, Shengya", "affiliation": "Universite De Lorraine" }, { "name": "Meng, Fanwei", "affiliation": "Northeastern University at Qinhuangdao" }, { "name": "Delattre, Cédric", "affiliation": "Université De Lorraine (IUT De Longwy)" }, { "name": "Zemouche, Ali", "affiliation": "CRAN UMR CNRS 7039, University of Lorraine" } ], "keywords": [ "Observer design", "Distributed nonlinear control" ], "abstract": "This paper addresses the state estimation problem for a class of interconnected nonlinear multi-agent systems (MAS). The block--triangular structure inherent to platoons results in highly coupled complex error dynamics. These coupled dynamics are difficult to analyze directly, presenting a significant challenge for observer design. The innovation of this work lies in the quadratic form of the decomposed error that enables the transformation of the coupled quadratic forms from the Lyapunov analysis into a tractable convex optimization problem, that can be solved by a set of Linear Matrix Inequalities (LMIs). The obtained result provides a constructive design of a distributed high--gain observer that ensures exponential convergence while providing lower bounds on all observer gains. The effectiveness of the proposed design is validated through simulations on a vehicle platoon model.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA15.2", "code": "ThA15.2", "title": "Observer-Based Control of Nonlinear Coupled Vehicle Dynamics with State Dependent Measurement Uncertainties (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA15", "sessionTitle": "Advances in Estimation and Observer Design: From Theory to Emerging Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Arezki, Hasni", "affiliation": "UPHF" }, { "name": "Sentouh, Chouki", "affiliation": "LAMIH UMR CNRS 8201, Université Polytechnique Hauts-De-France, Valenciennes, France" }, { "name": "Popieul, Jean-Christophe", "affiliation": "University of Valenciennes/LAMIH" } ], "keywords": [ "Output feedback nonlinear control", "Robust estimation", "Stability of nonlinear systems" ], "abstract": "This paper presents an observer-based feedback control strategy for nonlinear coupled vehicle dynamics affected by state-dependent measurement uncertainties. The proposed observer structure accounts for the nonlinear distortions introduced by imperfect sensors, while the control law ensures both stability and accurate trajectory tracking of the vehicle. The stability analysis relies on the convexity principle and the mean value theorem, enabling the transformation of the nonlinear terms into linear representations through Jacobian matrices evaluated at specific operating points. The stability of the resulting augmented system is established via a set of Linear Matrix Inequalities~(LMIs), derived under Lipschitz continuity and convex polytopic descriptions of the Jacobians. The proposed framework guarantees asymptotic stability and provides a systematic procedure for designing both the observer and the controller gains. Simulation results on a nonlinear single-track vehicle model demonstrate the effectiveness of the approach in reconstructing unmeasured states and maintaining robust stability despite state-dependent sensor uncertainties.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA15.3", "code": "ThA15.3", "title": "A Modulating Functions-Based Recursive Finite-Memory Volterra State Estimator for Nonlinear Systems (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA15", "sessionTitle": "Advances in Estimation and Observer Design: From Theory to Emerging Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Wei, Yanqiao", "affiliation": "Yanshan University" }, { "name": "Liu, Da-Yan", "affiliation": "INSA Centre Val De Loire, Campus De Bourges" }, { "name": "Hua, Changchun", "affiliation": "Yanshan Univ" }, { "name": "Wei, Xing", "affiliation": "Anyang Institute of Technology" }, { "name": "Liu, Hao-Ran", "affiliation": "Yanshan University" } ], "keywords": [ "Robust estimation", "Nonlinear observers and filters", "Application of nonlinear analysis and design" ], "abstract": "In this paper, the state of a class of Lipschitz nonlinear systems with an observable linear part is estimated using a modulating functions-based recursive finite-memory Volterra state estimator. To this end, the linear part of the system is first transformed into an observable canonical form. Then, by applying the generalized modulating functions method, the system state is implicitly characterized by a set of nonlinear Volterra integral equations of the second kind over a sliding time window. This formulation naturally attenuates measurement noise through integral smoothing. For discrete-time noisy output measurements, a numerical quadrature scheme is used to approximate the integral equations, leading to a recursive state reconstruction algorithm that does not require knowledge of the initial conditions. Then, the estimation errors are analyzed. In particular, error bounds are derived for the recursive estimation errors. Finally, a simulation example demonstrates that the proposed method provides robust and non-asymptotic estimation of both the state and disturbances.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA15.4", "code": "ThA15.4", "title": "Computation of the Weighted Kalman Filter Via Pseudo-Differential Operators (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA15", "sessionTitle": "Advances in Estimation and Observer Design: From Theory to Emerging Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Ruiz, Adrián", "affiliation": "Universitetet I Stavanger" }, { "name": "Rotondo, Damiano", "affiliation": "Universitetet I Stavanger" } ], "keywords": [ "Observer design" ], "abstract": "The weighted Kalman filter (WKF) is a recently introduced variant of the extended Kalman filter that replaces the standard Jacobian-based linearization with Gaussian-weighted integration. Until now, this reliance on integration has represented a major obstacle to the applicability of the WKF to general nonlinear systems. This paper shows that recent developments in probability theory, specifically those concerning the analytical evaluation of expectations involving nonlinear functions of Gaussian random vectors and higher-order moments, open new avenues for deriving a fully analytical and computationally efficient formulation of the WKF. The proposed approach eliminates the need for multidimensional numerical integration, thereby substantially reducing the computational complexity of the WKF. Simulation results obtained for a nonlinear quadratic system and for a nonlinear system with non-polynomial nonlinearities illustrate the effectiveness and applicability of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA15.5", "code": "ThA15.5", "title": "Learning with Unknown Input Observers for Robust Nonlinear Estimation (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA15", "sessionTitle": "Advances in Estimation and Observer Design: From Theory to Emerging Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Nguyen, Quang Huy", "affiliation": "University Lorraine" }, { "name": "Li, Qi", "affiliation": "Université De Lorraine" }, { "name": "Zemouche, Ali", "affiliation": "CRAN UMR CNRS 7039, University of Lorraine" }, { "name": "Rafaralahy, Hugues", "affiliation": "Université De Lorraine" }, { "name": "Haddad, Madjid", "affiliation": "SEGULA Technologies" } ], "keywords": [ "Observer design", "Nonlinear observers and filters", "Nonlinearity learning from data" ], "abstract": "This paper proposes a hybrid estimator for vehicle systems with unmodeled dynamics. A generalized unknown input observer provides bounded physics-based estimates when classical rank conditions are relaxed through output derivatives. These estimates supervise a neural adaptive observer that learns a state-dependent approximation of the unknown input. LMI conditions certify mathcal{H}^{1} and mathcal{L}_{2} estimation bounds, and vehicle simulations show improved trajectory tracking and tire-force residual reconstruction.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA15.6", "code": "ThA15.6", "title": "KalMRACO: Unifying Kalman Filtering and Model Reference Adaptive Control for Robust Control and Estimation (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA15", "sessionTitle": "Advances in Estimation and Observer Design: From Theory to Emerging Applications", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Fosso, Lauritz Rismark", "affiliation": "SINTEF Ocean" }, { "name": "Holden, Christian", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Ohrem, Sveinung Johan", "affiliation": "SINTEF Ocean" } ], "keywords": [ "Adaptive control design", "Observer design", "Application of nonlinear analysis and design" ], "abstract": "A common assumption when applying the Kalman filter is a priori knowledge of the system parameters. These parameters are not necessarily known, and this may limit the real-world applicability of the Kalman filter. The well-established Model Reference Adaptive Controller (MRAC) utilizes a known reference model and ensures that the input-output behavior of a potentially unknown system converges to that of the reference model. We present KalMRACO, a unification of Kalman filtering and MRAC leveraging the reference model of MRAC as the Kalman filter system model, thus eliminating, to a large degree, the need for knowledge of the underlying system parameters in the application of the Kalman filter. We also introduce the concept of blending estimated states and measurements in the feedback law to ensure stability during the initial transient. KalMRACO is validated through simulations and lab trials on an underwater vehicle. Results show superior tracking of the reference model state, observer state convergence, and noise mitigation properties.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA16.1", "code": "ThA16.1", "title": "Recognition of Ensemble Systems through Aggregated Measurements", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA16", "sessionTitle": "Modeling, Simulation and Control of Distributed Parameter Systems I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Hu, Ningyuan", "affiliation": "Tongji University" }, { "name": "Miao, Wei", "affiliation": "Washington University in St. Louis" }, { "name": "Cheng, Gong", "affiliation": "Tongji University" }, { "name": "Li, Jr-Shin", "affiliation": "Washington University in St. Louis" } ], "keywords": [ "System identification and adaptive control of distributed parameter systems" ], "abstract": "This paper investigates how to distinguish ensemble systems from their collective behavior using statistical methods in reproducing kernel Hilbert spaces (RKHS). We develop a nonparametric framework for comparing ensemble systems by computing the maximum mean discrepancy (MMD) between their aggregated measurements, without requiring any prior knowledge of the underlying dynamics. The framework naturally extends to clustering multiple unknown ensembles solely from their aggregated observations. Numerical experiments demonstrate the reliability and robustness of the proposed approach across a variety of ensemble models with distinct dynamical structures.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA16.2", "code": "ThA16.2", "title": "A Port-Hamiltonian Model for Coupled Vocal Fold Elasticity, Contact, and Diffusion-Driven Swelling (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA16", "sessionTitle": "Modeling, Simulation and Control of Distributed Parameter Systems I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Ponce, Cristobal", "affiliation": "Universidad Técnica Federico Santa María" }, { "name": "Parra, Jesús Alberto", "affiliation": "Universidad Técnica Federico Santa Maria" }, { "name": "Ramirez, Hector", "affiliation": "Universidad Tecnica Federico Santa Maria" }, { "name": "Peterson, Sean Daniel", "affiliation": "University of Waterloo" }, { "name": "Zañartu, Matias", "affiliation": "Universidad Técnica Federico Santa María" } ], "keywords": [ "Distributed parameters port Hamiltonian systems", "Lagrangian and Hamiltonian systems", "Interconnected nonlinear systems" ], "abstract": "A nonlinear continuous model for vocal fold dynamics based on the port-Hamiltonian systems (PHS) framework is presented. The model couples two-dimensional elastodynamics with a diffusion equation for fluid concentration, where local tissue deformation drives changes in concentration, producing volumetric swelling. This formulation enables the simulation of diffusion-driven tissue hydration and inflammation, and is further coupled to aerodynamic loading via a one-dimensional Bernoulli pressure model, while mechanical contact between the folds is incorporated through a nonlinear damping-based penalty method. A contact-induced swelling criterion, based on dissipated power, is tested to evaluate the onset and progression of inflammation under sustained phonation. Numerical experiments illustrate the capability of the model to capture the complex interplay between tissue mechanics, airflow-induced forces, contact phenomena, and diffusion-driven swelling, providing an energy-based framework for studying pathological vocal fold behaviors.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA16.3", "code": "ThA16.3", "title": "Conduction-Diffusion in N-Dimensional Settings As Irreversible Port-Hamiltonian Systems (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA16", "sessionTitle": "Modeling, Simulation and Control of Distributed Parameter Systems I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Mora, Luis A.", "affiliation": "University of Waterloo" }, { "name": "Le Gorrec, Yann", "affiliation": "FEMTO-ST, SupMicroTech Besançon" }, { "name": "Ramirez, Hector", "affiliation": "Universidad Tecnica Federico Santa Maria" }, { "name": "Matignon, Denis", "affiliation": "ISAE" } ], "keywords": [ "Distributed parameters port Hamiltonian systems", "Boundary control of distributed parameter systems", "Lagrangian and Hamiltonian systems" ], "abstract": "This work extends previous 1D irreversible port-Hamiltonian system (IPHS) formulations to boundary-controlled ND distributed parameter systems describing conduction–diffusion fluid phenomena. Within a unified and thermodynamically consistent framework, we show that conduction and diffusion can be represented through a single coherent structure that preserves global energy balance and ensures a correct characterization of entropy production. The resulting formulation provides a foundation for the systematic modeling and control of complex multi-physical processes governed by coupled transport mechanisms in N-dimensions. In the longer term, this framework opens the door to structure-preserving numerical schemes capable of enforcing thermodynamic principles directly at the discretized level.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA16.4", "code": "ThA16.4", "title": "Irreversible Port-Hamiltonian Formulations for 1-Dimensional Fluid Systems (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA16", "sessionTitle": "Modeling, Simulation and Control of Distributed Parameter Systems I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Ouardi, Ahlam", "affiliation": "University Mohammed Vi Polytechnic" }, { "name": "Sarkar, Arijit", "affiliation": "Brandenburg University of Technology Cottbus - Senftenberg" }, { "name": "Ramirez, Hector", "affiliation": "Universidad Tecnica Federico Santa Maria" }, { "name": "Le Gorrec, Yann", "affiliation": "FEMTO-ST, SupMicroTech Besançon" } ], "keywords": [ "Distributed parameters port Hamiltonian systems", "Boundary control of distributed parameter systems", "Lagrangian and Hamiltonian systems" ], "abstract": "The Irreversible Port-Hamiltonian Systems (IPHS) framework is extended to the modelling of non-isentropic fluids with viscous dissipation in the Eulerian description. Building on earlier IPHS formulations for diffusion-driven and non-convective distributed systems, it is shown that convective transport can be consistently encompassed by the framework by modifying the underlying differential operators. After revisiting the constitutive relations of non-isentropic fluids in both Eulerian and Lagrangian coordinates, it is demonstrate how these systems fit within an extended IPHS formulation. Furthermore, an extended parametrisation of the boundary port variables which ensures that the first and second laws of Thermodynamics are fulfilled allows to define a general class of boundary controlled IPHS.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA16.5", "code": "ThA16.5", "title": "Port-Hamiltonian Rayleigh Beam Models on Lagrangian Subspaces (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA16", "sessionTitle": "Modeling, Simulation and Control of Distributed Parameter Systems I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Ponce, Cristobal", "affiliation": "Universidad Técnica Federico Santa María" }, { "name": "Ramirez, Hector", "affiliation": "Universidad Tecnica Federico Santa Maria" }, { "name": "Le Gorrec, Yann", "affiliation": "FEMTO-ST, SupMicroTech Besançon" }, { "name": "Wu, Yongxin", "affiliation": "Université Marie Et Louis Pasteur" } ], "keywords": [ "Distributed parameters port Hamiltonian systems", "Lagrangian and Hamiltonian systems", "Lyapunov methods" ], "abstract": "Rayleigh beam models are of significant practical interest for the simulation and control of flexible structures due to their balance of accuracy and complexity. Unlike previous approaches based on kinematic constraints, this work derives the Rayleigh beam by degenerating the shear elastic energy of a Timoshenko beam model. This results in an equivalent reduced-state descriptor port-Hamiltonian system on a Stokes–Lagrange structure. Following a different approach, a second non-descriptor Stokes–Lagrange representation is presented, more closely related to an Euler–Bernoulli formulation with differential constitutive relations. Finally, a mixed finite element discretization is proposed that mirrors the degeneracy mechanism to transform a non-descriptor infinite-dimensional Timoshenko beam model into a descriptor Rayleigh beam approximation by driving the shear strain interpolation functions to zero.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA17.1", "code": "ThA17.1", "title": "Robustness of Lienard Systems in Constant Delays (I)", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Complex Dynamics in Time-Delay Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Aleksandrov, Alexander", "affiliation": "Applied Mathematics and ControlProcesses, St.PetersburgStateUniversity" }, { "name": "Efimov, Denis", "affiliation": "Inria" }, { "name": "Ping, Xubin", "affiliation": "Xidian University" }, { "name": "Fridman, Emilia", "affiliation": "Tel-Aviv Univ" } ], "keywords": [ "Nonlinear time-delay systems", "Lyapunov methods", "Stability of nonlinear systems" ], "abstract": "For mechanical systems in the Lienard canonical form of the model with constant delays, the problem of robust stability analysis is considered. It is assumed that all nonlinearities are homogeneous functions of different degrees. For this class of systems, stability conditions are obtained, first, for a delay-free model, and next, they are developed to the delayed case. To this end, different Lyapunov--Krasovskii functionals are constructed. Applying the averaging theory, the influence of time-varying perturbations is taken into account. The results are illustrated by simulations for a mechanical system.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA17.2", "code": "ThA17.2", "title": "Coprime Factorizations for a Class of Neutral Systems with a Chain of Poles Clustering the Imaginary Axis (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Complex Dynamics in Time-Delay Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Bonnet, Catherine", "affiliation": "Saclay Inria Centre" }, { "name": "Do, Duc Duy", "affiliation": "Inria Saclay Center" }, { "name": "Yamamoto, Yutaka", "affiliation": "Kyoto University" } ], "keywords": [ "Linear time-delay systems", "Robust controller synthesis", "Control of complex systems" ], "abstract": "We consider in this paper a class of single-input single-output delay systems of neutral type with transfer functions with one delay inducing a chain a poles clustering the imaginary axis from left of right. For a large class of subsystems, we provide coprime factorizations over H_infty which guarantee H_infty-stabilization properties and are a first step towards their robust H_infty-stabilization.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA17.3", "code": "ThA17.3", "title": "The Logistic Differential Equation with Two Independent Delays (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Complex Dynamics in Time-Delay Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Krauskopf, Bernd", "affiliation": "University of Auckland" }, { "name": "Mancini, Renzo", "affiliation": "University of Auckland" } ], "keywords": [ "Nonlinear time-delay systems", "Stability of nonlinear systems" ], "abstract": "We study the logistic equation when its two terms each feature a delay. The two delays, sigma and tau, are the only parameters of this nonlinear delay differential equation, and we show that its bifurcation diagram in the (tau, sigma)-plane features regions with complicated dynamics, including transitions to chaotic attractors. This system arose, after rescaling, as a conceptual model for the Atlantic Meridional Overturning Circulation. However, it can also be seen as a generalization of the well-known Hutchinson-Wright equation, and we show how all complicated dynamics `vanishes' as tau is decreased to zero.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA17.4", "code": "ThA17.4", "title": "Static Gain Adaptive Controller Design for Nonlinear Uncertain Time Delay Systems (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Complex Dynamics in Time-Delay Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Li, Wenjie", "affiliation": "Qufu Normal University" }, { "name": "Bu, Chaoen", "affiliation": "Qufu Normal University" }, { "name": "Zhang, Zhengqiang", "affiliation": "Qufu Normal University" } ], "keywords": [ "Nonlinear time-delay systems", "Adaptive control design" ], "abstract": "This paper investigates the control problem of a class of nonlinear systems subject to unknown time-varying delays, input saturation, and external disturbances. To effectively handle the challenges introduced by the unknown time delay, a novel Lyapunov-Krasovskii functional (LKF) is constructed. A set of static gain functions is developed to compensate for the delayed states, while a auxiliary subsystem is employed to address the input saturation. By integrating these techniques within a backstepping framework, n static gain functions are systematically designed to guarantee the stability of the closed-loop system. It is proven that all closed-loop signals remain bounded and ultimately converge to a small neighborhood around the origin, while the output tracking error approaches zero. Finally, simulation results are provided to demonstrate the effectiveness and robustness of the proposed control scheme.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA17.5", "code": "ThA17.5", "title": "Optimal Control Analysis of the Disability Labor Model with Time-Delay (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Complex Dynamics in Time-Delay Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Chanosot, Phachara", "affiliation": "Department of Mathematics, Faculty of Science, Chiang Mai University" }, { "name": "Wongkaew, Suttida", "affiliation": "Department of Mathematics, Faculty of Science, Chiang Mai University" }, { "name": "Niamsup, Piyapong", "affiliation": "Chiang Mai University" } ], "keywords": [ "Nonlinear time-delay systems", "Optimal control theory", "Applications of optimal control" ], "abstract": "This paper develops a delay differential equation model for Thailand’s disability labor market and examines the effect of delayed policy implementation. A fixed delay is used as an aggregate representation of the implementation lag in employment support. Based on this model, an optimal control problem is formulated in which a time-dependent intervention is used to reduce unemployment while balancing policy cost. The model is calibrated using Thai disability labor market data from 2022--2025, and numerical simulations are performed to illustrate the resulting dynamics. The results suggest that, under the calibrated parameter set, a front-loaded intervention strategy can reduce unemployment more effectively than a static policy over the planning horizon. These findings provide a quantitative perspective on delayed disability employment policy and may support further discussion of policy design in Thailand.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA17.6", "code": "ThA17.6", "title": "Failure of Slow Deterministic Dynamical Systems in the Presence of Negligible Noise (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA17", "sessionTitle": "Dynamics and Control of Time Delay Systems: Complex Dynamics in Time-Delay Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Bachrathy, Daniel", "affiliation": "Budapest University of Technology and Economics" } ], "keywords": [ "Linear time-delay systems", "Robust time-delay systems" ], "abstract": "In the analysis of dynamical systems, it is a common and convenient assumption that infinitesimally small noise has a negligible effect on deterministic behaviour. However, the present work highlights that for systems with slow dynamics - slowly oscillating between stable and unstable states - the application of a purely deterministic approach is fundamentally flawed. Our research explains the sharp contradiction between idealised analytical calculations and experimental or professional numerical simulation results. Any numerical integration method, even purely due to finite machine number representation introduces a minimal amount of noise into the system. When a system with slow parameter variation enters an unstable quasi-static domain, this infinitesimally small noise acts as an equivalent noise intensity that is exponentially magnified within a finite time. This mechanism leads to a premature loss of finite-time stability (FTS), generating vibrations that prevent the attainment of the apparently stable deterministic state. We demonstrate that if the exponential magnification of noise leads to system failure even in state-of-the-art numerical simulations, then under the unavoidable environmental noise of real physical measurements (e.g., 0.1% noise in cutting forces), deterministic stability is an absolute illusion. To address this, we investigate the temporal evolution of the second moment (covariance) using matrix-free direct simulations, providing a framework for handling the hidden instability and stochastic resonance of slow time-delayed systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA18.1", "code": "ThA18.1", "title": "Attribute-Based Modeling for Life Cycle Assessment: From Linking Components to Improvement Implications (I)", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA18", "sessionTitle": "Sustainable and Circular Manufacturing in the Digitized World I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Faraji Abdolmaleki, Shoeib", "affiliation": "Ecole Central De Nantes" }, { "name": "Eslami, Yasamin", "affiliation": "Ecole Centrale De Nantes" }, { "name": "Hilloulin, Benoit", "affiliation": "Nantes Université, École Centrale De Nantes" }, { "name": "Rozière, Emmanuel", "affiliation": "Nantes Université, École Centrale De Nantes" }, { "name": "da Cunha, Catherine", "affiliation": "Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004" } ], "keywords": [ "Sustainable and circular manufacturing systems", "Sustainable and circular supply chain and production" ], "abstract": "Life Cycle Assessment (LCA) is essential for environmental analysis, but its application is hampered by inconsistency, subjectivity, and poor traceability. Current LCA tools lack measurable links between configuration choices (approaches, methods, databases, and tools) and performance outcomes (accuracy, reliability). This study introduces an attribute-based modeling framework to enhance LCA outcomes. Drawing on seismic attribute analysis, the framework decomposes LCA elements into a component–attribute–implication hierarchy. It systematically investigates the causes of inaccuracy in LCA results, linking the selection of specific LCA elements to challenges in reliability. The framework defines measurable attributes for core LCA elements and maps their causal relationships to quantifiable improvement implications. A conceptual and measurable attribute–implication matrix connects configuration decisions with expected performance outcomes. This approach establishes a reasoning basis for LCA configuration, improving traceability and reducing subjectivity. It lays the groundwork for integrating LCA with decision-support and knowledge-based systems, thus contributing to the systematic advancement of LCA practice.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA18.2", "code": "ThA18.2", "title": "A Decision-Support Framework for Enhancing the Reliability and Accuracy of Life Cycle Assessment and Product Carbon Footprint (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA18", "sessionTitle": "Sustainable and Circular Manufacturing in the Digitized World I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Faraji Abdolmaleki, Shoeib", "affiliation": "Ecole Central De Nantes" }, { "name": "Neupane, Bishwash", "affiliation": "Nantes Université, École Centrale De Nantes" }, { "name": "Adjei Mensah, Michael", "affiliation": "Nantes Université, École Centrale De Nantes" }, { "name": "Eynard, Benoit", "affiliation": "UTC" }, { "name": "Le Duigou, Julien", "affiliation": "UTC" }, { "name": "Hilloulin, Benoit", "affiliation": "Nantes Université, École Centrale De Nantes" }, { "name": "Rozière, Emmanuel", "affiliation": "Nantes Université, École Centrale De Nantes" } ], "keywords": [ "Sustainable and circular manufacturing systems", "Sustainable and circular supply chain and production" ], "abstract": "Life Cycle Assessment (LCA) and Product Carbon Footprint (PCF) provide essential insights for environmental decision-making, yet their reliability and accuracy remain challenged by inconsistencies in methods, data sources, and digital integration. This paper presents a decision-support conceptual framework designed to enhance the accuracy and reliability of LCA/PCF studies. The framework is derived from a systematic literature review and industrial watch, identifying critical bottlenecks across methods, tools, and data management practices. It integrates three layers as business applications, decision support, and knowledge base within three core modules— (1) a smart connector for data acquisition and integration (interoperability module), (2) an inference engine, (3) AI-based learning for predictive reasoning (2 and 3 form decision-aid module) for structured information management. Together, it creates an interoperable environment bridging data-driven and knowledge-driven approaches. The framework serves as a foundation for the next generation of digital and intelligent LCA systems, promoting traceability, consistency, and informed sustainability decisions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA18.3", "code": "ThA18.3", "title": "An Ontology Structure for Printed Circuit Boards Design for Disassembly Analysis (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA18", "sessionTitle": "Sustainable and Circular Manufacturing in the Digitized World I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "El Warraqi, Laila", "affiliation": "Politecnico Di Milano" }, { "name": "Negri, Elisa", "affiliation": "Politecnico Di Milano" }, { "name": "Terzi, Sergio", "affiliation": "Politecnico Di Milano" }, { "name": "Rosa, Paolo", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Sustainable and circular manufacturing systems", "Data-driven and AI-based modelling of production and logistics" ], "abstract": "The growing volume of electronic waste has emphasized the urgency of developing design strategies that enable efficient end-of-life (EoL) recovery. Printed Circuit Boards (PCBs), as a central part of most electronic devices, present a particular challenge due to their structural complexity, heterogeneity, and reliance on permanent joining methods, which complicate disassembly. To address this challenge, this paper starts by presenting how to close the loop between PCB design and disassembly and presents an ontology structure aimed at supporting PCB design for disassembly, by semantically linking design decisions with disassembly requirements. The ontology formalizes knowledge around three core dimensions: i) product structure, including PCB components, materials, and connection types; ii) disassembly process knowledge, such as required tools and disassembly metrics; and iii) design implications to provide eco-design feedback to support circular design strategies. By providing a structured, machine-interpretable representation of disassembly knowledge, the proposed ontology aims to close the loop between PCB design and EoL strategies and demonstrates the potential of semantic technologies to enhance circularity in electronics.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA18.4", "code": "ThA18.4", "title": "Integrating Digital Twins and Digital Product Passports: A Research Agenda for Circular Lifecycle Decision-Making (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA18", "sessionTitle": "Sustainable and Circular Manufacturing in the Digitized World I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Franciosi, Chiara", "affiliation": "Université De Lorraine, CNRS, CRAN, F-54000, Nancy, France" }, { "name": "Marange, Pascale", "affiliation": "University of Nancy" }, { "name": "Ansari, Fazel", "affiliation": "Vienna University of Technology (TU Wien)" }, { "name": "Voisin, Alexandre", "affiliation": "Université De Lorraine, CNRS, CRAN" }, { "name": "Iung, Benoît", "affiliation": "Lorraine University" } ], "keywords": [ "Sustainable and circular manufacturing systems" ], "abstract": "Manufacturers are increasingly required to adopt circular strategies (CS) to reduce resource consumption and environmental impacts. Effective decision-making at a product’s End-of-Usage/End-of-Life, however, depends on the availability of reliable lifecycle information and adequate decision-support systems, both of which are often missing. To address these challenges, the European Union is introducing the Digital Product Passport (DPP) as part of the Ecodesign for Sustainable Products Regulation, aiming to provide comprehensive product information across value chains, like its origin, materials, environmental impact, and disposal recommendations. Recent studies highlight the need for dynamic DPPs capable of integrating evolving product data along the lifecycle. However, current DPP frameworks seem mainly static, remain mostly conceptual and offer limited guidance for supporting circularity decisions. In parallel, Digital Twins (DTs) are increasingly recognized as promising enablers for enriching DPPs with accurate, real-time lifecycle data; however, the combined use of DT and DPP remains an emerging and insufficiently explored research domain. This paper investigates the state of the art on the possible integrated use of DTs and DPPs to support dynamic, data-driven decision-making towards circularity objectives. A research agenda is proposed to guide future research and developments in this field.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA18.5", "code": "ThA18.5", "title": "AI-Enabled Circular Manufacturing across the Industrial Equipment Lifecycle Assessment (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA18", "sessionTitle": "Sustainable and Circular Manufacturing in the Digitized World I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 216", "authors": [ { "name": "Ferreira, Jose", "affiliation": "Faculdade De Ciências E Tecnologia, FCT, UniversidadeNovade Lisboa" }, { "name": "Mendonca, Joao Pedro", "affiliation": "MEtRICs Mechanical Engineering and Resource Sustainability Center" }, { "name": "Jardim-Goncalves, Ricardo", "affiliation": "UNINOVA - Instituto De Desenvolvimento De Novas Tecnologias" } ], "keywords": [ "Cyber-physical production systems", "Intelligent manufacturing systems", "Advanced manufacturing and remanufacturing technologies" ], "abstract": "Manufacturing systems are increasingly forced to reconcile productivity, resilience, and sustainability in the face of growing environmental and social constraints. Circular manufacturing has therefore emerged as a strategic paradigm for the transition from linear production models to closed-loop lifecycle management. In this context, digital technologies associated with Industry 4.0 play a central role, enabling transparency, optimisation, and informed decision-making throughout the lifecycle of industrial equipment. This paper presents a defined framework to support the assessment of sustainable circular manufacturing, validated through four industrial pilot projects in the metal, stone, plastic, and food sectors by the AIDEAS project. The proposed approach is based on the ESIA (Environmental and Social Impact Assessment) framework, designed to identify, predict, and mitigate the negative effects of a project on the environment and local communities. This paper presents the ESTEIA framework, which defines how to conduct a structured impact assessment based on key performance indicators, analysing environmental, economic, social, and technological effects. Finally, a case study from the company D2Tech is presented to demonstrate how ESTEIA was applied in the factory and to assess its impact.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA19.1", "code": "ThA19.1", "title": "Neural Network--Based Distributed Adaptive Fault--Tolerant Containment Control for Multi--QUAV Formations (I)", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA19", "sessionTitle": "Large-Scale Complex Systems: Analysis and Control I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Zhang, Meng", "affiliation": "Southeast University" }, { "name": "Linan, Wang", "affiliation": "Southea University" }, { "name": "Xu, Long", "affiliation": "RMIT University" }, { "name": "Wen, Guanghui", "affiliation": "Southeast University" } ], "keywords": [ "Complex dynamic systems" ], "abstract": "This paper investigates distributed adaptive fault-tolerant containment control for multi--quadrotor unmanned aerial vehicle (QUAV) formations subject to unknown nonlinearities, partial actuator loss, and input bias. To address these challenges, a neural network (NN)--based control scheme is proposed. NNs are employed to approximate the unknown dynamics online, while adaptive update laws are designed to compensate for the actuator faults. A distributed control protocol is formulated by integrating the NN approximation with relative state information from neighboring vehicles. The proposed controller guarantees that follower QUAVs converge to the convex hull spanned by the leaders, ensuring all signals in the closed loop system remain bounded. Numerical simulations validate the effectiveness of the proposed strategy.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA19.2", "code": "ThA19.2", "title": "Output Tracking of Impulsive Logical Dynamical Systems (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA19", "sessionTitle": "Large-Scale Complex Systems: Analysis and Control I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Yang, Xinrong", "affiliation": "Academy of Mathematics and Systems Science, Chinese Academy of Sciences" }, { "name": "Li, Haitao", "affiliation": "Shandong Normal University" } ], "keywords": [ "Complex dynamic systems" ], "abstract": "本文在混合索引模型框架下研究了冲动逻辑动力系统（ILDS）的输出跟踪问题。首先，揭示当跳跃转移矩阵为幂零矩阵时，ILDS是前向完备的。其次，通过构建一组可达集合，建立了ILDS输出追踪的必要且充分条件。最后，通过采用完整的可达集族，提出了一种合适的方法来设计相应的状态反馈控制，使ILDS能够实现输出跟踪。", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA19.3", "code": "ThA19.3", "title": "On the Necessity of Similarity-Based Interactions for Opinion Consensus (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA19", "sessionTitle": "Large-Scale Complex Systems: Analysis and Control I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Zhang, Qi", "affiliation": "East China University of Science and Technology" }, { "name": "Yang, Zixuan", "affiliation": "Shanghai University" }, { "name": "Wang, Lin", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Yang, Wen", "affiliation": "East China University of Science and Techonology" }, { "name": "Wang, Xiaofan", "affiliation": "Shanghai University" } ], "keywords": [ "Complex dynamic systems" ], "abstract": "This paper investigates whether similarity-based interactions are necessary for coordinators to guide opinion consensus in the Deffuant-Weisbuch (DW) model. We introduce two complementary types of coordinators: an inclusive coordinator (IC), which interacts with agents based on opinion similarity, and a strategic coordinator (SC), which interacts based on opinion dissimilarity. For both coordinators, we derive a sufficient condition under which their introduction guarantees that consensus becomes the unique equilibrium of the DW model. Extensive simulations reveal distinct operational regimes for the two types of coordinators. When the DW model rarely reaches consensus by itself, the SC is more effective in accelerating convergence, indicating that similarity-based interactions are not necessary. In contrast, in high-consensus regimes, the IC achieves faster consensus.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA19.4", "code": "ThA19.4", "title": "Distributed Cooperative Moving Path-Following Control for Underactuated Autonomous Surface Vehicles Based on Guiding Vector Fields (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA19", "sessionTitle": "Large-Scale Complex Systems: Analysis and Control I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Wang, Shuwang", "affiliation": "Southeast University" }, { "name": "Wen, Guanghui", "affiliation": "Southeast University" }, { "name": "Shen, Han", "affiliation": "Southeast University" } ], "keywords": [ "Complex dynamic systems", "Decentralized and distributed control for large-scale systems" ], "abstract": "This paper investigates the distributed cooperative moving path-following (MPF) problem for underactuated autonomous surface vehicles (ASVs) with uncertainties. The proposed cooperative guidance strategy comprises guidance signals, path variable updating laws, and virtual control inputs. Specifically, the guidance signals and path variable updating laws are devised by leveraging moving guidance vector fields to achieve cooperative path-following performance. The virtual control inputs are designed for the accurate tracking of guidance signals in the presence of uncertainties, which include internal modeling errors and external disturbances. Rigorous analysis guarantees the convergence of the cooperative MPF errors for the multi-ASV system from any initial states. Finally, numerical simulations are presented to validate the effectiveness of the proposed controller.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA19.5", "code": "ThA19.5", "title": "Distributed Trust-Based Weight Adjustment for Resilient Consensus against Dishonest Agents (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA19", "sessionTitle": "Large-Scale Complex Systems: Analysis and Control I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Zhang, Jing", "affiliation": "Nanjing Normal University" }, { "name": "Lu, Jianquan", "affiliation": "Southeast University" }, { "name": "Ho, Daniel W. C.", "affiliation": "City Univ. of Hong Kong" }, { "name": "Hadjicostis, Christoforos", "affiliation": "University of Cyprus" } ], "keywords": [ "Complex dynamic systems", "Decentralized and distributed control for large-scale systems", "Interconnected dynamical systems" ], "abstract": "This paper studies resilient consensus of multi-agent systems in the presence of dishonest agents. A trust-based distributed weight-adjustment mechanism is proposed, in which each normal agent evaluates its in-neighbors and updates the weights associated with them, using a penalty parameter and a regulation factor. Under mild connectivity conditions, convergence of the normal agents to a consensus value is established. Unlike state-of-the-art approaches, the proposed approach imposes less stringent requirements on network connectivity and relies solely on one-hop neighbor information. Simulation results demonstrate the advantages of the proposed method over MSR-type algorithms under certain attack scenarios.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA19.6", "code": "ThA19.6", "title": "Cooperative Predefined-Time Target Surrounding Control of Multiple Autonomous Surface Vehicles (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA19", "sessionTitle": "Large-Scale Complex Systems: Analysis and Control I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Lu, Hang", "affiliation": "Southeast University" }, { "name": "Shen, Han", "affiliation": "Southeast University" }, { "name": "Wen, Guanghui", "affiliation": "Southeast University" } ], "keywords": [ "Complex dynamic systems", "Decentralized and distributed control for large-scale systems", "Interconnected dynamical systems" ], "abstract": "This paper investigates the problem of cooperative predefined-time target surrounding for multiple autonomous surface vehicles (ASVs). A predefined-time control framework is developed to ensure that all the error dynamics of ASVs converge to zero within a rigorously specified time, independent of the initial conditions. The control design follows a multi-layer structure, where predefined-time kinematic controllers are first constructed, and then combined with a predefined-time kinetic controller to guarantee closed-loop convergence performance. The proposed scheme enables the ASVs to establish a coordinated and stable surrounding formation around a moving target. Simulation results validate the effectiveness of the proposed control scheme.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA20.1", "code": "ThA20.1", "title": "Row-Weighted Replay Lifelong Dictionary Learning for Multimode Process Monitorin (I)", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA20", "sessionTitle": "Leveraging AI for Next-Generation Industrial Alarm Systems: Advanced Data Analytics, Causality Inference, and Pretrained Models I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Xu, Yuan", "affiliation": "Beijing University of Chemical Technology" }, { "name": "Ye, Cheng-Shu", "affiliation": "Beijing University of Chemical Technology" }, { "name": "Zhu, Qun-Xiong", "affiliation": "Beijing University of Chemical Technology" }, { "name": "Zhang, Yang", "affiliation": "Beijing University of Chemical Technology" }, { "name": "He, Yanlin", "affiliation": "College of Information Science and Technology, Beijing University of Chemical Technology" }, { "name": "Ke, Wei", "affiliation": "Macao Polytechnic University" }, { "name": "Zhang, Ming-Qing", "affiliation": "Beijing University of Chemical Technology" } ], "keywords": [ "Monitoring, performance assessment, and fault detection in chemical process control", "Process performance monitoring/statistical process control", "Machine learning and artificial intelligence in chemical process control" ], "abstract": "Operating data frequently exhibit significant mode drift due to condition switching and load disturbances. As a result, conventional monitoring models built on static assumptions are prone to catastrophic forgetting and limited knowledge transfer. To overcome these challenges, we propose a row-weighted replay lifelong dictionary learning method (RrLDL) for multimodal process monitoring. RrLDL incorporates a variable contribution driven selective memory mechanism to mitigate feature-level steady-state forgetting, where a row-weighted constraint reinforces the discriminative power of individual process variables during knowledge retention and incremental learning. Furthermore, a compact replay strategy is adopted, prioritizing samples dominated by high-contribution variables to retain critical mode features. Numerical simulation and Tennessee Eastman benchmark experiments demonstrate that RrLDL achieves superior multimodal anomaly monitoring accuracy and enhanced cross-mode generalization compared with representative baseline methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA20.2", "code": "ThA20.2", "title": "Dual-Source Hybrid RAG for Industrial Alarm Knowledge Parsing and Operational Decision Support (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA20", "sessionTitle": "Leveraging AI for Next-Generation Industrial Alarm Systems: Advanced Data Analytics, Causality Inference, and Pretrained Models I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Pang, Long", "affiliation": "China University of Geosciences" }, { "name": "Hu, Wenkai", "affiliation": "China University of Geosciences" }, { "name": "Zhang, Lijun", "affiliation": "Stellenbosch University" } ], "keywords": [ "AI methods for FDI/FTC", "Monitoring, performance assessment, and fault detection in chemical process control", "Process performance monitoring/statistical process control" ], "abstract": "Industrial alarm systems often suffer from excessive alarms, which overwhelm operators and hinder timely response. Existing studies mainly focus on alarm system design and alarm event analysis, but rarely leverage auxiliary knowledge such as operational manuals to support operators’ decision-making during alarm handling. Motivated by such an issue, this paper proposes a dual-source hybrid Retrieval-Augmented Generation (RAG) approach for industrial alarm knowledge parsing and operational decision support. The contributions are twofold: First, a dual-source heterogeneous encoding method is proposed to convert Alarm & Event (A&E) data into sparse vectors and operational manuals into dense embeddings. Second, a hybrid retrieval strategy is designed by integrating sparse lexical matching with dense semantic retrieval. The effectiveness of the proposed method is demonstrated through validation on alarm data and related operational manuals from a public simulation model.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA20.3", "code": "ThA20.3", "title": "A Physics-Informed Semantic Domain Adaptation Method for Cross-Condition Bearing Fault Diagnosis (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA20", "sessionTitle": "Leveraging AI for Next-Generation Industrial Alarm Systems: Advanced Data Analytics, Causality Inference, and Pretrained Models I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Chen, Ning", "affiliation": "China University of Geosciences" }, { "name": "Hu, Wenkai", "affiliation": "China University of Geosciences" }, { "name": "Li, Yupeng", "affiliation": "China University of Geosciences" }, { "name": "Wan, Xiongbo", "affiliation": "China University of Geosciences" } ], "keywords": [ "Fault detection and isolation methods", "Monitoring, performance assessment, and fault detection in chemical process control", "Process performance monitoring/statistical process control" ], "abstract": "Cross-condition bearing fault diagnosis remains challenging because vibration patterns vary substantially under different operating conditions, and most existing methods depend mainly on data-driven features with limited physical interpretability. This paper proposes a physics-informed semantic domain adaptation (PISDA) method for cross-condition bearing fault diagnosis. Specifically, bearing semantics are extended with an adjustable bandwidth to form a continuous semantic field, based on which an interpretable physics-informed convolution layer is designed to provide physically meaningful decision outputs. In addition, a lightweight dual-branch architecture is devised, where the semantic branch and a general branch operate in parallel to jointly ensure physical consistency and discriminative capability. The effectiveness of the proposed PISDA is demonstrated by a case study with a public dataset. The results show that the proposed model achieves superior performance under various operating conditions while maintaining physical interpretability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA20.4", "code": "ThA20.4", "title": "Spatio-Temporal Fusion Variational Graph Convolutional Shrinkage Network for Industrial Process Fault Diagnosis (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA20", "sessionTitle": "Leveraging AI for Next-Generation Industrial Alarm Systems: Advanced Data Analytics, Causality Inference, and Pretrained Models I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Zhang, Chuan", "affiliation": "School of Information Science and Technology, Beijing University of Chemical Technology" }, { "name": "Zhang, Ming-Qing", "affiliation": "Beijing University of Chemical Technology" }, { "name": "Luo, Yi", "affiliation": "Chinese Institute of Coal Science" }, { "name": "Ke, Wei", "affiliation": "Macao Polytechnic University" }, { "name": "Zhu, Qun-Xiong", "affiliation": "Beijing University of Chemical Technology" }, { "name": "He, Yanlin", "affiliation": "College of Information Science and Technology, Beijing University of Chemical Technology" }, { "name": "Zhang, Yang", "affiliation": "Beijing University of Chemical Technology" }, { "name": "Xu, Yuan", "affiliation": "Beijing University of Chemical Technology" } ], "keywords": [ "Monitoring, performance assessment, and fault detection in chemical process control", "Machine learning and artificial intelligence in chemical process control", "Health/condition monitoring in processes" ], "abstract": "The performance of industrial process fault diagnosis is often degraded by noise of varying intensities during data acquisition and by ambiguous fault class boundaries. To address these challenges, we propose a spatio-temporal fusion variational graph convolutional shrinking network (STVGCSN). In the spatial domain, a gating-based adaptive soft-thresholding function is integrated into the graph convolutional network to suppress noise of varying intensities, while in the temporal domain, a one-dimensional convolutional neural network is used to extract temporal dependencies. Moreover, a variational autoencoder architecture is employed to fully exploit both supervised and unsupervised information, thereby mitigating challenges caused by ambiguous fault boundaries. Finally, an attention-based strategy is further introduced to dynamically weight multiple loss terms, enabling balanced optimization across multiple objectives. Experimental results on two representative chemical process datasets demonstrate that the proposed model STVGCSN delivers superior performance in fault diagnosis tasks.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA20.5", "code": "ThA20.5", "title": "Beyond Accuracy: Evaluating Classification Explainability in Industrial Alarm Systems (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA20", "sessionTitle": "Leveraging AI for Next-Generation Industrial Alarm Systems: Advanced Data Analytics, Causality Inference, and Pretrained Models I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Hedayati, Amin", "affiliation": "Isfahan University of Technology" }, { "name": "Roohi, Mohammad", "affiliation": "University of Alberta" }, { "name": "Izadi, Iman", "affiliation": "Isfahan University of Technology" }, { "name": "Wang, Jiandong", "affiliation": "Shandong University of Science and Technology" } ], "keywords": [ "Monitoring, performance assessment, and fault detection in chemical process control", "AI methods for FDI/FTC", "Machine learning and artificial intelligence in chemical process control" ], "abstract": "Reliable fault diagnosis in industrial processes requires not only accurate classification but also trustworthy and actionable explanations for decision support. This paper addresses the need for validating the interpretability of machine learning models used in alarm-based monitoring. We use fault-specific permutation importance and introduce the Root Cause Alignment Score (text{RCAS}), a new metric designed to quantify the correspondence between a classifier's feature importance and known physical root causes. By applying the proposed interpretability assessment framework to a set of classification models on alarm data generated from the Tennessee Eastman Process (TEP), the trade-off between predictive performance and diagnostic interpretability was evaluated. The results show that complex ensemble models, despite higher predictive performance, can exhibit lower RCAS values, indicating that the explanations from these models may be less aligned with known physical root causes. In contrast, models of lower complexity, such as support vector machines, achieved the highest diagnostic validity, though often at the cost of reduced classification performance. This study highlights that, in addition to conventional performance metrics, the RCAS can guide the selection among well-performing classifiers to achieve interpretability that directly supports operator decision-making for effective fault mitigation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA20.6", "code": "ThA20.6", "title": "Wrapping the Engineering Data Funnel into a Neuro-Symbolic Agentic Loop for Comprehensive Data Extraction from Engineering Design Documents", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA20", "sessionTitle": "Leveraging AI for Next-Generation Industrial Alarm Systems: Advanced Data Analytics, Causality Inference, and Pretrained Models I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Schoch, Nicolai", "affiliation": "ABB AG Corporate Research" }, { "name": "Ashiwal, Virendra", "affiliation": "ABB Corporate Research Center Germany" }, { "name": "Elsheikh, Mohamed", "affiliation": "ABB" } ], "keywords": [ "AI tools in automation engineering and operation", "Digital twins for cyber physical systems", "Model driven engineering of control systems" ], "abstract": "Engineering projects depend on the processing of unstructured, multimodal design documents such as P&IDs, Control Narratives, and IO lists. Converting these into structured, machine-readable representations remains a major challenge. In a previous work, we presented the Engineering Data Funnel (EDF) system, which already demonstrated significant progress in automating multimodal engineering data processing. However, its single-pass extraction approach and limited reasoning capabilities left gaps in information completeness and consistency. To address these limitations, building on EDF, we here introduce the Extended Engineering Data Funnel (xEDF), which wraps EDF into a Neuro-Symbolic Agentic Loop. xEDF iteratively applies symbolic reasoning, gap analysis, and targeted re-extraction to maximize information completeness and semantic consistency, optionally involving human experts. Evaluation of xEDF shows that EDF got significantly improved, and it demonstrates how combining neural flexibility with symbolic precision enables an effective and more reliable and comprehensive engineering data processing for instantiation of industrial plant digital twins.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA21.1", "code": "ThA21.1", "title": "Voltage Control in Partially Observable Distribution Grids Using Adaptive Hybrid Reinforcement Learning (I)", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA21", "sessionTitle": "AI Applications for Smart Power & Energy Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Bouchkati, Sarra", "affiliation": "RWTH Aachen University, Institute for High Voltage Equipment and Grids, Digitalization and Energy Economics" }, { "name": "Kortmann, Steffen", "affiliation": "RWTH Aachen University" }, { "name": "Sabirov, Ramil", "affiliation": "RWTH Aachen University" }, { "name": "Ulbig, Andreas", "affiliation": "RWTH Aachen University" } ], "keywords": [ "Electrical distribution systems", "Distributed optimization for smart grids", "Solar energy" ], "abstract": "Reinforcement learning (RL) is increasingly explored for power system control, yet deploying purely learned policies remains challenging due to partial observability and data inefficiency in complex distribution grids. We apply the Contextualized Hybrid Ensemble Q-learning (CHEQ) framework to centralized voltage control in low-voltage distribution grids, combining a Sequential Droop Controller (SDC) prior with a learned RL policy. An uncertainty-driven mixing mechanism, estimated from a critic ensemble, adaptively adjusts the relative contribution of each: granting the RL agent full authority in well-explored operating conditions, and deferring to the reliable SDC prior when uncertainty is high. Experiments on a realistic low-voltage benchmark grid demonstrate that the proposed approach eliminates voltage violations more effectively than the SDC and pure RL baseline while substantially reducing active power curtailment, confirming that reactive power is prioritized before resorting to generation curtailment.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA21.2", "code": "ThA21.2", "title": "Achievements of Generations Inheritance and Optimization (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA21", "sessionTitle": "AI Applications for Smart Power & Energy Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Vlachogiannis, George J.", "affiliation": "National Technical University of Athens (NTUA)" }, { "name": "Vita, Vasiliki", "affiliation": "** Department of Electrical and Electronic Engineering Educators, School of Pedagogical and Technological Education (ASPETE)" }, { "name": "Vlachogiannis, John G.", "affiliation": "Smart Sustainable Social Innovations Single Member P.C" }, { "name": "Robba, Michela", "affiliation": "University of Genoa" }, { "name": "Lee, Kwang Y.", "affiliation": "Baylor University" } ], "keywords": [ "Electrical transmission systems", "Energy management systems" ], "abstract": "This paper introduces a novel evolutionary algorithm which simulates how the achievements of generations are inherited and optimized (AGIO) during the evolution of human’s family trees. The AGIO supposes a number of progressed family trees which have one offspring at each generation (single family trees). Each individual in a family tree with good intention tends to improve the inherited achievement by his ancestors. The achievement of each family tree is maximized based on the capability of its individuals. The capability of each individual modulated in accordance with creativity, personality, random/normal conditions of the generation and the inherited achievements. The novel AGIO algorithm can solve a number of engineering problems, where the state variables are considered as capabilities of individuals and the objective values as achievements of family trees. In this paper, the results of reactive power and voltage controls obtained by AGIO are compared with those given by the very popular PSO and the evolutionary algorithm of Grey-Wolf Optimizer demonstrating the superiority of the first.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA21.3", "code": "ThA21.3", "title": "Robust EIS Frequency Features for SOC-Invariant SOH Estimation in Batteries (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA21", "sessionTitle": "AI Applications for Smart Power & Energy Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Lee, Jongmun", "affiliation": "Pohang University of Science and Technology" }, { "name": "Kim, Joonhee", "affiliation": "Pohang University of Science and Technology" }, { "name": "Han, Soohee", "affiliation": "Pohang University of Science and Technology" } ], "keywords": [ "Health aware control in processes", "Energy storage systems", "Real time simulators for energy systems" ], "abstract": "Battery EIS characteristics vary substantially with SOC, causing impedance-based features selected at a single SOC to lose reliability under different operating conditions. This study identifies SOC-invariant frequency features in the mid to high-frequency region through correlation analysis using cells aged at two temperature conditions. The four selected features were validated through 24-fold cross-validation and five randomized seeds, achieving an RMSE of 0.505% and a standard deviation of 0.021%, compared with the baseline (0.823%, 0.050%). These results demonstrate significant improvements in accuracy, robustness to SOC variation, and reproducibility, confirming the proposed features’ potential for reliable SOH estimation in practical applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA21.4", "code": "ThA21.4", "title": "A Graph Attention-Based Reinforcement Learning Framework for Robust Placement of PMU-Integrated Hybrid Measurement Systems", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA21", "sessionTitle": "AI Applications for Smart Power & Energy Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Zhou, Chenyu", "affiliation": "Institute of Science Tokyo" }, { "name": "Zhou, Xu", "affiliation": "Sichuan University" }, { "name": "Ishizaki, Takayuki", "affiliation": "Tokyo Institute of Technology" } ], "keywords": [ "Distributed optimization for smart grids", "Power systems stability", "Electrical distribution systems" ], "abstract": "This paper introduces a graph attention network-proximal policy optimization (GAT-PPO) framework for optimal placement of hybrid measurement devices in power systems. A stochastic power system observability model is proposed, which unifies heterogeneous measurements and uncertain constraints to ensure robust system observability amidst uncertainties caused by renewable generation. The challenge is first formulated as a stochastic optimization problem, which is then framed as a Markov decision process (MDP) to find a robust placement policy. A robust training approach simulates the stochastic failure of zero-injection buses (ZIBs), enabling the development of resilient, cost-effective measurement strategies that enhance state estimation accuracy in renewable-integrated power systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA21.5", "code": "ThA21.5", "title": "Strategic Bidding in Competitive and Adversarial Electricity Markets Using No-Regret Learning Algorithms", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA21", "sessionTitle": "AI Applications for Smart Power & Energy Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Islam, Md Mainul", "affiliation": "Texas A&M University" }, { "name": "Takiddin, Abdulrahman", "affiliation": "Florida State University" }, { "name": "Ismail, Muhammad", "affiliation": "Tennessee Technological University" }, { "name": "Hasan, Kurban", "affiliation": "Hamad Bin Khalifa University" }, { "name": "Serpedin, Erchin", "affiliation": "Texas A&M Univ" } ], "keywords": [ "Energy market", "Distributed optimization for smart grids", "Control and management of energy systems" ], "abstract": "This paper studies strategic bidding in network-constrained electricity markets from an online learning perspective. In repeated DC optimal power flow auctions on the IEEE 14-bus system, generators choose bid multipliers and adapt them over 200 rounds using full-information no-regret algorithms. We implement Follow-the-Leader (FTL), Follow-the-Perturbed-Leader (FTPL), Multiplicative Weight Update (MWU), Regret Matching Plus (RM+), Discounted Regret Matching (DRM), and Optimistic RM+ (Opt-RM+), and compare them with truthful bidding and a welfare-maximizing correlated-equilibrium benchmark. Mean welfare remains close to the truthful and benchmark value of about 5720/h, with less than 1% loss even for MWU. MWU obtains the highest mean profit, about 106/h compared with about 81/h under truthful bidding, but has the largest average external regret, about 2.3/h, and persistent volatility. FTPL and Opt-RM+ provide the strongest welfare–regret trade-off by increasing profit while keeping regret near 0.1–0.2/h. Overall, carefully selected no-regret bidding rules preserve near-benchmark welfare while improving generator profitability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA21.6", "code": "ThA21.6", "title": "Data-Driven Control Approach for Dual Active Bridge DC/DC Converters", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA21", "sessionTitle": "AI Applications for Smart Power & Energy Systems", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Nguyen, Ngoc Nam", "affiliation": "Seoul National University of Science and Technology" }, { "name": "Lee, Young Il", "affiliation": "Seoul National Univ of Science and Technology" } ], "keywords": [ "Power electronics", "Real time simulators for energy systems", "Electric vehicles and charging stations" ], "abstract": "This paper explores a direct data-driven control (DDDC) strategy for a dualactive bridge (DAB) DC/DC converter employing single-phase-shift (SPS) modulation. In this proposed approach, the nonlinear term of the DAB DC/DC converter is defined as the control input, establishing a one-to-one relationship between the control input and the phaseshift ratio. This formulation enables the application of the DDDC method. The fundamentals of the DDDC approach are first presented, focusing on determining a stabilizing matrix for the closed-loop system. However, since the obtained solution may not be unique, an optimal DDDC approach is subsequently proposed to optimize the controller gain and introduce a tuning factor. In particular, a closed-loop data-driven controller with an integral tracking error term is developed, and its gains are analytically derived using Lyapunov theory to guarantee system stability. The proposed control approach is validated through real-time simulations on a Typhoon HIL 404 platform interfaced with a TMS320F28379D DSP microcontroller, demonstrating its effectiveness.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA22.1", "code": "ThA22.1", "title": "Numerical Characteristics of Evaluation of Demand Response As Swing Option Using Least Squares Monte-Carlo Simulation (I)", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA22", "sessionTitle": "Advanced Methods for Active Distribution Networks under Smart Grids", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Yamaguchi, Nobuyuki", "affiliation": "Tokyo University of Science" } ], "keywords": [ "Demand response", "Energy market", "Energy management systems" ], "abstract": "Recently, pricing for electricity contracts has been becoming increasingly difficult due to changes in electricity demand due to the large-scale introduction of solar power generation and increased volatility in international energy commodity prices. Under these circumstances, demand response (DR) can be expected to become a highly flexible and desirable contract for buyers and sellers in the current situation of increasing uncertainty. In this study, the Least Squares Monte-Carlo Simulation method (LSM) is applied to value the DR concluded between an electricity retailer and a consumer who has batteries. Numerical experiments confirm that increasing the number of trials in the Monte Carlo simulation also makes the DR value evaluation more accurate. For the range of parameters given in this study, the relative standard deviation of the DR value was found to be less than 1 percent when the number of trials was 100,000.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA22.2", "code": "ThA22.2", "title": "Reactive Power Control Method of Smart Inverter Considering LRT and SVR Operation in Distribution Systems (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA22", "sessionTitle": "Advanced Methods for Active Distribution Networks under Smart Grids", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Iwase, Kazumi", "affiliation": "Nagoya Institute of Technology" }, { "name": "Iwatsuki, Takuto", "affiliation": "Nagoya Institute of Technology" }, { "name": "Aoki, Mutsumi", "affiliation": "Nagoya Institute of Technology" }, { "name": "Takamura, Tetsuta", "affiliation": "Chubu Electric Power Company, Incorporated" }, { "name": "Yamada, Fujihiro", "affiliation": "Chubu Electric Power Co., Inc" } ], "keywords": [ "Electrical distribution systems" ], "abstract": "In recent years, an increasing number of renewable energy sources, such as photovoltaic power generation (PV), have been interconnected to power systems. The output fluctuations of these sources have made it difficult to maintain appropriate voltage in power systems. The authors have previously investigated ways to maintain appropriate voltage by using the reactive power control of a PV inverter (smart inverter SI). This paper describes an optimal reactive power control method for a power system consisting of multiple distribution lines, taking into account the reduction of tap operations of Load Ratio control Transformers (LRTs) and Step Voltage Regulators (SVRs), the reduction of distribution line losses, and the fairness of the lifetime among multiple SIs. The authors also compare different impedance of distribution lines and consider the optimal reactive power output setting conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA22.3", "code": "ThA22.3", "title": "Data-Driven Distribution Network Reconfiguration Using a Dual-Stage Dual-Population Evolutionary Algorithm (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA22", "sessionTitle": "Advanced Methods for Active Distribution Networks under Smart Grids", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Sekizaki, Shinya", "affiliation": "Hiroshima University" }, { "name": "Hayashida, Tomohiro", "affiliation": "Hiroshima University" } ], "keywords": [ "Electrical distribution systems", "Solar energy" ], "abstract": "This paper proposes a data-driven distribution network reconfiguration optimization method designed to address renewable energy source (RES) uncertainty. Unlike overly conservative robust optimization in our previous works, the proposed method integrates data-driven distributionally robust optimization into a constrained multiobjective evolutionary algorithm (CMOEA) for solving the reconfiguration problem. By leveraging historical PV data and employing an approximated chance-constrained approach to model uncertainty, the proposed method achieves scalable and stable search performance. The efficiency of the proposed method is validated through computational experiments on a large-scale 118-bus distribution network model subject to severe constraints arising from RES uncertainties.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA22.4", "code": "ThA22.4", "title": "An Effective Energy Management System for Active Distribution Networks with Hybrid Storage, Electric Vehicle Uncertainty, and Voltage Control (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA22", "sessionTitle": "Advanced Methods for Active Distribution Networks under Smart Grids", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Sasaki, Yutaka", "affiliation": "Hiroshima University" }, { "name": "Bedawy, Ahmed", "affiliation": "Hiroshima University (Japan) & South Valley University (Egypt)" }, { "name": "Zoka, Yoshifumi", "affiliation": "Hiroshima University" }, { "name": "Krifa, Chiraz", "affiliation": "Hiroshima University" }, { "name": "Yorino, Naoto", "affiliation": "Hiroshima University" } ], "keywords": [ "Hydrogen systems for energy generation and storage", "Electric vehicles integration in energy networks", "Distributed optimization and control for smart cities" ], "abstract": "Active Distribution Networks (ADNs) with high penetration of renewable generation, energy storage, and electric vehicles (EVs) require advanced Energy Management Systems (EMSs) capable of handling operational uncertainty while maintaining power quality. This paper presents a unified EMS framework for ADNs that integrates neural-network-based forecasting, hybrid battery-hydrogen energy storage scheduling, stochastic EV uncertainty modeling, and decentralized voltage control. The proposed EMS comprises coordinated functional modules for forecasting, supply-demand management, handling EV uncertainty, and multi-agent voltage regulation. EV arrival/departure times, travel distances, and initial state-of-charge are modeled probabilistically, and representative scenarios are generated using Monte Carlo simulation and reduced using a Wasserstein-distance method. A two-stage stochastic optimization framework is then used for day-ahead scheduling and real-time operational adjustment through model predictive control (MPC). Voltage regulation is achieved using sensitivity-based coordination of on-load tap changers, step voltage regulators, and inverter reactive power support. Although these functions operate independently in the present study, the framework demonstrates a pathway toward an integrated EMS capable of jointly managing distributed energy resources, voltage regulation, and EV uncertainty in ADNs. Simulation results demonstrate that the proposed EMS reduces operating cost and imbalance energy while maintaining acceptable voltage profiles under uncertain EV behavior and renewable generation variability. The framework provides a scalable and practical solution for future ADNs with high penetration of inverter-based resources and EVs.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA22.5", "code": "ThA22.5", "title": "Resilient Frequency Regulation of Interconnected Power Systems with Wind Power under Markovian Cyber-Attacks", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA22", "sessionTitle": "Advanced Methods for Active Distribution Networks under Smart Grids", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Long, Yue", "affiliation": "University of Electronic Science and Technology of China" }, { "name": "Liu, Qidong", "affiliation": "University of Electronic Science and Technology" }, { "name": "Li, Tieshan", "affiliation": "University of Electronic Science and Technology of China" } ], "keywords": [ "Cybersecurity in smart grids", "Power systems stability", "Wind power" ], "abstract": "This paper addresses the design of a decentralized resilient controller for frequency regulation in Multi-Area Power Systems integrated with wind power generation. Diverging from standard load frequency control design methods, we first propose a refined equivalent model that explicitly incorporates the equality constraint arising from the lossless power conservation across inter-area tie-lines. Building upon this formulation, the system, operating under Denial-of-Service attacks resulting from network openness, is modeled as a Markov Jump System, specifically considering the non-ideal scenario where the attack transition probabilities are partially unknown. Subsequently, by employing the Cone Complementarity Linearization approach, we present a systematic design scheme for a controller that ensures effective frequency regulation despite cyber-attacks and unknown attack evolution patterns. Finally, the efficacy of the proposed methodology is validated through simulations on a three-area power system.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA22.6", "code": "ThA22.6", "title": "Flexibility Capacity Approximation and Aggregation for Microgrid Control Application", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA22", "sessionTitle": "Advanced Methods for Active Distribution Networks under Smart Grids", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Thobie, Niels", "affiliation": "CentraleSupélec" }, { "name": "Sadou, Nabil", "affiliation": "SUPELEC" }, { "name": "Gueguen, Herve", "affiliation": "CentraleSupelec" } ], "keywords": [ "Demand response", "Energy management systems", "Distributed optimization for smart grids" ], "abstract": "With the electrification of our uses and the massive integration of intermittent renewable production sources, demand-side flexibility is a way to maintain balance between production and consumption. This paper presents an individual flexibility capacity approximation that ensure confidentiality and low communication effort. This method is derived from the homothetic approximation technique. Our method is compared to other methods through a microgrid control problem. The results show that it provides a good tradeoff between fidelity and computation time, which makes it efficient for a massive group of buildings.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA23.1", "code": "ThA23.1", "title": "A Causal Robust Probabilistic Principal Component Regression for Soft Sensing (I)", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA23", "sessionTitle": "Next-Generation Intelligent Modeling, Monitoring and Optimization for Modern Industrial Processes I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Yao, Yating", "affiliation": "China University of Petroleum (east China)" }, { "name": "Yu, Hongjian", "affiliation": "China University of Petroleum (east China)" }, { "name": "Shao, Weiming", "affiliation": "China University of Petroleum (East China)" }, { "name": "Wei, Chihang", "affiliation": "Hangzhou Normal University" }, { "name": "Yuan, Xiaofeng", "affiliation": "Central South University" } ], "keywords": [ "Soft sensors in MMM systems", "Machine learning and artificial intelligence in MMM process control", "Machine learning and artificial intelligence in chemical process control" ], "abstract": "Robust probabilistic latent variable models (RPLVMs), with excellent tolerance of outlying data, are widely used in industrial processes for soft sensing. However, the existing RPLVMs neglect the process causalities among process variables, degrading the generalization performance and model interpretability. To this end, a causal robust probabilistic principal component regression (Cau-RPPCR) is proposed. In the Cau-RPPCR, the causal relationships among process variables are first analyzed and a novel RPLVM structure incorporating the physical causal priors is thereupon designed. Then, an effcient semisupervised training algorithm, based on expectation–maximization, is exploited for the Cau-RPPCR. The performance of the Cau-RPPCR is evaluated on a numerical example and an actual industrial process.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA23.2", "code": "ThA23.2", "title": "LSTM-MHDA-iTransformer Based Soft Sensor Modeling and Its Application to a Hydrocracking Unit (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA23", "sessionTitle": "Next-Generation Intelligent Modeling, Monitoring and Optimization for Modern Industrial Processes I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Huang, Deyang", "affiliation": "East China University of Science and Technology" }, { "name": "Cao, Yue", "affiliation": "East China University of Science and Technology" }, { "name": "Liu, Yurong", "affiliation": "East China University of Science and Technology" }, { "name": "Peng, Xin", "affiliation": "East China University of Science and Technology" }, { "name": "Li, Zhi", "affiliation": "East University of Science and Technology" }, { "name": "Gui, Weihua", "affiliation": "Central South University" }, { "name": "Jiang, Zhaohui", "affiliation": "Central South University" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Machine learning and artificial intelligence in chemical process control" ], "abstract": "Soft sensing techniques have been extensively utilized in industrial applications for estimating key quality variables, thereby providing strong support for process control and optimization. However, the typical features of data generated in industrial processes such as the dynamics and nonlinearity pose significant challenges to traditional soft sensing methods, limiting their ability to extract these features accurately. Consequently, the prediction of key quality variables becomes unreliable. In this work, a hybrid model of LSTM-MHDA-iTransformer is proposed, where these correlations and significant nonlinearity of sequential process data are considered. First, an LSTM network is employed to extract local information from the process data. A modified iTransformer module is then applied to further capture global information. By integrating these components through a feature fusion mechanism, the proposed model achieves collaborative multi-scale modeling, which effectively combines micro-scale variations with macro-scale trends. Finally, compared with five soft sensor models in a real Chinese hydrocracking unit, the developed model achieves greater predictive accuracy and demonstrates superior overall forecasting capability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA23.3", "code": "ThA23.3", "title": "Modeling of Dynamic Knowledge Embedding Based on Improved PINN for Fiber Spinning Industrial Processes (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA23", "sessionTitle": "Next-Generation Intelligent Modeling, Monitoring and Optimization for Modern Industrial Processes I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Yang, Shuo", "affiliation": "Donghua University" }, { "name": "Xie, Ruimin", "affiliation": "Donghua University" }, { "name": "Zhou, Feier", "affiliation": "Donghua University" }, { "name": "Guo, Fan", "affiliation": "Nanjing Institute of Technology" }, { "name": "Zhao, Chenwei", "affiliation": "Shanxi University" }, { "name": "Liu, Tong", "affiliation": "University of Sheffield" } ], "keywords": [ "Machine learning and artificial intelligence in chemical process control", "Machine learning and artificial intelligence in MMM process control", "Process modeling, identification, and estimation techniques" ], "abstract": "Modelling of spinning coagulation represents a crucial task in the wet spinning process of carbon fiber precursor filaments. This paper proposes an ARES-PINN (Adaptive Reweighting and Restart Strategy for PINNs) method based on a dynamic mechanism model for the wet spinning coagulation process. This approach addresses the current model's requirements for subsequent determination of optimal grid accuracy and computational complexity issues. This framework employs an adaptive loss weighting strategy and a restart approach for parameter initialization. By embedding physical laws (in the form of partial differential equations and boundary conditions) within the neural network's loss function, the model and method's efficacy are validated through a case study of a specific ternary polymer system. Dynamic simulation results demonstrate the method's performance under perturbations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA23.4", "code": "ThA23.4", "title": "Long-Term Series Forecasting for Industrial Processes Based on Temporal-Frequency Decomposition Network (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA23", "sessionTitle": "Next-Generation Intelligent Modeling, Monitoring and Optimization for Modern Industrial Processes I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Wang, Yue", "affiliation": "Beijing University of Chemical Technology" }, { "name": "Geng, Zhiqiang", "affiliation": "Beijing University of Chemical Technology" }, { "name": "Hu, Xuan", "affiliation": "Beijing University of Chemical Technology" }, { "name": "Wang, Mengzhi", "affiliation": "Beijing University of Chemical Technology" }, { "name": "Cai, Lei", "affiliation": "Beijing University of Chemical Technology" }, { "name": "Han, Yongming", "affiliation": "Beijing University of Chemical Technology" } ], "keywords": [ "Monitoring, performance assessment, and fault detection in chemical process control", "Model-predictive and optimization-based control in chemical processes", "Process modeling, identification, and estimation techniques" ], "abstract": "Long-Term Series Forecasting (LTSF) is of great significance for energy efficiency analysis and long-term optimization control in industrial production. However, the complex non-stationarity and dynamic time-varying characteristics of industrial process data pose substantial challenges to industrial LTSF tasks. Therefore, this paper proposes a Temporal-Frequency Decomposition Network (TFD-Net) for industrial process LTSF tasks. The TFD-Net innovatively adopts a parallel dual-stream architecture to separately forecast the non-stationary and stationary information in industrial process data. Specifically, the Feature Decoupling Module (FDM) uses Fourier transform to decouple the time series, obtaining stationary and non-stationary components. A simple multi-layer perceptron (MLP) is utilized to predict non-stationary features. Meanwhile, the feature extraction module based on the seasonal-trend decomposition is utilized to capture stationary components. And the TFD-Net effectively captures non-stationary features of data while retaining detailed stationary feature, significantly improving prediction accuracy. Finally, the TFD-Net is validated on five benchmarks and an actual industrial production dataset. The experimental results show that compared with the current baselines, the TFD-Net achieves state-of-the-art results, with a reduction of at least 11.7% in the mean square error and 11.0% in the mean absolute error, which can effectively guide industrial production.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA23.5", "code": "ThA23.5", "title": "A Neural Network Reduced-Order Model for Nonlinear MPC of Melt Pool Area in Directed Energy Deposition (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA23", "sessionTitle": "Next-Generation Intelligent Modeling, Monitoring and Optimization for Modern Industrial Processes I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Mjalled, Ali", "affiliation": "Ruhr University Bochum" }, { "name": "Kulik, Jann", "affiliation": "Ruhr-Universität Bochum" }, { "name": "Dyrska, Raphael", "affiliation": "Ruhr-Universität Bochum" }, { "name": "Monnigmann, Martin", "affiliation": "Ruhr-Universität Bochum" } ], "keywords": [ "Machine learning and artificial intelligence in MMM process control", "Industrial applications of process control", "MMM process modeling, identification, and estimation techniques" ], "abstract": "Laser-based directed energy deposition is an additive manufacturing process that builds 3D parts by melting metal wire or powder with a concentrated laser. This work presents a data-driven reduced-order model (ROM) to predict the temperature field during this process. The developed ROM combines a dimensionality reduction step using proper orthogonal decomposition (POD) and a long short-term memory (LSTM) network to predict the temporal evolution in the reduced space. We demonstrate that the ROM achieves superior prediction accuracy for temperature profiles and melt pool geometry using 64 POD modes compared to a dynamic mode decomposition with control (DMDc) model. Furthermore, we integrate the developed ROM into a nonliner model predictice control (MPC) framework to achieve reference tracking of the melt pool area by controlling the laser power, a task which would be computationally intractable using a high-fidelity PDE model.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA23.6", "code": "ThA23.6", "title": "A Linear-Quadratic Optimization Model for Sustainable Closed-Loop Supply Chains", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA23", "sessionTitle": "Next-Generation Intelligent Modeling, Monitoring and Optimization for Modern Industrial Processes I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Silva Filho, Oscar Salviano", "affiliation": "Retired Researcher, CTI Renato Archer" }, { "name": "Andres, Frederic Henri Nicolas", "affiliation": "National Institute for Informatics" } ], "keywords": [ "Production and operations management", "Supply chain management in manufacturing", "Simulation and optimization in production, operations and services" ], "abstract": "This paper presents a novel stochastic optimization model for long-term production planning in hybrid supply chains that combine manufacturing, remanufacturing, and recycling under uncertainty. The model introduces a unified chance-constrained framework that captures dynamic interactions among three inventory types and four production processes, extending conventional deterministic or single-stream approaches. It is reformulated as a deterministic linear–quadratic equivalent, enhancing tractability while preserving uncertainty in demand and return flows. This structure supports efficient solution methods within standard optimization tools and enables sensitivity analyses of recovery parameters. A numerical case study illustrates how varying recycling ratios affect optimal production, inventory, and cost over the long term. The results quantify the trade-off between manufacturing and remanufacturing, offering insights for robust and sustainable production planning. Overall, the framework integrates stochastic modeling and hybrid supply chain control, advancing decision-making for closed-loop and circular production systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA24.1", "code": "ThA24.1", "title": "Weather-Forecast-Driven Hydrological Modeling for Subsurface Drainage Control", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA24", "sessionTitle": "Sensing and Control in Agriculture", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Jaouen, Pierre", "affiliation": "University of Oulu" }, { "name": "Läpikivi, Miika", "affiliation": "Natural Resources Institute Finland" }, { "name": "Liimatainen, Maarit", "affiliation": "Natural Resources Institute Finland" }, { "name": "Liedes, Toni", "affiliation": "University of Oulu" }, { "name": "Ikonen, Enso", "affiliation": "University of Oulu" } ], "keywords": [ "Farmland irrigation and drainage control", "Water resource system modeling and control", "Modeling and estimation in agriculture" ], "abstract": "Hydrological models used by model-based control for drained field water management operate with weather forecasts, yet few studies have compared drained field models considering forecasts and limited calibration data. A linear model, a non-linear conceptual model, and a distributed physics-based model were calibrated and evaluated using field data from four subsurface-drained blocks. Short-term daily simulations driven by weather forecasts showed that all models outperformed a baseline assumption of constant groundwater depth. However, forecast uncertainty constrained the performance of all models. These findings suggest that dynamic modeling improves groundwater depth predictions and that conceptual models, more practical than physics-based models for real-world agricultural control or monitoring applications, can perform as well as physics-based models in forecast-driven short-term predictions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA24.2", "code": "ThA24.2", "title": "Plant Growth Estimation with a Camera-Based Vegetation Index Mapping System for Agricultural Ground Vehicles (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA24", "sessionTitle": "Sensing and Control in Agriculture", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Pindl, Lukas", "affiliation": "Technical University of Munich" }, { "name": "Maier, Michael", "affiliation": "Technische Universität München" }, { "name": "Oksanen, Timo", "affiliation": "Technical University of Munich" } ], "keywords": [ "Computer vision in agriculture", "Sensing and perception in agriculture" ], "abstract": "This work presents a camera-based sensor for mapping plant growth over an agricultural field. The sensor can be used on ground-based vehicles like a tractor and relies on RTK-GNSS to correctly merge many multispectral images onto one map. NDVI is used as an index to estimate plant growth, but the approach can be used with other indices as well depending on the camera. The images from the camera are projected onto the estimated ground plane using perspective projection. This computationally simple approach allows for real-time processing of all images on the field even with low-end hardware. The results are compared to a commercially established vehicle-mounted sensor. Absolute values are hard to compare, but both sensors show similar trends. The camera-based approach also allows for filtering of ground and non-ground areas, potentially reducing the impact of crop density on the average measured NDVI.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA24.3", "code": "ThA24.3", "title": "A Control Approach to Autonomous Cultivation with Online Soil-Condition Estimation (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA24", "sessionTitle": "Sensing and Control in Agriculture", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Uvesten, Viktor", "affiliation": "Linköping University, Väderstad AB" }, { "name": "Enqvist, Martin", "affiliation": "Linköping University" } ], "keywords": [ "Control in precision agriculture", "Modeling and estimation in agriculture", "Sensing and perception in agriculture" ], "abstract": "Autonomy is essential for addressing many of the future challenges in agriculture. In autonomous soil cultivation, the process of preparing the field before sowing, knowledge of the spatial soil conditions is crucial for achieving stable and efficient operation. This paper formulates a new control problem for a general single-tool cultivation machine, where the goal is to estimate the spatially varying soil conditions online while maintaining high control performance. To handle the partially unknown system dynamics, a consistent estimation method based on the Instrumental Variables (IV) framework is proposed. A simulation study demonstrates that the method accurately identifies the soil-condition function and clearly outperforms the classical Least Squares (LS) approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA24.4", "code": "ThA24.4", "title": "Multi-Stage Grapevine Leaf Area Estimation Using LiDAR and RGB Based U-Net Segmentation", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA24", "sessionTitle": "Sensing and Control in Agriculture", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Bleser, Joseph", "affiliation": "Hochschule Geisenheim University" }, { "name": "Tsoulias, Nikos", "affiliation": "Hochschule Geisenheim University" }, { "name": "Madni, Syed Shaham", "affiliation": "Hochschule Geisenheim University" }, { "name": "Paraforos, Dimitrios S.", "affiliation": "Geisenheim University" } ], "keywords": [ "Modeling and estimation in agriculture", "Computer vision in agriculture", "Sensing and perception in agriculture" ], "abstract": "Against the backdrop of the increasing need for techniques to monitor and determine the canopy structure in viticulture, this work deals with the search for a method for analysing canopies in vineyards, considering LiDAR and RGB data, and investigating the development of leaf areas across several stages of vine development. To this end, LiDAR and RGB data were combined in a first step to generate artificial RGB-D data. These were used to train two U-Net models, one using RGB data (U-NET_RGB) the other using RGB-D data (U-NET_RGB-D). Since accuracy and Dice Score values for U-NET_RGB were higher, it was used to estimate the foreground canopy from Eden Viewer RGB frames, of different growing stages of the vine and different defoliation levels, and calculate the corresponding leaf wall area. The probability density distribution was plausible and in line with other related studies.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA24.5", "code": "ThA24.5", "title": "Tactile Predictive Pushing of Unstable Hanging Fruit for Agricultural Robots (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA24", "sessionTitle": "Sensing and Control in Agriculture", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Jo, Yuseung", "affiliation": "Chonnam National University" }, { "name": "Park, Yonghyun", "affiliation": "Gwangju Institute of Science and Technology (GIST)" }, { "name": "Lee, Sechang", "affiliation": "Chonnam National University" }, { "name": "Son, Hyoung Il", "affiliation": "Chonnam National University" } ], "keywords": [ "Robotic manipulation of agricultural materials", "Agricultural robotics", "Sensing and perception in agriculture" ], "abstract": "This paper presents a tactile predictive pushing framework for gentle cluster-envelope clearing of unstable hanging fruit in dense agricultural canopies. A secondary arm equipped with a laminate capacitive tactile array uses sparse intensity signals to estimate a local contact-center surrogate, a directional pseudo-force, and a rate-weighted moment surrogate. These cues bias a Cartesian TCP controller for short-range lateral displacement of a grape-cluster envelope while limiting contact-surrogate peaks. Experiments over 20 repeated trials on the same grape cluster show smoother trajectories and shorter high-contact duration than a visual-feedback teleoperation baseline.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA24.6", "code": "ThA24.6", "title": "Observer-Based Grain Moisture Control for In-Silo Storage", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA24", "sessionTitle": "Sensing and Control in Agriculture", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Munteanu, Iulian", "affiliation": "Grenoble Alpes University, GIPSA-Lab" }, { "name": "Sacala, Ioan Stefan", "affiliation": "University Politehnica Bucharest" }, { "name": "Arghira, Nicoleta", "affiliation": "University Politehnica of Bucharest" }, { "name": "Fagarasan, Ioana", "affiliation": "Univ POLITEHNICA of Bucharest" }, { "name": "Schuler, Ana Sophia", "affiliation": "Universitatea Politehnica Bucuresti" } ], "keywords": [ "Modeling and estimation in agriculture", "Automation for post harvest technology", "Control in precision agriculture" ], "abstract": "This paper proposes an observer-based control engineering approach for in-silo drying and moisture content regulation of cereal grains. By using a suitable linearized finite-dimensional approximation of drying process and the two-time-scale nature of in-silo drying phenomena which dynamically decouples intergranular air humidity and grain moisture content evolutions, a two-loops cascaded control structure is built. A linear observer is used to complete previous control studies by estimating the non-measurable moisture content values in the grain mass. The grain drying process is driven by controlling intergranular air humidity within an inner loop built around a PI controller. The averaged moisture content is controlled within an outer loop containing a state feedback and an integral driving action, its feedback being based on grains moisture estimate. Performance of the proposed control approach has been validated via numerical simulation. The results enable control implementation on a real-world grain silo.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA25.1", "code": "ThA25.1", "title": "Online Hemodynamic Prediction for General Anesthesia Using Neural Controlled Differential Equations (I)", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA25", "sessionTitle": "Digital Twins and Diagnostics of the Human Cardiovascular System", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Fregolent, Mattia", "affiliation": "University of Brescia" }, { "name": "Schiavo, Michele", "affiliation": "University of Brescia" }, { "name": "Latronico, Nicola", "affiliation": "University of Brescia" }, { "name": "Paltenghi, Massimiliano", "affiliation": "Spedali Civili Di Brescia" }, { "name": "Rampazzo, Mirco", "affiliation": "Universita Degli Studi Di Padova" }, { "name": "Visioli, Antonio", "affiliation": "University of Brescia" } ], "keywords": [ "Biomedical system modeling, identification, and simulation", "Digital twins in healthcare, model-based therapeutics", "Pharmacokinetics, tracer kinetic modelling and drug delivery" ], "abstract": "Maintaining hemodynamic stability during general anesthesia is essential for ensuring patient safety. However, accurately predicting blood pressure responses to anesthetic drugs remains a major challenge due to strong inter- and intra-patient variability and the presence of unforeseeable surgical events. As a result, existing models often show substantial mismatches when tested on real-world surgical data, limiting their suitability for incorporation into multivariable, model-based control systems. In this study, we introduce a framework for intraoperative hemodynamic prediction based on neural controlled differential equations. Leveraging their ability to learn non-autonomous dynamics from irregularly sampled clinical time series, and through a straightforward architectural adjustment, the model can be deployed online to update its latent state in real time as new patient observations become available. By jointly considering drug concentration trajectories and blood pressure measurements, the model continuously realigns its predictions with the patient’s changing physiological condition. This approach lays the groundwork for future automated, multivariable anesthesia control strategies implemented within a digital twin setting.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA25.2", "code": "ThA25.2", "title": "CT-Based Classification of Symptomatic vs. Asymptomatic Carotid Plaques Using Schrödinger Spectrum Features (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA25", "sessionTitle": "Digital Twins and Diagnostics of the Human Cardiovascular System", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Juan Manuel, Vargas", "affiliation": "INRIA Saclay" }, { "name": "Wang, Louise", "affiliation": "Hôpital Européen Georges-Pompidou HEGP" }, { "name": "Piedelièvre, Alix", "affiliation": "Hôpital Européen Georges-Pompidou HEGP" }, { "name": "Goudot, Guillaume", "affiliation": "Hôpital Européen Georges-Pompidou HEGP" }, { "name": "Davaine, Jean Michel", "affiliation": "Hôpital Européen Georges-Pompidou HEGP" }, { "name": "Laleg, Taous-Meriem", "affiliation": "Inria" } ], "keywords": [ "Biomedical and medical imaging, image processing, visualization", "Biomedical signal measurement and processing" ], "abstract": "This paper presents a novel methodology for classifying symptomatic and asymptomatic carotid artery plaques from CT images using quantum-inspired features. The proposed approach applies two-dimensional semi-classical signal analysis (2D-SCSA) to extract spectral features from each slice and subsequently constructs spatial sequences that track the evolution of these features across the entire volume. Statistical and frequency-domain descriptors computed from these spatial sequences capture three-dimensional morphological and textural characteristics of the plaques. Using stratified group K-fold cross-validation with hyperparameter tuning, multiple machine-learning models are trained on the extracted features. Experimental results on real clinical data confirm the effectiveness of this hierarchical feature-extraction strategy, demonstrating its strong potential to improve clinical diagnosis and risk assessment of carotid artery disease.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA25.3", "code": "ThA25.3", "title": "A Discrete-Time Bayesian Filter for Robust Heart Rate Variability Analysis (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA25", "sessionTitle": "Digital Twins and Diagnostics of the Human Cardiovascular System", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "O'Sullivan, Ryan", "affiliation": "University of Canterbury" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" }, { "name": "Pretty, Christopher", "affiliation": "University of Canterbury" } ], "keywords": [ "Biomedical signal measurement and processing" ], "abstract": "Heart rate variability (HRV) is a key marker of autonomic nervous system function, but its reliability depends critically on accurate detection of R–R intervals from ECG signals. Conventional peak detection algorithms are highly susceptible to noise, motion artefact, and missed or spurious beats, often requiring manual correction and limiting suitability for real\u0002time clinical use. While Bayesian filtering has previously been applied to heart rate estimation, it has not been used for full HRV extraction or to fuse multiple noisy detectors. We present a discrete-time Bayesian histogram filtering framework for robust HRV analysis that integrates peak candidates from multiple sensors or detection algorithms. The method models detector outputs as Gaussian observations and applies a physiologically informed transition model based on adaptive R–R interval predictions. At each step, the filter produces a posterior distribution over likely beat locations, enabling automated peak selection, rejection of outliers, and recovery from missed detections. We evaluate the approach on synthetic and real ECG datasets with annotated ground truth. Results demonstrate improved accuracy and substantially greater robustness to severe noise and detection failures compared with standard algorithms, particularly in worst-case epochs. The method provides a principled foundation for reliable real-time HRV processing in clinical and wearable settings.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA25.4", "code": "ThA25.4", "title": "Relative Local Stiffness Estimation in a Planar Section of an Artery (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA25", "sessionTitle": "Digital Twins and Diagnostics of the Human Cardiovascular System", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Bhave, Ashish", "affiliation": "Institute of Technical Medicine, Furtwangen University" }, { "name": "Agahi, Behrouz", "affiliation": "Furtwangen University, Institute of Technical Medicine" }, { "name": "Moeller, Knut", "affiliation": "Furtwangen University" } ], "keywords": [ "Biomedical system modeling, identification, and simulation", "Digital twins in healthcare, model-based therapeutics", "Biomedical and medical imaging, image processing, visualization" ], "abstract": "Arterial narrowing and stiffening are root causes of poor heart health, myocardial infarction and peripheral vessel diseases. An in-vivo sensor-actuator system embedded on inflatable elastomer balloon is under development at Furtwangen University. The signals from the sensing segments on balloon are intended to provide a tactile assessment of tissue stiffness and shape, which can be further used to generate decision aids for a Vascular surgeon. This 2D study focused on implementing a Finite Element model to evaluate differential strains on lumen surface of 4 partly calcified arteries captured using an inflatable balloon setup embedded with 128 strain sensing elements. The deformation analysis of the balloon shows few elements initially conforming to stiffer unhealthy section of vessel lumen shift over to the section with normal stiffness as balloon pressure was increased. An analytical method was developed that tracks the maximum/minimum strain on elements and the corresponding sensing element number. A simplified calculation further allows estimation of relative local strains of the tissue region via balloon elements and therefore relative stiffness. The unhealthy region underwent relative strains ranging from 0.3 to 0.47 compared to the healthy region as observed from the Finite Element Analysis model whereas the analytical method computed the same in range of 0.4 to 0.5. It could be shown from simulation that an in-plane assessment involving inter-regional stiffness behaviour could be obtained. This sensor system could be used to estimate local tissue health and generate better informed decision aids.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA25.5", "code": "ThA25.5", "title": "Validation of a Personalized Cardiovascular Two-Channel Transfer Function on Virtual Data (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA25", "sessionTitle": "Digital Twins and Diagnostics of the Human Cardiovascular System", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Cerdeira, Alice Elizabeth", "affiliation": "University of Canterbury" }, { "name": "Murphy, Liam", "affiliation": "University of Canterbury" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" } ], "keywords": [ "Biomedical system modeling, identification, and simulation", "Biomedical signal measurement and processing", "Decision support and control in medicine" ], "abstract": "Cardiovascular management in the intensive care unit (ICU) is difficult and often sub-optimal. Model-based methods offer the opportunity to turn a relatively low number of clinically available measurements into a clearer picture to guide care. However, to use models effectively, the aortic pressure and conditions around the heart, which are not directly measured, must be known. This paper presents a method of identifying model parameters of two validated single-channel arterial transfer function models in parallel, to estimate central aortic pressure (Pao,est) from two peripheral arterial measurements. The method reduces the need for invasive pressures to be measured for use in cardiovascular models, particularly the three-chamber model, which requires the mean, range, and contractility of the aortic pressure as an output. The method uses MATLAB’s built-in genetic algorithm to minimize the difference between the output waves of two arterial transfer functions, using the pressure measurements and foot-to-foot time difference between the pressure waves. The method was implemented on 100 virtual patients from the HeaMod online database with known ground truth for quantitative validation, followed by two patients of clinical data from Christchurch Hospital ICU for qualitative validation. The range, mean and contractility error was calculated between the known and estimated virtual patients to assess method accuracy. For the clinical data, visual inspection of the waveforms and objective function evaluation were used. The method performed well in in-silico data, but precision in real clinical measures and their relative pulse transit time difference were a limitation in evaluating clinical patient-specific aortic pressures.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA25.6", "code": "ThA25.6", "title": "Toward a Bedside Cardiovascular Digital Twin: Clinically Feasible Identification of a Three-Chamber Lumped-Parameter Model (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA25", "sessionTitle": "Digital Twins and Diagnostics of the Human Cardiovascular System", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Murphy, Liam", "affiliation": "University of Canterbury" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" }, { "name": "Cushway, James", "affiliation": "University of Canterbury" }, { "name": "Desaive, Thomas", "affiliation": "University of Liege" } ], "keywords": [ "Digital twins in healthcare, model-based therapeutics", "Healthcare management, disease control, critical care", "Biomedical signal measurement and processing" ], "abstract": "Lumped-parameter cardiovascular models combine routine patient data with mathematical representations of circulatory physiology to estimate otherwise unmeasurable hemodynamic parameters. When individualised using patient-specific measurements, these models can yield clinically actionable insight beyond raw bedside signals. The three-chamber model (TCM) offers such potential, but its standard formulation requires direct aortic and ventricular measurements absent from routine ICU practice. This study introduces a clinically feasible TCM implementation (TCM_CF) that estimates the missing model inputs from available ICU data, and evaluates its performance against a full-measurement version (TCM_FM) using invasive reference measurements.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA26.1", "code": "ThA26.1", "title": "State of Health Estimation for a Maritime Vessel Battery Using Only Operational Data", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA26", "sessionTitle": "Marine Power, Propulsion and Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Jelovic, Matteas", "affiliation": "TU Wien" }, { "name": "Engel, Georg", "affiliation": "AVL LIST GmbH" }, { "name": "Koegeler, Hans-Michael", "affiliation": "AVL LIST GmbH" }, { "name": "Kozek, Martin", "affiliation": "Vienna University of Technology" }, { "name": "Hametner, Christoph", "affiliation": "TU Wien" } ], "keywords": [ "Marine renewable energy systems", "Dependability in marine systems", "Modelling, identification and control in marine systems" ], "abstract": "Safe operation of electrified vehicles requires precise state of charge estimation and long-term degradation assessment. This work presents a framework that simultaneously estimates state of charge and tracks capacity degradation using only operational field data without requiring laboratory-controlled conditions or special test procedures. The method combines an Extended Kalman Filter with meta-heuristic optimization and feature-based filtering to reduce variability from operational fluctuations. Validation using four years of maritime vessel data demonstrates robust agreement with reference capacity tests. These results confirm the framework's suitability for state of health monitoring in maritime applications where controlled excitation and extensive testing are not yet available.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA26.2", "code": "ThA26.2", "title": "Disturbance-Observer-Based Fuzzy Sliding Mode Control for Nonlinear Offshore Steel Jacket Platform with Wave Force", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA26", "sessionTitle": "Marine Power, Propulsion and Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Li, Jiarui", "affiliation": "Shanghai Maritime University" }, { "name": "Yang, Yekai", "affiliation": "Donghua University" }, { "name": "Zhang, Zhina", "affiliation": "East China University of Science and Technology" }, { "name": "Cao, Zhiru", "affiliation": "Shanghai University" } ], "keywords": [ "Modelling, identification and control in marine systems" ], "abstract": "This paper investigates the anti-disturbance sliding mode control problem for an offshore steel jacket platform under external irregular wave forces. To capture the inherent nonlinearity, a fuzzy modeling approach is employed to represent this system as a T-S fuzzy model. A nonlinear disturbance observer is then designed to estimate and counteract mismatched wave disturbances. By establishing design criteria for the observer gain, the estimation error of the wave forces is proven to exponentially converge to zero. Utilizing the disturbance estimation, an integral-type sliding surface and a corresponding sliding mode controller are developed, ensuring both reachability of the specified sliding surface and boundedness of sliding motion. Finally, simulation results on vibration reduction are presented to evaluate the performance of the proposed disturbance-rejection control strategy.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA26.3", "code": "ThA26.3", "title": "Fuzzy Fractional-Order Nonsingular Terminal Sliding Mode Control for Frequency Stability in Shipboard Microgrids", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA26", "sessionTitle": "Marine Power, Propulsion and Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Nafisifar, Maedeh", "affiliation": "Univesity of Zanjan" }, { "name": "Jozeslami, Matin", "affiliation": "Univesity of Zanjan" }, { "name": "Rouhani, Seyyed Hossein", "affiliation": "National Kaohsiung University of Science and Technology, Kaohsiung" }, { "name": "Jalilvand, Abolfazl", "affiliation": "Professor, University of Zanjan, Zanjan, Iran" }, { "name": "Fekih, Afef", "affiliation": "Univ of Louisiana at Lafayette" }, { "name": "Mobayen, Saleh", "affiliation": "National Yunlin University of Science and Technology" } ], "keywords": [ "Marine system guidance, navigation and control", "Power and propulsion in marine systems", "Modelling, identification and control in marine systems" ], "abstract": "The integration of renewable energy sources into shipboard microgrids introduces model uncertainties and external disturbances that affect system stability and performance. This paper presents a continuous adaptive control design that combines fuzzy rules with nonsingular terminal sliding mode concepts for frequency regulation in shipboard microgrids. Moreover, we build a dynamic model in the presence of uncertainties and disturbances with an online mechanism to estimate unknown bounds using an adaptive gain. The controller proposed is powerful and realizes finite-time acquisition with improved robust tracking. Simulation results support the improvement in frequency stability, supply disturbance rejection and provide robustness results comparing this method with conventional controllers.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA26.4", "code": "ThA26.4", "title": "A Nonlinear Model Predictive Controller for Reactivity Controlled Compression Ignition in Marine Engines", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA26", "sessionTitle": "Marine Power, Propulsion and Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Pohjola, Jeremias", "affiliation": "Aalto University" }, { "name": "Modabberian, Amin", "affiliation": "Aalto University" }, { "name": "Raisi Esfarjani, Mohammad", "affiliation": "University of Vaasa" }, { "name": "Talebi Sheikhsarmast, Amir", "affiliation": "University of Vaasa" }, { "name": "Vasudev, Aneesh", "affiliation": "University of Vaasa" }, { "name": "Visala, Arto", "affiliation": "Aalto University, ELEC School" }, { "name": "Hyvönen, Jari", "affiliation": "Engine Research and Technology Development at Wärtsilä Marine Solutions" }, { "name": "Mikulski, Maciej", "affiliation": "University of Vaasa" } ], "keywords": [ "Modelling, identification and control in marine systems", "Power and propulsion in marine systems" ], "abstract": "In this study, a nonlinear model predictive control (NMPC) framework is developed to control the combustion phasing and the indicated mean effective pressure (IMEP) of reactivity controlled compression ignition (RCCI) process by adjusting total fuel energy and blend ratio (BR) of low and high reactivity fuels in fuel injection. The controller is evaluated with a nonlinear dynamic process model and benchmarked against PID controllers. Despite high tracking accuracy for both control frameworks, NMPC achieves faster response for both combustion phasing and IMEP (within 10 cycles) and lower steady-state error (below 0.5 crank-angle-degree and 1 bar) in the presence of uncertainties. This improves control towards more efficient RCCI combustion.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA26.5", "code": "ThA26.5", "title": "Stochastic MPC with Power Directionality Constraints: Application to Ocean Wave Energy Conversion (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA26", "sessionTitle": "Marine Power, Propulsion and Energy Systems", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Shell, Jonathan", "affiliation": "University of Michigan" }, { "name": "Scruggs, Jeff", "affiliation": "University of Michigan" }, { "name": "Simmons, Jeremy", "affiliation": "University of Minnesota" }, { "name": "Van de Ven, James", "affiliation": "University of Minnesota" } ], "keywords": [ "Marine system guidance, navigation and control", "Modelling, identification and control in marine systems", "Power and propulsion in marine systems" ], "abstract": "We develop a technique for Model Predictive Control (MPC) for physical systems in which the actuators exhibit Power Directionality Constraints (PDCs). Such constraints restrict the flow of actuation power to be exclusively absorptive at all times. In ocean wave energy conversion, such constraints emerge in many technologies using hydraulic power trains to extract and transmit the harvested wave power. MPC techniques can be used to achieve near-optimal power generation performance for Wave Energy Converters (WECs), but incorporation of PDCs into an MPC framework is challenging due to their nonconvexity. To address this, we propose the use of convex overbounding technique. Although sub-optimal, this technique results in a provable lower bound on mean power generation which holds regardless of the MPC receding horizon length. We demonstrate the implementation of the algorithm in simulation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA27.1", "code": "ThA27.1", "title": "Active Observer and Estimation Method for Spacecraft Systems with Multiple Uncertainties (I)", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA27", "sessionTitle": "Collaborative Mission Planning and Intelligent Control for Large-Scale Constellations", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Gan, Gena", "affiliation": "Beijing University of Posts and Telecommunications" }, { "name": "Chu, Ming", "affiliation": "Beijing University of Posts and Telecommunications" }, { "name": "Zhang, Huayu", "affiliation": "Beijing University of Posts and Telecommunications" }, { "name": "Lin, Shaoqi", "affiliation": "Beijing University of Posts and Telecommunications" } ], "keywords": [ "AI for aircraft and spacecraft navigation, guidance and control", "Aerospace mission control and operations", "Control of multi satellite systems" ], "abstract": "This paper proposes an active output-feedback state-estimation method for nonlinear spacecraft systems subject to multiple unknown uncertainties. A novel observer structure is introduced in which a compensation signal, generated by online optimisation, dynamically corrects the estimate. The cost function incorporates a cooperative coupling term between the control command and the compensation signal, allowing the observer to exploit control intent as prior information and react in a feed-forward manner. The associated Hamilton–Jacobi–Bellman equation is solved online via an adaptive dynamic programming (ADP) scheme based on neural-network value-function approximation. The uniform ultimate boundedness of the estimation error is established by Lyapunov analysis, and the ultimate bound is given in closed form. Numerical simulations against the extended Kalman filter, a high-order sliding-mode observer, and a reinforcement-learning-based extended state observer confirm faster convergence, higher steady-state accuracy and stronger robustness of the proposed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA27.2", "code": "ThA27.2", "title": "The First On-Orbit Experiment of a Dual-Comb Ranging System: Enabling Future Precision Measurement for Spacecraft Formation and Beyond (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA27", "sessionTitle": "Collaborative Mission Planning and Intelligent Control for Large-Scale Constellations", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Xie, Yongchun", "affiliation": "Beijing Institute of Control Engineering" }, { "name": "Gu, Yingying", "affiliation": "Beijing Institute of Control Engineering" } ], "keywords": [ "Control of multi satellite systems", "Condition monitoring and maintenance of aerospace systems", "Space exploration and transportation" ], "abstract": "The advancement of space missions, such as satellite formation flying, gravitational field measurement, and on-orbit servicing, has created an urgent demand for absolute distance measurement technologies characterized by high precision, high update rate, and a large dynamic range. This paper reports the first on-orbit experiment of a dual-comb ranging system designed for space applications, aiming to validate its feasibility and performance under real-space engineering conditions, thereby laying the foundation for future space-based ranging applications. The dual-comb system was externally mounted on the forward compartment of the Tianzhou-9 cargo spacecraft. After docking with the space station, the system performed laser ranging using a corner reflector installed at the station’s docking interface. This experiment assessed the system’s adaptability to harsh space environments, including microgravity and significant external thermal fluctuations. Key parameters of the dual-comb system include a repetition rate of 50 MHz, a laser wavelength of 1560 nm, and an acquisition rate of 1 kHz achieved through optical asynchronous sampling. The system demonstrated a ranging precision of 2.15 μm at 10 Hz in orbit. These results fully verify the robust ranging capability and engineering potential of the dual-comb ranging technology in outer space. The success of this first space-borne experiment marks a critical transition of dual-comb ranging technology from laboratory demonstration to practical engineering applications in space. It opens a new chapter for future high-precision space ranging and shows broad promise for application including precision spectroscopy, time-frequency transmission, and laser communications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA27.3", "code": "ThA27.3", "title": "A Multi-Spacecraft Small-Body Image Reconstruction Method Based on Neural Radiance Fields (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA27", "sessionTitle": "Collaborative Mission Planning and Intelligent Control for Large-Scale Constellations", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Li, Shuai", "affiliation": "Beijing Institute of Technology" }, { "name": "Zhu, Shengying", "affiliation": "Institute of Deep Space Exploration, School of Aerospace Engineering, Beijing Institute of Technology" }, { "name": "Liang, Zixuan", "affiliation": "Beijing Institute of Technology" }, { "name": "Shao, Wei", "affiliation": "Qingdao University of Science & Technology" }, { "name": "Cui, Pingyuan", "affiliation": "Beijing Institute of Technology" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "AI for aircraft and spacecraft navigation, guidance and control" ], "abstract": "Reconstruction of small bodies is essential for vision-based spacecraft navigation. Conventional 3D methods require many well-illuminated images and heavy offline processing to obtain high-fidelity shape models. Deep learning-based approaches, such as NeRF, have recently attracted increasing attention, but robust multi-view reconstruction under perturbed camera poses remains challenging. This paper addresses this issue by introducing a multi-view depth consistency constraint that jointly refines camera poses and the radiance field. Experiments on simulated multi-spacecraft data demonstrate high-quality reconstruction under complex camera trajectories with significant pose perturbations and achieves superior performance to existing methods in novel-view rendering.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA27.4", "code": "ThA27.4", "title": "Tube Model Predictive Control for Dual-Satellite Electromagnetic Formation Flying with Coil-Current Constraints (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA27", "sessionTitle": "Collaborative Mission Planning and Intelligent Control for Large-Scale Constellations", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Wu, Yueyang", "affiliation": "Nankai University" }, { "name": "Meng, Bin", "affiliation": "Beijing Institute of Control Engineering" }, { "name": "Liu, Zhongxin", "affiliation": "Nankai University" }, { "name": "Ni, Yuan-Hua", "affiliation": "Nankai University" } ], "keywords": [ "Control of multi satellite systems", "Guidance, navigation and control of aircraft and spacecraft", "Nonlinear and optimal automotive control" ], "abstract": "This paper presents a tube-MPC scheme for dual-satellite electromagnetic formation flying with hard bounds on coil currents. The relative motion is modeled by Clohessy—Wiltshire dynamics with disturbances, and the electromagnetic interaction is linearized into a time-varying input matrix. A robust positively invariant ellipsoidal tube is computed in the relative position--velocity space and used to tighten both the inter-satellite distance constraints and the current limits, ensuring constraint satisfaction for all admissible disturbances. A quadratic terminal cost and an ellipsoidal terminal set guarantee recursive feasibility and exponential convergence of the nominal trajectory, while the real trajectory remains within a bounded neighborhood of the reference. A convex geomagnetic disturbance torque penalty is also included in the cost, enabling geomagnetic attenuation within the same convex optimization framework.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA27.5", "code": "ThA27.5", "title": "A Multi-Region Coverage Planning Algorithm for Agile Satellites Based on Regional Completion Ratio (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA27", "sessionTitle": "Collaborative Mission Planning and Intelligent Control for Large-Scale Constellations", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Ding, Mengfang", "affiliation": "Harbin Institute of Technology" }, { "name": "Zhou, Qingrui", "affiliation": "CAST" }, { "name": "Qiu, Huaxin", "affiliation": "Beihang University" } ], "keywords": [ "Aerospace mission control and operations", "Guidance, navigation and control of aircraft and spacecraft", "Control of multi satellite systems" ], "abstract": "This paper addresses the multi-region coverage problem for agile Earth observation satellite constellations and proposes a Regional Completion Ratio–Adaptive Large Neigh borhood Search (RCR-ALNS) algorithm. The method introduces the concept of Regional Completion Ratio (RCR), which quantifies the theoretical upper bound of satellite coverage capability through grid-based regional discretization. Based on this representation, an Adaptive Large Neighborhood Search (ALNS) framework is developed to globally optimize task combinations with dynamic neighborhood operator selection. Simulation results show that, in scenarios involving heterogeneous agile satellites and multiple observation regions, the proposed RCR-ALNS algorithm outperforms traditional strip-based methods in coverage, reward–cost ratio, and resource utilization efficiency.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA27.6", "code": "ThA27.6", "title": "Improved Progressive Envelopment Method for Multi-Satellite Complex Region Planning (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA27", "sessionTitle": "Collaborative Mission Planning and Intelligent Control for Large-Scale Constellations", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Zhang, Hangning", "affiliation": "Beijing Institute of Control Engineering" }, { "name": "Meng, Bin", "affiliation": "Beijing Institute of Control Engineering" }, { "name": "Ni, Yuan-Hua", "affiliation": "Nankai University" } ], "keywords": [ "Control of multi satellite systems", "Mission planning and decision making for AVs", "Multi-vehicle systems" ], "abstract": "This paper studies multi-satellite strip planning for imaging constellations over complex irregular regions under full-coverage constraints. The problem is formulated as constrained set covering on a hexagonal grid; a matroid structure together with a polymatroid utility supports a greedy approximation analysis of progressive envelopment (PGE). Intrinsic grid-point values with polar and anti-polar radius link the progressive envelopment construction to a one-step greedy strip-selection rule, and we have demonstrated the upper limit of the strip length given by PGE relative to the optimal value. To reduce overlap-heavy outputs from PGE, we propose pros-and-cons pairs (PCP), a coordination scheme that reallocates redundant overlap among strips while preserving low computational cost. Randomized multi scenario simulations show that PCP reduces total strip length substantially, which supports the proposed coordination design.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA28.1", "code": "ThA28.1", "title": "Auction-Based Responsibility Allocation for Scalable Decentralized Safety Filters in Cooperative Multi-Agent Collision Avoidance", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA28", "sessionTitle": "Guidance, Navigation and Control for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Autenrieb, Johannes", "affiliation": "German Aerospace Center (DLR)" }, { "name": "Spiller, Mark", "affiliation": "DLR" } ], "keywords": [ "Guidance, navigation and control for AVs", "Aerial and space robotics", "Multi-vehicle systems" ], "abstract": "This paper proposes a scalable decentralized safety filter for multi-agent systems based on high-order control barrier functions (HOCBFs) and auction-based responsibility allocation. While decentralized HOCBF formulations ensure pairwise safety under input bounds, they face feasibility and scalability challenges as the number of agents grows. Each agent must evaluate an increasing number of pairwise constraints, raising the risk of infeasibility and making it difficult to meet real-time requirements. To address this, we introduce an auction-based allocation scheme that distributes constraint enforcement asymmetrically among neighbors based on local control effort estimates. The resulting directed responsibility graph guarantees full safety coverage while reducing redundant constraints and per-agent computational load. Simulation results confirm safe and efficient coordination across a range of network sizes and interaction densities.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA28.2", "code": "ThA28.2", "title": "Safe Multi-Agent UAV-UGV Rendezvous in Dynamic Environments", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA28", "sessionTitle": "Guidance, Navigation and Control for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Masry, Ghewa", "affiliation": "Université De Haute-Alsace - IRIMAS" }, { "name": "Vieira, David", "affiliation": "Université De Haute-Alsace" }, { "name": "Orjuela, Rodolfo", "affiliation": "Université De Haute-Alsace, IRIMAS UR7499" }, { "name": "Meurer, Thomas", "affiliation": "Karlsruhe Institute of Technology (KIT)" }, { "name": "Basset, Michel", "affiliation": "Université De Haute-Alsace" } ], "keywords": [ "Guidance, navigation and control for AVs", "Multi-vehicle systems", "Trajectory tracking and path following for AVs" ], "abstract": "Multi-agent systems (MAS) offer significant advantages that can enhance performance across various domains. However, a critical challenge is to guarantee the agents' safety while navigating environments with both static and dynamic obstacles. This paper presents a distributed model predictive control (MPC) framework to coordinate multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) to rendezvous at predefined locations while simultaneously avoiding collisions with obstacles and other agents. An obstacle aggregation strategy is adopted to reduce computational complexity and enhance scalability. Simulation results demonstrate the effectiveness of the proposed approach to safely rendezvous in a dynamic environment.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA28.3", "code": "ThA28.3", "title": "Uncertainty-Aware Off-Road Global Planning Via Bayesian Dynamic Feasibility Learning", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA28", "sessionTitle": "Guidance, Navigation and Control for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Pan, Wei", "affiliation": "Tongji University" }, { "name": "Yang, Mingzhou", "affiliation": "Tongji University" }, { "name": "Zhang, Lin", "affiliation": "Tongji University" }, { "name": "Wang, Han", "affiliation": "Tongji University" }, { "name": "Zhou, Lanqi", "affiliation": "Tongji University" }, { "name": "Chen, Hong", "affiliation": "Tongji University" } ], "keywords": [ "Trajectory and path planning for AVs", "Guidance, navigation and control for AVs", "Autonomous vehicles" ], "abstract": "In complex off-road environments, vehicle dynamics are highly sensitive to surface conditions. Traditional global path planners based on nominal models fail to describe the execution deviations induced by soft terrains, making the planned paths prone to failure during execution. To address this issue, this paper proposes an execution-uncertainty-aware global planning framework based on a Bayesian Neural Network (BNN). A high-fidelity off-road vehicle dataset is constructed using the Chrono simulator, upon which a BNN is trained to learn the probabilistic feasibility and turning-radius distribution under different terrain conditions. The learned probabilistic model is then tightly coupled with Kino-RRT, where feasibility probability and curvature risks are incorporated into each node-expansion step, together with a speed backoff mechanism that adaptively adjusts the expansion strategy in regions with high uncertainty. Experimental results show that in dense-obstacle and soft-terrain scenarios, the proposed method achieves an average improvement of approximately 19% in smoothness, avoids collisions caused by execution uncertainty, and significantly enhances the consistency between planned and executed trajectories.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA28.4", "code": "ThA28.4", "title": "Real-Time Mixed-Integer Motion Planning and Trajectory Tracking Control for Autonomous Driving", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA28", "sessionTitle": "Guidance, Navigation and Control for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Poursajad, Mahdi", "affiliation": "Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau" }, { "name": "Al Khatib, Mohammad", "affiliation": "Technical University of Kaiserslautern" }, { "name": "Bajcinca, Naim", "affiliation": "University of Kaiserslautern" } ], "keywords": [ "Trajectory and path planning for AVs", "Guidance, navigation and control for AVs", "Autonomous vehicles" ], "abstract": "This paper proposes a mixed-integer model predictive control method for computing collision-free reference trajectories for autonomous driving in environments densely populated with stationary obstacles. The outputs of the high-level motion planner are passed to an operational low-level predictive controller. While the planning layer uses a point-mass vehicle model, the control layer is designed using a kinematic single-track model. In addition, polytope constraints with binary variables are introduced to enable a less conservative computation of safe active road regions and collision-free reference trajectories. The performance of the integrated control system is demonstrated in challenging driving scenarios with many obstacles, resulting in a large number of integer variables. High-fidelity closed-loop simulations show that the proposed scheme maintains real-time computational efficiency even in these demanding settings.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA28.5", "code": "ThA28.5", "title": "Variable L0 Guidance Strategy: Enlarged Operational Envelope and Path-Following", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA28", "sessionTitle": "Guidance, Navigation and Control for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Shivam, Amit", "affiliation": "SYSTEC-ISR-Porto, ARISE, Faculty of Engineering, University of Porto, Porto, Portugal" }, { "name": "Fernandes, Manuel C. R .M.", "affiliation": "Universidade Do Porto" }, { "name": "Fontes, Fernando A. C. C.", "affiliation": "Universidade Do Porto" }, { "name": "Fagiano, Lorenzo", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Trajectory tracking and path following for AVs", "Guidance, navigation and control for AVs", "Motion control for AVs" ], "abstract": "This paper presents a geometric and theoretical study of an exponentially varying look-ahead parameter for UAV path-following guidance. Conventional guidance laws with a fixed look-ahead distance often drive the vehicle into turn-rate saturation when the heading or cross-track error is large, leading to constrained maneuvers and higher control effort. The proposed variable L_0 strategy reshapes the look-ahead profile so that the guidance command adapts to the evolving tracking error geometry. A detailed investigation shows that this adaptation significantly enlarges the region in which the commanded turn rate remains unsaturated, allowing the vehicle to operate smoothly over a broader range of error conditions. For a sample simulation scenario, the unsaturated operational envelope increases by more than 70% relative to the constant L_0 formulation. These geometric insights translate to smoother trajectories, earlier recovery from saturation, and reduced control effort. Simulation studies on straight-line and elliptical paths demonstrate the merits of the variable look-ahead strategy, highlighting its control-efficient and reliable path-following performance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA28.6", "code": "ThA28.6", "title": "Lam'e Curve Path Generation for Robust Surveillance and Tracking", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA28", "sessionTitle": "Guidance, Navigation and Control for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Shivam, Amit", "affiliation": "SYSTEC-ISR-Porto, ARISE, Faculty of Engineering, University of Porto, Porto, Portugal" }, { "name": "Gallo, Alexander J.", "affiliation": "Politecnico Di Milano" }, { "name": "Incremona, Gian Paolo", "affiliation": "Politecnico Di Milano" }, { "name": "Ferrara, Antonella", "affiliation": "University of Pavia" } ], "keywords": [ "Trajectory tracking and path following for AVs", "Trajectory and path planning for AVs", "Guidance, navigation and control for AVs" ], "abstract": "This paper presents a solution to the robust surveillance guidance for uncrewed aerial vehicles (UAVs) using a continuous bounded curvature Lamé path, in the presence of disturbances. First, a novel algorithm to generate Lamé curves with guaranteed curvature bounds is proposed, relying on some heuristics to obtain close-to-minimal path length. Then, an arcsine vector field guidance method combined with sliding mode control is introduced to make the UAV reach and track the desired path in finite time. Simulation results and comparative studies with respect to an existing method in the literature show that the proposed approach provides both computational and performance enhancements.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA29.1", "code": "ThA29.1", "title": "Data-Driven Shamanskii Iteration for Linear Quadratic Gaussian Games (I)", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA29", "sessionTitle": "Recent Progress in Mean-Field Game Theory and Applications", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Liu, Yiheng", "affiliation": "Northeastern University" }, { "name": "Gao, Weinan", "affiliation": "Northeastern University" } ], "keywords": [ "Large-scale and networked optimization problems", "Adaptive control design", "Data-driven robust control" ], "abstract": "This paper studies continuous-time linear quadratic Gaussian games in a data-driven sense, where each agent is coupled with other agents through its cost function, and the system models of these agents are unknown. To solve the linear quadratic Gaussian game problem, it is usually necessary to solve algebraic Riccati equations. However, traditional successive approximation algorithms for solving algebraic Riccati equations face the challenge of slow convergence rate. Therefore, this paper proposes a learning algorithm based on Shamanskii iteration, aiming to accelerate the convergence rate of solving algebraic Riccati equations by using the first-order Fr′echet derivative. Compared with policy iteration approaches, the Shamanskii iteration method achieves a faster convergence rate. In addition, a data-driven Shamanskii iteration method is introduced, which eliminates the need for system dynamics, thus providing a more efficient solution in practice. Simulation results demonstrate that the Shamanskii iteration method improves the convergence rate compared with traditional policy iteration methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA29.2", "code": "ThA29.2", "title": "alpha-Potential Games for Decentralized Control of Heterogeneous Connected and Automated Vehicles (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA29", "sessionTitle": "Recent Progress in Mean-Field Game Theory and Applications", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Di, Xuan", "affiliation": "Columbia University" }, { "name": "Hu, Anran", "affiliation": "Columbia University" }, { "name": "Wang, Zhexin", "affiliation": "Columbia University" }, { "name": "Zhang, Yufei", "affiliation": "Imperial College London" } ], "keywords": [ "Differential or dynamic games", "Stochastic optimal control problems", "Distributed robust controller synthesis" ], "abstract": "Designing scalable and safe control strategies for large populations of connected and automated vehicles (CAVs) requires accounting for strategic interactions among heterogeneous agents under decentralized information. While dynamic games provide a natural modeling framework, computing Nash equilibria (NEs) in large-scale settings remains challenging, and existing mean-field game approximations rely on restrictive assumptions that fail to capture collision avoidance and heterogeneous behaviors. This paper proposes an alpha-potential game framework for decentralized CAV control. We show that computing alpha-NE reduces to solving a decentralized control problem, and derive tight bounds of the parameter alpha based on interaction intensity and asymmetry. We further develop scalable policy gradient algorithms for computing alpha-NEs using decentralized neural-network policies. Numerical experiments demonstrate that the proposed framework accommodates diverse traffic flow models and effectively captures collision avoidance, obstacle avoidance, and agent heterogeneity.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA29.3", "code": "ThA29.3", "title": "Model-Free Strategy Design Approach to Risk-Sensitive Mean-Field Games (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA29", "sessionTitle": "Recent Progress in Mean-Field Game Theory and Applications", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Zhang, Yuexi", "affiliation": "Dalian University of Technology" }, { "name": "Guo, Wanying", "affiliation": "Dalian University of Technology" }, { "name": "Xu, Zhenhui", "affiliation": "Institute of Science Tokyo" }, { "name": "Shen, Tielong", "affiliation": "Dalian University of Technology" }, { "name": "Moon, Jun", "affiliation": "Hanyang University" } ], "keywords": [ "Design methods for data-based control", "Learning methods for optimal control", "Differential or dynamic games" ], "abstract": "This paper investigates linear quadratic (LQ) risk-sensitive mean-field games (MFG), aiming to propose a model-free distributed strategy design method. Based on the Nash certainty equivalence (NCE) principle, a model-free hierarchical learning framework is proposed for a single agent. The key component of proposed approach involves two gain matrixs, adjoint ordinary differential equation (ODE), and mean-field (MF) state and a three-layer decoupled structure: the inner layer employs integral reinforcement learning (IRL) to embed the algebraic Riccati equation (ARE) and the adjoint equation, achieving model-free iteration; the middle layer updates the adjoint state weights using basis function approximation and Bellman's principle; the outer layer iteratively approximates the MF state based on the fixed-point principle, thereby forming a fully data-driven distributed optimal policy. A numerical example is illustrated to demonstrate the proposed result.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA29.4", "code": "ThA29.4", "title": "LQG Mean Field Games with Covariance-Matrix-Dependent Cost Coefficients (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA29", "sessionTitle": "Recent Progress in Mean-Field Game Theory and Applications", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Gao, Shuang", "affiliation": "Polytechnique Montreal" }, { "name": "Malhame, Roland P.", "affiliation": "Ecole Poly. De Montreal" } ], "keywords": [ "Stochastic optimal control problems", "Differential or dynamic games", "Decentralized control" ], "abstract": "This paper studies linear quadratic Gaussian (LQG) Mean Field Games (MFGs) where coefficients of quadratic cost functions depend on the covariance matrix of the population’s state distribution. Such formulations allow for modelling agents whose costs are not only sensitive to the instantaneous population state average but also the population state dispersion, which serves as a measure of instantaneous risk. The calculation of the possible MFG equilibria involves solving (i) two nonlinearly coupled differential equations (one Riccati equation and the other a differential Lyapunov equation for the covariance matrix evolution) and (ii) an additional Riccati equation for the local feedback gain. Sufficient conditions for the existence and the uniqueness of an MFG equilibrium solution are established.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA29.5", "code": "ThA29.5", "title": "Scalable Method for Mean Field Control with Kernel Interactions Via Random Fourier Features (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA29", "sessionTitle": "Recent Progress in Mean-Field Game Theory and Applications", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Cao, Zhongyuan", "affiliation": "NYU Shanghai" }, { "name": "Das, Kaustav", "affiliation": "Monash University" }, { "name": "Langrené, Nicolas", "affiliation": "Beijing Normal-Hong Kong Baptist University" }, { "name": "Lauriere, Mathieu", "affiliation": "New York University Shanghai" } ], "keywords": [ "Stochastic optimal control problems", "Infinite-dimensional multi-agent systems and networks", "Numerical methods for optimal control" ], "abstract": "We develop a scalable algorithm for mean field control problems with kernel interactions by combining particle system simulations with random Fourier feature approximations. The method replaces the quadratic-cost kernel evaluations by linear-time estimates, enabling efficient stochastic gradient descent for training feedback controls in large populations. We provide theoretical complexity bounds and demonstrate through crowd motion and flocking examples that the approach preserves control performance while substantially reducing computational cost. The results indicate that random feature approximations offer an effective and practical tool for high dimensional and large scale mean field control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA29.6", "code": "ThA29.6", "title": "Laplexion Mean Field Games on Compact Riemannian Manifolds", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA29", "sessionTitle": "Recent Progress in Mean-Field Game Theory and Applications", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 122", "authors": [ { "name": "Zhang, Tao", "affiliation": "McGill University" }, { "name": "Caines, Peter E.", "affiliation": "McGill Univ" }, { "name": "Huang, Minyi", "affiliation": "Carleton University" } ], "keywords": [ "Multi-agent systems", "Control over networks", "Stochastic differential equations" ], "abstract": "This paper develops a mean field game (MFG) framework on compact Riemannian manifolds based on a vertexon limit for embedded network sequences. The empirical vertex measures and their weak limits are formalized. Under local geodesic displacement moment conditions, the weighted graph Laplacian on sparse embeddings converges to the Laplace--Beltrami operator under isotropic network regime. On this basis, the HJB--FPK system of such a Laplexion MFG (LMFG) is derived, and existence and uniqueness of classical solutions on finite horizons using Hölder estimates on compact manifolds is established. A spectral method using the Laplace--Beltrami eigenbasis decouples the PDEs into countably many ODE modes, enabling tractable computation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA30.1", "code": "ThA30.1", "title": "A Cepstrum-Based, Vehicle and Speed-Independent Road Roughness Index from Inertial Sensor Data (I)", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA30", "sessionTitle": "JO: Parameter Identification and Monitoring", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Gelmini, Simone", "affiliation": "Politecnico Di Milano" }, { "name": "Leoni, Jessica", "affiliation": "Politecnico Di Milano" }, { "name": "Centurioni, Marco", "affiliation": "Politecnico Di Milano" }, { "name": "Pivaro, Nicola", "affiliation": "Politecnico Di Milano" }, { "name": "Strada, Silvia", "affiliation": "Politecnico Di Milano" }, { "name": "Tanelli, Mara", "affiliation": "Politecnico Di Milano" }, { "name": "Savaresi, Sergio", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Information processing and decision support in transportation", "Modeling, supervision, control and diagnosis of automotive systems", "Artificial intelligence in transportation" ], "abstract": "Objective road quality estimation is essential for smart infrastructure. Using everyday vehicles with low-cost inertial sensors provides a cost-effective and scalable solution. However, vertical acceleration signals are influenced by vehicle dynamics and speed, producing biased estimates. This issue has been addressed by proposing a novel approach that, using cepstral analysis, separates road-induced and vehicle behaviors. Specifically, thanks to a one-time, model-free calibration, it identifies the latter, providing an unbiased estimate of the first. Last, cepstral features also provide robustness to speed. Validation with naturalistic data across vehicles, drivers, and roads demonstrates accuracy and robustness, highlighting its potential for large-scale road monitoring.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA30.2", "code": "ThA30.2", "title": "Variational Bayesian Fusion Filtering Based on Credibility Framework (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA30", "sessionTitle": "JO: Parameter Identification and Monitoring", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Bai, Jingguang", "affiliation": "Shanghai Maritime University; Qiandao Lake Institute of Science" }, { "name": "Ge, Quanbo", "affiliation": "Nanjing University of Information Science & Technology" }, { "name": "Huang, Yanjun", "affiliation": "University of Waterloo" } ], "keywords": [ "Kalman filtering", "Probabilistic and Bayesian methods for system identification", "Estimation and filtering" ], "abstract": "Variational Bayesian (VB) filtering has seen numerous methodological improvements in recent years, particularly in addressing nonlinearity and non-Gaussian uncertainty. However, while these advancements have enhanced precision and computational efficiency, a critical gap remains in the systematic analysis of credibility theory for VB-based filtering processes. To address this issue, this paper introduces credibility theory into the VB filtering framework, establishing a mathematical relationship between parameter uncertainty and filtering result reliability. We further bridge VB credibility analysis with classical Kalman filtering's noise error estimation mechanisms. By comparatively integrating these two perspectives, we develop a multi-filter adaptive estimation scheme that dynamically adjusts noise parameter confidence bounds. This hybrid strategy not only improves robustness against parameter inaccuracies but also achieves superior filtering accuracy in nonlinear scenarios compared to standalone VB or Kalman methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA30.3", "code": "ThA30.3", "title": "Alarm Flood Classification Using a Hybrid Model and Time-Encoded Histograms (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA30", "sessionTitle": "JO: Parameter Identification and Monitoring", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Najafi, Amirhossein", "affiliation": "University of Alberta" }, { "name": "Roohi, Mohammad", "affiliation": "University of Alberta" }, { "name": "Chen, Tongwen", "affiliation": "University of Alberta" } ], "keywords": [ "Monitoring, performance assessment, and fault detection in chemical process control", "AI methods for FDI/FTC", "Reliability and safety in processes" ], "abstract": "Alarm floods in industrial plants can lead to severe disruptions, making it challenging for operators to efficiently identify and address underlying issues. Key challenges include varying flood lengths, redundancy among correlated alarms, the need to preserve temporal relationships, and the reliance of many existing methods on preprocessing steps such as chattering removal, which require manual tuning and configuration. Analyzing the patterns within alarm floods provides valuable insights for root cause analysis, enabling operators to implement safety measures and improve plant reliability. This paper presents a novel approach for classifying alarm floods using a neural network-based framework designed to address these challenges. To handle the variability in alarm flood lengths, a histogram-based encoding method is proposed. This method embeds the time hierarchy by introducing an exponential multiplier to the histogram data, ensuring that temporal relationships among alarms are preserved without requiring complex alignment techniques. The proposed method employs a hybrid neural architecture in which one network encodes alarm data into a compact latent space by reducing redundancies, while a second network performs classification within this space for accurate prediction. The performance of the proposed approach is evaluated against benchmark methods, showing notable improvements in both classification accuracy and computational efficiency. By addressing the challenges of varying alarm lengths and embedding temporal hierarchies, this work provides a robust solution for alarm flood classification.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA30.4", "code": "ThA30.4", "title": "A Recursive Parameter Identification Algorithm for Nonlinear Errors-In-Variables Models (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA30", "sessionTitle": "JO: Parameter Identification and Monitoring", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Koide, Hugo", "affiliation": "University of Poitiers" }, { "name": "Vayssettes, Jérémy", "affiliation": "ISAE" }, { "name": "Mercère, Guillaume", "affiliation": "Poitiers University" } ], "keywords": [ "Time/parameter varying system identification", "Estimation and filtering", "Nonlinear system identification" ], "abstract": "Recursive identification of model parameters from data is an essential tool in adaptive signal processing and adaptive control applications. For general nonlinear regression problems where errors are present in both dependent and independent variables, the errors-in-variables (EIV) model is frequently employed to avoid a biased estimation of the system parameters. This work presents a recursive identification algorithm for nonlinear EIV models which is derived from an offline sequential quadratic programming solution. The performance of the proposed algorithm is illustrated on benchmark simulation examples. The results demonstrate the numerical efficiency of the recursive algorithm, as well as its ability to track time-varying system parameters.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA30.5", "code": "ThA30.5", "title": "LPV Kalman Filter Design for Quasi-LPV Systems with Unmeasurable Gain Scheduling Functions: Application to Leak Diagnosis (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA30", "sessionTitle": "JO: Parameter Identification and Monitoring", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Hernández Gómez, Octavio Adrián", "affiliation": "Centro De Investigación Y De Estudios Avanzados Del Instituto Politécnico Nacional (Cinvestav)" }, { "name": "Ruiz-Leon, Javier", "affiliation": "CINVESTAV Guadalajara" }, { "name": "Delgado Aguiñaga, Jorge Alejandro", "affiliation": "Instituto Tecnológico Y De Estudios Superiores De Occidente" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "De los Santos Ruiz, Ildeberto", "affiliation": "Tecnologico Nacional De Mexico / I. T. Tuxtla Gutierrez" }, { "name": "Navarro Díaz, Adrián", "affiliation": "Instituto Tecnológico Y De Estudios Superiores De Monterrey" } ], "keywords": [ "Time/parameter varying system identification", "Fault detection and diagnosis", "Kalman filtering" ], "abstract": "This paper addresses the problem of observer design with unmeasurable gain scheduling functions for Quasi-Linear Parameter Varying (Quasi-LPV) models through a LPV Kalman Filter. The proposed methodology rewrites the system as dependent on the estimated scheduling vector. This requires finding a set of offline optimal filter gains that are robust against uncertainties such as perturbations and noises, while ensuring stability and convergence at each vertex by means of a polytopic formulation. An experimental application for non-concurrent water leak diagnosis using a database is performed to validate the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA30.6", "code": "ThA30.6", "title": "Observer Design for Lactic Acid Bacteria Population Balances with Non–uniformly Delayed Measurements (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA30", "sessionTitle": "JO: Parameter Identification and Monitoring", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Lepsien, Arthur", "affiliation": "University of Hohenheim" }, { "name": "Schaum, Alexander", "affiliation": "University of Hohenheim" }, { "name": "Holtorf, Lucas", "affiliation": "Kiel University" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Batch and semi-batch process control", "Advanced process control" ], "abstract": "The paper addresses the problem of providing estimates of the cell mass distribution density, glucose and lactate concentration as well as total biomass concentration in a lactic acid mfermentation process based on quasi continuous measurements of optical density, pH and conductivity, as well as sampled measurements of the cell size distribution. Based on a continuous-time cell mass structured population balance equation the problem is solved using a cascade of two Extended Kalman Filters (EKFs), one on the quasi continuous, i.e., periodic high frequency sampling time scale and one on the slowly sampled one, with explicit compensation of the known but non-uniformly distributed measurement delay, receiving additionally the state estimates from the first EKF. The performance of the proposed approach is demonstrated using a real–time implementation within batch experiments with Stroptococcus thermophilus.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA31.1", "code": "ThA31.1", "title": "Physics-Informed Neural Network Surrogate Modeling of Single Particle Model for Lithium-Ion Batteries", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA31", "sessionTitle": "Modeling, Control, Design and Optimisation for Battery Electric Vehicles", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Zhuang, Yi", "affiliation": "Aalborg University" }, { "name": "Zheng, Yusheng", "affiliation": "Aalborg University" }, { "name": "Che, Yunhong", "affiliation": "MIT" }, { "name": "Teodorescu, Remus", "affiliation": "Aalborg University" } ], "keywords": [ "Modeling, supervision, control and diagnosis of automotive systems", "Electric and solar vehicles", "AI and learning-based control for automotive systems" ], "abstract": "Physics-based models play a key role in battery management, yet face challenges in real-time applications due to the high computational cost of solving coupled algebraic-partial differential equations. To accelerate model simulation, this study benchmarks three physics-informed neural network (PINN) architectures for surrogate modeling of the single particle model of lithium-ion batteries, including two conventional PINN architectures and a DeepONet-based architecture. Both the accuracy and generalization of these PINNs are evaluated and compared under various current conditions. The results highlight the potential of PINNs in modeling battery physics but also reveal limitations of conventional PINN architectures under dynamic current conditions. Among them, the Fourier-enhanced PI-DeepONet achieves superior generalization and offers nearly a 10× speedup compared with numerical solvers. This work provides a practical foundation for developing generalizable physics-informed surrogate models for battery-management applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA31.2", "code": "ThA31.2", "title": "Data-Selective Online Battery Identification Using Extended Time Regular Expressions", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA31", "sessionTitle": "Modeling, Control, Design and Optimisation for Battery Electric Vehicles", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Weinreich, Nicolai André", "affiliation": "Aalborg University" }, { "name": "Muñiz, Marco", "affiliation": "Aalborg University" }, { "name": "Mikučionis, Marius", "affiliation": "Aalborg University" }, { "name": "Guldstrand Larsen, Kim", "affiliation": "Aalborg University, Denmark" }, { "name": "Teodorescu, Remus", "affiliation": "Aalborg University" } ], "keywords": [ "Automotive system identification and modelling", "Vehicle dynamic systems", "Electric and solar vehicles" ], "abstract": "In this paper, we propose a data-efficient online battery identification method which targets highly informative battery cell data segments based on the driving pattern of the vehicle. We consider the case of a vehicle driving on/off a motorway and construct an Extended Time Regular Expression (ETRE) to detect data segments fitting these driving patterns. Simulation results indicate that by only using up to 10.71% of the data on average, the proposed method provides a low-bias and low-variance estimator under non-negligible current and voltage noise compared to other conventional estimation algorithms.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA31.3", "code": "ThA31.3", "title": "PSO-Based Energy Management with Battery Thermal Observer for Hybrid Energy Storage in Electric Vehicles", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA31", "sessionTitle": "Modeling, Control, Design and Optimisation for Battery Electric Vehicles", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Asnai, Fatimazahra", "affiliation": "ERERA, National High School of Arts and Crafts, Mohammed V University of Rabat, Rabat, Morocco" }, { "name": "Ouadi, Hamid", "affiliation": "Mohammed V University" }, { "name": "Yazidi, Amine", "affiliation": "Laboratory of Innovative Technologies (LTI, UR 3899), University of Picardie Jules Verne, 80000 Amiens, France" }, { "name": "El Bakali, Saida", "affiliation": "ERERA, ENSAM, Mohammed V University, Rabat, Morocco" }, { "name": "Gheouany, Saad", "affiliation": "ERERA, National School of Arts and Crafts, Mohammed V University, Rabat, Morocco" } ], "keywords": [ "Vehicle dynamic systems", "Automotive system identification and modelling" ], "abstract": "The energy management in electric vehicles (EVs) is essential for improving their driving range, performance, and the lifespan of energy storage components. This study proposes an intelligent onboard energy management strategy that optimizes power sharing between a lithium-ion battery and a supercapacitor, while accounting for the technological limitation related to the battery’s internal temperature. To overcome the difficulty of directly measuring this temperature, a state observer was developed to estimate it from the measured surface temperature of the battery.The proposed management strategy is based on a multi-objective optimization framework integrating: i) meeting the average driving power demand through the battery, ii) minimizing battery power fluctuations, and iii) maintaining the supercapacitor’s state of charge at its nominal value. A constraint on the battery’s internal temperature is included to ensure safe and efficient operation. A comparative analysis shows that relying solely on external temperature is not sufficient to guarantee battery safety and performance. A constraint that is too relaxed on surface temperature may allow the internal temperature to reach destructive levels, with an observed thermal overshoot of up to 20◦C. Conversely, an overly strict constraint leads to excessive use of the supercapacitor, resulting in an exploitation rate reaching nearly 70%, which significantly degrades the overall energy-management performance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA31.4", "code": "ThA31.4", "title": "Predictive Thermal Derating of an Automotive PMSM", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA31", "sessionTitle": "Modeling, Control, Design and Optimisation for Battery Electric Vehicles", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Fischer, Andreas", "affiliation": "TU Wien" }, { "name": "Baumann, Martin", "affiliation": "Automation and Control Institute (ACIN), TU Wien (TUW)" }, { "name": "Kemmetmueller, Wolfgang", "affiliation": "TU Wien" }, { "name": "Steinboeck, Andreas", "affiliation": "TU Wien" } ], "keywords": [ "Nonlinear and optimal automotive control", "Engine and powertrain modeling and control", "Electric and solar vehicles" ], "abstract": "Permanent magnet synchronous motors (PMSMs) in electric vehicles must balance high power demands with thermal constraints to prevent overheating and ensure reliability. Conventional derating methods can be conservative, limiting motor performance too much because of overestimated thermal loads. This paper presents a derating approach based on model predictive control (MPC) that incorporates motor thermal dynamics and insulation aging considerations. The MPC integrates a prediction method for the future torque demand and motor speed to increase the available torque by including additional model-based knowledge. Simulation studies demonstrate improved power utilization by the presented approach compared to conventional derating methods while satisfying thermal constraints.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA31.5", "code": "ThA31.5", "title": "Cost-Optimal Hybrid Battery Pack Sizing in Electric Trucks", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA31", "sessionTitle": "Modeling, Control, Design and Optimisation for Battery Electric Vehicles", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Yu, Yan Feng", "affiliation": "TRATON AB" }, { "name": "Bessman, Alexander", "affiliation": "Traton AB" }, { "name": "Höckerdal, Erik", "affiliation": "TRATON AB" }, { "name": "Frisk, Erik", "affiliation": "Linköping University" }, { "name": "Krysander, Mattias", "affiliation": "Linköping University" } ], "keywords": [ "Hybrid, electric and alternative drive vehicles" ], "abstract": "Reconfigurable battery systems, which divide the pack into controllable units, enable hybrid battery packs (HBPs) that combine multiple cell technologies to meet the diverse performance demands of electric trucks. This study presents a linear programming (LP) framework for cost-optimal HBP sizing, enabling rapid and interpretable trade-off analysis prior to detailed vehicle simulation. From LP geometry, analytical conditions are derived that reveal when high-energy and high-power hybridization is beneficial. Parametric analysis and case studies show how variations in key design parameters shift the optimal configuration. The framework is further extended with a mixed-integer LP formulation that includes hybridization penalties and quantifies when HBPs remain cost-competitive compared to single-technology packs. Together, these results offer practical design guidelines for early-stage battery sizing in electric trucks.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA31.6", "code": "ThA31.6", "title": "Optimal Sizing of a SOFC Based Truck Powertrain", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA31", "sessionTitle": "Modeling, Control, Design and Optimisation for Battery Electric Vehicles", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Poujol, Marin", "affiliation": "University of Orleans" }, { "name": "Cottin, Willy", "affiliation": "Univ. Orléans" }, { "name": "Colin, Guillaume", "affiliation": "Univ. Orléans" }, { "name": "Charlet, Alain", "affiliation": "Univ. Orléans" } ], "keywords": [ "Hybrid, electric and alternative drive vehicles", "Modeling and simulation of transportation systems", "Automatic control, optimization, real-time operations in transportation" ], "abstract": "This article develops a two-stage optimization process using Bayesian Experimental Design (BED) and optimal control methodology for powertrain sizing. This approach is applied to a electrified truck powertrain composed of a Solid Oxide Fuel Cell (SOFC) and a Waste Heat Recovery System (WHRS) used as a Range Extender (RE) for a Battery Electric Vehicle (BEV) architecture. BED is used to find the best solution in terms of sizing while Dynamic Programming ensures fuel consumption minimisation. Moreover, the powertrain must supply the Heating, Ventilation, and Air Conditioning (HVAC) and auxiliary loads while achieving a three days mission. The optimization process shows that an inappropriate powertrain sizing can double fuel consumption compared to the best one. The optimal sizing tends towards a small battery and a large RE design with an overall powertrain efficiency around 39 %.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA32.1", "code": "ThA32.1", "title": "AST-PI-FxTSMC for Robust AUV Trajectory Tracking in Disturbed Environments", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA32", "sessionTitle": "Aerial, Field, and Marine Robotics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Close, Jack", "affiliation": "Queen's University Belfast" }, { "name": "Van, Mien", "affiliation": "Queen's University Belfast" }, { "name": "Wei, Chongfeng", "affiliation": "University of Leeds" } ], "keywords": [ "Aerial, field, and marine robotics", "Autonomous navigation", "Mechatronic system estimation, identification, control" ], "abstract": "As Autonomous Underwater Vehicles (AUVs) are working in unstructured underwater environments, their performance is heavily affected by model uncertainties and environmental disturbances. This paper proposes a novel adaptive super-twisting (AST) PI-fixed-time sliding mode control (PI-FxTSMC), which integrates a PI law, FxTSMC controller and AST to improve the robustness of the system against model uncertainty and disturbance. The global stability and convergence of the system is proven using Lyapunov methods. Simulation results for a 6DOF AUV demonstrate fast, predictable convergence, tight tracking, and markedly reduced chattering under random, varying disturbance, with bounded control effort. The design requires only measured pose and velocity, supporting embedded AUV guidance, navigation and control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA32.2", "code": "ThA32.2", "title": "Traveling Wave Optimization with Adaptive Stability for Robotic Fish", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA32", "sessionTitle": "Aerial, Field, and Marine Robotics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Wu, Shuangpeng", "affiliation": "Zhejiang University" }, { "name": "Lu, Shuda", "affiliation": "Zhejiang University" }, { "name": "Jiang, Daiyang", "affiliation": "ZheJiangUniversity" }, { "name": "Tang, Yuyan", "affiliation": "Zhejiang University" }, { "name": "Zhu, Yizheng", "affiliation": "Zhejiang University" }, { "name": "Sun, Yu", "affiliation": "Zhejiang University" }, { "name": "Liu, Xinrui", "affiliation": "Zhejiang University" }, { "name": "Zhang, Jingran", "affiliation": "Zhejiang University" }, { "name": "Xiong, Rong", "affiliation": "Zhejiang University" }, { "name": "Zheng, Xingwen", "affiliation": "Zhejiang University" } ], "keywords": [ "Aerial, field, and marine robotics", "High-performance motion control systems", "Mechatronic system modeling, design, optimization" ], "abstract": "The paper presents an optimization-based control method for robotic fish driven by body/caudal fin (BCF) propulsion. While BCF-mode robots outperform conventional autonomous underwater vehicles (AUVs) in efficiency and stealth, kinematic-based controllers or CPG-based controllers demonstrate inherent limitations in energy utilization and directional stability. Our method enhances swimming stability by dynamically estimating the simplified dynamic coefficients and unmodeled disturbances through model reference adaptive control (MRAC) and minimizing body oscillations via optimal control. Experimental results demonstrate improved tracking accuracy and forward velocity compared to traditional methods. Additionally, the proposed control method demonstrates strong generalization capability and can be readily adapted to different BCF-mode robotic platforms.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA32.3", "code": "ThA32.3", "title": "Flip-Assisted Collision Recovery Framework under Drastic Torque Disturbances for Quadrotors", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA32", "sessionTitle": "Aerial, Field, and Marine Robotics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Wu, Delong", "affiliation": "Beijing Institute of Technology" }, { "name": "Yang, Qingkai", "affiliation": "Beijing Institute of Technology" }, { "name": "Shi, Yangxi", "affiliation": "Beijing Institute of Technology" }, { "name": "Liang, Xinkai", "affiliation": "Beijing Institute of Technology" }, { "name": "Yang, Yuzhe", "affiliation": "Beijing Institute of Technology" }, { "name": "Zhao, Xinyue", "affiliation": "Beijing Insititute of Technology" }, { "name": "Fang, Hao", "affiliation": "Beijing Institute of Technology" } ], "keywords": [ "Aerial, field, and marine robotics", "High-performance motion control systems", "Task and motion planning" ], "abstract": "Quadrotors often crash when they encounter drastic contacts. External-force-centric collision recovery or disturbance rejection methods are generally ineffective in handling such contacts, as the uncontrolled tumbles caused by external torques are the main cause of crashes. Motivated by this insight, this paper presents a novel collision recovery framework. A recovery strategy criterion based on the thrust direction is designed to autonomously select between braking rotational motions and executing a compliant flip maneuver. Subsequently, a spatial-temporal joint planner generates an optimal flip trajectory to revise the original references. Furthermore, a full-attitude model predictive controller based on error states is proposed to execute aggressive trajectories and ultimately stabilize the system. Real-world experiments demonstrate that our collision recovery framework proactively manages attitude tumbles, exhibiting significant robustness under drastic disturbances.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA32.4", "code": "ThA32.4", "title": "Robust Control Barrier Function Design for Nonlinear Control-Affine Systems with Uncertainties: Application to Obstacle Avoidance Control of a Vehicle Defined on SE(3)", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA32", "sessionTitle": "Aerial, Field, and Marine Robotics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Niu, Hongjiao", "affiliation": "Tsinghua University," } ], "keywords": [ "Aerial, field, and marine robotics", "High-performance motion control systems", "Task and motion planning" ], "abstract": "For a nonlinear control-affine system with bounded uncertainties, this study investigates a approach for the safety-critical control using Control Barrier Functions (CBFs). The CBF method generally relies on an accurate system dynamic model. The system's uncertainties, including unmodeled dynamic uncertainties, controller structured parameter uncertainties, and unknown external disturbances, easily lead to the failure of safety guarantees. To this end, by leveraging the boundness of uncertainties, the constraint condition corresponding to the CBF is strengthened into a convex constraint. Combining with the quadratic programming (QP) method, a convex optimization is formulated, from which an analytical solution is derived. Thus a approach for coordinating the task control and safe control of a nonlinear control-affine system with uncertainties is proposed. Then, this approach is applied to the simultaneous stabilization and obstacle avoidance control for a vehicle modeled on the Lie group SE(3), a representation widely adopted for mechanical systems in practical engineering. With the introduction of an obstacle avoidance corrective control input, the vehicle achieves safe collision avoidance in the presence of obstacles while ensuring the stabilization control task is accomplished. Finally, numerical simulation experiments have effectively verified the effectiveness of this approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA32.5", "code": "ThA32.5", "title": "Feasible Force Set Shaping for a Payload-Carrying Platform Consisting of Tiltable Multiple UAVs Connected Via Passive Hinge Joints", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA32", "sessionTitle": "Aerial, Field, and Marine Robotics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Ito, Takumi", "affiliation": "National Institute of Advanced Industrial Science and Technology" }, { "name": "Kawashima, Hayato", "affiliation": "Institute of Science Tokyo" }, { "name": "Funada, Riku", "affiliation": "Kyoto University" }, { "name": "Sampei, Mitsuji", "affiliation": "The Polytechnic University of Japan" } ], "keywords": [ "Aerial, field, and marine robotics", "Mechatronic system modeling, design, optimization", "Robotic grasping and manipulation" ], "abstract": "This paper presents a method for shaping the feasible force set of a payload-carrying platform using multiple Unmanned Aerial Vehicles (UAVs) and proposes a control law exploiting the resulting redundancy. The UAVs are connected to the payload through passive hinge joints, whose angles are controlled by differential rotor thrust. The shape of the force set is deformable by adjusting the tilt angles of the UAVs. The proposed method ensures that the feasible force set encompasses the required shape, enabling the platform to generate force redundantly.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA32.6", "code": "ThA32.6", "title": "S2CamP: Smart View-Frustum Sampling for Camera-Parameter Preconditioning", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA32", "sessionTitle": "Aerial, Field, and Marine Robotics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Zhou, Jiachen", "affiliation": "Karlsruhe Institute of Technology" }, { "name": "Zhang, Yifei", "affiliation": "Karlsruhe Institute of Technology, Intelligent Sensor-Actuator-Systems Laboratory" }, { "name": "Kruggel-Emden, Harald", "affiliation": "Technical University of Berlin" }, { "name": "Hanebeck, Uwe", "affiliation": "Karlsruhe Institute of Technology (KIT)" } ], "keywords": [ "Robot perception and sensing", "Human machine cooperation & integration", "Aerial, field, and marine robotics" ], "abstract": "This paper is concerned with the joint optimization of camera parameters and Neural Radiance Field (NeRF). We analyze the Jacobian of the camera projection function using proxy 3D points sampled deterministically within a normalized camera view frustum. A Frolov–Fibonacci lattice combined with spherical inverse transforms yields low-dispersion samples and a sensitivity Gram matrix. From this matrix, we derive a linear reparameterization of the camera parameters that serves as a preconditioning transform, approximately whitening their sensitivities and reducing cross-parameter couplings. On a real-world benchmark, the proposed method improves camera-parameter accuracy and rendering quality while using only 10% of the samples required by a random-sampling baseline.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA33.1", "code": "ThA33.1", "title": "Optimal W-Infinity Control of Prandtl-Ishlinskii Hysteresis Model Via Weak Derivatives (I)", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA33", "sessionTitle": "JO-MECH: Mechatronic Systems and Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Cardoso, Daniel Neri", "affiliation": "Federal University of Minas Gerais" }, { "name": "Abreu, Petrus Emmanuel Oliveira Gomes Brant", "affiliation": "Federal University of Minas Gerais" }, { "name": "Raffo, Guilherme Vianna", "affiliation": "Federal University of Minas Gerais" } ], "keywords": [ "High-performance motion control systems", "Mechatronic system estimation, identification, control", "Micro and nano mechatronic systems" ], "abstract": "This work proposes a novel robust optimal W-infinity controller for dynamic systems exhibiting hysteresis modeled by the Prandtl-Ishlinskii operator. The approach utilizes the framework of weighted Sobolev spaces W_m,p,Gamma, which enables a rigorous synthesis using weak derivatives to handle the inherent non-differentiable and input non-affine nature of hysteresis. Through this formulation, the Prandtl-Ishlinskii operator is recast as a bounded uncertainty multiplying the input rate, allowing for the design of the robust optimal controller via linear matrix inequalities. The resulting method guarantees W_3,2,Gamma-stability with a W_infinity-gain bound. A numerical study on a piezoelectric actuator model validates the effectiveness of the proposed approach, demonstrating asymptotic tracking while simultaneously attenuating both hysteresis effects and external disturbance with straightforward implementation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA33.2", "code": "ThA33.2", "title": "Understanding Driving Risks for Older Drivers with Large Language Models (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA33", "sessionTitle": "JO-MECH: Mechatronic Systems and Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Yoshihara, Yuki", "affiliation": "Nagoya University" }, { "name": "Jiang, Linjing", "affiliation": "Nagoya University" }, { "name": "Karatas, Nihan", "affiliation": "Nagoya University" }, { "name": "Kanamori, Hitoshi", "affiliation": "NAGOYA University" }, { "name": "Harada, Asuka", "affiliation": "Institutes of Innovation for Future Society, Nagoya University" }, { "name": "Tanaka, Takahiro", "affiliation": "Nagoya University" } ], "keywords": [ "Human mechatronics and human-machine interaction", "Human AI integration", "Human machine safety" ], "abstract": "Understanding driving risks for older drivers is a critical challenge for traffic safety. Recent advances in multimodal large language models (LLMs) raise the possibility that such models may support scene-level interpretation beyond object detection. However, little is known what extent current LLMs can emulate the integrated, human-like judgments required for older driver diagnostics. Here we show that multi-shot prompting enables a LLM to label static dashcam images with accuracy above chance level across traffic density, intersection visibility, and stop-sign presence. We found lower recall for busy traffic scenes and stop-sign presence, indicating a conservative response tendency consistent with prior LLM-based medical studies. Our results demonstrate a LLM can approximate human judgments of traffic scenes when guided by well-designed prompts. More broadly, these results highlight the potential of multimodal LLMs to reduce the burden of large-scale data screening while keeping experts in the loop for final judgments. Extending such models to video inputs and newer architectures may enable automatic identification of driving scenes that warrant closer assessment of older drivers.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA33.3", "code": "ThA33.3", "title": "Steel Wire Rope Fault Detection Via 1D Convolutional Neural Networks (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA33", "sessionTitle": "JO-MECH: Mechatronic Systems and Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Sonzogni, Giulia", "affiliation": "Università Degli Studi Di Bergamo" }, { "name": "De Carli, Stefano", "affiliation": "University of Bergamo" }, { "name": "Previtali, Davide", "affiliation": "University of Bergamo" }, { "name": "Mazzoleni, Mirko", "affiliation": "University of Bergamo" }, { "name": "Previdi, Fabio", "affiliation": "Universita' Degli Studi Di Bergamo" } ], "keywords": [ "Mechatronic system fault detection, diagnostics, hardware-in-the-loop simulation" ], "abstract": "Traditional fault detection methods for Steel Wire Ropes (SWRs) require specific hardware and dismounting the ropes from the motion system in which they are installed, resulting in machinery downtime and increased operational costs. In this paper, we focus on SWRs used in servomechanisms and propose a novel 1D convolutional neural network to classify the health condition of steel wire ropes. Our proposal leverages only servomechanism sensor signals (e.g., currents, velocities, and accelerations), preventing interruptions to the motion system. We experimentally validate our approach on a real hoisting system, demonstrating superior accuracy over standard machine learning methods. Additionally, we use the gradient-weighted class activation mapping explainability tool to identify the key frequency components driving fault detection, thereby enabling the selection of an effective sampling frequency. The proposed method offers a practical, non-destructive, and efficient fault diagnosis tool for industry, significantly reducing maintenance downtime and improving machinery availability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA33.4", "code": "ThA33.4", "title": "Design of a 3D-Printed Continuum Robot with Convergent Compliant Joints for Balanced Stress Distribution (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA33", "sessionTitle": "JO-MECH: Mechatronic Systems and Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Wen, Runting", "affiliation": "Technical University of Munich" }, { "name": "Wen, Zhen", "affiliation": "Technical University of Munich" }, { "name": "Lueth, Tim C.", "affiliation": "Technical University of Munich" }, { "name": "Sun, Yilun", "affiliation": "Tongji University" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "Soft robotics", "Mechatronics for robotic systems" ], "abstract": "The application of tendon-driven continuum robots (TDCR) has rapidly expanded across various engineering fields due to their flexibility and dexterity. Discrete-jointed continuum robots are typically fabricated as segmented modules interconnected by joints, often resulting in extended prototyping timelines and elevated manufacturing costs. Besides, with identical compliant joints along the backbone, the real bending shape of the robot usually is an arc with variable curvature, resulting in uneven stress distribution along robot’s backbone. To cope with these problems, we propose a 3D-printed continuum robot incorporating convergent compliant joints, enabling monolithic fabrication and achieving balanced stress distribution along the backbone. Kinematic and kinetostatic analyses demonstrate the flexible manipulation, trajectory accuracy, and sufficient stiffness of the FDM-printed TDCR under varying tendon tensions and external forces. In addition, simulation and experimental results validate that the convergent compliant joint design improves stress distribution along the backbone.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA33.5", "code": "ThA33.5", "title": "Self-Excited Vibration Suppression of Spline-Shaft System with a Piezo-Actuated Smart Dry Friction Damper (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA33", "sessionTitle": "JO-MECH: Mechatronic Systems and Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Cao, Peng", "affiliation": "National Key Laboratory of Science and Technology on Helicopter Transmission" }, { "name": "Wang, Dan", "affiliation": "NUAA" }, { "name": "Zhu, Rupeng", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Chen, Weifang", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Song, Liyao", "affiliation": "Nanjing University of Aeronautics and Astronautics" } ], "keywords": [ "Smart structures and vibration control" ], "abstract": "Floating splines are widely used in helicopter tail transmission systems due to their simplicity, reliability, and high specific power. However, long-term operation leads to lubrication degradation, which increases tooth surface friction and may even introduce self-excited vibrations, thereby affecting system stability. To address this issue, a self-excited vibration suppression method for a supercritical spline-shaft system based on a piezo-actuated smart dry friction damper is proposed. The proposed method integrates piezoelectric actuation control into the conventional dry friction damper of the helicopter transmission shaft system. A dynamic model of the supercritical shaft system incorporating a floating spline and a dry friction damper is established, and numerical simulations are conducted to reveal the self-excited vibration characteristics. The coupled dynamic response is further derived to verify the feasibility of suppressing self-excited vibration through the damper. Finally, the structural design and control strategy of the piezo-actuated smart dry friction damper are presented. The findings provide new insights and theoretical references for vibration control and damper design of spline-shaft systems in helicopter.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA33.6", "code": "ThA33.6", "title": "On the Achievable Stability Margin for Resonant Systems with Negative Imaginary Controllers: MEMS Force Sensor Example (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:30-11:50", "sessionCode": "ThA33", "sessionTitle": "JO-MECH: Mechatronic Systems and Control", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Dadkhah, Diyako", "affiliation": "University of Texas at Dallas" }, { "name": "Petersen, Ian R", "affiliation": "The Australian National University" }, { "name": "Moheimani, S.O. Reza", "affiliation": "University of Texas at Dallas" } ], "keywords": [ "Smart structures and vibration control", "Micro and nano mechatronic systems", "Mechatronic system estimation, identification, control" ], "abstract": "This paper presents a root locus analysis to determine the maximum achievable stability margin for three types of standard single input single output negative imaginary controllers when applied to a single mode undamped resonant plant. The controllers considered are integral resonant, positive position, and phase-lead controllers. It is found that both the integral resonant controllers and the positive position controllers can achieve only a finite stability margin, with the positive position controllers achieving a higher stability margin than the integral resonant controllers. It is also found that at least in theory, phase lead controllers could achieve an arbitrarily large stability margin. The results are also validated experimentally on an experimental platform involving a lightly damped SISO MEMS force sensor.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA34.1", "code": "ThA34.1", "title": "Adaptive LQI Strategy for PMSM FOC Control Used in Robotics Applications (I)", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA34", "sessionTitle": "JO-MECH: Human-Robot Interaction", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Torres Rodríguez, Iván Jesús", "affiliation": "ARC-ULL" }, { "name": "Marsà-Fargas, Jordi", "affiliation": "ARC" }, { "name": "Martin-Hernandez, Luis Daniel", "affiliation": "ARC" }, { "name": "Pérez-Díaz, C. Adrián", "affiliation": "ARC" }, { "name": "Candelo-Zuluaga, Carlos", "affiliation": "Arquimea Research Center S.L" }, { "name": "Sanz Merodio, Daniel", "affiliation": "Arquimea Research Center" }, { "name": "Toledo, Jonay", "affiliation": "Univ of La Laguna" }, { "name": "Lopez, Miguel", "affiliation": "ARQUIMEA Research Center" } ], "keywords": [ "Human-robot interaction", "Humanoid and legged robots" ], "abstract": "High-performance actuation is essential in robotics, particularly for Permanent Magnet Synchronous Motor (PMSM) drives used in dynamic platforms. Traditionally, low-level motor control has relied on Proportional–Integral (PI) controllers due to their simplicity and low computational demand. However, PI controllers require meticulous per-operating-point tuning and lack robustness against mechanical disturbances, often leading to excessive overshoot or sluggish dynamic responses. In this work, we present a gain-scheduled Linear–Quadratic–Integral (LQI) control strategy for PMSMs. The controller precomputes optimal LQI gains at multiple linearisation points across the motor's operating range and selects them online via a lookup table, enabling an automated tuning procedure that covers the entire operational envelope. Experimental evaluation on a commercial PMSM platform demonstrates that the proposed approach achieves competitive bandwidth with significantly lower overshoot compared to PI control, and substantially superior disturbance rejection—reducing perturbation duration by up to an order of magnitude. The method remains computationally feasible for real-time execution on a mid-range STM32G4 microcontroller, making it a practical alternative for PMSM drives in dynamic robotic actuators.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA34.2", "code": "ThA34.2", "title": "UWB-Based Dynamic Safe Zones for Human-Robot Interaction in Industrial Environments: An Interoperability Study (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA34", "sessionTitle": "JO-MECH: Human-Robot Interaction", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Fruchtenicht, Diego Alberto", "affiliation": "Federal University of Rio Grande Do Sul - UFRGS" }, { "name": "Ceriotti, Vinícius Cella", "affiliation": "Federal University of Rio Grande Do Sul - UFRGS" }, { "name": "Dos Santos Roque, Alexandre", "affiliation": "Halmstad University, Federal University of Rio Grande Do Sul - UFRGS" }, { "name": "Pohren, Daniel", "affiliation": "DHP Systems" }, { "name": "Pignaton de Freitas, Edison", "affiliation": "Federal University of Rio Grande Do Sul" } ], "keywords": [ "Human-robot interaction", "Task and motion planning", "Robot perception and sensing" ], "abstract": "The growing integration of Ultra-Wideband (UWB) chips into modern smartphones by leading brands such as Apple, Samsung, and Google expands its potential applications, particularly in Internet of Things (IoT) devices. Building on this potential, this study explores the interoperability between the Qorvo UWB chip and the U1 chip in Apple smartphones, with the aim of applying these technologies within industrial robotic cell environments to enhance operator safety through the definition of dynamic safe zones. The study was conducted using a KUKA KR6 R700 robotic cell in a controlled laboratory setting. In this setup, a Qorvo DW3000EVB board, connected to a Nordic nRF52840-DK board and integrated into the robotic cell setup as a fixed reference point, communicated with an Apple smartphone via UWB technology to gather distance data between the smartphone and the robotic cell. The results demonstrated that signal obstruction, particularly when the operator’s body blocked the line of sight, significantly degrades ranging accuracy, with the Mean Absolute Error (MAE) reaching up to 0.633 meters, especially when the smartphone was positioned at chest height (P2). Conversely, in unobstructed conditions, the MAE was significantly lower, reaching a minimum of 0.133 meters.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA34.3", "code": "ThA34.3", "title": "Contact-Aware Hierarchical MPC-QP Framework for Hexapod Locomotion (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA34", "sessionTitle": "JO-MECH: Human-Robot Interaction", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Ryu, Sangsoo", "affiliation": "Myongji University" }, { "name": "Hur, Seongyong", "affiliation": "Myongji University" }, { "name": "Kong, Wonung", "affiliation": "MYONGJI UNIVERSITY" }, { "name": "Choi, Dongil", "affiliation": "Myungji Univ" } ], "keywords": [ "Humanoid and legged robots", "Task and motion planning", "Mechatronics for robotic systems" ], "abstract": "This paper presents a contact-aware hierarchical control framework for hexapod locomotion. A Linear Inverted Pendulum Model (LIPM)-based MPC planner runs at 100 Hz to generate CoM and ZMP references while constraining the ZMP inside a contact-dependent support polygon. A centroidal ground-reaction-force QP and a revised WBC task QP operate at 250 Hz to regulate body motion, contact forces, and stance-foot consistency, while a 500 Hz CTC layer provides torque-level tracking. MuJoCo simulation results verify CoM tracking, ZMP feasibility, and real-time multi-thread execution.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA34.4", "code": "ThA34.4", "title": "Apparent Inertia Reduction Via Force Sensorless Impedance Control (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:50-11:10", "sessionCode": "ThA34", "sessionTitle": "JO-MECH: Human-Robot Interaction", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Gerlagh, Bart", "affiliation": "University of Twente" }, { "name": "Nijenhuis, Marijn", "affiliation": "University of Twente" }, { "name": "Hakvoort, Wouter", "affiliation": "University of Twente" } ], "keywords": [ "Mechatronics for robotic systems", "Human-robot interaction" ], "abstract": "In interactive robot tasks, virtual compliance at the interaction port is often achieved using impedance control. The impedance controller can be extended with force feedback to adjust the inertia at the interaction port, classically done using a force sensor. In cases where forces cannot be measured accurately, a disturbance observer (DOB) can be employed. In this work, we propose to extend the classical inertia shaping controller with a DOB and investigate the limits of mass rendering. We analyse stability using Hurwitz analysis. We also compare the DOB-based impedance controller with optimal results from H2 and μ-synthesis on a dedicated 2-Degrees-of-Freedom (DoF) experimental setup with an internal parasitic mode. The results highlight a clear trade-off between inertia reduction, robust stability and performance. Although the DOB-based controller provides an intuitive", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA34.5", "code": "ThA34.5", "title": "A Hybrid CNN-LSTM Encoder-Decoder for Gait Mode Detection in Lower-Limb Exoskeletons: A Systematic Benchmark with Two Foot-Mounted IMUs (I)", "day": "Thursday", "date": "August 27, 2026", "time": "11:10-11:30", "sessionCode": "ThA34", "sessionTitle": "JO-MECH: Human-Robot Interaction", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Ferhat, Malak", "affiliation": "Université Paris Est Creteil" }, { "name": "Pasini, Kevin", "affiliation": "IRT-SystemX" }, { "name": "Koulal, Yidhir Aghiles", "affiliation": "RT-SystemX" }, { "name": "Oukhellou, Latifa", "affiliation": "Université Gustave Eiffel" }, { "name": "Mohammed, Samer", "affiliation": "Université Paris-Est Créteil - UPEC" } ], "keywords": [ "Wearable robotics", "Human-robot interaction" ], "abstract": "Reliable gait mode detection is essential for safe and adaptive control of lower-limb exoskeletons. This paper proposes a hybrid CNN–LSTM encoder–decoder architecture that combines convolutional spatial feature extraction with recurrent temporal modeling for real-time classification of five locomotion modes—level walking, ramp ascent, ramp descent, stair ascent, and stair descent—using only two foot-mounted inertial measurement units (IMUs). A systematic benchmark is conducted across four deep learning architectures (CNN, LSTM, LSTM encoder–decoder, and the proposed CNN–LSTM encoder–decoder) and three input representations (raw IMU signals, expert-engineered kinematic features, and a hybrid combination). Models are evaluated on a dataset of ten subjects using leave-one-subject-out cross-validation, and further validated in real-time experiments on four unseen subjects. Results show that the proposed CNN–LSTM encoder–decoder consistently achieves the best performance, with F1-scores up to 0.985 in offline evaluation and near-perfect classification in real-time conditions, demonstrating strong generalization to new subjects and environments. These findings confirm the effectiveness of hybrid spatio-temporal architectures for robust, lightweight gait mode detection in adaptive exoskeleton control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA35.1", "code": "ThA35.1", "title": "Fixed-Time Sliding Mode Control for an Uncertain Flexible Link Manipulator with Saturated Actuator (I)", "day": "Thursday", "date": "August 27, 2026", "time": "09:50-10:10", "sessionCode": "ThA35", "sessionTitle": "JO-MECH: Soft Robotics and Manipulators", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Karami, Hamede", "affiliation": "University of Zanjan" }, { "name": "Bayat, Farhad", "affiliation": "Zanjan University" }, { "name": "Mobayen, Saleh", "affiliation": "National Yunlin University of Science and Technology" }, { "name": "Fekih, Afef", "affiliation": "Univ of Louisiana at Lafayette" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Robotic learning and adaptation", "Mechatronic system fault detection, diagnostics, hardware-in-the-loop simulation" ], "abstract": "This paper presents a novel fast, fixed-time, nonsingular sliding-mode controller to improve target-tracking accuracy and reduce vibrations in flexible-link manipulators. The effects of uncertainties, disturbances, and actuator saturation are accounted for in the dynamic equations of the flexible-link manipulator. These practical issues, along with the complex dynamic equations, impose significant challenges on the tracking controller design for flexible link manipulators. Despite these complexities, the proposed controller guarantees closed-loop stability based on the Lyapunov theorem. An adaptation mechanism is employed to eliminate the necessity of knowing the upper bound of the uncertainties. Furthermore, an auxiliary function is employed to overcome the actuator's saturation constraint. Finally, it is proven that using the proposed controller, the tracking errors will converge to zero within a fixed time, independent of the initial conditions. The superiorities of this approach are compared with a recent state-of-the-art method. The simulation results and experimental validations confirm the efficiency and success of the proposed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA35.2", "code": "ThA35.2", "title": "Adaptive Control of 1 DOF Flexible-Link Robot with Two Lumped Masses Based on Algebraic Identification (I)", "day": "Thursday", "date": "August 27, 2026", "time": "10:10-10:30", "sessionCode": "ThA35", "sessionTitle": "JO-MECH: Soft Robotics and Manipulators", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [ { "name": "Ben Ftima, Selma", "affiliation": "UCLM" }, { "name": "Gharab, Saddam", "affiliation": "UCLM" }, { "name": "Feliu-Batlle, Vicente", "affiliation": "Univ of Castilla-La Mancha. CIF: Q-1368009E" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "Robot perception and sensing", "Adaptive and adaptable automation" ], "abstract": "This work presents an adaptive control strategy for a massless flexible-link robot with two lumped masses. Due to their sensitivity to parameter variations, flexible-link manipulators are prone to instability when controller parameters are not accurately tuned. To address this issue, a nested-loop adaptive control architecture is proposed, where the inner loop controls the motor angle and the outer loop regulates the tip position through the base moment. A novel algebraic identification algorithm is integrated for real-time parameter estimation, ensuring fast convergence and robustness against strain gauge disturbances. The identified parameters are used to adaptively adjust the controller gains. Simulation and experimental results confirm the effectiveness of the proposed approach in improving speed, accuracy, and robustness, with potential extension to flexible arms with distributed mass and multiple vibration modes.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Th-ThA35.3", "code": "ThA35.3", "title": "Experimental Validation of Model-Based Collocated Control for Shape Regulation in El", "day": "Thursday", "date": "August 27, 2026", "time": "10:30-10:50", "sessionCode": "ThA35", "sessionTitle": "JO-MECH: Soft Robotics and Manipulators", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 324", "authors": [], "keywords": [], "abstract": "", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_4.html" }, { "id": "Fr-FrM00.1", "code": "FrM00.1", "title": "From Event-Triggered to Neuromorphic Control: A System-Theoretic Perspective", "day": "Friday", "date": "August 28, 2026", "time": "08:30-09:30", "sessionCode": "FrM00", "sessionTitle": "From Event-Triggered to Neuromorphic Control: A System-Theoretic Perspective", "sessionType": "Plenary Session", "room": "Auditorium", "authors": [ { "name": "Heemels, Maurice", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Adaptive control design" ], "abstract": "Control systems are traditionally designed using continuous-time or time-triggered sampled signals, where information is encoded in amplitudes. Recently, event-triggered control has shown that control actions can be generated asynchronously and only when needed, thereby improving efficiency in networked and embedded implementations. Neuromorphic control, inspired by the functioning of biological neurons, pushes this paradigm to its extreme: control signals consist of sequences of fixed-amplitude spikes, so information is encoded entirely in the timing of spiking events. In this talk, we address the fundamental question of how feedback controllers can be designed when the only control freedom lies in spike timing. We present a framework in which neuromorphic controllers are modeled as hybrid systems, combining continuous evolution with discrete state jumps induced by spikes. Based on this viewpoint, we discuss complementary design approaches, including Lyapunov-based triggering strategies and emulation-based constructions that approximate continuous feedback laws. Together, these results provide building blocks toward a design theory for spiking control systems. We illustrate these design ideas through a nuclear fusion application, where plasma fueling through pellet injection yields inherently impulsive actuation, making neuromorphic feedback strategies highly relevant. The talk concludes with an outlook on this exciting research area.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA01.1", "code": "FrA01.1", "title": "Advanced Battery Modeling, Monitoring, and Control for Emerging Applications (I)", "day": "Friday", "date": "August 28, 2026", "time": "09:50-11:50", "sessionCode": "FrA01", "sessionTitle": "Advanced Battery Modeling, Monitoring and Control for Emerging Applications", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Fang, Huazhen", "affiliation": "Michigan State University" } ], "keywords": [ "Advanced process control" ], "abstract": "The world is on the cusp of a new era of electrification across different sectors of industry and economy. A key technology driving this transformation is lithium-ion batteries. As the best available power source, lithium-ion batteries provide high energy/power density and long cycle life. As they find every-growing use in electric vehicles, electric aircraft, grid storage and autonomous platforms, demands for their performance and safety have been rising. Systems and control theory can play a key role in meeting the needs to advance the application of battery systems, resulting in provable progresses. This tutorial-style workshop is designed to provide a deep, structured introduction to the state of the art and new frontiers in battery modeling, monitoring and control, with a focus on integrating physical insights and control-theoretic rigor with practical implementation. The workshop will be particularly relevant to researchers and practitioners within the systems and control community looking to expand their research towards developing and applying control-theoretic methods for lithium-ion batteries and emerging battery-powered systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA02.1", "code": "FrA02.1", "title": "The Multi-Dimensional Bisection Method (MDBM) (I)", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:20", "sessionCode": "FrA02", "sessionTitle": "Best Practice of Efficient Stability Chart Calculations: Advanced Multi-Dimensional Bisection and Sparse Semi-Discretization", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Bachrathy, Daniel", "affiliation": "Budapest University of Technology and Economics" } ], "keywords": [ "Adaptive control design" ], "abstract": "Generating high-resolution stability charts typically requires exhaustive point-by-point grid sweeps across a multi-dimensional parameter space, leading to exponential computational costs. This paper introduces the Multi-Dimensional Bisection Method (MDBM) as a robust, highly efficient alternative to overcome this curse of dimensionality. Implemented as a well-known, highly optimised, and mature open-source package in Julia and in Matlab, MDBM is designed to implicitly find and trace complex multi-dimensional boundaries, such as stability borders and envelopes of parametric families. By evaluating constraints on hypercube vertices and utilising localised iterative refinement, MDBM restricts computational effort solely to the vicinity of the actual boundaries, reducing processing times from hours to seconds. In this session, we will go beyond the core mechanics to explore the finer settings, extra features, and advanced properties of this well-established method, demonstrating how to extract maximum performance when charting stability boundaries in delayed dynamical systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA02.3", "code": "FrA02.3", "title": "Optimizing Performance: Fastest Decay & Robustness Margins (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:20", "sessionCode": "FrA02", "sessionTitle": "Best Practice of Efficient Stability Chart Calculations: Advanced Multi-Dimensional Bisection and Sparse Semi-Discretization", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Bachrathy, Daniel", "affiliation": "Budapest University of Technology and Economics" } ], "keywords": [ "Adaptive control design" ], "abstract": "While identifying the marginal stability boundary is critical, practical engineering applications require optimizing performance and ensuring robustness against parameter uncertainties. This section extends the framework to performance optimisation by analysing swept regions and envelopes of parametric families. Instead of using brute-force discretisation to map out performance drop-offs, we propose a unified boundary handling method based on non-linear parameter reparameterization and global directional constraints. This enables us to formulate the tracking of the \"fastest decay\" rate (such as the spectral abscissa) and the calculation of explicit robustness margins as a single, constrained system. By utilising a component-wise mapping that vanishes at interval endpoints, we eliminate the combinatorial overhead of analysing lower-dimensional boundary strata separately. When integrated with advanced boundary-tracking algorithms like the Multi-Dimensional Bisection Method (MDBM) , this approach bypasses the tracking of irrelevant interior curves and isolates only the active, exposed boundaries of the robust parameter domains.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA02.4", "code": "FrA02.4", "title": "Time-Domain Methods: Multipliction Free Semi-Discretization (MF-SD) (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:20-11:50", "sessionCode": "FrA02", "sessionTitle": "Best Practice of Efficient Stability Chart Calculations: Advanced Multi-Dimensional Bisection and Sparse Semi-Discretization", "sessionType": "Tutorial Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Bachrathy, Daniel", "affiliation": "Budapest University of Technology and Economics" } ], "keywords": [ "Adaptive control design" ], "abstract": "For systems with time-periodic parameters—such as machining chatter with spindle speed variation or delayed networked controls—stability is governed by Floquet theory. Traditional time-domain techniques, like the standard Semi-Discretization method, approximate the infinite-dimensional Monodromy operator but suffer from high computational demands (typically cubic or quadratic complexity) due to repeated matrix multiplications over discrete time intervals. This paper presents the Multiplication-Free Semi-Discretization (MFSD) method, a novel numerical approach that compiles the discretized governing equations into a single, highly sparse generalized eigenvalue problem. Inspired by collocation techniques, MFSD completely circumvents successive matrix multiplications, reducing the computational complexity to linear time. The paper will demonstrate how MFSD breaks traditional computational bottlenecks, enabling near-real-time spectral analysis and stability prediction for complex time-periodic delayed systems. Furthermore, we will highlight how this architectural breakthrough extends to stochastic delayed systems; by reformulating the second-moment dynamics, the computational complexity of the stochastic semi-discretization method has been successfully reduced from the traditional quartic complexity to just quadratic, opening new frontiers for robust control design under random perturbations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA03.1", "code": "FrA03.1", "title": "Tracking Control of Discrete-Time Strict-Feedback Systems with Mismatched Disturbances Using a FAS Method", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA03", "sessionTitle": "Applications of FAS Theory in Discrete Systems and Specialized Scenarios", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Zhang, Da-Wei", "affiliation": "Southern University of Science and Technology" }, { "name": "Liu, Guo-Ping", "affiliation": "Southern University of Science and Technology" } ], "keywords": [ "Control using FAS approach" ], "abstract": "This study investigates the tracking control for discrete-time strict-feedback systems with the mismatched disturbances by means of a fully actuated system (FAS) method. Firstly, an equivalent transformation is constructed to convert the discrete-time strict-feedback systems with the mismatched disturbances into a class of input-delay FASs with the lumped disturbance. Then, a high-order disturbance observer (HODO) is designed by using a difference operator and its high-order form to achieve the accurate estimation of the lumped disturbance via a less conservatism assumption. With the help of the FAS method, a predictive proportional-integral (PI) control with the disturbance compensation is designed to implement the desired tracking control with eliminating the open-loop nonlinearities and compensating for the input delays. A sufficient criterion is presented to analyze the bounded stability and asymptotic tracking of the closed-loop FASs. Finally, a simulation of the Chua's circuit is shown to verify the effectiveness.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA03.2", "code": "FrA03.2", "title": "Model-Free Control for Flexible Joint Robots: A Fully Actuated System Approach", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA03", "sessionTitle": "Applications of FAS Theory in Discrete Systems and Specialized Scenarios", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Li, Shunli", "affiliation": "Harbin Institute of Technology" }, { "name": "Duan, Guang-Ren", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Control using FAS approach", "Fully-actuated systems in industry", "Global fully actuated systems" ], "abstract": "A model-free controller that employing only the link angle measurement is developed for flexible joint robots, accompanied by a rigorous stability analysis. Notably, the developed controller guarantees stabilization from arbitrary practical initial conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA03.3", "code": "FrA03.3", "title": "Fully Actuated System Approach-Based Safety-Critical Control for Uncertain Manipulator", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA03", "sessionTitle": "Applications of FAS Theory in Discrete Systems and Specialized Scenarios", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Fan, Jinpeng", "affiliation": "Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and T" }, { "name": "Ren, Weijie", "affiliation": "Southern University of Science and Technology" }, { "name": "Duan, Guang-Ren", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Control using FAS approach", "Global fully actuated systems", "Fully-actuated systems in industry" ], "abstract": "Most existing control barrier function (CBF) strategies for robotic manipulators either suffer from complex optimization arising from inertia matrix coupling or demand precise model knowledge unavailable in practice. To address these limitations, we propose a robust safety-critical control framework for uncertain manipulators with strict multiple constraints, built upon the fully actuated system (FAS) approach. A robust integral of the sign of the error (RISE) disturbance observer is incorporated to achieve exponential estimation of lumped uncertainties, yielding a computable time-varying error bound that directly reduces conservatism in safety certification. By exploiting the FAS transformation, the nonlinear manipulator dynamics are converted into a perturbed double-integrator structure with respect to a virtual control input, whereupon all safety constraints reduce to decoupled, component-wise affine inequalities that eliminate inertia-matrix coupling from the optimization. The resulting strictly convex quadratic program (QP) guarantees high-precision trajectory tracking under simultaneous position and velocity constraints. Simulation results on a two-link planar manipulator validate the effectiveness of the proposed framework.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA03.4", "code": "FrA03.4", "title": "Input Compensation for Discrete-Time Fully Actuated Systems with Time-Varying Delay", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA03", "sessionTitle": "Applications of FAS Theory in Discrete Systems and Specialized Scenarios", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Cui, Kaixin", "affiliation": "Taiyuan University of Technology" }, { "name": "Lu, Hao", "affiliation": "Harbin Institute of Technology" }, { "name": "Xu, Xinying", "affiliation": "Taiyuan University of Technology" } ], "keywords": [ "Global fully actuated systems", "Control using FAS approach", "Predictive control of fully-actuated systems" ], "abstract": "This paper presents a fully actuated system (FAS) predictive control method for stabilizing discrete-time nonlinear systems with time-varying input delays. The design integrates FAS approach with an online predictor that actively compensates for the variable delay by forecasting system states based on current delay measurements. The control law is synthesized by imposing a desired linear dynamics on the predicted future state, thereby aligning the delayed actuation with the intended control objective. Stability is rigorously guaranteed under Lipschitz nonlinearity and bounded delay variation. Simulations show the proposed method achieves faster convergence, lower steady-state error, and more efficient control compared to conventional delay-ignoring or fixed-delay compensation strategies, offering a systematic solution for time-delay discrete-time high-order FASs (DT-HOFASs).", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA03.5", "code": "FrA03.5", "title": "Strict Safety Tracking Control of WMR Using FAS Structure", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA03", "sessionTitle": "Applications of FAS Theory in Discrete Systems and Specialized Scenarios", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Gao, Yang", "affiliation": "Southeast University" }, { "name": "Zhang, Zhongcai", "affiliation": "Qufu Normal University" }, { "name": "Wu, Yuqiang", "affiliation": "Qufu Normal Univ" } ], "keywords": [ "Sub-fully actuated systems", "Global fully actuated systems", "Control using FAS approach" ], "abstract": "Traditional control barrier function (CBF)-based controllers may become infeasible when the CBF control direction vanishes, preventing the enforcement of safety constraints. In this work, the problem is resolved by transforming the wheeled mobile robot (WMR) into a fully actuated system (FAS). Nevertheless, when the FAS form are used to achieve tracking, the position loss of WMR is inevitable. To avoid this issue, the nominal controller and the associated control Lyapunov function (CLF) are constructed using only a subset of the FAS structure inherent to the WMR. A disturbance-observer-enhanced CLF-CBF quadratic program ensures feasibility under disturbances while guaranteeing both tracking performance and safety. Simulation results validate the effectiveness of the proposed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA04.1", "code": "FrA04.1", "title": "Generative Design of Stabilizing Controllers with Diffusion Models: The Youla Approach", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA04", "sessionTitle": "LLMs for Modeling, Control, and Controller Synthesis", "sessionType": "Regular Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Cercola, Matteo", "affiliation": "Politecnico Di Milano" }, { "name": "Materassi, Donatello", "affiliation": "University of Minnesota" }, { "name": "Formentin, Simone", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Data-efficient control via foundation models" ], "abstract": "Designing controllers that simultaneously achieve strong performance and provable closed-loop stability remains a central challenge in control engineering. This work introduces a diffusion-based generative framework for linear controller synthesis grounded in the Youla–Kučera parameterization, enabling the construction of stabilizing controllers by design. The diffusion model learns a conditional mapping from plant dynamics and desired performance metrics to feasible Youla parameters, guaranteeing internal stability while flexibly accommodating user-specified targets. Trained on synthetically generated stable SISO plants with fixed-order Youla parameters, the proposed approach reliably synthesizes controllers that meet prescribed sensitivity and settling-time specifications on previously unseen systems. To the best of our knowledge, this work provides the first demonstration that diffusion models can generate stabilizing controllers, combining rigorous control-theoretic guarantees with the versatility of modern generative modeling.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA04.2", "code": "FrA04.2", "title": "Benchmark for Planning and Control with Large Language Model Agents: Blocksworld with Model Context Protocol", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA04", "sessionTitle": "LLMs for Modeling, Control, and Controller Synthesis", "sessionType": "Regular Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Jobs, Niklas", "affiliation": "Helmut Schmidt University" }, { "name": "Vieira da Silva, Luis Miguel", "affiliation": "Helmut Schmidt University" }, { "name": "Somashekaraiah, Jayanth", "affiliation": "Helmut-Schmidt-Universität" }, { "name": "Weigand, Maximilian", "affiliation": "Helmut Schmidt University" }, { "name": "Kube, David", "affiliation": "Siemens AG" }, { "name": "Gehlhoff, Felix", "affiliation": "Helmut Schmidt University" } ], "keywords": [ "LLMs for modeling and control" ], "abstract": "Industrial automation increasingly requires flexible control strategies that can adapt to changing tasks and environments. Agents based on Large Language Models (LLMs) offer potential for such adaptive planning and execution but lack standardized benchmarks for systematic comparison. We introduce a benchmark with an executable simulation environment representing the Blocksworld problem providing five complexity categories. By integrating the Model Context Protocol (MCP) as a standardized tool interface, diverse agent architectures can be connected to and evaluated against the benchmark without implementation-specific modifications. A single-agent implementation demonstrates the benchmark's applicability, establishing quantitative metrics for comparison of LLM-based planning and execution approaches.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA04.3", "code": "FrA04.3", "title": "Activation Control of State Space Model-Based LLMs Using Control Barrier Function", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA04", "sessionTitle": "LLMs for Modeling, Control, and Controller Synthesis", "sessionType": "Regular Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Kim, Kisong", "affiliation": "Institute of Science Tokyo" }, { "name": "Sasahara, Hampei", "affiliation": "The University of Tokyo" }, { "name": "Imura, Jun-ichi", "affiliation": "Institute of Science Tokyo" } ], "keywords": [ "LLMs for modeling and control" ], "abstract": "This paper proposes an activation control method for Large Language Model (LLM) safety, especially for Mamba which is the representative model constructed with the State Space Models (SSMs). Unlike the Reinforcement Learning from Human Feedback (RLHF) or prompt engineering, our proposed method directly manipulates the internal activations of the LLM, and it needs no fine tuning. The existing activation engineering methods are the static approach, such as simple addition of the fixed concept vector to the activation. The proposed method overcomes this indiscriminate steering and gives flexibility. At first, the Control Barrier Function (CBF) is defined with the determination of the unsafe set in the activation space of SSM layers. Then, the expert channels are also determined by the contrastive prompt's activations. Finally, the activations are controlled when they enter the predetermined unsafe set. The control inputs are calculated based on the current states so as to drive the activations out of the unsafe set. The experiments with mamba-2.8b model and Real Toxicity Prompts dataset show that the proposed method enhances the safety of LLMs.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA04.4", "code": "FrA04.4", "title": "In-Context Learning for Zero-Shot Speed Estimation of BLDC Motors", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA04", "sessionTitle": "LLMs for Modeling, Control, and Controller Synthesis", "sessionType": "Regular Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Colombo, Alessandro", "affiliation": "Politecnico Di Milano" }, { "name": "Busetto, Riccardo", "affiliation": "IDSIA USI-SUPSI" }, { "name": "Breschi, Valentina", "affiliation": "Eindhoven University of Technology" }, { "name": "Forgione, Marco", "affiliation": "SUPSI-USI" }, { "name": "Piga, Dario", "affiliation": "SUPSI-USI" }, { "name": "Formentin, Simone", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "LLMs for modeling and control", "Data-efficient control via foundation models" ], "abstract": "Accurate speed estimation in sensorless brushless DC motors is essential for high-performance control and monitoring, yet conventional model-based approaches struggle with system nonlinearities and parameter uncertainties. In this work, we describe a transformer-based in-context learning framework to perform zero-shot speed estimation using only electrical measurements. By training the filter offline on simulated motor trajectories, we enable real-time inference on unseen real motors without retraining, eliminating the need for explicit system identification. Experimental results demonstrate that our method outperforms traditional Kalman filter-based estimators, especially in low-speed regimes that are crucial during motor startup.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA04.5", "code": "FrA04.5", "title": "Zero-Shot Sim-To-Real In-Context Learning of Speed Controllers for BLDC Motors", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA04", "sessionTitle": "LLMs for Modeling, Control, and Controller Synthesis", "sessionType": "Regular Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Colombo, Alessandro", "affiliation": "Politecnico Di Milano" }, { "name": "Delcaro, Giacomo", "affiliation": "Politecnico Di Milano" }, { "name": "Busetto, Riccardo", "affiliation": "IDSIA USI-SUPSI" }, { "name": "Poli, Enrico", "affiliation": "STMicroelectronics" }, { "name": "Lombardi, Prospero", "affiliation": "STMicroelectronics" }, { "name": "Marano, Vincenzo", "affiliation": "STMicroelectronics" }, { "name": "Formentin, Simone", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "LLMs for modeling and control", "Data-efficient control via foundation models" ], "abstract": "We address the problem of high-performance speed control for BLDC motors under unknown and varying dynamics, where conventional robust or adaptive designs often require conservative tuning or extensive experimentation. We propose a Sim-to-Real In-Context Learning framework in which a Transformer policy, trained exclusively in simulation via Domain Randomization, acts as a zero-shot meta-controller that adapts directly from recent input--output data, without parameter identification or online adaptation laws. Experiments on a real BLDC platform with large inertial variations show that the learned controller generalizes reliably across operating conditions. Comparative results further demonstrate that it matches the performance of state-of-the-art black-box optimization while requiring no real-world training iterations, thus shifting the burden of adaptation from online experimentation to offline computation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA04.6", "code": "FrA04.6", "title": "A-SID: Agent-Based System Identification Via Large Language Models and Tool Orchestration", "day": "Friday", "date": "August 28, 2026", "time": "11:30-11:50", "sessionCode": "FrA04", "sessionTitle": "LLMs for Modeling, Control, and Controller Synthesis", "sessionType": "Regular Session", "room": "Convention Hall - Room 104", "authors": [ { "name": "Talaei, Behrouz Kiani", "affiliation": "Imperial College London" }, { "name": "Vyas, Javal", "affiliation": "Imperial College London" }, { "name": "Mercangöz, Mehmet", "affiliation": "Imperial College London" } ], "keywords": [ "LLMs for modeling and control", "Development of assistant systems for manufacturing systems", "Explainability and safety of LLM-based controllers" ], "abstract": "We present A-SID (Agent-based System Identification), a proof-of-concept framework that combines a large language model with a Python-based execution environment for automated data preprocessing and linear model identification. Python routines first compute statistical diagnostics describing missing data, trends, noise levels, and outliers. These diagnostics are then passed to the LLM (OpenAI GPT4), which generates a structured preprocessing plan and executable code for subsequent Auto-Regressive with eXogenous input (ARX) model estimation and validation. The framework is evaluated on four synthetic input–output datasets covering ideal linear data, corrupted linear data, severe data anomalies, and nonlinear dynamics. The results show that A-SID can execute the complete identification workflow without manual intervention and that LLM-guided preprocessing improves ARX model accuracy in the linear cases. However, the nonlinear case also shows an important limitation: inappropriate preprocessing can remove genuine system dynamics while still improving numerical fit metrics. The study therefore demonstrates the feasibility of LLM-orchestrated system-identification workflows while highlighting the need for stronger validation of agent decisions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA05.1", "code": "FrA05.1", "title": "Safe Backup Control Synthesis for a Multirotor System", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:05", "sessionCode": "FrA05", "sessionTitle": "LB: Robotics", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Kim, Byeongjun", "affiliation": "Seoul National University" }, { "name": "Kong, Youngkyoung", "affiliation": "Seoul National University" }, { "name": "Kim, H. Jin", "affiliation": "Seoul National Univ" } ], "keywords": [ "Aerial, field, and marine robotics", "Autonomous navigation" ], "abstract": "This paper presents a safe backup control method for multirotor systems, which generates a time-optimal trajectory to a predefined safe set and integrates a safety filter to ensure constraint satisfaction in real time. The approach enables rapid recovery from potentailly unsafe states while maintaining dynamic feasibility, adaptively adjusting the recovery horizon based on the current state to maximize the operational envelope. Simulation results demonstrate that the proposed controller guarantees safety under input and state constraints while effectively keeping the system within the safe operating envelope.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA05.2", "code": "FrA05.2", "title": "Geometric Attitude Control on SO(3) with Control Contraction Metric", "day": "Friday", "date": "August 28, 2026", "time": "10:05-10:20", "sessionCode": "FrA05", "sessionTitle": "LB: Robotics", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Eom, Dohyun", "affiliation": "Seoul National University" }, { "name": "Kim, H. Jin", "affiliation": "Seoul National Univ" } ], "keywords": [ "Aerial, field, and marine robotics", "Autonomous navigation" ], "abstract": "This paper presents a geometric attitude tracking controller on SO(3) based on the control contraction metric (CCM) framework. Unlike conventional Lyapunov-based methods that guarantee stability around a fixed equilibrium, the proposed approach ensures exponential convergence of differential dynamics along trajectories. A Riemannian metric is constructed for rigid-body rotational dynamics, and a feedback law satisfying the contraction condition is derived. Numerical results validate the contraction property through eigenvalue analysis.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA05.3", "code": "FrA05.3", "title": "Consideration of Actuator Dynamics for Multirotor Control", "day": "Friday", "date": "August 28, 2026", "time": "10:20-10:35", "sessionCode": "FrA05", "sessionTitle": "LB: Robotics", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Lee, Jaewoo", "affiliation": "Seoul National University" }, { "name": "Lee, Jinwoo", "affiliation": "Seoul National University" }, { "name": "Lee, Hyun Gyu", "affiliation": "University of Illinois Urbana-Champaign" }, { "name": "Kim, Yeonjoon", "affiliation": "Seoul National University" }, { "name": "Kim, H. Jin", "affiliation": "Seoul National Univ" }, { "name": "Lee, Dongjae", "affiliation": "Kyung Hee University" } ], "keywords": [ "Aerial, field, and marine robotics", "High-performance motion control systems", "Task and motion planning" ], "abstract": "Accurate and robust control of multirotor systems requires consideration of actuator dynamics, particularly during aggressive maneuvers and rapid command changes. Most existing control strategies assume instantaneous actuator response and neglect rotor and, if applicable, servo dynamics in the stability analysis. This assumption can degrade performance and lead to instability under fast transients. This paper proposes a control framework that explicitly incorporates actuator dynamics into the closed-loop design. A backstepping-based approach is employed to address the cascaded structure between rigid-body and actuator dynamics. This formulation is particularly advantageous for variable-tilt platforms, which experience heterogeneous actuation delays due to the distinct responses of rotors and servos. Supported by a rigorous Lyapunov-based stability analysis and robustness guarantees against actuator time constant uncertainties, the effectiveness of the proposed method is demonstrated through numerical simulations. The results demonstrate improved tracking performance compared to baseline method that neglect actuator dynamics.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA05.4", "code": "FrA05.4", "title": "Occlusion-Aware Apple Detection with Vision-Language-Model Assisted Selective Human-In-The-Loop Correction", "day": "Friday", "date": "August 28, 2026", "time": "10:35-10:50", "sessionCode": "FrA05", "sessionTitle": "LB: Robotics", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Rathore, Divya", "affiliation": "Cornell University" }, { "name": "Loganathan Girija, Divyanth", "affiliation": "Cornell University" }, { "name": "Karkee, Manoj", "affiliation": "Cornell University" } ], "keywords": [ "Agricultural robotics", "Computer vision in agriculture", "Sensing and perception in agriculture" ], "abstract": "This study proposes a hybrid perception framework that integrates occlusion-aware apple detection with Vision Language-Model reasoning for human-in-the-loop supervision during robotic apple harvesting. A transformer-based RF-DETR model performs occlusion-based apple detection, while a probabilistic calibration scheme identifies predictions with low reliability. These ambiguous cases are provided to VLM for semantic occlusion reasoning and selectively escalated to human supervision when uncertainty or disagreement between RF-DETR and VLM decision persists. Results demonstrated occlusion-aware detection mAP@50 of 86.2%, with calibration allowing 47% of detections to be directly accepted. VLM showed 74.2% classification accuracy on uncertain cases, highlighting promise for selective human-in-the-loop correction.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA05.5", "code": "FrA05.5", "title": "Constrained Exploration for Cooperative Multi-Agent Reinforcement Learning", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:05", "sessionCode": "FrA05", "sessionTitle": "LB: Robotics", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Son, Sungil", "affiliation": "Seoul National University" }, { "name": "Jung, Hoseong", "affiliation": "Seoul National University" }, { "name": "Oh, Dahyun", "affiliation": "Seoul National University" }, { "name": "Kim, H. Jin", "affiliation": "Seoul National Univ" } ], "keywords": [ "AI-powered robotics", "Human machine cooperation & integration", "Robotic learning and adaptation" ], "abstract": "Cooperative multi-agent reinforcement learning often relies on intrinsic rewards to discover coordination, yet naive reward mixing can distort the learning signal and degrade team performance. We propose a constrained exploration framework that maximizes exploration subject to a conservative non-degradation constraint on extrinsic task return, solved via an epigraph reformulation with adaptive exploration budgets. This approach separates intrinsic rewards from task objectives and regulates exploration through task feasibility. Our approach consistently outperforms strong baselines and induces novel cooperative strategies in multi-agent tasks.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA05.6", "code": "FrA05.6", "title": "Autonomous Navigation Using Bio-Inspired Soft Sensors for Close-Proximity Perception", "day": "Friday", "date": "August 28, 2026", "time": "11:05-11:20", "sessionCode": "FrA05", "sessionTitle": "LB: Robotics", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Ozturk, Emre", "affiliation": "Hacettepe University" }, { "name": "Uyanik, Ismail", "affiliation": "Hacettepe University" }, { "name": "Kumbay Yildiz, Solen", "affiliation": "Hacettepe University" } ], "keywords": [ "Soft robotics", "Robot perception and sensing", "Autonomous navigation" ], "abstract": "This paper presents a low-cost, bio-inspired soft robotic antenna designed for close-proximity depth sensing, modeled after the tactile functionality of insect antennae. Fabricated from a flexible silicone rubber substrate with embedded microchannels containing conductive carbon paste, the sensor utilizes resistance-based feedback to estimate distance. The sensor's performance is characterized through time-domain analysis and modeled using a fifth-order recursive least squares estimation (RLSE) algorithm. The effectiveness of the soft antenna is demonstrated through an autonomous wheeled robot executing Bug1, Bug2, and TangentBug algorithms for wall-following and obstacle avoidance. Results show that the soft antenna provides reliable distance sensing and accurate obstacle detection, offering a resilient and scalable solution for bio-inspired perception in autonomous systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA05.7", "code": "FrA05.7", "title": "Whole-Body Motion Planning and Safety-Critical Control for Aerial Manipulation", "day": "Friday", "date": "August 28, 2026", "time": "11:20-11:35", "sessionCode": "FrA05", "sessionTitle": "LB: Robotics", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Yang, Lin", "affiliation": "Nanyang Technological University" }, { "name": "Lee, Jinwoo", "affiliation": "Seoul National University" }, { "name": "Campolo, Domenico", "affiliation": "Nanyang Technological University (NTU) Singapore" }, { "name": "Kim, H. Jin", "affiliation": "Seoul National Univ" }, { "name": "Byun, Jeonghyun", "affiliation": "Seoul National University" } ], "keywords": [ "Aerial, field, and marine robotics", "Task and motion planning", "High-performance motion control systems" ], "abstract": "Aerial manipulation combines the maneuverability of multirotors with the dexterity of robotic arms to perform complex tasks in cluttered spaces. Yet planning safe, dynamically feasible trajectories remains difficult due to whole-body collision avoidance and the conservativeness of common geometric abstractions such as bounding boxes or ellipsoids. We present a whole-body motion planning and safety-critical control framework for aerial manipulators built on superquadrics (SQs). Using an SQ-plus-proxy representation, we model both the vehicle and obstacles with differentiable, geometry-accurate surfaces. Leveraging this representation, we introduce a maximum-clearance planner that fuses Voronoi diagrams with an equilibrium-manifold formulation to generate smooth, collision-aware trajectories. We further design a safety-critical controller that jointly enforces thrust limits and collision avoidance via high-order control barrier functions. In simulation, our approach outperforms sampling-based planners in cluttered environments, producing faster, safer, and smoother trajectories and exceeding ellipsoid-based baselines in geometric fidelity. Actual experiments on a physical aerial-manipulation platform confirm feasibility and robustness, demonstrating consistent performance across simulation and hardware settings. The video can be found at https://youtu.be/hQYKwrWf1Ak.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA06.1", "code": "FrA06.1", "title": "A Unified Adaptive Observer Design Framework for LPV Systems Subject to Sampled Data Measurements", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA06", "sessionTitle": "Adaptive Observer Design", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Benoudiba, Amayas", "affiliation": "IBISC-Lab, Evry Val d'Essonne University" }, { "name": "Ait oufroukh, Naima", "affiliation": "IBISC" }, { "name": "Ahmed Ali, Sofiane", "affiliation": "IBISC Laboratory" }, { "name": "Ichalal, Dalil", "affiliation": "IBISC-Lab, Evry Val d'Essonne University" } ], "keywords": [ "Adaptive observer design", "Time/parameter varying system identification", "Discrete event modeling and simulation" ], "abstract": "This paper addresses adaptive observer design for Linear Parameter Varying (LPV) systems with sampled outputs and time-varying unknown parameters. The proposed observer combines a robust Multiple Proportional Integral (MPI) structure with a closed-loop output predictor to jointly estimate the states and unknown parameters while compensating for sampled measurements. The design is formulated through delay-dependent Linear Matrix Inequalities (LMIs), ensuring convergence of the estimation errors to zero and providing an upper bound on the Maximum Allowable Sampling Period (MASP). Nonlinearities are characterized by One-Sided Lipschitz (OSL) and Quadratic Inner-Boundedness (QIB) conditions, yielding less conservative results than classical Lipschitz-based designs. A numerical example illustrates the effectiveness of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA06.2", "code": "FrA06.2", "title": "Unknown Input Observer Based Control for Bilinear Switched Systems", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA06", "sessionTitle": "Adaptive Observer Design", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Aliyev, Aydin", "affiliation": "IMT Nord Europe" }, { "name": "Arango Restrepo, Juan Pablo", "affiliation": "IMT Nord Europe SERI SN" }, { "name": "Etienne, Lucien", "affiliation": "IMT Lille-Douai" }, { "name": "Duviella, Eric", "affiliation": "IMT Lille Douai" } ], "keywords": [ "Adaptive observer design", "Hybrid and switched systems modeling" ], "abstract": "Observation and control of switched systems have been widely studied in control theory, most of the work focuses on switched linear systems where each active mode is an LTI plant. However, this assumption can be conservative for many practical applications. This paper extends the analysis to switched bilinear systems, which more accurately capture the interaction between inputs and states in processes such as chemical reactors, flexible mechanical structures, and electrical machines. In this work, we propose numerically tractable, sufficient conditions for gain synthesis of Luenberger-Like Observers, Unknown Input Observers and unknown input reconstruction, using both common and non-common Lyapunov function approaches. The analysis considers both dwell-time constraints and arbitrary switching between active modes. A case study on a photovoltaic–thermal system illustrates the effectiveness and practical relevance of the proposed methodology.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA06.3", "code": "FrA06.3", "title": "Parameterized Observers for Distributed State Estimation of Jointly Observable Uncertain Linear Systems", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA06", "sessionTitle": "Adaptive Observer Design", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Yang, Xianzhi", "affiliation": "Beijing Institute of Technology" }, { "name": "Zhang, Lan", "affiliation": "Beijing Institute of Technology" }, { "name": "Deng, Fang", "affiliation": "Beijing Institute of Technology" }, { "name": "Chen, Jie", "affiliation": "Beijing Institue of Technology" }, { "name": "Lu, Maobin", "affiliation": "Beijing Institute of Technology" } ], "keywords": [ "Adaptive observer design", "Multi-agent systems", "Distributed control and estimation" ], "abstract": "This paper proposes a constructive distributed adaptive observer for discrete-time jointly observable uncertain linear systems over directed networks. A discrete-time system decomposition method is developed to mitigate the effects of unknown parameters. The proposed distributed adaptive observer consists of a parameterized observer for the observable-subsystem state and time-varying unobservable-subsystem dynamics, together with two nonlinear mappings that link the unknown parameters and the system states to the states of these dynamics. By establishing a parametric representation of the measurement output, the parameter estimation problem is converted into a parameter identification problem and is then solved via a gradientdescent method. By Lyapunov analysis, we show the asymptotic stability of the estimation error system, ensuring distributed state estimation despite system uncertainties and the joint observability condition.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA06.4", "code": "FrA06.4", "title": "On the Design of a Homogeneous Observer for Unicycle Models", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA06", "sessionTitle": "Adaptive Observer Design", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Ushirobira, Rosane", "affiliation": "Inria" }, { "name": "Efimov, Denis", "affiliation": "Inria" }, { "name": "Renzaglia, Alessandro", "affiliation": "INRIA" }, { "name": "Simonin, Olivier", "affiliation": "INSA De Lyon" } ], "keywords": [ "Adaptive observer design" ], "abstract": "This paper investigates the problem of estimating the orientation of mobile robots described by a unicycle dynamics and using position measurements. By applying transformations to both coordinates and time, we transform the dynamics into a chained form of nonholonomic systems, which is weighted homogeneous. We then propose a homogeneous observer that converges in finite time under mild restrictions on robot velocities, and we analyze its properties in the original time domain. The efficiency of the new observer is illustrated by comparative simulations with popular solutions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA06.5", "code": "FrA06.5", "title": "Combined MIMO MRAC with Least-Squares Estimator", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA06", "sessionTitle": "Adaptive Observer Design", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Costa, Ramon R.", "affiliation": "Federal University of Rio De Janeiro" }, { "name": "Hsu, Liu", "affiliation": "COPPE - Federal Univ of Rio De Janeiro" }, { "name": "Lizarralde, Fernando", "affiliation": "Federal Univ. of Rio De Janeiro" }, { "name": "Peixoto, Alessandro Jacoud", "affiliation": "COPPE/Federal University of Rio De Janeiro (UFRJ)" } ], "keywords": [ "Model reference adaptive control", "Linear system identification" ], "abstract": "A model-reference adaptive control (MRAC) scheme with fast output tracking is combined with an external parameter estimator for multi-input-multi-output (MIMO) systems. The controller design is based on the SDU approach. Fast adaptive tracking is achieved by reducing the error equation relative degree to zero and employing a standard gradient-type adaptive law driven by the tracking error. The external parameter estimator uses a least-squares algorithm driven by prediction error. The key challenge in combining controller and estimator parameters is their dimensional incompatibility. The novelty of this work lies in the mechanism developed to integrate these two estimators into a stable adaptive control algorithm. Simulation results for a 2-input 2-output system with 14 parameters demonstrate remarkable convergence properties.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA06.6", "code": "FrA06.6", "title": "Online Estimation-Based Adaptive Control of Fixed-Wing UAVs under Relaxed Excitation", "day": "Friday", "date": "August 28, 2026", "time": "11:30-11:50", "sessionCode": "FrA06", "sessionTitle": "Adaptive Observer Design", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Kar, Jigyansa", "affiliation": "IIT Bombay" }, { "name": "Maity, Arnab", "affiliation": "Indian Institute of Technology Bombay" } ], "keywords": [ "Nonlinear adaptive control", "Model reference adaptive control", "Filtering and smoothing" ], "abstract": "Accurate online identification of aerodynamic coefficients is essential for stable tracking performance and safe operation across varying UAV operation, yet classical adaptive estimators require persistent excitation (PE), a condition rarely met during typical flight manoeuvres. Recent advances have shown that incorporating memory filters can relax PE to an interval-excitation requirement. Based on this idea, this paper introduces an extended Dynamic Regressor Extension and Mixing (DREM) scheme for the nonlinear longitudinal dynamics of a fixed-wing UAV. The method transforms the aircraft equations into filtered regression models and employs an enhanced mixing strategy to relax the PE requirement to an interval/initial excitation (IE) condition. This results in fully decoupled scalar regressions, parameter convergence, and stable performance with low-excitation manoeuvres. Simulation studies demonstrate that the proposed estimator retrieves lift, drag, and pitching-moment coefficients with more accurate convergence than classical DREM under relaxed excitation, enabling more accurate and adaptive downstream control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA07.1", "code": "FrA07.1", "title": "Distributed Recursive Binary Identification under Tampering and Non-Persistent Excitation (I)", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA07", "sessionTitle": "Distributed Estimation and Information Fusion Over Sensor Networks", "sessionType": "Invited Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Guo, Jian", "affiliation": "The Hong Kong Polytechnic University" }, { "name": "Zhang, Ji-Feng", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Distributed control and estimation", "Quantized systems", "Linear system identification" ], "abstract": "In this paper, we consider distributed parameter estimation with binary observations under measurement-side tampering, where each node observes a thresholded output whose label may be flipped and exchanges information over a communication graph. We develop a distributed recursive projection algorithm based on the diffusion strategy. Without imposing independence, stationarity, or Gaussian assumptions, we establish an almost sure logarithmic bound for a Lyapunov-type estimation error. Under a mild cooperative excitation condition, the estimates of all nodes are strongly consistent, even when each individual node is non-exciting. Simulations on a jointly exciting network corroborate the theory and show that the proposed algorithm converges, whereas non-cooperative and tampering-unaware baselines do not.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA07.2", "code": "FrA07.2", "title": "On Robust Distributed Pseudolinear Kalman Filter for Bearings-Only Tracking under Measurement Outliers (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA07", "sessionTitle": "Distributed Estimation and Information Fusion Over Sensor Networks", "sessionType": "Invited Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Luo, Bote", "affiliation": "Chinese Academy of Sciences" }, { "name": "Xue, Wenchao", "affiliation": "Chinese Academy of Sciences, Beijing 100190," }, { "name": "Dong, Ruifeng", "affiliation": "University of the Chinese Academy of Sciences" }, { "name": "Guo, Tong", "affiliation": "Institute of Optoelectronic Technology, Chinese Academy of Sciences" }, { "name": "Xie, Hui", "affiliation": "Shanghai Institute of Technical Physics, Chinese Academy of Sciences" }, { "name": "Mao, Yao", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Distributed control and estimation", "Cyber security networked control", "Quantized systems Profile: Invited Session" ], "abstract": "In bearings-only target tracking, the nonlinearity of the measurement model amplifies the detrimental impact of measurement outliers. Consequently, distributed sensor networks become highly sensitive to these outliers, which can severely degrade both local estimation and network‑wide fusion accuracy. To address this, this work proposes a robust distributed pseudolinear Kalman filter (RD‑PLKF) for planar tracking that combines a bias‑corrected weighted least‑squares estimator with an adaptive outlier suppression mechanism. Rigorous stability analysis proves uniform boundedness of the mean‑square error under mild network connectivity and outlier assumptions. Numerical simulations demonstrate that the proposed RD‑PLKF maintains high tracking accuracy, strong robustness, and low computational cost across a range of outlier intensities, outperforming conventional pseudolinear Kalman filter and other robust filtering schemes.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA07.3", "code": "FrA07.3", "title": "Distributed State Estimation with Binary-Valued Communication (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA07", "sessionTitle": "Distributed Estimation and Information Fusion Over Sensor Networks", "sessionType": "Invited Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Li, Mengqi", "affiliation": "University of Science and Technology Beijing" }, { "name": "Liu, Wenke", "affiliation": "University of Science and Technology Beijing" }, { "name": "Zhang, Qingxiang", "affiliation": "University of Science and Technology Beijing" }, { "name": "Jia, Rui-Zhe", "affiliation": "University of Science and Technology Beijing" }, { "name": "Guo, Jin", "affiliation": "University of Science and Technology Beijing" } ], "keywords": [ "Distributed control and estimation", "Cyber security networked control", "Quantized systems" ], "abstract": "This paper mainly investigates the distributed state estimation problem for multi-sensor networks under communication constraints. Considering the limited bandwidth and communication resources, we propose a binary-valued data transmission scheme and the corresponding distributed filter, which overcomes the drawback of current methods that require nodes to exchange the high-dimensional state information. Moreover, the filter requires only a single data exchange among nodes per iteration to synchronize observation updates and achieve consensus of state estimates. Finally, we provide proofs for the stability of the filtering algorithm, which have been validated through simulation examples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA07.4", "code": "FrA07.4", "title": "Supervisory Measurement-Guided Noise Covariance Estimation: Discussing Forward and Reverse Differentiation (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA07", "sessionTitle": "Distributed Estimation and Information Fusion Over Sensor Networks", "sessionType": "Invited Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Li, Haoying", "affiliation": "Chinese University of Hong Kong, Shenzhen" }, { "name": "Peng, Yifan", "affiliation": "The Chinese University of Hong Kong. Shen Zhen" }, { "name": "Wu, Yuchi", "affiliation": "Shanghai University" }, { "name": "Wu, Junfeng", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Linear system identification", "Kalman filtering" ], "abstract": "Reliable state estimation depends on accurately modeled noise covariances, which are difficult to determine in practice. This paper formulates the noise covariance estimation as a bilevel optimization problem that factorizes the joint likelihood of primary and supervisory measurements to reconcile information exploitation with computational tractability. The factorization converts the nested Bayesian dependency into a Markov-chain structure, allowing efficient computation. At the lower level, a Kalman filter with state augmentation performs such computation. Meanwhile, closed-form forward and reverse differentiation provide efficient gradients for the upper-level updates, and we compare the two modes’ space and time complexities to inform their practical selection. The upper level subsequently refines the noise covariances to guide the lower-level estimation. Taken together, the proposed algorithms offer a systematic and computationally efficient approach to noise covariance estimation in linear Gaussian systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA07.5", "code": "FrA07.5", "title": "Joint Bit Allocation and Parameter Estimation for Bandwidth-Constrained Distributed Sensor Network (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA07", "sessionTitle": "Distributed Estimation and Information Fusion Over Sensor Networks", "sessionType": "Invited Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Li, Xin", "affiliation": "Chinese Academy of Sciences" }, { "name": "Shao, Mingjie", "affiliation": "Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences" }, { "name": "Zhao, Yanlong", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Nonlinear system identification", "Quantized systems" ], "abstract": "This paper studies the joint optimization problem of bit allocation and parameter estimation in distributed sensor networks, where sensors transmit quantized measurements to a fusion center under a total bit budget imposed on backhaul links. By leveraging the Bussgang decomposition, we formulate a covariance-based mean-square-error minimization problem that jointly optimizes the linear estimator and the bit allocation among sensors. Since the resulting problem is a non-convex mixed-integer program, direct global optimization is computationally prohibitive. To address this difficulty, an alternating minimization algorithm is developed for the continuous relaxation problem, followed by local search to obtain a feasible integer bit allocation. Simulation results show that the proposed method outperforms equal bit allocation and achieves accuracy close to exhaustive enumeration with much lower computational cost.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA08.1", "code": "FrA08.1", "title": "Finite-Time Adaptive Convergence in the Dynamic Error Model under Persistent or Interval Excitation (I)", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA08", "sessionTitle": "Adaptation and Identification with Improved Transient Performance and Accelerated Convergence", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Gerasimov, Dmitry", "affiliation": "ITMO University" }, { "name": "Nikiforov, Vladimir O.", "affiliation": "ITMO University" } ], "keywords": [ "Model reference adaptive control", "Linear system identification" ], "abstract": "The paper addresses the problem of parametric convergence enhancement up to finite time convergence (FTC) in schemes of direct adaptation that stem from the dynamic error model with measurable state. FTC is achieved under persistent or interval excitation conditions. In contrast to the majority of adaptation schemes with FTC, the main advantages of the proposed FTC mechanism consist in the following: 1) the proposed mechanism is compatible with standard algorithms of adaptation designed for a dynamic error model; 2) zeroing of the control error is always achieved for any bounded regressor without any additional conditions (like persistent or interval excitation, or like not-in-L_1 condition) what is important for direct adaptive control; 3) the FTC alertness is preserved under persistent excitation condition.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA08.2", "code": "FrA08.2", "title": "An Enhancement of Adaptive Observer Accuracy Based on the Heavy-Ball Algorithm (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA08", "sessionTitle": "Adaptation and Identification with Improved Transient Performance and Accelerated Convergence", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Ríos, Héctor", "affiliation": "SECIHTI - Instituto Tecnológico De La Laguna" }, { "name": "Efimov, Denis", "affiliation": "Inria" }, { "name": "Ushirobira, Rosane", "affiliation": "Inria" } ], "keywords": [ "Adaptive observer design", "Estimation and filtering" ], "abstract": "This paper presents a design of a heavy–ball algorithm–based adaptive observer for the simultaneous estimation of states and constant parameters in a class of uncertain nonlinear systems subject to external disturbances. The proposed estimator consists of a Luenberger–like observer for state estimation and a heavy–ball–based algorithm for identifying unknown constant parameters. For the ideal case, the adaptive observer estimates the actual values of both state and parameter vectors with an exponential convergence rate, possessesing the input–to–state stability property with respect to bounded external disturbances. The closed–loop stability analysis can be performed using a Lyapunov function approach under conventional conditions on the system’s persistence of excitation. The effectiveness of the proposed estimation algorithm is demonstrated through simulation results, which highlight an improvement in the accuracy compared to a conventional adaptive observer.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA08.3", "code": "FrA08.3", "title": "Adaptive Prescribed Performance Control with Regulation-Triggered Batch Identifier and Extended State Observers (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA08", "sessionTitle": "Adaptation and Identification with Improved Transient Performance and Accelerated Convergence", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Shen, Jiajun", "affiliation": "Beihang University" }, { "name": "Liu, Yuxiao", "affiliation": "Beihang University" }, { "name": "Wang, Wei", "affiliation": "Beihang University" }, { "name": "Yan, Jiaqi", "affiliation": "Beihang University" } ], "keywords": [ "Nonlinear adaptive control", "Neural and fuzzy adaptive control", "Event-based control" ], "abstract": "This paper presents an adaptive prescribed performance control scheme for uncertain systems. The certainty equivalence prescribed performance controller is formulated by leveraging the error transformation method. The radial basis function neural networks are employed to approximate the unstructured uncertainties. A batch identifier is established, which makes full use of historical excitation information to update the estimate of optimal weight vector when the regulation-triggered condition is met. The extended state observers are employed to compensate for the approximation errors and environmental noises. It is demonstrated that the prescribed performance is achieved and the stiff differential equation problem is mitigated.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA08.4", "code": "FrA08.4", "title": "Composite Learning Robot Control with Prediction-Guided Directional Forgetting (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA08", "sessionTitle": "Adaptation and Identification with Improved Transient Performance and Accelerated Convergence", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Shi, Tian", "affiliation": "Southeast University" }, { "name": "Wang, Qian", "affiliation": "Sun Yat-Sen University" }, { "name": "Li, Shihua", "affiliation": "Southeast University" }, { "name": "Pan, Yongping", "affiliation": "Nanyang Technological University" } ], "keywords": [ "Nonlinear adaptive control" ], "abstract": "Parameter convergence is crucial for improving the stability and robustness of adaptive robot control, and exploiting online data memory is a natural approach to enhancing parameter estimation. This paper proposes a directional forgetting-based composite learning robot control (DF-CLRC) method to achieve parameter convergence under a condition of interval excitation that is strictly weaker than persistent excitation. In the DF-CLRC, the forgetting rate is adjusted using a directional forgetting rate matrix, and the excitation time is updated based on a torque prediction error, thereby exploiting online data memory more effectively to achieve time-varying parameter estimation. Simulations on a seven-degrees-of-freedom robot have verified the performance of the proposed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA08.5", "code": "FrA08.5", "title": "Dynamic Memory Event-Based Low-Complexity Predefined-Time Control for Teleoperation Systems with Actuator Faults and Complex Output Constraint", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA08", "sessionTitle": "Adaptation and Identification with Improved Transient Performance and Accelerated Convergence", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Longnan, Li", "affiliation": "Harbin Engineering University" }, { "name": "Guo, Shaofan", "affiliation": "Institute of Xi'an Aerospace Solid Propulsion Technology" }, { "name": "Zhang, Lanyong", "affiliation": "Harbin Engineering University" }, { "name": "Yang, Chenguang", "affiliation": "University of the West of England" } ], "keywords": [ "Teleoperation", "Adaptive and adaptable automation", "High-performance motion control systems" ], "abstract": "Synchronization tracking control of teleoperation systems is crucial for ensuring reliable remote operation in complex environments. However, in practice, such systems often suffer from limited communication bandwidth and complex output constraints, while actuator faults may further degrade the system’s control performance and even lead to instability. To this end, we develop a dynamic memory event-based adaptive practical predefined-time fault-tolerant control scheme. First, to relax the strict assumption on initial conditions in existing constraint methods while simultaneously accommodating various types of output constraints, a virtual control term integrating a shifting function and unified transformation functions is constructed. Second, two novel adaptive update laws with predefined-time convergence and low complexity are developed to compensate the system's lumped uncertainties and to solve the unknown control gain issue induced by actuator faults, respectively. Unlike finite- or fixed-time control schemes, the convergence-time upper bound of the developed approach is independent of the system's initial conditions and can be tuned via a single parameter. Simulation results show that, for any initial conditions, the developed method can constrain the system output within the boundary constraints after a predefined time, while further reducing communication resources, even in the presence of actuator faults.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA09.1", "code": "FrA09.1", "title": "Mobile Source Identification in 1D Advection–Diffusion Equation", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA09", "sessionTitle": "Physics Informed and Grey Box Model Identification I", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Akil, Doaa", "affiliation": "UGA" }, { "name": "Georges, Didier", "affiliation": "Grenoble Institute of Engineering and Management - Univ. Grenoble Alpes" }, { "name": "Millet, Olivier", "affiliation": "Université De La Rochelle" } ], "keywords": [ "Physics informed and grey box model identification", "Active learning and experiment design", "Diffusion process" ], "abstract": "This article presents an inverse source problem for the 1D advection-diffusion equation. From noisy data collected by a network of fixed sensors, we compute both the initial condition and the parameters of a moving Gaussian source. The problem is formulated as a variational optimization framework, resulting in a state equation, an adjoint equation, and gradient expressions calculated via a discrete adjoint of an implicit upwind scheme. A BFGS algorithm is used to update all unknowns. Numerical tests, including a fixed source case with optimal sensor placement, demonstrate the reconstruction accuracy and the essential role of sensor location.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA09.2", "code": "FrA09.2", "title": "Improved PINNs for Solving Forward and Inverse Problems of Nonlinear Fractional-Order Diffusion PDEs", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA09", "sessionTitle": "Physics Informed and Grey Box Model Identification I", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Ge, Fudong", "affiliation": "Tianjin University" }, { "name": "Chen, YangQuan", "affiliation": "University of California, Merced" }, { "name": "Wang, Haoyu", "affiliation": "Tianjin University" }, { "name": "Song, Weijing", "affiliation": "China University of Geosciences" }, { "name": "Tu, Junwen", "affiliation": "China University of Geosciences" } ], "keywords": [ "Physics informed and grey box model identification", "Diffusion process", "Machine and deep learning for system identification" ], "abstract": "The purpose of this paper is to improve physics-informed neural networks (PINNs) algorithm for solving forward and inverse problems of nonlinear fractional-order diffusion partial differential equations (PDEs). Toward this aim, we first utilize the finite difference L_1 method on non-uniform meshes to embed fractional-order derivative into PINNs for achieving a higher accuracy, while making up the drawback that automatic differentiation is invalid to fractional-order derivatives. Solving algorithms via the improved PINNs for forward and inverse problems of nonlinear fractional-order diffusion PDEs are then presented. Subsequently, we take fractional-order Fisher equation as an examples to verify the effectiveness and robustness of our obtained results. These numerical results allow us to see that our improved PINNs can efficiently deal with the forward and inverse problems of nonlinear fractional-order diffusion PDEs.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA09.3", "code": "FrA09.3", "title": "Rethinking Physics-Informed Regression Beyond Training Loops and Bespoke Architectures", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA09", "sessionTitle": "Physics Informed and Grey Box Model Identification I", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Sabug, Lorenzo Jr", "affiliation": "Imperial College" }, { "name": "Kerrigan, Eric C.", "affiliation": "Imperial College London" } ], "keywords": [ "Physics informed and grey box model identification", "Filtering and smoothing", "Learning methods for control" ], "abstract": "We revisit the problem of physics-informed regression, and propose a method that directly computes the state at the prediction point, simultaneously with the derivative and curvature information of the existing samples. We frame each prediction as a constrained optimisation problem, leveraging multivariate Taylor series expansions and explicitly enforcing physical laws. Such an approach makes the role of physical assumptions transparent: the governing equations enter the optimisation problem through equality constraints on the relevant differential quantities, rather than absorbed into physics-loss minimisation terms. Our comparative benchmarks on a reaction–diffusion system demonstrate predictive accuracy that is on par with a neural network–based approach, while exchanging the requirement for a single training phase for the execution of separate prediction computations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA09.4", "code": "FrA09.4", "title": "Curriculum-Learned Vanishing Stacked Residual PINNs for Hyperbolic PDE State Reconstruction", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA09", "sessionTitle": "Physics Informed and Grey Box Model Identification I", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Eshkofti, Katayoun", "affiliation": "KTH" }, { "name": "Barreau, Matthieu", "affiliation": "KTH" } ], "keywords": [ "Physics informed and grey box model identification", "Iterative and repetitive learning control", "Distributed control and estimation" ], "abstract": "Modeling distributed dynamical systems governed by hyperbolic partial differential equations (PDEs) remains challenging due to discontinuities and shocks that hinder convergence of traditional physics-informed neural networks (PINNs). The recently proposed vanishing stacked residual PINN (VSR-PINN) embeds a vanishing-viscosity mechanism within stacked residual refinements, enabling a smooth transition from the parabolic to hyperbolic regime. This paper integrates three curriculum-learning methods into VSR-PINN: primal-dual (PD) optimization, causality progression, and adaptive sampling. The PD strategy balances physics and data losses, the causality scheme unlocks deeper stacks by respecting temporal and gradient evolution, and adaptive sampling targets high residuals. Numerical experiments on traffic reconstruction confirm that enforcing causality systematically reduces the median point-wise MSE and its variability across runs, yielding improvements of nearly one order of magnitude over non-causal training in both the baseline and PD variants.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA09.5", "code": "FrA09.5", "title": "Identification of Port-Hamiltonian Differential-Algebraic Equations from Input-Output Data", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA09", "sessionTitle": "Physics Informed and Grey Box Model Identification I", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Hagelaars, Noortje", "affiliation": "Eindhoven University of Technology" }, { "name": "van Otterdijk, Gé Jan Ember", "affiliation": "Eindhoven University of Technology" }, { "name": "Moradi, Sarvin", "affiliation": "Eindhoven University of Technology" }, { "name": "Tóth, Roland", "affiliation": "Eindhoven University of Technology" }, { "name": "Jaensson, Nick", "affiliation": "Eindhoven University of Technology" }, { "name": "Schoukens, Maarten", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Physics informed and grey box model identification", "Linear system identification" ], "abstract": "Many models of physical systems, such as mechanical and electrical networks, exhibit algebraic constraints that arise from subsystem interconnections and underlying physical laws. Such systems are commonly formulated as differential-algebraic equations (DAEs), which describe both the dynamic evolution of system states and the algebraic relations that must hold among them. Within this class, port-Hamiltonian differential-algebraic equations (pH-DAEs) offer a structured, energy-based representation that preserves interconnection and passivity properties. This work introduces a data-driven identification method that combines port-Hamiltonian neural networks (pHNNs) with a differential-algebraic solver to model such constrained systems directly from noisy input–output data. The approach preserves the passivity and interconnection structure of port-Hamiltonian systems while employing a backward Euler discretization with Newton’s method to solve the coupled differential and algebraic equations consistently. The performance of the proposed approach is demonstrated on a DC power network, where the identified model accurately captures system behaviour and maintains errors proportional to the noise amplitude, while providing reliable parameter estimates.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA09.6", "code": "FrA09.6", "title": "Physics-Guided Recurrent State-Space Neural Networks for Multi-Step Prediction", "day": "Friday", "date": "August 28, 2026", "time": "11:30-11:50", "sessionCode": "FrA09", "sessionTitle": "Physics Informed and Grey Box Model Identification I", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Li, Ruiyuan", "affiliation": "Delft University of Technology" }, { "name": "Seth, Ajay", "affiliation": "Delft University of Technology" }, { "name": "Kok, Manon", "affiliation": "Delft University of Technology" } ], "keywords": [ "Physics informed and grey box model identification", "Machine and deep learning for system identification", "Nonlinear system identification" ], "abstract": "State-space models are traditionally based on physical knowledge, but multi-step predictions from these physical models can be poor due to model inaccuracy. Black-box deep learning has shown promise as an alternative. However, these methods rely on the availability of large datasets and potentially available physical knowledge is neglected. We propose the PG-RSSNN, a physics-guided recurrent state-space neural network that incorporates recurrent structures to enable the use of non-saturating activation functions in multi-step prediction. It mitigates the vanishing gradients and eliminates the risk of numerical divergence in training seen in existing structures that feed back state estimates. Results across multiple systems with various physical model imperfections, from linear state-space models with Gaussian noise to a robotic arm and a cascaded water tank system, show that the proposed PG-RSSNN maintains stable training behavior, and improves multi-step predictions, as compared with black-box neural networks and physics-only models, even with limited training data and when physical models are only partially known.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA10.1", "code": "FrA10.1", "title": "End-To-End ILC for Repetitive Untrackable Tasks: A Cooperative Game Perspective (I)", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA10", "sessionTitle": "Recent Advances in Iterative Learning and Repetitive Control I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Zhuang, Zhihe", "affiliation": "Jiangnan University" }, { "name": "González, Rodrigo A.", "affiliation": "Eindhoven University of Technology" }, { "name": "Tao, Hongfeng", "affiliation": "Jiangnan University" }, { "name": "Paszke, Wojciech", "affiliation": "University of Zielona Gora" }, { "name": "Oomen, Tom", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Iterative and repetitive learning control" ], "abstract": "An inherent assumption of perfect tracking in iterative learning control (ILC) is that there exists an ILC input such that the generated output can track the desired trajectory reference. This assumption may fail in practice, which gives rise to desired but untrackable tasks. This paper gives an end-to-end ILC design for repetitive untrackable tasks in closed-loop systems. The reference input is trial-to-trial updated together with the ILC feedforward input based on the measurement data. This two-player behavior of the closed-loop ILC system is investigated from a cooperative game perspective. A sufficient condition for the two-player end-to-end ILC to have a lower cost than the one-player norm optimal ILC (NOILC) is discovered. Finally, a numerical example is given to verify the effectiveness of the developed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA10.2", "code": "FrA10.2", "title": "A Flexible Modular-Based Iterative Learning Control Design (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA10", "sessionTitle": "Recent Advances in Iterative Learning and Repetitive Control I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Hobson, Daniel", "affiliation": "University of Southampton" }, { "name": "Chu, Bing", "affiliation": "University of Southampton" }, { "name": "Cai, Xiaohao", "affiliation": "University of Southampton" } ], "keywords": [ "Iterative and repetitive learning control", "Learning methods for control" ], "abstract": "In many common industrial applications, Iterative Learning Control is a suitable technique to achieve accurate tracking of a reference trajectory. Using the principle of repeated attempts at a task, the previous behaviour can be used to generate an improved control sequence that achieves increasing tracking accuracy over these multiple trials. In our previous work Hobson et al. (2025) we take inspiration from the structure and performance of biological motion control systems (sensorimotor systems), leading to a design that learns to generate control signals using a linear combination of parametric ‘modules’. In Hobson et al. (2025) we assume that the primitive functions (forming the ‘modules’) are known in advance. However, the biological systems that provide the inspiration appear to be more flexible, with the basis functions themselves changing during learning and also being learnt. In this flexible setting, not all of these modules are necessarily known a priori and it is not possible to apply existing methods because they require that these modules be pre-defined and fixed. In this paper, we propose a flexible algorithm that permits modification of these modules between trials and provide convergence guarantees. We then demonstrate improved learning performance using a numerical example where a low-fidelity model is learnt quickly and then refined to increase accuracy while maintaining superior convergence speed.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA10.3", "code": "FrA10.3", "title": "Robustness of Norm Optimal Iterative Learning Control to Nonlinear Input Characteristics (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA10", "sessionTitle": "Recent Advances in Iterative Learning and Repetitive Control I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Owens, David H.", "affiliation": "The Univ of Sheffield" }, { "name": "Chu, Bing", "affiliation": "University of Southampton" } ], "keywords": [ "Iterative and repetitive learning control" ], "abstract": "Norm Optimal Iterative Learning Control is an established optimization-based, iterative methodology for constructing inputs for a linear system that generates, exactly, a desired reference signal. It is designed for applications that use a combination of offline, model-based computation with plant operation to generate tracking data to assess the progress of the iterations. It has well defined convergence and robustness properties to linear modelling errors. This paper provides an analysis of the robustness of the algorithm to nonlinear input characteristics, producing a simple algebraic test relating robustness to parameters that characterize the nonlinearity, the plant model and the optimization process. This is a problem with significant practical importance but there is little understanding in the literature. The analysis uses an operator theoretical methodology in Hilbert space. This gives the results great generality. In particular, they cover continuous and discrete state space tracking, end-point and intermediate point problems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA10.4", "code": "FrA10.4", "title": "Iterative Simultaneous Learning of Feedforward and Reference Signals for Output-Constrained Coarse-Fine Systems (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA10", "sessionTitle": "Recent Advances in Iterative Learning and Repetitive Control I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Suzuki, Rikuto", "affiliation": "The University of Tokyo" }, { "name": "Tsurumoto, Kentaro", "affiliation": "The University of Tokyo" }, { "name": "Ohnishi, Wataru", "affiliation": "The University of Tokyo" }, { "name": "Kenjo, Atsushi", "affiliation": "ADTEC Engineering Co., Ltd" }, { "name": "Kita, Toshiki", "affiliation": "ADTEC Engineering Co., Ltd" }, { "name": "Tanaka, Yoneta", "affiliation": "ADTEC Engineering Co., Ltd" } ], "keywords": [ "Iterative and repetitive learning control" ], "abstract": "A coarse-fine system, consisting of coarse and fine systems, is widely employed in industries that require long stroke, yet fast and precise motion. In this paper, we propose a framework that jointly learns the feedforward signal and the reallocation of reference signals with explicit consideration of output constraints via Iterative Learning Control. The proposed method minimizes the overall tracking error by cooperatively compensating for tracking errors in the fine and coarse systems through coordinated task allocation between the two systems. The effectiveness of the proposed method is demonstrated through simulations on an output-constrained coarse-fine system, with comparison against existing approaches.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA10.5", "code": "FrA10.5", "title": "2D Optimization Based Iterative Learning Control Design for Uncertain Linear Differential Systems with Input Saturation (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA10", "sessionTitle": "Recent Advances in Iterative Learning and Repetitive Control I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Pakshin, Pavel", "affiliation": "Arzamas Polytechnic Institute of R.E. Alekseev NSTU" }, { "name": "Emelianova, Julia", "affiliation": "Arzamas Polytechnic Institute of R.E. Alekseev NSTU" }, { "name": "Rogers, Eric", "affiliation": "Univ of Southampton" } ], "keywords": [ "Iterative and repetitive learning control" ], "abstract": "The paper develops an iterative learning control law for uncertain linear differential systems with actuator saturation. A new 2D systems-based design is developed by applying gradient optimization and vector Lyapunov functions from the stability theory of repetitive processes. The resulting control law provides improved performance compared with existing ones. An example demonstrates the effectiveness of the new design.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA10.6", "code": "FrA10.6", "title": "Repetitive Control for Cancellation of Tollmien-Schlichting Waves (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:30-11:50", "sessionCode": "FrA10", "sessionTitle": "Recent Advances in Iterative Learning and Repetitive Control I", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Rolen, Abigail", "affiliation": "Rensselaer Polytechnic Institute" }, { "name": "Mishra, Sandipan", "affiliation": "Rensselaer Polytechnic Institute" }, { "name": "Amitay, Michael", "affiliation": "Rensselaer Polytechnic Institute" } ], "keywords": [ "Iterative and repetitive learning control", "Filtering and smoothing", "Learning methods for control" ], "abstract": "Tollmien-Schlichting waves are flowfield instabilities that can eventually evolve into turbulence, which can degrade aircraft performance. This paper presents a repetitive control approach for the attenuation of Tollmien–Schlichting (TS) waves through active flow control. Since the TS-wave behavior depends on flow conditions that may change over time, we pair the repetitive controller with an on-line period detection algorithm. This results in a repetitive controller where the internal model is time-varying. Therefore, we derive sufficient conditions under which the controller remains stable, for time-varying or incorrectly detected periods. For validation, high fidelity CFD simulations are conducted wherein TS waves are artificially generated by an upstream actuator and canceled downstream through the control scheme. The disturbance attenuation performance of the proposed solution is benchmarked against a state-of-the-art strategy, namely a Filtered-x Least Mean Squares (FxLMS) controller. The repetitive controller is shown to deliver comparable performance to FxLMS, while eliminating the need for a second upstream reference sensor (which is necessary for the FxLMS). At least 92% amplitude attenuation of both single-frequency and periodic TS waves is demonstrated in the high-fidelity simulation environment.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA13.1", "code": "FrA13.1", "title": "Bilinear Heat Pump Models in MPC-Based Energy-Optimal Building Operation with Integer Inputs", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA13", "sessionTitle": "Applications of Optimal Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Lammersmann, Benedikt", "affiliation": "Ruhr University Bochum" }, { "name": "Dadras Javan, Shahriar", "affiliation": "Ruhr University of Bochum, Chair of Automatic Control and System Theory" }, { "name": "Monnigmann, Martin", "affiliation": "Ruhr-Universität Bochum" } ], "keywords": [ "Model predictive control", "Control of hybrid systems", "Applications of optimal control" ], "abstract": "We employ model predictive control to reduce the energy cost of a building by optimizing electrical and thermal energy flows jointly over a horizon. The discrete-controllable heat pump possesses nonlinear behavior that is modeled by a first-order approximation, resulting in overall bilinear system dynamics. The resulting optimal control problem is then represented as an equivalent mixed-integer linear program. Additionally, a linear-continuous relaxation is incorporated to enable long-horizon optimizations. A simulative case study demonstrates the high potential of the proposed approach, yielding performance comparable to the theoretical optimum while being mindful of the computational effort.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA13.2", "code": "FrA13.2", "title": "Event-Triggered Reference Governor with Deep Reinforcement Learning for Constrained Quadrotor UAV Control (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA13", "sessionTitle": "Applications of Optimal Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Wang, Rong", "affiliation": "Jilin University" }, { "name": "Chen, Dong", "affiliation": "Michigan State University" }, { "name": "Nie, Zifei", "affiliation": "Kyushu University" }, { "name": "Chen, Hong", "affiliation": "Tongji University" }, { "name": "Gong, Xun", "affiliation": "Jilin University" } ], "keywords": [ "Model predictive control", "Applications of optimal control", "Learning methods for optimal control" ], "abstract": "Optimization-based methods are essential for enforcing state constraints and ensuring flight safety in unmanned aerial vehicles (UAVs). However, predictive optimization techniques that explicitly handle multivariable constraints often suffer from high computational cost due to the nonlinear and strongly coupled UAV dynamics. This paper proposes an event-triggered reference governor (ET-RG) for UAV control that enforces constraints within an optimal control framework. To balance performance and efficiency, a reinforcement learning (RL)-based triggering mechanism is introduced to activate the RG adaptively only when necessary. Simulations on an autonomous quadrotor hovering task demonstrate that the proposed ET-RG achieves constraint-compliant tracking while significantly reducing computational burden compared to conventional reference governors.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA13.3", "code": "FrA13.3", "title": "Game-Theoretic Learning-Based Mitigation of Insider Threats", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA13", "sessionTitle": "Applications of Optimal Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Xu, Gehui", "affiliation": "Academy of Mathematics and Systems Science, Chinese Academy of Sciences" }, { "name": "Chen, Kaiwen", "affiliation": "Imperial College London" }, { "name": "Parisini, Thomas", "affiliation": "Imperial C., Aalborg U. & Univ. of Trieste" }, { "name": "Malikopoulos, Andreas", "affiliation": "Cornell University" } ], "keywords": [ "Applications of optimal control", "Differential or dynamic games", "Adaptive control design" ], "abstract": "An insider is defined as a team member who covertly deviates from the team’s optimal collaborative control strategy in pursuit of a private objective, while maintaining an outward appearance of cooperation. Such insider threats can severely undermine cooperative systems: subtle deviations may degrade collective performance, jeopardize mission success, and compromise operational safety. This paper presents a comprehensive framework for identifying and mitigating insider threats in cooperative control settings. We introduce an insider-aware, game-theoretic formulation in which the insider’s hidden intention is parameterized, allowing the threat identification task to be reformulated as a parameter estimation problem. To address this challenge, we employ an online indirect dual adaptive control approach that simultaneously infers the insider’s control strategy and counteracts its negative influence. By injecting properly designed probing signals, the resulting mitigation policy asymptotically recovers the nominal optimal control law -- one that would be achieved under full knowledge of the insider’s objective. Simulation results validate the effectiveness of the proposed identification–mitigation framework and illustrate its capability to preserve team performance even in the presence of covert adversarial behavior.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA13.4", "code": "FrA13.4", "title": "Berk–Nash Equilibrium and Learning for Satellite Orbital Pursuit–Evasion Games", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA13", "sessionTitle": "Applications of Optimal Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Zhu, Quanyan", "affiliation": "New York University" } ], "keywords": [ "Applications of optimal control", "Differential or dynamic games" ], "abstract": "We study orbital pursuit and evasion games in which each spacecraft operates with a misspecified model of relative motion and updates it from noisy measurements. We introduce a Berk Nash framework in which each player designs a linear quadratic Gaussian controller for a subjective model and adjusts its parameters by minimizing an innovation based Kullback–Leibler divergence. We establish existence of equilibrium and implement a two sided learning scheme. A three dimensional Clohessy–Wiltshire case study demonstrates convergence toward pseudo true parameters and closed loop behavior that closely matches the classical zero sum benchmark, offering a principled representation of learning and bounded rationality in space engagements.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA13.5", "code": "FrA13.5", "title": "A Generalized Slack Variables Method for Solving the Dynamic Economic Dispatch Problem", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA13", "sessionTitle": "Applications of Optimal Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Grimaldi, Riccardo Alessandro", "affiliation": "Imperial College London" }, { "name": "Astolfi, Alessandro", "affiliation": "King Abdullah University of Science and Technology (KAUST)" } ], "keywords": [ "Applications of optimal control", "Control barrier functions and state space constraints" ], "abstract": "A new methodology for solving the Dynamic Economic Dispatch problem, based on the use of slack variables, is proposed. The approach provides a systematic procedure for transforming the Dynamic Economic Dispatch problem into an extended unconstrained optimal control problem, the solutions of which directly yield solutions to the original problem. A key advantage of the proposed method is its ability to handle an arbitrary number of state constraints. Two simple examples illustrate the theory.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA13.6", "code": "FrA13.6", "title": "Online Learning-Based Predictive-Triggered Control for Mobile Robots", "day": "Friday", "date": "August 28, 2026", "time": "11:30-11:50", "sessionCode": "FrA13", "sessionTitle": "Applications of Optimal Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Ma, Kai", "affiliation": "Xi'an University of Architecture and Technology" }, { "name": "Chen, Jiaxuan", "affiliation": "Xi'an University of Architecture and Technology" }, { "name": "He, Ning", "affiliation": "Xi'an University of Architecture and Technology" }, { "name": "Liu, Jinfeng", "affiliation": "University of Alberta" } ], "keywords": [ "Model predictive control", "Real-time optimal control", "Applications of optimal control" ], "abstract": "Model predictive control (MPC) has attracted considerable attention in robotic systems due to its ability to explicitly handle state and input constraints. However, MPC performance depends on model accuracy, and the need to repeatedly solve an optimal control problem (OCP) often prevents fast robotic systems from obtaining the optimal control sequence in time. To address this issue, this paper proposes an online learning-based predictive-triggered MPC approach. This approach first integrates the predictions obtained from online learning into the MPC cost function, thereby improving the control performance of the robotic system. Then, a parallel computation mechanism is employed to advance the OCP solution ahead of the control-update instant, thereby relaxing the implicit assumption that the OCP can be solved instantaneously in standard MPC. Importantly, sufficient conditions are derived to guarantee algorithmic feasibility and closed-loop stability of the robotic system. Finally, simulation studies demonstrate the effectiveness of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA14.1", "code": "FrA14.1", "title": "A Multi-Objective Optimization Framework for Efficient Tuning and Comparative Analysis of Controllers in Multivariable Processes (I)", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA14", "sessionTitle": "Multi-Objective Optimization Techniques in Control Systems Engineering", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Huilcapi Subia, Victor Manuel", "affiliation": "Universidad Politécnica Salesiana" }, { "name": "Herrero Durá, Juan Manuel", "affiliation": "Polytechnic Univ of Valencia" }, { "name": "Blasco, Xavier", "affiliation": "Polytechnic Univ of Valencia" }, { "name": "Pajares, Alberto", "affiliation": "Universitat Politecnica De Valencia" } ], "keywords": [ "Soft computing and robust intelligent control" ], "abstract": "This paper proposes a methodological framework for tuning multivariable controllers and globally evaluating their performance using a multiobjective optimization approach. The methodology was applied to a multivariable system with two inputs and two outputs, in which Proportional-Integral (PI) and Dynamic Matrix Controllers (DMC) were tuned. Two analysis scenarios were established, each revealing relevant information to a designer regarding the choice of controller type and/or control loops appropriate for stabilizing the system. The DMC controller exhibits superior performance compared to the diagonal and off-diagonal PI controllers evaluated in this paper and is considered a reference controller. This comparison enables a designer to analyze the trade-off between the simplicity of implementation and performance in detail, highlighting scenarios in which a PI design concept may be more suitable than a more complex predictive solution proposed by a DMC design concept.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA14.2", "code": "FrA14.2", "title": "Techno-Economic Models Identification of a Hybrid Inverter Solar–Battery System Using a Multi-Objective Optimization Approach (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA14", "sessionTitle": "Multi-Objective Optimization Techniques in Control Systems Engineering", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Herrero Durá, Juan Manuel", "affiliation": "Polytechnic Univ of Valencia" }, { "name": "Blasco, Xavier", "affiliation": "Polytechnic Univ of Valencia" }, { "name": "Sanchis, Javier", "affiliation": "Polytechnical Univ of Valencia" }, { "name": "Simarro, Raul", "affiliation": "Universidad Politécnica De Valencia" } ], "keywords": [ "Soft computing and robust intelligent control" ], "abstract": "This work investigates two alternative models—a detailed and a simplified one—for a hybrid solar-battery inverter system using a multi-objective optimization approach. Both models are identified and validated with real measurement data. To mitigate outlier effects, the objective function minimizes the mean error after removing extreme values. Results show both models accurately capture system behavior, with mean errors below 20W for battery power and 2% for SOC. Slightly higher SOC errors appear in the simplified model when prioritizing battery performance. The results highlight the effectiveness of multi-objective optimization for comparing model structures and assessing trade-offs.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA14.3", "code": "FrA14.3", "title": "Multi-Objective Parameter Tuning and Performance Comparison of PI and Fuzzy Controllers (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA14", "sessionTitle": "Multi-Objective Optimization Techniques in Control Systems Engineering", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Alvarado, Alejandro", "affiliation": "Pontifícia Universidade Católica Do Paraná" }, { "name": "Reynoso-Meza, Gilberto", "affiliation": "Pontificia Universidade Católica De Paraná" } ], "keywords": [ "Bio-inspired algorithms and optimization-based control", "Fuzzy and neural systems in control" ], "abstract": "This work presents a comparative analysis between a Takagi–Sugeno type-1 fuzzy controller in a MIMO configuration and the classical PI controller tuned using the BLT methodology. The study focuses on three well-known benchmark plants: Wood–Berry, Tyreus Stabilizer, and Wardle & Wood, all characterized by strong loop interactions. The tuning of the fuzzy controller is carried out through a multiobjective optimization framework, which simultaneously minimizes the Integral of Absolute Error (IAE) and the Total Variation (TV). Eleven independent optimization runs are performed per plant, and the best-performing solution is selected according to the median hypervolume metric. The results demonstrate favorable tracking performance and reduced control effort when compared with the BLT-based PI controllers. Pareto fronts, trajectory-tracking responses, and control-signal profiles are presented for the median-hypervolume solutions, highlighting the improved trade-off achieved by the proposed fuzzy strategy.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA14.4", "code": "FrA14.4", "title": "Tuning of Proportional Integral Derivative Acceleration (PIDA) Controllers with Multi-Objective Optimization (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA14", "sessionTitle": "Multi-Objective Optimization Techniques in Control Systems Engineering", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Feican-Campoverde, Christian", "affiliation": "Pontificia Universidade Católica Do Paraná" }, { "name": "Reynoso-Meza, Gilberto", "affiliation": "Pontificia Universidade Católica De Paraná" } ], "keywords": [ "Bio-inspired algorithms and optimization-based control", "Soft computing and robust intelligent control" ], "abstract": "The Proportional–Integral–Derivative (PID) controller remains widely adopted in industry due to its simplicity and robustness, yet its performance may degrade in high-order or strongly coupled two-input two-output (TITO) processes. To address these limitations, the Proportional–Integral–Derivative Acceleration (PIDA) controller extends the PID structure by adding an acceleration term. This work proposes a sequential multi-objective optimization framework for tuning the PIDA controller. By progressively refining the Pareto front, the method enhances convergence and improves decision making. Results demonstrate that the optimized PIDA configuration achieves superior trade-offs between tracking performance and control effort when compared with alternative controller designs.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA14.5", "code": "FrA14.5", "title": "A Multi-Modal Multi-Objective Optimization Perspective in Multivariable PI Control (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA14", "sessionTitle": "Multi-Objective Optimization Techniques in Control Systems Engineering", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Reynoso-Meza, Gilberto", "affiliation": "Pontificia Universidade Católica De Paraná" }, { "name": "Aguirre, Hernan", "affiliation": "Shinshu University" }, { "name": "Alvarado, Alejandro", "affiliation": "Pontifícia Universidade Católica Do Paraná" }, { "name": "Feican-Campoverde, Christian", "affiliation": "Pontificia Universidade Católica Do Paraná" } ], "keywords": [ "Bio-inspired algorithms and optimization-based control" ], "abstract": "This paper investigates the role of multimodal optimization in multivariable PI controller tuning and proposes a pipeline that integrates multi-objective and multimodal search strategies. In multivariable control, the simultaneous optimization of several performance and control-action objectives often leads to many-objective formulations, which complicate convergence and diversity preservation in evolutionary algorithms. To address this issue, we employ an aggregation strategy to reduce the dimensionality of the objective space while preserving interpretability for decision-making. Experimental results demonstrate that the reduced two-objective formulation yields comparable hypervolume performance to the full six-objective problem. Additionally, the analysis reveals that the aggregated Pareto front exhibits multimodality: several distinct decision vectors produce equivalent performance levels. By applying an emph{a posteriori} multimodal search around preferred Pareto-optimal solutions, we show that these alternatives can be systematically identified and visualized. This broadens the multi-criteria decision-making process, allowing the engineer to explore structurally different controller configurations that deliver similar trade-offs between performance and control effort.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA14.6", "code": "FrA14.6", "title": "Multi-Objective Evaluation of PI Controller Tuning Methodologies for UFOPTD System (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:30-11:50", "sessionCode": "FrA14", "sessionTitle": "Multi-Objective Optimization Techniques in Control Systems Engineering", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Campos Reyes, Faride Yamil", "affiliation": "Tecnológico Nacional De México Campus Veracruz" }, { "name": "García Alvarado, Miguel Ángel", "affiliation": "Tecnológico Nacional De México Campus Veracruz" }, { "name": "Ángeles-Sánchez, Laura Elimelet", "affiliation": "Master’s in Biotechnology, Universidad Del Papaloapan, Circuito Central 200 Parque Industrial, Tuxtepec 68301, Oaxaca, México" }, { "name": "Reynoso-Meza, Gilberto", "affiliation": "Pontificia Universidade Católica De Paraná" }, { "name": "Carrillo-Ahumada, J.", "affiliation": "Universidad Del Papaloapan" } ], "keywords": [ "Model driven engineering of control systems" ], "abstract": "Unstable systems have long attracted significant interest within the automatic control community. Their particular characteristics often demand greater attention during tuning compared to stable systems. The methodology followed in this work consisted of deriving several PI controllers for an unstable system and evaluating them in terms of stability, robustness, and performance before applying the multi-objective Promethee framework. Each tuning criterion can yield its own best solution, yet these results may still be enhanced through very specific parameter variations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA16.1", "code": "FrA16.1", "title": "Dynamic Feedback Stabilization of Combustion Oscillations in a Rijke Tube", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA16", "sessionTitle": "Modeling, Simulation and Control of Distributed Parameter Systems IV", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Zhang, Yu-Long", "affiliation": "Beijing Institute of Technology" }, { "name": "Wang, Jun-Min", "affiliation": "Beijing Institute of Technology" }, { "name": "Li, Donghai", "affiliation": "Tsinghua University" }, { "name": "Zhu, Min", "affiliation": "Tsinghua University" } ], "keywords": [ "Control of distributed parameter systems", "Boundary control of distributed parameter systems", "Linear systems" ], "abstract": "In this paper, we consider dynamic feedback stabilization of combustion oscillations in a Rijke tube. The combustion oscillations in the Rijke tube are modeled by the linearized Euler equations of gas dynamics, with the heat release rate described as a pointwise source term governed by an ordinary differential equation. Based on the extended state observer (ESO) in active disturbance rejection control (ADRC), finite-dimensional dynamic boundary and pointwise controllers are designed to suppress combustion oscillations. The stability analysis of the closed-loop system consists of two parts: first, the Nyquist criterion is applied to confirm that the real parts of all closed-loop eigenvalues are negative under specific feedback gains; second, operator semigroup theory combined with the Riesz basis approach demonstrates the validity of the spectrum-determined growth condition, thereby establishing exponential stability of the closed-loop system. The Nyquist plot is also utilized to develop a methodology for computing the stabilizing parameter region of the controller. Numerical simulations are conducted to validate the effectiveness of the proposed control design.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA16.2", "code": "FrA16.2", "title": "Cross-Directional Modelling and Control of Slot-Die Battery Electrode Coating (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA16", "sessionTitle": "Modeling, Simulation and Control of Distributed Parameter Systems IV", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Kim, Hyuntae", "affiliation": "University of Oxford" }, { "name": "Kempf, Idris", "affiliation": "University of Oxford" } ], "keywords": [ "Control of distributed parameter systems", "Model reduction of distributed parameter systems", "System identification and adaptive control of distributed parameter systems" ], "abstract": "As global battery demand increases, real-time process control becomes increasingly important for battery electrode manufacturing, yet slot-die lines are still mostly manually operated in open loop. This paper develops a physics-based modelling-and-control pipeline for film-thickness regulation. Computational fluid dynamics (CFD) simulations provide the data from which a low-order cross-directional model is identified and calibrated. Numerical simulations demonstrate close agreement between the CFD and the cross-directional model, which is used to design a controller that can be used in both real-time, automated feedback operation and manual feedforward operation during line commissioning.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA16.3", "code": "FrA16.3", "title": "Transfer Learning-Based Moving Horizon Estimation for Schrödinger Equation with Limited Data (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA16", "sessionTitle": "Modeling, Simulation and Control of Distributed Parameter Systems IV", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Xie, Junyao", "affiliation": "University of Guelph" } ], "keywords": [ "Integration of ML/AI for control of DPS", "Systems theoretic properties of distributed parameter systems", "Optimization-based estimation and control" ], "abstract": "This manuscript proposes a novel transfer learning-based moving horizon estimation method for state/output estimation of an infinite-dimensional system (i.e., target system) with limited output measurements, by leveraging the output measurements from another infinite-dimensional system (i.e., source system). This study is motivated by the fact that practical applications often suffer from data unavailability issues due to sensor hardware failure and/or harsh operating conditions. Practical applications such as cooperative sensing in parallel pipelines/reactors and autonomous vehicles indeed provide natural settings in which data from a twin system can compensate for insufficient monitoring data in another system. This manuscript leverages the idea of transfer learning to borrow the monitoring data from the source system for the state estimation of the target system. In particular, a transfer moving horizon estimation algorithm is proposed for output estimation in the presence of process and measurement disturbances as well as inequality hard constraints, and its stability analysis is further provided. The proposed method is demonstrated on a Schrödinger equation example through numerical simulation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA16.4", "code": "FrA16.4", "title": "Boundary Consensus of Reaction-Diffusion Multi-Agent Systems under Restricted Observation", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA16", "sessionTitle": "Modeling, Simulation and Control of Distributed Parameter Systems IV", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Yan, Xu", "affiliation": "Southeast University" }, { "name": "Cao, Jinde", "affiliation": "Southeast Univ" } ], "keywords": [ "Infinite-dimensional multi-agent systems and networks", "Boundary control of distributed parameter systems", "Observer design" ], "abstract": "This paper investigates the consensus problem for multi-agent systems modeled by reaction-diffusion partial differential equations. Considering the practical constraints that complete state information is often unavailable and that control inputs can only be applied at the boundary of the spatial domain, a boundary control strategy based on partial spatial domain measurements is proposed. First, to address the issue of unmeasurable states, an observer is designed using measurement information from piecewise spatial subdomains or discrete points. Subsequently, a distributed boundary control protocol is developed based on the estimated states to ensure consensus. By employing the Lyapunov direct method and Poincaré-Wirtinger inequalities, sufficient conditions for the asymptotic stability of the closed-loop error system are derived and formulated as linear matrix inequalities. Finally, the effectiveness of the proposed method is validated through numerical simulations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA16.5", "code": "FrA16.5", "title": "Observer-Based State Feedback Controller for a Mindlin Plate Model in Port-Hamiltonian Framework (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA16", "sessionTitle": "Modeling, Simulation and Control of Distributed Parameter Systems IV", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Diaz, Ignacio", "affiliation": "FEMTO-ST" }, { "name": "Le Gorrec, Yann", "affiliation": "FEMTO-ST, SupMicroTech Besançon" }, { "name": "Wu, Yongxin", "affiliation": "Université Marie Et Louis Pasteur" } ], "keywords": [ "Distributed parameters port Hamiltonian systems", "Observer design", "Passivity-based control" ], "abstract": "This paper applies an early lumped observer‐based state‐feedback (OBSF) control design methodology, originally developed for one‐dimensional (1D) boundary‐controlled port‐Hamiltonian systems, to a two‐dimensional (2D) boundary‐controlled Mindlin plate. To this end, the 2D port‐Hamiltonian Mindlin plate model is first introduced and then discretized using a structure‐preserving finite‐difference method on staggered grids. A controllability decomposition is subsequently applied to identify the controllable modes of the discretized model. Furthermore, the state-feedback and observer gains are designed so that the OBSF controller is strictly positive real. This guarantees the stability of the closed-loop system when the finite-dimensional OBSF controller is interconnected with the 2D boundary-controlled Mindlin plate. Numerical simulations are finally presented to illustrate the effectiveness of the proposed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA16.6", "code": "FrA16.6", "title": "State Estimation for a Class of 2-D Semilinear Distributed Parameter Systems Using Boundary Collocated Outputs (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:30-11:50", "sessionCode": "FrA16", "sessionTitle": "Modeling, Simulation and Control of Distributed Parameter Systems IV", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Ge, Fudong", "affiliation": "Tianjin University" }, { "name": "Chen, YangQuan", "affiliation": "University of California, Merced" }, { "name": "Zuo, Zhiqiang", "affiliation": "Tianjin University" }, { "name": "Song, Weijing", "affiliation": "China University of Geosciences" } ], "keywords": [ "Observer design", "Control of distributed parameter systems", "Analytic design" ], "abstract": "The aim of this paper is to deal with the state estimation problem of a class of two-dimensional (2-D) semilinear distributed parameter systems governed by parabolic partial differential equations (PDEs) with space-dependent diffusivity. For this, the boundary collocated outputs, which only measure the boundary linear information of the studied PDEs, are considered. We then propose a Luenberger-type PDE observer and investigate exponential stability of the resulting observer error system by utilizing Lyapunov stability analysis theory and the linear matrix inequalities (LMIs). Simulation results are finally presented to verify the effectiveness of our methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA17.1", "code": "FrA17.1", "title": "Reinforcement Learning-Based Multi-Agent Simulation Framework for Smart Sales and Operations Planning (I)", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA17", "sessionTitle": "Simulation Modeling, Machine Learning and Optimization Algorithms to Support Decision Making in Production, Logistics, and Supply Chain Management", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Laissaoui, Akram Badreddine", "affiliation": "INSA Lyon" }, { "name": "Arbaoui, Taha", "affiliation": "INSA Lyon" }, { "name": "Ladier, Anne-Laure", "affiliation": "INSA Lyon, Université Lumière Lyon 2, Université Claude Bernard Lyon 1, Université Jean Monnet Saint-Etienne, DISP UR4570, 69621" }, { "name": "Benichou, Alain", "affiliation": "Renault Group" }, { "name": "Hamou, Khaled", "affiliation": "INSA Lyon" } ], "keywords": [ "Data-driven and AI-based modelling of production and logistics", "Simulation and optimization in production, operations and services", "Production and operations management" ], "abstract": "The Sales and Operations Planning process faces high uncertainties and complex supply–demand interactions, complicating decision-making. We propose a Reinforcement Learning-based multi-agent simulation framework, validated by domain experts, to model and simulate realistic market behaviors, supply dynamics, and production processes. A Soft Actor-Critic agent learns adaptive policies, optimizing production and supply decisions under stochastic conditions. Experiments show that the Reinforcement Learning agent effectively responds to fluctuations in both demand and supply. This framework supports robust scenario planning and enables automated policy generation, demonstrating the potential of Reinforcement Learning to enhance decision-making in complex Sales and Operations Planning environments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA17.2", "code": "FrA17.2", "title": "Semantic Parsing of Manufacturing Layouts Via Hybrid AI Agents for Automated Simulation Model Generation (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA17", "sessionTitle": "Simulation Modeling, Machine Learning and Optimization Algorithms to Support Decision Making in Production, Logistics, and Supply Chain Management", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Ganesh, Purushothaman", "affiliation": "Otto-Von-Guericke-University Magdeburg" }, { "name": "Kute, Sanket", "affiliation": "Otto-Von-Guericke-University Magdeburg" }, { "name": "Lang, Sebastian", "affiliation": "Fraunhofer Institute for Factory Operation and Automation IFF" } ], "keywords": [ "Data-driven and AI-based modelling of production and logistics", "Simulation and optimization in production, operations and services", "Production and operations management" ], "abstract": "Developing discrete-event simulation (DES) models is resource-intensive and requires significant domain expertise. However, instantiating model structure from existing 2D layouts remains largely manual and non-value-added. We propose a hybrid multi-agent system leveraging semantic parsing to automatically digitize static layouts. The architecture integrates computer vision for component identification and Large Language Models (LLMs) for semantic context extraction, including material flow pathways and operational parameters. Deterministic agents structure this information into Core Manufacturing Simulation Data (CMSD) XML schema, eliminating manual layout replication. Such automated generation capabilities are particularly valuable for B2B digital industrial platforms serving small and medium enterprises (SMEs), enabling rapid supply chain scenario evaluation and reconfiguration during demand fluctuations and disruptions without requiring deep simulation expertise.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA17.3", "code": "FrA17.3", "title": "Virtual Commissioning of Autonomous Mobile Robots in Accordance with VDA 5050: Architecture and Implementation (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA17", "sessionTitle": "Simulation Modeling, Machine Learning and Optimization Algorithms to Support Decision Making in Production, Logistics, and Supply Chain Management", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Kute, Sanket", "affiliation": "Otto-Von-Guericke-University Magdeburg" }, { "name": "Verbeet, Richard", "affiliation": "Bosch Rexroth AG, Ulm" }, { "name": "Müller, Marcel", "affiliation": "Otto Von Guericke University Magdeburg" }, { "name": "Reggelin, Tobias", "affiliation": "Otto Von Guericke University Magdeburg" }, { "name": "Lang, Sebastian", "affiliation": "Fraunhofer Institute for Factory Operation and Automation IFF" } ], "keywords": [ "Manufacturing plant simulation, control and optimization", "Cyber-physical production systems" ], "abstract": "The increasing diversity of autonomous mobile robots (AMRs) in industrial environments creates significant challenges regarding interoperability. While the VDA 5050 standard addresses operational interoperability by defining a common communication interface between AMRs and fleet management systems (FMS), its application in the planning and commissioning phases remains underexplored. This paper proposes a virtual commissioning (VCOM) architecture designed to streamline the integration of VDA 5050-compliant AMRs using material flow simulation. It presents a generalized system architecture that interfaces simulation software with an FMS, alongside a practical middleware-based solution for simulation environments lacking native MQTT support. The approach is validated through a demonstrator integrating Bosch Rexroth’s Active Shuttle Management System with Visual Components software via Node-RED. Results indicate that this architecture facilitates the early verification of control logic, the identification of deadlock scenarios, and the acceleration of the validation process through simulation running faster than real time.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA17.4", "code": "FrA17.4", "title": "A Morphological Framework for Synchronising Digital Twins and Physical Systems in Production and Logistics (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA17", "sessionTitle": "Simulation Modeling, Machine Learning and Optimization Algorithms to Support Decision Making in Production, Logistics, and Supply Chain Management", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Galka, Stefan", "affiliation": "OTH - Ostbayerische Technische Hochschule Regensburg" }, { "name": "Reggelin, Tobias", "affiliation": "Otto Von Guericke University Magdeburg" } ], "keywords": [ "Simulation and optimization in production, operations and services", "Smart production and logistics in manufacturing", "Industry X.0 for production and logistics" ], "abstract": "Digital twins are gaining increasing importance in production and logistics, particularly when simulation-based models are employed for decision support. Despite a wide range of existing approaches, a systematic categorisation of the possible forms of synchronisation between the physical system and its virtual counterpart – an essential characteristic of digital twins – has so far been lacking. This paper addresses this gap by developing a classification of relevant aspects of synchronisation and illustrating it through exemplary application cases. The paper demonstrates how this structuring supports the conceptual design of appropriate synchronisation mechanisms and highlights the design elements that must be considered when implementing simulation-based digital twins in industrial environments. The proposed morphology is intended as a starting point for further scientific discussion", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA17.5", "code": "FrA17.5", "title": "Improving Urban Logistics Traffic Simulations through Vehicle-Count Data Calibration (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA17", "sessionTitle": "Simulation Modeling, Machine Learning and Optimization Algorithms to Support Decision Making in Production, Logistics, and Supply Chain Management", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Bouazza, Wassim", "affiliation": "Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France" } ], "keywords": [ "Supply chain and logistics engineering, simulation and optimization", "Simulation and optimization in production, operations and services", "Logistics and warehouse management" ], "abstract": "Reliable transportation planning is a cornerstone of industrial logistics, particularly for Just-In-Time (JIT) operations and strict service level agreements. However, simulation-based planning tools often suffer from a ``realism gap'' due to poorly calibrated background traffic, leading to overly optimistic schedules and operational failures. This paper bridges this gap by introducing the Score-based Adaptive Recharge Algorithm (SARA), a data-driven method that calibrates microscopic traffic simulations against real-world sensor data. Applied to a microscopic simulation model of Nantes, France, SARA reveals severe congestion dynamics, such as localized bottlenecks and time-dependent variability that naive models fail to capture. By grounding the simulation in real-world sensor data provided by Nantes Métropole Open-Data API, it is demonstrated that uncalibrated models underestimate delivery times by up to SI{40}{percent} during peak hours, rendering them unsuitable for robust industrial planning. In summary, this comprehensive end-to-end framework provides logistics planners with the realistic ground truth required to optimize Time-Dependent Vehicle Routing Problems and establish operational supply chain reliability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA17.6", "code": "FrA17.6", "title": "Insourcing Additive Manufacturing to Enhance Spare Parts Supply Chain Resilience: A Preliminary Simulation Study (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:30-11:50", "sessionCode": "FrA17", "sessionTitle": "Simulation Modeling, Machine Learning and Optimization Algorithms to Support Decision Making in Production, Logistics, and Supply Chain Management", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Lolli, Francesco", "affiliation": "University of Modena and Reggio Emilia" }, { "name": "Bazzanini, Giovanni", "affiliation": "University of Modena and Reggio Emilia" }, { "name": "Coruzzolo, Antonio Maria", "affiliation": "University of Modena and Reggio Emilia" }, { "name": "Zhao, Qian", "affiliation": "University of Modena and Reggio Emilia" }, { "name": "Balugani, Elia", "affiliation": "University of Modena and Reggio Emilia" } ], "keywords": [ "Supply chain and logistics engineering, simulation and optimization", "Supply chain management in manufacturing", "Production and operations management" ], "abstract": "Supply chains have faced significant disruptions due to unforeseen events like the COVID-19 pandemic, semiconductor shortages, and natural disasters, highlighting the need for enhanced supply chain resilience. This study investigates the role of additive manufacturing (AM) in improving the resilience of spare parts supply chains (SPSC), especially during low-frequency, high-impact events. While existing research has explored AM's impact on specific supply chain (e.g. medical), there is a lack of quantitative studies focusing on enhancing SPSC resilience. To address this gap, we conducted a simulation study to evaluate the benefits of insourced AM for spare part production in terms of supply chain resilience under different disruption scenarios. The results indicate that incorporating AM into SPSC can significantly enhance their resilience by ensuring quick recovery and sustained high machinery availability while reducing management costs of up to 90.26%. This study adds simulation-based quantitative evidence on the resilience benefits of insourced AM in a modeled SPSC under low-frequency, high-impact (LFHI) supplier disruptions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA19.1", "code": "FrA19.1", "title": "Scalable Consensus Condition for a Linear Multi-Agent System with Information Processing Delays (I)", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA19", "sessionTitle": "Large-Scale Complex Systems: Analysis and Control IV", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Lizzio, Fausto Francesco", "affiliation": "Politecnico Di Torino" }, { "name": "Capello, Elisa", "affiliation": "Politecnico Di Torino, CNR-IEIIT" }, { "name": "Fujisaki, Yasumasa", "affiliation": "The University of Osaka" } ], "keywords": [ "Decentralized and distributed control for large-scale systems", "Large-scale complex systems", "Interconnected dynamical systems" ], "abstract": "This paper provides a scalable consensus condition for large-scale linear multi-agent systems subject to information-processing delays. The agents are homogeneous Single-Input–Single-Output systems and interact over a directed communication graph that contains a spanning tree. The consensus condition is given through a stability region in the complex plane of the Laplacian matrix eigenvalues. The existence of such a stable region is given in terms of a delay threshold, which is not a critical delay in the classical sense. Different from similar approaches in the literature, in which the critical delay has to be computed for every disagreement mode of the system, the delay threshold in this work is linked to the agents' dynamics and not to the network topology. As the number of agents does not affect the treatment, the consensus condition is scalable. Several numerical examples illustrate the result.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA19.2", "code": "FrA19.2", "title": "A Games-In-Games Approach for Hybrid Robust Control of Cyber-Physical Systems", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA19", "sessionTitle": "Large-Scale Complex Systems: Analysis and Control IV", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Pan, Yunian", "affiliation": "New York University" }, { "name": "Zhu, Quanyan", "affiliation": "New York University" } ], "keywords": [ "Hierarchical control", "Composite systems", "Systems-of-systems" ], "abstract": "We present a games-in-games architecture for cyber-physical systems in which a physical plant is regulated in continuous time, while a cyber defender and attacker steer the generator of a Markov jump mode process. The construction produces a finite-horizon piecewise deterministic Markov process (PDMP) whose inner layer is a continuous-time Isaacs game and whose outer layer is a stochastic game over mode distributions. We derive the associated coupled Hamilton-Jacobi-Isaacs (HJI) equations via a Dynkin argument, prove well-posedness under mild assumptions, and specialize the theory to linear-quadratic data, yielding mode-coupled Riccati equations and a monotonicity property with respect to cyber hardening. A voltage regulation study showcases that the game-enabled generator shaping reduces the compromised dwelling time without performance downgrading.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA19.3", "code": "FrA19.3", "title": "Hierarchical Group-Optimal Equilibria in Congestion Games: Existence, Preservation, and Stabilization (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA19", "sessionTitle": "Large-Scale Complex Systems: Analysis and Control IV", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Wang, Yanfei", "affiliation": "Shandong University" }, { "name": "Feng, Jun-e", "affiliation": "Shandong University" } ], "keywords": [ "Interconnected dynamical systems", "Hierarchical control", "Complex dynamic systems" ], "abstract": "This paper introduces a novel solution concept termed the Hierarchical Group-Optimal Equilibrium (HGOE) to characterize the intergroup competition and intragroup cooperation in group-based congestion games. Specifically, we establish the existence of HGOEs by proving that such games inherently possess a potential structure. However, this potential structure may be disrupted when resource failures are introduced, making the search for HGOEs challenging. This motivates us to analyze how failure probabilities affect these equilibria and derive sufficient conditions under which the original HGOEs and weak HGOEs from the failure-free game remain preserved. Furthermore, an event-triggered control scheme is developed via an algebraic state-space approach, which guarantees global stabilization to a set of epsilon-HGOEs with time-optimal performance. The results have been applied to a resource allocation problem in network function virtualization.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA19.4", "code": "FrA19.4", "title": "Optimum Adaptation of a Steiner Network", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA19", "sessionTitle": "Large-Scale Complex Systems: Analysis and Control IV", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Rosenberg, Manou", "affiliation": "Curtin University" }, { "name": "Ye, Mengbin", "affiliation": "Adelaide University" }, { "name": "Anderson, Brian D.O.", "affiliation": "Australian National Univ" } ], "keywords": [ "Large-scale complex systems" ], "abstract": "The Euclidean Steiner tree problem, normally posed in two dimensions, seeks to connect a set of prescribed terminal nodes by placing additional nodes, known as Steiner points, with edges connecting such nodes either to another Steiner point or a terminal node, and with the placements minimising the sum of all the edge lengths of the associated tree. We consider a problem in which we start with a known solution to a Steiner tree problem, and the terminal positions are then perturbed. A first-order approximation theorem is established for efficiently updating the Steiner point positions to recover a Steiner tree solution after the perturbations to terminal nodes. A numerical example that uses a stepwise application for a large perturbation illustrates the effectiveness of our approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA19.5", "code": "FrA19.5", "title": "Logical Matrix Factorization towards Robust Stabilization of Boolean Control Networks with Function Perturbation (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA19", "sessionTitle": "Large-Scale Complex Systems: Analysis and Control IV", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Li, Haitao", "affiliation": "Shandong Normal University" }, { "name": "Li, Wenrong", "affiliation": "Shandong Normal University" }, { "name": "Zhao, Guodong", "affiliation": "Shandong Normal University" }, { "name": "Zhang, Xiangbo", "affiliation": "Georgia Institute of Technology" }, { "name": "Wang, Yuanhua", "affiliation": "Shandong Normal University" } ], "keywords": [ "Large-scale complex systems" ], "abstract": "This article develops the logical matrix factorization technique to explore the robust stabilization of Boolean control networks (BCNs) subject to function perturbation. Firstly, the index set of factorized structure matrix is obtained, based on which, a size-reduced system is constructed which remains part of the transition information for the original BCN. Secondly, the equivalence of state-feedback stabilization (SFS) between the size-reduced system and the original BCN is derived. Then, the impact of function perturbation on the SFS of original BCN is converted into that of size-reduced system. Thirdly, a perturbed position index matrix is constructed for the size-reduced system, and some criteria are proposed for the robust SFS of BCNs subject to function perturbation. Finally, the validity of obtained results is supported by the model of lac operon in the Escherichia coli.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA19.6", "code": "FrA19.6", "title": "Finite-Time Bipartite Consensus for Matrix-Weighted Multi-Agent Systems with Asymmetric Saturation (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:30-11:50", "sessionCode": "FrA19", "sessionTitle": "Large-Scale Complex Systems: Analysis and Control IV", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Li, Pengyuan", "affiliation": "Dalian Maritime University" }, { "name": "Li, Runshuang", "affiliation": "Dalian Maritime University" }, { "name": "Xia, Weiguo", "affiliation": "Dalian University of Technology" } ], "keywords": [ "Large-scale complex systems" ], "abstract": "This paper investigates the global matrix-weighted finite-time bipartite consensus (FTBC) problem of multi-agent systems (MASs) subject to asymmetric saturation constraints, where zero saturation bounds are considered. Distributed control laws using matrix weights are proposed, and sufficient conditions to guarantee the global FTBC are derived. The theoretical analysis are validated by numerical examples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA20.1", "code": "FrA20.1", "title": "Toward Agentic Automation: Multi-Agent Implementation of the SmartFactory Reference Architecture (I)", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA20", "sessionTitle": "20 Years Smart Factory – Lessons Learned and Future Challenges", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Jungbluth, Simon", "affiliation": "Technologie-Initiative SmartFactoryKL E.V" }, { "name": "Gösling, Henning", "affiliation": "German Research Center for Artificial Intelligence" }, { "name": "Ruskowski, Martin", "affiliation": "German Research Center for Artificial Intelligence" } ], "keywords": [ "Intelligent manufacturing systems", "Cyber-physical production systems", "Production and operations management" ], "abstract": "The SmartFactory Reference Architecture proposes software agents on top of interoperable, semantic descriptions of resources and products based on Asset Administration Shells and OPC UA. Using these standardized interfaces, agents can adapt to various production environments to plan, control, and monitor flexible processes. By integrating language models, an agent becomes a so-called AI agent, enabling natural language-based interaction with human operators. Within this work, a comprehensive Multi-Agent System (MAS) for flexible Industry 4.0 environments is presented. The MAS defines autonomous agents in a distributed data space for services, products, and resources, allowing these agents to coordinate tasks and allocate resources in a dynamic production environment.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA20.2", "code": "FrA20.2", "title": "Semantic-Driven Digital Twins and Smart Applications in Industry 4.0: Toward AI Agents (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA20", "sessionTitle": "20 Years Smart Factory – Lessons Learned and Future Challenges", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Steinmetz, Charles", "affiliation": "ABB Corporate Research Center Germany" }, { "name": "Juhlin, Prerna", "affiliation": "ABB Corporate Research Center Germany" }, { "name": "Morgan Pereira, Pedro Henrique", "affiliation": "SENAI Institute of Innovation in Integrated Solutions in Metal Mechanics" }, { "name": "Pereira, Carlos Eduardo", "affiliation": "Federal Univ. of Rio Grande Do Sul - UFRGS" } ], "keywords": [ "Intelligent manufacturing systems", "Human-technology integration in manufacturing", "Model-driven enterprise-system engineering" ], "abstract": "Industry 4.0 systems increasingly rely on heterogeneous Digital Twin representations, creating challenges for synchronization, semantic interoperability, and human supervision across engineering and operational models. This paper proposes a conceptual architecture for Agentic Industry 4.0 systems in which specialized AI agents coordinate model synchronization across heterogeneous representations such as AutomationML, Asset Administration Shells, and simulation models. The architecture combines semantic resources, standardized interaction mechanisms, and human-in-the-loop supervision to support adaptable and traceable model updates. Large Language Models (LLMs) are positioned mainly as supporting components for natural interaction and context-sensitive mapping assistance, rather than as fully autonomous decision makers. A proof-of-concept implementation based on an industrial motor use case illustrates event-driven coordination, model access through MCP-based interfaces, and user-approved synchronization updates. The implementation demonstrates the feasibility of the architecture while also revealing current limitations regarding semantic complexity, robustness evaluation, and scalability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA20.3", "code": "FrA20.3", "title": "Federated Learning in Digital Supply Networks (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA20", "sessionTitle": "20 Years Smart Factory – Lessons Learned and Future Challenges", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Khan, Md Irfan", "affiliation": "University of South Carolina" }, { "name": "Little, Wade", "affiliation": "University of South Carolina" }, { "name": "Farahani, Mojtaba A.", "affiliation": "University of South Carolina" }, { "name": "Wuest, Thorsten", "affiliation": "University of South Carolina" } ], "keywords": [ "Data-driven and AI-based modelling of production and logistics" ], "abstract": "Digital Supply Networks (DSNs) are rapidly evolving, driven by a variety of Industry 4.0 technologies, generating vast amounts of heterogeneous, distributed, and often sensitive data across suppliers, manufacturers, logistics providers, users, and many other participants. Yet, traditional, centralized Machine Learning (ML) approaches struggle to harness the opportunity embedded in this diverse data due to, e.g., privacy concerns, competitive barriers, and regulatory constraints. Federated Learning (FL) emerges as a promising solution to this obstacle, enabling collaborative model training without raw data sharing, thereby preserving privacy while enhancing predictive capabilities across decentralized supply networks. This paper provides a synthesis of recent advancements in FL for DSNs, examining how this technology tackles challenges such as data heterogeneity, information asymmetry, cross-border regulatory constraints to enable privacy-preserving collaboration, and data-driven decision making across decentralized supply networks. Furthermore, we discuss how FL can transform DSNs by fostering secure collaboration, enhancing scalability, and building resilience across geographically dispersed and operationally diverse partners. Finally, challenges of introducing FL in DSNs, including incentive alignment, personalization, model convergence under non-independent and identically distributed (non-IID) data, and future research directions toward adaptive, trustworthy, and sustainable DSNs are identified to guide future research and exploration in this domain.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA20.4", "code": "FrA20.4", "title": "System Configuration Spaces: From Legacy Factories to Factories of the Future", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA20", "sessionTitle": "20 Years Smart Factory – Lessons Learned and Future Challenges", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Leite Patrão, Rafael", "affiliation": "TU Delft" }, { "name": "Negenborn, Rudy", "affiliation": "Delft University of Technology" }, { "name": "Napoleone, Alessia", "affiliation": "Delft University of Technology" } ], "keywords": [ "Manufacturing plant simulation, control and optimization", "Manufacturing engineering and management", "Large-scale complex systems" ], "abstract": "Manufacturing systems face increasing challenges from uncertain regulatory and geopolitical landscapes, pressuring factories to continuously adapt operations and internal design to remain“future-proof” and satisfy demand. Workstation configuration can describe both design and operational aspects, indicating what can be produced and associated costs and time. In this paper, we present a system-wide configuration representation, formalised as a metric space. We propose a method to construct such a System Configuration Space (SCS) from data available to most legacy manufacturing systems. An illustrative example shows how the SCS can help decision-makers better understand a system’s configurations and how this information can be used by optimization and simulation methods to solve decision problems, such as capacity allocation and investment.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA20.5", "code": "FrA20.5", "title": "20 Years of Smart Factories - Lessons Learned and Future Challenges (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA20", "sessionTitle": "20 Years Smart Factory – Lessons Learned and Future Challenges", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Zuehlke, Detlef", "affiliation": "German Research Center for Artificial Intelligence" } ], "keywords": [ "Cyber-physical production systems", "Intelligent manufacturing systems", "Industrial artificial intelligence" ], "abstract": "In 2005, the world’s first smart factory was founded and then built in Kaiserslautern, Germany, as a PPP (public-private partnership). In close cooperation between industry, academia, and politics, a test bed for smart factory technologies was set up, which then became a blueprint for similar activities worldwide. This activity was presented in a plenary paper at the IFAC World Congress 2008 in Seoul Zuehlke (2008, 2010) and thus became internationally known. The preliminary work carried out here and then disseminated by IFAC ultimately led to the term Industry 4.0 in 2011. This marked the beginning of the triumphant advance of Industry 4.0 in all industrialized countries around the world. Twenty years have passed since then, so it is appropriate to take stock. The papers in this session provide an overview of the activities of smart factory technologies in different countries and in different fields of application.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA21.1", "code": "FrA21.1", "title": "Wind Turbine Fault Diagnosis Using Structural Analysis and Optimized-Rectangular GPR Interval Estimation (I)", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA21", "sessionTitle": "JO-CEP: Wind Power and Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Perez-Perez, Esvan De Jesus", "affiliation": "Tecnologico Nacional De Mexico, Instituto Tecnologico De Tuxtla Gtz" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" }, { "name": "De los Santos Ruiz, Ildeberto", "affiliation": "Tecnologico Nacional De Mexico / I. T. Tuxtla Gutierrez" }, { "name": "Guzman Rabasa, Julio Alberto", "affiliation": "Instituto Tecnologico De Tuxtla Gutierrez" }, { "name": "Valencia-Palomo, Guillermo", "affiliation": "Instituto Tecnológico De Hermosillo" } ], "keywords": [ "Structural analysis/quantitative methods for FDI/FTC", "Wind power", "Data-driven methods for FDI/FTC" ], "abstract": "This paper presents a hybrid fault diagnosis framework for a utility-scale 5 MW wind turbine (FAST-based model) that combines physics-guided structural analysis with data-driven interval estimation. Analytical Redundancy Relations (ARRs) are derived to generate residuals from measured/estimated variables. To provide robust, tight uncertainty bounds, an Optimized-Rectangular Gaussian Process Regression (OR-GPR) is introduced that learns asymmetric, input-dependent prediction intervals with target empirical coverage while minimizing mean width. Residuals are computed by comparing sensor measurements with OR-GPR predictions, and faults are detected using a combination of interval-based thresholds and Cumulative Sum (CUSUM) control charts. Experiments on representative scenarios show earlier detection and lower false alarms compared with conventional GPR intervals and fixed-threshold method, demonstrating the effectiveness of ARR-guided residuals with OR-GPR for wind turbine condition monitoring.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA21.2", "code": "FrA21.2", "title": "A Robust qLPV NMPC Framework with Artificial States Initialisation: Experimental Application to a Scaled Wind Turbine (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA21", "sessionTitle": "JO-CEP: Wind Power and Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Morato, Marcelo Menezes", "affiliation": "Cnrs / Gipsa-Lab / Uga" }, { "name": "Da Silva, Samira Liana", "affiliation": "Universidade Federal De Santa Catarina" }, { "name": "Santos, Tito", "affiliation": "Federal University of Bahia" }, { "name": "Normey-Rico, Julio Elias", "affiliation": "Federal Univ of Santa Catarina" } ], "keywords": [ "Wind power", "Control and management of energy systems" ], "abstract": "Several works have proposed Nonlinear Model Predictive Control (NMPC) schemes using quasi-Linear Parameter Varying (qLPV) embeddings. Despite recent advances, many results simply rely on gain-scheduling (i.e. frozen predictions) or using the practical iterative implementation from (Cisneros et al., 2016). However, if the involved prediction uncertainties are not considered, the control becomes neither optimal nor robust. In this work, we exploit the recent idea introduced by K ̈ohler and Zeilinger (2025) as a pragmatic way to ensure input- to-state stability (ISS) and recursive feasibility in qLPV NMPC. In particular, we let the state prediction assume an artificial initial value, and then penalise the corresponding deviation to the real sampled state in the optimisation cost. We debate how this simple relaxation indeed enables ISS, despite the inherent prediction mismatch due to the unavailable scheduling trajectories. We also discuss direct extensions for when load disturbances are present and for the reference tracking case. The method is experimentally validated using a scaled wind turbine benchamrk.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA21.3", "code": "FrA21.3", "title": "Wind Tunnel Demonstration of Gust-Aware Control on a Scaled Model Wind Turbine (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA21", "sessionTitle": "JO-CEP: Wind Power and Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Phadnis, Mandar", "affiliation": "University of Colorado Boulder" }, { "name": "Petrović, Vlaho", "affiliation": "University of Oldenburg" }, { "name": "Pao, Lucy Y.", "affiliation": "University of Colorado Boulder" } ], "keywords": [ "Wind power", "Control and management of energy systems" ], "abstract": "Wind turbulence is the dominant disturbance in wind turbine operation. Wind gusts can drive large fluctuations in generator speed and structural loads. Generator overspeed events can trigger wind turbine shutdowns, reducing energy production and increasing the cost of wind energy. Specific wind sequences, such as a lull followed by a sharp positive ramp, are particularly prone to causing overspeed peaks, especially in near- and above-rated operation. Gust-aware control strategies have been explored to detect such events and adjust the turbine operation accordingly. This paper presents an experimental validation of gust-aware control strategies in a wind tunnel using a scaled wind turbine and a state-of-the-art actively controlled wind grid. The baseline wind turbine controller is augmented with gust-measure-enabled rating rules that can (i) pre-emptively derate the turbine when the gust measures indicate an incoming gust, thus limiting generator overspeed, and (ii) boost the power set point when the gust measures indicate calmer, steadier conditions that allow the turbine to safely increase energy capture. Two gust measures are computed online from local inflow signals. Performance is evaluated under prescribed (a) repeating gust and (b) turbulent wind patterns. The tested controllers are blind to the inflow test conditions in all cases. The gust-aware controllers consistently execute anticipatory derating prior to gust arrivals, reducing generator speed peaks, while enabling systematic power boosts during steady wind intervals. Across both inflow families, the rating rules achieve the intended condition-dependent modulation of power, experimentally demonstrating the feasibility of gust-aware power management. Blade-load responses, however, are sensitive to decision thresholds and timing, indicating that careful tuning is needed to fully realize overspeed mitigation and energy gains while managing structural loads. The results support gust-measure-driven control as a potential pathway to improved operational resilience and opportunistic energy capture that warrants further exploration.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA21.4", "code": "FrA21.4", "title": "Nash Bargaining for Power-Fatigue Co-Optimization in Wake-Affected Wind Farms: A Learning-Aided Approach (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA21", "sessionTitle": "JO-CEP: Wind Power and Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Liu, Yiming", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Wang, Zhaojian", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Huang, Ruanming", "affiliation": "State Grid Shanghai Municipal Electric Power Company, Shanghai, China" }, { "name": "Yang, Bo", "affiliation": "Department of Automation, Shanghai Jiao Tong University, Shanghai" } ], "keywords": [ "Wind power", "Control and management of energy systems" ], "abstract": "This paper proposes a multi-objective control framework for wake-affected wind farms to manage the trade-off between power maximization and fatigue load minimization. The conflicting objectives are formulated using Nash Bargaining theory, providing a fair, Pareto-efficient solution without heuristic weight tuning. A Warm-started Proximal Alternating Direction Method of Multipliers (W-PADMM) algorithm is proposed to efficiently solve the bargaining problem, which embeds a learning-aided mechanism using a Long Short-Term Memory (LSTM) network to proactively guide the optimization. Case studies on a 9-turbine wind farm using historical operational data validate that the algorithm achieves a superior power-fatigue balance with significant gains in computational efficiency.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA21.5", "code": "FrA21.5", "title": "Wind Turbine Inflow Estimation Via Nested, Self-Calibrating EKF: A Field Test (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA21", "sessionTitle": "JO-CEP: Wind Power and Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Onnen, David", "affiliation": "ForWind - University of Oldenburg" }, { "name": "Joshi, Raghawendra", "affiliation": "ForWind - University of Oldenburg" }, { "name": "Wölk, Philipp", "affiliation": "Leibniz Universität Hannover, Institute of Turbomachinery and Fluid Dynamics" }, { "name": "Kühn, Martin", "affiliation": "University of Oldenburg" }, { "name": "Petrović, Vlaho", "affiliation": "University of Oldenburg" } ], "keywords": [ "Wind power", "Control and management of energy systems", "Process modeling, identification, and estimation techniques" ], "abstract": "Modern wind turbines need high situational awareness for decision making and control scheduling. It allows for a trade-off between greedy power maximisation and further control objectives such as load alleviation or grid compliance. The paper formulates a nested Extended Kalman Filter that is able to precisely reconstruct and distinguish the inflow wind and load-relevant structural dynamics of a wind turbine. The formulation is directly motivated by the demands of field applicability, thus robust, low in computational costs and flexible with respect to sensor availability or calibration drifts. The estimator is field-tested on a utility-scale commercial turbine. It shows good agreement with independent reference measurements, namely a hub-mounted scanning lidar for the spatially resolved inflow wind field and camera-based digital image correlation for the structural movements.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA21.6", "code": "FrA21.6", "title": "ChebGCN-NA-LSTM-Based Regional Collaborative Wind Power Forecasting (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:30-11:50", "sessionCode": "FrA21", "sessionTitle": "JO-CEP: Wind Power and Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Liu, Xutao", "affiliation": "North China University of Technology" }, { "name": "Zhou, Meng", "affiliation": "North China University of Technology" }, { "name": "Wang, Jing", "affiliation": "North China University of Technology (NCUT)" }, { "name": "Puig, Vicenç", "affiliation": "Universitat Politècnica De Catalunya (UPC)" } ], "keywords": [ "Wind power", "Forecasting of power supply and demand" ], "abstract": "To address the problem of complex spatiotemporal correlations between the target wind farm and neighboring wind farms, as well as the difficulty of fully characterizing spatial dependencies with a single graph structure in regional multi-wind-farm forecasting, this paper proposes a Chebyshev Graph Convolutional Network with Node Adaptation Long Short-Term Memory (ChebGCN-NA-LSTM) method for wind power forecasting. The proposed method first employs multi static graphs to characterize spatial relationships among wind farms. Based on these graph structures, ChebGCN is used to extract high-order spatial features, whereas a node-adaptive mechanism complements the static graphs by capturing latent spatial dependencies. Furthermore, LSTM is incorporated to model temporal dependencies. Finally, experimental results show that the proposed method can effectively improve the accuracy and robustness of target wind farm power forecasting.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA22.1", "code": "FrA22.1", "title": "Parametric Modelling and Optimal Estimation of Airway Pressure in Mechanically Ventilated Patients with Spontaneous Effort: Separating Patient Effort and Baseline Pressure (I)", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA22", "sessionTitle": "Modeling and Diagnostics of the Respiratory System I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Zhao, Yadian", "affiliation": "University of Canterbury" }, { "name": "Zhou, Cong", "affiliation": "Chinese Academy of Sciences" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" } ], "keywords": [ "Modeling and control in mechanical ventilation", "Digital twins in healthcare, model-based therapeutics" ], "abstract": "Spontaneous efforts during mechanical ventilation significantly affect airway pressure and complicate the accurate assessment of respiratory mechanics. This study presents a novel non-invasive method based on parametric modelling and optimization that can directly reconstruct both baseline pressure and spontaneous effort from the airway pressure. The method was validated using real clinical data. Results shown robust performance under diverse conditions, and substantial reduction in variability of estimated resistance and elastance (R: 42% → 14%; E: 45% → 16%). This work establishes a foundation for intelligent, effort-aware mechanical ventilation monitoring.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA22.2", "code": "FrA22.2", "title": "Mapping Respiratory System Function Using a Single Novel and Non-Invasive Pulmonary Function Test (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA22", "sessionTitle": "Modeling and Diagnostics of the Respiratory System I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Guy, Ella F. S.", "affiliation": "University of Canterbury" }, { "name": "Hill, Jordan F.", "affiliation": "University of Canterbury" }, { "name": "Wallace, Lauren", "affiliation": "Te Whatu Ora – Waitaha" }, { "name": "Kelly, Paul", "affiliation": "Te Whatu Ora – Waitaha" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" } ], "keywords": [ "Biomedical system modeling, identification, and simulation", "Medical devices, systems and solutions", "Digital twins in healthcare, model-based therapeutics" ], "abstract": "Chronic respiratory disease poses significant clinical and quality of life burden on a global scale. The ability to track and self-manage respiratory disease is limited by the pulmonary function testing tools available to provide a comprehensive, repeatable, and reliable respiratory assessment, outside of a specialist clinical setting. Outlined, is a proposed framework for monitoring lung health in chronic respiratory disease outside of clinical settings. This new comprehensive pulmonary function test, designed to elucidate simulated changes in key respiratory parameters, is investigated in this study using a mechanical test lung. The results of this paper demonstrate an ability to describe set changes in model terms, as well as informing continued development of hardware and planned clinical trial protocols. This paper provides a foundation for patient-specific automated respiratory disease tracking. Therefore, has the potential to improve patient care, adherence to therapy, and precision / patient-specificity of care.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA22.3", "code": "FrA22.3", "title": "An Independent Pressure-Flow Sensor Module for Mechanical Ventilation Treatment Waveform Capture (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA22", "sessionTitle": "Modeling and Diagnostics of the Respiratory System I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Keong, Jin", "affiliation": "Monash University Malaysia" }, { "name": "Ang, Christopher Yew Shuen", "affiliation": "Monash University Malaysia" }, { "name": "Lee, Darren Tze Huei", "affiliation": "Monash University Malaysia" }, { "name": "Chiew, Yeong Shiong", "affiliation": "Monash University" } ], "keywords": [ "Medical devices, systems and solutions", "Biomedical signal measurement and processing", "Modeling and control in mechanical ventilation" ], "abstract": "Access to continuous airway pressure (P) and flow (𝑉̇) waveforms is important for assessing respiratory mechanics during mechanical ventilation (MV) treatment. Yet, such data are often difficult to obtain due to ventilator-specific interfaces and closed communication protocols. This study proposes a compact, ventilator-independent pressure–flow sensor module (P𝑉̇ sensor module) integrated with a portable data acquisition system for capturing high-fidelity mechanical ventilation waveforms. The system was tested against a validated reference device using a mechanical test lung across multiple respiratory system elastance and resistance configurations in both volume and pressure control ventilation modes. Flow measurements from the sensor closely matched ventilator output with an absolute percentage error at <10%, while expected pressure differences were observed due to proximal placement. Respiratory mechanics estimated using the single-compartment model showed variable agreement with the reference system across conditions, with discrepancies increasing under more extreme mechanical loads. Overall, the proposed P𝑉̇ sensor module demonstrated reliable waveform capture and the ability to track trends in respiratory mechanics, supporting its potential use in continuous monitoring, research applications, and the development of advanced analytics such as digital-twin modelling and clinical decision support systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA22.4", "code": "FrA22.4", "title": "Non-Invasive, Continuous, Venous Oxygen Saturation and Oxygen Extraction Estimation from the Internal Jugular Vein (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA22", "sessionTitle": "Modeling and Diagnostics of the Respiratory System I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Hill, Jordan F.", "affiliation": "University of Canterbury" }, { "name": "Hess, Aaron S.", "affiliation": "New Zealand Blood Association" }, { "name": "Pretty, Christopher", "affiliation": "University of Canterbury" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" } ], "keywords": [ "Medical devices, systems and solutions", "Biomedical signal measurement and processing", "Clinical trial, clinical validation" ], "abstract": "Venous blood oxygen saturation (SvO2) measurements are critical for monitoring oxygen use. Currently, accurate SvO2 measurement requires invasive blood sampling. Conventional peripheral pulse oximetry techniques cannot measure SvO2 because peripheral veins are not pulsatile. This study explores the potential for non-invasive SvO2 estimation using internal jugular vein (IJV) pulsations detected by a flexible sensor array, to thus exploit standard pulse oximetry. A prototype sensor was developed to detect pulsatile signals from the carotid artery and IJV on the right-hand side of the neck. The sensor's performance was evaluated in 6 healthy adult subjects with arterial oxygen saturation (SaO2) measured using a commercial pulse oximeter and SvO2 estimated using the detected IJV pulse and breathing modulations. These estimates were combined to determine the subjects' cerebral oxygen extraction ratio (O₂ER). The sensor reliably detected pulsations in both upright and supine positions, with an average SvO2 of 65.70-80.91% with standard deviations of 1.67% and 4.26% for the IJV pulse and breathing modulations respectively, corresponding to an O2ER of 0.18-0.33 (±0.03). Differences in IJV pulse and breathing modulation estimations, as well as between upright and supine positions, are likely due to the influence of crosstalk between the carotid artery and IJV. The proof-of-concept sensor demonstrated the ability to continuously and non-invasively monitor SvO2 and O₂ER, producing estimates within expected literature ranges. Further validation with a larger cohort and comparison to blood gas analysis is necessary to confirm the sensor's accuracy, along with methods to mitigate crosstalk between arteries and veins.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA22.5", "code": "FrA22.5", "title": "Clinical Validation and Testing of Volumetric Capnography Via Hysteresis Loop Analysis (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA22", "sessionTitle": "Modeling and Diagnostics of the Respiratory System I", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Hastings, Samuel", "affiliation": "University of Canterbury" }, { "name": "Guy, Ella F. S.", "affiliation": "University of Canterbury" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" } ], "keywords": [ "Biomedical system modeling, identification, and simulation", "Biomedical signal measurement and processing" ], "abstract": "Current models for volumetric capnography (VCap) curve analysis fail to capture nonlinear phase III dynamics associated with dysfunctional breathing. Hysteresis loop analysis (HLA) addresses this error by decomposing a VCap curve into a minimal number of linear segments, capturing nonlinearities as they appear. This paper compares HLA against VCap analysis via Functional Approximation based on the Levenberg-Marquardt algorithm (FA-LMA) to identify airway dead space (VDAW) and the slope of phase III (SIII) using publicly available clinical data. Both methods perform equally well on this clinical data. However, FA-LMA consistently overestimates SIII compared to HLA, suggesting different performance against non-linear phase IIIs in clinical breaths. Overall, HLA accurately identifies clinically relevant parameters from VCap curves in clinical data, and is well posed to adapt to atypical VCap curves associated with dysfunctional breathing. Full testing on a dedicated respiratory cohort is justified by these results.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA23.1", "code": "FrA23.1", "title": "Reinforcement Learning-Assisted Terminal Cost Approximation for QP-Formulated Economic Model Predictive Control (I)", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA23", "sessionTitle": "Machine Learning for Process Control & Optimization", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Lee, Hoseong", "affiliation": "Seoul National University" }, { "name": "Choi, Wonhyeok", "affiliation": "Seoul National University" }, { "name": "Lee, Jong Min", "affiliation": "Seoul National University" } ], "keywords": [ "Machine learning and artificial intelligence in chemical process control", "Model-predictive and optimization-based control in chemical processes", "Advanced process control" ], "abstract": "Capturing long-horizon economic effects in EMPC can require extended prediction horizons or detailed nonlinear models, increasing the online problem size and modeling effort. This paper proposes a reinforcement learning (RL)-assisted terminal cost approximation for QP-formulated EMPC, in which a low-rank quadratic terminal cost embeds long-horizon economic information while retaining a short prediction horizon and the QP structure. For a continuous stirred-tank reactor (CSTR) with economically optimal periodic operation, the proposed method recovers cyclic behavior, improves the discounted return by 21% over short-horizon EMPC, and reduces computation time by about 90% relative to a nonlinear EMPC benchmark.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA23.2", "code": "FrA23.2", "title": "Design of Experiments for Identification That Maximizes the Performance of Model Predictive Control (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA23", "sessionTitle": "Machine Learning for Process Control & Optimization", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Oshima, Masanori", "affiliation": "Technical University of Ilmenau" }, { "name": "Kim, Sanghong", "affiliation": "Tokyo University of Agriculture and Technology" }, { "name": "Shardt, Yuri A.W.", "affiliation": "Technical University of Ilmenau" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes", "Process modeling, identification, and estimation techniques", "Machine learning and artificial intelligence in chemical process control" ], "abstract": "A prediction model that achieves the high performance required for model predictive control (MPC) can be obtained by system identification using high-quality data. Such high-quality data can be acquired from design of experiments (DoE), which reduces the confidence region of the model parameters by minimizing a geometric quantity of the region. However, such an index of the confidence region does not directly quantify the performance of MPC. This paper proposes MPC-oriented DoE that maximizes the expected setpoint-tracking performance of MPC with the prediction model estimated by the prediction error method. The proposed DoE index is calculated by Monte Carlo simulation of the closed-loop system, where a process with parameters sampled from the confidence region is controlled by the MPC. The proposed DoE index is minimized using Bayesian optimization. In the case study, the prediction model obtained using MPC-oriented DoE provided the control performance comparable to the best control performance of the prediction models for the existing three DoE methods, i.e., D-optimal, E-optimal, and A-optimal DoE.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA23.3", "code": "FrA23.3", "title": "Surrogate-Assisted Reinforcement Learning Control of NMP Solvent Recovery by Batch Distillation (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA23", "sessionTitle": "Machine Learning for Process Control & Optimization", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Oh, Tae Hoon", "affiliation": "UNIST" } ], "keywords": [ "Machine learning and artificial intelligence in chemical process control", "Advanced process control", "Batch and semi-batch process control" ], "abstract": "This work presents a surrogate-assisted reinforcement learning framework for optimal control of N-methyl-2-pyrrolidone (NMP) recovery by batch distillation in lithium-ion battery manufacturing. A first-principles dynamic model of a pilot-scale column is calibrated with plant data and approximated by a physics-constrained Neural ordinary differential equation surrogate. A Double Deep Q-Network agent uses this surrogate to learn reflux-ratio trajectories and batch termination decisions under a 99.99 wt% NMP purity constraint. Two reward formulations are considered: minimization of production cost and minimization of CO2-equivalent emissions, both evaluated at an industrial scale via techno-economic and life-cycle calculations. Compared with fixed reflux operation, the learned policies reduce specific production cost by up to 3.8% and specific CO2-equivalent emissions by up to 57%, demonstrating that reinforcement-learning-based control can simultaneously improve economic and environmental performance of solvent recycling processes.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA23.4", "code": "FrA23.4", "title": "Addressing Terminal Constraints in Data-Driven Demand Response Scheduling (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA23", "sessionTitle": "Machine Learning for Process Control & Optimization", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Bloor, Maximilian", "affiliation": "Imperial College London" }, { "name": "White, Martha", "affiliation": "University of Alberta" }, { "name": "del Rio-Chanona, Ehecatl Antonio", "affiliation": "Imperial College London" }, { "name": "Tsay, Calvin", "affiliation": "Imperial College London" } ], "keywords": [ "Machine learning and artificial intelligence in chemical process control", "Advanced process control" ], "abstract": "Electrified chemical processes are incentivized by exposure to time-varying electricity markets to operate flexibly, but participating in demand response schemes can require satisfying terminal constraints over long horizons. Specifically, terminal constraints may be required when computing optimal schedules in order to preserve dynamic stability. Model-based optimization methods are computationally costly, and data-driven scheduling via reinforcement learning (RL) faces severe credit-assignment challenges. We integrate Goal-Space Planning (GSP) with Deep Deterministic Policy Gradient (DDPG), using learned temporally abstract models over discrete subgoals to propagate value across extended horizons. Using a simulated air separation benchmark, we demonstrate the proposed approach improves sample efficiency over standard DDPG while satisfying terminal storage constraints, mitigating myopic control behavior.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA23.5", "code": "FrA23.5", "title": "Towards Optimal Power Management in Hybrid LNG Vessels: A Comparative Study of Optimal Control Strategies (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA23", "sessionTitle": "Machine Learning for Process Control & Optimization", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Abdalla, Ahmed", "affiliation": "University of British Columbia" }, { "name": "Kirchen, Patrick", "affiliation": "University of British Columbia" }, { "name": "Gopaluni, Bhushan", "affiliation": "University of British Columbia" } ], "keywords": [ "Energy management systems" ], "abstract": "This study evaluates model predictive control (MPC), equivalent emissions minimization strategy (EEMS), and deep reinforcement learning (DRL) for energy management in an LNG hybrid-powered vessel using real sailing data. A modified EEMS with a hyperbolic tangent penalty is proposed to improve battery state of charge (SOC) regulation. A constrained-loss TD3 method is introduced, which enforces operational limits in the actor loss and requires no SOC model or power demand forecasting. Results show that the proposed EEMS and TD3 approaches achieve an emissions reduction performance comparable to MPC while maintaining healthy SOC levels. These findings highlight practical and scalable energy management systems for low-emission hybrid ship operation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA23.6", "code": "FrA23.6", "title": "Neural Network Self-Tuning LADRC Multi-Zone Temperature Control System in Large-Sized Monolithic Silicon Epitaxy Equipment", "day": "Friday", "date": "August 28, 2026", "time": "11:30-11:50", "sessionCode": "FrA23", "sessionTitle": "Machine Learning for Process Control & Optimization", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Suo, Jiazhe", "affiliation": "Zhejiang University" }, { "name": "Jin, Bo", "affiliation": "Zhejiang University" } ], "keywords": [ "Real-time optimization and control in chemical processes", "Advanced process control", "Industrial applications of process control" ], "abstract": "Temperature control in large-sized monolithic silicon epitaxy equipment reactor is a significant challenge due to strong nonlinearities, time delays, and cross-coupling among multiple heating zones. Currently, ADRC-based multi-zone temperature control systems suffer from repeative parameter tuning and fixed controller parameters across zones, which prevents the controllers from responding promptly to setpoint changes or variations in nonlinear dynamics. This study proposes a radial basis function (RBF) neural network–based self-tuning Linear Active Disturbance Rejection Control (LADRC) multi-zone temperature control system. During processes, the RBF neural network performs real-time system identification, and a gradient descent algorithm is employed to adaptively adjust the LADRC parameters of all four zones. Simulation results demonstrate that the proposed self-tuning LADRC temperature control system achieves faster settling and rise times, as well as reduced overshoot. Finally, experiments confirm that the proposed system can deliver high-performance temperature control without manual parameter tuning.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA24.1", "code": "FrA24.1", "title": "Multimode Data-Driven Process Fault Detection under Distributional Uncertainty", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA24", "sessionTitle": "Process Monitoring, Fault Detection and Diagnosis", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Kammammettu, Sanjula", "affiliation": "University of Alberta" }, { "name": "Li, Zukui", "affiliation": "University of Alberta" } ], "keywords": [ "Monitoring, performance assessment, and fault detection in chemical process control" ], "abstract": "Process monitoring of complex, multivariate systems presents a significant challenge when the process is operated at multiple operating conditions. In such cases, distinguishing a valid change in operating conditions from abnormal process deviations may be treated as a multimode process fault detection problem. Some methods to address this problem model the process using a multimode probability distribution. In practical applications, the true distribution, or indeed a good estimate of the same, may not be readily available to the user. Such inexact information on the probability distribution lead to poor fault detection performance that may further lead to process operations degradation. A distributionally robust design of fault detection systems is preferable in the face of ambiguous uncertainty. In this work, we propose a Bayesian fusion approach using Gaussian mixture models (GMM) and introduce a distributional uncertainty model based on optimal transport distance between GMMs. We formulate a worst-case performance evaluation problem and propose a detection threshold optimization algorithm. The effectiveness of the proposed method is demonstrated through a simulated example.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA24.2", "code": "FrA24.2", "title": "Mutual Information-Guided Spatio-Temporal Mamba Model for Industrial Fault Detection", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA24", "sessionTitle": "Process Monitoring, Fault Detection and Diagnosis", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Zhang, Zhibo", "affiliation": "College of Control Science and Engineering, China University of Petroleum (East China)" }, { "name": "Yao, Bohan", "affiliation": "China University of Petroleum (East China)" }, { "name": "Deng, Xiaogang", "affiliation": "China University of Petroleum" }, { "name": "Wang, Ping", "affiliation": "China University of Petroleum" } ], "keywords": [ "Monitoring, performance assessment, and fault detection in chemical process control", "Fault detection and isolation methods" ], "abstract": "The traditional Mamba model has demonstrated its success in the field of fault detection. However, it focuses on temporal characteristics but omits spatial dependencies among multivariate variables. To fully utilize spatial information and enhance anomaly detection performance, this paper proposes an improved Mamba model, called the mutual information-guided spatio-temporal Mamba (MiST-Mamba). First, the method constructs a multi-scale mutual information-based convolutional encoder, which measures interdependencies among input variables through multiple mutual information matrices and further applies convolution operations to capture spatial features. The obtained spatial features are then fed into a spatio-temporal Mamba module, which employs a selective state-update mechanism to represent the long-term temporal dynamics of spatial features. Finally, a convolutional decoder is used to construct the reconstruction residuals, which serve as anomaly scores for anomaly detection. Experiments on the Tennessee Eastman benchmark demonstrate that the proposed method outperforms the traditional Mamba model in terms of fault detection performance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA24.3", "code": "FrA24.3", "title": "A Fault Root Cause Diagnosis Framework Based on Full Time Scales Granger Causality Algorithm", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA24", "sessionTitle": "Process Monitoring, Fault Detection and Diagnosis", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Chen, Rui", "affiliation": "Tongji University" }, { "name": "Liang, Shu", "affiliation": "Tongji University, School of Electronics and Information Engineering" }, { "name": "Li, Pan", "affiliation": "Tongji University" }, { "name": "Zhou, Yuanqiang", "affiliation": "Tongji University" }, { "name": "Xu, Jia", "affiliation": "Tongji University" }, { "name": "Gao, Furong", "affiliation": "Hong Kong Univ of Sci & Tech" } ], "keywords": [ "Monitoring, performance assessment, and fault detection in chemical process control", "Process modeling, identification, and estimation techniques", "Industrial applications of chemical process control" ], "abstract": "In non-stationary industrial processes, fault variables often exhibit complex causal interactions across multiple time scales, posing significant challenges for accurate root cause diagnosis (RCD). To address this problem, this study proposes an RCD algorithm based on full time scales Granger causality analysis, enabling accurate root cause identification and comprehensive characterization of fault propagation mechanisms. Specifically, the original time series is decomposed into multi-scale intrinsic mode functions using multivariate variational mode decomposition, minimizing both frequency-domain bandwidth and reconstruction error. An error correction term is then incorporated into a structural equation model parameterized by multilayer perceptrons to mitigate the adverse effects of non-stationarity on causality identification. Instantaneous causal relationships inferred at the original time scale through variational inference are integrated with those inferred across multiple scales to construct a comprehensive causal diagram among fault variables. Experimental results from a real-world injection molding process demonstrate that the proposed algorithm accurately identifies root cause variables.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA24.4", "code": "FrA24.4", "title": "Conditional Time-Domain Diffusion Prediction Via Multimodal Fusion for Predictive Maintenance in Non-Stationary Industrial Processes", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA24", "sessionTitle": "Process Monitoring, Fault Detection and Diagnosis", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Zhao, Chaoliang", "affiliation": "Dalian University of Technology" }, { "name": "Zhu, Li", "affiliation": "Dalian University of Technology" }, { "name": "Cui, Shujie", "affiliation": "Dalian University of Technology, School Control Science and Engineering" }, { "name": "Chen, Junghui", "affiliation": "Chung-Yuan Christian Univ" } ], "keywords": [ "Monitoring, performance assessment, and fault detection in chemical process control", "Predictive maintenance and equipment condition monitoring", "Machine learning and artificial intelligence in chemical process control" ], "abstract": "Predictive maintenance has demonstrated significant efficacy in minimizing resource losses and maintaining production continuity in advanced manufacturing environments. However, traditional approaches face substantial challenges when applied to industrial IoT systems characterized by non-stationary time series with high-dimensional, multi-source, noisy, and spatiotemporally correlated sensor data. Key limitations include the inability to capture dynamic interdependencies due to independent channel assumptions, insufficient integration of domain expertise, and absence of uncertainty quantification, which collectively elevate operational risks. To address these challenges, this paper introduces a text-prompt-guided temporal diffusion transformer model incorporating static-dynamic graph learning for predictive maintenance in non-stationary industrial processes. The model’s primary contribution is its adaptive modeling of multivariate coupling relationships through a spatiotemporal interaction network. Through customized text prompts and modal alignment constraints, the conditional guidance mechanism coordinates extraction of long-term and short-term dependencies with uncertainty-aware prediction during the diffusion denoising process. This approach substantially enhances the reliability of long-horizon predictions for industrial applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA24.5", "code": "FrA24.5", "title": "Model-Based Viscosity Estimation Using Pressure Pulsation Dynamics", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA24", "sessionTitle": "Process Monitoring, Fault Detection and Diagnosis", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Otte, Julian", "affiliation": "Ruhr University Bochum" }, { "name": "Leonow, Sebastian", "affiliation": "Ruhr University Bochum" }, { "name": "Monnigmann, Martin", "affiliation": "Ruhr-Universität Bochum" } ], "keywords": [ "Process modeling, identification, and estimation techniques", "Industrial applications of process control" ], "abstract": "In situ monitoring of fluid viscosity is of high interest for efficient process control, but measuring the viscosity of fluids is challenging and expensive. Progressing cavity pumps serve as valuable data source, because they induce a mild pressure pulsation that contains information on the fluid viscosity. We present a model-based approach to estimating fluid viscosity using only the easy-to-measure pressure pulsation at the progressing cavity pump. We derive a lumped-parameter model that relates the pump dynamics to the fluid viscosity, and introduce a phase-locked-loop-enhanced extended Kalman filter to estimate the viscosity online. We validate our approach using measurement data from a laboratory test stand and demonstrate its effectiveness in monitoring fluid viscosity across a range of operating conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA25.1", "code": "FrA25.1", "title": "3D Electrical Impedance Tomography Reconstruction Using Periodic-Activation Neural Networks (I)", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA25", "sessionTitle": "Biomedical and Medical Imaging, Image Processing, Visualization", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Choi, Jungeui", "affiliation": "Escola Politecnica Da USP" }, { "name": "Emura, Hector Shin", "affiliation": "EPUSP" }, { "name": "Duran, Guilherme C.", "affiliation": "EPUSP" }, { "name": "Tsuzuki, Marcos de Sales Guerra", "affiliation": "University of Sao Paulo" } ], "keywords": [ "Biomedical and medical imaging, image processing, visualization", "Biomedical signal measurement and processing", "Medical devices, systems and solutions" ], "abstract": "Electrical Impedance Tomography (EIT) seeks to recover internal conductivity distributions from boundary voltage measurements, but the inverse problem is severely ill-posed and highly sensitive to noise. This work investigates sinusoidal and hyperbolic–sinusoidal neural networks for domain-dependent 3D EIT reconstruction. The proposed formulation provides a representation capable of capturing both smooth and high-frequency spatial features. Results using a synthetic dataset containing 3D phantoms show that the hyperbolic–sinusoidal model offers sharper boundary recovery, more accurate conductivity contrast, and higher resilience to measurement noise compared to the standard sinusoidal model. These findings indicate that periodic-activation neural networks form a promising framework for high-resolution 3D EIT reconstruction.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA25.2", "code": "FrA25.2", "title": "Breast Surface Reconstruction from Multi-View Images Via Laplacian-Regularized Implicit Differentiable Rendering (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA25", "sessionTitle": "Biomedical and Medical Imaging, Image Processing, Visualization", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Sun, Yuwei", "affiliation": "University of Canterbury" }, { "name": "Zhou, Cong", "affiliation": "Chinese Academy of Sciences" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" } ], "keywords": [ "Digital twins in healthcare, model-based therapeutics" ], "abstract": "Accurate three-dimensional (3D) modelling of the breast surface is one basis of emerging breast cancer diagnosis and risk assessment systems. Typical explicit geometric approaches, such as structured-light scanning and stereo vision, require complex multi-camera hardware and calibration, which limits easy, robust integration into routine clinical workflows. This study investigates an implicit differentiable rendering (IDR) framework for breast surface reconstruction and introduces a normal-based Laplacian regularization term tailored to the smooth geometry of soft tissue. The proposed method reconstructs a signed distance field of the breast from multi-view RGB images acquired with a single camera and optimises the surface using only image-level supervision, without ground-truth 3D meshes or precise calibration. Experiments on a breast phantom with three random initialisations show the Laplacian-regularized model (L-IDR) consistently outperforms the original IDR baseline, achieving lower Chamfer distance and equal or higher PSNR across all seeds. Error histograms, cumulative error distributions and boxplots further show L-IDR shifts the point-wise error distribution towards smaller values, shortens high-error tails, yielding smoother reconstructed surfaces. Ablation studies indicate a moderate Laplacian weight and Fourier feature resolution provide a favourable balance between geometric fidelity and regularisation. These results suggest Laplacian-regularized implicit differentiable rendering offers a low-cost, calibration-free and flexible solution for breast surface reconstruction, and a promising step towards lightweight, image-based 3D breast modelling methods to complement a range of emerging breast cancer diagnostics.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA25.3", "code": "FrA25.3", "title": "Gaussian Decomposition of EIT Reconstructions with Different Inclusion Geometries (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA25", "sessionTitle": "Biomedical and Medical Imaging, Image Processing, Visualization", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Hall, Henry Wayne", "affiliation": "University of Canterbury" }, { "name": "Holder-Pearson, Lui", "affiliation": "University of Canterbury" }, { "name": "Zhou, Cong", "affiliation": "Chinese Academy of Sciences" }, { "name": "Moeller, Knut", "affiliation": "Furtwangen University" }, { "name": "Desaive, Thomas", "affiliation": "University of Liege" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" } ], "keywords": [ "Biomedical and medical imaging, image processing, visualization", "Digital twins in healthcare, model-based therapeutics", "Biomedical signal measurement and processing" ], "abstract": "Electrical Impedance Tomography (EIT) is an emerging medical imaging modality. It is non-invasive and operates by injecting small amounts of current into the body and measuring the induced boundary voltages. These voltages identify an inverse problem that allows the estimation of internal body impedances. Due to the large ill-posed nature of the inverse problem and the physics associated with the induced electric field, inclusions with different geometries may appear similar in reconstructed images. This lack of resolution limits the clinical applicability of EIT. Inclusions with different vertical geometries were reconstructed using an open-source EIT device. Each inclusion was moved through 25 different positions in a saline phantom. The reconstructed images were processed by considering a 1D slice of impedance values and approximating it with a Gaussian function. The parameters of the Gaussian function were used to find patterns in type and position of the inclusion. When the 1D slice was coincident with the target the impedances had one peak and small amounts of ringing making it a good approximation (R^2≥0.93). The Gaussian parameters showed repeatable patterns as the inclusion was moved around the phantom. The amplitude was the only parameter that changed with inclusions. The mean changed based on the horizontal position of the inclusion, and the standard deviation was consistent between inclusions. This simplified method is unable to reliably distinguish between complex geometries due to an ambiguity between the impacts of volume and changes in vertical shape. However, the position and size of the inclusions can be recovered from the 1D slices. Thus, the same information about the inclusion can be determined from a 1D set of impedances as a 2D image.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA25.4", "code": "FrA25.4", "title": "Diagnostic Algorithm Development for a Digital Imaging Elasto-Tomography Breast Cancer Screening System (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA25", "sessionTitle": "Biomedical and Medical Imaging, Image Processing, Visualization", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Couper, Samantha", "affiliation": "University of Canterbury" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" }, { "name": "Zhou, Cong", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Biomedical and medical imaging, image processing, visualization", "Medical devices, systems and solutions", "Healthcare management, disease control, critical care" ], "abstract": "Globally, 1 in 9 women experience breast cancer during their lifetime. Although mammography is the current large-scale screening modality, compliance is reduced due to radiation exposure, discomfort, and invasiveness. Digital Imaging Elasto-Tomography is an emerging radiation-free, non-invasive and portable screening technology designed to improve equity of access and outcomes for underserved populations. The surface motion response of the breast under a low-amplitude, mechanical vibration is measured with cameras and lasers. Based on the 400-1000% stiffness contrast between healthy and cancerous tissue, automated diagnostic methods analyse tissue properties and detect potential cancers. The capability of the detection algorithm is thus a key focus area to ensure high diagnostic accuracy. Further development and validation of an existing stiffness-based diagnostic algorithm is presented for this technology. Hysteresis Loop Analysis (HLA) was used to estimate tissue stiffness. Improved phantom breasts that better mimic the boundary between healthy tissue and stiff inclusions, as well as clinical data, were used to test the optimised algorithm. The Empirical Cumulative Distribution Function (ECDF) of the HLA stiffness enabled quantitative observation of abnormal regions of tissue, through skew, asymmetry about the centre, and the proportion of motion points above an empirically determined threshold. Future research efforts will aim to improve motion reconstruction success to enable diagnostic testing of higher amplitude data, more extensive clinical data testing and algorithm optimisation. Conclusions from the ECDF stiffness analysis show promise for improving the diagnostic algorithm capability of this technology, ultimately enhancing potential to improve breast screening equity and outcomes for women.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA25.5", "code": "FrA25.5", "title": "Camera Calibration and Imaging Methods for a Digital Imaging Elasto-Tomography Breast Cancer Screening System (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA25", "sessionTitle": "Biomedical and Medical Imaging, Image Processing, Visualization", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Couper, Samantha", "affiliation": "University of Canterbury" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" }, { "name": "Zhou, Cong", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Biomedical and medical imaging, image processing, visualization", "Medical devices, systems and solutions", "Healthcare management, disease control, critical care" ], "abstract": "For women worldwide, breast cancer has both the highest incidence and highest mortality of all cancers. Mammography is the current large scale screening modality. However, screening compliance is reduced by radiation exposure, discomfort and invasiveness. Digital Imaging Elasto-Tomography is an emerging radiation free, non-invasive and portable breast cancer screening technology aimed at improving access and outcomes for underserved populations. A low amplitude, steady-state, mechanical vibration is applied at the nipple to measure the surface motion response. With the 400-1000% contrast in stiffness between healthy and cancerous tissue, underlying tissue properties and potential cancers can be detected. The imaging system and calibration methods are thus a key stress area for ensuring accuracy and quality. The full imaging system and methods of calibration are presented for this technology. Camera and laser calibrations were validated visually and through performance metrics such as RMS error and inter-beam angle. These calibrations were used to evaluate surface motion reconstruction success, through imaging of four silicone phantoms at various frequencies [25Hz, 30Hz, 35Hz, 40Hz, 45Hz] and amplitudes [1mm, 1.5mm, 2mm], producing a total of 60 data sets. Phantoms with stiff inclusions had reconstruction success rates of > 80%, compared to only 26.7% in the healthy phantom, likely due to the deteriorated surface affecting laser correspondence. Across the 19 failures, the most common was disagreement between the laser correspondence and surface model, driven by unpredictable surface and light interactions. Future research efforts will focus on improving laser calibration inaccuracies and analysing phantom geometry to better reflect human breast variability. A higher quality laser model and more representative phantom breasts should enhance motion reconstruction reliability, ultimately strengthening the potential of this technology to increase breast screening equity and outcomes for women.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA25.6", "code": "FrA25.6", "title": "Absolute Electrical Impedance Tomography Reconstruction Using Adjoint State Method with Wasserstein Metric (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:30-11:50", "sessionCode": "FrA25", "sessionTitle": "Biomedical and Medical Imaging, Image Processing, Visualization", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Házi, Balázs", "affiliation": "Budapest University of Technology Economics" }, { "name": "Benyo, Balazs", "affiliation": "Budapest University of Technology and Economics" }, { "name": "Lovas, András", "affiliation": "Kiskunhalas Semmelweis Hospital" }, { "name": "Szlávecz, Ákos", "affiliation": "Budapest University of Technology and Economics" } ], "keywords": [ "Medical devices, systems and solutions", "Biomedical and medical imaging, image processing, visualization" ], "abstract": "Electrical Impedance Tomography (EIT) is widely used for bedside lung monitoring, but clinical imaging is still dominated by differential reconstructions. This paper investigates an absolute EIT reconstruction method based on the adjoint state method and a quadratic Wasserstein data misfit. A Complete Electrode Model-based thorax phantom with lung-like conductivity inhomogeneities was used to generate simulated boundary voltages, and the proposed method was compared with a conventional EIDORS time-difference Gauss–Newton reconstruction. The Wasserstein–adjoint reconstruction recovered the global lung-like structure more faithfully than the differential baseline, achieving a best structural similarity index measure (SSIM) of 0.874, correlation of r = 0.876, and mean absolute error (MAE) of 0.075 when iterative Gaussian smoothing was included. The reconstruction metrics converged after approximately 1000 iterations, and the custom implementation required about one minute for 1500 iterations on the investigated hardware. A Gaussian-noise analysis simulating measurement noise further showed gradual metric degradation with increasing voltage perturbation, supporting robustness under moderate noise while highlighting the need for future validation with mesh, electrode model, and experimental mismatch.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA26.1", "code": "FrA26.1", "title": "Large Language Model-Based Solution for Single-Origin Single-Destination Vehicle Routing Problems (I)", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA26", "sessionTitle": "Advances in Optimal Control, Learning, Data-Driven Control and Decision-Making for Vehicle Autonomy", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Zhou, Kailin", "affiliation": "Dalian University of Technology" }, { "name": "Xu, Fuguo", "affiliation": "Dalian University of Technology" }, { "name": "Shen, Tielong", "affiliation": "Dalian University of Technology" }, { "name": "Wang, Lei", "affiliation": "Dalian University of Technology" } ], "keywords": [ "Artificial intelligence in transportation", "Planning, management and security in transportation", "Automatic control, optimization, real-time operations in transportation" ], "abstract": "This paper proposes a large language model (LLM)-based approach for solving vehicle routing problems, with a single-origin, single-destination route network. In this LLM-based solution, different minimization targets, such as traveling distance, traveling time, and energy consumption, can be achieved. To derive the optimal route, the LLM abstracts and processes real-world historical traffic data, including GPS positions, real-time speeds, and energy consumption rates, from an Excel file. Then, prompt engineering is conducted using natural language to generate algorithms in Python. Simulation results show the effectiveness of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA26.2", "code": "FrA26.2", "title": "Data-Driven Predictive Control for Autonomous Vehicle Trajectory Tracking: A Comparative Study (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA26", "sessionTitle": "Advances in Optimal Control, Learning, Data-Driven Control and Decision-Making for Vehicle Autonomy", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Liu, Changjie", "affiliation": "Tongji University" }, { "name": "Li, Nan", "affiliation": "Tongji University" }, { "name": "Liu, Zhuolin", "affiliation": "Tongji University" }, { "name": "Qiao, Meihao", "affiliation": "Tongji University" }, { "name": "Zhang, Haobo", "affiliation": "Tongji University" }, { "name": "Chen, Hong", "affiliation": "Tongji University" }, { "name": "Han, Kyoungseok", "affiliation": "Hanyang University" } ], "keywords": [ "Trajectory and path planning for AVs", "Nonlinear and optimal automotive control", "Autonomous vehicles" ], "abstract": "Data-Driven Predictive Control (DPC) has emerged as a promising paradigm for directly constructing controllers from measured data, bypassing explicit model identification. It has attracted increasing attention in recent years, with notable advances in both theory and practice. Rooted in behavioral systems theory and Willems’ fundamental lemma, most developments have focused on linear systems, yet a number of extensions have been proposed for nonlinear problems. This paper presents a comparative study of three representative nonlinear DPC strategies applied to autonomous vehicle trajectory tracking. The comparison is carried out along three dimensions: (i) sensitivity to data quality, (ii) tracking accuracy, and (iii) computational efficiency. The evaluation is performed using both a simplified vehicle dynamics model and a high-fidelity CarSim model. The results highlight the effectiveness and limitation of each strategy, confirming their real-time feasibility and offering practical guidance for method selection in trajectory tracking applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA26.3", "code": "FrA26.3", "title": "Game-Theory-Inspired Autonomous Racing with Online Opponent Learning and Adaptive Strategic Planning (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA26", "sessionTitle": "Advances in Optimal Control, Learning, Data-Driven Control and Decision-Making for Vehicle Autonomy", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Ji, Kyoungtae", "affiliation": "Hanyang University" }, { "name": "Bae, Sangjae", "affiliation": "Honda Research Institute, USA" }, { "name": "Li, Nan", "affiliation": "Tongji University" }, { "name": "Kim, Youngki", "affiliation": "University of Michigan-Dearborn" }, { "name": "Han, Kyoungseok", "affiliation": "Hanyang University" } ], "keywords": [ "Mission planning and decision making for AVs", "Multi-vehicle systems", "Trajectory and path planning for AVs" ], "abstract": "Strategic reasoning in autonomous racing has emerged as a critical challenge for multi-agent systems, requiring vehicles to anticipate opponent behaviors while executing aggressive maneuvers. It has attracted increasing attention in recent years, with notable advances in game-theoretic planning and opponent modeling. Based on game-theoretic approaches, most research has assumed perfect knowledge of opponent rationality levels, yet this assumption is unrealistic as drivers exhibit time-varying strategic behaviors that must be inferred online. This paper presents a comparative study of adaptive opponent modeling strategies combining Level-k game theory with Hidden Markov Model (HMM)-based learning. The comparison is carried out along two dimensions: (i) strategic performance with fixed-level opponents and (ii) adaptability to dynamic strategy-switching opponents. The framework infers opponent rationality levels through real-time forward filtering and continuously adapts transition probabilities via Baum-Welch learning using forward-backward algorithm. Experimental validation includes competitive scenarios with dynamic level-switching opponents. Results show that online adaptation achieves superior performance compared to baseline fixed-level strategies across evaluation scenarios. The findings provide insights into the benefits of adaptive opponent modeling for multi-agent autonomous racing.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA26.4", "code": "FrA26.4", "title": "Knapsack-Based Online Sensor Selection for Vehicle State Estimation (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA26", "sessionTitle": "Advances in Optimal Control, Learning, Data-Driven Control and Decision-Making for Vehicle Autonomy", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Han, Jehyeop", "affiliation": "KAIST" }, { "name": "Kang, Minhee", "affiliation": "KAIST" }, { "name": "Colombo, Alessandro", "affiliation": "Politecnico Di Milano" }, { "name": "Farina, Marcello", "affiliation": "Politecnico Di Milano" }, { "name": "Ahn, Heejin", "affiliation": "KAIST" } ], "keywords": [ "Information processing and decision support in transportation", "Kalman filtering techniques in automotive control" ], "abstract": "As connected and autonomous driving technologies advance, vehicles increasingly rely on data from external sensors. Although this information can enhance state estimation, processing all available streams imposes significant communication and computational costs. To address this challenge, we introduce a Sensor Management Center (SMC) that selects a low-cost subset of external sensors in real time while satisfying chance-constrained error bounds derived from an Extended Kalman Filter (EKF) covariance. We formulate the selection problem as a multidimensional minimum knapsack problem and adopt a deficiency-weighted greedy algorithm as an approximate yet efficient solution. The proposed approach is validated through MATLAB simulations and experiments on a 1:15-scale cooperative driving testbed.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA26.5", "code": "FrA26.5", "title": "Constrained Sampling MPC for Safe Contact-Rich Control: From Exploration to Precision Via Hybrid Refinement (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA26", "sessionTitle": "Advances in Optimal Control, Learning, Data-Driven Control and Decision-Making for Vehicle Autonomy", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Wang, Chenghao", "affiliation": "Northeastern Univ" }, { "name": "Romeres, Diego", "affiliation": "Mitsubishi Electric Research Laboratories" }, { "name": "Schperberg, Alexander", "affiliation": "Mitsubishi Electric Research Laboratories" }, { "name": "Li, Na (Lina)", "affiliation": "SEAS Harvard" }, { "name": "Wang, Yebin", "affiliation": "Mitsubishi Electric Research Laboratories" } ], "keywords": [ "Trajectory and path planning for AVs", "Autonomous mobile robots" ], "abstract": "Safe robotic control in contact-rich environments requires navigating highly non-convex optimization landscapes while enforcing safety constraints and achieving task-level precision. Annealing-based sampling MPC methods provide fast exploration of complex solution spaces, but they lack principled mechanisms for constraint handling and struggle to reach the tight tolerances demanded by factory manipulation tasks. We propose Constrained Annealing-based Sampling MPC (CAS-MPC), a unified framework that combines a constrained annealing-based sampling control with gradient-based refinement. First, we introduce a primal–dual weight reshaping scheme that incorporates inequality constraints into annealing-based sampling MPC, ensuring collision avoidance while preserving sample diversity that trajectory-filtering approaches lose. Second, we propose a hybrid refinement strategy that transitions from fast sampling-based exploration to high-precision control: when far from the goal, CAS-MPC leverages its sampling capability for robust global exploration, and when near the goal it switches to Sequential Linear–Quadratic MPC for millimeter-level pose refinement. We validate our approach on a Unitree Go2 quadruped navigating around obstacles and on a Fetch mobile manipulator performing SE(3) reaching tasks, demonstrating both safe operation and precise goal achievement.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA26.6", "code": "FrA26.6", "title": "CARE Planner for Constrained Attention and Risk-Aware Planning in Imitation-Based Autonomous Driving (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:30-11:50", "sessionCode": "FrA26", "sessionTitle": "Advances in Optimal Control, Learning, Data-Driven Control and Decision-Making for Vehicle Autonomy", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Kim, Jiyun", "affiliation": "Korea Advanced Institute of Science and Technology (KAIST)" }, { "name": "Choi, Kyunghwan", "affiliation": "Korea Advanced Institute of Science and Technology" } ], "keywords": [ "Trajectory and path planning for AVs", "Learning and adaptation in autonomous vehicles", "Autonomous vehicles" ], "abstract": "Most imitation learning planners for autonomous driving are still supervised using displacement-based criteria that emphasize average proximity to the expert trajectory, even when safer alternatives exist. CARE Planner addresses this limitation by extending CAR Planner with risk-aware multimodal supervision. The proposed model preserves constrained ego-state attention to maintain robustness against shortcut-learning-driven attention collapse. It also introduces a clearance-based tail-risk score that guides supervision mode selection and soft targets over trajectory modes. On the nuPlan benchmark, CARE Planner improves overall performance and safety-related metrics over strong baselines, indicating that risk-aware supervision improves the reliability of multimodal imitation planning in challenging scenarios.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA27.1", "code": "FrA27.1", "title": "Bearing-Only Formation Tracking Control of Euler-Lagrange Systems with Parametric Uncertainties and Exogenous Disturbances", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA27", "sessionTitle": "Trajectory Tracking and Path Following for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Cheah, Hong Liang", "affiliation": "UNSW" }, { "name": "Deghat, Mohammad", "affiliation": "University of New South Wales" } ], "keywords": [ "Multi-vehicle systems", "Trajectory tracking and path following for AVs", "Autonomous vehicles" ], "abstract": "This paper presents a novel bearing-only formation tracking control law for Euler–Lagrange systems. Compared with single- or double-integrator dynamics, Euler–Lagrange systems provide a more realistic representation of physical agents. The proposed control law enables follower agents to achieve the desired formation and track moving leaders without requiring inter-agent communication or bearing rate measurements. Furthermore, it effectively rejects time-varying disturbances without prior knowledge of their upper bounds. A sufficient condition for collision avoidance among agents is also established. Lastly, a numerical simulation is provided to demonstrate the effectiveness of the proposed control law.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA27.2", "code": "FrA27.2", "title": "HyRS-RL: Hybrid Control for Automated Parking in Dynamic Environments Via Reeds-Shepp Guided RL", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA27", "sessionTitle": "Trajectory Tracking and Path Following for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Zhang, Yifan", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Xu, Yunwen", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Li, Dewei", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Ma, Aoyun", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Trajectory and path planning for AVs", "Autonomous vehicles", "Trajectory tracking and path following for AVs" ], "abstract": "Autonomous driving technology continues to evolve, yet it faces severe challenges in highly dynamic and long-horizon scenarios. Existing rule-based methods offer high precision but lack adaptability to changing environments. Conversely, learning-based methods demonstrate strong robustness but often suffer from low terminal accuracy and convergence difficulties. Furthermore, while segmented approaches that separate the approach phase from the parking phase reduce task complexity, they frequently encounter instability during state transitions between phases. To address these limitations, we propose a hybrid end-to-end parking framework that combines Reinforcement Learning with Reeds-Shepp curve guidance. This framework utilizes Reeds-Shepp curves to provide geometric priors that accelerate agent convergence, while leveraging the high-level decision capabilities of Reinforcement Learning to handle dynamic interactions. By adaptively fusing the control outputs from both components, the long-horizon and dynamic parking tasks is performed within a unified framework. Experimental results demonstrate that the proposed method significantly outperforms traditional baselines. Specifically, the parking success rate reaches 98% in scenarios with 4 dynamic obstacles and remains at 89% in complex interactive scenarios involving 8 dynamic obstacles. This study validates the effectiveness of the hybrid driving strategy in solving long-horizon complex parking problems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA27.3", "code": "FrA27.3", "title": "Global-Local Planning and Tracking Control for Micro-Vehicles", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA27", "sessionTitle": "Trajectory Tracking and Path Following for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Wang, Yingcan", "affiliation": "Gunma University" }, { "name": "Zuo, Xiaozhuo", "affiliation": "Gunma University" }, { "name": "Kamal, Md Abdus Samad", "affiliation": "Gunma University" }, { "name": "Yamada, Kou", "affiliation": "Gunma Univ" } ], "keywords": [ "Trajectory and path planning for AVs", "Trajectory tracking and path following for AVs", "Autonomous vehicles" ], "abstract": "This paper presents a novel integrated path planning and motion control system for a micro-vehicle that employs an improved A* algorithm with the Dynamic Window Approach (DWA) for path and trajectory planning and Model Predictive Control (MPC) for tracking. The proposed A* algorithm improves the efficiency and safety of path planning through adaptive neighborhood search, dynamic weighting, and an obstacle-proximity penalty. The proposed DWA algorithm incorporates a soft-constraint subfunction of relative motion, besides steering angle constraints, to generate smooth and feasible trajectories. Finally, MPC is used to achieve accurate and stable tracking control. The simulation results show that the proposed framework can generate safe and smooth trajectories in complex obstacle environments while maintaining high tracking accuracy and real-time feasibility.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA27.4", "code": "FrA27.4", "title": "Adaptive Sparse Gaussian Process Model Predictive Control for Robust Tracking in Autonomous Sweepers", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA27", "sessionTitle": "Trajectory Tracking and Path Following for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Zhang, Runxi", "affiliation": "Tongji University" }, { "name": "Chen, Xingru", "affiliation": "CowaRobot" }, { "name": "Li, Wenhao", "affiliation": "Tongji University" }, { "name": "Liao, Wenlong", "affiliation": "COWAROBOT" }, { "name": "Jin, Bo", "affiliation": "Tongji University" } ], "keywords": [ "Trajectory tracking and path following for AVs", "Autonomous vehicles" ], "abstract": "Autonomous street sweepers require high-precision trajectory tracking to ensure effective cleaning and curb-collision avoidance. Standard Model Predictive Control (MPC) frameworks using nominal kinematic models struggle with significant, non-stationary mis-matches caused by unmodeled dynamics from road-crown slopes, friction variations, and tireslip effects. Although Gaussian Processes (GP) can learn such dynamics, their static, offline-tuned hyperparameters cannot adapt to changing conditions, limiting safety guarantees. This paper proposes a unified control framework integrating an Adaptive Sparse Gaussian Process (ASGP) with application-specific probabilistic chance constraints. The ASGP employs a forgetting factor to track time-varying residuals, while the chance constraints stem from the sweeping mechanism’s geometry, ensuring true safety and task boundaries. The framework is validated in simulation and on a full-scale commercial sweeper. Simulation and real-world experiments demonstrate improved tracking accuracy and robust safety performance, meeting the application’s precision requirements.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA27.5", "code": "FrA27.5", "title": "Enhanced Geometric Tracking Control of Quad-Rotors Via Virtual Frame Transformation on SE(3)", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA27", "sessionTitle": "Trajectory Tracking and Path Following for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Liu, Xudong", "affiliation": "National University of Defense Technology" }, { "name": "Cao, Su", "affiliation": "National University of Defense Technology" }, { "name": "Yu, Li", "affiliation": "National University of Defense Technology" }, { "name": "Yu, Huangchao", "affiliation": "National University of Defense Technology" }, { "name": "Wang, Xiangke", "affiliation": "National University of Defense Technology" } ], "keywords": [ "Trajectory tracking and path following for AVs", "Autonomous vehicles", "Motion control for AVs" ], "abstract": "The dynamics of quad-rotors under complex maneuvers present significant challenges due to their strong coupling and underactuation. This paper introduces a dual-quaternion–based modeling framework that provides a compact representation of quad-rotor dynamics on SE(3). By integrating geometric tracking control with a virtual frame transformation, the proposed method effectively mitigates the inherent underactuation of quad-rotor systems. Building on this formulation, a two-layer control architecture is developed, consisting of: (1) an outer-loop position and attitude tracking controller, and (2) an inner-loop twist stabilization controller. Numerical simulations and comparative studies validate the effectiveness of the proposed approach, demonstrating stable trajectory tracking at speeds up to 14 m/s and significantly outperforming conventional geometric controllers in high-speed and complex maneuvering scenarios.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA27.6", "code": "FrA27.6", "title": "Asynchronous Distributed Formation Control for USVs with Ocean Current and Limited Communication", "day": "Friday", "date": "August 28, 2026", "time": "11:30-11:50", "sessionCode": "FrA27", "sessionTitle": "Trajectory Tracking and Path Following for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Wang, Yu", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Wu, Jing", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Long, Chengnian", "affiliation": "Shanghai Jiao Tong University" } ], "keywords": [ "Trajectory tracking and path following for AVs", "Marine system guidance, navigation and control", "Multi-vehicle systems" ], "abstract": "This paper addresses the distributed formation control of multiple unmanned surface vehicles (USVs) subject to unknown ocean currents and communication bandwidth constraints. To reduce communication load, an asynchronous event-triggered mechanism is introduced at the communication layer, eliminating the need for continuous monitoring inherent in synchronous or continuous transmission schemes. For the kinematic subsystem, an Extended State Observer based guidance law is developed to compensate for current-induced deviations and ensure accurate trajectory tracking. For the dynamic subsystem, an adaptive control law based on a linear analytical quantization model is proposed, which allows controller design without prior knowledge of the input quantization parameters, which avoids the restrictive assumption of fixed quantizer settings. Simulation results verify the effectiveness of the proposed strategy.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA28.1", "code": "FrA28.1", "title": "Control-Theoretic Analysis of Neurovascular Dynamics in Alzheimer’s Disease Using FNIRS (I)", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA28", "sessionTitle": "JO-JSC: Biomedical System Modeling, Identification, and Simulation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Hong, Keum-Shik", "affiliation": "Pusan National University" }, { "name": "Kang, Min-Kyoung", "affiliation": "Pusan National University" }, { "name": "Yong-Il, Shin", "affiliation": "Pusan National University Yangsan Hospital" }, { "name": "Kim, Ho Kyung", "affiliation": "Pusan National University" } ], "keywords": [ "Biomedical and medical imaging, image processing, visualization", "Biomedical signal measurement and processing", "Biomedical system modeling, identification, and simulation" ], "abstract": "Alzheimer’s disease (AD) is increasingly seen as both structural degeneration and disruption of brain network dynamics. Yet, quantitative neurovascular dynamics characterization is limited. We propose a control-theoretic framework to examine neurovascular complexity using resting-state fNIRS. Data were collected from 83 participants: healthy controls (HC, n = 27), mild cognitive impairment (MCI, n = 37), and AD (n = 19). Sliding-window nonlinear complexity measures—Higuchi’s fractal dimension, spectral entropy, and wavelet entropy—were computed for each channel to construct complexity-coupling networks based on inter-channel temporal coordination. Static topology, dynamic descriptors (mean, variability, range, temporal dependence), and state-based metrics from k-means clustering were analyzed. Task–rest reconfiguration and cognitive performance associations were evaluated. AD showed reduced network integration and selective loss of positive complexity coupling. Decreased temporal variability and state entropy, with fewer visited states, indicate diminished dynamic flexibility and state space contraction. Additionally, AD exhibited reduced task-induced network reconfiguration, reflecting impaired neurovascular adaptability. Among measures, spectral entropy–based networks, especially in deoxyhemoglobin signals, showed the strongest group discrimination and robust cognitive score associations. These findings show AD involves collapse of neurovascular dynamic complexity, with reduced spectral diversity, increased temporal rigidity, and constrained state-space dynamics. The framework offers a sensitive, mechanistically interpretable biomarker for tracking disease progression.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA28.2", "code": "FrA28.2", "title": "Enhancing Myoelectric Control Using a Hybrid Frisch Scheme and Non-Negative Matrix Factorization Model for sEMG-Based Hand Motion Regression (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA28", "sessionTitle": "JO-JSC: Biomedical System Modeling, Identification, and Simulation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Meattini, Roberto", "affiliation": "University of Bologna" }, { "name": "Monti, Francesco", "affiliation": "University of Bologna" }, { "name": "Bertozzi, Luca", "affiliation": "University of Bologna" }, { "name": "Sohrabi, Hamid", "affiliation": "University of Bologna" }, { "name": "Bargellini, Davide", "affiliation": "University of Bologna" }, { "name": "Diversi, Roberto", "affiliation": "University of Bologna" } ], "keywords": [ "Biomedical signal measurement and processing", "Control of physiological and clinical variables", "Medical devices, systems and solutions" ], "abstract": "Continuous myoelectric control of prosthetic hands requires robust regression from surface electromyographic (sEMG) signals, which are affected by noise, cross-talk, and overlapping muscle activity. We propose a hybrid approach combining the Frisch scheme, an Errors-in-Variables method, with Non-Negative Matrix Factorization (NMF) for muscle synergy extraction. The Frisch stage improves the statistical consistency of sEMG data prior to factorization, enabling more reliable continuous control signal estimation. The method was evaluated against standard and sparse NMF variants on data from five subjects performing four gestures. Results show that the proposed Frisch+NMF approach achieves lower reconstruction error, demonstrating the benefit of noise-aware preprocessing for myoelectric control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA28.3", "code": "FrA28.3", "title": "A Model of Stem Cell Dynamics with Carrying Capacity: The Role of Feedback on Proliferation Rate (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA28", "sessionTitle": "JO-JSC: Biomedical System Modeling, Identification, and Simulation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Borri, Alessandro", "affiliation": "Istituto Di Analisi Dei Sistemi Ed Informatica \"A. Ruberti\" (IASI), Consiglio Nazionale Delle Ricerche (CNR)" }, { "name": "Palumbo, Pasquale", "affiliation": "University of Milano-Bicocca" }, { "name": "Singh, Abhyudai", "affiliation": "University of Delaware" } ], "keywords": [ "Biomedical system modeling, identification, and simulation" ], "abstract": "Stem cells play a crucial role in biomedical research, offering remarkable potential for regenerative medicine, disease modeling, and drug discovery. Their ability to self-renew and differentiate into specialized cell types makes them essential for tissue repair and regeneration. This note explores a basic model of the differentiation/proliferation mechanisms while accounting for the maximum population size the environment can sustainably support due to limiting resources - i.e., the carrying capacity. Regulatory mechanisms affecting the proliferation rate are investigated using both deterministic and stochastic approaches. The deterministic analysis identifies regions of the parameter space that ensure a stable balance between stem and differentiated cells, while the stochastic approach provides valuable insights suggesting that a positive feedback on the proliferation rate leads to lower fluctuations in the accumulation of differentiated cells.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA28.4", "code": "FrA28.4", "title": "Qualitative Behavior Analysis of a Model Underlying the Warburg Effect (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA28", "sessionTitle": "JO-JSC: Biomedical System Modeling, Identification, and Simulation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Palumbo, Pasquale", "affiliation": "University of Milano-Bicocca" }, { "name": "Brotti, Susanna", "affiliation": "Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza Della Scienza 2, 20126 Milan" }, { "name": "Singh, Raghvendra", "affiliation": "Department of Chemical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India" } ], "keywords": [ "Biomedical system modeling, identification, and simulation" ], "abstract": "The Warburg effect describes the preference of highly proliferating cells (like cancer cells) for aerobic glycolysis and lactate production despite oxygen availability. In a recent paper, Jaiswal and Singh (2024) proposed that this behavior arises from a negative feedback loop linking cytoplasmic NADH levels and cell proliferation. Their model integrates glycolysis, oxidative phosphorylation, and pyruvate-to-lactate conversion to explain how the NADH/NAD+ ratio governs proliferation and quiescence. Here, we propose the qualitative behavior analysis, showing how quiescent and non quiescent equilibria arise according to model parameters. The corresponding bifurcation diagrams provide new biological insights on cellular behavior and pave the way to further investigation on the cellular machinery leading to the Warburg effect.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA28.5", "code": "FrA28.5", "title": "Knowledge-Guided Recurrent Neural Networks for Glucose-Insulin Dynamics Modeling (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA28", "sessionTitle": "JO-JSC: Biomedical System Modeling, Identification, and Simulation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "De Carli, Stefano", "affiliation": "University of Bergamo" }, { "name": "Licini, Nicola", "affiliation": "University of Bergamo" }, { "name": "Previtali, Davide", "affiliation": "University of Bergamo" }, { "name": "Previdi, Fabio", "affiliation": "Universita' Degli Studi Di Bergamo" }, { "name": "Ferramosca, Antonio", "affiliation": "Univeristy of Bergamo" } ], "keywords": [ "Biomedical system modeling, identification, and simulation", "Artificial pancreas or organs" ], "abstract": "Mathematical models of glucose–insulin dynamics are essential for managing type 1 diabetes. Their applications extend to closed-loop control, forecasting glucose trajectories, anticipating and detecting hypo- and hyperglycemia, and supporting real-time decision-making. In this work, we introduce the Compartmental Recurrent Neural Network (COMP-RNN), which advances the Biologically-Informed Recurrent Neural Network (BI-RNN) framework for glucose–insulin dynamics modeling. The COMP-RNN extends the data-driven strengths of the BI-RNN by embedding physiological structure directly into the model architecture. Specifically, it leverages structured recurrent networks aligned with canonical physiological compartments and incorporates prior physiological knowledge into training through an augmented cost function. The COMP-RNN is trained and validated on in silico cohorts. Compared to both BI-RNN and a benchmark linear model, the proposed approach achieves higher predictive accuracy and improved parameter efficiency, while better reflecting the underlying physiological system.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA28.6", "code": "FrA28.6", "title": "A Quantitative Design Guideline for Biomolecular Positive Feedback Systems (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:30-11:50", "sessionCode": "FrA28", "sessionTitle": "JO-JSC: Biomedical System Modeling, Identification, and Simulation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Kumar, Vinod", "affiliation": "Indian Institute of Technology Kanpur" }, { "name": "Sen, Shaunak", "affiliation": "Indian Institute of Technology Delhi" } ], "keywords": [ "Biomedical system modeling, identification, and simulation", "Dynamics and control of gene expression and metabolic pathways" ], "abstract": "Feedback is at the core of biological systems found in medicine and in biotechnology. While design metaphors from control engineering are widely used to understand negative feedback in such systems, they are relatively uncommon for positive feedback, especially for biomolecular circuits. Here, we extended a block diagram modelling framework for the design of positive feedback. We found a quantitative design guideline for the strength of the positive feedback, which when wrapped around a saturation function can give a threshold and a hysteretic response. The critical feedback strength was inversely proportional to the saturation value and directly proportional to the input scale where saturation starts. We found that this saturation-threshold-hysteresis hierarchy persisted in a realistic model of a positively autoregulated gene. We showed how this classical model fitted well in a block diagram framework with multiplicative feedback and derived expressions for the critical feedback strength in terms of the saturation parameters. The dependence of the critical feedback on the parameters matched with the obtained design guideline. To complete a rigorous workflow, we discussed how Groebner Bases computations and an Interval Newton algorithm can be used to provide validated numerical solutions in biological positive feedback systems. These results should be helpful in the analysis and design of biomolecular systems with applications to the control of biomedical systems and in biotechnology.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA30.1", "code": "FrA30.1", "title": "Health-Aware Predictive Energy Management for Non-Road Fuel Cell Electric Vehicles (I)", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA30", "sessionTitle": "JO: Monitoring, Performance Assessment, and Fault Detection in Control Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Köppel, Dominik", "affiliation": "TU Wien" }, { "name": "Jakubek, Stefan M.", "affiliation": "Technical Univ. of Vienna/Austria" }, { "name": "Hametner, Christoph", "affiliation": "TU Wien" } ], "keywords": [ "Hybrid, electric and alternative drive vehicles", "Nonlinear and optimal automotive control", "Engine and powertrain modeling and control" ], "abstract": "Advancing fuel cell systems for non-road applications requires addressing key challenges in durability and fuel efficiency. This paper presents a health-aware predictive energy management strategy for fuel cell non-road vehicles. This strategy explicitly integrates component degradation and fuel economy within a multi-objective energy management optimization. Its performance is evaluated using real-world wheel loader driving data across various operating scenarios. Full-lifetime investigations accounting for progressive component degradation demonstrate that the presented approach enables improved fuel cell and battery lifetime balancing. Compared to a purely fuel-minimizing strategy, it significantly extends the vehicle’s service life while maintaining high fuel efficiency.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA30.2", "code": "FrA30.2", "title": "Structure-Aware LSTM–GATv2: Causal Discovery and Fault Diagnosis Via Adversarial Learning (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA30", "sessionTitle": "JO: Monitoring, Performance Assessment, and Fault Detection in Control Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Modir Rousta, Mohammadhossein", "affiliation": "University of Alberta" }, { "name": "Memarian, Alireza", "affiliation": "University of Alberta" }, { "name": "Huang, Biao", "affiliation": "Univ. of Alberta" } ], "keywords": [ "Machine learning and artificial intelligence in chemical process control", "Monitoring, performance assessment, and fault detection in chemical process control", "AI methods for FDI/FTC" ], "abstract": "Robust causal inference under noise and nonlinear dynamics is essential for industrial fault detection and root-cause diagnosis. A unified framework is presented that integrates Granger causality for structural priors, LSTM-based temporal encoding, GATv2-driven graph refinement, and FGSM adversarial training. The approach concurrently performs forecasting, detection, and diagnosis within a structure-aware pipeline. Evaluation on synthetic datasets and the Tennessee Eastman Process reveals substantial performance gains over Granger-only baselines in both graph reconstruction and root-cause localization. Adversarial augmentation yields consistent improvements across all metrics, with particularly strong gains in precision and noise resilience, validating the framework's reliability for industrial diagnostic applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA30.3", "code": "FrA30.3", "title": "A Digital Twin of Evaporative Thermo-Fluidic Process in Fixation Unit of DoD Inkjet Printers (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA30", "sessionTitle": "JO: Monitoring, Performance Assessment, and Fault Detection in Control Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Toolhally, Samarth", "affiliation": "Eindhoven University of Technology" }, { "name": "Roelofs, Joeri", "affiliation": "Canon Production Printing, Technical University Eindhoven" }, { "name": "Weiland, Siep", "affiliation": "Eindhoven Univ. of Tech" }, { "name": "Das, Amritam", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Mechatronics for advanced manufacturing and energy systems", "Mechatronic system estimation, identification, control", "Application of mechatronic principles" ], "abstract": "In inkjet printing, optimal paper moisture is critical for print quality and is achieved through hot‑air impingement in a fixation unit. This paper presents a modular digital twin of the fixation unit that models the thermo‑fluidic drying process and enables real‑time monitoring of its spatio‑temporal behavior. The digital twin is formulated as an infinite‑dimensional state estimator that infers unmeasured thermal states from limited sensor data while remaining robust to external disturbances. Modularity is realized through a graph‑theoretic model in which each subsystem is represented by PDEs, with evaporation modeled as a nonlinear boundary effect via a Linear Fractional Representation. Using the Partial Integral Equation (PIE) framework, a unified approach for simulation, analysis, and estimator synthesis is developed and validated with data from a commercial inkjet printer. An mathcal{H}_{infty}‑optimal Luenberger estimator is synthesized to estimate internal thermal states, together forming a digital twin that enables spatio‑temporal monitoring capabilities beyond those available in traditional printing systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA30.4", "code": "FrA30.4", "title": "Back-Pressure Meets Game Theory: A Markovian Perspective for Urban Traffic (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA30", "sessionTitle": "JO: Monitoring, Performance Assessment, and Fault Detection in Control Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Choutri, Salah Eddine", "affiliation": "New York University Abu Dhabi (NYUAD)" }, { "name": "Djehiche, Boualem", "affiliation": "Royal Technical University of Stockholm" }, { "name": "Jabari, Saif", "affiliation": "New York University Abu Dhabi" } ], "keywords": [ "Queuing systems and performance model", "Stochastic control", "Control over networks" ], "abstract": "This paper presents a new formulation of the back-pressure control problem based on continuous-time Markov chains defined over a discrete state space, combined with a game-theoretic framework for urban traffic networks. Queue pressure differences in the back-pressure scheme are embedded in the jump intensities of the Markov chains, yielding an equivalent stochastic reformulation of the problem as a non-cooperative game. The resulting equilibrium corresponds to a control strategy that governs the evolution of the underlying continuous-time Markov chains. Simulation results illustrate the qualitative behavior of the proposed framework under time-varying traffic demand and validate the auxiliary Markov-chain reformulation of the back-pressure objective. Rather than focusing on numerical benchmarking, the contribution of this work lies in providing a new theoretical perspective that unifies stochastic modeling and game-theoretic decision-making in the context of urban traffic control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA30.5", "code": "FrA30.5", "title": "Water Network Clogging Detection and Localization (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA30", "sessionTitle": "JO: Monitoring, Performance Assessment, and Fault Detection in Control Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Molnö, Victor", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Mascherpa, Michele", "affiliation": "KTH Kungliga Tekniska Högskolan" }, { "name": "Kallesøe, Carsten Skovmose", "affiliation": "Grundfos" }, { "name": "Sandberg, Henrik", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Water distribution systems", "Fault detection and isolation methods" ], "abstract": "We formulate the pipe clogging detection and localization problem as a hydraulic resistance parameter estimation task. We derive conditions on the demands, which the system operator may control through pumps, under which resistance estimation admits a unique solution. We perform resistance estimation in a simulated version of a well-field in Viborg, Denmark, under progressively increasing clogging, demonstrating that the theoretical conditions accurately predict the estimation performance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA30.6", "code": "FrA30.6", "title": "Effect on Traction Performance of Filtering Algorithms for the Electric Tractor During Plow Tillage (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:30-11:50", "sessionCode": "FrA30", "sessionTitle": "JO: Monitoring, Performance Assessment, and Fault Detection in Control Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Siddique, Md Abu Ayub", "affiliation": "Eco-Friendly Hydrogen Electric Tractor & Agricultural Machinery Institute, Chungnam National University, Daejeon 34134, Republic" }, { "name": "Baek, Seung-Yun", "affiliation": "North Dakota State University" }, { "name": "Kim, Yong-Joo", "affiliation": "Chungnam National University" } ], "keywords": [ "Kalman filtering techniques in automotive control", "Electric and solar vehicles", "Vehicle dynamic systems" ], "abstract": "This study evaluates the effect of filtering algorithms on the traction performance of a single-motor electric tractor during plow tillage. A 19-kW electric tractor equipped with telemetry-based wheel torque meters and proximity sensors was used to measure axle torque and rotational speeds. Two filtering approaches—Kalman Filter Algorithm (KFA) and an Artificial Neural Network (ANN) filter—were applied to estimate axle torque, and their effectiveness was assessed against measured torque data. The estimated torque was then used to compute traction-related indicators, including axle power, net traction force, and tractive efficiency (TE). Results showed that KFA closely matched the measured axle torque across the operation, demonstrating stable dynamic response and minimal overshoot. ANN produced moderate accuracy but showed greater fluctuation during rapid load transitions. In contrast, the unfiltered dataset significantly overestimated torque, leading to unstable traction behavior. Average TE values were 30.42%, 29.81%, 28.90%, and 17.10% for measured, KFA, ANN, and unfiltered data, respectively, with statistical analysis confirming no significant difference between measured and KFA values. These findings demonstrate that KFA is an effective filtering method for improving real-time load estimation and traction performance during plow tillage. The results provide essential insights for developing more efficient motor control and energy management strategies for electric tractors.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA31.1", "code": "FrA31.1", "title": "TOOFAB: A Toolbox for Fast Battery Simulation", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA31", "sessionTitle": "Demonstration: Control Systems and Applications", "sessionType": "Demonstration Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Weldeghebreal, Elionai", "affiliation": "Eindhoven University of Technology" }, { "name": "le Roux, Francis Anne", "affiliation": "Eindhoven University of Technology" }, { "name": "Khalik, Zuan", "affiliation": "ASML" }, { "name": "Bergveld, Henk Jan", "affiliation": "Eindhoven University of Technology" }, { "name": "Donkers, M.C.F. (Tijs)", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Energy storage systems", "Real time simulators for energy systems" ], "abstract": "Battery modeling plays an important role in the application of Lithium-ion batteries as it provides important information for system design and control purposes. Physics-based models, such as the Doyle-Fuller-Newman model, are useful for more advanced applications such as ageing-aware charging as they provide information on the internal states of the battery. In this paper, we present an open-source physics-based battery modeling toolbox in Matlab, called TOOFAB (TOOlbox for FAst Battery simulation). This paper includes an overview of the numerical implementation of the Doyle-Fuller-Newman battery model, a thermal model, an ageing model and a parameter estimation procedure. We provide a detailed description of the toolbox features and functions and we further provide example use cases, including one example of model parameter estimation and one example of simulation of ageing.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA31.2", "code": "FrA31.2", "title": "TU/e Remote Labs: A Platform for Flexible and Scalable Control Education", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA31", "sessionTitle": "Demonstration: Control Systems and Applications", "sessionType": "Demonstration Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Rap, Jake Erno Willem", "affiliation": "Eindhoven University of Technology" }, { "name": "Ozkan, Leyla", "affiliation": "Technical University of Eindhoven" }, { "name": "Donkers, M.C.F. (Tijs)", "affiliation": "Eindhoven University of Technology" }, { "name": "Hendrix, W.H.A. (Will)", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Internet based control education", "Control education laboratories", "Control engineering curricula" ], "abstract": "Practical laboratory work is essential in control education, but its logistics do not scale well with growing student numbers and limited, expensive hardware. Standard remote-desktop solutions offer remote access but lack isolation, scheduling, and administrative control of laboratory setups. This paper presents TU/e Remote Labs, a platform that provides controlled, web-based access to real laboratory setups through virtualized environments and automated experiment session management. We describe the system architecture, the interactive and queued experiment workflows, and the integration of the platform into several control courses, supported by usage data demonstrating its flexibility and scalability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA31.3", "code": "FrA31.3", "title": "A New Class of Atomic Force Microscope: Park Systems FX40", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA31", "sessionTitle": "Demonstration: Control Systems and Applications", "sessionType": "Demonstration Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Kang, Chul-Goo", "affiliation": "Konkuk Univ" }, { "name": "Jo, Ah-Jin", "affiliation": "Park Systems Corp" }, { "name": "Ahn, Byoung-Woon", "affiliation": "Park Systems Corp" } ], "keywords": [ "Micro and nano mechatronic systems", "Mechatronic system modeling, design, optimization", "High-performance motion control systems" ], "abstract": "Park Systems FX40 is the latest innovation in atomic force microscopy (AFM), designed for high-resolution imaging of small samples. Its low noise floor, minimal thermal drift, and enhanced mechanical stability enable highly precise and reliable measurements. Key features include automatic probe exchange, automatic laser beam alignment, and a sample-view camera. With a powerful controller featuring an 8-channel lock-in amplifier and 5 MHz bandwidth for advanced signal processing, the FX40 supports a broad set of cutting-edge AFM modes. This paper demonstrates the enhanced performance and improved user-friendliness of the FX40 system.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA31.4", "code": "FrA31.4", "title": "Algebraic MPC Toolbox: Theory and Realization", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA31", "sessionTitle": "Demonstration: Control Systems and Applications", "sessionType": "Demonstration Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Ulukir, Talha", "affiliation": "Turkish Aerospace" }, { "name": "Dursun, Ufuk", "affiliation": "Ford Otosan" }, { "name": "Ustoglu, Ilker", "affiliation": "Istanbul Technical University" } ], "keywords": [ "Model predictive control", "Applications of optimal control", "Real-time optimal control" ], "abstract": "This paper introduces the Algebraic Model Predictive Control (A-MPC) toolbox for Simulink, developed for Linear Time-Invariant (LTI) systems. Although conventional Model Predictive Control (MPC) offers significant advantages, its industrial adoption is limited due to the computational burden of online optimization algorithms. This limitation often results in reduced real-world performance or increased product costs. To address this issue, a new algebraic formulation has been developed and implemented in the toolbox. The method reformulates constraints into a continuous form using the hyperbolic tangent function, enabling the application of first-order necessary conditions to the optimal control problem. Furthermore, the method has been extended with additional control options to enhance overall performance depending on the application, including Disturbance Input, Integral Action, and Rate Limiter. Since the proposed approach eliminates online optimization and relies entirely on algebraic computation, the computation time is significantly reduced. Simulation results for several use cases are presented and evaluated in terms of control performance and computation time, demonstrating the improved efficiency and effectiveness of the toolbox. The Algebraic MPC Toolbox is available at https://github.com/TalhaUlukir/AMPC.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA31.5", "code": "FrA31.5", "title": "Safe Reinforcement Learning for SMR Autonomous Load-Following: A PINN-Augmented RL-Classical Ensemble Framework with CBF Safety Certification", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA31", "sessionTitle": "Demonstration: Control Systems and Applications", "sessionType": "Demonstration Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Park, Ilhoon", "affiliation": "GNP System Co., Ltd" } ], "keywords": [ "Power plant control", "Nuclear power", "Digital twins for power and process systems" ], "abstract": "This paper proposes a hierarchical ensemble control framework for safe autonomous load-following of Small Modular Reactors (SMRs), integrating reinforcement learning (RL) with classical robust control under formal safety certification. A Physics-Informed Neural Network (PINN) digital twin provides a differentiable surrogate model enabling model-based policy gradient optimization with ~ 10X sample efficiency improvement. Parallel H∞ robust and SAC-CMDP controllers are adaptively blended by a meta-controller with Lyapunov-guaranteed switching stability, while a Control Barrier Function (CBF) safety filter enforces nuclear constraints via real-time quadratic programming. Gain margin analysis confirms that fixed-gain PID—standard practice in current PWR/SMR operations—loses stability margins below 50% power, providing the key engineering motivation for the multi-controller architecture. Simulation results on a 24-state first-principles SMR digital twin under 10% model–plant mismatch demonstrate 75.6% IAE reduction over baseline PID with zero safety constraint violations and 5×faster settling.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA32.1", "code": "FrA32.1", "title": "Continuous-Time Estimation of Deformation and Dynamic Parameters for Cosserat Rods under Free Vibration", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA32", "sessionTitle": "Smart Materials Based Mechatronic Systems and Structures: From Innovative Design to Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Herschmann, Samuel", "affiliation": "UKAEA" }, { "name": "Forbes, James", "affiliation": "McGill University" }, { "name": "Sloth, Christoffer", "affiliation": "Aalborg University" }, { "name": "Zhang, Kaiqiang", "affiliation": "UKAEA" }, { "name": "Skilton, Robert", "affiliation": "UK Atomic Energy Authority" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Mechatronic system modeling, design, optimization", "Soft robotics" ], "abstract": "Accurate modelling of flexible robotic structures is essential for achieving precise control in many engineering applications. Parameter estimation for nonlinear flexible systems becomes particularly challenging when measurements of dynamic deformation are limited and the system’s initial states are unknown. This paper introduces a continuous-time parameter and deformation trajectory estimation method for robotic payloads modelled as Cosserat rods, using temporal and spatial basis functions. The method enables identification of parameters that enter the dynamics nonlinearly from limited measurements. Simulation and experimental validation on a flexible manipulator payload equipped with a tip-mounted IMU demonstrate accurate reconstruction of both deformation and wrench responses, highlighting strong performance under limited sensing conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA32.2", "code": "FrA32.2", "title": "Robust Motion Control of Shape Memory Alloy Wire Actuators Via Bilinear Matrix Inequalities (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA32", "sessionTitle": "Smart Materials Based Mechatronic Systems and Structures: From Innovative Design to Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Brandi, Giuseppe", "affiliation": "Polytechnic University of Bari" }, { "name": "Priuli, Alberto", "affiliation": "Saarland University" }, { "name": "Massenio, Paolo Roberto", "affiliation": "Polytechnic University of Bari" }, { "name": "Naso, David", "affiliation": "Politecnico Di Bari" }, { "name": "Rotondo, Damiano", "affiliation": "Universitetet I Stavanger" }, { "name": "Rizzello, Gianluca", "affiliation": "Saarland University" } ], "keywords": [ "Mechatronic system estimation, identification, control", "High-performance motion control systems", "Mechatronics for robotic systems" ], "abstract": "Shape Memory Alloy (SMA) actuators are widely used in mechatronics and robotics due to their high energy density, flexibility, and relatively large strain. However, their nonlinear and hysteretic behavior poses great challenges for reliable deployment in real-world scenarios. This work presents a model-based robust control approach for SMA actuators. The rate-dependent hysteretic response of the actuator is described via a Wiener model, combining a linear time-invariant dynamics with a static hysteresis operator. While this model captures well the actuator's response, it does not allow direct compensation of the hysteresis at the plant's input via an inverse model. To address this issue, we introduce a compensator-free robust motion control strategy that treats the hysteresis as a bounded time-varying uncertainty, and achieves set-point regulation with a prescribed decay rate in the full actuator range. The robust controller consists of a PI law, whose gains are systematically tuned via a bilinear matrix inequality optimization. Experimental validation confirms the effectiveness of the closed-loop scheme.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA32.3", "code": "FrA32.3", "title": "Design and Modeling of a HASEL Actuator-Based Micro Parallel Robot (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA32", "sessionTitle": "Smart Materials Based Mechatronic Systems and Structures: From Innovative Design to Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Feregrino, Agustin", "affiliation": "Université Marie Et Louis Pasteur" }, { "name": "Cisneros, Nelson", "affiliation": "FEMTO-ST" }, { "name": "Lefevre, Alexis", "affiliation": "FEMTO-ST" }, { "name": "Wu, Yongxin", "affiliation": "Université Marie Et Louis Pasteur" }, { "name": "Le Gorrec, Yann", "affiliation": "FEMTO-ST, SupMicroTech Besançon" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "Mechatronic system estimation, identification, control", "Mechatronics for robotic systems" ], "abstract": "This paper presents the mechatronic design, modeling, and experimental validation of a three-degree-of-freedom (3-DOF) micro parallel robot with a prismatic–spherical (3PS) topology actuated by Hydraulically Amplified Self-Healing Electrostatic (HASEL) actuators. Each actuator provides prismatic motion, while a compliant interface forms spherical joints. A prototype was built and tested using laser tracking. A port-Hamiltonian (PH) model combined with forward kinematics (FKM) captures the nonlinear dynamics, and inverse kinematics (IKM) estimates actuator inputs. Parameters were identified using nonlinear grey-box (NLGB) estimation, yielding a compact model suitable for control design.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA32.4", "code": "FrA32.4", "title": "Stiffness Anisotropy-Based Modeling for a Tendon-Driven Notched Continuum Robot (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA32", "sessionTitle": "Smart Materials Based Mechatronic Systems and Structures: From Innovative Design to Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Yu, Zuoqing", "affiliation": "Nanjing University of Aeronautics and Astronautics (NUAA)" }, { "name": "Duan, Yuzhou", "affiliation": "NUAA" }, { "name": "Shaoshuai, Kang", "affiliation": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Rakotondrabe, Micky", "affiliation": "University of Toulouse Alliance" }, { "name": "Ling, Jie", "affiliation": "Nanjing University of Aeronautics and Astronautics (NUAA)" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "Medical and rehabilitation robotics", "Mechatronics for robotic systems" ], "abstract": "With the inherent compliance and having an internal channel for tool delivery, tendon-driven notched continuum robots (TDNCRs) are widely used in the medical and industrial domains. However, the notched design introduces bending stiffness anisotropy in TDNCRs, which has not been explicitly considered in existing models. Therefore, we propose a mechanics-based model that accounts for stiffness anisotropy. Starting from the computation framework of area moment of inertia (AMI), both planar and spatial deformations are analyzed, and the mechanics-based model is developed based on Euler–Bernoulli beam theory. A prototype of TDNCR was designed, and experiments on bending angle and bending plane angle were conducted to validate the model. The root-square-mean errors (RSMEs) for bending angle are within 2 degrees, and the deviation for bending plane angle is within 4 degrees, validating the proposed model.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA32.5", "code": "FrA32.5", "title": "Robust Sampled-Data Model-Free Adaptive Control for a Piezoelectric Robotic Manipulator (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA32", "sessionTitle": "Smart Materials Based Mechatronic Systems and Structures: From Innovative Design to Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Naghdi, Maryam", "affiliation": "Isfahan University of Technology" }, { "name": "Izadi, Iman", "affiliation": "Isfahan University of Technology" }, { "name": "Ling, Jie", "affiliation": "Nanjing University of Aeronautics and Astronautics (NUAA)" }, { "name": "Rakotondrabe, Micky", "affiliation": "University of Toulouse Alliance" } ], "keywords": [ "High-performance motion control systems", "Micro and nano mechatronic systems", "Mechatronics for robotic systems" ], "abstract": "This paper presents a robust sampled-data model-free adaptive control (RSDMFAC) strategy specifically designed for precise position control of actuators in a robotic hand, using only position feedback. The proposed method is simple to implement, computationally efficient, and entirely independent of parameter identification. This makes it highly suitable for applications requiring rapid and accurate precise manipulations, where computational efficiency is crucial. The proposed approach ensures high performance in closed-loop systems, maintaining robustness even in the presence of external disturbances, sensor noise, and uncertainties in system dynamics. Experimental validation demonstrates the effectiveness of the control algorithm, with results highlighting its capability to achieve precise positioning and stability under challenging real-world conditions. This method offers a reliable and scalable solution for advanced robotic manipulation tasks in environments where high precision and adaptability are required.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA32.6", "code": "FrA32.6", "title": "Data-Driven Dynamic Modeling of a Tendon-Actuated Continuum Robot", "day": "Friday", "date": "August 28, 2026", "time": "11:30-11:50", "sessionCode": "FrA32", "sessionTitle": "Smart Materials Based Mechatronic Systems and Structures: From Innovative Design to Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Hansen, Harald Minde", "affiliation": "CERN" }, { "name": "Sæbø, Bjørn Kåre", "affiliation": "Norwegian University of Science and Technology (NTNU)" }, { "name": "Pettersen, Kristin Y.", "affiliation": "Norwegian Univ. of Science and Tech" }, { "name": "Gravdahl, Jan Tommy", "affiliation": "Norwegian University of Science and Technology (NTNU)" }, { "name": "Di Castro, Mario", "affiliation": "CERN" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Soft robotics", "Mechatronic system modeling, design, optimization" ], "abstract": "Developing dynamic models for tendon-driven continuum robots is challenging due to their nonlinear, high-dimensional, and friction-dominated dynamics. This paper presents a comparative study of data-driven system identification methods—including N4SID, ARX, and SINDYc—for modeling a tendon-actuated continuum robot with rolling joints developed at CERN. Despite the high number of joints of the robot, experimental analysis reveals that a two-degree-of-freedom dynamic model can accurately capture the system dynamics, owing to strong kinematic dependencies between the joints. The models are validated against experimental data, and used in the design of a model predictive controller, demonstrating their feasibility for real-time control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA33.1", "code": "FrA33.1", "title": "Energy-Based Control of a Dielectric Elastomer Cardiac Assist Device (I)", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA33", "sessionTitle": "JO-MECH: Mechatronic System Estimation and Control I", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Hammoud, Amal", "affiliation": "University of Franche-Comté" }, { "name": "Liu, Ning", "affiliation": "FEMTO-ST Institute" }, { "name": "Le Gorrec, Yann", "affiliation": "FEMTO-ST, SupMicroTech Besançon" }, { "name": "Civet, Yoan", "affiliation": "EPFL" }, { "name": "Perriard, Yves", "affiliation": "Ecole Polytechnique Fédérale De Lausanne (EPFL)" } ], "keywords": [ "Mechatronic system estimation, identification, control" ], "abstract": "This paper is concerned by the port-Hamiltonian modeling and control of a dielectric elastomer actuator designed for use in a cardiac assist device. The proposed nonlinear model captures the actuator’s hyperelastic material behavior, viscoelastic damping, and electromechanical coupling, and remains valid for large deformations up to 40%. An original Interconnection and Damping Assignment Passivity-Based Control strategy is developed to achieve closed-loop stabilization at a desired position. The accuracy of the multiphysics model and the performances of the proposed controller are experimentally validated.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA33.2", "code": "FrA33.2", "title": "Proportional-Integral Takagi-Sugeno Fuzzy Observer for Vehicle Dynamics Estimation (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA33", "sessionTitle": "JO-MECH: Mechatronic System Estimation and Control I", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Ifqir, Sara", "affiliation": "CRIStAL Lab, Centrale Lille Institute" }, { "name": "Nguyen, Anh-Tu", "affiliation": "INSA Hauts-De-France, Université Polytechnique Hauts-De-France" }, { "name": "Fagninou, Aaron", "affiliation": "University of Haute-Alsace, IRIMAS UR7499" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Autonomous navigation" ], "abstract": "This paper addresses the design and validation of robust estimation strategies for intelligent vehicle systems, with a particular emphasis on lateral velocity and tire-road interaction force estimation. A Takagi-Sugeno (TS) fuzzy model is used to represent the nonlinear vehicle dynamics, and a new proportional-integral (PI) observer is developed based on this representation. The observer synthesis is reformulated as a convex optimization problem under linear matrix inequality (LMI) constraints, and its performance is formally guaranteed using Lyapunov stability theory. The proposed observer is validated using real data collected from a Renault ZOE experimental platform. The results demonstrate high estimation accuracy and improved reliability compared with existing methods across a wide range of driving scenarios, highlighting the suitability of the approach for advanced driver assistance systems (ADAS) and autonomous driving applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA33.3", "code": "FrA33.3", "title": "Discrete Trajectory Tracking Prescribed-Time Control for Wheeled Mobile Robots (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA33", "sessionTitle": "JO-MECH: Mechatronic System Estimation and Control I", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Rodríguez-Arellano, Jesus", "affiliation": "Instituto Politécnico Nacional" }, { "name": "Aguilar, Luis T.", "affiliation": "Instituto Politecnico Nacional" }, { "name": "Miranda-Colorado, Roger", "affiliation": "Secihti-CINVESTAV" }, { "name": "Monroy-Rodriguez, Roald", "affiliation": "Instituto Politecnico Nacional" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Autonomous navigation", "Mechatronics for mobility systems" ], "abstract": "The versatility of Wheeled Mobile Robots in industry and research has motivated the development of advanced control strategies. One of their main tasks is trajectory tracking, which aims to track a time-varying path to reach a desired pose. However, wheeled mobile robots are inherently subject to external and internal uncertainties, hence degrading their performance in real-world scenarios. To address this issue, this work proposes a prescribed-time controller combined with a twisting control to compensate for matched disturbances. Moreover, the proposed scheme employs the Euler-forward discretization method, which makes the tracking error converge asymptotically to zero despite the effects of kinematic disturbances, sampling time increments, and the zero-order-hold effect. Closed-loop stability is demonstrated via a discrete Lyapunov analysis. In addition, a further analysis is performed to determine the exact sampling instant at which the closed-loop system becomes unstable, and a reset is performed to avoid instability. Finally, extensive numerical simulations validate the proposed scheme by varying initial conditions, introducing external disturbances, and increasing the initial sample time.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA33.4", "code": "FrA33.4", "title": "Identification and Control of ROV Attitude and Heave: A Compact Approach Using Modified Relay Feedback Test (I)", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA33", "sessionTitle": "JO-MECH: Mechatronic System Estimation and Control I", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Ibrahim, M. Y.", "affiliation": "Khalifa University" }, { "name": "Rehan, Ahmed", "affiliation": "Khalifa University" }, { "name": "Chehadeh, Mohamad", "affiliation": "The Petroleum Institute" }, { "name": "Boiko, Igor", "affiliation": "Khalifa University of Science and Technology" }, { "name": "Zweiri, Yahya", "affiliation": "Khalifa University" } ], "keywords": [ "Mechatronic system estimation, identification, control", "High-performance motion control systems", "Mechatronics for robotic systems" ], "abstract": "We present a practical identification-to-control workflow for the attitude (roll, pitch) and depth (heave) channels of a BlueROV2 using the Modified Relay Feedback Test (MRFT). MRFT enables the generation of test oscillations at predetermined phase lags of the plant, producing oscillation data (amplitude–frequency) across phases set by the MRFT parameter. The induced oscillations occur in the second and third quadrants of the complex plane; notably, while a linearized physics-based model with force as input cannot yield phase lags below −180◦, the MRFT response in the second quadrant reveals the presence of additional actuator and sensor dynamics, critical for accurate controller design. The measured oscillation data are mapped to frequency-response points via the describing-function method, providing comprehensive dynamic samples inclusive of these effects. These points enable Nyquist-domain fitting for system identification, from which a compact low-order complex-domain model is obtained that balances magnitude and phase errors. Using this identified model, a Type-B PID controller is tuned. The resulting gains deliver accurate set-point tracking in experiments with and without the presence of waves, demonstrating the robustness inherent to MRFT-based identification. Overall, the pipeline—MRFT sampling at several phase lags, compact complex-fit identification, and ITAE-based PID tuning—offers a fast, instrumentation-light path from field data to implementable attitude and depth control on ROV platforms. A demonstration video of the experimental results is available at Ibrahim (2025).", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA33.5", "code": "FrA33.5", "title": "Parametric Data-Driven Feedforward Control for Horizontal Boom Motion of a Rotary Crane System (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA33", "sessionTitle": "JO-MECH: Mechatronic System Estimation and Control I", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Nshama, Enock William", "affiliation": "University of Dar Es Salaam" }, { "name": "Msukwa, Mathew Renny", "affiliation": "University of Dar Es Salaam" }, { "name": "Takahashi, Hideki", "affiliation": "Kobelco Construction Machinery Co., Ltd" }, { "name": "Uchiyama, Naoki", "affiliation": "Toyohashi University of Technology" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Mechatronic system modeling, design, optimization" ], "abstract": "This paper presents a parametric data-driven feedforward control method for horizontal boom motion of a rotary crane system. System parameter data are obtained as a linear least square error minimization solution. An angular velocity-based potential field predicts parameter values corresponding to instantaneous reference velocities. The parameter predictions are used as inverse dynamics to generate the feedforward control signal. Computation time and experimental results illustrate the proposed method's effectiveness practicality in tracking performance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA33.6", "code": "FrA33.6", "title": "Tilt-Based Aberration Estimation in Transmission Electron Microscopy (I)", "day": "Friday", "date": "August 28, 2026", "time": "11:30-11:50", "sessionCode": "FrA33", "sessionTitle": "JO-MECH: Mechatronic System Estimation and Control I", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "van Hulst, Jilles", "affiliation": "Eindhoven University of Technology" }, { "name": "Franken, Erik", "affiliation": "Thermo Fisher Scientific" }, { "name": "Janssen, Bart", "affiliation": "Thermo Fisher Scientific" }, { "name": "Heemels, Maurice", "affiliation": "Eindhoven University of Technology" }, { "name": "Antunes, Duarte", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Mechatronic system modeling, design, optimization" ], "abstract": "Transmission electron microscopes (TEMs) enable atomic-scale imaging but suffer from aberrations caused by lens imperfections and environmental conditions, reducing image quality. These aberrations can be compensated by adjusting electromagnetic lenses, but this requires accurate estimates of the aberration coefficients, which can drift over time. This paper introduces a method for the estimation of aberrations in TEM by leveraging the relationship between an induced tilt of the electron beam and the resulting image shift. The method uses a Kalman filter (KF) to estimate the aberration coefficients from a sequence of image shifts, while accounting for the drift of the aberrations over time. The applied tilt sequence is optimized by minimizing the trace of the predicted error covariance in the KF, which corresponds to the A-optimality criterion in experimental design. The resulting non-convex optimization problem is solved using a gradient-based, receding-horizon approach with multi-starts. The proposed method is validated on a real TEM set-up. The results show that optimized patterns significantly outperform naive approaches. A direct comparison with a Zemlin tableau implementation (Zemlin et al., 1978) shows that the proposed method achieves comparable or higher image quality on amorphous specimens, while additionally extending to non-amorphous specimens where the Zemlin tableau cannot operate.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA34.1", "code": "FrA34.1", "title": "Trajectory Tracking Controller for Omnidirectional Robots Subject to Disturbances", "day": "Friday", "date": "August 28, 2026", "time": "09:50-10:10", "sessionCode": "FrA34", "sessionTitle": "Mechatronics for Robotic Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Burgueño Ruvalcaba, Marco Antonio", "affiliation": "Centro De Investigación Científica Y Estudios Superiores De Ensenada Baja California" }, { "name": "Garcia Covarrubias, David Antonio", "affiliation": "CICESE" }, { "name": "Pliego, Javier", "affiliation": "Centro De Investigación Científica Y De Educación Superior De Ensenada" }, { "name": "Montañez Molina, Carlos", "affiliation": "Centro De Investigación Científica Y De Educación Superior De Ensenada" }, { "name": "Arteaga, Marco A.", "affiliation": "UNAM" } ], "keywords": [ "Mechatronic system estimation, identification, control", "High-performance motion control systems", "Aerial, field, and marine robotics" ], "abstract": "This paper addresses the pose tracking of omnidirectional mobile robots subject to external perturbations. We consider the mobile robot as a rigid body whose configuration space is the Special Euclidean group SE(2). We propose a nonlinear disturbance observer to estimate the external perturbations. The estimated signals are combined with a nonlinear trajectory tracking law that guarantees asymptotic convergence to the reference time-varying pose. Experimental results validate the proposed control strategy.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA34.2", "code": "FrA34.2", "title": "Event-Triggered Composite Compensation Strategy for Balance and Disturbance Rejection of Wheeled Bipedal Robots", "day": "Friday", "date": "August 28, 2026", "time": "10:10-10:30", "sessionCode": "FrA34", "sessionTitle": "Mechatronics for Robotic Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Cao, Haixin", "affiliation": "Nankai University" }, { "name": "Lu, Biao", "affiliation": "Nankai University, Tianjin, China" }, { "name": "Fang, Yongchun", "affiliation": "Nankai Univ" }, { "name": "Liu, Rui", "affiliation": "Nankai University" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Mechatronics for robotic systems" ], "abstract": "This paper investigates balance control for underactuated wheeled bipedal robots (WBRs), where conventional feedforward compensation is insufficient for disturbance rejection. To simultaneously handle external disturbances and parameter uncertainties, an event-triggered composite compensation framework is proposed. Specifically, a nonlinear disturbance observer is first employed to estimate the lumped disturbance. An event-triggered mechanism then filters this estimation to facilitate accurate parameter identification. The resulting parameters are employed to refine the reference commands of the model predictive controller (MPC), thus integrating underactuation-aware compensation with recursive optimization. The effectiveness of the proposed approach is validated through comprehensive hardware experiments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA34.3", "code": "FrA34.3", "title": "Time-Series Anomaly Detection for Mobile Robots in Automotive Active Safety Testing Using an RNN-VAE", "day": "Friday", "date": "August 28, 2026", "time": "10:30-10:50", "sessionCode": "FrA34", "sessionTitle": "Mechatronics for Robotic Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Meyer, Henrik", "affiliation": "Volkswagen AG, Leibniz University Hannover" }, { "name": "Raguse, Karsten", "affiliation": "Volkswagen AG" }, { "name": "Colombo, Armando Walter", "affiliation": "Hochschule Emden/Leer" }, { "name": "Seel, Thomas", "affiliation": "Leibniz Universität Hannover" }, { "name": "Ehlers, Simon F. G.", "affiliation": "Leibniz University Hannover" } ], "keywords": [ "Mechatronic system fault detection, diagnostics, hardware-in-the-loop simulation", "Mechatronics for mobility systems", "Mechatronic system estimation, identification, control" ], "abstract": "Mobile robots, like the ultra-flat overrunable (UFO) robot platform, used in automotive active safety tests, currently lack self-diagnostic capabilities necessary to detect present hardware defects. This circumstance can lead to more severe failures, causing expensive repairs and operational downtime. This work proposes, for the first time, a reconstruction-based time-series anomaly detection model for these mobile robots, considering defect classes such as unevenly worn full-rubber tires or damaged dampers. Unlike prior publications, the proposed approach leverages the vast quantities of unlabeled data generated during routine operation through a simple pre-training step. Furthermore, it optimizes the hyperparameters of the implemented gated recurrent unit-based variational autoencoder (GRU-VAE) and evaluates both a stateless, windowed training approach and one using truncated backpropagation through time (TBPTT). The model’s generalization capabilities are demonstrated by successfully detecting six defect types, with four of them not present in the data used for hyperparameter optimization and threshold selection. This is validated using a test set collected from five system instances at various points over a period of several months, achieving an F1 score of 0.936, indicating strong practical viability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA34.4", "code": "FrA34.4", "title": "Dart-Catching Cable-Driven Parallel Robot: Forward Kinematics", "day": "Friday", "date": "August 28, 2026", "time": "10:50-11:10", "sessionCode": "FrA34", "sessionTitle": "Mechatronics for Robotic Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Schwegel, Michael", "affiliation": "TU Wien" }, { "name": "Feiler, Georg", "affiliation": "Technical University Vienna" }, { "name": "Knechtelsdorfer, Ulrich", "affiliation": "TU Wien, ACIN" }, { "name": "Kugi, Andreas", "affiliation": "TU Wien" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "High-performance motion control systems", "Mechatronics for robotic systems" ], "abstract": "In this paper, we present a cable-driven parallel robot (CDPR) capable of catching darts at arbitrary, predefined segments of a standard tournament dartboard, repeatedly and with high accuracy. The robot employs a previously published novel lightweight CDPR design that minimizes moving masses, thereby enabling high accelerations and precise positioning. To fully exploit the potential of this design, an efficient solution to the forward kinematics problem is necessary. We propose a computationally efficient algorithm for forward kinematics, provide its derivation and analysis, and validate the approach through experiments with the dart-catching robot. A video demonstration is available online: https://youtu.be/_WwZZbF93H4", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA34.5", "code": "FrA34.5", "title": "IMU to Joint Extrinsic Calibration of Articulated Link Pairs for Heavy-Duty Machinery", "day": "Friday", "date": "August 28, 2026", "time": "11:10-11:30", "sessionCode": "FrA34", "sessionTitle": "Mechatronics for Robotic Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Mirjalili, Amirsaman", "affiliation": "Tampere University" }, { "name": "Kowsari, Elham", "affiliation": "Postdoctoral Researcher" }, { "name": "Baumann, Dominik", "affiliation": "Aalto University" }, { "name": "Ghabcheloo, Reza", "affiliation": "Tampere University" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Robot perception and sensing", "Aerial, field, and marine robotics" ], "abstract": "Precise state estimation for heavy-duty machinery requires accurate sensor placement; however, manual calibration is error-prone. We propose a probabilistic framework for estimating the extrinsic parameters, namely lever arms and joint axes, using only IMU data. By unifying rigid-body constraints within a factor graph, our method accounts for measurement noise and resolves translational gauge ambiguity via geometric regularization. High-fidelity simulations of a forestry grapple demonstrate robust performance, achieving sub-degree axis accuracy (RMSE 0.244^circ) and sub-centimeter lever-arm precision (RMSE 5.29 mm). This formulation enables a plug-and-play magnetometer-free solution and demonstrates the feasibility of self-calibration for industrial systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrA34.6", "code": "FrA34.6", "title": "Filtered Safety Control of Flexible Payloads with Deflection Constraints", "day": "Friday", "date": "August 28, 2026", "time": "11:30-11:50", "sessionCode": "FrA34", "sessionTitle": "Mechatronics for Robotic Systems", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Park, Younghwa", "affiliation": "Maersk Mc-Kinney Moller Institute, University of Southern Denmark" }, { "name": "Herschmann, Samuel", "affiliation": "UKAEA" }, { "name": "Milella, Ferdinando", "affiliation": "United Kingdom Atomic Energy Authority" }, { "name": "Sloth, Christoffer", "affiliation": "Aalborg University" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Mechatronics for robotic systems", "Smart structures and vibration control" ], "abstract": "This paper presents a filtered high-order control barrier function (FHOCBF) for control-affine systems, applied to enforce safety constraints on the deflection of flexible payloads. A reduced-order model is constructed using the floating-frame-of-reference formulation combined with Craig–Bampton reduction to capture dominant elastic modes. An extended Kalman filter reconstructs the rigid–flexible states from collocated force measurements. To mitigate vibration during motion, a trajectory optimization scheme incorporating input shaping produces vibration-aware reference commands. Safety constraints on payload deflection are enforced through a FHOCBF, which prevents excessive deformation while avoiding the vibration amplification typically caused by classical HOCBFs. Simulation studies and experiments demonstrate that the proposed filtered safety-critical architecture achieves precise, vibration-aware, and deflection-constrained control of flexible payloads.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB01.1", "code": "FrB01.1", "title": "Adaptive Optimal Resource Allocation for Isolation Interventions: Flattening the Curve (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB01", "sessionTitle": "JO: Optimal Control and Optimization", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Arnouss, Mohamed", "affiliation": "Avignon University" }, { "name": "Hayel, Yezekael", "affiliation": "Avignon University" }, { "name": "Allali, Karam", "affiliation": "University Hassan II of Casablanca" } ], "keywords": [ "Healthcare management, disease control, critical care", "Decision support and control in medicine", "Intensive and chronic care or treatment" ], "abstract": "Economic savings achieved through targeted isolation avoid additional disease burdens and effectively address the disease-economy trade-offs in epidemic control. In this study, we use phase-space analysis to derive the explicit solution of the optimal control problem that minimize the infection peak given budget limitation. The optimal policy obtained is an adaptive control where the isolation rate dynamically adjusts according to the current epidemic state. We show that targeted isolation control policy achieves the same infection peak as transmission reduction policies under equivalent budgets, while avoiding broad socio-economic disruptions. Additionally, we show through numerical simulations that the control resolves the epidemic faster and reduces total infections. This demonstrates that targeted isolation can strike a balance between public health and economic stability, offering actionable insights for public health decisions moving forward", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB01.2", "code": "FrB01.2", "title": "Second-Order Policy Gradient Methods for the Linear Quadratic Regulator (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB01", "sessionTitle": "JO: Optimal Control and Optimization", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Valaei, Amirreza", "affiliation": "Aktus AI" }, { "name": "Bahari Kordabad, Arash", "affiliation": "Max Planck Institute for Software Systems: MPI SWS" }, { "name": "Soudjani, Sadegh", "affiliation": "Max Planck Institute for Software Systems" } ], "keywords": [ "Optimal control theory", "Learning methods for optimal control", "Numerical methods for optimal control" ], "abstract": "Policy gradient methods are a powerful family of reinforcement learning algorithms for continuous control that optimize a policy directly. However, standard first-order methods often converge slowly. Second-order methods can accelerate learning by using curvature information, but they are typically expensive to compute. The linear quadratic regulator (LQR) is a practical setting in which key quantities, such as the policy gradient, admit closed-form expressions. In this work, we develop second-order policy gradient algorithms for LQR by deriving explicit formulas for both the approximate and exact Hessians used in Gauss--Newton and Newton methods, respectively. Numerical experiments show a faster convergence rate for the proposed second-order approach over the standard first-order policy gradient baseline.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB01.3", "code": "FrB01.3", "title": "Active Mode Discrimination and Control for Probabilistic Modes and Bounded Uncertainties (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB01", "sessionTitle": "JO: Optimal Control and Optimization", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Niu, Ruochen", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Hu, Chuan", "affiliation": "McMaster University" }, { "name": "Wu, Mingyu", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Yang, Jufeng", "affiliation": "Jiangsu University" } ], "keywords": [ "Optimization-based estimation and control", "Model validation", "Uncertain systems" ], "abstract": "Accurate mode and fault discrimination are essential for ensuring system safety and operational efficiency. However, reliable identification is often challenged by confounding disturbances, bounded uncertainties, and modeling inaccuracies. Although recent advances in machine learning can estimate the likelihood of mode occurrences, these approaches generally fail to provide deterministic guarantees, especially under worst-case conditions. To address this limitation, we propose a probabilistically informed active mode discrimination framework that incorporates mode probability priors into the active input design. A probing strategy is developed for systems with probabilistic modes under bounded uncertainties, where a control objective is integrated to minimize the impact of probing on system performance. Furthermore, deterministic discrimination conditions and feasible minimal-time criteria are established to reduce computational complexity. The overall problem is formulated as a mixed-integer linear/quadratic programming (MILP/MIQP) problem solvable by standard optimization solvers.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB01.4", "code": "FrB01.4", "title": "Model-Based Gradient Estimation and Extremum Seeking Control for Continuous Time Dynamical Systems (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB01", "sessionTitle": "JO: Optimal Control and Optimization", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Lopez-Caamal, Fernando", "affiliation": "Universidad De Guanajuato" }, { "name": "Cea-Barcia, Glenda Edith", "affiliation": "Department of Environmental Sciences" }, { "name": "Regalado-Aguirre, Juan Alberto", "affiliation": "Posgrado En Biociencias" }, { "name": "Torres, Ixbalank", "affiliation": "Universidad De Guanajuato" } ], "keywords": [ "Real-time optimization and control in chemical processes", "Advanced process control", "Model-predictive and optimization-based control in chemical processes" ], "abstract": "In this paper we aim to steer the output of a continuous-time dynamical system to a state in which a performance index is minimised. To this end, we first obtain a dynamical system that models the gradient of such performance index with respect to the control input of the plant. In general, such model for the gradient depends on the plant's model, which we assume available. Then, we design a control law that ushers the gradient to zero, and the input and output values to the optimal condition, despite of the presence of unknown inputs which shift the location of the optimum state. Such control law updates continuously as they depend on the instantaneous value of the gradient. To show applicability, we perform the numerical simulation of a fermentation process.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB01.5", "code": "FrB01.5", "title": "A Neural Network Model for Chance-Constrained Optimization of Water Distribution Systems under Uncertainty (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB01", "sessionTitle": "JO: Optimal Control and Optimization", "sessionType": "Regular Session", "room": "Convention Hall - Room 101", "authors": [ { "name": "Duong, Julia", "affiliation": "Technische Universität Ilmenau" }, { "name": "Korder, Kristina", "affiliation": "Technische Universität Ilmenau" }, { "name": "Li, Pu", "affiliation": "Technische Universität Ilmenau" } ], "keywords": [ "Water distribution systems", "Big data and machine learning applied to smart cities" ], "abstract": "Chance-constrained optimization (CCOPT) of water distribution systems (WDSs) under uncertainty typically relies on computationally expensive Monte Carlo simulations to evaluate the chance constraints. This paper presents an artificial neural network (ANN)-based CCOPT framework for WDS, in which a trained neural network surrogate replaces Monte Carlo simulations for evaluating chance constraints, enabling real-time capable optimization of both hydraulic and water quality objectives. The method integrates a nonlinear programming (NLP) optimizer with an ANN model to generate operation scenarios for estimating state variables such as pressure and water age. Based on the assessment of constraint violations, a heuristic rule is designed to update the weighting parameter in the objective function. This enables adaptive refinement of the NLP formulation based on the predicted outcomes. Tests on a benchmark WDS model demonstrate the prediction accuracy being higher than 97% and a reduction in computation time over 99% compared to the Monte Carlo based method. This highlights the potential of the proposed approach for real-time CCOPT of large-scale WDSs, providing water utilities with rapid, uncertainty-aware decision support for joint pressure and water age control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB02.1", "code": "FrB02.1", "title": "Q-Learning-Based Stochastic Model Predictive Control for Green Ammonia Production (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB02", "sessionTitle": "JO: Controller Synthesis", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Park, Hyun Min", "affiliation": "Seoul National University" }, { "name": "Oh, Tae Hoon", "affiliation": "UNIST" }, { "name": "Lee, Jong Min", "affiliation": "Seoul National University" } ], "keywords": [ "Control and optimization for sustainability and energy systems", "Model-predictive and optimization-based control in chemical processes", "Machine learning and artificial intelligence in chemical process control" ], "abstract": "Green ammonia production systems powered by intermittent renewable energy must meet periodic demand under tight unit and storage constraints. We propose Q-learning-based stochastic model predictive control, a methodology integrating a stochastic model predictive control framework with a Q-function as the terminal cost. The proposed method explicitly enforces hard constraints, effectively manages both short-term and long-term disturbances, and offers significant advantages in terms of on-line computational speed. Simulation results show that the proposed method outperforms Nonlinear Model Predictive Control, Double Deep Q-Network, and Q-learning-based Model Predictive Control baselines. The proposed method achieves the lowest total cost, minimal soft constraint penalties, and eliminates both tank overflow and ammonia demand shortfall, enabling practical, real-time operation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB02.2", "code": "FrB02.2", "title": "Continuous Adaptive Barrier Function-Based PID Sliding Mode Control for UAVs under Actuator Faults (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB02", "sessionTitle": "JO: Controller Synthesis", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Askari Sepestanaki, Mohammadreza", "affiliation": "National Yunlin University of Science and Technology" }, { "name": "Pouzesh, Mohsen", "affiliation": "National Yunlin University of Science and Technology" }, { "name": "Mobayen, Saleh", "affiliation": "National Yunlin University of Science and Technology" }, { "name": "Najafi, Amin", "affiliation": "University of Zanjan" }, { "name": "Jamadi, Mohammad", "affiliation": "National Yunlin University of Science and Technology" }, { "name": "Fekih, Afef", "affiliation": "Univ of Louisiana at Lafayette" } ], "keywords": [ "Guidance, navigation and control of aircraft and spacecraft", "Condition monitoring and maintenance of aerospace systems", "Aerial and space robotics" ], "abstract": "This paper develops a proportional-integral-derivative (PID)-based sliding mode framework with an adaptive barrier mechanism for quadrotor Unmanned Aerial Vehicles (UAVs) operating under uncertainty and actuator faults. The barrier term is used to bound the states and drive them to a small neighborhood of the reference in finite time without needing upper bounds on disturbances. A PID switching manifold accelerates transient response during both the reaching and sliding phases. To limit chattering while maintaining robustness, smooth, continuous control inputs are used. Simulations of a faulted quadrotor under severe disturbances demonstrate accurate tracking, finite-time convergence of the sliding variables, and stable behavior even with reduced actuator efficiency. The results indicate improved transients, lower control effort, and better disturbance rejection compared to a standard adaptive sliding mode control benchmark.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB02.3", "code": "FrB02.3", "title": "Robust Cascade Control for Electro-Hydraulic Brake Systems Based on a Triple-Step Approach and Nonlinear Integral Sliding Mode Control (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB02", "sessionTitle": "JO: Controller Synthesis", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Zhang, Sumin", "affiliation": "Jilin University" }, { "name": "Jin, Xiaosong", "affiliation": "Jilin University" }, { "name": "He, Rui", "affiliation": "Jilin University" } ], "keywords": [ "Nonlinear and optimal automotive control", "Adaptive and robust control of automotive systems", "Modeling, supervision, control and diagnosis of automotive systems" ], "abstract": "The electro-hydraulic brake (EHB) system presents considerable control challenges due to model uncertainties, friction, and time-varying nonlinearities. This paper proposes a robust cascade control framework combining a nonlinear triple-step approach and nonlinear integral sliding mode control (NISMC). A data-driven model compensates for time-varying pressure-flow characteristics in the pressure loop. Simultaneously, an extended state observer-based NISMC actively rejects lumped disturbances in the servo loop. Hardware-in-the-loop (HIL) experiments verify the strategy, demonstrating an 18.6% faster response, 30.8% improved accuracy, and 68% reduction in cumulative error, significantly enhancing EHB system robustness.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB02.4", "code": "FrB02.4", "title": "Derivative-Free Policy Iteration for Adaptive LQR with Time-Varying Perturbations (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB02", "sessionTitle": "JO: Controller Synthesis", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Ch, Jayant", "affiliation": "Indraprastha Institute of Information Technology, Delhi" }, { "name": "Basu Roy, Sayan", "affiliation": "Indraprastha Institute of Information Technology Delhi" } ], "keywords": [ "Optimal control theory", "Adaptive control design", "Uncertain systems" ], "abstract": "This paper presents a policy iteration algorithm for adaptive robust control of nominal continuous-time linear systems with completely unknown dynamics. We utilize a dual-layer filtering architecture to enable derivative-free, on-policy learning without explicit system identification. The algorithm is shown to be inherently robust to bounded, time-varying perturbations in both state and input matrices. We derive explicit, iteration-dependent stability bounds using Lyapunov theory, proving that the algorithm remains uniformly ultimately bounded (UUB), and converge to a bounded neighborhood of the nominal optimal solution. The approach relies only on a finite-interval excitation condition and is verifiable online, ensuring a simple yet robust data-driven control framework.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB02.5", "code": "FrB02.5", "title": "Optimal Situational Control Algorithms for Fixed-Wing UAVs (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB02", "sessionTitle": "JO: Controller Synthesis", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Mitin, Fedor", "affiliation": "BSTU VOENMEH" }, { "name": "Kabanov, Sergei", "affiliation": "Baltic State Technical University" } ], "keywords": [ "Real-time optimal control" ], "abstract": "The paper addresses the problem of optimal control of fixed-wing unmanned aerial vehicles (UAVs) under varying terminal conditions and external disturbances. A control structure is developed based on Pontryagin's maximum principle, followed by the implementation of an algorithm for adaptive correction of the control structure parameters. Simulations of both single UAV flights and group takeoffs using the leader-follower scheme were conducted. Numerical experiments demonstrate that the developed algorithms ensure high accuracy in meeting terminal conditions and robustness to measurement noise and external disturbances. The results have practical applications in monitoring tasks and the organization of communication networks.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB02.6", "code": "FrB02.6", "title": "Parallel Dynamic Programming for Conic Linear Quadratic Control (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB02", "sessionTitle": "JO: Controller Synthesis", "sessionType": "Regular Session", "room": "Convention Hall - Room 102", "authors": [ { "name": "Zhang, Luyao", "affiliation": "TU Delft" }, { "name": "Bravo, Gabriel", "affiliation": "Dartmouth College" }, { "name": "Plancher, Brian", "affiliation": "Dartmouth College and Barnard College, Columbia University" }, { "name": "Grammatico, Sergio", "affiliation": "Delft Univ. of Tech" } ], "keywords": [ "numerical methods for optimal control", "applications of optimal control", "model predictive control" ], "abstract": "Linear Quadratic (LQ) control problems are at the heart of linear control theory and Model Predictive Control (MPC). While performant, standard approaches to solving such problems are inherently serial, limiting real-time scalability despite the parallel computing power available on modern multi-core CPUs. Contributing to addressing this challenge and motivated by ``divide and conquer'' strategies, we present a parallel-in-time approach that solves computationally demanding conic optimal control problems through the use of the alternating direction method of multipliers (ADMM). In particular, we formulate the inner primal update of ADMM as an LQ problem and split the reformulated problem along the time horizon. This enables us to derive a variant of the Riccati recursion using dynamic programming to solve each subproblem in parallel. Numerical benchmarks on two real-world applications demonstrate as much as a 5x speedup compared to existing related approaches on multi-core CPU hardware.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB03.1", "code": "FrB03.1", "title": "Fully Actuated System Approach for Servo Motor with Communication Delay Via Disturbance Observer and State Predictor", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB03", "sessionTitle": "Applications of FAS Theory in Aircraft and Unmanned Aerial Vehicles", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Chen, Shengjia", "affiliation": "Southern University of Science and Technology" }, { "name": "Liu, Haowen", "affiliation": "Southern University of Science and Technology" }, { "name": "Duan, Guang-Ren", "affiliation": "Harbin Institute of Technology" }, { "name": "Li, Ping", "affiliation": "Southern University of Science and Technology" } ], "keywords": [ "Control using FAS approach", "Fully-actuated systems in industry" ], "abstract": "Communication delay in permanent magnet synchronous motor servo systems significantly compromise system stability and tracking accuracy. To achieve high-precision motion control of servo motor subject to communication delay and external disturbances, this paper proposes a fully actuated system approach based on a state predictor. First, a lead compensated disturbance observer is designed to estimate disturbances. Second, a reducedorder Luenberger observer is developed to estimate the derivative of disturbance. Next, the disturbance and its derivative are used to compute state prediction compensation, improving prediction accuracy. Finally, based on the predicted states, a fully actuated system control scheme is designed to achieve fast tracking performance in the closed-loop system. Simulation results validate the effectiveness and superiority of the proposed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB03.2", "code": "FrB03.2", "title": "High-Order Fully Actuated Robust Control Approach for the Passively-Tilting Dual-Frame Hexacopter", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB03", "sessionTitle": "Applications of FAS Theory in Aircraft and Unmanned Aerial Vehicles", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Cao, Mingye", "affiliation": "Harbin Institute of Technology" }, { "name": "Liu, Jiajun", "affiliation": "Harbin Institute of Technology" }, { "name": "Zhu, Yimin", "affiliation": "Harbin Institute of Technology" }, { "name": "Zhang, Lixian", "affiliation": "Harbin Institute of Technology" }, { "name": "Wu, Tong", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Control using FAS approach", "Global fully actuated systems", "Fully-actuated systems in industry" ], "abstract": "This paper proposes a high-order fully actuated (HOFA) robust control approach for the passively-tilting dual-frame hexacopter (PTDF-H) to improve its motion accuracy and stability. The PTDF-H achieves full 6-DoF control via passive tilting enabled by universal joints, optimizing energy use through internal thrust counteraction. A robust controller is designed based on the HOFA system approach, which ensures precise trajectory tracking and disturbance rejection, with stability verified via Lyapunov theory. Dynamics modeling and control allocation are derived, and simulations demonstrate the effectiveness of the controller.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB03.3", "code": "FrB03.3", "title": "Robust Control of a Tilt-Rotor Quadrotor UAV Via High-Order Fully-Actuated System Approaches", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB03", "sessionTitle": "Applications of FAS Theory in Aircraft and Unmanned Aerial Vehicles", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Liu, Xiaorui", "affiliation": "China, Harbin Institue of Technology" }, { "name": "Zhang, Linchen", "affiliation": "Harbin Institute of Technology" }, { "name": "Cao, Mingye", "affiliation": "Harbin Institute of Technology" }, { "name": "Zhang, Lixian", "affiliation": "Harbin Institute of Technology" }, { "name": "Zhu, Yimin", "affiliation": "Harbin Institute of Technology" }, { "name": "Yang, Jianan", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Global fully actuated systems", "Control using FAS approach", "Fully-actuated systems in industry" ], "abstract": "This paper presents a robust trajectory-tracking controller for a tilt-rotor fully actuated quadrotor. Starting from a six-degree-of-freedom model that includes gyroscopic effects, tilt angles, and tilt rates, the position and attitude dynamics are reformulated as high-order fully actuated systems. A robust controller is then designed to handle bounded force/torque disturbances and the time-varying input mapping. Lyapunov analysis guarantees convergence of tracking errors to adjustable invariant sets. Simulations of circular trajectory tracking under disturbances demonstrate accurate position tracking, attitude stability, and bounded control effort, highlighting the effectiveness of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB03.4", "code": "FrB03.4", "title": "FAS Approaches: Iterative Learning Predictive Control for Spacecraft Fly-Around Batch Processes", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB03", "sessionTitle": "Applications of FAS Theory in Aircraft and Unmanned Aerial Vehicles", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Wang, Xiubo", "affiliation": "Northeastern University" }, { "name": "He, Xinyi", "affiliation": "Northeastern University" }, { "name": "Xu, Lixue", "affiliation": "Harbin Institute of Technology" }, { "name": "Meng, Fanwei", "affiliation": "Northeastern University at Qinhuangdao" }, { "name": "Guo, Ge", "affiliation": "Northeastern University" } ], "keywords": [ "Predictive control of fully-actuated systems", "Global fully actuated systems", "Control using FAS approach" ], "abstract": "This paper proposes an iterative learning predictive control (ILPC) strategy based on fully actuated system (FAS) approaches for spacecraft fly-around batch processes. In contrast to conventional approaches that focus solely on time-domain control, the proposed fully-actuated ILPC (FA-ILPC) framework extends the control strategy to time-batch dual physical domain. A time–batch mixed dynamical model for spacecraft fly-around mission is first established. By using the fully actuation of the dual physical domain spacecraft system, the double-difference operators with a regulation system is introduced, then a decoupled mixed-difference linear closed-loop predictive model is constructed. This model incorporates both historical difference information and the current states in the time and batch domains. Based on this formulation, the predictive optimization is converted into two decoupled optimization problems subject to three sets of linear matrix inequality (LMI) constraints, which ensures stability in both time and batch domains. Finally, the simulation results further verify the effectiveness of the proposed FA-ILPC strategy for spacecraft fly-around batch processes.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB03.5", "code": "FrB03.5", "title": "A Unidirectionally Connected FAS Approach for 6-DOF Quadrotor Control", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB03", "sessionTitle": "Applications of FAS Theory in Aircraft and Unmanned Aerial Vehicles", "sessionType": "Regular Session", "room": "Convention Hall - Room 103", "authors": [ { "name": "Ren, Weijie", "affiliation": "Southern University of Science and Technology" }, { "name": "Liu, Haowen", "affiliation": "Southern University of Science and Technology" }, { "name": "Duan, Guang-Ren", "affiliation": "Harbin Institute of Technology" } ], "keywords": [ "Unidirectionally connected FASs", "Sub-fully actuated systems", "Control using FAS approach" ], "abstract": "This paper proposes a unidirectionally connected fully actuated system (UC-FAS) approach for the sub-stabilization and tracking control of 6-DOF quadrotors, tackling limitations both in state-space and FAS framework to some extent. The framework systematically converts underactuated quadrotor dynamics into a UC-FAS model, unifying the existing different FAS transformation ways. By eliminating estimation of the high-order derivatives of control inputs, a drawback of current methods, the UC-FAS model simplifies controller design and enables direct eigenstructure assignment for closed-loop dynamics. Simulations demonstrate precise 6-DOF tracking performance. This work bridges theoretical FAS approach advancements with practical implementation needs, offering a standardized paradigm for nonlinear quadrotor control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB05.1", "code": "FrB05.1", "title": "Hybrid Metaheuristic Optimization of Distributed Control System Hardware Architecture with Model-Based Verification", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:25", "sessionCode": "FrB05", "sessionTitle": "LB: Distributed and Networked Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Zakirzyanov, Ruslan", "affiliation": "NEXT Engineering" } ], "keywords": [ "Bio-inspired algorithms and optimization-based control", "Model driven engineering of control systems", "Cyber physical systems" ], "abstract": "Large-scale chemical plants rely on distributed process control systems (PCS) comprising numerous processing units, communication modules, and I/O devices interconnected via industrial networks. The design of a cost-efficient and reliable hardware architecture under incomplete knowledge of plant dynamics, control algorithms, and timing requirements remains a challenging combinatorial optimization problem. This paper proposes a formal model for distributed control system hardware architecture synthesis. A hybrid ant colony-based metaheuristic framework is developed to construct feasible hierarchical architectures. The proposed approach is validated on a large-scale sulfuric acid plant control system case study. Plant parameters are identified from operational data, system stability is analyzed, and a controller synthesis is performed based on the optimized architecture. The results demonstrate the feasibility of the approach and confirm that the obtained architecture satisfies structural and dynamic performance requirements.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB05.2", "code": "FrB05.2", "title": "RL-Based Worker Assignment in Paced Mixed-Model Assembly Line", "day": "Friday", "date": "August 28, 2026", "time": "13:25-13:40", "sessionCode": "FrB05", "sessionTitle": "LB: Distributed and Networked Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Liu, Yichen", "affiliation": "Delft University of Technology" }, { "name": "Sharifi Kolarijani, Mohamad Amin", "affiliation": "Delft University of Technology" }, { "name": "Hashemi-Petroodi, S. Ehsan", "affiliation": "KEDGE Business School" } ], "keywords": [ "Consensus and reinforcement learning control", "Markov decision process", "Learning methods for control" ], "abstract": "We consider worker assignment in a paced mixed-model assembly line (WAMAL) where the task-station layout is fixed with a stochastic incoming product sequence. We focus on reallocating workers during the operation. In particular, we allow walking workers to move between stations at the end of each takt, subject to zoning and staffing constraints. We model this decision process as a Markov decision process (MDP) whose state describes the current product types along the line, together with the previous worker allocation. The stage cost combines a penalty for worker movement with a bottleneck objective based on the maximum station load, where station loads are computed using local scheduling models that capture parallel and collaborative work. To handle the resulting large state and action spaces, we learn reassignment policies from simulation using reinforcement learning frameworks, including Q-learning, Double DQN, PPO, and GRPO. Experiments on a small and a large instance show that the learned policies improve upon a random feasible-assignment baseline and remain computationally practical, and advantageous against policy iterations as the problem size grows.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB05.3", "code": "FrB05.3", "title": "Targeted Topological Cluster Realization Via Minimal Edge Modification", "day": "Friday", "date": "August 28, 2026", "time": "13:40-13:55", "sessionCode": "FrB05", "sessionTitle": "LB: Distributed and Networked Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Ma, Jeongmin", "affiliation": "Gwangju Institute of Science and Technology" }, { "name": "Ahn, Hyo-Sung", "affiliation": "Gwangju Institute of Science and Technology (GIST)" } ], "keywords": [ "Consensus", "Control of networks", "Control over networks" ], "abstract": "This paper discusses the problem of modifying a given directed consensus network so that it forms designated topological clusters through minimal edge modifications. Most existing studies on clustering phenomena focus primarily on identifying clusters in a given network, while relatively little attention has been paid to modifying a given network so that clusters are formed in a desired configuration. We define this network design problem and present initial ideas toward an efficient approach for realizing targeted topological clusters.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB05.4", "code": "FrB05.4", "title": "On Controlling Network Epidemic Models Via Targeted and Partial Node Isolation", "day": "Friday", "date": "August 28, 2026", "time": "13:55-14:10", "sessionCode": "FrB05", "sessionTitle": "LB: Distributed and Networked Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Volpe, Deborah", "affiliation": "National Institute of Geophysics and Vulcanology" }, { "name": "Orlandi, Giacomo", "affiliation": "Politecnico Di Torino" }, { "name": "Boggio, Mattia", "affiliation": "Politecnico Di Torino" }, { "name": "Turvani, Giovanna", "affiliation": "Politecnico Di Torino" }, { "name": "Novara, Carlo", "affiliation": "Politecnico Di Torino" }, { "name": "Zino, Lorenzo", "affiliation": "Politecnico Di Torino" } ], "keywords": [ "Control over networks" ], "abstract": "We deal with the problem of controlling the spread of an epidemic disease on a network in an optimal manner by restricting human mobility from or to one or multiple locations. In particular, we consider a susceptible--infected--removed discrete-time network epidemic model, in which we encapsulate a control action that captures mobility reductions or bans via temporally weakening or removing links from the network (either unidirectionally or bidirectionally). To optimally tradeoff the burden on the healthcare system and the social and economic costs associated with mobility restrictions, we cast an optimization problem. However, the discrete nature of mobility interventions hinders the tractability of the optimization problem with classical methods. Here, we address it by formalizing as a Quadratic Unconstrained Binary Optimization (QUBO) problem, which is efficiently solved using the growing potentialities of quantum computing. Our approach and its robustness in the presence of uncertainty is demonstrated on a realistic case study.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB05.5", "code": "FrB05.5", "title": "Consensus-Based Dual-Decomposition Algorithm on Event-Triggered and Quantized Networks", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:25", "sessionCode": "FrB05", "sessionTitle": "LB: Distributed and Networked Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Takata, Tomoyuki", "affiliation": "The University of Osaka" }, { "name": "Hayashi, Naoki", "affiliation": "Osaka University" }, { "name": "Inuiguchi, Masahiro", "affiliation": "Osaka University" } ], "keywords": [ "Distributed optimization", "Control over networks", "Consensus" ], "abstract": "This paper proposes a communication-efficient distributed optimization method for dynamic multi-agent systems in which multiple agents share a common resource represented by a coupling constraint. In conventional distributed optimization, frequent exchange of high-precision variables can impose a substantial communication burden. To mitigate this issue, we develop a distributed dual-decomposition algorithm that operates over event-triggered and quantized networks. We provide a theoretical analysis and derive sufficient conditions under which the proposed algorithm converges to an optimal solution despite quantization errors and intermittent communications. Numerical experiments on a vehicle-allocation problem in a car-sharing scenario validate the effectiveness of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB05.6", "code": "FrB05.6", "title": "Distributed Consensus of Multi-Agent Systems Via Log-Sum-Exp Smoothing for Non-Differentiable Objectives", "day": "Friday", "date": "August 28, 2026", "time": "14:25-14:40", "sessionCode": "FrB05", "sessionTitle": "LB: Distributed and Networked Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Ito, Yuta", "affiliation": "Meiji University" }, { "name": "Ichihara, Hiroyuki", "affiliation": "Meiji University" } ], "keywords": [ "Large-scale and networked optimization problems", "Non-smooth and discontinuous optimal control", "Distributed nonlinear control" ], "abstract": "This paper proposes a consensus control method for multi-agent systems using distributed optimization with non-differentiable objectives. To address chattering issues in the subgradient of the objective, this paper introduces log-sum-exp smoothing with a smoothing parameter. This enables smooth exploration in the early phase and achieves strict consensus convergence as the parameter approaches zero. Numerical examples and mobile robot experiments demonstrate the effectiveness of the proposed method in achieving consensus among agents and in robot formation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB05.7", "code": "FrB05.7", "title": "Quality versus Popularity: Underlying Network Formation Mechanisms of Online Social Platforms", "day": "Friday", "date": "August 28, 2026", "time": "14:40-14:55", "sessionCode": "FrB05", "sessionTitle": "LB: Distributed and Networked Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Xu, Yuchen", "affiliation": "Peking University" }, { "name": "Mei, Wenjun", "affiliation": "Peking University" } ], "keywords": [ "Social networks and opinion dynamics" ], "abstract": "This paper investigates the structural dynamics of online platforms transitioning from popularity-driven to quality-based regimes. Subject to finite attention constraints (M), we analytically prove that quality-based growth intrinsically exacerbates system inequality. Analytically, we prove that for M ge 3, this regime yields higher Gini coefficients than its popularity-driven counterpart. These mechanisms define a continuous spectrum of across empirical online social networks, revealing a fundamental trade-off between quality-based popularity-based paradigm. Our results provide a mechanistic framework for understanding structural stratification in constrained digital ecosystems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB05.8", "code": "FrB05.8", "title": "Balancing Sustainability and Output in Renewable-Resource Differential Games Via a Fairness-Competition Lever", "day": "Friday", "date": "August 28, 2026", "time": "14:55-15:10", "sessionCode": "FrB05", "sessionTitle": "LB: Distributed and Networked Systems", "sessionType": "Regular Session", "room": "Convention Hall - Room 105", "authors": [ { "name": "Han, Yi", "affiliation": "Peking University" }, { "name": "Mei, Wenjun", "affiliation": "Peking University" } ], "keywords": [ "Econometric models and methods", "Game theories", "Control and automation to improve social and political stability" ], "abstract": "Sustainable exploitation of renewable common-pool resources remains challenging when natural regeneration interacts with strategic extraction by self-interested agents. While most studies emphasize specific regulatory instruments, the dynamic role of redistribution principles, e.g., the tension between fairness-oriented sharing and competition-driven incentives, remains less understood within a unified framework. In this paper, we study a two-player renewable-resource differential game with a linear redistribution rule indexed by a parameter k, which continuously interpolates between pooling and textit{winner-takes-more} incentives. Focusing on symmetric stationary feedback Nash equilibria (FNE), we establish the existence and uniqueness of a globally defined continuous feedback equilibrium and characterize its structural regimes. Comparative statics show that increasing k raises the equilibrium steady-state stock and enhances long-run sustainability, whereas decreasing k improves instantaneous payoffs and short-run incentives. Numerical simulations further identify parameter regions in which stronger pooling maximizes discounted welfare. These findings reveal a fundamental intertemporal trade-off between sustainability and short-run incentives, highlighting redistribution intensity as a structural governance lever in dynamic resource management.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB06.1", "code": "FrB06.1", "title": "Robust Multiple-Object Tracking for Legged Robots Via Vibration-Aware Motion Compensation", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:25", "sessionCode": "FrB06", "sessionTitle": "LB: Machine Learning and Robotics", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "DongHun, Kim", "affiliation": "Jeonbuk National University" }, { "name": "Jo, Hyunggi", "affiliation": "Jeonbuk National University" } ], "keywords": [ "Humanoid and legged robots", "Robot perception and sensing", "Autonomous navigation" ], "abstract": "Quadruped robot-induced vibrations cause camera shake, which reduces the alignment (IoU) between Kalman predictions and detections in Tracking-by-Detection (TBD) multi-object tracking (MOT). This destabilizes data association and increases ID switches. Existing online TBD trackers often assume camera stability, making it difficult to distinguish ego-motion from object motion in robot navigation environments. Additionally, global motion compensation (GMC) using sparse optical flow and RANSAC-based affine estimation can become unstable under high vibrations due to a low inlier ratio, leading to poorer association performance. To address this, this paper proposes a translational global motion estimation method using the median of background feature point displacement vectors and a rGMC that accounts for wrap-around in panoramic images. In the QuadTrack 14 sequences, rGMC improves the HOTA score from 31.48 to 33.54 compared to standard affine-RANSAC GMC, and reduces ID switches from 970 to 854.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB06.2", "code": "FrB06.2", "title": "Evaluation of Physics-Based Static Estimators for Industrial Distillation Column under Small Sample Using Submodels", "day": "Friday", "date": "August 28, 2026", "time": "13:25-13:40", "sessionCode": "FrB06", "sessionTitle": "LB: Machine Learning and Robotics", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Torgashov, Andrei", "affiliation": "Institute of Automation and Control Processes FEB RAS" } ], "keywords": [ "Industrial applications of chemical process control", "Advanced process control", "Monitoring, performance assessment, and fault detection in chemical process control" ], "abstract": "The paper deals with the development of static estimators (SEs) for industrial distillation column quality indicators using a physics-based nonlinear model. SEs are also known in industry as soft sensors. One of the main obstacles to their construction is the small training sample (TS). Unlike existing approaches based on TS extensions, this paper proposes to use a physics-based model in SEs. SEs include sub- or auxiliary models for feed composition obtaining in addition to material and energy balances, phase equilibria, and models of mass transfer efficiency at separation stages. Submodel calibration issues and the advantages of the proposed approach over existing methods are discussed.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB06.3", "code": "FrB06.3", "title": "A Process Optimality Graph for Prescriptive Analytics Generated Using Established Machine Learning Methods", "day": "Friday", "date": "August 28, 2026", "time": "13:40-13:55", "sessionCode": "FrB06", "sessionTitle": "LB: Machine Learning and Robotics", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Louw, Tobi", "affiliation": "Stellenbosch University" }, { "name": "Schulze-Hulbe, Alexander", "affiliation": "Stellenbosch University" }, { "name": "Bradshaw, Steven", "affiliation": "Stellenbosch University" } ], "keywords": [ "Monitoring, performance assessment, and fault detection in chemical process control", "Process performance monitoring/statistical process control", "Machine learning and artificial intelligence in chemical process control" ], "abstract": "We present the optimality graph as a simple prescriptive analytic based on historical plant data to suggest operator actions for improved process performance. The approach enables identification of modes from plant data, generation of descriptive mode labels, and identification of drivers (actions or disturbances) for transition between modes. During online operation, the approach maps current operating conditions to a previously defined process mode and suggests actions to shift the process to a more desirable mode. Importantly, we avoid developing Yet Another Machine Learning Algorithm (YAMLA) and rely exclusively on well-established methods readily available in popular libraries in the hope that this will encourage wide-spread adoption. We demonstrate the approach on an industrial iron ore flotation data set.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB06.4", "code": "FrB06.4", "title": "Human-In-The-Loop Neuro-Symbolic Drift Anticipation for Reliable Visual SLAM", "day": "Friday", "date": "August 28, 2026", "time": "13:55-14:10", "sessionCode": "FrB06", "sessionTitle": "LB: Machine Learning and Robotics", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Nam, Junhyun", "affiliation": "Incheon National University" }, { "name": "Jo, Wonse", "affiliation": "Incheon National University" } ], "keywords": [ "Robot perception and sensing", "Autonomous navigation", "Human-robot interaction" ], "abstract": "This paper introduces Hybrid DeepSEE (HDS), a Human-in-the-Loop (HITL) neuro-symbolic framework for proactive drift anticipation in Visual SLAM (V-SLAM). While data-driven models offer predictive power, their ”black-box” nature often yields physically inconsistent outputs in out-of-distribution (OOD) environments. To address this, HDS integrates neural drift risk estimation with symbolic constraint reasoning. By utilizing a Large Language Model (LLM) as a reasoning bridge, the framework translates qualitative human context into interpretable symbolic constraints. Building upon this architecture, we propose a superior drift anticipation framework that ensures enhanced reliability and consistency in Visual SLAM", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB06.5", "code": "FrB06.5", "title": "Contact-State Estimation for Residual Reinforcement Learning of Vision-Language-Action Models", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:25", "sessionCode": "FrB06", "sessionTitle": "LB: Machine Learning and Robotics", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Im, Sohyeon", "affiliation": "Kyungpook National University" }, { "name": "Lee, Sangmoon", "affiliation": "Kyungpook National University" } ], "keywords": [ "Robotic grasping and manipulation", "Robotic learning and adaptation", "Task and motion planning" ], "abstract": "Pretrained Vision–Language–Action (VLA) policies can generate semantically meaningful manipulation trajectories, but they remain brittle when physical contact with deformable objects is not maintained during execution. In garment manipulation, a policy may initially establish contact with a garment but later lose it while continuing the nominal trajectory, leading to slip or drop failures. This paper studies contact robustness of pretrained VLA policies in deformable garment manipulation. We formulate contact failure as a postcontact transition event. Specifically, a drop is detected when a valid closed-contact state has previously been established, but the contact proxy is later lost while the gripper remains closed. Since the ideal object-in-hand state is not directly observable for deformable garments, we estimate a measurable contact proxy from gripper states, gripper collider geometry, and cloth particle positions. Based on this proxy, we define contact-based evaluation metrics, including drop rate and post-contact violation rate, to quantify failures that are not captured by task success alone. Our formulation provides a practical basis for analyzing pretrained VLA policies and designing contact-aware residual reinforcement learning rewards for deformable object manipulation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB06.6", "code": "FrB06.6", "title": "Dynamic Modelling and Parameter Identification of a Salicylic Acid Biosensor (Late-breaking/Discussion Paper)", "day": "Friday", "date": "August 28, 2026", "time": "14:25-14:40", "sessionCode": "FrB06", "sessionTitle": "LB: Machine Learning and Robotics", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Aymerich, Alejandro", "affiliation": "Universitat Politecnica De Valencia" }, { "name": "Arboleda-Garcia, Mario Andres", "affiliation": "Universitat Politècnica De Valencia" }, { "name": "Boada, Yadira", "affiliation": "Universitat Politècnica De València" }, { "name": "Vignoni, Alejandro", "affiliation": "Universitat Politècnica De Valencia" } ], "keywords": [ "Synthetic biology", "Modelling, parameter identification and state estimation in biosystems", "Kinetic modelling, analysis and optimization of metabolism" ], "abstract": "We present an expanded modelling and identification study of a whole-cell biosensor for salicylic acid (SA), based on the canonical NahR/pSal transcriptional system. A mechanistic ODE framework is formulated, mathematically reduced, and calibrated using dynamic time-series fluorescence and growth data. The identified model reproduces both activation and repression-like behaviours across SA concentrations, offering mechanistic insight aligned with molecular evidence. The model is designed for DBTL workflows and supports transfer to alternative bacterial hosts.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB06.7", "code": "FrB06.7", "title": "PINN-Based Shape Estimation for Deformable Linear Objects under Contact", "day": "Friday", "date": "August 28, 2026", "time": "14:40-14:55", "sessionCode": "FrB06", "sessionTitle": "LB: Machine Learning and Robotics", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Lee, Giwan", "affiliation": "Chonnam National University" }, { "name": "Hong, Ayoung", "affiliation": "Chonnam National University" } ], "keywords": [ "Robotic learning and adaptation", "AI-powered robotics" ], "abstract": "This paper proposes a shape estimation method for deformable linear objects (DLOs) under contact conditions using a physics-informed neural network (PINN). By incorporating physical constraints based on the Cosserat rod formulation into the loss function, the network efficiently learns DLO shapes using boundary node data. The framework is evaluated on stiff and flexible DLO datasets generated via simulation. Experimental results demonstrate that the PINN improves shape estimation accuracy and geometric smoothness compared to a baseline data-driven neural network under external contacts.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB06.8", "code": "FrB06.8", "title": "Temporally Coupled Policy Learning in Resource-Constrained Hybrid Control", "day": "Friday", "date": "August 28, 2026", "time": "14:55-15:10", "sessionCode": "FrB06", "sessionTitle": "LB: Machine Learning and Robotics", "sessionType": "Regular Session", "room": "Convention Hall - Room 106", "authors": [ { "name": "Jung, Hoseong", "affiliation": "Seoul National University" }, { "name": "Kim, H. Jin", "affiliation": "Seoul National Univ" } ], "keywords": [ "Robotic learning and adaptation", "AI-powered robotics", "Aerial, field, and marine robotics" ], "abstract": "In this paper, we address resource-constrained hybrid control problems in which discrete resource-usage decisions constrain subsequent continuous control actions over a finite horizon. We propose an information-theoretic temporal action representation that maximizes alignment between state-discrete decision histories and future continuous maneuver segments. These learned representations are discretized into a compact mode code to condition the continuous controller for multi-modal closed-loop behavior. In simulation benchmarks with strict action budgets, the resulting controller improves task success and resource efficiency over representative hybrid-control baselines.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB07.1", "code": "FrB07.1", "title": "Non-Equilibrium MAV-Capture-MAV Via Time-Optimal Planning and Reinforcement Learning (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB07", "sessionTitle": "Advances in Distributed Control and Estimation for Complex Networked Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Zheng, Canlun", "affiliation": "Westlake University" }, { "name": "Guo, Zhanyu", "affiliation": "Westlake University" }, { "name": "Yin, Zikang", "affiliation": "Westlake University" }, { "name": "Wang, Chunyu", "affiliation": "Westlake University" }, { "name": "Wang, Zhikun", "affiliation": "The Westlake University" }, { "name": "Zhao, Shiyu", "affiliation": "Westlake University" } ], "keywords": [ "Learning methods for control", "Model reference adaptive control", "Nonlinear adaptive control" ], "abstract": "This paper addresses intercepting highly maneuverable micro aerial vehicles (MAVs) using a compact platform with a custom launcher. We compare Time-Optimal Planning, which generates aggressive energy-efficient trajectories, with Reinforcement Learning, which offers superior robustness to dynamic uncertainties. Simulations and real-world experiments validate the RL-based approach, demonstrating reliable capture under unstable, high-speed conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB07.2", "code": "FrB07.2", "title": "KC-MAPF: A Kinematically-Constrained MARL Approach for Multi-Agent Path Finding (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB07", "sessionTitle": "Advances in Distributed Control and Estimation for Complex Networked Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Su, Peiyuan", "affiliation": "Southern University of Science and Technology" }, { "name": "Lin, Zhiyun", "affiliation": "Southern University of Science and Technology" } ], "keywords": [ "Multi-agent systems", "Distributed reinforcement learning" ], "abstract": "We propose KC-MAPF, a Multi-Agent Reinforcement Learning (MARL) framework for Multi-Agent Path Finding (MAPF) with physical constraints. The framework extends the edge state representation to uniformly model kinematic and volumetric constraints, enabling efficient collision resolution during MARL training and execution via pre-computed conflict sets. Its core Dual Graph Attention Network (Dual-GAT) decouples pathfinding from collision avoidance, allowing the agent to separately learn kinematic feasibility and collision avoidance. Experiments show the method resolves complex deadlocks and exhibits strong generalization to more complex scenarios.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB07.3", "code": "FrB07.3", "title": "Resource-Efficient Distributed Online Mapping with Incremental Gaussian Mixture Model (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB07", "sessionTitle": "Advances in Distributed Control and Estimation for Complex Networked Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Li, Le", "affiliation": "Zhejiang University" }, { "name": "Zheng, Ronghao", "affiliation": "Zhejiang University" }, { "name": "Zhang, Senlin", "affiliation": "Zhejiang University" }, { "name": "Liu, Meiqin", "affiliation": "Zhejiang University" } ], "keywords": [ "Multi-agent systems", "Distributed control and estimation", "Distributed optimization" ], "abstract": "Collaborative mapping in unknown environments poses persistent challenges for multi-robot systems, particularly when onboard memory capacity and communication bandwidth are severely constrained. To address these limitations, we propose an Incremental Gaussian Mixture Model (IGMM) framework that adapts to streaming observations through single-pass point cloud processing. Unlike conventional Expectation–Maximization (EM)-based Gaussian Mixture Model (GMM) construction, our method performs incremental updates without iterative optimization or historical point cloud storage, thereby enabling memory-efficient online mapping. Based on this incremental formulation, we introduce a distributed GMM fusion mechanism that allows neighboring robots to share their Gaussian components and merge those that overlap, thereby eliminating redundancy and reducing communication overhead. Experiments in both simulated and real-world environments demonstrate that our approach achieves accurate environmental modeling while significantly lowering memory usage and communication cost.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB07.4", "code": "FrB07.4", "title": "Distributed Complete Coverage Control for Collaborative Environmental Monitoring Using Control Barrier Function (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB07", "sessionTitle": "Advances in Distributed Control and Estimation for Complex Networked Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Lin, Ziqian", "affiliation": "Zhejiang University" }, { "name": "Zheng, Ronghao", "affiliation": "Zhejiang University" }, { "name": "Zhang, Senlin", "affiliation": "Zhejiang University" }, { "name": "Liu, Meiqin", "affiliation": "Zhejiang University" } ], "keywords": [ "Distributed control and estimation", "Multi-agent systems", "Control under communication constraints" ], "abstract": "We propose a distributed coverage control algorithm for collaborative monitoring by a team of quadcopters equipped with downward facing cameras. While optimizing for maximum coverage quality, the algorithm ensures that complete area coverage is maintained. Rather than improving the coverage rate of entire area directly, we concentrate on achieving seamless coverage along the boundary and combine it with hole prevention between neighboring quadcopters. The conditions for ensuring coverage of edges and vertices are formulated and transformed into motion constraints with control barrier functions. Owing to the identical mathematical form of the control barrier functions between a pair of quadcopters, these motion constraints can be computed in a distributed manner. The effectiveness and performance of the algorithm are validated via numerical simulations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB07.5", "code": "FrB07.5", "title": "Consistent Distributed Kalman Filter for Sensor Network Systems with Extended Split Covariance Intersection (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB07", "sessionTitle": "Advances in Distributed Control and Estimation for Complex Networked Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Bai, Mingming", "affiliation": "Zhejiang University" }, { "name": "Xu, Jinming", "affiliation": "Zhejiang University" }, { "name": "Huang, Yulong", "affiliation": "Harbin Engineering University" }, { "name": "He, Jiacheng", "affiliation": "University of Electronic Science and Technology of China" } ], "keywords": [ "Estimation and filtering", "Kalman filtering", "Multi-agent systems" ], "abstract": "Instead of fusing conservative upper bounds of the entire local estimates as in classical covariance intersection (CI) fusion, this paper presents an extended split CI (ESCI) based distributed Kalman filter for sensor networks to alleviate the conservativeness, where the local estimate is decomposed into three mutually independent components: an unknown correlated (UC) part, a known common part, and an uncorrelated part, and only the UC components are fused conservatively. Markedly reduced conservativeness and improved estimation accuracy are thereby induced. Theoretical foundation including fusion unbiasedness and consistency are further established. The existing CI-based and split-CI-based distributed Kalman filters are recovered as special cases of the proposed framework. Simulation results for partially observed sensor networks demonstrate the resulting performance gains.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB07.6", "code": "FrB07.6", "title": "Split-Spectrum Based Distributed Control of Linear Multi-Channel Systems (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB07", "sessionTitle": "Advances in Distributed Control and Estimation for Complex Networked Systems", "sessionType": "Invited Session", "room": "Convention Hall - Room 107", "authors": [ { "name": "Liu, Xi-Ming", "affiliation": "Southern University of Science and Technology" }, { "name": "Wang, Lili", "affiliation": "Southern University of Science and Technology" } ], "keywords": [ "Distributed control and estimation", "Control of networks" ], "abstract": "This paper addresses the distributed feedback control problem for continuous-time multi-channel linear systems with sensing and actuation distributed over a network of agents. Under joint controllability, joint observability, and a fixed strongly connected communication graph, we develop a distributed output feedback control architecture based on a split-spectrum observer. The proposed design extends the classical certainty equivalence principle to the distributed setting and explicitly handles the coupling effects induced by local feedback inputs. By exploiting a spectral decomposition that separates locally observable and unobservable estimation error dynamics, the method allows independent placement of observable modes and high gain consensus regulation of unobservable components. It is shown that, for any prescribed exponential decay rate, the observer and controller gains can be selected such that all agents’ estimation errors converge to zero at least at the desired rate, even in the presence of feedback-induced perturbations. A simulation example verifies the effectiveness of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB08.1", "code": "FrB08.1", "title": "Adaptive Prediction Theory for MLMS with Applications to Judicial Sentencing (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB08", "sessionTitle": "Interdisciplinary Advances in Stochastic/Nonlinear Systems Identification: Methods, Theory, and Applications to Judicial Sentencing Modeling", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Jin, Yifei", "affiliation": "State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences" }, { "name": "Zheng, Xin", "affiliation": "Chinese Academy of Sciences" }, { "name": "Guo, Lei", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Time/parameter varying system identification", "Physics informed and grey box model identification" ], "abstract": "This paper is motivated by the fact that existing research on judicial sentencing prediction predominantly relies on end-to-end models, which often neglect the inherent sentencing logic and lack interpretability. To address this challenge, we make three key contributions: First, we propose a novel Saturated Mechanistic Sentencing (SMS) model, in which sentencing decisions are represented under saturated observation, thereby embedding the legal logic derived from China’s Criminal Law and providing inherent interpretability. This SMS model can be transferred to a saturated stochastic linear regression (SSLR) model, for which we introduce the corresponding Momentum Least Mean Squares (MLMS) adaptive algorithm to account for real-data with possible drifting distribution. Second, for the MLMS-based adaptive predictor, we establish a mathematical theory for a general class of SSLR models on the accuracy of adaptive prediction without resorting to any stationarity and independence assumptions on the data, including the best possible upper bound for the prediction accuracy achievable by the best predictor designed in the known parameters case. Third, empirical evaluation on real-world data demonstrates that our approach achieves a prediction accuracy that is close to the best possible theoretical upper bound, validating both the model's suitability and the algorithm's effectiveness.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB08.2", "code": "FrB08.2", "title": "L_1-Based Adaptive Identification under Quantized Observations with Applications (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB08", "sessionTitle": "Interdisciplinary Advances in Stochastic/Nonlinear Systems Identification: Methods, Theory, and Applications to Judicial Sentencing Modeling", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Zheng, Xin", "affiliation": "Chinese Academy of Sciences" }, { "name": "Jin, Yifei", "affiliation": "Chinese Academy of Sciences" }, { "name": "Liu, Yujing", "affiliation": "Academy of Mathematics and Systems Science, Chinese Academy of Sciences" }, { "name": "Guo, Lei", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Nonlinear system identification", "Quantized systems" ], "abstract": "Quantized observations are ubiquitous in a wide range of applications across engineering and social sciences, and algorithms based on the L1-norm are well recognized for their robustness to outliers compared with their L2-based counterparts. Nevertheless, adaptive identification methods that combine quantized observations with L1-optimization remain largely underexplored. Motivated by this gap, we develop a new L1-based adaptive identification algorithm specifically designed for quantized observations. Without relying on the traditional persistent excitation condition, we establish global convergence of the parameter estimates and show that the average regret asymptotically vanishes as the data size increases. Finally, we apply the proposed algorithm to the probation-decision prediction problem in judicial sentencing using a real-world dataset, demonstrating its superior performance and practical significance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB08.3", "code": "FrB08.3", "title": "Adaptive Identification and Prediction of Large Regression Models for Multi-Class Tasks with Applications to Confusing Crime Classification (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB08", "sessionTitle": "Interdisciplinary Advances in Stochastic/Nonlinear Systems Identification: Methods, Theory, and Applications to Judicial Sentencing Modeling", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Dai, Ruifen", "affiliation": "Data Science Institute, Shandong University" }, { "name": "Yang, Lingyan", "affiliation": "Discipline Inspection and Supervision School, Shandong University" }, { "name": "Wang, Fang", "affiliation": "Shandong University" } ], "keywords": [ "Nonlinear system identification", "Estimation and filtering", "Machine and deep learning for system identification" ], "abstract": "This paper develops adaptive learning methods for large regression models under multi-class observations, and applies them to the confusing crime classification task. We propose a Two-Step Adaptive Extended Quasi-Newton (TSAEQN) algorithm that estimates growing-dimensional parameter vectors and predicts multi-class outcomes without the need to store historical data. Moreover, we establish the strong consistency of the parameter estimates under non-persistent excitation conditions, which are notably weaker than traditional assumptions such as independent and identically distributed or periodicity. By analyzing the asymptotic order of accumulated regret, we show that the proposed adaptive predictors can achieve accurate multi-class predictions without requiring any data excitation conditions. Finally, confusing crime classification experiments based on real judicial judgment data demonstrate the effectiveness of our algorithm in terms of both predictive performance and model size selection.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB08.4", "code": "FrB08.4", "title": "Adaptive Regulation of Wiener Systems with General Nonlinearities in Output Sensors (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB08", "sessionTitle": "Interdisciplinary Advances in Stochastic/Nonlinear Systems Identification: Methods, Theory, and Applications to Judicial Sentencing Modeling", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Ding, Mingxia", "affiliation": "AMSS. Chinese Academy of Science" }, { "name": "Zhao, Wenxiao", "affiliation": "Academy of Mathematics and Systems Science, Chinese Acedemy of Sciences" } ], "keywords": [ "Nonlinear adaptive control", "Stochastic adaptive control" ], "abstract": "This work addresses the adaptive regulation of the Wiener system subject to general output sensor nonlinearities, including saturation, dead-zone, polynomial-type nonlinearity, and other typical forms. First, the existence of an optimal control law is established, and it is shown that the optimal regulation control is equivalent to estimating the zero point of a function associated with the Wiener system. Second, following a direct control approach, a stochastic approximation-type regulator is developed using output measurements with the general nonlinear sensors without the need for explicit identification of the Wiener system. Furthermore, the optimality of the regulator is established. Numerical examples are provided to demonstrate the effectiveness of the developed regulator.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB08.6", "code": "FrB08.6", "title": "Gradient-Based Adaptive Prediction and Control for Nonlinear Stochastic Systems (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB08", "sessionTitle": "Interdisciplinary Advances in Stochastic/Nonlinear Systems Identification: Methods, Theory, and Applications to Judicial Sentencing Modeling", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Liu, Yujing", "affiliation": "Academy of Mathematics and Systems Science, Chinese Academy of Sciences" }, { "name": "Zheng, Xin", "affiliation": "Academy of Mathematics and Systems Science, Chinese Academy of Sciences" }, { "name": "Liu, Zhixin", "affiliation": "Academy of Mathematics and Systems Sciences" }, { "name": "Guo, Lei", "affiliation": "Chinese Academy of Sciences" } ], "keywords": [ "Estimation and filtering", "Stochastic adaptive control", "Nonlinear system identification" ], "abstract": "This paper investigates gradient-based adaptive prediction and control for nonlinear stochastic dynamical systems under a weak convexity condition on the prediction-based loss. This condition accommodates a broad range of nonlinear models in control and machine learning such as saturation functions, sigmoid and ReLU activation functions, and standard classification models. Without requiring any excitation condition of the system data, we establish global convergence of the proposed adaptive predictor and derive explicit rates for its asymptotic performance. Furthermore, under a classical nonlinear minimum-phase condition and with a linear growth bound on the nonlinearities, we establish the convergence rate of the resulting closed-loop control error. Finally, we demonstrate the effectiveness of the proposed adaptive prediction algorithm on a real-world judicial sentencing dataset, and further evaluate the adaptive control performance via a numerical simulation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB08.8", "code": "FrB08.8", "title": "Distributed Estimation of Stochastic Large Models with Binary-Valued Measurements (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB08", "sessionTitle": "Interdisciplinary Advances in Stochastic/Nonlinear Systems Identification: Methods, Theory, and Applications to Judicial Sentencing Modeling", "sessionType": "Invited Session", "room": "Convention Hall - Room 108", "authors": [ { "name": "Wang, Ying", "affiliation": "KTH Royal Institute of Technology," }, { "name": "Gan, Die", "affiliation": "Nankai University" }, { "name": "Zhao, Yanlong", "affiliation": "Chinese Academy of Sciences" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" } ], "keywords": [ "Estimation and filtering", "Quantized systems", "Multi-agent systems" ], "abstract": "This paper investigates the distributed estimation problem for stochastic large models with unknown infinite parameters under binary-valued measurements. A diffusion-type distributed recursive projection algorithm with an increasing order is proposed, to handle the infinite dimensionality of the parameters and the information loss in binary-valued measurements. Specifically, this algorithm transforms infinite-dimensional estimation into a sequence of finite-dimensional ones with growing orders, utilizes binary-valued innovation to construct direction information of estimates, and adopts diffusion strategy to fuse neighboring estimates to reducing signal requirements. The almost sure convergence of the algorithm is established without requiring independence or stationarity of the regressors, thereby accommodating the correlated feedback signals commonly encountered in control systems. The results extend existing works on finite-dimensional parameter estimation. A numerical example is provided to illustrate the algorithm and demonstrate the joint effect of the sensors.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB09.1", "code": "FrB09.1", "title": "Physics-Informed Recurrent Neural Networks for Efficient Modeling of Rail-Vehicle Dynamics", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB09", "sessionTitle": "Physics Informed and Grey Box Model Identification II", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Zeipel, Henrik", "affiliation": "Paderborn University, Faculty of Mechanical Engineering, Dynamics and Mechatronics" }, { "name": "Schuette, Jan", "affiliation": "Paderborn University, Faculty of Mechanical Engineering, Dynamics and Mechatronics" }, { "name": "Sextro, Walter", "affiliation": "University of Paderborn" } ], "keywords": [ "Physics informed and grey box model identification", "Machine and deep learning for system identification", "Nonlinear system identification" ], "abstract": "Efficient models of dynamical systems require both time-efficient predictions and data-efficient identification, especially under sparse measurement conditions. As existing methods often trade off physical consistency, speed, and data efficiency, this work proposes a Physics-Informed Recurrent Neural Network (PIRNN), enabling fast inference and robust generalization. The method integrates a discrete physics loss derived from a state-space model (SSM), training-progress adaptive sampling of collocation points in the PIRNNs latent-space, and a mixed incremental prediction scheme for heterogeneous system behavior. Numerical experiments of this general modeling framework on the example of monorail vehicle dynamics show that the presented PIRNN yields SSM-level accuracy while being seven times faster than a run-time optimized SSM. Furthermore, the PIRNN improves generalization in low-data regimes.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB09.2", "code": "FrB09.2", "title": "Horizon Selection in Physics-Enhanced Neural ODEs: Theoretical Insights and Flux Linkage Application", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB09", "sessionTitle": "Physics Informed and Grey Box Model Identification II", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Montecchio, Giulio", "affiliation": "Robert Bosch GmbH" }, { "name": "Hartmann, Benjamin", "affiliation": "Robert Bosch GmbH" }, { "name": "Reimann, Sven", "affiliation": "Robert Bosch GmbH" }, { "name": "Manderla, Maximilian", "affiliation": "Robert Bosch GmbH" }, { "name": "Achterhold, Jan", "affiliation": "Robert Bosch GmbH" }, { "name": "Görges, Daniel", "affiliation": "University of Kaiserslautern" } ], "keywords": [ "Physics informed and grey box model identification", "Machine and deep learning for system identification", "Nonlinear system identification" ], "abstract": "The integration horizon during the training plays a critical role in Physics-Enhanced Neural Ordinary Differential Equations. We draw conclusions about horizon extension in the training of Neural Ordinary Differential Equations based on classical nonlinear system identification of input-output models. In light of this insight, we propose a framework that exploits longer horizons to reduce bias in physical parameter estimates, extracts residual information from data, and acts as a regularizer improving generalization. In the learning of a model for permanent magnet synchronous machine, the method is used to jointly estimate the flux map and the resistance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB09.3", "code": "FrB09.3", "title": "Topology-Guided Physics-Informed Learning of District Heating Networks with Guaranteed Conservation Laws", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB09", "sessionTitle": "Physics Informed and Grey Box Model Identification II", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Sgadari, Corrado", "affiliation": "Politecnico Di Milano" }, { "name": "Bianchi, Federico", "affiliation": "Ricerca Sul Sistema Energetico - RSE SpA" }, { "name": "Polimeni, Simone", "affiliation": "Ricerca Sul Sistema Energetico" }, { "name": "La Bella, Alessio", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Physics informed and grey box model identification", "Machine and deep learning for system identification", "Nonlinear system identification" ], "abstract": "This work presents a novel physics-informed learning approach for district heating networks (DHNs), enabling reliable models identification from standard operational measurements. DHNs exhibit large-scale complex dynamics, depending on many physical parameters often not available in practice, and difficult to be learned with conventional identification techniques. The proposed physics-informed learning approach exploits the inherent DHNs modelling structure, enabling to obtain accurate data-based models physically consistent with system topology and conservation laws. The method is tested on a validated simulator of the real district heating network located at Ricerca sul Sistema Energetico (RSE) facility, demonstrating significantly improved accuracy and reliability compared with existing data-driven techniques.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB09.4", "code": "FrB09.4", "title": "Efficient Physics-Informed Kriging for Nonlinear Systems Forecasting", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB09", "sessionTitle": "Physics Informed and Grey Box Model Identification II", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Carnerero, A. Daniel", "affiliation": "The University of Osaka" }, { "name": "Ramirez, Daniel R.", "affiliation": "Univ. of Sevilla" }, { "name": "Alamo, Teodoro", "affiliation": "Universidad De Sevilla" } ], "keywords": [ "Physics informed and grey box model identification", "Nonlinear system identification", "Data-driven control theory" ], "abstract": "This paper introduces an efficient physics-informed Kriging framework for the forecasting of nonlinear dynamical systems. Traditional Kriging methods, while powerful for data-driven modeling, often function as black-box predictors that neglect underlying physical knowledge. To overcome this limitation, we propose a two-step methodology that integrates first-principles constraints without compromising computational efficiency. First, a nominal Kriging prediction is obtained purely from data. Then, this prediction is refined through a projection-based reconciliation step that enforces physical constraints by solving a linearized optimization problem. This approach achieves better performance than the traditional Kriging methods while maintaining computational costs. The effectiveness of the method is demonstrated through numerical experiments on a double pendulum system, where the proposed method yields improved prediction accuracy over standard Kriging predictors, with only a minor increase in computation time. These results highlight the potential of the proposed scheme for real-time forecasting in nonlinear and physically constrained systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB09.5", "code": "FrB09.5", "title": "Dissipative Latent Residual Physics-Informed Neural Networks for Modeling and Identification of Electromechanical Systems", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB09", "sessionTitle": "Physics Informed and Grey Box Model Identification II", "sessionType": "Regular Session", "room": "Convention Hall - Room 109", "authors": [ { "name": "Long, Youyuan", "affiliation": "Italy Institute of Technology" }, { "name": "Solak, Gokhan", "affiliation": "Italian Institute of Technology, Genoa" }, { "name": "Ajoudani, Arash", "affiliation": "Italian Institute of Technology" } ], "keywords": [ "Physics informed and grey box model identification", "Nonlinear system identification", "Machine and deep learning for system identification" ], "abstract": "Accurate dynamical modeling is essential for control of embodied systems, yet first-principle models of electromechanical systems often fail to capture complex dissipative effects such as friction, stray losses, and structural damping. While residual-learning physics-informed neural networks (PINNs) can effectively augment imperfect first-principle models with data-driven components, the residual terms are typically implemented as unconstrained multilayer perceptrons (MLPs), which may inadvertently inject artificial energy into the system. To more faithfully model the dissipative dynamics, we propose DiLaR-PINN, a dissipative latent residual PINN designed to learn unmodeled dissipative effects in a physically consistent manner. Structurally, the residual network operates only on unmeasurable (latent) state components and is parameterized in a skew–dissipative form that guarantees non-increasing energy. To enable stable and data-efficient training when only part of the state is measurable, we further develop a recurrent rollout training scheme with a curriculum-based sequence length extension strategy. Experiments on a real-world helicopter system show that DiLaR-PINN more accurately captures dissipative effects and achieves superior long-horizon prediction performance compared to multiple baselines.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB10.1", "code": "FrB10.1", "title": "Modifier Adaptation Based Iterative Learning Control Design (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB10", "sessionTitle": "Recent Advances in Iterative Learning and Repetitive Control II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Shen, Haonan", "affiliation": "University of Southampton" }, { "name": "Chu, Bing", "affiliation": "University of Southampton" } ], "keywords": [ "Iterative and repetitive learning control" ], "abstract": "Iterative learning control (ILC) is a high-performance control design method to improve the tracking performance of systems that operate repeatedly. Compared to model-free ILC design, model-based ILC algorithms generally have better convergence performance by using system model information in the control updating law. However, obtaining an accurate system model can be challenging or expensive (if not impossible at all) in practice. As a result, only an approximate, nominal model can be obtained, the use of which in model-based ILC design can inevitably degrade the algorithm’s performance or even lead to divergence. To address this issue, this paper proposes a novel ILC algorithm based on a recently developed feasible-side globally convergent modifier adaptation (MA) design, which, unlike traditional MA algorithms, has convergence guarantees. The proposed algorithm achieves monotonic convergence of the tracking error norm to the minimum possible value achievable by the real plant, while guaranteeing that the updated control inputs satisfy plant constraints, in clear contrast with the existing design methods. A rigorous convergence analysis is provided, followed by simulation examples demonstrating the effectiveness of the proposed algorithm.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB10.2", "code": "FrB10.2", "title": "Sparse Iterative Learning Control: Frequency-Domain Design Approach (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB10", "sessionTitle": "Recent Advances in Iterative Learning and Repetitive Control II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Tsurumoto, Kentaro", "affiliation": "The University of Tokyo" }, { "name": "Ickenroth, Tjeerd", "affiliation": "Eindhoven University of Technology" }, { "name": "Ohnishi, Wataru", "affiliation": "The University of Tokyo" }, { "name": "Oomen, Tom", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Iterative and repetitive learning control" ], "abstract": "In iterative learning control (ILC), significant trial-varying disturbances lead to inefficient implementations and performance deterioration. The aim of this paper is to develop a frequency-domain ILC framework, explicitly enforcing a sparse structure in the learned input signal. This is achieved by taking a two-step approach: first, a learning update is computed using frequency-domain filters; and second, an l1-regularized optimization problem is solved to promote the desired sparse structure. By utilizing frequency-domain design in ILC, the robustness to model uncertainty is guaranteed, while the enforced sparse structure leads to efficient implementation and performance improvement. The framework is validated on a typical motion system subject to considerable measurement noise, confirming its effectiveness in practical applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB10.3", "code": "FrB10.3", "title": "Data-Driven Iterative Learning Control for Batch Processes Designed Using Repetitive Process Stability Theory (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB10", "sessionTitle": "Recent Advances in Iterative Learning and Repetitive Control II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Maniarski, Robert", "affiliation": "University of Zielona Góra" }, { "name": "Paszke, Wojciech", "affiliation": "University of Zielona Gora" }, { "name": "Rogers, Eric", "affiliation": "Univ of Southampton" }, { "name": "Zhuang, Zhihe", "affiliation": "Jiangnan University" }, { "name": "Tao, Hongfeng", "affiliation": "Jiangnan University" }, { "name": "Liu, Tao", "affiliation": "Dalian University of Technology (DLUT)" } ], "keywords": [ "Iterative and repetitive learning control", "Data-driven control theory" ], "abstract": "This paper makes novel contributions to the field of data-driven iterative learning control for batch processes. The basis is system dynamics described using only input-state-output measurements collected during an open-loop experiment. The control design procedure is subsequently formulated as a repetitive process, as it must account for the interaction between batch-to-batch error and the transient response across batches. The resulting design procedure produces a stabilizing output feedback controller in the time domain and a feedforward controller that guarantees monotonic convergence in the batch-to-batch domain. Additionally, all required computations can be performed effectively using convex optimization procedures subject to linear matrix inequality constraints. A numerical example demonstrates the application of the new results.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB10.4", "code": "FrB10.4", "title": "Iterative Tuning of Nonlinear Model Predictive Control for Robotic Manufacturing Tasks (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB10", "sessionTitle": "Recent Advances in Iterative Learning and Repetitive Control II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Ingole, Deepak", "affiliation": "ZHAW Zurich University of Applied Sciences" }, { "name": "Bhend, Valentin", "affiliation": "ZHAW" }, { "name": "Murali Ganesh, Shiva Ganesh", "affiliation": "ZHAW Zurich University of Applied Sciences" }, { "name": "Döbrich, Oliver", "affiliation": "ZHW" }, { "name": "Rupenyan, Alisa", "affiliation": "ZHAW Zurich University for Applied Sciences" } ], "keywords": [ "Adaptive gain scheduling autotuning control and switching control", "Iterative and repetitive learning control", "Nonlinear adaptive control" ], "abstract": "Manufacturing processes are often affected by environmental drift and system wear, which can require controller re-tuning even for repetitive operations. This paper presents an iterative learning framework for the automatic tuning of Nonlinear Model Predictive Control (NMPC) weighting matrices based on task-level performance feedback. Inspired by normoptimal Iterative Learning Control (ILC), the proposed method adjusts the NMPC weights Q and R across task repetitions to minimize key performance indicators (KPIs) related to tracking accuracy, control smoothness, and saturation. Unlike gradient-based approaches, which require differentiating through the NMPC solver, the proposed method uses an empirical sensitivity matrix that enables structured weight updates without analytic derivatives. The framework is validated in simulation on a UR10e robot performing carbon fiber winding on a tetrahedral core. The results show that the proposed approach converges to near-optimal tracking performance (RMSE within 0.3% of offline Bayesian Optimization (BO)) in only 4 online repetitions, compared with 100 offline evaluations required by the BO algorithm. The method provides a practical solution for adaptive NMPC tuning in repetitive robotic tasks by combining the precision of carefully optimized controllers with the flexibility of online adaptation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB10.5", "code": "FrB10.5", "title": "Iterative Learning Control Design for Linear Parameter Varying Feedforward Controller and Disturbance Observer (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB10", "sessionTitle": "Recent Advances in Iterative Learning and Repetitive Control II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Kong, Taejune", "affiliation": "DGIST" }, { "name": "Oomen, Tom", "affiliation": "Eindhoven University of Technology" }, { "name": "Oh, Sehoon", "affiliation": "DGIST" } ], "keywords": [ "Iterative and repetitive learning control", "Time/parameter varying system identification", "Data-driven control theory" ], "abstract": "This paper presents an iterative learning control (ILC) framework for integrated optimization of parameter-varying feedforward (FF) controller and disturbance observer (DOB) signals in linear parameter varying (LPV) motion systems. Both signals are parameterized by basis functions with scheduling-dependent coefficients, and are updated across trials by minimizing a kernel-regularized prediction error cost function. The resulting learning law jointly refines parameter-varying inverse dynamics and disturbance estimation. Simulations on a mass–spring–damper LPV system demonstrate improved tracking accuracy and disturbance rejection compared with conventional linear time invariant (LTI) ILC and fixed-model FF controller and DOB design.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB10.6", "code": "FrB10.6", "title": "Performance-Driven Feedforward Selection: A Sparsity-Promoting Approach (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB10", "sessionTitle": "Recent Advances in Iterative Learning and Repetitive Control II", "sessionType": "Open Invited Track Session", "room": "Convention Hall - Room 110", "authors": [ { "name": "Ickenroth, Tjeerd", "affiliation": "Eindhoven University of Technology" }, { "name": "Cerullo, Armando", "affiliation": "Eindhoven University of Technology" }, { "name": "Oomen, Tom", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Iterative and repetitive learning control", "Learning methods for control" ], "abstract": "Feedforward compensation enables unparalleled control performance if the structure is carefully selected. The aim is to develop a data-driven approach to iteratively learn a high performance feedforward controller that automatically identifies and suppresses sinusoidal disturbances with unknown frequencies and amplitudes. The presented method overparameterizes the feedforward signal, and efficiently selects a sparse subset via sparse optimization to identify the disturbance frequencies. The approach is validated on a simulation example and on a physical setup of a desktop A3-printer, demonstrating effective disturbance identification and suppression. It shows that sparsity enables efficient parameter selection for suppressing disturbances in view of control performance, which is not considered by pre-existing approaches.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB13.1", "code": "FrB13.1", "title": "Fuzzy Preview Sliding Mode Control for Vehicle Path-Following Systems under Deception Attacks (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB13", "sessionTitle": "JO-EAAI: Applications of Optimal Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Lee, Yongjun", "affiliation": "Korea University, Department of Electrical and Computer Engineering" }, { "name": "Ahn, Woo Jin", "affiliation": "Inha University" }, { "name": "Jang, Sunho", "affiliation": "Korea Institute of Robotics and Technology Convergence" }, { "name": "Choi, Hyun Duck", "affiliation": "Seoul National University of Science and Technology" }, { "name": "Lim, Myo-Taeg", "affiliation": "Korea Univ" } ], "keywords": [ "Applications of optimal control", "Adaptive control design", "Sliding mode control" ], "abstract": "This paper proposed a robust preview sliding mode control for vehicle path-following systems with actuator failures and cyber-attacks. Leveraging advances in onboard computers and signal processing, the preview steering control effectively utilizes future road curvature to improve the path-tracking performance and system robustness. In addition, the finite-time extended dissipativity is satisfied under input and output constraints, ensuring driving safety within a finite interval. Meanwhile, The core fuzzy sliding mode scheme attenuates the influence of actuator failures and randomly occurring deception attacks of network communication. CarSim-Simulink co-simulation demonstrates the effectiveness of the proposed controller under cyber attacks.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB13.2", "code": "FrB13.2", "title": "Constrained Performance Boosting Control for Nonlinear Systems (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB13", "sessionTitle": "JO-EAAI: Applications of Optimal Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Giacomelli, Gianluca", "affiliation": "Eindhoven University of Technology" }, { "name": "Saccani, Danilo", "affiliation": "École Polytechnique Fédérale De Lausanne (EPFL)" }, { "name": "Weiland, Siep", "affiliation": "Eindhoven Univ. of Tech" }, { "name": "Ferrari-Trecate, Giancarlo", "affiliation": "Ecole Polytechnique Fédérale De Lausanne" }, { "name": "Breschi, Valentina", "affiliation": "Eindhoven University of Technology" } ], "keywords": [ "Applications of optimal control", "Learning methods for optimal control", "Optimization-based estimation and control" ], "abstract": "We present the Alternating Direction Method of Multipliers for Performance Boosting (ADMM-PB), an approach for designing neural controllers for constrained, stable nonlinear systems. This method builds on the Performance Boosting (PB) approach, an internal model control formulation that, exploiting a stable neural operator, guarantees closed-loop stability by construction and improves performance by optimizing its parameters offline. To handle constraints, we add auxiliary variables to the PB formulation and cast an ADMM-based algorithm. This algorithm alternates between a gradient-descent update of the controller parameters and a projection step that promotes the feasibility of the sampled trajectories. As a result, the proposed procedure handles constraints during training without altering the controller architecture or compromising its stability-by-design guarantees. Our numerical results show that, compared with a baseline based on barrier-inspired penalties in the loss, ADMM-PB achieves lower constraint violations, at the price of more conservative closed-loop behavior.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB13.3", "code": "FrB13.3", "title": "Data-Driven Preview-PID for Generator-Speed Regulation of a 5-MW Wind Turbine (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB13", "sessionTitle": "JO-EAAI: Applications of Optimal Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Rama, V Siva Brahmaiah", "affiliation": "Kyungpook National University" }, { "name": "Vijayan, Anjana", "affiliation": "Kyungpook National University" }, { "name": "Go, CheolJae", "affiliation": "Kyungpook National University" }, { "name": "Yang, Jung-Min", "affiliation": "Kyungpook National University" } ], "keywords": [ "Applications of optimal control", "Learning methods for optimal control", "Real-time optimal control" ], "abstract": "This paper presents a data-driven Preview-PID controller for generator-speed regulation of the nonlinear OpenFAST NREL 5-MW wind turbine under turbulent and gust conditions. The proposed method combines a bi-level Gaussian-noise residual network with a time-aware long short-term memory (BiGN-ResNet-T-LSTM) forecaster and a classical proportional-integral-derivative (PID) controller. The forecaster predicts the generator speed one step ahead, and this prediction is used as a virtual preview signal for anticipatory pitch correction. Pitch saturation and anti-windup are included to keep the pitch command within actuator limits. The method is evaluated in above-rated turbulent operation and under an extreme gust scenario. The results show improved transient regulation and reduced overshoot compared with PID and H-infinity controllers, while maintaining competitive performance against model predictive control (MPC) within a simpler feedback structure.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB13.4", "code": "FrB13.4", "title": "Deep Reinforcement Learning for Melt Pool Solidification Cooling Rate Control in Directed Energy Deposition (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB13", "sessionTitle": "JO-EAAI: Applications of Optimal Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Li, Kezi", "affiliation": "UBC" }, { "name": "Jin, Xiaoliang", "affiliation": "University of British Columbia" }, { "name": "Nagamune, Ryozo", "affiliation": "University of British Columbia" } ], "keywords": [ "Applications of optimal control", "Output regulation and tracking", "Integration of ML/AI for control of DPS" ], "abstract": "This manuscript investigates the use of deep reinforcement learning (RL) to control the solidification cooling rate (SCR) in directed energy deposition (DED) metal additive manufacturing (AM) process. Control of SCR is critical in metal AM as it determines mechanical properties of the final product. To track a specified target SCR trajectory, the RL agent optimizes key DED process parameters on a layer-by-layer basis, including laser power, scan velocity, and inter-layer dwell time. The agent employs an actor-critic architecture with deep neural networks, enabling direct optimization of continuous-valued process parameters. Moreover, the trained policy generalizes to previously unseen SCR trajectories and varying numbers of layers, demonstrating robustness and adaptability beyond the training domain. Simulation results demonstrate accurate SCR tracking within 2% of the target trajectories while reducing overall build time. By improving both mechanical performance and process efficiency, the proposed framework addresses a key gap in current research and provides practical value for industrial DED applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB13.5", "code": "FrB13.5", "title": "Prescribed Performance Optimal Control for Autonomous Aerial-Ground Heterogeneous Systems Via Safe Reinforcement Learning (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB13", "sessionTitle": "JO-EAAI: Applications of Optimal Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Liu, Zhucheng", "affiliation": "Northwestern Polytechnical University" }, { "name": "Yang, Feisheng", "affiliation": "Northwestern Polytechnical University" }, { "name": "Gong, Zhenyu", "affiliation": "Northwestern Polytechnical University" }, { "name": "Feng, Xiao", "affiliation": "Northwestern Polytechnical University" } ], "keywords": [ "Optimal control theory", "Control barrier functions and state space constraints", "Adaptive control design" ], "abstract": "This paper addresses formation control for multiple heterogeneous autonomous aerial and ground vehicles operating in an environment with obstacles. A novel performance-driven optimal safety control framework is proposed to minimize control costs while ensuring formation accuracy and satisfying safety constraints. Utilizing the prescribed performance control method with tunable error bounds, an optimal nominal control signal is generated to enable heterogeneous vehicles to track the leader's trajectory. Furthermore, based on control barrier functions and safety filters, a smooth controller is derived to ensure safety. Meanwhile, this controller is used to construct an auxiliary system for the online adaptive adjustment of the performance function. Finally, the optimal safety control strategy is obtained by solving the Hamilton-Jacobi-Bellman equation. A critic-only reinforcement learning algorithm is employed to learn the smooth safety controller online. Theoretical analysis shows that the algorithm can simultaneously ensure both system safety and performance requirements during formation tracking. Simulation results verify the effectiveness and superiority of the proposed algorithm.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB13.6", "code": "FrB13.6", "title": "AI-Augmented Density-Driven Optimal Control for Decentralized Environmental Mapping (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB13", "sessionTitle": "JO-EAAI: Applications of Optimal Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 211", "authors": [ { "name": "Lee, Kooktae", "affiliation": "Texas Tech University" }, { "name": "Martinez, Julian", "affiliation": "New Mexico Institute of Mining and Technology" } ], "keywords": [ "Optimal control theory", "Decentralized control", "Learning methods for optimal control" ], "abstract": "This paper presents an AI-augmented decentralized framework for multi-agent environmental mapping under limited sensing and communication. Conventional mapping strategies often rely on an accurate initial target distribution, yet such prior knowledge is typically unavailable or highly biased in unknown environments. To address this limitation, we introduce an adaptive mechanism enabling agents to iteratively reconstruct local spatial density estimates while maintaining coordinated coverage. A dual multilayer perceptron module, operating under a fully online-learning paradigm, continuously updates local mean-variance statistics and regulates virtual uncertainty to prevent local stagnation. Theoretical analysis establishes a convergence guarantee under the Wasserstein metric, and simulation results demonstrate that the proposed AI-augmented Density-Driven Optimal Control (D2OC) achieves robust, high-fidelity reconstruction of complex spatial distributions compared with conventional decentralized baselines.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB14.1", "code": "FrB14.1", "title": "Compliant Explicit Reference Governor for Contact Friendly Robotic Manipulators", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB14", "sessionTitle": "Safety Critical Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Gautam, Yaashia", "affiliation": "University of Colorado Boulder" }, { "name": "Briscoe-Martinez, Gilberto", "affiliation": "University of Colorado Boulder" }, { "name": "Mohan, Adhitya", "affiliation": "University of Colorado Boulder" }, { "name": "Nechyporenko, Nataliya", "affiliation": "University of Colorado Boulder" }, { "name": "Roncone, Alessandro", "affiliation": "University of Colorado Boulder" }, { "name": "Nicotra, Marco M.", "affiliation": "University of Colorado Boulder" } ], "keywords": [ "Application of nonlinear analysis and design", "Controller constraints and structure" ], "abstract": "This paper introduces the Compliant Explicit Reference Governor (CERG), a modular reference management system that enables robots to interact physically with their environment under provable guarantees. The CERG is an intermediate layer that can be placed between a high-level planner and a low-level controller: its role is to enforce operational constraints and to enable the smooth transition between free-motion and contact operations. The CERG ensures safety by limiting the total energy available to the robotic arm at the time of contact. In the absence of contact, however, the CERG does not penalize the system performance. Numerical examples as well as experiments on a real hardware setup showcase the behavior of the CERG for increasingly complex systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB14.2", "code": "FrB14.2", "title": "Whitney Control Barrier Functions: A Mesh-Based Geometric Approach Via Discrete Exterior Calculus", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB14", "sessionTitle": "Safety Critical Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Currier, Keith", "affiliation": "University of Florida" }, { "name": "Leal, Wilmer", "affiliation": "University of Florida" }, { "name": "Rauta, George", "affiliation": "University of Florida" }, { "name": "Copeland, Austin", "affiliation": "University of Florida" }, { "name": "Fairbanks, James", "affiliation": "University of Florida" }, { "name": "Dixon, Warren E.", "affiliation": "Univ of Florida" } ], "keywords": [ "Control barrier functions and state space constraints" ], "abstract": "This paper introduces Whitney Control Barrier Functions, a class of nonsmooth control barrier functions constructed via the simplicial triangulation of a safe set and analyzed using Discrete Exterior Calculus (DEC). The framework solves a Poisson–Dirichlet problem on the triangulation to produce a discrete barrier potential, which is extended exactly to continuous, piecewise-linear functions over the domain using Whitney 0-forms. We derive the corresponding nonsmooth CBF conditions that guarantees forward invariance for control-affine systems and an optimization-based feedback law with continuity guarantees. Simulation results on mathbb{R}^2 and a torus demonstrate a fully mesh-based geometric approach for safety on generalizable manifolds.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB14.3", "code": "FrB14.3", "title": "Designing Control Barrier Functions Using a Dynamic Backup Policy", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB14", "sessionTitle": "Safety Critical Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Freire, Victor", "affiliation": "University of Colorado Boulder" }, { "name": "Nicotra, Marco M.", "affiliation": "University of Colorado Boulder" } ], "keywords": [ "Control barrier functions and state space constraints", "Application of nonlinear analysis and design" ], "abstract": "This paper presents a systematic approach to construct control barrier functions for nonlinear control affine systems subject to arbitrary state and input constraints. Taking inspiration from the reference governor literature, the proposed method defines a family of backup policies, parametrized by the equilibrium manifold of the system. The control barrier function is defined on the augmented state-and-reference space: given a state-reference pair, the approach quantifies the distance to constraint violation at any time in the future. The proposed method is applied to an inverted pendulum on cart.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB14.4", "code": "FrB14.4", "title": "Converse Strict Control Barrier Certificate for Locally Lipschitz Continuous Systems", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB14", "sessionTitle": "Safety Critical Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Aoki, Haruto", "affiliation": "Tokyo University of Science" }, { "name": "Nakamura, Hisakazu", "affiliation": "Tokyo University of Science" } ], "keywords": [ "Control barrier functions and state space constraints", "Application of nonlinear analysis and design" ], "abstract": "Forward invariance is a fundamental property for safety in control engineering. We prove that, for locally Lipschitz continuous system, forward invariance is equivalent to the existence of a strict barrier function. Our result does not require forward completeness. Furthermore, building on this equivalence, we establish a converse theorem for strict control barrier functions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB14.5", "code": "FrB14.5", "title": "Explicit Control Barrier Function-Based Safety Filters and Their Resource-Aware Computation", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB14", "sessionTitle": "Safety Critical Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Mestres, Pol", "affiliation": "California Institute of Technology" }, { "name": "Mousavi, Shima Sadat", "affiliation": "California Institute of Technology" }, { "name": "Ong, Pio", "affiliation": "California Institute of Technology" }, { "name": "Yang, Lizhi", "affiliation": "California Institute of Technology" }, { "name": "Das, Ersin", "affiliation": "Illinois Institute of Technology" }, { "name": "Burdick, Joel W.", "affiliation": "California Inst. of Tech" }, { "name": "Ames, Aaron", "affiliation": "Caltech" } ], "keywords": [ "Control barrier functions and state space constraints", "Convex optimization" ], "abstract": "This paper studies the efficient implementation of safety filters that are designed using control barrier functions (CBFs), which minimally modify a nominal controller to render it safe with respect to a prescribed set of states. Although CBF-based safety filters are often implemented by solving a quadratic program (QP) in real time, the use of off-the-shelf solvers for such optimization problems poses a challenge in applications where control actions need to be computed efficiently at very high frequencies. In this paper, we introduce a closed-form expression for controllers obtained through CBF-based safety filters. This expression is obtained by partitioning the state-space into different regions, with a different closed-form solution in each region. We leverage this formula to introduce a resource-aware implementation of CBF-based safety filters that detects changes in the partition region and uses the closed-form expression between changes. We showcase the applicability of our approach in examples ranging from aerospace control to safe reinforcement learning.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB14.6", "code": "FrB14.6", "title": "The Effect of Control Barrier Functions on Energy Transfers in Controlled Physical Systems", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB14", "sessionTitle": "Safety Critical Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 212", "authors": [ { "name": "Califano, Federico", "affiliation": "University of Twente" }, { "name": "Zanella, Riccardo", "affiliation": "University of Twente" }, { "name": "Macchelli, Alessandro", "affiliation": "Univ. of Bologna - Italy" }, { "name": "Stramigioli, Stefano", "affiliation": "University of Twente" } ], "keywords": [ "Control barrier functions and state space constraints", "Lagrangian and Hamiltonian systems", "Passivity-based control" ], "abstract": "Using a port-Hamiltonian formalism, we show the effects of safety-critical control implemented with control barrier functions (CBFs) on the power balance of controlled physical systems. The presented results will provide novel tools to design CBFs inducing desired energetic behaviors of the closed-loop system, including non-trivial damping injection effects and non-passive control actions, effectively injecting energy into the system in a controlled manner. Simulations validate the presented results.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB15.1", "code": "FrB15.1", "title": "Recursive Coefficient System Identification for Fractional-Order LTV Systems (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB15", "sessionTitle": "Fractional Order Differentiation in Modeling and Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Duhé, Jean-Francois", "affiliation": "Universidad De Panamá, Facultad De Informática Electrónica Y Comunicación" }, { "name": "Victor, Stephane", "affiliation": "Univ. Bordeaux" } ], "keywords": [ "Linear fractional-order systems", "Linear systems" ], "abstract": "Continuous-time system identification has proven to have several advantages over discrete-time model identification (Garnier and Wang (2008)). It is possible to preserve physical meaning of the parameters in some cases, and it is also possible to avoid numerical precision issues inherent to discretization. On the other hand, if one considers continuous-time fractional-order systems, complex dynamics can be modeled. Some system identification studies have been performed in order to identify continuous-time LPV models by means of recursive system identification. However, there is not always a measurable scheduling variable in order to apply this formalism. This study considers fractional-order linear time-varying systems and the well known recursive prediction error method will be used for coefficient estimation in order to evaluate the parameter tracking capabilities of the method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB15.2", "code": "FrB15.2", "title": "Differentiable Programming for Fractional System Identification (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB15", "sessionTitle": "Fractional Order Differentiation in Modeling and Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Matychyn, Ivan", "affiliation": "University of Warmia and Mazury in Olsztyn" }, { "name": "Onyshchenko, Viktoriia", "affiliation": "University of Warmia and Mazury" } ], "keywords": [ "Linear fractional-order systems", "Optimization-based estimation and control", "Linear systems" ], "abstract": "This paper discusses identification of fractional-order systems using the technique of differentiable programming. We illustrate this approach by implementing the well-established L1 finite difference method for solving a fractional differential equation natively within the PyTorch framework. This makes the entire numerical solver end-to-end differentiable. We demonstrate that this allows for the use of automatic differentiation (AD) to compute the exact gradient of the solution trajectory with respect to the system parameters. This gradient is then fed into standard optimizers like Adam to rapidly and robustly solve the inverse problem. We validate this ``differentiable solver'' by first comparing its gradients to those of an analytical Mittag--Leffler solution and then successfully identifying the lambda parameter in a fractional relaxation model from noisy synthetic data, jointly identifying both lambda and the fractional order alpha, and comparing the AD-based optimizer with conventional finite-difference and gradient-free methods in terms of accuracy and runtime.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB15.3", "code": "FrB15.3", "title": "Fractional-Order Momentum Enhanced Accelerated Stochastic Gradient Descent", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB15", "sessionTitle": "Fractional Order Differentiation in Modeling and Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Chen, Yuquan", "affiliation": "Hohai University" }, { "name": "Wenchao, Hong", "affiliation": "Hohai University" }, { "name": "Chen, YangQuan", "affiliation": "University of California, Merced" }, { "name": "Wang, Bing", "affiliation": "Hohai University" } ], "keywords": [ "Linear fractional-order systems", "Linear systems" ], "abstract": "Deep neural network training often suffers from slow convergence and limited global search capability. This paper develops a new fractional-order stochastic gradient descent with momentum (FOSGDM) optimizer to improve the performance of traditional SGDM optimizer. SGDM is firstly reformulated as a second-order dynamical system with gradient feedback, and the FOSGDM is designed by replacing the integer-order differential operator with a fractional-order one. Then an implicit Euler discretization is further employed to derive a computationally efficient iterative algorithm. Experimental results finally show that the proposed method achieves faster convergence, lower loss, and higher accuracy compared with classical SGDM, demonstrating improved optimization efficiency and generalization performance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB15.4", "code": "FrB15.4", "title": "Fractional Proportional-Derivative Consensus Protocols for Double Integrator and Damped Oscillator Multi-Agent Systems (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB15", "sessionTitle": "Fractional Order Differentiation in Modeling and Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Butcher, Eric", "affiliation": "University of Arizona" }, { "name": "Maadani, Mohammad", "affiliation": "University of Arizona" } ], "keywords": [ "Linear fractional-order systems", "Decentralized control", "Linear systems" ], "abstract": "Necessary and sufficient stability bounds on the feedback gains are obtained for linear fractional PD^alpha consensus protocols applied to multi-agent systems of double integrators and damped oscillators on directed and rooted coupling topologies, where a reference model is employed when the fractional relative velocities are unavailable. For consensus stability the fractional order alpha lies in a subset of the interval [0,2], while it is shown that a decreased lower bound on the velocity gain and lower integrated control effort are enabled with a non-integer fractional order. The analytical stability bounds are illustrated with examples, while bounds for integer-order consensus protocols and undirected graphs are obtained as special cases.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB15.5", "code": "FrB15.5", "title": "Fractional Order Control of Pneumatic Syringe-Based Micropipette Operation with Dead-Zone Input (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB15", "sessionTitle": "Fractional Order Differentiation in Modeling and Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Zhang, Yujie", "affiliation": "Nankai University" }, { "name": "Li, Bingxin", "affiliation": "Tianjin University of Commerce" }, { "name": "Liu, Yaowei", "affiliation": "Nankai University" }, { "name": "Zhao, Xin", "affiliation": "Nankai University" } ], "keywords": [ "Linear fractional-order systems", "Application of nonlinear analysis and design", "Lyapunov methods" ], "abstract": "Micropipette aspiration and injection techniques are widely applied in biological and medical fields. In this paper, we propose a fractional order dead-zone model to describe the nonlinear viscoelastic dynamics of the interface position in a pneumatic syringe-based micropipette system. A fractional order sliding mode controller is designed to regulate the gas-liquid interface (GLI) and cytoplasm-liquid interface (CLI) in the micropipette which is connected to a pneumatic syringe motor (PSM), taking into account the dead-zone input characteristics of the system. The stability of the closed-loop system is analyzed using Lyapunov methods. Simulation and experimental results demonstrate the effectiveness of the proposed control scheme in achieving GLI and CLI position tracking despite the presence of dead-zone nonlinearity.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB15.6", "code": "FrB15.6", "title": "Global Terrestrial Temperature Modeling by Using Fractional Models with Output-Error Method (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB15", "sessionTitle": "Fractional Order Differentiation in Modeling and Control", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 213", "authors": [ { "name": "Victor, Stephane", "affiliation": "Univ. Bordeaux" }, { "name": "Bounouh, Aziz", "affiliation": "IMS" }, { "name": "Malti, Rachid", "affiliation": "Univ. Bordeaux" } ], "keywords": [ "Linear fractional-order systems", "Linear systems" ], "abstract": "The global terrestrial system is a very complex system and modeling it through the global temperature output is indeed challenging. Temperature estimation results from complex diffusion phenomena and as the fractional operator is a very well suited operator to model such phenomena thanks to its long memory property and parameter compactness, it is proposed to use such an operator for climate change modeling. Continuous-time system identification is proposed by using an output-error (OE) model for multiple-input single-output (MISO) fractional order systems. When the model structure is assumed known, in the sense when the differentiation orders are assumed known, only the coefficients are estimated by using the MISO-oe algorithm extended to fractional MISO systems. For unknown differentiation orders, the differentiation orders are estimated together with the coefficients with a gradient-based algorithm for all parameter (both coefficients and differentiation orders) estimation. Finally, the terrestrial climate change is identified with fractional models on real input/output terrestrial climate data which provide a very good fitness of the global Earth temperature, as compared to classic (rational) models with the same number of parameters. Moreover, it should de noted that the data, both input and output temperature ones, are used from 1850 up to 2024, which considers the latest known data.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB16.1", "code": "FrB16.1", "title": "Control of MAS Modeled by Wave Equation (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB16", "sessionTitle": "Modeling, Simulation and Control of Distributed Parameter Systems V", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Wurm, Jens", "affiliation": "UMIT – Private University for Health Sciences, Medical Informatics and Technology" }, { "name": "Woittennek, Frank", "affiliation": "UMIT - Private University for Health Sciences, Medical Informatics and Technology" } ], "keywords": [ "Control of distributed parameter systems", "Backstepping control of distributed parameter systems", "Control of hyperbolic systems and conservation laws" ], "abstract": "The consensus problem for a multi-agent system (MAS) subject to a line graph communication topology is considered, with the agents modeled as nonholonomic unicycle-type mobile robots. The control task is to maintain a desired formation. Using dynamic feedback linearization, the agent dynamics are transformed into simple double-integrator models. Assuming a line graph communication topology and a sufficiently high number of agents, the consensus problem is approximated by a wave-type partial differential equation (PDE). Depending on the desired formation, this PDE may be unstable. To stabilize the tracking error dynamics, a backstepping-based boundary control law is proposed. To this end, the wave equation is rewritten as a system of two first-order partial integro-differential equations (PIDEs) coupled to a boundary ordinary differential equation (ODE). Numerical simulations demonstrate the effectiveness of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB16.2", "code": "FrB16.2", "title": "Temperature-Driven Optimal Control of Concrete Curing Based on Coupled Partial Differential Equations (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB16", "sessionTitle": "Modeling, Simulation and Control of Distributed Parameter Systems V", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Ratke, Denis", "affiliation": "Karlsruhe Institute of Technology" }, { "name": "Meurer, Thomas", "affiliation": "Karlsruhe Institute of Technology (KIT)" }, { "name": "Schwarz, Yannik", "affiliation": "Ruhr University Bochum" }, { "name": "Sanio, David", "affiliation": "Ruhr University Bochum" }, { "name": "Mark, Peter", "affiliation": "Ruhr University Bochum" } ], "keywords": [ "Optimal control of PDE systems", "Applications of optimal control", "Numerical methods for optimal control" ], "abstract": "Controlled thermal curing of high-performance concrete (HPC) is addressed. The hardening process is described by a thermo-chemical model represented as a coupled system of nonlinear partial differential equations (PDEs) that account for temperature and hydration evolution during curing. Based on this model, a PDE-constrained optimal control problem (OCP) is formulated to achieve targeted hydration profiles. The OCP is solved numerically using automatic differentiation and the adjoint method. The resulting optimal boundary control distribution reveals the spatial arrangement of heating and cooling zones within the structure, demonstrating the effectiveness of the proposed approach in achieving precise curing control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB16.3", "code": "FrB16.3", "title": "Generic Model Control of Caputo Time-Fractional Linear Diffusion-Reaction Systems", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB16", "sessionTitle": "Modeling, Simulation and Control of Distributed Parameter Systems V", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Maidi, Ahmed", "affiliation": "Universite Mouloud MAMMERI" }, { "name": "Paulen, Radoslav", "affiliation": "Slovak University of Technology in Bratislava" }, { "name": "Corriou, Jean-Pierre", "affiliation": "ENSIC" } ], "keywords": [ "Control of distributed parameter systems", "Output regulation/tracking for distributed parameter systems", "Boundary control of distributed parameter systems" ], "abstract": "This paper extends generic model control to a time-fractional linear diffusion-reaction system within the late lumping framework. Both distributed and boundary control problems are investigated for an output defined as a spatially weighted average of the state. In the distributed control case, the design is straightforward since the characteristic index is finite. In contrast, in the boundary control case, where the characteristic index is infinite, an equivalent pointwise control problem is derived, enabling controller design. The developed controllers yield, in closed loop, a fractional linear lumped-parameter system. Thus, building on existing results concerning the stability of fractional ordinary differential equations, a tuning procedure is proposed for selecting the proportional and integral gains of the controller. The effectiveness of the controllers, in terms of output tracking and disturbance rejection, is validated through numerical simulations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB16.4", "code": "FrB16.4", "title": "Pre-Compensation Strategies for Pressure-Driven Microfluidic Flow in Elastic-Walled Channels", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB16", "sessionTitle": "Modeling, Simulation and Control of Distributed Parameter Systems V", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Ströhle, Timo", "affiliation": "Karslruher Institut Für Technologie" }, { "name": "Petit, Nicolas", "affiliation": "MINES Paris, PSL University" } ], "keywords": [ "Control of distributed parameter systems", "Control of hyperbolic systems and conservation laws", "Optimization-based estimation and control" ], "abstract": "The article studies a fundamental problem in of pressure-driven fluid mechanics is a channel having elastic walls. The compliance of the walls leads to complex fluid–structure interactions, which are well known to practitioners in microfluidics. A linear distributed-parameter model is proposed, from which non-rational transfer functions are derived. The properties of the system are analyzed, with particular attention to echo effects in the step response and overshoot in the outlet flow rate. Several motion-planning techniques are proposed to suppress high-frequency ringing and echoes through transient pre-compensation. In their most general form, using numerical optimization, the presented methods account for practical constraints such as limitations on the magnitude and rate of change of the inlet pressure.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB16.5", "code": "FrB16.5", "title": "Stabilization of a Chain of 3 Hyperbolic PDEs with 2 Inputs in Arbitrary Position (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB16", "sessionTitle": "Modeling, Simulation and Control of Distributed Parameter Systems V", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Braun, Adam", "affiliation": "CNRS, CentraleSupelec, Université Paris-Saclay" }, { "name": "Auriol, Jean", "affiliation": "CNRS, CentraleSupélec, Université Paris-Saclay" }, { "name": "Brivadis, Lucas", "affiliation": "Université Paris-Saclay, CNRS, CentraleSupélec" } ], "keywords": [ "PDEs for time delay systems", "Boundary control of distributed parameter systems", "Infinite-dimensional multi-agent systems and networks" ], "abstract": "This paper addresses the stabilization of a chain of three coupled hyperbolic partial differential equations actuated by two control inputs applied at arbitrary nodes of the network. With the exception of configurations where one input is located at an endpoint, cases that are already well studied in the literature, all admissible two-input configurations are treated in this paper within a unified framework. The proposed approach relies on a backstepping transformation combined with a reformulation of the closed-loop dynamics as an Integral Difference Equation (IDE). This IDE representation reveals a common structural pattern across configurations and clarifies the role played by delayed dynamics in the stability analysis. Within this formulation, the stabilization problem can be handled using existing IDE control techniques. For most configurations, the stabilization of the PDE system requires an approximate spectral controllability assumption. Remarkably, one specific configuration can be stabilized without imposing any additional spectral condition. In contrast, we also provide an explicit example of a configuration for which the required spectral controllability property fails to hold.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB16.6", "code": "FrB16.6", "title": "Data-Driven Safe Control of Strict-Feedback Linear Systems with Input Delay (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB16", "sessionTitle": "Modeling, Simulation and Control of Distributed Parameter Systems V", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 214", "authors": [ { "name": "Zhao, Zhenxu", "affiliation": "Xiamen University" }, { "name": "Wang, Ji", "affiliation": "Xiamen University" } ], "keywords": [ "PDEs for time delay systems", "System identification and adaptive control of distributed parameter systems", "Backstepping control of distributed parameter systems" ], "abstract": "This paper presents a data-driven safe control design for linear strict-feedback systems subject to unknown plant parameters, disturbances, and input delay. We employ Koopman-based Krylov Dynamic Mode Decomposition (DMD) to reconstruct system dynamics and batch least-squares to identify parameters in the input channel. Based on these, a backstepping controller incorporating Control Barrier Functions (CBFs) is synthesized. The proposed approach guarantees: 1) finite-time identification of a substantial number of unknown parameters; 2) exponential output tracking with rigorous safety guarantee, where the safe set is identical to the original one after a finite time. Efficacy is demonstrated via a vehicle collision avoidance application.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB17.1", "code": "FrB17.1", "title": "Structured Obsolescence Management Reference Model, STORM (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB17", "sessionTitle": "A Key for Sustainable Longevity: Integrating Physical Asset Management and Obsolescence Management", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Mokraoui, Salah", "affiliation": "ISAE-Supméca Institut Supérieur De Mécanique De Paris" }, { "name": "Zolghadri, Marc", "affiliation": "Supmeca-Paris" }, { "name": "Ben Brahim, Imen", "affiliation": "ISAE-SUPMECA" }, { "name": "Besbes, Mariem", "affiliation": "ISAE-SUPMECA" }, { "name": "Liu, Yan", "affiliation": "College of Mathematics and Computer Science, Shantou University" } ], "keywords": [ "Enterprise architecture", "Enterprise interoperability", "Systems-of-systems" ], "abstract": "According to IEC 62402, obsolescence is defined as the transition of an item from a state of availability to a state of unavailability with respect to its original specifications provided by the supplier. This paper introduces a Structured Obsolescence Management Reference Model (STORM), designed to represent the close interconnections between the Obsolescence Management (OM) function and other internal organizational functions, suppliers and customers across the value chain. STORM specifies these interrelations throughout the product life cycle. The STORM framework is grounded in an extensive review of the literature, international standards, and guidebooks addressing obsolescence management practices.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB17.2", "code": "FrB17.2", "title": "Schema-Constrained, Agentic LLM Pipeline for Hierarchical Fault Knowledge Extraction from Manufacturing Texts (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB17", "sessionTitle": "A Key for Sustainable Longevity: Integrating Physical Asset Management and Obsolescence Management", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Indiran, Hanu Priya", "affiliation": "University of Cambridge" }, { "name": "Parlikad, Ajith Kumar", "affiliation": "University of Cambridge" } ], "keywords": [ "Intelligent manufacturing systems", "Industrial artificial intelligence", "Hierarchical control" ], "abstract": "High-value manufacturing depends critically on understanding how process deviations influence functional metrics, trigger failure modes, and ultimately lead to test failures. The Hierarchical Interaction and Fault Abstraction Framework (HIFAF) provides a principled structure for representing these interactions, yet much of the relevant knowledge remains embedded in unstructured technical reports and defect descriptions. This paper investigates how large language models (LLMs) can operationalise this latent knowledge by automatically populating a HIFAF-style schema, and introduces methodological enhancements to improve extraction quality. The approach employs schema-constrained prompting to enforce valid entity and relation types, a two-pass entity->relation pipeline that separates hierarchical entity typing from causal relation inference, and a minimal domain-specific causal prior encoded as a compact knowledge base. To assess diagnostic utility, the paper defines graph-level fault-consistency metrics that evaluate whether the extracted graphs capture coherent transitions from process deviations through functional metrics and failure modes to observable fault events. Experiments with Claude Sonnet 4 on 48 semiconductor manufacturing text segments spanning chemical mechanical planarisation, lithography, etching and deposition indicate that schema constraints substantially reduce structurally invalid outputs, and that the two-pass variant improves the recovery of ambiguous entity classes, while a filtered relation F1 score of 0.66 suggests that upstream entity typing is the primary bottleneck. This study provides preliminary evidence that LLMs can serve as effective front-ends for hierarchical fault abstraction when guided by lightweight structural and semantic controls.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB17.3", "code": "FrB17.3", "title": "A Decision-Support Approach Via Bayesian Networks Using the ARIANE Method for Obsolescence Risk Managment (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB17", "sessionTitle": "A Key for Sustainable Longevity: Integrating Physical Asset Management and Obsolescence Management", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Ben Brahim, Imen", "affiliation": "ISAE-SUPMECA" }, { "name": "Besbes, Mariem", "affiliation": "ISAE-SUPMECA" }, { "name": "Zolghadri, Marc", "affiliation": "Supmeca-Paris" }, { "name": "Kanu, Chibueze", "affiliation": "Nigerian Airspace Management Agency" }, { "name": "Theillet, Christophe", "affiliation": "ABMI" }, { "name": "Dechamp, François", "affiliation": "ABMI" } ], "keywords": [ "Maintenance engineering, management and services" ], "abstract": "This research combines Bayesian Networks with ARIANE (Analysis of obsolescence Risks, Impacts, criticality and their surveillANcE), a method inspired by FMEA and tailored to obsolescence management. This hybrid approach enables the modeling of obsolescence effects on system availability while supporting decision-making under uncertainty. By capturing causal dependencies and simulating multiple intervention strategies, it provides a powerful tool for assessing risk evolution over time and identifying optimal mitigation actions to ensure sustained system performance and availability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB17.5", "code": "FrB17.5", "title": "Adaptation of Remanufacturing Operations for Enabling Industrial Repurposing: A Conceptual Framework Integrating Design Activities (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB17", "sessionTitle": "A Key for Sustainable Longevity: Integrating Physical Asset Management and Obsolescence Management", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "de La Morinerie, Pierre", "affiliation": "Nantes University" }, { "name": "Laroche, Florent", "affiliation": "Ecole Centrale De Nantes" }, { "name": "Cardin, Olivier", "affiliation": "LS2N UMR CNRS 6004 - Nantes University - IUT De Nantes" } ], "keywords": [ "Sustainable and circular manufacturing systems", "Sustainable and circular supply chain and production" ], "abstract": "Product obsolescence raises major environmental and industrial challenges, reinforcing the need for circular strategies such as repurposing. While repurposing is recognized as a promising approach for extending product value beyond its original use, its industrial implementation remains limited, lacking frameworks covering the full process from sourcing to market. This paper proposes a conceptual framework for industrial repurposing, mainly adapted from remanufacturing frameworks. The process introduces a dedicated design stage to identify new functions, allocate viable cores, and integrate repurposed subassemblies into new product architectures. It also highlights the need for flexible sourcing, optimized core allocation algorithms, and dedicated marketing approaches, as repurposed products cannot be positioned as strictly new or used. Although conceptual, the proposed framework provides a basis for industrializing repurposing within established enterprises and contributes to strategies aimed at mitigating obsolescence.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB17.6", "code": "FrB17.6", "title": "An Indicator-Based Strategic Framework for Industrial Asset Lifecycle Extension (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB17", "sessionTitle": "A Key for Sustainable Longevity: Integrating Physical Asset Management and Obsolescence Management", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Zappa, Sofia", "affiliation": "Politecnico Di Milano" }, { "name": "Roda, Irene", "affiliation": "Politecnico Di Milano" }, { "name": "Franciosi, Chiara", "affiliation": "Université De Lorraine, CNRS, CRAN, F-54000, Nancy, France" }, { "name": "Macchi, Marco", "affiliation": "Politecnico Di Milano" }, { "name": "Voisin, Alexandre", "affiliation": "Université De Lorraine, CNRS, CRAN" } ], "keywords": [ "Sustainable and circular manufacturing systems", "Viable and resilient supply chain and production", "Manufacturing engineering and management" ], "abstract": "Within asset management, companies must support circular economy objectives, yet end-of-life decisions for industrial assets remain fragmented and qualitative. Although lifecycle extension (LE) strategies - reuse, repair, refurbishment, remanufacturing, and recycling - are well established, no consolidated indicator framework exists to assess their appropriateness under different LE scenarios. This paper addresses this gap by developing a literature-based set of LE-relevant indicators and evaluating how indicator criticality conditions influence strategy appropriateness through a five-point semantic scale. Results reveal distinct patterns across strategies, clarifying how indicator conditions shape feasible and context-consistent options and providing a structured basis for future data-driven and multi-criteria LE decision approaches.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB17.7", "code": "FrB17.7", "title": "An AI-Enhanced Resilience Framework for Obsolescence-Driven Disruptions (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB17", "sessionTitle": "A Key for Sustainable Longevity: Integrating Physical Asset Management and Obsolescence Management", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 215", "authors": [ { "name": "Huitel, Rémy", "affiliation": "Sector Group" }, { "name": "Eslami, Yasamin", "affiliation": "Ecole Centrale De Nantes" }, { "name": "da Cunha, Catherine", "affiliation": "Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004" }, { "name": "Remond, Olivier", "affiliation": "Sector Group" } ], "keywords": [ "Maintenance engineering, management and services", "Industrial artificial intelligence", "Viable and resilient supply chain and production" ], "abstract": "Obsolescence is a growing source of disruption in long-life and high-reliability systems, where the unavailability of components, materials, or software can lead to degraded performance, increased costs, or total loss of function. Existing obsolescence management practices rely on manual data collection, irregular updates, and system-level strategies that are difficult to sustain over multi-decade lifecycles. Therefore, the system can often become inconsistent and in stress in times obsolescence occurs and put the system existence in danger. To that point, this study explores how Artificial Intelligence (AI) can enhance system resilience in form of a framework and by enabling a proactive and component-level monitoring for obsolescence. Which helps decision makers predict an obsolescence of the component at early stages and make decision accordingly. The article is inspired by empirical studies, best practices and comes from industrial experience. It discusses practical constraints, including confidentiality requirements and reliability of AI outputs alongside the necessity of human interaction where AI is not the most reliable source for decision making. At the end, the framework shows how integration of AI to the obsolescence management can impact system’s resilience and sustainable strategic management.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB19.1", "code": "FrB19.1", "title": "Assessing Performance Tradeoffs in Hierarchical Organizations Using a Diffusive Coupling Model (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB19", "sessionTitle": "Large-Scale Complex Systems: Analysis and Control V", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Zino, Lorenzo", "affiliation": "Politecnico Di Torino" }, { "name": "Ye, Mengbin", "affiliation": "Adelaide University" }, { "name": "Anderson, Brian D.O.", "affiliation": "Australian National Univ" } ], "keywords": [ "Large-scale complex systems", "Complex dynamic systems", "Interconnected dynamical systems" ], "abstract": "We study a continuous-time dynamical system of nodes diffusively coupled over a hierarchical network to examine the performance tradeoffs that organizations face while achieving coordination and sharing information across layers. After defining a network structure that captures real-world features of hierarchical organizations, we use linear systems and perturbation theory to characterize the rate of convergence to a consensus, and how effectively information propagates through the network, depending on the breadth of the organization and the strength of inter-layer communication, highlighting a fundamental performance tradeoff. Namely, networks that favor fast coordination will have decreased ability to share information that is generated in the lower layers of the organization and is to be passed up the hierarchy.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB19.2", "code": "FrB19.2", "title": "Signed DeGroot–Friedkin Model (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB19", "sessionTitle": "Large-Scale Complex Systems: Analysis and Control V", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Razaq, Muhammad Ahsan", "affiliation": "Linköping University" }, { "name": "Luan, Yangyang", "affiliation": "Wuhan University" }, { "name": "Altafini, Claudio", "affiliation": "Linkoping University" } ], "keywords": [ "Large-scale complex systems", "Complex dynamic systems", "Interconnected dynamical systems" ], "abstract": "This paper investigates the stability and convergence properties of the signed DeGroot--Friedkin (DF) model. The model captures the evolution of self-appraisals---agents' perceptions of their own social power---within a network where influence can be both cooperative and competitive. We establish sufficient conditions under which the system converges to a unique fixed social power.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB19.3", "code": "FrB19.3", "title": "Distributed Online Aggregative Optimization with Coupled Inequality Constraints Over Unbalanced Digraphs (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB19", "sessionTitle": "Large-Scale Complex Systems: Analysis and Control V", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Tan, Jin", "affiliation": "Northeastern University" }, { "name": "Zhang, Kunpeng", "affiliation": "Northeastern University" }, { "name": "Xu, Lei", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Yi, Xinlei", "affiliation": "College of Electronics and Information Engineering, Tongji University" }, { "name": "Wen, Guanghui", "affiliation": "Southeast University" }, { "name": "Meng, Ziyang", "affiliation": "Tsinghua University" }, { "name": "Cao, Ming", "affiliation": "University of Groningen" }, { "name": "Yang, Tao", "affiliation": "Northeastern University" } ], "keywords": [ "Large-scale complex systems", "Decentralized and distributed control for large-scale systems" ], "abstract": "This paper studies the distributed online aggregative optimization problem over unbalanced digraphs, where each agent’s local cost function varies over time and depends not only on its own decision variable but also on an aggregate variable. Coupled inequality constraints are also considered. To address this problem, a distributed online primal--dual push-sum aggregative gradient tracking algorithm is proposed. Under convex global cost functions, the proposed algorithm achieves a dynamic regret of order O(max{T^p1, V*_T, V^g_T}), where p1 ∈ (1/2, 1), which is sublinear when both V*_T and V^g_T grow sublinearly. In addition, a constraint violation of order O(T^((1+p2)/2)) is established with p2 ∈ (1/2, 1), which is also sublinear. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed algorithm.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB19.4", "code": "FrB19.4", "title": "Hierarchical Distributed Least-Distance Formation Tracking for High-Order Nonlinear Systems with Faster Convergence (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB19", "sessionTitle": "Large-Scale Complex Systems: Analysis and Control V", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Sun, Haoran", "affiliation": "Shanghai University" }, { "name": "Zhang, Huixin", "affiliation": "Shanghai University" }, { "name": "Wang, Xiaofan", "affiliation": "Shanghai University" } ], "keywords": [ "Large-scale complex systems", "Hierarchical control", "Complex dynamic systems" ], "abstract": "This paper proposes a hierarchical distributed control framework to achieve least-distance formation tracking for high-order nonlinear multi-agent systems. A fundamental challenge in such problems is that the regularization term required for optimal planning decays over time, rendering the system vulnerable to disturbances. To overcome this, the proposed architecture decouples optimal planning from robust tracking. At the upper level, a Tikhonov-regularization-based planner is designed to minimize the aggregate distance to the target while preserving the formation, for which we provide a quantitative analysis of the convergence rate. At the lower level, to handle coupled nonlinearities and unknown control gains, a barrier-function-based control scheme is introduced. Crucially, this scheme is designed to enforce a tracking convergence rate faster than the upper-level evolution, ensuring that the physical agents can effectively synchronize with the virtual planner despite the decaying regularization. Numerical simulations validate that the proposed framework achieves precise formation tracking with enhanced robustness against nonlinear disturbances.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB19.5", "code": "FrB19.5", "title": "Dynamics for Weighted and Weakly Pareto Nash Equilibria in Multi-Objective Population Games (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB19", "sessionTitle": "Large-Scale Complex Systems: Analysis and Control V", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Mitsumoto, Kio", "affiliation": "Osaka University" }, { "name": "Wada, Takayuki", "affiliation": "University of Hyogo" }, { "name": "Fujisaki, Yasumasa", "affiliation": "The University of Osaka" } ], "keywords": [ "Large-scale complex systems", "Interconnected dynamical systems", "Complex dynamic systems" ], "abstract": "Multi-objective population dynamics are introduced and analyzed in this paper. A multi-objective population game is formulated by assigning vector-valued payoffs to each strategy. The notion of multi-objective population dynamics is defined as the evolution of strategy distributions driven by such payoffs. Two classes of dynamics are studied. First, weighted multi-objective population dynamics are obtained by scalarization of the vector-valued payoffs and by applying classical Smith dynamics. For the dynamics, convergence to weighted Nash equilibria is characterized. Second, minimum-type multi-objective population dynamics is constructed so that its equilibria coincide with weakly Pareto Nash equilibria. The stability of equilibria under these dynamics is analyzed under potential game assumptions. For this purpose, the concept of Pareto potential games, in which each component game admits a potential function, is introduced and used to build Lyapunov functions. The proposed dynamics are illustrated by a numerical example that highlights the difference between weighted Nash equilibria and weakly Pareto Nash equilibria.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB19.6", "code": "FrB19.6", "title": "A Hierarchical Evolutionary Game Model of Trade Wars with Aspiration and Bankruptcy Dynamics (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB19", "sessionTitle": "Large-Scale Complex Systems: Analysis and Control V", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 217", "authors": [ { "name": "Chen, Hao", "affiliation": "Shanghai Jiaotong University" }, { "name": "Wang, Lin", "affiliation": "Shanghai Jiao Tong University" }, { "name": "Zhang, Guanglin", "affiliation": "Donghua University" } ], "keywords": [ "Large-scale complex systems", "Interconnected dynamical systems", "Complex dynamic systems" ], "abstract": "We propose a hierarchical evolutionary game model to investigate trade competition under multiscale interactions, bounded rationality, and extinction dynamics. Leaders represent macro-level policy-makers, while followers denote micro-level trading entities embedded in scale-free networks with stochastic cross-group links. Strategies evolve via aspiration-driven stochastic updating, and bankruptcy induces irreversible exit. Numerous simulations reveal that high aspiration secures trade advantage, while asymmetric tariffs act as powerful extinction accelerators. Network clustering and degree heterogeneity further regulate resilience and dominance. These mechanisms remain robust in bipolar systems with multiple small economies and strategic trade diversion. The framework provides a nonlinear dynamical perspective on trade wars, dominance transitions, and global competition.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB20.1", "code": "FrB20.1", "title": "Towards the Development of Digital Ethical Twins in Smart Manufacturing: Conceptual Foundations and Research Opportunities", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB20", "sessionTitle": "Challenges in Reconfigurable, Flexible or Agile Manufacturing Systems", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Liu, Yinling", "affiliation": "University of Lorraine" }, { "name": "Hind, Bril El-Haouzi", "affiliation": "University of Lorraine" } ], "keywords": [ "Human-centered production and logistics", "Smart production and logistics in manufacturing", "Intelligent manufacturing systems" ], "abstract": "Few Digital Twins (DTs) provide real-time control over their physical counterparts within the context of smart manufacturing. One of the main reasons could be that humans do not sufficiently trust the decisions made by DTs. To address this challenge, we first introduce the concept of Digital Ethical Twins (DETs) — DTs capable of conducting ethical reasoning. An overview of related work is then performed to analyze ethics in industry and identify the challenges of developing DETs. Finally, a formal definition of DET is provided to specify its fundamental components. The findings indicate that promising research opportunities exist in shaping visions, conceptual frameworks, engineering methodologies, and formal methods to advance the development of DETs.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB20.2", "code": "FrB20.2", "title": "A Structured Decision Model for the Selection of Production Organization Forms in SMEs", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB20", "sessionTitle": "Challenges in Reconfigurable, Flexible or Agile Manufacturing Systems", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Zimmermann, Jonas", "affiliation": "University of Applied Sciences Magdeburg-Stendal" }, { "name": "Reuß, Maximilian", "affiliation": "Frauhofer IFF" }, { "name": "Dreher, Manuel David", "affiliation": "Fraunhofer-Institut Für Fabrikbetrieb Und -Automatisierung IFF" }, { "name": "Behrendt, Fabian", "affiliation": "Magdeburg-Stendal University of Applied Sciences, Germany" }, { "name": "Glistau, Elke", "affiliation": "Otto-Von-Guericke-Univesity Magdeburg" } ], "keywords": [ "Production and operations management", "Manufacturing engineering and management", "Logistics and warehouse management" ], "abstract": "Manufacturing companies today are confronted with various global trends and increasing demands. Small and medium-sized enterprises (SMEs) in particular must react flexibly often through structural adaptions of their production systems. However, many SMEs lack the necessary capacities to proactively plan such structural transformations, making the selection of suitable production organizations forms (POFs) increasingly challenging. To effectively support this decision-making, structured, holistic, and scientifically grounded decision-models are needed. The aim of this paper is therefore to develop a decision- model that enables to systematically advise SMEs on the most appropriate POF.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB20.3", "code": "FrB20.3", "title": "Exact Methods for Energy-Cost Optimization in Configuration Planning (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB20", "sessionTitle": "Challenges in Reconfigurable, Flexible or Agile Manufacturing Systems", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Peña, Quentin", "affiliation": "LIMOS, Mines Saint-Etienne" }, { "name": "Delorme, Xavier", "affiliation": "Mines Saint-Etienne" } ], "keywords": [ "Production and operations management", "Simulation and optimization in production, operations and services", "Sustainable and circular supply chain and production" ], "abstract": "Reconfigurable Manufacturing Systems (RMS) provide an effective response to uncertainty in production planning. When designing an RMS, it is essential to account for operational costs, such as energy costs under Time-of-Use pricing. This leads to a multi-objective bi-level optimization problem that jointly considers line balancing and configuration planning. Current optimization methods rely on solving many Linear Programs (LP) to compute the optimal configuration planning for a given balancing. We propose two exact, efficient methods: an improved LP-based approach using sensitivity analysis, and an iterative method exploiting configuration properties. These methods can be integrated into existing matheuristics to achieve substantial computational savings.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB20.4", "code": "FrB20.4", "title": "Switching Control of Production-Distribution Systems", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB20", "sessionTitle": "Challenges in Reconfigurable, Flexible or Agile Manufacturing Systems", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Hou, Tan", "affiliation": "Queen's University Belfast" }, { "name": "Athanasopoulos, Nikolaos", "affiliation": "Queen's University Belfast" }, { "name": "McLoone, Seán Francis", "affiliation": "Queen's University Belfast" } ], "keywords": [ "Supply network dynamics and control", "Production and operations management", "Complex dynamic systems" ], "abstract": "We investigate the switching control of production–distribution systems, in which each plant is modeled as a set of discrete-time single integrators, and the interconnections between them are subject to transportation-induced time delays and customer-intent driven changes. We show that the production–distribution system with time delay and constrained switching can be transformed into an equivalent delay-free switched system whose dynamics depend only on the switching signal at the current time instant. Building upon this representation, we derive sufficient conditions that ensure state boundedness and constraint satisfaction throughout the system’s evolution. Moreover, we propose a periodic switching strategy together with a distribution law that ensures feasibility, which paves the way for future optimisation-oriented studies. A numerical example is provided to illustrate the effectiveness of the proposed approach.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB20.5", "code": "FrB20.5", "title": "A Holistic Conceptual Framework for Performance Assessment of Circular Supply Chain Management (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB20", "sessionTitle": "Challenges in Reconfigurable, Flexible or Agile Manufacturing Systems", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Tamak, Sundeep", "affiliation": "Ecole Centrale De Nantes" }, { "name": "Eslami, Yasamin", "affiliation": "Ecole Centrale De Nantes" }, { "name": "da Cunha, Catherine", "affiliation": "Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004" } ], "keywords": [ "Sustainable and circular supply chain and production", "Sustainable and circular manufacturing systems" ], "abstract": "The present work proposes a conceptual performance assessment framework of circular supply chains, addressing the critical need for holistic sustainability evaluation within the circular economy paradigm. Through a systematic literature review and analysis of existing frameworks, the study identifies gaps in current performance measurement approaches and proposes a holistic, multi-dimensional framework that integrates the triple bottom line, circular economy strategies and circular supply chain processes to effectively assess the sustainability performance of a circular supply chain. By facilitating a granular and comprehensive assessment of the circular supply chain’s sustainability performance, the proposed framework supports decision-making, fosters systemic integration of circular economy strategies, and promotes sustainable supply chains.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB20.6", "code": "FrB20.6", "title": "Agility, Resilience, and Business Continuity in Manufacturing: Enablers and Operational Performance", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB20", "sessionTitle": "Challenges in Reconfigurable, Flexible or Agile Manufacturing Systems", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 218", "authors": [ { "name": "Meechang, Kunruthai", "affiliation": "Mines Saint-Etienne, LIMOS UMR 6158" }, { "name": "De Benedittis, Julien", "affiliation": "Mines Saint-Etienne, COACTIS" }, { "name": "Pero, Margherita", "affiliation": "Politecnico Di Milano" }, { "name": "Medini, Khaled", "affiliation": "Ecole Des Mines De Saint Etienne" } ], "keywords": [ "Viable and resilient supply chain and production", "Digital supply chain and production", "Manufacturing engineering and management" ], "abstract": "Manufacturing firms increasingly face disruptions, demand volatility, and operational uncertainty, yet limited empirical evidence explains how agility, resilience, and business continuity jointly relate to operational performance. This study addresses this gap by examining whether these three capabilities are associated with operational performance and whether organisational enablers mediate these relationships. A conceptual model was tested using survey data from 270 manufacturing companies. Reliability and validity were assessed before testing direct and mediated relationships through regression-based mediation analysis. The findings indicate that enablers, particularly strategy and stakeholder relationships, alongside process and technology, mediate the association between these capabilities and operational performance measured through flexibility, speed, quality, and reliability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB21.1", "code": "FrB21.1", "title": "Disturbance-Adaptive Finite-Time Control of Three-Phase Rectifiers (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB21", "sessionTitle": "JO-CEP: Power Systems and Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Omiloli, Koto Andrew", "affiliation": "Florida State University" }, { "name": "Vedula, Satish", "affiliation": "Florida State University" }, { "name": "Olajube, Ayobami", "affiliation": "Florida State University" }, { "name": "Anubi, Olugbenga", "affiliation": "Florida State University" } ], "keywords": [ "Power electronics", "Power systems stability", "Energy management systems" ], "abstract": "Three-phase AC–DC rectifiers are fundamental components in modern power electronics systems, yet achieving rapid voltage regulation and precise current tracking under load and grid disturbances remains challenging due to nonlinear dynamics and measurement uncertainties. This paper presents a finite-time control method for three-phase AC–DC rectifiers that achieves millisecond-scale regulation of DC-link voltage and grid currents under varying conditions. The proposed design employs a transformed augmented error-state dynamics model, extending the voltage dynamics to a two-state system to construct an adaptive sliding surface that guarantees fast finite-time convergence. A nonlinear sliding-mode voltage regulator with an online disturbance estimator ensures rapid and robust voltage tracking, while a fast current controller achieves finite-time dq-axis current tracking with minimal chattering. Theoretical results establishes finite-time stability and provides explicit gain selection conditions. Simulation results demonstrate up to 99.40% and 87.5% reductions in voltage and current convergence times, respectively, compared to conventional robust controllers. Laboratory experiments further validate the approach, showing 33.33% lower voltage ripple, 33.33% faster rise time, and 32.43% reduced steady-state error relative to a recent method. These results confirm improvements in transient performance, convergence, and overall system stability, highlighting the method’s practical applicability for high-performance rectifier control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB21.2", "code": "FrB21.2", "title": "GRU-Enhanced Extended-State Kalman Filter for Online Disturbance Estimation in MPC of Supercritical Cogeneration Units (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB21", "sessionTitle": "JO-CEP: Power Systems and Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Guo, Mengmeng", "affiliation": "National University of Singapore" }, { "name": "Hao, Yongsheng", "affiliation": "Southeast University" }, { "name": "Wu, Zhe", "affiliation": "National University of Singapore" }, { "name": "Sun, Li", "affiliation": "Southeast University" } ], "keywords": [ "Power plant control", "Control and management of energy systems", "Power systems stability" ], "abstract": "High-performance model predictive control (MPC) for complex industrial processes critically depends on accurate state estimation and robust disturbance rejection. Conventional observers like the extended-state Kalman filter (ESKF) are effective but struggle with the nonlinear, non-stationary disturbances inherent in systems such as supercritical cogeneration units, leading to degraded control performance. This paper proposes a novel gated recurrent unit (GRU)-enhanced ESKF-MPC framework to address this limitation. The core of our methodology is a synergistic integration of a GRU network with the ESKF. The GRU is trained online to learn and predict the future evolution of the estimation error, providing a proactive disturbance forecast that corrects and enhances the ESKF's estimates. This adaptive observer is then embedded within an MPC scheme built upon a control-oriented model identified using generalized binary noise signals. Comprehensive simulations on a 350 MW supercritical cogeneration unit validate the proposed strategy. Compared to a conventional ESKF-MPC benchmark during wide-range load changes, the GRU-ESKF-MPC reduced the average integral absolute error (IAE) for main steam pressure, intermediate-point temperature, and heat supply flow rate by 21.6%, 24.9%, and 7.3%, respectively. Furthermore, under significant disturbances including coal quality uncertainty, the proposed method achieved a 31.5% reduction in average IAE. These results confirm that integrating a GRU provides a powerful, adaptive mechanism for proactive disturbance compensation, significantly enhancing the robustness and performance of MPC in industrial applications.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB21.3", "code": "FrB21.3", "title": "Fully Distributed Prescribed-Time Secondary Control for DC Microgrid (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB21", "sessionTitle": "JO-CEP: Power Systems and Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Yu, Tao", "affiliation": "Southeast University" }, { "name": "Cao, Yang", "affiliation": "Southeast University" }, { "name": "Gong, Xin", "affiliation": "Southeast University" }, { "name": "Xu, Dezhi", "affiliation": "School of Electrical Engineering, Southeast University" }, { "name": "Sun, Yonghui", "affiliation": "City University of Hong Kong" } ], "keywords": [ "Power systems stability", "Control and management of energy systems" ], "abstract": "This paper proposes a fully distributed secondary control strategy for islanded DC microgrids that restores the DC-bus voltage and enforces proportional current sharing within a user-specified deadline. The controller embeds a time-base scaling and edge-adaptive gains into a dynamic average-consensus law, guaranteeing prescribed-time convergence independent of initial conditions while requiring only neighbor-to-neighbor communication. A Lyapunov analysis establishes strict finite-time regulation of both the bus-voltage deviation and sharing error, without invoking global topology bounds at run time. Compared with fixed-time and sliding-mode baselines, the method achieves predictable settling to zero error at the deadline with a simple implementation. The effectiveness is validated on a four-converter islanded DC microgrid, where voltage restoration and current sharing align exactly with the prescribed time.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB21.4", "code": "FrB21.4", "title": "mathcal{H}_2 Gain-Scheduling DOF with mathcal{D}-Stability Applied to Electronic Systems (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB21", "sessionTitle": "JO-CEP: Power Systems and Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "S A, Valdivia", "affiliation": "Universidad De Talca" }, { "name": "Fuentes, Roberto M.", "affiliation": "University of Talca" }, { "name": "Marciel, Esteban", "affiliation": "University of Talca" }, { "name": "Baier, Carlos", "affiliation": "Universidad De Talca" }, { "name": "Morais, Cecília F.", "affiliation": "University of Campinas" }, { "name": "Palma, Jonathan M.", "affiliation": "UTalca | Universidad De Talca" } ], "keywords": [ "Power systems stability", "Power electronics", "Real time simulators for energy systems" ], "abstract": "This paper establishes novel synthesis conditions in terms of linear matrix inequalities (LMIs) for the design of a full-order dynamic output-feedback (DOF) controller for continuous-time Linear Parameter-Varying (LPV) systems. The proposed approach simultaneously minimizes the mathcal{H}_2-guaranteed cost and ensures that the closed-loop eigenvalues (for a fixed operation point) stay within a predefined convex region of the complex plane. Also, the new conditions combine LMIs with fine-tuned scalar parameters to obtain less conservative results. The effectiveness of the control strategy is demonstrated through a Hardware-in-the-Loop (HIL) simulation of a DC-DC converter.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB21.5", "code": "FrB21.5", "title": "History Matching Predictive Control for HVAC Chiller Sequencing and Comfort-Aware Cooling Optimization (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB21", "sessionTitle": "JO-CEP: Power Systems and Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Liu, Yiren", "affiliation": "Lingnan University" }, { "name": "Mo, Yanfang", "affiliation": "Lingnan University" }, { "name": "Qin, S. Joe", "affiliation": "Lingnan University, Hong Kong" }, { "name": "Li, Jicheng", "affiliation": "City University of Hong Kong" } ], "keywords": [ "Smart buildings and building automation", "Big data and machine learning applied to smart cities", "IoT for cities" ], "abstract": "Buildings in Hong Kong account for nearly 80% of the city’s total energy use, with HVAC systems consuming most of it and costing over HK12.3 billion annually. Inefficient chiller switching during transitional weather often causes excessive energy waste. This study proposes a predictive control framework for dynamic chiller sequencing. A Scalable DEMMFL model predicts cooling load under varying strategies, while a History-Matching Cooling Classification method identifies under-, balanced-, or over-cooling states. By prioritizing balanced-cooling conditions, the framework achieves temperature-responsive control, reducing energy consumption while maintaining comfort and advancing smart building energy management.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB21.6", "code": "FrB21.6", "title": "Integral Active Disturbance Rejection Control for Microgrid Load Frequency Regulation under Cyberattacks (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB21", "sessionTitle": "JO-CEP: Power Systems and Control", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 311", "authors": [ { "name": "Altaf, Aatif", "affiliation": "Centralesupelec" }, { "name": "Khalin, Anatolii", "affiliation": "CentraleSupélec Rennes" }, { "name": "Bourdais, Romain", "affiliation": "CentraleSupelec - IETR" } ], "keywords": [ "Cybersecurity in smart grids", "Power systems stability" ], "abstract": "The growing integration of communication technologies in smart grids has increased their vulnerability to cyberattacks, thereby threatening frequency stability. These issues require resilient control strategies. This paper proposes an integral active disturbance rejection control design for load frequency control in a microgrid under cyberattacks. Unlike active disturbance rejection control, this method incorporates a separate integral action in the feedback loop to complement the extended state observer, enhancing robustness, disturbance rejection, steady state accuracy, and resiliency against sensor and actuator attacks. An Input-to-State Stability analysis is presented to establish stability guarantees for the IADRC based load frequency control system under bounded disturbances. Controller and observer tuning is formulated as an optimization problem, and the gains are optimized using whale migration optimization.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB22.1", "code": "FrB22.1", "title": "A Self-Consistent Breath-Wise Modeling Framework for Dynamic Ventilation Analysis (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB22", "sessionTitle": "Modeling and Diagnostics of the Respiratory System II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Liu, Jieyu", "affiliation": "Univeristy of Canterbury" }, { "name": "Zhou, Cong", "affiliation": "Chinese Academy of Sciences" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" }, { "name": "Ang, Christopher Yew Shuen", "affiliation": "Monash University Malaysia" }, { "name": "Chiew, Yeong Shiong", "affiliation": "Monash University" } ], "keywords": [ "Digital twins in healthcare, model-based therapeutics", "Modeling and control in mechanical ventilation", "Medical devices, systems and solutions" ], "abstract": "Weaning is a key part of mechanical ventilation (MV) care in the intensive care unit (ICU), where delayed weaning increases length of MV, length of stay, and resultant cost and mortality. Accurate evaluation of patient-specific respiratory mechanics is important for adjusting ventilator support and informing weaning-related decisions. Typical assessments are very intermittent and miss dynamic changes in lung mechanics and recruitment. There are thus fewer opportunities to appropriately reduce pressure support as respiratory condition evolves. This study proposes a breath-by-breath self-consistent update framework to estimate inspiratory elastance, k_(2 ), and identifies the optimal PEEP during mechanical ventilation, potentially every breath, where falling PEEP would indicate the potential to wean is increasing as patients are weaned from low PEEP. Airway pressure and volume data from each breath are used to construct a baseline Hysteresis Loop Model (HLM), which is a clinically validated, predictive digital twin model. A bidirectional PEEP scanning strategy predicts how k_(2 )would change under different PEEP settings, and the PEEP producing the lowest predicted elastance is selected for the next update. In parallel, a stochastic model built from breath-to-breath elastance trends in clinical data from a large cohort, maps this predicted value to a next-breath estimate. A clinical dataset of 200 breaths was used to evaluate feasibility. Elastance trends tracked respiratory changes, suggesting potential for real-time assessment of elastance, PEEP, and thus weaning potential. The overall results validate the approach in-silico and provide justification for clinical testing.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB22.2", "code": "FrB22.2", "title": "Safe Range for Lung Elastance: Necessity and Feasibility (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB22", "sessionTitle": "Modeling and Diagnostics of the Respiratory System II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Sun, Qianhui", "affiliation": "University of Liege" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" }, { "name": "Zhou, Cong", "affiliation": "Chinese Academy of Sciences" }, { "name": "Desaive, Thomas", "affiliation": "University of Liege" } ], "keywords": [ "Control of physiological and clinical variables", "Decision support and control in medicine", "Modeling and control in mechanical ventilation" ], "abstract": "Lung elastance is of great interest in ventilation care reflecting and assessing lung stiffness and recruitability. An optimal positive end expiratory pressure (PEEP) level is suggested to be at where elastance yields its minimum among applied levels with overall best balance and compromise between benefits and harms. Other indices to assess alveoli status and risk are proposed and most of them provide a safe range for clinical application. While elastance is observed to have very small difference between a few PEEP levels, no research has studied whether they are acceptable (safe) or not. In this study, 19 volume-controlled ventilation (VCV) patients’ data are analyzed. Two commonly used lung elastance indices, respiratory system elastance (Ers) and dynamic elastance (Edyn), are extracted and analyzed with a clinical validated index, stress index (SI). Low correlations between both elastance indices towards SI with Pearson r ≤ 0.42 and Spearman rs ≤ 0.66. However, the safe range for SI is strongly linked to small Ers and Edyn differences (within 5-10% of its minimum), whereas the small difference does not guarantee a safe SI. Clinical indices which are not limited to VCV patients such as SI and more clinical data under diverse conditions should be examined to further investigate this observation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB22.3", "code": "FrB22.3", "title": "Detection of Simulated Apnoeas through Respiratory and Oxygenation Monitoring for Improved PAP Algorithms (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB22", "sessionTitle": "Modeling and Diagnostics of the Respiratory System II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Hill, Jordan F.", "affiliation": "University of Canterbury" }, { "name": "Guy, Ella F. S.", "affiliation": "University of Canterbury" }, { "name": "Pretty, Christopher", "affiliation": "University of Canterbury" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" } ], "keywords": [ "Biomedical signal measurement and processing", "Modeling and control in mechanical ventilation", "Digital twins in healthcare, model-based therapeutics" ], "abstract": "Obstructive sleep apnoea (OSA) is characterised by repeated airway collapse during sleep, leading to partial or complete interruptions of airflow. Early detection of obstructions can improve treatment outcomes. However, current positive airway pressure (PAP) devices can miss subtle changes in obstructions. This study evaluated whether combining airway pressure and flow measurements with arterial and venous oxygenation could detect obstruction-like changes during simulated apnoeas. Twenty (N=20) healthy adults performed normal breathing and voluntary breath-hold tasks while using a CPAP device set at 0, 4, and 8 cmH₂O positive end-expiratory pressure (PEEP). An inline venturi sensor measured airway pressure and flow. A prototype neck-worn reflectance sensor captured arterial and venous pulses from the carotid artery and jugular veins, respectively. Normal breathing and breath-hold periods were extracted and analysed to estimate flow, pressure, arterial oxygen saturation (SpaO₂), venous oxygen saturation (SpvO₂) and oxygen extraction ratio (O₂ER). During breath holds, airflow decreased by 23.4 ± 15.4 % for 10-second holds and 19.2 ± 12.7 % for 20-second holds at 4 cmH₂O PEEP. At 8 cmH₂O PEEP, reductions were smaller at 12.6 ± 9.7 % and 10.3 ± 13.1 %, respectively. SpaO₂ ranged from 84.6 to 94.1 %, while SpvO₂ ranged from 62.5 to 79.7 %. The oxygen extraction ratio varied between 0.16 and 0.34, which are all within normal ranges, with SpaO2 lower due to venous influence. These results demonstrate airway and optical sensor measurements can detect airflow reductions and dynamic oxygenation changes during simulated apnoeas. This multimodal sensor approach is stable and repeatable, justifying further development for early detection of airway obstruction and personalised, real-time OSA monitoring for better closed-loop PAP algorithms.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB22.4", "code": "FrB22.4", "title": "In-Silico Mechanical Power Investigation During Mechanical Ventilation Treatment (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB22", "sessionTitle": "Modeling and Diagnostics of the Respiratory System II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Rubbi, Syed Shafayat", "affiliation": "Monash University Malaysia" }, { "name": "Tan, Chee Pin", "affiliation": "Monash University" }, { "name": "Ang, Christopher Yew Shuen", "affiliation": "Monash University Malaysia" }, { "name": "Chiew, Yeong Shiong", "affiliation": "Monash University" } ], "keywords": [ "Modeling and control in mechanical ventilation", "Decision support and control in medicine", "Intensive and chronic care or treatment" ], "abstract": "Mechanical ventilation (MV) is an integral intensive care treatment that preserves the life of patients, especially in patients with diminished respiratory function. However, setting MV requires understanding the nuances of key MV parameters. Some parameters govern the energy transferred to the patient lungs during MV treatment and as such, inappropriate MV setting increases the risk of ventilator-induced lung injury (VILI). This study aims to investigate the quantitative relationship between MV parameters, and the mechanical power (MP) transmitted to the lungs. A single compartment model was used to simulate the mechanical behaviour of the respiratory system during MV. The airway pressure of a patient during MV was simulated using a single-compartment lung model. Simulations were conducted by varying the key MV parameters to analyse their effects on mechanical power. Results showed that MP increases proportionally with parameters such as respiratory elastance, respiratory resistance, PEEP, tidal volume and respiratory rate, however the effect was more pronounced with respiratory elastance. Isolines of driving pressure and minute ventilation were used to visualise how variations in respiratory rate and tidal volume contribute to MP. Analysis of the relationships between ventilator parameters and MP offers valuable insight into how optimised parameter selection may improve ventilation safety and mitigate the risk of VILI.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB22.5", "code": "FrB22.5", "title": "Methods and Validation Testing for Volumetric Capnography Via Hysteresis Loop Analysis (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB22", "sessionTitle": "Modeling and Diagnostics of the Respiratory System II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Hastings, Samuel", "affiliation": "University of Canterbury" }, { "name": "Guy, Ella F. S.", "affiliation": "University of Canterbury" }, { "name": "Chase, J. Geoffrey", "affiliation": "University of Canterbury" } ], "keywords": [ "Biomedical system modeling, identification, and simulation", "Biomedical signal measurement and processing" ], "abstract": "Volumetric capnography (VCap) is a non-invasive method of quantifying dead space and ventilation-perfusion mismatch. Current VCap analysis assumes 3 phases in a VCap curve but neglects additional non-linearities associated with dysfunctional breathing. This paper introduces a novel application of hysteresis loop analysis (HLA) to address these issues in an efficient manner suitable for real-time. HLA decomposes capnography curves into a minimal number of piecewise linear segments with minimal error, allowing non-linearities associated with dysfunction to be identified breath-to-breath. HLA is compared to the accepted VCap analysis method using the Levenberg-Marquardt algorithm to assess key clinical variables of airway dead space (VDAW) and the slope of phase III (SIII) from simulated data with known ground truth. Both methods perform equally well on simulated data with a known ground truth. However, HLA provides a simple alternative to the state-of-the-art as it can adapt to atypical VCap curves seen in dysfunctional breathing without changing the model. Overall, HLA accurately assesses nonlinear capnography curves impacted by respiratory dysfunction. Full generalisability remains to be prospectively validated on clinical data as justified by the results here.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB22.6", "code": "FrB22.6", "title": "Hybrid CNN+ViT Architecture for Accurate Interstitial Lung Disease Classification", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB22", "sessionTitle": "Modeling and Diagnostics of the Respiratory System II", "sessionType": "Open Invited Track Session", "room": "Exhibition Center 1 - Room 312", "authors": [ { "name": "Palatka, József", "affiliation": "Obuda University" }, { "name": "Dénes-Fazakas, Lehel", "affiliation": "Óbuda University" }, { "name": "Biró, Attila", "affiliation": "Obuda University" }, { "name": "Kovacs, Levente", "affiliation": "Obuda University" }, { "name": "Szilagyi, Laszlo", "affiliation": "Obuda University" } ], "keywords": [ "Biomedical and medical imaging, image processing, visualization", "Biomedical signal measurement and processing", "Decision support and control in medicine" ], "abstract": "This paper investigates the classification of Interstitial Lung Disease (ILD) using a hybrid model architecture incorporating a convolutional neural network (CNN) and a Visual Transformer (ViT). Several configurations were evaluated, including variants with different numbers of filters, transformer layers, and dense units. The results demonstrate that the CNN+ViT combination is capable of achieving high accuracy and stability, some models reaching F1-scores above 0.97 and AUC values around 0.99. Models with larger CNN filter sizes achieved better generalization, while simpler architectures with fewer transformer layers outperformed deeper and more complex ones. The findings confirm that the proposed CNN+ViT architecture provides an effective and robust solution for ILD image classification, establishing a solid foundation for future developments with larger CNN backbones and extended training durations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB23.1", "code": "FrB23.1", "title": "Adaptive Tube-Enhanced Multi-Stage Model Predictive Control for Bolus Feeding Cultivation of E.coli (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB23", "sessionTitle": "Modeling and Optimization in Bioprocesses", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Kim, Bum Jin", "affiliation": "Incheon National University" }, { "name": "Luna, Martin", "affiliation": "CONICET-UTN" }, { "name": "Martinez, Ernesto Carlos", "affiliation": "CONICET-UTN" }, { "name": "Cruz Bournazou, Mariano Nicolas", "affiliation": "TU-Berlin, DataHow AG" }, { "name": "Kim, Jong Woo", "affiliation": "Incheon National University" } ], "keywords": [ "Biological and pharmaceutical systems", "Batch and semi-batch process control", "Model-predictive and optimization-based control in chemical processes" ], "abstract": "High-throughput mini-bioreactor platforms rely on pulse-fed cultivations that are highly sensitive to model mismatch and parametric uncertainty. We present an adaptive tube-enhanced multi-stage model predictive control (ATEMS MPC) framework for robust bolus feeding of E.coli on such a platform, building on a nominal MPC–moving horizon estimation (MHE) scheme. Parameter uncertainty estimated by the MHE is represented by a set of scenarios, which are propagated in a multi-stage MPC to design pulse-feeding trajectories. An adaptive tube layer tightens state and input constraints according to the current uncertainty, ensuring robust satisfaction of dissolved oxygen and substrate bounds. In silico studies for the fed-batch cultivation of E.coli demonstrate that the proposed framework yields a robust feeding strategy that effectively handles process variability and ensures strict satisfaction of oxygen constraints despite model uncertainty.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB23.2", "code": "FrB23.2", "title": "Robust Operation of High-Throughput Phenotyping Experiments Using Deep Reinforcement Learning (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB23", "sessionTitle": "Modeling and Optimization in Bioprocesses", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Lange, Christoph", "affiliation": "Technische Universität Berlin" }, { "name": "Luna, Martin", "affiliation": "CONICET-UTN" }, { "name": "Mione, Federico", "affiliation": "Ingar (conicet / Utn)" }, { "name": "Hassfurther, Rosa", "affiliation": "Technische Universit¨at Berlin, Chair of Bioprocess Engineering" }, { "name": "Martinez, Ernesto", "affiliation": "Technical University of Berlin" }, { "name": "Cruz Bournazou, Mariano Nicolas", "affiliation": "TU-Berlin, DataHow AG" } ], "keywords": [ "Biological and pharmaceutical systems", "Real-time optimization and control in chemical processes", "Batch and semi-batch process control" ], "abstract": "Strain screening in bioprocess development requires robust control of parallel fed-batch experiments across diverse microbial strains. We present a Proximal Policy Optimization (PPO)-based deep reinforcement learning agent that modifies a reference glucose feeding profile to maximize final biomass concentration while satisfying a dissolved oxygen tension (DOT) constraint. Training with ODE parameter perturbations (domain randomization) produces a policy that generalizes across diverse E. coli strain phenotypes without retraining. Simulation results demonstrate that a single domain-randomized agent outperforms both a fixed reference profile and a non-randomized RL agent in biomass yield and DOT constraint satisfaction across a wide range of strain metabolic responses.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB23.3", "code": "FrB23.3", "title": "Self-Adaptive Nonlinear Model Predictive Control with Unscented Kalman Filter Integration for Mammalian Cell Perfusion Culture (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB23", "sessionTitle": "Modeling and Optimization in Bioprocesses", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Kim, Dongkyu", "affiliation": "Seoul National University" }, { "name": "Park, Siyang", "affiliation": "Seoul National University" }, { "name": "Hong, Moo Sun", "affiliation": "Seoul National University" } ], "keywords": [ "Biological and pharmaceutical systems", "Advanced process control", "Process modeling, identification, and estimation techniques" ], "abstract": "Perfusion culture is a promising platform for intensified biomanufacturing, enabling high cell density and sustained operation. However, its automated control remains challenging because of nonlinear dynamics and the complexity of cell culture kinetics. In this study, a nonlinear model predictive control (NMPC) framework combined with a parameter-adaptive unscented Kalman filter (UKF) is developed, with emphasis placed on real-time estimation-based adaptation rather than extensive prior biological model development. The UKF continuously updates kinetic parameters and disturbance terms, allowing the control model to adapt during operation. Case studies show accurate tracking of viable cell density, glucose concentration, and working volume while enforcing the cell-specific perfusion rate constraint. In addition, Monte Carlo simulations demonstrate robustness of the model parameter estimation and control strategy under kinetic variability. These results indicate that the proposed adaptive NMPC framework provides a practical basis for automating mammalian perfusion cultures.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB23.4", "code": "FrB23.4", "title": "Switching-Time Bioprocess Control with Pulse-Width-Modulated Optogenetics (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB23", "sessionTitle": "Modeling and Optimization in Bioprocesses", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Espinel-Ríos, Sebastián", "affiliation": "University College Dublin" } ], "keywords": [ "Biological and pharmaceutical systems", "Machine learning and artificial intelligence in chemical process control", "Advanced process control" ], "abstract": "Biotechnology can benefit from dynamic control to improve production efficiency. In this context, optogenetics enables modulation of gene expression using light as an external input, allowing fine-tuning of protein levels to unlock dynamic metabolic control and regulation of cell growth. Optogenetic systems can be actuated by light intensity. However, relying solely on intensity-driven control (i.e., signal amplitude) may fail to properly tune optogenetic bioprocesses when the dose-response relationship (i.e., light intensity versus gene-expression strength) is steep. In these cases, tunability is effectively constrained to either fully active or fully repressed gene expression, with little intermediate regulation. Pulse-width modulation can alleviate this issue by alternating between fully ON and OFF light intensity within forcing periods, thereby smoothing the average response and enhancing process controllability. Optimizing pulse-width-modulated optogenetics entails a switching-time optimal control problem with a binary input over multiple forcing periods. While this can be formulated as a mixed-integer optimization problem on a refined control grid with monotonic input constraints, the number of decision variables can grow rapidly with increasing control-grid resolution within forcing periods and with the total number of forcing periods, complicating the task. Here, we propose an alternative solution based on reinforcement learning. We parametrize control actions via the duty cycle, a continuous proxy variable that encodes the ON-to-OFF switching time within each forcing period, thereby respecting the intrinsic binary nature of the light intensity while avoiding fine-grid binary decision variables.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB23.5", "code": "FrB23.5", "title": "Offset-Free Nonlinear Model Predictive Control for a Fed-Batch Bioreactor", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB23", "sessionTitle": "Modeling and Optimization in Bioprocesses", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 313", "authors": [ { "name": "Hatami, Ehsan", "affiliation": "Graz University of Technology" }, { "name": "Celikovic, Selma", "affiliation": "Research Center Pharmaceutical Engineering" }, { "name": "Wilfling, Katrina", "affiliation": "RCPE GmbH" }, { "name": "Rehrl, Jakob", "affiliation": "Salzburg University of Applied Sciences, Salzburg, Austria" }, { "name": "Horn, Martin", "affiliation": "Graz University of Technology" }, { "name": "Steinberger, Martin", "affiliation": "Graz University of Technology" } ], "keywords": [ "Model-predictive and optimization-based control in chemical processes", "Process modeling, identification, and estimation techniques" ], "abstract": "Biopharmaceutical process control demands strategies capable of handling nonlinear dynamics, disturbances, and incomplete process knowledge. This work presents a control framework that integrates hybrid modeling with advanced predictive control. The process model combines first-principles equations with neural networks to capture additional dynamics and improve prediction accuracy. This hybrid structure balances physical interpretability with data-driven flexibility, ensuring reliable representation of the fed-batch bioreactor. To enhance robustness against uncertainty and disturbances, an offset-free nonlinear model predictive control (NMPC) scheme is implemented. The offset-free formulation compensates for plant–model mismatch and ensures long-term setpoint tracking. Numerical studies demonstrate improved tracking and disturbance rejection compared to standard NMPC, confirming the potential of neural-network-based hybrid models for reliable and efficient bioprocess control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB24.1", "code": "FrB24.1", "title": "Genetic Algorithm Allocation for Multicore Partitioned Mixed-Criticality Real-Time Systems", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB24", "sessionTitle": "AI Methods for FDI/FTC", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Ortiz, Luis", "affiliation": "Universitat Politècnica De València" }, { "name": "Fontalba, Marc", "affiliation": "Universitat Politècnica De València" }, { "name": "Guasque, Ana", "affiliation": "Universitat Politecnica De Valencia" }, { "name": "Balbastre, Patricia", "affiliation": "Universitat Politècnica De València" }, { "name": "Simo, Jose", "affiliation": "UPV" }, { "name": "Crespo, Alfons", "affiliation": "Universidad Politecnica De Valencia" } ], "keywords": [ "Cyberphysical security in processes", "Reliability and safety in processes", "AI methods for FDI/FTC" ], "abstract": "This paper presents a genetic algorithm (GA) approach for efficient task allocation in multicore partitioned mixed-criticality real-time systems (MCRTS). Traditional methods struggle to balance resource utilisation with schedulability across criticality levels. The GA encodes task-to-core mappings as chromosomes, with fitness functions ensuring utilisation balance and schedulability under worst-case scenarios. Through evolutionary operators—selection, crossover, and mutation—the algorithm efficiently explores the solution space. Simulation results demonstrate superior performance over heuristic methods, achieving higher schedulability success rates and improved core utilisation, making it suitable for dynamic workload variations.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB24.2", "code": "FrB24.2", "title": "Multi-Node State Prediction of Industrial Steam Network Based on Flow-Directed Message Passing Neural Network", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB24", "sessionTitle": "AI Methods for FDI/FTC", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Wang, Zixu", "affiliation": "Dalian University of Technology" }, { "name": "Wang, Ze", "affiliation": "Dalian University of Technology" }, { "name": "Han, Zhongyang", "affiliation": "Dalian University of Technology" }, { "name": "Zhao, Jun", "affiliation": "Dalian University of Technology" }, { "name": "Wang, Wei", "affiliation": "Dalian University of Technology" }, { "name": "Dong, HongXin", "affiliation": "Dalian University of Thechology" } ], "keywords": [ "Process performance monitoring/statistical process control", "Thermal systems modelling", "Energy communities" ], "abstract": "Process industries typically involve large-scale steam networks with numerous users and rapidly varying demands. Accurate prediction of the operational states of such networks is essential for optimizing the supply–demand balance and improving the efficiency of overall system. Considering the physical flow-direction characteristics of steam transport, a Flow-Directed Message Passing Neural Network (FD-MPNN) for the state prediction of steam networks is proposed in this study. First, the network topology is modeled as a directed graph according to the physical flow-direction of steam, and a physics–data fusion feature construction method is designed. Then, based on the paths from the source node to the load node in the directed graph, a series of subgraphs with ordered hierarchies are constructed, thereby adaptively determining the number of layers of the graph neural network model. Subsequently, the generation–aggregation–update process for the message is deployed in each subgraph, ensuring consistency between the information propagation direction and the actual physical steam flow-direction in the network. Furthermore, to model the non-stationary response process of the source-end variations transmission towards load-ends, the original states are introduced into the message generation stage and integrated with the updated features. Finally, to verify the performance of the proposed model, the multi-node state prediction is conducted using an actual industrial steam network as a case, in which the boundary conditions and initial states are designed based on real industrial requirements. The experimental results show that the proposed FD-MPNN model consistently outperforms traditional data-driven methods.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB24.3", "code": "FrB24.3", "title": "A Novel Lightweight Deep Model for Monitoring Key-Performance-Indicator Related Faults", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB24", "sessionTitle": "AI Methods for FDI/FTC", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Hao, Weichen", "affiliation": "Beijing University of Chemical Technology" }, { "name": "Li, Dazi", "affiliation": "Beijing University of Chemical Technology" }, { "name": "Karimi, Hamid Reza", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Fault detection and isolation methods", "Data-driven methods for FDI/FTC", "Process performance monitoring/statistical process control" ], "abstract": "A key challenge in monitoring key-performance-indicator (KPI) related faults for dynamic industrial processes is achieving interpretable monitoring of at a low computational cost. Independent variable analysis (IVA), as an emerging KPI-related fault monitoring method, alleviates the dimensionality challenges of traditional multivariate statistical analysis, it still has limitations in deep dynamic feature extraction. This study presents a novel lightweight deep model (LDM), termed cascade temporal IVA with IVA (C-TIVA-IVA), which offers three advantages. First, as an LDM, C-TIVA-IVA reduces complexity by stacking linear layers to replace computationally expensive parameter tuning of deep neural networks. Second, it overcomes the dimensionality challenges of existing LDMs by integrating process variables and KPIs at each layer into a joint matrix for decomposition. Finally, with the introduction of the proposed TIVA model, C-TIVA-IVA addresses the limitations of existing LDMs in monitoring dynamic processes. Experimental results on a real industrial process dataset demonstrate the superior performance of the proposed method.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB24.4", "code": "FrB24.4", "title": "Structural Methods for Testable Signal Sets in Data-Driven Fault Diagnosis", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB24", "sessionTitle": "AI Methods for FDI/FTC", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Krysander, Mattias", "affiliation": "Linköping University" }, { "name": "Jung, Daniel", "affiliation": "Linköping University" } ], "keywords": [ "Structural analysis/quantitative methods for FDI/FTC", "Data-driven methods for FDI/FTC", "Fault detection and isolation methods" ], "abstract": "Structural methods have been widely used in model-based fault diagnosis for diagnosability analysis and for the design of diagnosis systems. Recently, it has also been proven useful in the design of data-driven models, e.g., neural networks, for residual generation. However, existing structural analysis methods do not provide consistent fault-isolability results when combined with machine learning, due to learning ambiguities. This paper illustrates the challenges posed by existing structural analysis methods and the properties of structurally overdetermined sets, and presents new results and methods for the design of data-driven residual generation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB24.5", "code": "FrB24.5", "title": "Robustness and Safety Analysis of ReLU Neural Networks", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB24", "sessionTitle": "AI Methods for FDI/FTC", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Cabral, Leonardo", "affiliation": "Universidade De Caxias Do Sul (UCS)" }, { "name": "Valmorbida, Giorgio", "affiliation": "L2S, CentraleSupelec" }, { "name": "Gomes Da Silva Jr, Joao Manoel", "affiliation": "Universidade Federal Do Rio Grande Do Sul (UFRGS)" } ], "keywords": [ "AI tools in automation engineering and operation", "Fuzzy and neural systems in control", "Reinforcement learning and deep learning in control" ], "abstract": "Given the broad application of neural networks in control and classification tasks, certifying their robustness against input perturbations is essential. A common approach to ensure robustness is to verify whether a set of admissible inputs yields outputs within a prescribed safety set, in which case the neural network is called safe. This paper introduces a novel convex Semidefinite Programming (SDP) test for certifying the safety of neural networks with ReLU activation functions. The proposed SDP test is based on an exact characterization of both the neural network and the ReLU activation function, leading to less conservative results compared to benchmark methods based on sector inequalities. The effectiveness of the proposed method is illustrated through numerical examples.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB24.6", "code": "FrB24.6", "title": "LSTM-Based Pilot-Induced Oscillation Prediction (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB24", "sessionTitle": "AI Methods for FDI/FTC", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 314", "authors": [ { "name": "Ünen, Can", "affiliation": "Bilkent University" }, { "name": "Yildiz, Yildiray", "affiliation": "Bilkent University" } ], "keywords": [ "AI for aircraft and spacecraft navigation, guidance and control", "Guidance, navigation and control of aircraft and spacecraft", "Flight dynamics modelling and identification" ], "abstract": "This paper introduces a Long Short-Term Memory (LSTM)-based system to predict pilot-induced oscillations (PIO) in rotorcraft. The LSTM-based network predicts PIO dynamics with the help of recent histories of pilot reference, tracking error, attitude response, pilot inputs, and actuator motion. An integrated loss term is designed to help capture the PIO dynamics. The system is trained on data from human-in-the-loop experiments and simulations. The results indicate that the proposed PIO prediction approach together with the carefully designed loss function provides an efficient early-warning tool for rotorcraft PIO, with the capability of distinguishing over 60% of PIO cases.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB25.1", "code": "FrB25.1", "title": "ECG Signal Denoising in AWGN Using a Variable Step-Size LMS Adaptive Filter", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB25", "sessionTitle": "Biomedical System Modeling, Identification, and Simulation", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Park, Jeongmin", "affiliation": "POSTECH" }, { "name": "Hong, Hye Seung", "affiliation": "POSTECH" }, { "name": "Park, PooGyeon", "affiliation": "Pohang Univ. of Sci. & Tech" } ], "keywords": [ "Biomedical signal measurement and processing" ], "abstract": "Electrocardiogram (ECG) signals provide essential information about cardiac activity, but they are often contaminated by noise degrading diagnostic quality. While baseline wander and power-line interference have been widely studied, additive white Gaussian noise (AWGN) arising from electronic circuits and wireless transmission in wearable ECG systems remains relatively unexplored. This paper addresses ECG denoising under AWGN using a variable step-size least mean square (VSS-LMS) adaptive filter. The proposed method dynamically adjusts the step size to improve the quality of ECG signals over the general LMS algorithm. Simulation results using ECG data confirm the effectiveness of the proposed approach in removing AWGN.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB25.2", "code": "FrB25.2", "title": "MS-TANet: Multi-Scale Temporal Attention for EEG Direction Decoding", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB25", "sessionTitle": "Biomedical System Modeling, Identification, and Simulation", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Liu, Yixin", "affiliation": "Beihang University" }, { "name": "Luo, Junhua", "affiliation": "Beihang University" }, { "name": "Tang, Ning", "affiliation": "ITMO University" }, { "name": "Wang, Zeyu", "affiliation": "Faculty of Computer Science and Control Systems, BMSTU Russia" }, { "name": "Wang, Lingling", "affiliation": "Beihang University" }, { "name": "Fu, Li", "affiliation": "School of Automation Science and Electrical Engineering, Beihang University" } ], "keywords": [ "Biomedical signal measurement and processing", "Biomedical system modeling, identification, and simulation", "Rehabilitation engineering and healthcare delivery" ], "abstract": "Decoding directional motion intention from EEG is essential for non-invasive brain–computer interfaces but remains difficult due to signal non-stationarity and diverse temporal patterns. We present the Multi-Scale Temporal Attention Network (MS-TANet), which integrates parallel multi-scale convolutions with multi-head self-attention to capture both local dynamics and global dependencies. Evaluated on a vestibular navigation dataset of 20 subjects, MS-TANet achieves a subject-independent accuracy of 93.58%, outperforming conventional CNN- and TCN-based models. This establishes MS-TANet as a robust solution for precise, real-time BCI navigation tasks under non-stationary conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB25.3", "code": "FrB25.3", "title": "Inferring Gene Regulatory Network Dynamics from Limited Snapshot Data for Ultra-Early Disease Treatment", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB25", "sessionTitle": "Biomedical System Modeling, Identification, and Simulation", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Xu, Zhenhui", "affiliation": "Institute of Science Tokyo" }, { "name": "Sasahara, Hampei", "affiliation": "The University of Tokyo" }, { "name": "Imura, Jun-ichi", "affiliation": "Institute of Science Tokyo" } ], "keywords": [ "Biomedical system modeling, identification, and simulation", "Biological networks inference and modelling" ], "abstract": "The early detection of critical transitions in gene regulatory networks is essential for timely medical intervention. However, identifying the underlying network dynamics in the pre-disease stage is challenging because only a few destructive snapshot measurements are typically available. Motivated by the need to recover the system dynamics that governs ultra-early disease treatment, this paper proposes a moment-difference-based identification method for discrete-time stochastic linear systems that operates effectively under severe data limitations. We find out that both the state matrix and constant vector can be uniquely identified using only four snapshot datasets during the short pre-disease stage under mild conditions, which require an invertible state matrix, a large steady-state covariance, and full-rank excitation of the transformed mean difference vectors within each eigenvalue block. Simulation studies further demonstrate that the method accurately reconstructs high-dimensional gene regulatory network dynamics and recovers the dominant eigenvector, providing a promising foundation for ultra-early disease intervention design.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB25.4", "code": "FrB25.4", "title": "Bioinformatics-Inspired Pathway Modeling and Adaptive Control for Brain Tumor MRI Classification Using Hybrid Deep Learning and Biostatistics", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB25", "sessionTitle": "Biomedical System Modeling, Identification, and Simulation", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Biró, Attila", "affiliation": "Obuda University" }, { "name": "Kovacs, Levente", "affiliation": "Obuda University" }, { "name": "Szilagyi, Laszlo", "affiliation": "Obuda University" } ], "keywords": [ "Biomedical system modeling, identification, and simulation", "Biomedical and medical imaging, image processing, visualization", "Biomedical signal measurement and processing" ], "abstract": "Brain tumor diagnosis from MRI remains challenging due to heterogeneous tumor morphology, limited annotated datasets, and the lack of interpretable, uncertainty-aware decision mechanisms. This paper presents a hybrid biomedical systems framework that integrates deep learning, bioinformatics-inspired pathway modeling, biostatistics, and adaptive control for robust tumor classification using the Bangladesh Brain Cancer MRI Dataset (6056 images; glioma, meningioma, general tumor). High-dimensional convolutional features and handcrafted morphology–texture–spectral descriptors are aggregated into five imaging pathways analogous to biological regulatory modules, enabling structured phenotype representation and statistical hypothesis testing via ANOVA and mixed-effects models. A latent-state embedding (UMAP/PCA) further reveals tumor-specific clusters. The pathways are fused with a CNN classifier to form a hybrid diagnostic model, while Monte Carlo dropout enables uncertainty quantification. A feedback control law dynamically adjusts the decision threshold to maintain clinically acceptable confidence levels. Grad-CAM–based heatmaps and pseudo-masks provide interpretable localization of discriminative tumor regions. Although classification performance improvements over CNN-only baselines are modest, the proposed framework substantially improves interpretability, uncertainty awareness, calibration robustness, and control-oriented decision support, demonstrating the value of combining systems engineering principles with bioinformatics and machine learning for medical image diagnostics in low-resource environments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB25.5", "code": "FrB25.5", "title": "Real-Time Decomposition of Active Sensing and Tracking Motions Via a Structure-Aware Filtering Framework", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB25", "sessionTitle": "Biomedical System Modeling, Identification, and Simulation", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Aydin, Emin Yusuf", "affiliation": "Graduate School of Science and Engineering, Bioengineering Division, Hacettepe University, Ankara, Türkiye" }, { "name": "Yilmaz, Onurcan", "affiliation": "Hacettepe University" }, { "name": "Öztürk, Mustafa", "affiliation": "Department of Elektrical and Electronics Engineering , Middle East Technical University , Ankara , Türkiye" }, { "name": "DoĞan, Hasan", "affiliation": "Middle East Technical Univerrsity" }, { "name": "Uyanik, Ismail", "affiliation": "Hacettepe University" } ], "keywords": [ "Biomedical system modeling, identification, and simulation", "Biomedical signal measurement and processing", "Dynamics and control of gene expression and metabolic pathways" ], "abstract": "Active sensing animals often generate high-frequency movements that are superimposed on task-level behavior, making their functional role difficult to quantify in real time. This paper introduces a structure-aware adaptive FIR filter that decomposes weakly electric fish motion into low-frequency tracking and high-frequency active-sensing components. The filter is derived analytically from a frequency-sampling formulation and operates causally at the behavioral sampling rate. Simulations and ROS2-based real-time tests show that the filter suppresses targeted frequencies with low latency. Closed-loop experiments demonstrate that scaling and reinjecting the extracted high-frequency component into the refuge trajectory systematically modulates movement variance and spectral content. The framework provides a real-time tool for causal manipulation of self-generated sensing movements in biological and bio-inspired control systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB25.6", "code": "FrB25.6", "title": "Persistence Analysis of Haematopoietic Cells Dynamics with Multi-Stage Maturation", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB25", "sessionTitle": "Biomedical System Modeling, Identification, and Simulation", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 315", "authors": [ { "name": "Zenati, Abdelhafid", "affiliation": "School of Mathematics, Computer Science and Engineering, City University of London" }, { "name": "Youcef-Toumi, Kamal", "affiliation": "Massachusetts Institute of Technology" } ], "keywords": [ "Biomedical system modeling, identification, and simulation", "Dynamics and control of biologically motivated nonlinear systems", "Life cycle analysis/assessment for biosystems" ], "abstract": "This paper investigates the persistence, or positive steady-state (PSS) stability, of a nonlinear haematopoiesis model with multi-stage cellular maturation and distributed delays. The PSS has direct biological relevance, representing either regulated blood production or a dormant leukaemic state, making its characterisation clinically important. Direct stability analysis of the delayed nonlinear system is difficult; therefore, the key idea here is reformulating the delayed nonlinear cascade into an analytically tractable averaged system that faithfully captures its dynamics. Using the General Averaging Theorem, we derive a delay-free averaged model, for which necessary and sufficient conditions for global asymptotic stability (GAS) are obtained via the cascade stability theorem. Contraction theory is then employed to relate the averaged and original delayed systems, enabling us to distinguish the original system asymptotic convergence to equilibrium from sustained oscillations, the latter being biologically associated with pathological conditions such as chronic myeloid leukaemia and cyclic neutropenia. Numerical simulations support the theoretical results and illustrate the two possible dynamical behaviours.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB26.1", "code": "FrB26.1", "title": "A Dataset and Benchmark for Maritime Vessel Tracking with an Overview of Recent Advances in Maritime MOT and ReID (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB26", "sessionTitle": "Perception and Situational Awareness for Autonomous Ships", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Fabijanic, Matej", "affiliation": "Faculty of Electrical Engineering and Computing Zagreb" }, { "name": "Ferreira, Fausto", "affiliation": "University of Zagreb" } ], "keywords": [ "Perception and filtering in marine systems", "Robotic vision for AVs", "AI and embodied-AI in marine systems" ], "abstract": "Vessel identification and tracking are key tasks in coastal zones, where monitoring supports both maritime safety and environmental protection. Progress in this area is constrained by the lack of publicly available datasets that capture realistic vessel interactions, occlusions, and appearance variability across vessel types. In this work, we introduce a new multi-object tracking (MOT) dataset collected from a fixed shoreline camera overlooking a busy strait in the Adriatic Sea. The dataset contains 13,493 annotated frames and 65 unique vessels. The footage includes several occlusion and reappearance events relevant for reidentification (ReID). We evaluate four widely used tracking-by-detection methods: ByteTrack, BoT-SORT, StrongSORT, and BoostTrack++. All methods use a fine-tuned YOLOv9e detector. BoT-SORT achieves the highest overall accuracy, while ByteTrack performs competitively despite not using ReID features. All tested trackers struggle with long-term occlusions. We identify the need for maritime-specific ReID models and improved domain-aware tracking strategies. The proposed dataset and benchmark establish a foundation for future research on maritime MOT and vessel ReID.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB26.2", "code": "FrB26.2", "title": "Prediction of Ship Trajectories for Collision Avoidance with a Transformer-Based Cross-Ship Attention Model (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB26", "sessionTitle": "Perception and Situational Awareness for Autonomous Ships", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Alfsen, Nils Petter", "affiliation": "NTNU" }, { "name": "Veksler, Aleksander", "affiliation": "NTNU" }, { "name": "Rokseth, Børge", "affiliation": "NTNU" }, { "name": "Johansen, Tor Arne", "affiliation": "Norwegian University of Science and Technology" } ], "keywords": [ "AI and embodied-AI in marine systems", "Autonomous marine systems and vehicles", "Perception and filtering in marine systems" ], "abstract": "A transformer-based machine learning model for AIS-based vessel trajectory prediction has recently been proposed. Our research adapts this machine learning model to automatic collision avoidance in ship navigation. While the original method considers prediction horizons of several hours, we adapted it to perform more accurate predictions within the horizons of tens of minutes, making it suitable for planning of collision avoidance maneuvers. The research considered feature engineering (e.g., ship type), training regimes to reduce exposure bias, and modeling interactions with other ships (cross-ship attention). The model was found to perform well across multiple vessel types, from pleasure crafts to cargo vessels, indicating generalized learning. It also showed ability to predict complex interaction scenarios, including collision avoidance maneuvers.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB26.3", "code": "FrB26.3", "title": "Exploring LLM Capabilities for Situational Understanding and COLREG Compliance on Real-World Maritime Navigation Scenarios (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB26", "sessionTitle": "Perception and Situational Awareness for Autonomous Ships", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Wirbel, Julius", "affiliation": "Technical University of Denmark (DTU)" }, { "name": "Hansen, Peter Nicholas", "affiliation": "Technical University of Denmark" }, { "name": "Clemmensen, Line", "affiliation": "University of Copenhagen" }, { "name": "Galeazzi, Roberto", "affiliation": "Technical University of Denmark" } ], "keywords": [ "AI and embodied-AI in marine systems", "Decision and support in marine systems", "Marine system guidance, navigation and control" ], "abstract": "Recently, Large Language Models (LLMs) have shown considerable capability for situational understanding, reasoning, and decision making in different domains, most notable in the automotive sector. Therefore, we explore current state-of-the-art LLMs as a tool for maritime navigation, which includes both codified rules in the Collision Regulations (COLREGs) and uncodified best practices summarized in the concept of ``Good Seamanship''. We construct a dataset consisting of 50 diverse, real-world navigation scenarios from AIS data, label scenarios with applicable COLREG rules, recommended actions, and the reasoning for the action. We explore a variety of different LLM architectures and sizes to determine their understanding of maritime navigation tasks as well as evaluate their reasoning capabilities in this domain. The results obtained indicate that the maritime navigation task remains difficult to solve without fine-tuning, even for larger online models.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB26.4", "code": "FrB26.4", "title": "Seeing above the Waves: A Modular Sensing Framework for Data Acquisition at Sea (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB26", "sessionTitle": "Perception and Situational Awareness for Autonomous Ships", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Schmidt, Jonathan Eichild", "affiliation": "Technical University of Denmark" }, { "name": "Wirbel, Julius", "affiliation": "Technical University of Denmark (DTU)" }, { "name": "Hansen, Peter Nicholas", "affiliation": "Technical University of Denmark" }, { "name": "Louedec, Morgan", "affiliation": "ENSTA Bretagne" }, { "name": "Westerdahl, Christian", "affiliation": "Technical University of Denmark" }, { "name": "Dagdilelis, Dimitrios", "affiliation": "Technical University of Denmark" }, { "name": "Galeazzi, Roberto", "affiliation": "Technical University of Denmark" } ], "keywords": [ "Sensors and actuators in marine systems", "Autonomous marine systems and vehicles", "Perception and filtering in marine systems" ], "abstract": "Advancing autonomy for surface vessels requires systematic evaluation of their sensing and perception subsystems. Yet, maritime environments impose unique challenges: sensor installation is constrained by vessel layout, environmental conditions such as fog or sea clutter are difficult to reproduce, and long-duration missions complicate data collection. This work addresses the question: How can we design a modular and reproducible sensor platform for maritime autonomy? We present a comprehensive design blueprint that incorporates diverse modalities - RADAR, LiDAR, IMU, GNSS, AIS, RGB and LWIR cameras, and weather sensors - to enhance environmental awareness and vessel proprioception. Supported by a dedicated ROS2-based software framework for data management, our modular platform enables long-term data collection, hardware-in-the-loop testing, and integration with existing sensors and algorithms. By unifying hardware design and data capture methodology, the platform enhances reproducibility and comparability across vessels and research projects. The proposed framework bridges engineering implementation and research methodology, providing the foundation for standardized, verifiable datasets essential to advancing situational awareness and autonomous maritime navigation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB26.5", "code": "FrB26.5", "title": "Dynamic Risk-Aware Framework for Autonomous Marine Navigation: Balancing Safety and COLREGs Compliance", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB26", "sessionTitle": "Perception and Situational Awareness for Autonomous Ships", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Sudharsan, Nataraj", "affiliation": "Texas A&M University" }, { "name": "Patil, Mayur Shivaji", "affiliation": "Texas A&M University" }, { "name": "Ammula, Veneela", "affiliation": "American Bureau of Shipping" }, { "name": "Tomdio, Jude", "affiliation": "American Bureau of Shipping" }, { "name": "Wang, Jin", "affiliation": "American Bureau of Shipping" }, { "name": "Rathinam, Sivakumar", "affiliation": "Texas A&M University" }, { "name": "Pagilla, Prabhakar R.", "affiliation": "Texas A&M University" } ], "keywords": [ "Autonomous marine systems and vehicles", "Marine system guidance, navigation and control", "Decision and support in marine systems" ], "abstract": "Safe and reliable navigation in autonomous systems depends on adherence to established navigational rules across all operational domains: air, land, and sea. In the maritime domain, the International Regulations for Preventing Collisions at Sea (COLREGs) provide the primary framework for guiding vessel interactions and enhancing overall safety. However, universal compliance remains challenging, as both human-operated and autonomous vessels may deviate from these rules. This uncertainty necessitates that autonomous ships not only comply with COLREGs but also detect and respond effectively to rule violations by surrounding vessels. This work provides a rule-abiding yet adaptive navigation framework that enforces COLREGs compliance under normal conditions, while permitting controlled, risk-aware behavior that is essential for collision avoidance when surrounding vessels violate these rules. The approach uses the velocity obstacle method and introduces a composite risk metric, integrating Distance at Closest Point of Approach (DCPA), Time to Closest Point of Approach (TCPA) and closing velocity between vessels, which is then utilized in a weighted optimization scheme to select the most suitable, safe and context-aware evasive maneuver. To balance safety and COLREGs compliance, a four-term cost function is employed in the optimization scheme that includes weighted cost terms for rule compliance, predicted risk, goal alignment and speed preference. The framework was evaluated and validated using standard Imazu test scenarios in a simulation environment, demonstrating robust collision-free and rule-compliant behavior in complex maritime environments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB26.6", "code": "FrB26.6", "title": "Learning Hazardous Maritime Scenarios through Adaptive Stress Testing with Contract-Based Design Method", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB26", "sessionTitle": "Perception and Situational Awareness for Autonomous Ships", "sessionType": "Invited Session", "room": "Exhibition Center 1 - Room 316", "authors": [ { "name": "Sitorus, Andreas Raja Goklas", "affiliation": "NTNU/ITK NTNU" }, { "name": "Adetunji, Aduragbemi Samuel", "affiliation": "Norwegian University of Science and Technology (NTNU)" }, { "name": "Tran, Hoang Anh", "affiliation": "Norwegian University of Science and Technology" }, { "name": "Rokseth, Børge", "affiliation": "NTNU" } ], "keywords": [ "AI and embodied-AI in marine systems", "Simulation and digital-twin in marine systems", "Maritime transport operation and automation" ], "abstract": "Autonomous ships must maintain safe navigation under uncertain and harsh environmental conditions; however, identifying and testing such scenarios in real-life situations is difficult, costly, and risky. Conventional simulation-based testing often lacks mechanisms for generating scenarios that maximize the likelihood of safety-critical events, because failures typically happen in frequently overlooked edge cases. We propose a novel Adaptive Stress Testing method that uses a reinforcement learning policy to steer disturbance models toward predefined failure events.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB27.1", "code": "FrB27.1", "title": "Adversarial UAV Decision-Making Via Knowledge-Augmented Safe Hierarchical Reinforcement Learning", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB27", "sessionTitle": "Mission Planning and Decision Making for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Zhang, Hongtu", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Peng, Gaoxiang", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Fan, Huijin", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Liu, Lei", "affiliation": "Huazhong University of Science and Technology" }, { "name": "Wang, Bo", "affiliation": "Huazhong University of Science and Techonology" } ], "keywords": [ "Mission planning and decision making for AVs", "AI for aircraft and spacecraft navigation, guidance and control" ], "abstract": "Decision-making for one-on-one 3D pursuit–evasion engagements between unmanned aerial vehicles is challenging because it requires interpretable high-level tactical decisions together with adaptive, continuous, safety-constrained control. We propose a knowledge-augmented safe hierarchical reinforcement learning framework that uses rule-based stage partitioning and maneuver selection to encode expert knowledge, and maneuver-specific policies with stage-aligned rewards and constraints for adaptive control. In adversarial simulations, the framework improves survival and neutralization rates, reduces constraint violations, and outperforms end-to-end proximal policy optimization, option-critic hierarchical reinforcement learning, and rule-based baselines.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB27.2", "code": "FrB27.2", "title": "Multi-Target UAV Assignment Problem and Coordinated Attack for a Swarm of UAVs", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB27", "sessionTitle": "Mission Planning and Decision Making for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Piet, Benjamin", "affiliation": "Mines Paris PSL - Institut Saint Louis (ISL)" }, { "name": "Strub, Guillaume", "affiliation": "French-German Research Institute of Saint-Louis (ISL)" }, { "name": "Changey, Sébastien", "affiliation": "Institut Franco-Allemand De Recherches De Saint-Louis" }, { "name": "Petit, Nicolas", "affiliation": "MINES Paris, PSL University" } ], "keywords": [ "Multi-vehicle systems", "Guidance, navigation and control for AVs", "Mission planning and decision making for AVs" ], "abstract": "This paper addresses the problem of optimal allocation of UAVs within a swarm to a set of targets. A Mixed-Integer Linear Programming (MILP) formulation is proposed to determine an optimal assignment while enforcing constraints on the number of attackers allocated to each target. The objective function is tailored to the application, balancing the minimization of average travel distance with limits on individual deviations from that average. The method is then applied as a procedure for multi-target coordinated attacks, enabling UAVs to strike each target simultaneously from different directions. Simulation results demonstrate the effectiveness of the proposed framework in generating feasible, synchronized attack plans under a variety of mission conditions. Notably, the MILP is solvable through its LP relaxation, enabling computation in polynomial time", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB27.3", "code": "FrB27.3", "title": "Reservation-Aware Event-Triggered Multi-Vehicle Path Planning on Resource Graphs for Irregular Time-Varying Road Networks", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB27", "sessionTitle": "Mission Planning and Decision Making for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Chu, Fanyuan", "affiliation": "Tongji University" }, { "name": "Sun, Mengge", "affiliation": "Tongji University" }, { "name": "Guo, Lulu", "affiliation": "Tongji University" } ], "keywords": [ "Multi-vehicle systems", "Trajectory and path planning for AVs", "Mission planning and decision making for AVs" ], "abstract": "This paper studies event-triggered cooperative path planning for vehicle fleets on irregular, time-varying road networks. A reservation-aware event-triggered multi-vehicle planning (REMP) framework is built on a resource graph that encodes road semantics, permissions and time-window closures, and uses soft reservations and congestion indicators for local replanning. On a 10x10 grid, REMP reduces completion and stall times by 39% and 59% relative to static planning. Relative to non-cooperative event-triggered replanning, it reduces completion time by 9% on the grid and 13% on a semi-rural OpenStreetMap network, while using roughly half as many accepted replanning updates.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB27.4", "code": "FrB27.4", "title": "Convolution-Based Grid Game-Theoretic Model: An Interactive Motion Planning Framework for Autonomous Driving", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB27", "sessionTitle": "Mission Planning and Decision Making for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Zhang, Chaojie", "affiliation": "Tongji University" }, { "name": "Liu, Qingwei", "affiliation": "University of Lincoln" }, { "name": "Zhang, Liangliang", "affiliation": "Tongji University" }, { "name": "Wang, Jun", "affiliation": "Tongji University" } ], "keywords": [ "Trajectory and path planning for AVs", "Autonomous vehicles", "Mission planning and decision making for AVs" ], "abstract": "Existing motion planners struggle with prohibitive computational costs in spatiotemporal searches and suboptimal interactions due to path-speed decoupling. This paper proposes an interactive motion planning framework using a convolution-based grid game-theoretic model under an Eulerian perspective. A Stackelberg game first assigns dynamic priorities, followed by a physics-aware Gibbs sampling process that refines a 3D payoff field to model probabilistic multi-agent interactions. By treating the vehicle contour as a convolution kernel, we generate a 4D feature map that enables highly efficient collision checking. This allows a real-time spatiotemporal hybrid A* algorithm to generate high-quality, interaction-aware trajectories. Simulations in parking and construction zones demonstrate that the proposed method significantly enhances both interaction safety and computational efficiency over state-of-the-art baselines.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB27.5", "code": "FrB27.5", "title": "Branch-Stochastic Model Predictive Control for Motion Planning under Multi-Modal Uncertainty with Scenario Clustering", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB27", "sessionTitle": "Mission Planning and Decision Making for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Xing, Zekun", "affiliation": "Technical University of Munich" }, { "name": "Chaudhari, Ramkrishna", "affiliation": "Technical University of Munich" }, { "name": "Leibold, Marion", "affiliation": "Technical University of Munich" }, { "name": "Wollherr, Dirk", "affiliation": "Technical University of Munich" }, { "name": "Buss, Martin", "affiliation": "Technische Universitaet Muenchen" } ], "keywords": [ "Trajectory and path planning for AVs", "Autonomous vehicles", "Mission planning and decision making for AVs" ], "abstract": "Motion planning for autonomous driving must account for multi-modal uncertainty in both the intentions and trajectories of surrounding vehicles. Handling uncertainty in a worst-case manner guarantees robustness but often leads to excessive conservatism. Stochastic Model Predictive Control (SMPC) reduces trajectory-level conservatism through chance constraints, yet remains conservative with respect to intention uncertainty since constraints must hold across all intentions. We present a novel combination of SMPC and the branching structure, enabling the planner to generate distinct trajectories for different possible intentions while maintaining safety under trajectory uncertainty. A novel scenario clustering is proposed to merge prediction scenarios based on high-level decision similarity, thereby ensuring real-time tractability. Furthermore, an adaptive branching-time computation postpones commitment to separate plans until intention uncertainty is sufficiently reduced. Simulation studies in challenging highway scenarios demonstrate that the proposed method improves safety, reduces conservatism, and achieves real-time computational performance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB27.6", "code": "FrB27.6", "title": "Multi-UAS Assignment for Inspection Missions", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB27", "sessionTitle": "Mission Planning and Decision Making for AVs", "sessionType": "Regular Session", "room": "Exhibition Center 1 - Room 317", "authors": [ { "name": "Wickers, Aaron", "affiliation": "Helmut-Schmidt-University / University of the Federal Armed Forces Hamburg" }, { "name": "Alpen, Mirco", "affiliation": "Helmut-Schmidt-University" }, { "name": "Horn, Joachim", "affiliation": "Helmut-Schmidt-University / University of the Federal Armed Forces Hamburg" } ], "keywords": [ "Trajectory and path planning for AVs", "Mission planning and decision making for AVs", "Multi-vehicle systems" ], "abstract": "Efficient coordination of multiple unmanned aerial systems (UAS) is essential for large-scale inspection missions. This paper presents a three-stage optimization framework for offline multi-UAS inspection planning that combines MILP-based clustering and agent assignment with energy-constrained multi-tour routing. The approach integrates realistic path costs, a data-driven energy model, and agent-specific inspection scores. In this way, large planning instances are reduced to a more tractable form while preserving relevant spatial, energetic, and mission-specific structure. Evaluation on a real bridge point cloud shows balanced assignments and more reliable time and energy estimates than a Euclidean baseline. Further, a Pareto analysis highlights the trade-off between inspection quality and operational time.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB28.1", "code": "FrB28.1", "title": "An Adaptive Variable Objective Prioritization MPC Strategy for Artificial Pancreas Systems (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB28", "sessionTitle": "JO-JSC: Healthcare Management, Disease Control, Critical Care", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Yu, Xia", "affiliation": "Northeastern University" }, { "name": "Liu, Hao", "affiliation": "Northeastern University" }, { "name": "Sun, Xiaoyu", "affiliation": "Northeasterun University" }, { "name": "Lu, Jingyi", "affiliation": "Shanghai Jiao Tong University Affiliated Sixth People's Hospital" }, { "name": "Zhou, Jian", "affiliation": "Shanghai Jiao Tong University Affiliated Sixth People's Hospital" }, { "name": "Li, Hongru", "affiliation": "Northeastern University" } ], "keywords": [ "Artificial pancreas or organs", "Decision support and control in medicine", "Intensive and chronic care or treatment" ], "abstract": "Artificial pancreas systems are crucial for effective blood glucose management in type 1 diabetes (T1D). Since the clinical importance of glycemic regulation objectives varies across physiological conditions and glucose risk states, we propose an adaptive Variable Objective Prioritization Model Predictive Control (VPMPC) strategy, where the adaptation is achieved through state-dependent adjustment of objective priorities. This strategy incorporates a physiological-condition dictionary to dynamically select lexicographic objective priorities according to current glucose regulation requirements. Unlike conventional fixed-priority MPC formulations, the proposed method allows the objective hierarchy to vary with physiological conditions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB28.2", "code": "FrB28.2", "title": "A Robust MPC Approach for Safer Insulin Dosing (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB28", "sessionTitle": "JO-JSC: Healthcare Management, Disease Control, Critical Care", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Licini, Nicola", "affiliation": "University of Bergamo" }, { "name": "Santos, Marcelo Alves", "affiliation": "University of Bergamo" }, { "name": "Previdi, Fabio", "affiliation": "Universita' Degli Studi Di Bergamo" }, { "name": "Ferramosca, Antonio", "affiliation": "Univeristy of Bergamo" } ], "keywords": [ "Artificial pancreas or organs", "Healthcare management, disease control, critical care", "Biomedical system modeling, identification, and simulation" ], "abstract": "We present a robust model predictive control (MPC) approach for safer insulin dosing in artificial pancreas systems. The method applies estimation-informed constraint tightening to maintain safety under disturbances and unmodeled dynamics. Safety margins, derived from estimator confidence, are enforced as tube-based tightening on state and input constraints. A zone control objective targets a glucose band with asymmetric weights that emphasize avoidance of low glucose. Simulation studies show fewer low glucose events and lower violation risk than a nominal MPC baseline, with comparable dosing effort. The contribution of this work is a robust tube-based zone MPC scheme that maps estimator uncertainty into safety margins for insulin dosing. This paper is a shortened, conference version of the journal article in (Licini et al 2026).", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB28.3", "code": "FrB28.3", "title": "A Comprehensive Evaluation of Imputation Methods for Retrospective Clinical Data Analysis (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB28", "sessionTitle": "JO-JSC: Healthcare Management, Disease Control, Critical Care", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Osman, Zulfadhli", "affiliation": "Universiti Sains Malaysia" }, { "name": "Suhaimi, Fatanah", "affiliation": "Universiti Sains Malaysia" }, { "name": "Mohamad, Ahmad Fakrurrozi", "affiliation": "Craniofacial and Biomaterial Sciences Cluster, Advanced Medical and Dental Institute, Universiti Sains Malaysia" } ], "keywords": [ "Control of physiological and clinical variables", "Biomedical signal measurement and processing", "Medical devices, systems and solutions" ], "abstract": "This study compares imputation methods to address the missingness in retrospective clinical data. A total of 547 surgical patient database from INSPIRE and PPUSMB databases, which consist of 15 vital parameters, were analysed using Orange software. Five imputation techniques, namely, simple tree, random forest, k-NN, average value and MICE, were evaluated with R², MSE, MAE and RE. The random forest method gained a high accuracy value and a lower percentage errors compared to other methods. To confirm the robustness of the imputation method, it is suggested that further validation is required, particularly for high-risk surgical patients.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB28.4", "code": "FrB28.4", "title": "Validation of a CFD–ABM Coupling Method for Infectious Disease Transmission in an Indoor Environment: Application to a COVID-19 Outbreak in a Restaurant (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB28", "sessionTitle": "JO-JSC: Healthcare Management, Disease Control, Critical Care", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Inghels, Clara", "affiliation": "Nantes University, Ecole Centrale Nantes, CNRS, LS2N, UMR 6004 ; Naval Group, Lorient, France" }, { "name": "Beghin, Clément", "affiliation": "Naval Group, Lorient, France" }, { "name": "da Cunha, Catherine", "affiliation": "Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004" }, { "name": "Billon-Denis, Emmanuelle", "affiliation": "DGA - French Procurement Agency, Vert-Le-Petit, France" }, { "name": "Gorgé, Olivier", "affiliation": "IRBA - French Armed Forces Biomedical Research Institute, Brétigny-Sur-Orge, France" }, { "name": "Duclos, Audrey", "affiliation": "Naval Group, Lorient, France" } ], "keywords": [ "Modeling and control in mechanical ventilation", "Biomedical system modeling, identification, and simulation" ], "abstract": "The COVID-19 pandemic has emphasized the importance of understanding airborne transmission mechanisms in indoor environments, where airflow strongly influences the risk of exposure. This paper presents the validation of a modelling methodology that couples Computational Fluid Dynamics (CFD) and Agent-Based Modelling (ABM), as developed in previous works. The methodology consists in discretizing the environment into airflow-homogeneous spatial areas, according to the CFD results, as inputs for the ABM model. This coupling aims to overcome the limitations of ABMs assuming “well-mixed and homogeneous air” in the space and of CFD models neglecting transmissions by direct contact between individuals. The proposed methodology is applied to the well-documented COVID-19 outbreak in a restaurant in Guangzhou, China, where airflow-driven transmission was identified as the dominant transmission mode. Numerical experiments are conducted to assess the influence of discretization granularity on model performance. The results show that coupling CFD with ABM allows realistic representations of the airflow heterogeneity and its impact on exposure to infection risk by comparison with a case study. This validation confirms the relevance of discretizing CFD results to enhance agent-based infectious disease modelling in indoor environments and the criterion to use for discretization.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB28.5", "code": "FrB28.5", "title": "Reconstruction of Pressure Support Ventilation Signals: A Virtual Patient Set (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB28", "sessionTitle": "JO-JSC: Healthcare Management, Disease Control, Critical Care", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Lindup, Kaelan", "affiliation": "Curtin University" }, { "name": "Bertoni, Michele", "affiliation": "Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia;" }, { "name": "Padula, Fabrizio", "affiliation": "Curtin University, School of Electrical Engineering, Computing and Mathematical Sciences" }, { "name": "Visioli, Antonio", "affiliation": "University of Brescia" } ], "keywords": [ "Modeling and control in mechanical ventilation", "Biomedical system modeling, identification, and simulation", "Digital twins in healthcare, model-based therapeutics" ], "abstract": "Protection of the lung and diaphragm is imperative to the safety of a patient receiving pressure support ventilation in ICUs. To this end, accurate modeling of the interactions that occur between the patient and the ventilator within a breath is a vital step. Modeling of these interactions may be useful to better understand interactions that compromise patient safety, test in-silico new techniques to estimate physiological signals, and ultimately deliver safe ventilation. However, the derivation of such a model from first principles is hindered by the presence of internal ventilator dynamics, which are both highly nonlinear and undisclosed by ventilator manufacturers. Instead, in this paper, interactions were derived from clinical data, considering 400 breaths per patient to construct a set of 10 virtual patients. A simple first-order system was utilized to describe the interactions, with appropriate correlations between the system's gain and time constant, and the magnitude of the patient's effort to generalize the model. In parallel, generalized patient respiratory effort profiles were derived by analyzing similarities in measured efforts. Reconstruction of ventilator waveforms, utilizing the virtual patients, was achieved with a median accuracy larger than 87% in the worst case. A potential use case is also presented, further demonstrating the value of the presented virtual patients for in-silico development and validation of novel techniques. The derived virtual patients are shared via an online repository, and sufficient information is provided for readers to derive additional virtual patients.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB28.6", "code": "FrB28.6", "title": "Patient-Specific Depth of Anesthesia Control Using Event-Based Model-Free Adaptive Design (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB28", "sessionTitle": "JO-JSC: Healthcare Management, Disease Control, Critical Care", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 121", "authors": [ { "name": "Noshad, Erfan", "affiliation": "Rouzbahan Institute of Higher Education, Sari, Iran" }, { "name": "Abbasi Nozari, Hasan", "affiliation": "Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran" }, { "name": "Castaldi, Paolo", "affiliation": "University of Bologna" } ], "keywords": [ "Modeling and control in mechanical ventilation", "Pharmacokinetics, tracer kinetic modelling and drug delivery", "Healthcare management, disease control, critical care" ], "abstract": "This study introduces a technique known as Event-Triggered Model-Free Adaptive Control (ET-MFAC), which is the first of its kind developed for the precise automated administration of propofol to regulate the Depth of Hypnosis (DoH), evaluated through the Bispectral Index (BIS). To address the difficulties encountered by model-based controllers in adapting to patient variability and complexity, our methodology, which is founded on Full Form Dynamic Linearization (FFDL), relies on data-driven principles. A mathematical model is utilized exclusively for the purpose of data collection within the BIS target range of 40-60. The control signal updates are triggered locally by significant state changes, with noise introduced to replicate real-world EEG artifacts. The controller is engineered to disregard this noise, adjusting the drug dosage only in response to substantial physiological alterations to guarantee smooth and safe anesthesia, thereby enhancing noise resilience and computational efficiency. The proposed method was compared with traditional MFAC and PID controllers, demonstrating superior performance as evidenced by lower RMSE, ISE, and IAE values. Additionally, it successfully completed the induction phase for 12 virtual patients, thereby minimizing the risk of patient awareness, and the proposed approach performed exceptionally well during the maintenance phase. The findings validate the feasibility of the framework for clinical implementation and its potential for future applications in tele-anesthesia.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB30.1", "code": "FrB30.1", "title": "Integrating Attack-Defense Graphs with On-Line Control to Enhance Cybersecurity of Smart University Campuses (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB30", "sessionTitle": "JO-CEP: Smart Buildings and Building Automation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Tsadikovich, Dmitry", "affiliation": "Bar-Ilan University" }, { "name": "Levner, Eugene", "affiliation": "Holon Institute of Technology" } ], "keywords": [ "Cybersecurity in smart cities" ], "abstract": "A smart university campus (SUC) is a cyber-physical system that integrates advanced technologies, such as the Internet of Things, artificial intelligence, smart sensors, and cloud services, to improve educational, research, and administrative processes. The fusion of intelligent physical and cyber elements creates complex cybersecurity challenges for SUCs. This study proposes a fast and practical method for integrating an attack-defense graph approach with online control to enhance the cybersecurity of SUCs. The proposed approach involves three stages. First, the most vulnerable intrusion path in the attack graph, representing the shortest (minimum time) attack path in the SUC, is determined. In the second stage, countermeasures are applied to defend the most vulnerable assets. Finally, the third stage serves as a control layer that implements regular preventive and corrective control/monitoring measures to ensure the proper functioning and effectiveness of deployed defenses in the face of their unexpected failures and violations. The proposed polynomial method was tested and validated using the analysis of a real ransomware attack on Maastricht University in 2019, confirming its effectiveness and applicability.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB30.2", "code": "FrB30.2", "title": "Optimal Multi-Objective Power Management and Sizing of Interconnected AC Microgrids Considering Voltage Regulation, Power Losses, Economic and Environmental Criteria (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB30", "sessionTitle": "JO-CEP: Smart Buildings and Building Automation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Gheouany, Saad", "affiliation": "ERERA, National School of Arts and Crafts, Mohammed V University, Rabat, Morocco" }, { "name": "Ouadi, Hamid", "affiliation": "Mohammed V University" }, { "name": "El Bakali, Saida", "affiliation": "ERERA, ENSAM, Mohammed V University, Rabat, Morocco" }, { "name": "Asnai, Fatimazahra", "affiliation": "ERERA, National High School of Arts and Crafts, Mohammed V University of Rabat, Rabat, Morocco" } ], "keywords": [ "Distributed optimization and control for smart cities", "Power systems stability", "Smart buildings and building automation" ], "abstract": "This study proposes an intelligent Active/Reactive Power Management and Sizing (AR-PMS) Strategy designed to optimize the operation of decentralized microgrids integrating photovoltaic (PV), wind turbine (WT), and battery energy storage system (BESS). A multi-objective particle swarm optimization (MOPSO) algorithm is employed to minimize investment cost, energy losses, and carbon emissions while improving voltage stability and energy efficiency. The optimization process generates Pareto-optimal configurations, from which three representative microgrid designs are identified through k-medoids clustering, enabling structured trade-off analysis and informed decision-making. The results, obtained from a year-long simulation of the IEEE- 33 bus system using real meteorological data from Rabat (Morocco), demonstrate significant improvements in network performance and stability under data uncertainties, including a 72% reduction in active and reactive power losses, a 56.4% decrease in CO2 emissions, and improved voltage profiles across all buses, with an average voltage deviation decreased by 63.29%.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB30.3", "code": "FrB30.3", "title": "A Multi-Objective Approach to Building Control Based on Vectorized Deep Transformer Q-Network (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB30", "sessionTitle": "JO-CEP: Smart Buildings and Building Automation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Verma, Richa", "affiliation": "Tallinn University of Technology" }, { "name": "Kaparin, Vadim", "affiliation": "Tallinn University of Technology" }, { "name": "Kumar, Dinesh", "affiliation": "German Aerospace Center" }, { "name": "Belikov, Juri", "affiliation": "Tallinn University of Technology" } ], "keywords": [ "Smart buildings and building automation", "Control and management of energy systems" ], "abstract": "Buildings consume nearly 40% of global energy, with more than half used for heating, ventilation, and air conditioning (HVAC) systems. Balancing energy efficiency and occupant comfort remains a major challenge, particularly under uncertain and partially observable conditions where objectives often conflict. This study introduces a transformer-based reinforcement learning approach that learns directly from vector-valued rewards. The method allows the controller to optimize comfort and energy performance at the same time without combining them into a single score. The approach was implemented and evaluated in the Building Optimization Performance Test (BOPTEST) Single Zone Commercial Hydronic environment, which acts as a digital twin of a real building. Results show that the proposed method produces smoother Pareto fronts and adapts more effectively to changing comfort and energy preferences compared with scalar reward baselines. These outcomes highlight the potential of transformer-based multi-objective reinforcement learning for achieving intelligent and energy-efficient building control.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB30.4", "code": "FrB30.4", "title": "A Robust Data-Driven MPC for Greenhouse Temperature Control (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB30", "sessionTitle": "JO-CEP: Smart Buildings and Building Automation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Mignoni, Nicola", "affiliation": "Politecnico Di Bari" }, { "name": "Zero, Enrico", "affiliation": "Università Degli Studi Di Genova" }, { "name": "Scarabaggio, Paolo", "affiliation": "Politecnico Di Bari" }, { "name": "Carli, Raffaele", "affiliation": "Politecnico Di Bari" }, { "name": "Sacile, Roberto", "affiliation": "Dibris - Unige - Italy" }, { "name": "Dotoli, Mariagrazia", "affiliation": "Politecnico Di Bari" } ], "keywords": [ "Thermal systems modelling", "Smart buildings and building automation" ], "abstract": "This paper addresses the problem of robust temperature control in greenhouses, where maintaining optimal thermal conditions is essential for crop productivity, while accounting for uncertainties in external factors, such as solar irradiance and ambient temperature. We propose a novel spatially distributed model for greenhouse temperature dynamics, formulated through a finite difference scheme, which incorporates volumetric heat sources from solar radiation and an inverter-based Heating, Ventilation, and Air Conditioning (HVAC) system. The HVAC contribution is represented using a normalized Gaussian kernel with directional weighting to capture air jet effects, ensuring convexity with respect to the control input, i.e., the fraction of cooling power. Solar irradiance is modeled as a boundary-adjacent volumetric source, accounting for the polyhedral geometry of a sloped-roof structure. Uncertainties are handled through a scenario-based robust model predictive control formulation, which preserves convexity and guarantees the optimality of the control action. Moreover, the proposed framework exploits the sparse structure of the model to ensure scalability. The effectiveness of the proposed approach is validated using data from a smart greenhouse located in Genova, Italy.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB30.5", "code": "FrB30.5", "title": "Optimization and Control of Energy Production and Structural Motion in a Floating Wind Farm Subject to Wake Effects (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB30", "sessionTitle": "JO-CEP: Smart Buildings and Building Automation", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 123", "authors": [ { "name": "Rafia, Hassan", "affiliation": "ERERA, National School of Arts and Crafts, Mohammed V University, Rabat, Morocco" }, { "name": "Ouadi, Hamid", "affiliation": "Mohammed V University" }, { "name": "Giri, Fouad", "affiliation": "University of Caen Normandie" }, { "name": "Chaoui, Fatima-Zahra", "affiliation": "ENSET, Université Mohammed V" }, { "name": "Boulal, Anis", "affiliation": "Smartilab EMSI, Rabat, Morocco" } ], "keywords": [ "Wind power", "Power systems stability", "Demand response" ], "abstract": "This article presents a real-time optimization strategy for a floating offshore wind farm composed of three NREL 5 MW turbines of the Hywind OC4 platform type. At the level of farm control, an optimizer based on a particle swarm optimization (PSO) algorithm combined with a recursive least squares (RLS) motion predictor that adapts the production model in order to capture the non-linearity and coupling with other motions. This optimizer generates motion-sensitive power and yaw references by anticipating the platform’s step dynamics and wake interactions. At the level of turbine control, an adaptive ANN–PI controller ensures fast and robust rotor speed tracking without the need for a model or training data . The proposed strategy simultaneously maximizes power production and decrease the platform’s pitching motion. The simulation results demonstrate significant performance improvements compared to a baseline demand-tracking strategy. In particular, pitch-rate fluctuations are reduced by approximately 26%, while downstream power production increases by about 20% with a variability reduction of around 27%. These results confirm that integrating structural feedback and wake-aware coordination enhances both motion stability and energy capture in floating wind farms.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB31.1", "code": "FrB31.1", "title": "Toward an LLM-Driven Framework for Task Planning and Reactive Execution in Human-Robot Collaborative Assembly", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB31", "sessionTitle": "Demonstration: Robotics and Autonomous Systems", "sessionType": "Demonstration Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Antonelli, Dario", "affiliation": "Politecnico Di Torino" }, { "name": "Yang, Xiaolang", "affiliation": "Tongji University" }, { "name": "Stylios, Chrysostomos", "affiliation": "University of Ioannina," }, { "name": "Tyrovolas, Marios", "affiliation": "University of Ioannina" }, { "name": "Mermigkis, Georgios", "affiliation": "Computer Engineering & Informatics Department, University of Patras" }, { "name": "Yang, Bo", "affiliation": "Chongqing University" }, { "name": "Liu, Xuemei", "affiliation": "Tongji University" }, { "name": "Georgoulas, George", "affiliation": "Technological Educational Institution of Epirus," } ], "keywords": [ "AI-powered robotics", "Robotic learning and adaptation", "Task and motion planning" ], "abstract": "This research work presents a comprehensive pipeline for autonomous manufacturing that bridges the gap between digital product data and physical robot execution. Building upon recent advancements in Large Language Models (LLMs) and Artificial Intelligence Planning, the proposed framework aims to automate the transition from Computer-Aided Design (CAD) and process plans to executable robot programs. By integrating semantic meta-modeling with reactive control structures, specifically Behavior Trees (BTs), we address the critical challenges of flexibility, interoperability, and error recovery in Robot Automation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB31.2", "code": "FrB31.2", "title": "A Prototyping Framework for Distributed Control of Multi-Robot Systems", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB31", "sessionTitle": "Demonstration: Robotics and Autonomous Systems", "sessionType": "Demonstration Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Memon, Junaid Ahmed", "affiliation": "University of Oxford" }, { "name": "Andre do Nascimento, Allan", "affiliation": "University of Oxford" }, { "name": "Margellos, Kostas", "affiliation": "University of Oxford" }, { "name": "Papachristodoulou, Antonis", "affiliation": "Univ of Oxford" } ], "keywords": [ "Control architecture for multi agent systems", "Control software architecture", "Model driven engineering of control systems" ], "abstract": "This paper presents a prototyping framework for distributed control of multi-robot systems, aimed at bridging theory and practical testing of distributed optimization algorithms. Using the Single Program, Multiple Data (SPMD) paradigm, the framework emulates distributed control on a single computer, with each core running the same algorithm using local states and neighbour-to-neighbour communication. We demonstrate the framework on a four-quadrotor position-swapping task using a non-cooperative game-theoretic distributed algorithm. Computational time and trajectory data are compared across the supported dynamics levels: a point-mass model, a high-fidelity quadrotor model, and an experimental hardware testbed using Crazyflie quadcopters. The results show that the framework provides a low-cost and accessible approach for validating distributed algorithms.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB31.3", "code": "FrB31.3", "title": "Webapp Platform for Learning and Research in Autonomous Vehicle Systems", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB31", "sessionTitle": "Demonstration: Robotics and Autonomous Systems", "sessionType": "Demonstration Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Pommier, Nicomedes", "affiliation": "Universidad Adolfo Ibáñez" }, { "name": "Escobar, Carlos", "affiliation": "Universidad Técnica Federico Santa María" }, { "name": "Fuentes Rojas, Cristian Alejandro", "affiliation": "UAI" }, { "name": "Vargas, Francisco J.", "affiliation": "Universidad Técnica Federico Santa María" }, { "name": "Peters, Andrés A.", "affiliation": "Universidad Adolfo Ibáñez" } ], "keywords": [ "Internet based control education", "Control education laboratories", "Adding games to control education to encourage participation" ], "abstract": "Small-scale autonomous vehicles are valuable educational tools, yet barriers like limited hardware access and steep learning curves hinder student engagement. This demonstration paper presents L.A.D, a browser-based educational platform that enables students to access simulations and physical Quanser QCar 2 via LAN, without ROS~2 installation. A centralized server runs Django backend, ROS~2 Docker containers, and React frontend. Students interact with a structured 12-module curriculum, node-based visual programming, and real-time robot control via WebSocket. Deployed at Universidad Adolfo Ibáñez and Universidad Federico Santa María with twelve QCar 2 units, this solution democratizes autonomous vehicle education.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB31.4", "code": "FrB31.4", "title": "RiFoLo: Rigidity Toolbox for Formations and Localization of Multi-Agent Systems", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB31", "sessionTitle": "Demonstration: Robotics and Autonomous Systems", "sessionType": "Demonstration Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Liu, Xuan", "affiliation": "Southern University of Science and Technology" }, { "name": "Liu, Yitong", "affiliation": "Southern University of Science and Technology" }, { "name": "Chen, Liangming", "affiliation": "Southern University of Science and Technology (SUSTech)" } ], "keywords": [ "Multi-agent systems", "Control of networks", "Distributed control and estimation" ], "abstract": "Rigidity theory plays a fundamental role in the analysis and synthesis of various networked multi-agent or multi-node systems, such as swarm robots, sensor networks, and protein structures. Although some significant conditions have been proposed for checking the rigidity of networks under typical constraints such as inter-agent distances, bearings, and angles, a comprehensive numerical toolbox capable of analyzing rigidity under different types of constraints is still lacking. Motivated by this, this paper introduces RiFoLo, an interactive toolbox for the rigidity analysis of multi-agent systems, with two particular applications, namely multi-agent formations and network localization. The toolbox provides an integrated platform for distance, bearing, and angle rigidity analysis. Through an intuitive graphical interface, users can define frameworks, check rigidity, and perform simulations of multi-agent formations and cooperative localization. With this toolbox, researchers can systematically analyze rigidity properties, and the resulting insights can guide the design of network topologies for multi-agent systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB31.5", "code": "FrB31.5", "title": "RoboticsLab: A Scalable Educational Platform for Learning ROS2 and Control in Robotics", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB31", "sessionTitle": "Demonstration: Robotics and Autonomous Systems", "sessionType": "Demonstration Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Mutti, Stefano", "affiliation": "SUPSI" }, { "name": "Gitardi, Diego", "affiliation": "SUPSI" }, { "name": "Baraldo, Stefano", "affiliation": "SUPSI" }, { "name": "Valente, Anna", "affiliation": "Institute of Systems and Technologies for Sustainable Production, University of Applied Sciences and Arts of Southern Switzerlan" } ], "keywords": [ "Repositories for control education", "Control education laboratories", "Continuing control education" ], "abstract": "This paper presents RoboticsLab, an educational sandbox platform designed to enhance learning in robotics and control through the integration of the Robot Operating System 2 (ROS2). The tool addresses the growing need for effective, practice-oriented learning by providing a virtual environment where learners can experiment with robotic systems and control algorithms in a realistic yet accessible setting. By combining theoretical concepts with interactive simulations, RoboticsLab bridges the gap between abstract knowledge and practical implementation, fostering deeper understanding and skill development. RoboticsLab offers a scalable framework for creating diverse environments for testing algorithms, reducing barriers to entry for students and practitioners while maintaining rigor in control and robotics education. Its modular design allows for diverse educational scopes, from introductory courses to advanced research projects. As industry and academia increasingly demand proficiency in ROS2 and autonomous systems, tools like RoboticsLab play a pivotal role in preparing the next generation of engineers and researchers. This paper details the platform's architecture and potential applications, demonstrating its value as a resource for interactive, competency-based learning in robotics and control. Github repository : url{https://github.com/automation-robotics-machines/Roboti csLab}", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB31.6", "code": "FrB31.6", "title": "Demonstration of Space Robot Teleoperation Over a Lossy and Delayed Network Using ATMOS", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB31", "sessionTitle": "Demonstration: Robotics and Autonomous Systems", "sessionType": "Demonstration Session", "room": "Exhibition Center 2 - Room 124", "authors": [ { "name": "Jang, Inkyu", "affiliation": "Seoul National University" }, { "name": "Marchesini, Gregorio", "affiliation": "KTH Royal Institute of Technology" }, { "name": "De Carli, Nicola", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Kim, Byeongjun", "affiliation": "Seoul National University" }, { "name": "Hwang, Sunwoo", "affiliation": "Seoul National University" }, { "name": "Kim, Dabin", "affiliation": "Seoul National University" }, { "name": "Krantz, Elias", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Kong, Youngkyoung", "affiliation": "Seoul National University" }, { "name": "Jiang, Frank J.", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Wong, Annika", "affiliation": "FleetMQ" }, { "name": "Roque, Pedro", "affiliation": "KTH Royal Institute of Technology, Stockholm, Sweden" }, { "name": "Sanjaya, Prasetyo Wibowo Laksono", "affiliation": "Institut Teknologi Bandung" }, { "name": "Bastianello, Nicola", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Dhullipalla, Mani Hemanth", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Johansson, Karl H.", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Shim, Hyungbo", "affiliation": "Seoul National University" }, { "name": "Dimarogonas, Dimos V.", "affiliation": "KTH Royal Institute of Technology" }, { "name": "Kim, H. Jin", "affiliation": "Seoul National Univ" } ], "keywords": [ "Teleoperation", "Aerial, field, and marine robotics" ], "abstract": "We present a demonstration showcasing the Autonomy Testbed for Multi-purpose Orbiting Systems (ATMOS), a planar spacecraft-analog robot designed for hardware-in-the-loop evaluation of guidance and control strategies in microgravity-like conditions. Using ATMOS as the physical test platform, we investigate the design, analysis, and performance evaluation of control architectures for remotely operated spacecraft under round-trip communication delays. In this work, we develop and experimentally validate a control strategy that combines state prediction and trajectory tracking control to perform a docking maneuver, accounting for time-varying random communication latency between ground operators and the ATMOS system. The demonstration includes a long-distance remote control experiment between Seoul and Stockholm, introducing realistic intercontinental delays and variability. The results highlight the capability of ATMOS to support rapid, reliable, and cost-effective testing of spacecraft teleoperation concepts, establishing a first step toward robust validation of on-orbit operations in microgravity-like environments.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB32.1", "code": "FrB32.1", "title": "Hybrid Position–Force Adaptive Control for Robot-Assisted Ultrasound Probe Manipulation (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB32", "sessionTitle": "Medical and Rehabilitation Robotics", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Sun, Sama", "affiliation": "National Taiwan University" }, { "name": "Lee, Yu-Hsiu", "affiliation": "National Taiwan University" } ], "keywords": [ "Human-robot interaction", "Robotic learning and adaptation", "Robotic grasping and manipulation" ], "abstract": "Ultrasound-guided radiofrequency ablation (RFA) is a critical therapy for hepatocellular carcinoma. However, its effects are often compromised by respiratory-induced abdominal motion which disrupts probe contact and image stability. To overcome the limitations of manual operation and conventional control, we established an experimental platform comprising a 6-DoF robotic manipulator interacting with a dynamic abdominal phantom. Within this setup, we implemented a Hybrid Position/Force Control architecture designed to decouple the regulation of contact force from the tracking of respiratory movement. This framework is further augmented by Adaptive Inverse Control (AIC) strategies, which utilize real-time estimation to proactively predict and cancel physiological disturbances. This paper is to demonstrate a robust control strategy capable of maintaining the optimal force for acoustic coupling and precise target tracking, thereby advancing the feasibility of autonomous robotic ultrasound in dynamic clinical settings.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB32.2", "code": "FrB32.2", "title": "Intelligent Robotic Injection System with Interferometric Force Sensing (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB32", "sessionTitle": "Medical and Rehabilitation Robotics", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Song, Cheol", "affiliation": "DGIST" }, { "name": "Cho, Gichan", "affiliation": "DGIST" }, { "name": "Im, Jintaek", "affiliation": "DGIST" }, { "name": "Na, Jongyeol", "affiliation": "DGIST" }, { "name": "Lee, Myung Ho", "affiliation": "DGIST" }, { "name": "Hyun-Jung, Kwon", "affiliation": "Asan Medical Center" } ], "keywords": [ "Mechatronic system modeling, design, optimization", "Biomedical and biomimetic mechatronic systems", "Medical and rehabilitation robotics" ], "abstract": "Accurate needle placement is crucial for the safety and effectiveness of epidural procedures. Traditional techniques such as the loss-of-resistance (LOR) method can be inconsistent, particularly in patients with narrowed epidural spaces, while fluoroscopic imaging exposes healthcare professionals to radiation. To address these challenges, this study introduces a novel epidural needle system incorporating an optical interferometry-based force sensor to enhance precision and safety. The proposed system integrates a fiber-optic force sensor into a commercially available epidural needle and employs a robotic injection mechanism powered by a piezoelectric actuator for controlled insertion. A graphical user interface (GUI) provides real-time feedback by detecting puncture points.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB32.3", "code": "FrB32.3", "title": "Preliminary Study on Compliant Serpentine Spring-Driven Snap Needle Insertion Unit for Interventional Pain Management (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB32", "sessionTitle": "Medical and Rehabilitation Robotics", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Hyun, Jaeho", "affiliation": "Asan Medical Center" }, { "name": "Yang, Bomi", "affiliation": "Asan Medical Center" }, { "name": "Cho, Yeongjun", "affiliation": "Asan Medical Center" }, { "name": "Choi, Jaesoon", "affiliation": "Asan Medical Center and University of Ulsan College of Medicine" }, { "name": "Moon, Youngjin", "affiliation": "Asan Medical Center, University of Ulsan" } ], "keywords": [ "Medical and rehabilitation robotics", "Mechatronics for robotic systems", "Biomedical and biomimetic mechatronic systems" ], "abstract": "In interventional pain management procedures, a needle must be accurately inserted through the skin and soft tissue to deliver medication to the target lesion. However, due to the viscoelastic properties of the skin, an initial penetration resistance occurs. Clinically, a rapid insertion technique is commonly employed to overcome this resistance. In this study, we propose a 3D-printed serpentine spring-based snap needle insertion unit that mimics this high-speed insertion mechanism. The proposed unit is based on a compliant mechanism that stores elastic energy through compression and instantaneously releases it via a mechanical trigger, thereby enabling high-speed needle insertion. To achieve this, the design equations of the serpentine spring were formulated, and the insertion velocity was evaluated according to variations in beam thickness and length. Comparative experiments were conducted using a silicone skin phantom to assess the proposed snap insertion method against a conventional constant-speed insertion approach. Experimental results demonstrated that the proposed structure exhibits effective high-speed insertion characteristics, confirming that compliant mechanisms can be effectively utilized in medical needle insertion systems.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB32.4", "code": "FrB32.4", "title": "A Parallelogram-Based Mechanism for MRI-Guided Abdominal Interventions (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB32", "sessionTitle": "Medical and Rehabilitation Robotics", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Wu, Chen-Yen", "affiliation": "National Taiwan University" }, { "name": "Lee, Yu-Hsiu", "affiliation": "National Taiwan University" } ], "keywords": [ "Medical and rehabilitation robotics", "Mechatronics for robotic systems", "Mechatronic system integration" ], "abstract": "This paper introduces the design, analysis, and prototype evaluation of a MRI-conditional robotic system for abdominal intervention with a mechanically remote center of motion (RCM). The system uses a decoupled dual-parallelogram architecture with flexure-based compliant joints to achieve compactness, reduced interference, and wear-free rotation. An inchworm insertion module with flexure grippers enables long-distance needle insertion within the limited MRI bore. The system features three active degrees of freedom (DOF), including two rotations for needle orientation and one translation for insertion, all driven by nonmagnetic pneumatic actuators. Finite element analysis shows that the compliant virtual pivot allows a pm24^circ rotational range while keeping the maximum stress below 85% of the material yield strength. RCM accuracy tests yielded RMS errors of 0.65 mm for pitch and 1.04 mm for yaw, indicating that the compliant mechanism can effectively replace conventional revolute pivots. The remaining errors are mainly attributed to manufacturing and assembly tolerances. The grippers provide a maximum puncture force of 13 N at 100 psi, which is sufficient to meet typical clinical needle insertion requirements. Closed-loop control experiments showed steady-state errors below 0.08 mm for both rotational axes, with maximum tracking errors under 1.3 mm. These results demonstrate that the proposed decoupled parallelogram--flexure architecture provides accurate RCM constraint, stable actuation, and effective motion control for compact and fully pneumatic robotic assistance in MRI-guided abdominal interventions.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB32.5", "code": "FrB32.5", "title": "Cable Tension-Based Force Estimation for CDPR-Based Needle Insertion (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB32", "sessionTitle": "Medical and Rehabilitation Robotics", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Son, Seongho", "affiliation": "Chonnam National University" }, { "name": "Jung, MyungJin", "affiliation": "Chonnam National University" }, { "name": "Kim, Mincheol", "affiliation": "Jeju National University" }, { "name": "Hong, Ayoung", "affiliation": "Chonnam National University" } ], "keywords": [ "Robot perception and sensing", "Medical and rehabilitation robotics", "Task and motion planning" ], "abstract": "A medical needle insertion procedure has traditionally been performed manually, relying on clinicians’ haptic perception to assess insertion force. However, manual operation may introduce variability due to hand tremor and the lack of quantitative force measurement. Although a robotic needle insertion system has been developed to overcome these limitations, the needle-tissue interaction force must be accurately estimated to provide appropriate force feedback to clinicians. In this paper, we present an external force estimation method for a cable-driven parallel robot (CDPR) based needle insertion system. Cable tensions are measured in real time using load cells installed on each cable, and the external force acting at the needle tip is estimated from the measured tensions through the kinematic relationship of the CDPR under a quasi-static assumption. Experimental validation demonstrates that the proposed method reliably captures clinically relevant force variations and enables effective monitoring of needle-tissue interaction during robotic needle insertion.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB32.6", "code": "FrB32.6", "title": "Mechanical Model of a Two-Section Concentric Tendon-Driven Continuum Robot (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:50-15:10", "sessionCode": "FrB32", "sessionTitle": "Medical and Rehabilitation Robotics", "sessionType": "Invited Session", "room": "Exhibition Center 2 - Room 321", "authors": [ { "name": "Kuncara, Ivan Adi", "affiliation": "Chonnam National University" }, { "name": "Hong, Ayoung", "affiliation": "Chonnam National University" } ], "keywords": [ "Soft robotics", "Mechatronics for robotic systems", "Robotic grasping and manipulation" ], "abstract": "The development of two-section continuum robots has increased due to their larger workspace and enhanced dexterity compared to single-section designs. Unlike conventional two-section continuum robots, this work adopts a concentrically tendon-driven configuration consisting of an inner and an outer section, with the inner section placed inside the outer section. While this architecture provides additional flexibility, it also introduces significant modeling challenges arising from inter-section tendon coupling. To address this issue, we propose a mechanical model that explicitly accounts for coupling effects between the two sections and external loads. The bending angles are computed from the internal forces and moments induced by tendon tension based on Euler–Bernoulli beam theory. The proposed model is evaluated through numerical simulations incorporating external loading. The results demonstrate that the proposed model captures the expected behavior of the continuum robot, as validated through comparison with the high-fidelity Cosserat rod model.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB33.1", "code": "FrB33.1", "title": "Data-Driven Modeling and Estimation of Beam Position Drift for Electron Beam Systems (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB33", "sessionTitle": "JO-MECH: Mechatronic System Estimation and Control II", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Schrom, Katharina", "affiliation": "TU Wien" }, { "name": "Deutschmann-Olek, Andreas", "affiliation": "TU Wien" }, { "name": "Falkensteiner, Roland", "affiliation": "Graz University of Technology" }, { "name": "Kugi, Andreas", "affiliation": "TU Wien" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Mechatronics for advanced manufacturing and energy systems" ], "abstract": "Electron beam systems (EBS) have reached a level of precision where residual beam position drifts, caused by thermal expansion, mechanical bending, and electronic effects, have become a dominant source of inaccuracy. These drifts arise from different interacting influence factors and are difficult to predict with first-principles models alone. Hence, this article presents a data-driven approach to model and estimate beam drift in EBS by including indirect ambient measurements. Principal component analysis is used to extract static impact variables from temperature, pressure, and other sensor data. These variables are embedded into a linear statespace model that accounts for dynamic effects, from which an adaptive Kalman filter is derived for real-time drift estimation between calibration measurements. The developed estimator avoids covariance windup, enforces parameter sparsity, and allows physically motivated constraints. Finally, the proposed method is validated by measurement data from a semiconductor electron beam tool, demonstrating accurate drift estimation in a wide range of scenarios.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB33.2", "code": "FrB33.2", "title": "Leveraging the Drive Motor for Mitigating Gear Mesh Vibration Using Adaptive Learning Rate FxLMS (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB33", "sessionTitle": "JO-MECH: Mechatronic System Estimation and Control II", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Dave, Sidharth", "affiliation": "Technical University of Darmstadt" }, { "name": "Nordmann, Rainer", "affiliation": "Technical University of Darmstadt" }, { "name": "Rinderknecht, Stephan", "affiliation": "Technische Universitaet Darmstadt" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Mechatronics for mobility systems", "Mechatronic system integration" ], "abstract": "This study presents and experimentally demonstrates two vibration control structures for mitigation of gear mesh vibration using the drive motor, which also serves as the prime mover. Using the drive motor for vibration mitigation eliminates the need for a dedicated actuator. However, the dual-role of the drive motor in speed-controlled systems can result in a conflict - where the speed controller counteracts the vibration controller. The two vibration control structures, designed for speed and torque control of systems, are based on an adaptive learning rate Filtered-x Least Mean Square algorithm and can be implemented as add-ons, without the need to reparametrise the motor controller.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB33.3", "code": "FrB33.3", "title": "Position Control Approaches for a Pneumatically Actuated Assistance System (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB33", "sessionTitle": "JO-MECH: Mechatronic System Estimation and Control II", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Ibrahim, Kaneewar", "affiliation": "University of Rostock" }, { "name": "Prabel, Robert", "affiliation": "University of Rostock" }, { "name": "Aschemann, Harald", "affiliation": "University of Rostock" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Mechatronics for robotic systems", "Application of mechatronic principles" ], "abstract": "Three nonlinear control approaches – backstepping, flatness-based control, and sliding mode control – are applied in this paper to stabilize the end-effector position of an assistance system with serial kinematics that is actuated by pneumatic artificial muscles (PAMs). The equations of motion are derived employing Lagrange’s equations of second kind, while two polynomial functions are identified to approximate the nonlinear muscle forces and volumes, respectively. All control designs are evaluated and compared in simulations with varying payloads and result in a promising tracking performance.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB33.4", "code": "FrB33.4", "title": "Modeling Shape Memory Alloy Hysteresis Using Hybrid Prandtl-Ishlinski Model and Long Short-Term Memory Networks (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB33", "sessionTitle": "JO-MECH: Mechatronic System Estimation and Control II", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Harb, Hussein", "affiliation": "Uttop" }, { "name": "Mauze, Benjamin", "affiliation": "ENIT, Unversity of Toulouse" }, { "name": "Rakotondrabe, Micky", "affiliation": "University of Toulouse Alliance" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Smart structures and vibration control", "Wearable robotics" ], "abstract": "This work introduces a new hybrid Prandtl–Ishlinskii (PI) model. It uses a classical PI (CPI) augmented with Long Short-Term Memory (LSTM) networks to represent the static non-linearity in Hammerstein approximation. This model is used to simulate and control hysteresis in shape memory alloys (SMAs). The hybrid formulation allows to capture rate- and amplitude-dependent hysteresis by combining the interpretability of the CPI model with the adaptability of recurrent neural networks. Linear dynamic and non-linearity static characteristics are identified through a rate- and amplitude-dependent system identification procedure and used to train the hybrid model. Experimental validation confirms that the proposed model achieves higher accuracy than CPI model in predicting complex hysteresis behaviors. An inverse-multiplicative controller built on the hybrid model further enhances displacement tracking performance. Results highlight the efficacy of combining phenomenological-based and data-driven approaches for accurate modeling and control of SMA for exoskeleton actuation.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB33.5", "code": "FrB33.5", "title": "Data-Driven NMPC of Grading Operations for Excavators: Approaches and Experimental Results (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB33", "sessionTitle": "JO-MECH: Mechatronic System Estimation and Control II", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 322", "authors": [ { "name": "Gottardini, Andrea", "affiliation": "ETEL S.A" }, { "name": "Cecchin, Leonardo", "affiliation": "Politecnico Di Milano" }, { "name": "Demir, Ozan", "affiliation": "Robert Bosch GmbH" }, { "name": "Fagiano, Lorenzo", "affiliation": "Politecnico Di Milano" } ], "keywords": [ "Mechatronic system estimation, identification, control", "Task and motion planning", "High-performance motion control systems" ], "abstract": "Hydraulic excavators are crucial for tasks like levelling and creating sloped surfaces, requiring high precision. Automation can boost productivity by improving accuracy and reducing reliance on skilled labour. However, the non-linearities and variability in hydraulic systems make controller design challenging. Two data-driven Model Predictive Control (MPC) approaches are presented, ideal for handling non-linear dynamics and system constraints: the first with a trajectory tracking formulation and the second with a path following one. The system model is based on Linear Local Models averaged by non-linear membership functions, trained with the LOcal LInear MOdel Tree (LOLIMOT) algorithm, an approach that is simple to linearize for efficient online implementation of the MPC laws. The data-driven nature of the model ensures flexibility and the ability to handle diverse real-world scenarios. The control system was tested on a full-scale JCB Hydradig 110W, the results demonstrate that the MPC approaches significantly outperform previous data-based controllers. Moreover, a comparison between the two MPC formulations indicates that path following outperforms trajectory tracking in this specific application.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB34.1", "code": "FrB34.1", "title": "Belief-Desire-Intention AI Agents for Dynamic and Reconfigurable Robot Modules", "day": "Friday", "date": "August 28, 2026", "time": "13:10-13:30", "sessionCode": "FrB34", "sessionTitle": "Social Robotics and Ethics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Witucki, Linus", "affiliation": "Karlsruhe Institute of Technology (KIT)" }, { "name": "Rösler, Jan Eike", "affiliation": "Karlsruher Institut Für Technologie (KIT)" }, { "name": "Barth, Mike", "affiliation": "Karlsruhe Institute of Technology (KIT)" } ], "keywords": [ "AI-powered robotics", "Human centered automation", "Human-robot interaction" ], "abstract": "Biopharmaceutical companies face distinct challenges in pre-development and early-stage research, resulting in low levels of laboratory automation. Despite the use of robots, complex automated systems require frequent reconfiguration and expert intervention incurring high maintenance costs and diverting resources from scientific discovery. To address these issues, this contribution introduces a hybrid architecture based on a modular robot system that integrates the Belief-Desire-Intention (BDI) model and information models with Large Language Models (LLMs), utilizing the Model Context Protocol (MCP) for direct robot communication. The implementation of a prototype BDI-LLM agent demonstrates a flexible, extensible, and easily deployable system capable of commissioning and reconfiguration with minimal programming skills.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB34.2", "code": "FrB34.2", "title": "Non-Normalized Shared-Constraint Dynamic Games for Human–Robot Collaboration with Asymmetric Responsibility", "day": "Friday", "date": "August 28, 2026", "time": "13:30-13:50", "sessionCode": "FrB34", "sessionTitle": "Social Robotics and Ethics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Pustilnik, Mark", "affiliation": "UC Berkeley" }, { "name": "Borrelli, Francesco", "affiliation": "University of California" } ], "keywords": [ "Human-robot interaction", "Human machine cooperation & integration", "Autonomous navigation" ], "abstract": "This paper proposes a dynamic game formulation for cooperative human–robot navigation in shared workspaces with obstacles, where the human and robot jointly satisfy shared safety constraints while pursuing a common task. A key contribution is the introduction of a emph{non-normalized equilibrium} structure for the shared constraints. This structure allows the two agents to contribute different levels of ``effort'' towards enforcing safety requirements such as collision avoidance and inter-players spacing. We embed this non-normalized equilibrium into a receding-horizon optimal control scheme.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB34.3", "code": "FrB34.3", "title": "Envelope Protection and Surgery-By-Wire: Translating Aviation Safety Concepts to Robotic Surgery (I)", "day": "Friday", "date": "August 28, 2026", "time": "13:50-14:10", "sessionCode": "FrB34", "sessionTitle": "Social Robotics and Ethics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Pal, Atanu", "affiliation": "Cambridge University Hospitals" }, { "name": "Tewary, Shreeya", "affiliation": "Cambridge" } ], "keywords": [ "Human-robot interaction", "Shared control", "Teleoperation" ], "abstract": "Robotic surgery replaces direct mechanical interaction between surgeon and patient with a digitally mediated interface, establishing a surgery-by-wire paradigm analogous to fly-by-wire systems in aviation. While current platforms primarily use this architecture for signal conditioning (e.g., motion scaling and tremor suppression), its potential for systematic safety enforcement remains underexplored. Inspired by flight envelope protection, this paper introduces the concept of a surgical envelope as a structured representation of safety constraints in robotic surgery. The surgical envelope defines a time-varying admissible set of system states, capturing haptic interaction limits, spatial workspace constraints, and scene-aware no-go regions. A constraint-based interpretation is proposed in which system actions are modified, where necessary, to remain within admissible safety limits while preserving surgeon intent, enabling integration of safety constraints within a human-in-the-loop control framework. The framework provides a unifying interpretation of existing techniques such as virtual fixtures, shared control, and constraint-based safety mechanisms, while highlighting trade-offs between safety, transparency, and operator autonomy. An example illustrates envelope-aware control in a simplified surgical scenario. The surgical envelope is presented as a foundational abstraction for interaction-aware safety in robotic surgery and a basis for future development of envelope-aware and context-adaptive control strategies.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB34.4", "code": "FrB34.4", "title": "SINRL: Socially Integrated Navigation with Reinforcement Learning Using Spiking Neural Networks", "day": "Friday", "date": "August 28, 2026", "time": "14:10-14:30", "sessionCode": "FrB34", "sessionTitle": "Social Robotics and Ethics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Tretter, Florian", "affiliation": "FZI Research Center for Information Technology" }, { "name": "Floegel, Daniel", "affiliation": "FZI Research Center for Information Technology" }, { "name": "Vasilache, Alexandru", "affiliation": "FZI Research Center for Information Technology" }, { "name": "Max, Grobbel", "affiliation": "FZI Forschungszentrum Informatik" }, { "name": "Becker, Jürgen", "affiliation": "KIT Karlsruhe Institute of Technology" }, { "name": "Hohmann, Soeren", "affiliation": "KIT" } ], "keywords": [ "Human-robot interaction", "Task and motion planning", "Robotic learning and adaptation" ], "abstract": "Integrating autonomous mobile robots into human environments requires human-like decision-making, energy-efficient and event-based computation. Despite progress, neuromorphic methods are rarely applied to Deep Reinforcement Learning (DRL) navigation approaches due to unstable training. We address this gap with a hybrid socially integrated DRL actor-critic approach that combines Spiking Neural Networks (SNNs) in the actor with Artificial Neural Networks (ANNs) in the critic and a neuromorphic feature extractor to capture temporal crowd dynamics and human-robot interactions. Our approach enhances social navigation performance and reduces estimated energy consumption by approximately 1.69 orders of magnitude.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" }, { "id": "Fr-FrB34.5", "code": "FrB34.5", "title": "SensHRPS: Sensing Comfortable Human-Robot Proxemics and Personal Space with Eye-Tracking (I)", "day": "Friday", "date": "August 28, 2026", "time": "14:30-14:50", "sessionCode": "FrB34", "sessionTitle": "Social Robotics and Ethics", "sessionType": "Regular Session", "room": "Exhibition Center 2 - Room 323", "authors": [ { "name": "Kushina, Nadezhda", "affiliation": "University of Kaiserslautern-Landau" }, { "name": "Watanabe, Ko", "affiliation": "German Research Center of Artificial Intelligence" }, { "name": "Kannan, Aarthi", "affiliation": "University of Kaiserslautern-Landau" }, { "name": "Ashok, Ashita", "affiliation": "University of Kaiserslautern-Landau" }, { "name": "Dengel, Andreas", "affiliation": "German Research Center for Artificial Intelligence" }, { "name": "Berns, Karsten", "affiliation": "Robotics Research Lab, University of Kaiserslautern-Landau" } ], "keywords": [ "Wearable computing systems", "Human-robot interaction", "Robotic learning and adaptation" ], "abstract": "Social robots must adjust to human proxemic norms to ensure user comfort and engagement. While prior research demonstrates that eye-tracking features reliably estimate comfort in human-human interactions, their applicability to interactions with humanoid robots remains underexplored. In this study, we investigate user comfort with the robot \"Ameca\" across four experimentally controlled distances (0.5 m to 2.0 m) using mobile eye-tracking and subjective reporting (N=19). We evaluate multiple machine and deep learning models to estimate comfort based on gaze features. Contrary to previous human-human studies where Transformer models excelled, a Decision Tree classifier achieved the highest performance (F1-score = 0.73), with the minimum minor axis of a pupil identified as the most important predictor. These findings suggest that physiological comfort thresholds in human-robot interaction differ from human-human dynamics and can be modelled using interpretable logic.", "url": "https://ifac.papercept.net/conferences/conferences/IFAC26/program/IFAC26_ContentListWeb_5.html" } ], "stats": { "paperCount": 3010, "sessionCount": 397, "keywordCount": 512, "dayCounts": { "Monday": 852, "Tuesday": 795, "Wednesday": 785, "Thursday": 246, "Friday": 332 }, "sessionTypeCounts": { "Plenary Session": 5, "Interactive Session": 919, "Regular Session": 1176, "Open Invited Track Session": 597, "Invited Session": 269, "Tutorial Session": 28, "Semi-Plenary Session": 5, "Demonstration Session": 11 }, "topKeywords": [ [ "Multi-agent systems", 143 ], [ "Mechatronic system estimation, identification, control", 89 ], [ "Data-driven control theory", 84 ], [ "Adaptive control design", 83 ], [ "Model predictive control", 81 ], [ "Advanced process control", 75 ], [ "Stability of nonlinear systems", 72 ], [ "Control and management of energy systems", 71 ], [ "Distributed control and estimation", 70 ], [ "Linear systems", 70 ], [ "Lyapunov methods", 67 ], [ "Process modeling, identification, and estimation techniques", 65 ], [ "Task and motion planning", 65 ], [ "Learning methods for optimal control", 62 ], [ "Learning methods for control", 61 ], [ "Linear system identification", 61 ], [ "Application of nonlinear analysis and design", 61 ], [ "Mechatronic system modeling, design, optimization", 61 ], [ "Optimal control theory", 61 ], [ "Power systems stability", 59 ], [ "Applications of optimal control", 58 ], [ "Machine learning and artificial intelligence in chemical process control", 57 ], [ "Control barrier functions and state space constraints", 55 ], [ "Observer design", 53 ], [ "Autonomous vehicles", 52 ], [ "Nonlinear system identification", 49 ], [ "High-performance motion control systems", 49 ], [ "Estimation and filtering", 49 ], [ "Consensus", 47 ], [ "Biomedical system modeling, identification, and simulation", 47 ] ] } }