| Prof. Li ChaiZhejiang University, China Professor Chai Li is currently a Qiushi Professor at Zhejiang University. He has been a Distinguished Young Scholar of Chinese National Science Foundation, and an expert honored with the State Council Special Allowance. He has won the Second Prize of Hubei Natural Science Award, and the honor of Hubei Province Outstanding Researcher. Professor Chai has been selected for prestigious talent programs such as Hubei High-End Talent Cultivation Program, Hubei New Century High-Level Talent Program, and MOE New Century Excellent Talents Support Program. His research interests include multi-agent systems, distributed optimization, graph signal processing, and networked control systems. He has published over 100 fully refereed papers in prestigious journals and leading conferences. He has served as the associate editor of Control and Decision, and Control Engineering. Title：Accelerated Average Consensus of Multi-agent Systems with Local Node Memory: Optimal Convergence Rate and Explicit Formulae Abstract：Previous researches have shown that adding local memory can accelerate the consensus of multi-agent systems. It is natural to ask questions like what is the fastest rate achievable by the M-tap memory acceleration, and what are the corresponding control parameters. This talk introduces a set of effective and previously unused techniques to analyze the convergence rate of accelerated consensus with M-tap memory of local nodes and to design the control protocols. These effective techniques, including the Kharitonov stability theorem, the Routh stability criterion and the robust stability margin, have led to the following new results: 1) the direct link between the convergence rate and the control parameters; 2) explicit formulas of the optimal convergence rate and the corresponding optimal control parameters for M ≤ 2 on a given graph; 3) the optimal worst-case convergence rate and the corresponding optimal control parameters for the memory M ≥ 1 on a set of uncertain graphs. We show that the acceleration with the memory M = 1 provides the optimal convergence rate in the sense of worst-case performance. Several numerical examples are given to demonstrate the validity and performance of the theoretical results. |
| Prof.Ai-Guo WuHarbin Institute of Technology (Shenzhen), China Professor Ai-Guo Wu is a recipient of the Class A Program of the Youth Science Fund and the Excellent Young Scholars Fund of the National Natural Science Foundation of China. He was supported by the Program for New Century Excellent Talents Program in Universities in 2011. His research interests include parameter introduction methods in control system design, fully actuated system control, time-delay system control, and spacecraft control. He received supports of many projects including the National Key Research and Development Program, the General Program of the National Natural Science Foundation of China, and Preeminent Youth Team Project of Guangdong Basic and Applied Basic Research Foundation. He has received many awards, including the Second Class Award of the National Natural Science Award of China (5th achiever), the Second Class Award of the Guangdong Provincial Natural Science Award (1st achiever), the Young Scientist Award from the Chinese Association of Automation, and the National Excellent Doctoral Dissertation Award from the Academic Degrees Committee of the State Council and the Ministry of Education of P. R. China. He serves as an editorial board member for journals such as IEEE Transactions on Cybernetics. Title：Multiple fundamental matrices with their applications for analysis and design of control systems Abstract：In analysis and design of control systems, the concept of fundamental matrices plays important roles. In the field of time-delays, the fundamental matrices have been widely utilized to stability analysis and design of predictor feedback control laws. A common feature of the existing results is that a single fundamental matrix is related to the considered system. Such a single-fundamental-matrix approach may be invalid for high-order systems and some systems with time-variant state-delays. To overcome this difficulty, the concept of multiple fundamental matrices has been presented. In this talk, a recent development of this new approach is reported. |
| Prof. Wangli HeEast China University of Science and Technology, China Wangli He is a Professor at East China University of Science and Technology. Her current research interests include networked multi-agent systems, distributed control, optimization and learning, electricity-hydrogen coupled energy systems and autonomous intelligent unmanned systems.She has published over 150 papers in prestigious academic journals and conferences, including IEEE Transactions, Automatica, and IEEE/CAA Journal of Automatica Sinica. She has led more than 10 major projects, including those under the Science and Technology Innovation 2030 - 'New Generation of Artificial Intelligence' program, the National Natural Science Foundation of China, and the Shanghai Carbon Neutrality Basic Research Special Zone Project. She has been recognized as an Elsevier China Highly Cited Scholar and listed in the Global Top 2% Scientists 'Lifetime Scientific Impact' ranking. Her awards include the First Prize of the Shanghai Natural Science Award, the Chinese Association of Automation Young Scientist Award, the First Prize of the National Teaching Achievement Award (Postgraduate Education), and the Science and Technology Progress Award (Innovative Team) of the Chinese Society for Instrument and Control. She has served as an Associate Editor for the international authoritative journals IEEE Transactions on Neural Networks (2020-2023) and IEEE Transactions on Industrial Informatics (2023-present). Title：Group Intelligence Empowers Optimal Regulation of Wind-Solar-Hydrogen-Storage Low-Carbon Energy Systems Abstract：Hydrogen holds a strategic supporting role in building a clean, low-carbon, safe and efficient energy system. The integration of green electricity and green hydrogen with green chemical production is driving the deep decarbonization transition of the industry through technological innovation and system optimization. Wind-solar-hydrogen-storage low-carbon energy systems have become a critical implementation pillar for China’s energy security and the “Dual Carbon” strategy. The large-scale and rapid deployment of variable renewable energy, heterogeneous energy storage, and complex market factors pose fundamental academic challenges to the research on optimal operation and control of power-hydrogen integrated energy systems. This report presents preliminary explorations on multi-scale mechanism modeling of electrolyzers (the key equipment for renewable hydrogen production), system capacity configuration and scheduling under wind-solar uncertainty, and peer-to-peer energy trading. It aims to establish the chain of “underlying modeling—cross-domain trading—intra-domain scheduling”, advance the deep integration of renewable energy into the energy system restructuring via power-hydrogen fusion innovation, and provide a feasible pathway for global green, low-carbon and sustainable development. |
 | Prof. Jiahu QinUniversity of Science and Technology of China, China Jiahu Qin is a Chair Professor at the University of Science and Technology of China and the Deputy Dean of the School of Deep Space Exploration. His primary research interests focus on the coordination, optimization, and decision-making of autonomous intelligent systems. In related fields, he has authored two English monographs published by Springer and over 100 papers in prestigious journals such asAutomatica andIEEE Transactions, while holding more than 50 authorized Chinese invention patents. Professor Qin has presided over more than 10 national and provincial-level projects, including the National Science Fund for Distinguished Young Scholars, the Excellent Young Scientists Fund, National Key Projects, the first batch of the Ministry of Education's Disciplinary Breakthrough Pilot Projects, and the Science and Technology Innovation 2030—"New Generation Artificial Intelligence" Major Project. His outstanding academic contributions have earned him numerous accolades, including the First Prize of the Natural Science Award from the Chinese Association of Automation (CAA) (Ranked 1st), the First Prize of the Technological Invention Award from the CAA (Ranked 1st), the First Prize of the Heilongjiang Provincial Natural Science Award (Ranked 3rd), the CAA Young Scientist Award, the Guan Zhao-Zhi Award from the Chinese Control Conference, and the Best Conference Paper Award from the IEEE Industrial Electronics Society. Currently, he serves as the Deputy Secretary-General of the CAA, Vice President of the Anhui Robotics Society, Vice Chair of the CAA Youth Working Committee, and Vice Chair of the Technical Committee on Modeling and Simulation of Intelligent IoT Systems under the China Simulation Federation. He also serves as an Editorial Board Member for several prominent journals, includingAutomatica,IEEE/ASME TMECH,IEEE TIE,IEEE TCNS, andActa Automatica Sinica. Title：Perception, Decision-Making, and Control for Autonomous UAVs in Complex Dynamic Environments Abstract：This talk presents the recent research progress of our group in perception, decision-making, and control for autonomous UAVs, as well as the application of related results to scenarios such as autonomous drone racing and aerial docking. |
| Prof. Biao LuoCentral South University, China Biao Luo, IEEE Senior Member, received the Ph.D. degree in control science and engineering from Beihang University, Beijing, China, in 2014. He is currently a Professor with the School of Automation, Central South University (CSU), Changsha, China. Before joining CSU, he was an Associate Professor and Assistant Professor with the Institute of Automation, Chinese Academy of Sciences, Beijing, China, from 2014 to 2018. He published 100+ papers, including top journals and conferences IEEE TPAMI, Automatica, AAAI, etc. He serves as an Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems, the Artificial Intelligence Review, and the Neurocomputing. He is the Vice-Chair of Adaptive Dynamic Programming and Reinforcement Learning Technical Committee, Chinese Association of Automation. His current research interests include intelligent control, reinforcement learning, deep learning, and decision-making. Title：Principles and Recent Advances on Off-policy Reinforcement Learning for Optimization Control Abstract：Off-policy reinforcement learning is able to learn the optimization control with system data generated by other behavior control policies. It overcomes the problems of inadequate exploration, inefficient data utilization, data collecting difficulty, etc., which makes off-policy learning control more practical and easy to realize. In this report, the principles and recent advantages about off-policy learning based on control methods are discussed based the number of controller/player involved, i.e., single-/two-/multi-player. |
| Prof. Shuai LiuShandong University, China Shuai Liu, a full professor at the School of Control Science and Engineering, Shandong University. He was selected for the National Thousand Young Talents Program (2017), and is also a Distinguished Taishan Scholar of Shandong Province, a Distinguished Young Scholar of Shandong University. His research interests include distributed optimization, intelligent control, optimal estimation, reinforcement learning, integrated energy systems, smart grids, and fault diagnosis. He has led and participated in numerous international and national research projects. He has authored over 100 SCI journal papers. He has received the Shandong Provincial Natural Science Award, the Natural Science Award and Science and Technology Progress Award of the Chinese Association of Automation (CAA), the Shandong Automation Society Natural Science Award etc. He has also been awarded multiple best paper awards. He serves as an Associate Editor for several top-tier journals, including IEEE Journal of Automatica Sinica (JAS) and IEEE Transactions on Cybernetics (T-CYBER). He is a member of the IEEE CSS Conference Editorial Board, as well as the IEEE CSS Technical Committees on Nonlinear Systems and Control and on Smart Cities. Title：Distributed Decision-Making and Learning in Complex Cyber-Physical Systems Abstract：Multi-agent decision-making in large-scale distributed systems and cyber-physical systems is often subject to incomplete information constraints, including limited information acquisition, asymmetric communication structures, unreliable feedback signals, and unknown or coupled system dynamics. Achieving efficient learning, cooperative optimization, and stable decision-making under such conditions is a fundamental problem in control, optimization, and game theory. This talk will present recent theoretical and algorithmic advances in distributed online optimization and complex dynamic games. First, in the context of distributed online optimization, the talk will introduce information-efficient learning mechanisms under limited information acquisition, complex communication structures, unreliable feedback, and system coupling, and will discuss their cooperative optimization capability, convergence performance, and robustness under local information. Then, for dynamic games in complex cyber-physical systems, the talk will discuss equilibrium seeking and distributed game learning under coupled nonlinearities and unknown dynamics, aiming to develop decision-making theories and implementable algorithmic frameworks suitable for incomplete-information environments. Overall, this talk aims to reveal how incomplete information affects decision-making mechanisms, convergence performance, and stability in multi-agent systems from the perspectives of information-limited learning, distributed cooperative optimization, and dynamic game-based decision-making, thereby providing a unified theoretical foundation for intelligent decision-making and cooperative control in complex distributed systems. |
