Prof. Huaicheng Yan

Shanghai University of Electric Power, China

Huaicheng Yan, Ph.D., Professor, Doctoral Supervisor.

He has been selected for the National-level Talents Program, the Young and Middle-aged Leading Talents in Science and Technology Innovation of the Ministry of Science and Technology of China, Shanghai Leading Talents, Shanghai Outstanding Academic Leader, and Clarivate Highly Cited Researcher.

His main research interests cover networked control systems, multi-agent systems, artificial intelligence, intelligent autonomous driving, control of robots, unmanned aerial vehicles and spacecraft, as well as cybersecurity.He has presided over more than 40 national and provincial/ministerial projects, including subjects of the National Key Research and Development Program of China and projects funded by the National Natural Science Foundation of China (NSFC). He has published over 200 SCI-indexed papers, among which more than 120 are featured in Automatica and IEEE Transactions journals. Over 20 of his papers have been listed as ESI Highly Cited Papers, and more than 10 as ESI Hot Papers. Two of his papers were selected into the "100 Most Influential International Academic Papers in China". He has won the Best Paper Award at domestic and international academic conferences 5 times, and has been granted or filed more than 30 national invention patents.

He currently serves as Associate Editor for multiple international journals, including IEEE Transactions on Neural Networks and Learning Systems and International Journal of Robotics and Automation. He has received 8 science and technology awards, including the Second Class Prize of Natural Science Award of the Ministry of Education of China, and the Second Class Prize of Shanghai Natural Science Award.

He is now a Member of the Technical Committee on Control Theory of the Chinese Association of Automation (CAA), Standing Member of the Youth Working Committee of CAA, Member of the Intelligent Robotics Professional Committee of the Chinese Association for Artificial Intelligence (CAAI), and Director of the Shanghai Automation Association.

Title：TBD

Abstract：TBD

Prof. Li Chai

Zhejiang 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. Jiahu Qin

University 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. Wangli He

East China University of Science and Technology, China

Wangli He. PhD, Professor, Doctoral Supervisor, Senior Member of IEEE. 

She is a recipient of the National Science Fund for Excellent Young Scholars, the Young Talent Support Project of the China Association for Science and Technology (CAST), the Shanghai Youth Science and Technology Rising Star Program, and the title of "March 8th Red Banner Bearer" of the Shanghai Education System. She has served as a Visiting Associate Professor at Tokyo Metropolitan University, Chair of the IEEE IES Technical Committee on Networked Control Systems and Applications, and Publication Chair of the 3rd International Symposium on Advanced Computational Intelligence and Intelligent Informatics. Her main research areas include distributed cooperative control and optimization of multi-agent systems, cooperative perception, control and decision-making of multi-robot/vehicle systems, as well as machine vision and machine learning. To date, she has published more than 60 papers in authoritative journals including Automatica, IEEE Transactions series journals, and journals of the American Physical Society (APS).

She has chaired multiple projects including the Key Research and Development Program subject of the Ministry of Science and Technology of China, the National Science Fund for Excellent Young Scholars, the General Program of the NSFC, and the Natural Science Foundation of Shanghai. She was awarded the First Prize of Shanghai Natural Science Award (2nd ranked) in 2019. She currently serves as an Associate Editor for international journals including IEEE Transactions on Neural Networks and Learning Systems and IEEE Journal of Emerging and Selected Topics in Industrial Electronics.

Title：TBD

Abstract：TBD

Prof. Biao Luo

Central South University, China

Biao LUO, Professor, Doctoral Supervisor.

Recipient of the National Excellent Youth Science Fund of China, Hunan Province Science and Technology Innovation Leading Talent, Recipient of Hunan Provincial Outstanding Youth Science Fund, IEEE Senior Member, and winner of the Excellent Doctoral Dissertation Award of the Chinese Association of Automation (CAA) in 2015.In recent years, he has published more than 80 academic papers in leading academic journals and conferences, including IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), IEEE Transactions on Cybernetics (IEEE TCYB), Automatica, and Acta Automatica Sinica.From 2014 to 2018, he served as Assistant Research Fellow and Associate Research Fellow at the Institute of Automation, Chinese Academy of Sciences (CASIA).

He currently serves as Deputy Director of the Technical Committee on Adaptive Dynamic Programming and Reinforcement Learning of the Chinese Association of Automation (CAA). He is currently (or has previously served as) Associate Editor for multiple international journals, including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Emerging Topics in Computational Intelligence, Artificial Intelligence Review, Neurocomputing, and Journal of Industrial & Management Optimization.

His research is mainly oriented to application scenarios including intelligent autonomous driving, unmanned aerial vehicles, robotics, quantitative trading, and smart grids, with core research directions covering intelligent control, decision-making and game theory, swarm collaboration and adversarial game, deep learning, reinforcement learning, intelligent autonomous decision-making, and intelligence augmentation.

Title：TBD

Abstract：TBD

Prof. Shuai Liu

Shandong University, China

Shuai Liu, Professor and Doctoral Supervisor at the School of Control Science and Engineering, Shandong University. He is a recipient of the National Overseas High-level Young Talent Program, and a Taishan Scholar Expert of Shandong Province. 

He received his PhD degree from Nanyang Technological University (NTU), Singapore in 2012. His research directions include distributed optimization, intelligent control, and reinforcement learning. He has published more than 100 SCI-indexed papers, and holds 20 authorized domestic and international patents.He has chaired or participated in multiple international and national-level projects, including 2 projects of the National Natural Science Foundation of Singapore, 1 research fund of Temasek Laboratories Singapore, 1 NSFC Innovative Research Group Project (as a key member), 2 NSFC Key Programs, 2 NSFC Joint Funds, 1 NSFC General Program, and 1 Major Innovation Engineering Project of Shandong Province. 

As the first principal completer, he has received the Second Prize of Shandong Provincial Natural Science Award, the Second Prize of CAA Natural Science Award, the First Prize of Natural Science Award of Shandong Association of Automation, and the Technical Invention Award of the Chinese Instrument and Control Society (CIS). He sits on the editorial board of leading control journals includingIEEE Transactions on Cybernetics (IEEE T-CYBER), and serves as Director or Member of multiple technical committees, including the Technical Committee on Swarm Intelligence and Cooperative Control of the Chinese Institute of Command and Control (CICC).

Title：TBD

Abstract：TBD