报告题目：Nonlinear and Robust Model Predictive Control Based on Neural Computation
报 告 人：Prof. Jun Wang， Chinese University of Hong Kong
报告时间：3月20日(周五)下午15：00
报告地点：南一楼中311室
邀 请 方：“多谱信息处理技术”国家级重点实验室
Abstract: Model predictive control (MPC) is a advanced control methodology widely accepted by both academics and industries. In this talk, nonlinear and robust MPC schemes will be presented based on feedforward and recurrent neural networks. To avoid the nonconvexity of problem formulation with nonlinear systems, the original nonconvex optimization problem associated with nonlinear MPC is first reformulated as a convex one by means of decomposition via Taylor expansion. An online supervised learning algorithm is initially developed for estimating the unknown residual term resulted from the decomposition. To save online computational time, offline supervised learning is also carried out based on feedforward neural networks for parameter estimation. The results are extended for robust MPC based on minimax and invariant-tube formulations. In addition, population-based collective neurodynamic optimization approach is developed for nonlinear MPC without linearization. The proposed neural network approaches have many desirable properties such as global convergence and low complexity. Simulation results of many examples are provided to demonstrate the effectiveness and performance of the proposed approaches.
Speaker’s Short Bio: Jun Wang is a Professor and the Director of the Computational Intelligence Laboratory in the Department of Mechanical and Automation Engineering at the Chinese University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, and University of North Dakota. He also held various short-term visiting positions at USAF Armstrong Laboratory (1995), RIKEN Brain Science Institute (2001), Huazhong University of Science and Technology (2006–2007), and Shanghai Jiao Tong University (2008-2011) as a Changjiang Chair Professor. Since 2011, he is a National Thousand-Talent Chair Professor at Dalian University of Technology on a part-time basis. He received a B.S. degree in electrical engineering and an M.S. degree in systems engineering from Dalian University of Technology, Dalian, China. He received his Ph.D. degree in systems engineering from Case Western Reserve University, Cleveland, Ohio, USA. His current research interests include neural networks and their applications. He published over 170 journal papers, 15 book chapters, 11 edited books, and numerous conference papers in these areas. He is the Editor-in-Chief of the IEEE Transactions on Cybernetics since 2014 and a member of the editorial board of Neural Networks since 2012. He also served as an Associate Editor of the IEEE Transactions on Neural Networks (1999-2009), IEEE Transactions on Cybernetics and its predecessor (2003-2013), and IEEE Transactions on Systems, Man, and Cybernetics – Part C (2002–2005), as a member of the editorial advisory board of International Journal of Neural Systems (2006-2013), as a guest editor of special issues of European Journal of Operational Research (1996), International Journal of Neural Systems (2007), Neurocomputing (2008, 2014), and International Journal of Fuzzy Systems (2010, 2011). He was an organizer of several international conferences such as the General Chair of the 13th International Conference on Neural Information Processing (2006) and the 2008 IEEE World Congress on Computational Intelligence, and a Program Chair of the IEEE International Conference on Systems, Man, and Cybernetics (2012). He has been an IEEE Computational Intelligence Society Distinguished Lecturer (2010-2012, 2014-2016). In addition, he served as President of Asia Pacific Neural Network Assembly (APNNA) in 2006 and many organizations such as IEEE Fellow Committee (2011-2012); IEEE Computational Intelligence Society Awards Committee (2008, 2012, 2014), IEEE Systems, Man, and Cybernetics Society Board of Directors (2013-2015), He is an IEEE Fellow, IAPR Fellow, and a recipient of an IEEE Transactions on Neural Networks Outstanding Paper Award and APNNA Outstanding Achievement Award in 2011, and Neural Networks Pioneer Award from IEEE Computational Intelligence Society (2014), among others.
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