Speaker: Academician Jun Wang
Title: Nonlinear and Robust Model Predictive Control Based on Collaborative Neurodyanmic Optimization
Time: 14:30 pm, May 24, 2023 (Wednesday)
Abstract:
Model predictive control (MPC) is an advanced control methodology widely accepted by both academics and industries. In this talk, nonlinear and robust MPC approaches will be presented based on multiple neural networks. To tackle the nonconvexity in the 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. In addition, a collaborative neurodynamic optimization approach is developed with several neurodynamic optimization models for nonlinear MPC without linearization. The results are extended for robust MPC based on minimax and invariant-tube formulations. Simulation results of many examples are provided to demonstrate the effectiveness and performance of the proposed approaches for the control of mechatronic systems.
Personal Introduction:
Jun Wang is the Chair Professor Computational Intelligence in the Department of Computer Science and School of Data Science at City University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, University of North Dakota, and the Chinese University of Hong Kong. He also held various short-term visiting positions at USAF Armstrong Laboratory, RIKEN Brain Science Institute, Dalian University of Technology, Huazhong University of Science and Technology, and Shanghai Jiao Tong University (Changjiang Chair Professor). He received a B.S. degree in electrical engineering and an M.S. degree in systems engineering from Dalian University of Technology and his Ph.D. degree in systems engineering from Case Western Reserve University. His current research interests include neural networks and their applications. He published over 200 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. 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 board of Neural Networks (2012-2014), editorial advisory board of International Journal of Neural Systems (2006-2013. 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 is an IEEE Fellow, IAPR Fellow, and an IEEE Systems, Man and Cybernetics Society Distinguished Lecturer (2017-2018), and was 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; IEEE Computational Intelligence Society Awards Committee; IEEE Systems, Man, and Cybernetics Society Board of Governors, He is a recipient of an IEEE Transactions on Neural Networks Outstanding Paper Award and APNNA Outstanding Achievement Award in 2011, Neural Networks Pioneer Award from IEEE Computational Intelligence Society in 2014, among other distinctions.
[Editor: Xiaohan Liu]