题目：Reinforcement-Learning Fuzzy-Model-Based Control Systems

时间：2024年11月25日 16:00-17:30

腾讯会议号：487494838

密码：481529

邀请人：何俊 教授（重大装备设计与控制工程研究所）

Biography

Hak-Keung Lam (Fellow, IEEE) received the B.Eng. (Hons.) and Ph.D. degrees from the Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, in 1995 and 2000, respectively. During the period of 2000 and 2005, he was with the Department of Electronic and Information Engineering, The Hong Kong Polytechnic University as Postdoctoral Fellow and Research Fellow, respectively. He joined as a Lecturer with King’s College London, London, U.K., in 2005 and is currently a Reader. His current research interests include intelligent control, computational intelligence, and machine learning.

Dr. Lam has served as a program committee member, international advisory board member, invited session chair, and publication chair for various international conferences and a reviewer for various books, international journals, and international conferences. He was an Associate Editor for IEEE Transactions on Circuits and Systems II: Express Briefs and is an Associate Editor for IEEE Transactions on Fuzzy Systems, IET Control Theory and Applications, International Journal of Fuzzy Systems, Neurocomputing, and Nonlinear Dynamics; and Guest Editor for a number of international journals. He is on the editorial board of the Journal of Intelligent Learning Systems and Applications, Journal of Applied Mathematics, Mathematical Problems in Engineering, Modelling and Simulation in Engineering, Annual Review of Chaos Theory, Bifurcations and Dynamical System, The Open Cybernetics and Systemics Journal, Cogent Engineering, and International Journal of Sensors, Wireless Communications and Control. He was named as a highly cited Researcher. He is a Coeditor of two edited volumes: Control of Chaotic Nonlinear Circuits (Singapore: World Scientific, 2009) and Computational Intelligence and Its Applications (Singapore: World Scientific, 2012); and author/coauthor of three monographs: Stability Analysis of Fuzzy-Model-Based Control Systems (New York, NY, USA: Springer, 2011), Polynomial Fuzzy Model Based Control Systems (New York, NY, USA: Springer, 2016), and Analysis and Synthesis for Interval Type-2 Fuzzy-Model-Based Systems (New York, NY, USA: Springer, 2016).

Abstract

The integration of reinforcement learning techniques with fuzzy model-based control systems is a powerful approach in control theory, yet research results and efforts remain limited. This presentation explores the synergy between reinforcement learning algorithms and fuzzy logic, aiming to optimize control strategies in complex and dynamic environments. The theoretical foundations and practical applications will be integrated, highlighting their ability to adapt and learn from experience while maintaining robust performance. Further research topics on combining fuzzy logic and reinforcement learning techniques will also be discussed.