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报告内容摘要：
Unlike the optimal control of linear systems, the optimal control of nonlinear systems often requires solving the nonlinear Hamilton-Jacobi-Bellman (HJB) equation instead of the Riccati equation. The discrete-time HJB (DTHJB) equation is more difficult to work with than the Riccati equation because it involves solving nonlinear partial difference equations. Though dynamic programming has been an useful computational technique in solving optimal control problems for many years, it is often computationally untenable to run it to obtain the optimal solution, due to the backward numerical process required for its solutions, i.e., the well-known "curse of dimensionality". A self-learning control scheme for unknown nonlinear discrete-time systems with discount factor in the cost function is developed for this purpose. An iterative adaptive dynamic programming algorithm via globalized dual heuristic programming technique is developed to obtain the optimal controller with convergence analysis. Neural networks are used as parametric structures to facilitate the implementation of the iterative algorithm, which will approximate at each iteration the cost function, the optimal control law, and the unknown nonlinear system, respectively. Simulation examples are provided to verify the effectiveness of the present self-learning control approach.

报告人简介：
Derong Liu received the Ph.D. degree in electrical engineering from the University of Notre Dame in 1994. He was a Staff Fellow with General Motors R&D Center, Warren, MI, from 1993 to 1995. He was an Assistant Professor in the Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, from 1995 to 1999. He joined the University of Illinois at Chicago in 1999, where he became a Full Professor of Electrical and Computer Engineering and of Computer Science in 2006. He was selected for the “100 Talents Program” by the Chinese Academy of Sciences in 2008. He has published nine books. Dr. Liu is the Editor-in-Chief of the IEEE Transactions on Neural Networks and an Associate Editor of the IEEE Transactions on Control Systems Technology, Neurocomputing and the International Journal of Neural Systems. He was an elected AdCom member of the IEEE Computational Intelligence Society (2006-2008). He received the Harvey N. Davis Distinguished Teaching Award from Stevens Institute of Technology (1997), the Faculty Early Career Development (CAREER) Award from the National Science Foundation (1999), the University Scholar Award from University of Illinois (2006), and the Overseas Outstanding Young Scholar Award from the National Natural Science Foundation of China (2008).