Adaptive Dynamic Programming: Single and Multiple Controllers

This book presents a class of novel optimal control methods and games schemes based on adaptive dynamic programming techniques. For systems with one control input, the ADP-based optimal control is designed for different objectives, while for systems with multi-players, the optimal control inputs are proposed based on games. In order to verify the effectiveness of the proposed methods, the book analyzes the properties of the adaptive dynamic programming methods, including convergence of the iterative value functions and the stability of the system under the iterative control laws. Further, to substantiate the mathematical analysis, it presents various application examples, which provide reference to real-world practices. 

Part I:  Preparation.- Introduction to optimal control and adaptive dynamic programming.- Neural networks implementation.- Part II:  Optimal control for system with single controller.- Finite-time optimal control.- Multi-objective optimal control.- Multiple actor-critic optimal control via ADP.- Optimal control for complex-valued nonlinear systems.- Chaotic systems optimal tracking control.- Part III:  Multi-player systems games.- Optimal control for unknown systems with disturbances.- Zero-sum differential games.- Non-Zero-Sum games.- Synchronization control for multi-agent games.

Ruizhuo Song received the Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2012. She was a postdoctoral fellow with University of Science and Technology Beijing, Beijing, China. She is currently an Associate Professor with the School of Automation and Electrical Engineering, University of Science and Technology Beijing. She was a Visiting Scholar with the Department of Electrical Engineering at University of Texas at Arlington, Arlington, TX, USA, from 2013 to 2014. Her current research interests include optimal control, multi-player games, neural-networks-based control, nonlinear control, wireless sensor networks, and adaptive dynamic programming and their industrial application. She has published over 40 journal and conference papers, and coauthored 2 monographs. 

Qinglai Wei received the B.S. degree in Automation, and the Ph.D. degree in control theory and control engineering, from the Northeastern University, Shenyang, China, in 2002 and 2009, respectively. From 2009--2011, he was a postdoctoral fellow with The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China. He is currently a professor of the institute. He has authored two books, and published over 60 international journal papers. His research interests include adaptive dynamic programming, neural-networks-based control, optimal control, nonlinear systems and their industrial applications.

Dr. Qing Li received his B.E. degree from North China University of Science and Technology, Tangshan, China, in 1993, and the Ph. D degree incontrol theory and its applications from University of Science and Technology Beijing, Beijing, China, in 2000. He is currently a Professor with the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China. He has been a visiting scholar at Ryerson University, Toronto, Canada, from February 2006 to February 2007. His research interests include intelligent control and intelligent optimization.