Springer Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems: 167 (Studies in Systems, Decision and Control, 167)

This book reports on the latest advances in adaptive critic control with robust stabilization for uncertain nonlinear systems. Covering the core theory, novel methods, and a number of typical industrial applications related to the robust adaptive critic control field, it develops a comprehensive framework of robust adaptive strategies, including theoretical analysis, algorithm design, simulation verification, and experimental results. As such, it is of interest to university researchers, graduate students, and engineers in the fields of automation, computer science, and electrical engineering wishing to learn about the fundamental principles, methods, algorithms, and applications in the field of robust adaptive critic control. In addition, it promotes the development of robust adaptive critic control approaches, and the construction of higher-level intelligent systems.   Review “The book presents results on learning-based robust adaptive critic control theory, including self-learning robust stabilization, data-driven robust optimal control, adaptive trajectory tracking, adaptive H1 control design. A general analysis for adaptive critic systems in terms of stability, convergence, optimality, and robustness under uncertain environment is covered.” (Alexandra Rodkina, zbMATH 1407.93006, 2019) From the Back Cover This book reports on the latest advances in adaptive critic control with robust stabilization for uncertain nonlinear systems. Covering the core theory, novel methods, and a number of typical industrial applications related to the robust adaptive critic control field, it develops a comprehensive framework of robust adaptive strategies, including theoretical analysis, algorithm design, simulation verification, and experimental results. As such, it is of interest to university researchers, graduate students, and engineers in the fields of automation, computer science, and electrical engineering wishing to learn about the fundamental principles, methods, algorithms, and applications in the field of robust adaptive critic control. In addition, it promotes the development of robust adaptive critic control approaches, and the construction of higher-level intelligent systems.   About the Author Dr. Ding Wang is an associate professor in The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. His main research interests cover adaptive and learning control systems, complex systems and intelligent control, neural networks and neural computing. Dr. Chaoxu Mu is an associate professor in school of electrical and information engineering, Tianjin University. Her research interests focus mainly on non-linear control theory and applications, adaptive dynamic programming and robust control.