Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems

Beschreibung

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. 
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. 
Provides a comprehensive and critical review of the latest advances in adaptive critic based robust control design for nonlinear systems Discusses data-based robust stabilization, trajectory tracking, and event-driven disturbance attenuation Includes theoretical analysis, algorithm design, simulation verification, and experimental results Focuses on robust adaptive critic control methods, and the construction of higher-level intelligent systems

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Ding Wang

Themen in »Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems«

Adaptive critic designs Adaptive/approximate dynamic programming Neural networks Optimal control Reinforcement learning Robust control

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“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)
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