Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems

ISBN-13: 9789811345937 / Angielski / Miękka / 2019 / 307 str.

Ding Wang; Chaoxu Mu

Recenzje

"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)

Spis treści

A Survey of Robust Adaptive Critic Control Design.- Robust Optimal Control of Nonlinear Systems with Matched Uncertainties.- Observer-Based Online Adaptive Regulation for a Class of Uncertain Nonlinear Systems.- Adaptive Tracking Control of Nonlinear Systems Subject to Matched Uncertainties.- Event-Triggered Robust Stabilization Incorporating an Adaptive Critic Mechanism.- An Improved Adaptive Optimal Regulation Framework with Robust Control Synthesis.- Robust Stabilization and Trajectory Tracking of General Uncertain Nonlinear Systems.- Event-Triggered Nonlinear H∞ Control Design via an Improved Critic Learning Strategy.- Intelligent Critic Control with Disturbance Attenuation for a Micro-Grid System.- Sliding Mode Design for Load Frequency Control with Power System Applications.

O autorze

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.

Blurb

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.