Deep Reinforcement Learning with Guaranteed Performance: A Lyapunov-Based Approach

Description

This book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy resolution of redundant manipulators with consideration of parameter uncertainty and periodic disturbances. It also reports on a series of systematic investigations on a near-optimal adaptive control method based on the Taylor expansion, neural networks, estimator design approaches, and the idea of sliding mode control, focusing on the tracking control problem of nonlinear systems under different scenarios. The book culminates with a presentation of two new redundancy resolution methods; one addresses adaptive kinematic control of redundant manipulators, and the other centers on the effect of periodic input disturbance on redundancy resolution. Each self-contained chapter is clearly written, making the book accessible to graduate students as well as academic and industrial researchers in the fields of adaptive and optimal control, robotics, and dynamic neural networks. Front Matter ....Pages i-xvii A Survey of Near-Optimal Control of Nonlinear Systems (Yinyan Zhang, Shuai Li, Xuefeng Zhou)....Pages 1-20 Near-Optimal Control with Input Saturation (Yinyan Zhang, Shuai Li, Xuefeng Zhou)....Pages 21-48 Adaptive Near-Optimal Control with Full-State Feedback (Yinyan Zhang, Shuai Li, Xuefeng Zhou)....Pages 49-95 Adaptive Near-Optimal Control Using Sliding Mode (Yinyan Zhang, Shuai Li, Xuefeng Zhou)....Pages 97-128 Model-Free Adaptive Near-Optimal Tracking Control (Yinyan Zhang, Shuai Li, Xuefeng Zhou)....Pages 129-165 Adaptive Kinematic Control of Redundant Manipulators (Yinyan Zhang, Shuai Li, Xuefeng Zhou)....Pages 167-197 Redundancy Resolution with Periodic Input Disturbance (Yinyan Zhang, Shuai Li, Xuefeng Zhou)....Pages 199-225