Description: Deep Reinforcement Learning with Guaranteed Performance : A Lyapunov-Based Approach, Hardcover by Zhang, Yinyan; Li, Shuai; Zhou, Xuefeng, ISBN 3030333833, ISBN-13 9783030333836, Like New Used, Free shipping in the US 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. Th 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 th accessible to graduate students as well as academic and industrial researchers in the fields of adaptive and optimal control, robotics, and dynamic neural networks.
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Book Title: Deep Reinforcement Learning with Guaranteed Performance : a Lyapunov-Based Approach
Number of Pages: Xvii, 225 Pages
Language: English
Publisher: Springer International Publishing A&G
Topic: Intelligence (Ai) & Semantics, Electrical, Robotics
Publication Year: 2019
Illustrator: Yes
Genre: Computers, Technology & Engineering
Item Weight: 18.9 Oz
Author: Shuai Li, Yinyan Zhang, Xuefeng Zhou
Item Length: 9.3 in
Book Series: Studies in Systems, Decision and Control Ser.
Item Width: 6.1 in
Format: Hardcover
