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Название: Deep Reinforcement Learning with Guaranteed Performance: A Lyapunov-Based Approach
Автор: Yinyan Zhang, Shuai Li
Издательство: Springer
Год: 2019 (2020 Edition)
Страниц: 237
Язык: английский
Формат: pdf (true)
Размер: 11.2 MB

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.

In the past decades, both optimal control and adaptive control have been widely investigated to solve control problems of nonlinear systems arising from engineering applications. Optimal control aims at finding a control law to drive a control system to a desired state while optimizing certain performance index with or without constraints. Adaptive control is a tool to handle parameter uncertainty or structure uncertainty of control systems. In most works, the two types of control methods are separated. Reinforcement learning and, in particular, deep reinforcement learning have attracted more and more research interest in recent years. Such type of learning methods could be a powerful tool for the control of nonlinear systems.

This book 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.

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