Reinforcement learning aided performance optimization of feedback control systems / Changsheng Hua.
2021
TJ216
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Title
Reinforcement learning aided performance optimization of feedback control systems / Changsheng Hua.
Author
Hua, Changsheng.
ISBN
9783658330347 (electronic bk.)
3658330341 (electronic bk.)
9783658330330 (print)
3658330333 (print)
3658330341 (electronic bk.)
9783658330330 (print)
3658330333 (print)
Imprint
Wiesbaden : Springer Vieweg, 2021.
Language
English
Description
1 online resource (139 pages)
Other Standard Identifiers
10.1007/978-3-658-33034-7 doi
Call Number
TJ216
Dewey Decimal Classification
629.8/3
Summary
Changsheng Hua proposes two approaches, an input/output recovery approach and a performance index-based approach for robustness and performance optimization of feedback control systems. For their data-driven implementation in deterministic and stochastic systems, the author develops Q-learning and natural actor-critic (NAC) methods, respectively. Their effectiveness has been demonstrated by an experimental study on a brushless direct current motor test rig. The author: Changsheng Hua received the Ph. D. degree at the Institute of Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Germany, in 2020. His research interests include model-based and data-driven fault diagnosis and fault-tolerant techniques.
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Includes bibliographical references.
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Description based on print version record.
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Reinforcement Learning Aided Performance Optimization of Feedback Control Systems.
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Table of Contents
Introduction
The basics of feedback control systems
Reinforcement learning and feedback control
Q-learning aided performance optimization of deterministic systems
NAC aided performance optimization of stochastic systems
Conclusion and future work.
The basics of feedback control systems
Reinforcement learning and feedback control
Q-learning aided performance optimization of deterministic systems
NAC aided performance optimization of stochastic systems
Conclusion and future work.