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Table of contents (6 chapters)
Keywords
About this book
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.Authors and Affiliations
About 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.
Bibliographic Information
Book Title: Reinforcement Learning Aided Performance Optimization of Feedback Control Systems
Authors: Changsheng Hua
DOI: https://doi.org/10.1007/978-3-658-33034-7
Publisher: Springer Vieweg Wiesbaden
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2021
Softcover ISBN: 978-3-658-33033-0Published: 04 March 2021
eBook ISBN: 978-3-658-33034-7Published: 03 March 2021
Edition Number: 1
Number of Pages: XIX, 127
Number of Illustrations: 53 b/w illustrations
Topics: Machine Learning, Computer Hardware, Input/Output and Data Communications, System Performance and Evaluation