Reinforcement Learning Aided Performance Optimization of Feedback Control Systems
(Sprache: Englisch)
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...
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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.
Inhaltsverzeichnis zu „Reinforcement Learning Aided Performance Optimization of Feedback Control Systems “
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.
Autoren-Porträt von Changsheng Hua
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.Bibliographische Angaben
- Autor: Changsheng Hua
- 2021, 1st ed. 2021, XIX, 127 Seiten, Maße: 14,8 x 21 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3658330333
- ISBN-13: 9783658330330
Sprache:
Englisch
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