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210407 ||| eng |
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|a 9783658330347
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|a Hua, Changsheng
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|a Reinforcement Learning Aided Performance Optimization of Feedback Control Systems
|h Elektronische Ressource
|c by Changsheng Hua
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| 250 |
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|a 1st ed. 2021
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| 260 |
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|a Wiesbaden
|b Springer Fachmedien Wiesbaden
|c 2021, 2021
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| 300 |
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|a XIX, 127 p. 53 illus
|b online resource
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|a 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
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| 653 |
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|a Electronic digital computers / Evaluation
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| 653 |
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|a Machine learning
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| 653 |
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|a System Performance and Evaluation
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| 653 |
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|a Machine Learning
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| 653 |
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|a Hardware Performance and Reliability
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| 653 |
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|a Computers
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| 653 |
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|a Input/Output and Data Communications
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| 653 |
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|a Computer input-output equipment
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 028 |
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|a 10.1007/978-3-658-33034-7
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| 856 |
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|u https://doi.org/10.1007/978-3-658-33034-7?nosfx=y
|x Verlag
|3 Volltext
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|a 006.31
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| 520 |
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|a 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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