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Reinforcement Learning Aided Performance Optimization of Feedback Control Systems [electronic resource] /

By: Contributor(s): Material type: TextTextPublisher: Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Vieweg, 2021Edition: 1st ed. 2021Description: XIX, 127 p. 53 illus. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783658330347
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 006.31 23
LOC classification:
  • Q325.5-.7
Online resources:
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.
In: Springer Nature eBookSummary: 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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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.

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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