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Reinforcement Learning Aided Performance Optimization of Feedback Control Systems

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Reinforcement Learning Aided Performance Optimization of Feedback Control Systems, Springer Vieweg
Von Changsheng Hua, im heise Shop in digitaler Fassung erhältlich

Produktinformationen "Reinforcement Learning Aided Performance Optimization of Feedback Control Systems"

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.

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.

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.

Artikel-Details

Anbieter:
Springer Vieweg
Autor:
Changsheng Hua
Artikelnummer:
9783658330347
Veröffentlicht:
03.03.21