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Reinforcement Learning Aided Performance Optimization of Feedback Control Systems de Changsheng Hua

Reinforcement Learning Aided Performance Optimization of Feedback Control Systems

Autor Changsheng Hua
en Limba Engleză Paperback – 4 mar 2021
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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Specificații

ISBN-13: 9783658330330
ISBN-10: 3658330333
Pagini: 127
Ilustrații: XIX, 127 p. 53 illus.
Dimensiuni: 148 x 210 mm
Greutate: 0.2 kg
Ediția:1st ed. 2021
Editura: Springer Fachmedien Wiesbaden
Colecția Springer Vieweg
Locul publicării:Wiesbaden, Germany

Cuprins

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.

Notă biografică

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.
 

Textul de pe ultima copertă

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.