Reinforcement Learning Aided Performance Optimization of Feedback Control Systems

Author:   Changsheng Hua
Publisher:   Springer Fachmedien Wiesbaden
Edition:   1st ed. 2021
ISBN:  

9783658330330


Pages:   127
Publication Date:   04 March 2021
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Reinforcement Learning Aided Performance Optimization of Feedback Control Systems


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Overview

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.

Full Product Details

Author:   Changsheng Hua
Publisher:   Springer Fachmedien Wiesbaden
Imprint:   Springer Vieweg
Edition:   1st ed. 2021
Weight:   0.454kg
ISBN:  

9783658330330


ISBN 10:   3658330333
Pages:   127
Publication Date:   04 March 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of 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.

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Author Information

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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