Reinforcement Learning Aided Performance Optimization of Feedback Control Systems

1. Auflage 2021
ISBN: 978-3-658-33033-0
Verlag: Springer

Buch, Englisch, 127 Seiten, Format (B × H): 148 mm x 210 mm, Gewicht: 202 g

Reihe: Lehrbuch

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.

Zielgruppe

Research

Autoren/Hrsg.

Hua, Changsheng

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik Hardware: Grundlagen und Allgemeines
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

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.

Über Autor(innen)

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.