Reinforcement Learning Aided Performance Optimization of Feedback Control Systems [electronic resource] /
Material type: TextPublisher: Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Vieweg, 2021Edition: 1st ed. 2021Description: XIX, 127 p. 53 illus. online resourceContent type:- text
- computer
- online resource
- 9783658330347
- 006.31 23
- Q325.5-.7
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.
There are no comments on this title.