Reinforcement learning aided performance optimization of feedback control systems
Hua, Changsheng
Reinforcement learning aided performance optimization of feedback control systems by Changsheng Hua - First edition 2021 - 1 recurso en línea (XIX, 127 páginas) 53 ilustraciones
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.
9783658330347
10.1007/978-3-658-33034-7 doi
Sistemas de control por realimentación
TJ216 / 2021 EB
Reinforcement learning aided performance optimization of feedback control systems by Changsheng Hua - First edition 2021 - 1 recurso en línea (XIX, 127 páginas) 53 ilustraciones
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.
9783658330347
10.1007/978-3-658-33034-7 doi
Sistemas de control por realimentación
TJ216 / 2021 EB