Reinforcement Learning Aided Performance Optimization of Feedback Control Systems
Hua, Changsheng.
Reinforcement Learning Aided Performance Optimization of Feedback Control Systems [electronic resource] / by Changsheng Hua. - 1st ed. 2021. - XIX, 127 p. 53 illus. online resource.
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
Machine learning.
Computers.
Computer input-output equipment.
Electronic digital computers--Evaluation.
Machine Learning.
Hardware Performance and Reliability.
Input/Output and Data Communications.
System Performance and Evaluation.
Q325.5-.7
006.31
Reinforcement Learning Aided Performance Optimization of Feedback Control Systems [electronic resource] / by Changsheng Hua. - 1st ed. 2021. - XIX, 127 p. 53 illus. online resource.
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
Machine learning.
Computers.
Computer input-output equipment.
Electronic digital computers--Evaluation.
Machine Learning.
Hardware Performance and Reliability.
Input/Output and Data Communications.
System Performance and Evaluation.
Q325.5-.7
006.31