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Reinforcement Learning Aided Performance Optimization of Feedback Control Systems
Reinforcement Learning Aided Performance Optimization of Feedback Control Systems
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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…
  • Leidėjas:
  • ISBN-10: 3658330333
  • ISBN-13: 9783658330330
  • Formatas: 14.8 x 21 x 0.9 cm, minkšti viršeliai
  • Kalba: Anglų

Reinforcement Learning Aided Performance Optimization of Feedback Control Systems (el. knyga) (skaityta knyga) | knygos.lt

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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.

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  • Autorius: Changsheng Hua
  • Leidėjas:
  • ISBN-10: 3658330333
  • ISBN-13: 9783658330330
  • Formatas: 14.8 x 21 x 0.9 cm, minkšti viršeliai
  • Kalba: Anglų

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.

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