Cantitate/Preț
Produs
Total produse
Vezi coșul
Reinforcement Learning Aided Performance Optimization of Feedback Control Systems de Changsheng Hua

Reinforcement Learning Aided Performance Optimization of Feedback Control Systems

Autor Changsheng Hua
en Limba Engleză Paperback – 4 mar 2021
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.

Citește tot Restrânge

Preț: 44724 lei

Preț vechi: 52616 lei
-15%

Puncte Express: 671

Carte tipărită la comandă

Livrare economică 01-15 iunie

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă

Specificații

ISBN-13: 9783658330330
ISBN-10: 3658330333
Pagini: 148
Ilustrații: XIX, 127 p. 53 illus.
Dimensiuni: 148 x 210 x 9 mm
Greutate: 0.2 kg
Ediția:1st edition 2021
Editura: SPRINGER VIEWEG
Locul publicării:Wiesbaden, Germany

Cuprins

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.

Textul de pe ultima copertă

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.