Adaptive Multi-Step Evaluation Design With Stability Guarantee for Discrete-Time Optimal Learning Control

This paper is concerned with a novel integrated multi-step heuristic dynamic programming (MsHDP) algorithm for solving optimal control problems. It is shown that, initialized by the zero cost function, MsHDP can converge to the optimal solution of the Hamilton-Jacobi-Bellman (HJB) equation. Then, th...

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Veröffentlicht in:	IEEE/CAA journal of automatica sinica Jg. 10; H. 9; S. 1797 - 1809
Hauptverfasser:	Wang, Ding, Wang, Jiangyu, Zhao, Mingming, Xin, Peng, Qiao, Junfei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway Chinese Association of Automation (CAA) 01.09.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Faculty of Information Technology,the Beijing Key Laboratory of Computational Intelligence and Intelligent System,the Beijing Laboratory of Smart Environmental Protection,and the Beijing Institute of Artificial Intelligence,Beijing University of Technology,Beijing 100124,China
Schlagworte:	Adaptive control Adaptive critic Algorithms artificial neural networks Convergence Cost function Dynamic programming Hamilton-Jacobi-Bellman (HJB) equation Heuristic algorithms Iterative methods Mathematical models multi-step heuristic dynamic programming multi-step reinforcement learning Neural networks Observers Optimal control Policies Stability analysis Stability criteria Time optimal control artificial neural networks Adaptive critic optimal control Hamilton-Jacobi-Bellman(HJB)equation multi-step heuristic dynamic pro-gramming multi-step reinforcement learning
ISSN:	2329-9266, 2329-9274
Online-Zugang:	Volltext
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