Adaptive Fault-Tolerant Tracking Control for Discrete-Time Multiagent Systems via Reinforcement Learning Algorithm

This article investigates the adaptive fault-tolerant tracking control problem for a class of discrete-time multiagent systems via a reinforcement learning algorithm. The action neural networks (NNs) are used to approximate unknown and desired control input signals, and the critic NNs are employed t...

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Veröffentlicht in:	IEEE transactions on cybernetics Jg. 51; H. 3; S. 1163 - 1174
Hauptverfasser:	Li, Hongyi, Wu, Ying, Chen, Mou
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.03.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Actuators Adaptive algorithms Adaptive control Algorithms Artificial neural networks Control stability Cost function Discrete time systems Discrete-time multiagent systems (MASs) Estimation Fault tolerance Fault tolerant systems fault-tolerant control Feedback control Machine learning Multi-agent systems Multiagent systems Neural networks neural networks (NNs) Reinforcement learning reinforcement learning algorithm Tracking control
ISSN:	2168-2267, 2168-2275, 2168-2275
Online-Zugang:	Volltext
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