Adaptive Fault-Tolerant Tracking Control for MIMO Discrete-Time Systems via Reinforcement Learning Algorithm With Less Learning Parameters

This paper is concerned with a reinforcement learning-based adaptive tracking control technique to tolerate faults for a class of unknown multiple-input multiple-output nonlinear discrete-time systems with less learning parameters. Not only abrupt faults are considered, but also incipient faults are...

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Veröffentlicht in:	IEEE transactions on automation science and engineering Jg. 14; H. 1; S. 299 - 313
Hauptverfasser:	Liu, Lei, Wang, Zhanshan, Zhang, Huaguang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptive critic design Adaptive systems Algorithms Control systems Cost function Discrete-time systems Fault tolerance fault tolerant control MIMO multiple-input multiple-output discrete-time systems Neural networks reinforcement learning algorithm Tracking control systems
ISSN:	1545-5955, 1558-3783
Online-Zugang:	Volltext
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