Event-Driven Guaranteed Cost Control Design for Nonlinear Systems With Actuator Faults via Reinforcement Learning Algorithm

This article presents a novel event-driven guaranteed cost control method for nonlinear systems subject to actuator faults. For the purpose of handling the problem of actuator faults and obtaining the event-driven approximate optimal guaranteed cost control approach for general nonlinear dynamics, t...

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Veröffentlicht in:	IEEE transactions on systems, man, and cybernetics. Systems Jg. 50; H. 11; S. 4135 - 4150
Hauptverfasser:	Zhang, Huaguang, Liang, Yuling, Su, Hanguang, Liu, Chong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.11.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Actuators Algorithms Approximation algorithms Control methods Control systems Cost function Dynamical systems Event-driven control fault tolerant control Faults guaranteed cost control Heuristic algorithms Machine learning Neural networks Nonlinear control Nonlinear dynamics Nonlinear systems Process control reinforcement learning (RL) Sliding mode control sliding mode control (SMC)
ISSN:	2168-2216, 2168-2232
Online-Zugang:	Volltext
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