A reinforcement learning methodology to hierarchical sliding‐mode surface H∞ control of nonlinear systems via a dynamic event‐triggered mechanism

Summary This paper addresses the problem of a hierarchical sliding mode surface (HSMS) H∞$$ {H}_{\infty } $$ control design for nonlinear systems via a dynamic event‐triggered mechanism. Initially, the HSMS containing the system states is constructed to enhance the system's response rate and ro...

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Veröffentlicht in:	Asian journal of control Jg. 27; H. 5; S. 2224 - 2242
Hauptverfasser:	Wang, Tengda, Karimi, Hamid Reza, Wang, Huanqing, Xu, Ning, Li, Lun, Zhao, Xudong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken Wiley Subscription Services, Inc 01.09.2025
Schlagworte:	Control methods Control systems Cost function dynamic event‐triggered mechanism H-infinity control hierarchical sliding‐mode surface technique H∞$$ {H}_{\infty } $$ control Machine learning neural network Nonlinear control Nonlinear systems reinforcement learning Tunnel diodes Zero sum games
ISSN:	1561-8625, 1934-6093
Online-Zugang:	Volltext
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