A novel practically designated-time fuzzy optimal control via reinforcement learning for stochastic constrained nonlinear systems

This paper investigates the practically designated time fuzzy optimal control problem for stochastic constrained nonlinear systems with input delay, using reinforcement learning (RL) algorithms. For the uncertain stochastic nonlinear system, a new practically designated time control scheme is propos...

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Veröffentlicht in:	Neurocomputing (Amsterdam) Jg. 654; S. 131315
Hauptverfasser:	Qiu, Lifang, Zhao, Junsheng, Sun, Zong-Yao, Mu, Chaoxu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 14.11.2025
Schlagworte:	Event-triggered mechanism Fuzzy logic system Practically designated-time stabilization Reinforcement learning algorithm State constraint Event-triggered mechanism Practically designated-time stabilization Fuzzy logic system Reinforcement learning algorithm State constraint
ISSN:	0925-2312
Online-Zugang:	Volltext
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