Combining hybrid metaheuristic algorithms and reinforcement learning to improve the optimal control of nonlinear continuous-time systems with input constraints

This paper proposes an innovative method for achieving optimal tracking control in nonlinear continuous-time systems with input constraints. The method combines reinforcement learning and hybrid metaheuristics to enhance the controller’s performance. Specifically, a hybrid metaheuristic algorithm is...

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Vydané v:	Computers & electrical engineering Ročník 116; s. 109179
Hlavní autori:	Khalili Amirabadi, Roya, Solaymani Fard, Omid
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.05.2024
Predmet:	Actor-critic neural network Hybrid metaheuristic algorithms Nonlinear systems Optimal tracking control Reinforcement learning Hybrid metaheuristic algorithms Optimal tracking control Nonlinear systems Actor-critic neural network Reinforcement learning
ISSN:	0045-7906, 1879-0755
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