A Fuzzy-Model-Based Approach to Optimal Control for Nonlinear Markov Jump Singularly Perturbed Systems: A Novel Integral Reinforcement Learning Scheme

A fuzzy-model-based approach is developed to investigate the reinforcement learning-based optimization for nonlinear Markov jump singularly perturbed systems. As the first attempt, an offline parallel iteration learning algorithm is presented to solve the coupled algebraic Riccati equations with sin...

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Veröffentlicht in:	IEEE transactions on fuzzy systems Jg. 31; H. 10; S. 1 - 7
Hauptverfasser:	Shen, Hao, Wang, Yun, Wang, Jing, Park, Ju H.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Circuits Fuzzy systems fuzzy-model-based approach Heuristic algorithms Iterative methods Machine learning Markov jump singularly perturbed systems Markov processes Nonlinear control Optimal control Optimization parallel algorithm Parallel algorithms Reinforcement learning Riccati equation Singular perturbation System dynamics Tunnel diodes
ISSN:	1063-6706, 1941-0034
Online-Zugang:	Volltext
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