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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Vydané v:IEEE transactions on fuzzy systems Ročník 31; číslo 10; s. 1 - 7
Hlavní autori: Shen, Hao, Wang, Yun, Wang, Jing, Park, Ju H.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.10.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Shrnutí: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 singular perturbation and jumping parameters. Furthermore, based on the integral reinforcement learning approach, a novel online parallel learning algorithm is proposed by employing the slow and fast sampled data simultaneously, where the impacts of stochastic jumping and ill-conditioned numerical problems are avoided. Meanwhile, the convergence of the proposed learning algorithms is proved. Finally, we present a tunnel diode circuit model to demonstrate the efficacy of the proposed methods.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2023.3265666