Multiobjective H2/H∞ Control Design for Nonlinear Stochastic Chaotic Systems via a Front-Squeezing LMIs-Constrained MOEA

This study proposes the multiobjective H 2 / H ∞ fuzzy control design for a nonlinear stochastic chaotic system via concurrently optimizing H 2 and H ∞ performance indices in a Pareto optimal sense. Using the Takagi–Sugeno fuzzy model to approximate the nonlinear stochastic chaotic system, the multi...

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Bibliographic Details
Published in:International journal of fuzzy systems Vol. 23; no. 8; pp. 2371 - 2383
Main Authors: Wu, Chien-Feng, Hsu, Chun-Fei, Hwang, Chi-Kuang
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2021
Springer Nature B.V
Subjects:
Algebra
Artificial Intelligence
Chaos theory
Compressing
Computational Intelligence
Computing costs
Controllers
Design
Engineering
Evolutionary algorithms
Fuzzy control
H-infinity control
Linear matrix inequalities
Management Science
Mathematical analysis
Multiple objective analysis
Nonlinear control
Operations Research
Optimization
Parents & parenting
Pareto optimization
Pareto optimum
Performance indices
Multiobjective
Pareto optimal solution
control
Takagi–Sugeno fuzzy model
Front-squeezing linear matrix inequalities-constrained multiobjective evolution algorithm
ISSN:1562-2479, 2199-3211
Online Access:Get full text
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Summary:This study proposes the multiobjective H 2 / H ∞ fuzzy control design for a nonlinear stochastic chaotic system via concurrently optimizing H 2 and H ∞ performance indices in a Pareto optimal sense. Using the Takagi–Sugeno fuzzy model to approximate the nonlinear stochastic chaotic system, the multiobjective H 2 / H ∞ fuzzy control design problem can be transformed into a linear matrix inequalities (LMIs)-constrained multiobjective optimization problem (an LMIs-constrained MOP). By the help of the LMIs-constrained multiobjective evolution algorithm (LMIs-constrained MOEA), one can obtain the Pareto optimal controller. However, the existing LMIs-constrained MOEA usually couples with a heavy computational load. This study proposes the front-squeezing LMIs-constrained MOEA to resolve such a computational cost problem. Finally, a simulation example is presented to verify the effectiveness of the proposed theories.
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ISSN:1562-2479
2199-3211
DOI:10.1007/s40815-021-01149-z