Multivariable adaptive fuzzy predictive control based on T-S model

Fu Cai Liu; Li Na Ren; Ping Li Lu

doi:10.3969/j.issn.1001-506X.2010.12.34

Multivariable adaptive fuzzy predictive control based on T-S model

Fu Cai Liu^*, Li Na Ren, Ping Li Lu

^*Corresponding author for this work

School of Automation

Yanshan University

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

A very good control effect algorithm of fuzzy predictive control based on nonlinear multivariable system is presented. Firstly, the nonlinear system of T-S model is identified by fast clustering and recursive least square method, and the linear model is gotten. Then the fuzzy generalized predictive controller based on linear system is designed to control the nonlinear object adaptively. The simulations for the variable nonlinear object with delay time and strong coupling show that this algorithm has good control effects.

Original language	English
Pages (from-to)	2660-2663
Number of pages	4
Journal	Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics
Volume	32
Issue number	12
DOIs	https://doi.org/10.3969/j.issn.1001-506X.2010.12.34
Publication status	Published - Dec 2010

Keywords

Adaptive control
Fast fuzzy clustering
Generalized predictive control
T-S model

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10.3969/j.issn.1001-506X.2010.12.34

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title = "Multivariable adaptive fuzzy predictive control based on T-S model",

abstract = "A very good control effect algorithm of fuzzy predictive control based on nonlinear multivariable system is presented. Firstly, the nonlinear system of T-S model is identified by fast clustering and recursive least square method, and the linear model is gotten. Then the fuzzy generalized predictive controller based on linear system is designed to control the nonlinear object adaptively. The simulations for the variable nonlinear object with delay time and strong coupling show that this algorithm has good control effects.",

keywords = "Adaptive control, Fast fuzzy clustering, Generalized predictive control, T-S model",

author = "Liu, {Fu Cai} and Ren, {Li Na} and Lu, {Ping Li}",

year = "2010",

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AU - Ren, Li Na

AU - Lu, Ping Li

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N2 - A very good control effect algorithm of fuzzy predictive control based on nonlinear multivariable system is presented. Firstly, the nonlinear system of T-S model is identified by fast clustering and recursive least square method, and the linear model is gotten. Then the fuzzy generalized predictive controller based on linear system is designed to control the nonlinear object adaptively. The simulations for the variable nonlinear object with delay time and strong coupling show that this algorithm has good control effects.

AB - A very good control effect algorithm of fuzzy predictive control based on nonlinear multivariable system is presented. Firstly, the nonlinear system of T-S model is identified by fast clustering and recursive least square method, and the linear model is gotten. Then the fuzzy generalized predictive controller based on linear system is designed to control the nonlinear object adaptively. The simulations for the variable nonlinear object with delay time and strong coupling show that this algorithm has good control effects.

KW - Adaptive control

KW - Fast fuzzy clustering

KW - Generalized predictive control

KW - T-S model

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JF - Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics

IS - 12

ER -

Multivariable adaptive fuzzy predictive control based on T-S model

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Keywords

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