State-Feedback H∞ Control for LPV System Using T-S Fuzzy Linearization Approach

This paper discusses the linear parameter varying (LPV) gain scheduling control problem based on the Takagi-Sugeno (T-S) fuzzy linearization approach. Firstly, the affine nonlinear parameter varying (ANPV) description of a class of nonlinear dynamic processes is defined; that is, at any scheduling p...

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Main Authors: Jizhen Liu, Yang Hu, Zhongwei Lin
Format: Article
Language:English
Published: Hindawi Limited 2013-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2013/169454
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spelling doaj-6960873a991949cdaf916c94f1de72012020-11-24T22:25:05ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472013-01-01201310.1155/2013/169454169454State-Feedback H∞ Control for LPV System Using T-S Fuzzy Linearization ApproachJizhen Liu0Yang Hu1Zhongwei Lin2State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, ChinaThis paper discusses the linear parameter varying (LPV) gain scheduling control problem based on the Takagi-Sugeno (T-S) fuzzy linearization approach. Firstly, the affine nonlinear parameter varying (ANPV) description of a class of nonlinear dynamic processes is defined; that is, at any scheduling parameter, the corresponding system is affine nonlinear as usual. For such a class of ANPV systems, a kind of developed T-S fuzzy modeling procedure is proposed to deal with the nonlinearity, instead of the traditional Jacobian linearization approach. More concretely, the evaluation system for the approximation ability of the novelly developed T-S fuzzy modeling procedure is established. Consequently, the LPV T-S fuzzy system is obtained which can approximate the ANPV system with required accuracy. Secondly, the notion of piecewise parameter-dependent Lyapunov function is introduced, and then the stabilization problem and the state-feedback H∞ control problem of the LPV T-S fuzzy system are studied. The sufficient conditions are given in linear matrix inequalities (LMIs) form. Finally, a numerical example is provided to demonstrate the availability of the above approaches. The simulation results show the high approximation accuracy of the LPV T-S fuzzy system to the ANPV system and the effectiveness of the LPV T-S fuzzy gain scheduling control.http://dx.doi.org/10.1155/2013/169454
collection DOAJ
language English
format Article
sources DOAJ
author Jizhen Liu
Yang Hu
Zhongwei Lin
spellingShingle Jizhen Liu
Yang Hu
Zhongwei Lin
State-Feedback H∞ Control for LPV System Using T-S Fuzzy Linearization Approach
Mathematical Problems in Engineering
author_facet Jizhen Liu
Yang Hu
Zhongwei Lin
author_sort Jizhen Liu
title State-Feedback H∞ Control for LPV System Using T-S Fuzzy Linearization Approach
title_short State-Feedback H∞ Control for LPV System Using T-S Fuzzy Linearization Approach
title_full State-Feedback H∞ Control for LPV System Using T-S Fuzzy Linearization Approach
title_fullStr State-Feedback H∞ Control for LPV System Using T-S Fuzzy Linearization Approach
title_full_unstemmed State-Feedback H∞ Control for LPV System Using T-S Fuzzy Linearization Approach
title_sort state-feedback h∞ control for lpv system using t-s fuzzy linearization approach
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2013-01-01
description This paper discusses the linear parameter varying (LPV) gain scheduling control problem based on the Takagi-Sugeno (T-S) fuzzy linearization approach. Firstly, the affine nonlinear parameter varying (ANPV) description of a class of nonlinear dynamic processes is defined; that is, at any scheduling parameter, the corresponding system is affine nonlinear as usual. For such a class of ANPV systems, a kind of developed T-S fuzzy modeling procedure is proposed to deal with the nonlinearity, instead of the traditional Jacobian linearization approach. More concretely, the evaluation system for the approximation ability of the novelly developed T-S fuzzy modeling procedure is established. Consequently, the LPV T-S fuzzy system is obtained which can approximate the ANPV system with required accuracy. Secondly, the notion of piecewise parameter-dependent Lyapunov function is introduced, and then the stabilization problem and the state-feedback H∞ control problem of the LPV T-S fuzzy system are studied. The sufficient conditions are given in linear matrix inequalities (LMIs) form. Finally, a numerical example is provided to demonstrate the availability of the above approaches. The simulation results show the high approximation accuracy of the LPV T-S fuzzy system to the ANPV system and the effectiveness of the LPV T-S fuzzy gain scheduling control.
url http://dx.doi.org/10.1155/2013/169454
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