Fuzzy normalization and stabilization for a class of nonlinear rectangular descriptor systems

Chong Lin; Jian Chen; Bing Chen; Lei Guo; Ziye Zhang

doi:10.1016/j.neucom.2016.09.034

Fuzzy normalization and stabilization for a class of nonlinear rectangular descriptor systems

Chong Lin^*
, Jian Chen
, Bing Chen
, Lei Guo
, Ziye Zhang

^*Corresponding author for this work

School of Automation Science and Electrical Engineering

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

51 Scopus citations

Abstract

This paper studies the problem of fuzzy normalization and stabilization for a class of rectangular descriptor systems in Takagi-Sugeno (T-S) fuzzy models. It delivers a feasible scheme for the design of proportional and derivative type dynamic compensator which ensures the closed-loop system normalized and admissible. The dynamic compensator parameters are computed by solving a set of quadratic matrix inequalities, and accordingly, an efficient algorithm is built to solve related matrix inequalities. Illustrative examples are given to show the effectiveness of the present results.

Original language	English
Pages (from-to)	263-268
Number of pages	6
Journal	Neurocomputing
Volume	219
DOIs	https://doi.org/10.1016/j.neucom.2016.09.034
State	Published - 5 Jan 2017

Keywords

Dynamic compensator
Normalization and stabilization
Rectangular descriptor systems
T-S fuzzy systems

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10.1016/j.neucom.2016.09.034

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abstract = "This paper studies the problem of fuzzy normalization and stabilization for a class of rectangular descriptor systems in Takagi-Sugeno (T-S) fuzzy models. It delivers a feasible scheme for the design of proportional and derivative type dynamic compensator which ensures the closed-loop system normalized and admissible. The dynamic compensator parameters are computed by solving a set of quadratic matrix inequalities, and accordingly, an efficient algorithm is built to solve related matrix inequalities. Illustrative examples are given to show the effectiveness of the present results.",

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author = "Chong Lin and Jian Chen and Bing Chen and Lei Guo and Ziye Zhang",

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AU - Chen, Jian

AU - Chen, Bing

AU - Guo, Lei

AU - Zhang, Ziye

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Y1 - 2017/1/5

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AB - This paper studies the problem of fuzzy normalization and stabilization for a class of rectangular descriptor systems in Takagi-Sugeno (T-S) fuzzy models. It delivers a feasible scheme for the design of proportional and derivative type dynamic compensator which ensures the closed-loop system normalized and admissible. The dynamic compensator parameters are computed by solving a set of quadratic matrix inequalities, and accordingly, an efficient algorithm is built to solve related matrix inequalities. Illustrative examples are given to show the effectiveness of the present results.

KW - Dynamic compensator

KW - Normalization and stabilization

KW - Rectangular descriptor systems

KW - T-S fuzzy systems

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U2 - 10.1016/j.neucom.2016.09.034

DO - 10.1016/j.neucom.2016.09.034

M3 - 文章

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SN - 0925-2312

VL - 219

SP - 263

EP - 268

JO - Neurocomputing

JF - Neurocomputing

ER -

Fuzzy normalization and stabilization for a class of nonlinear rectangular descriptor systems

Abstract

Keywords

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