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

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• 作者：Chong Lin^a ; ^{linchong_2004@hotmail.com}Author Vitae ; Jian Chen^aAuthor Vitae ; Bing Chen^aAuthor Vitae ; Lei Guo^bAuthor Vitae ; Ziye Zhang^c ; ^{zhangzy02@126.com}
• 关键词：Rectangular descriptor systems ; T-S fuzzy systems ; Normalization and stabilization ; Dynamic compensator
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：5 January 2017
• 年：2017
• 卷：219
• 期：Complete
• 页码：263-268
• 全文大小：520 K
• 卷排序：219

文摘

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.