Non-fragile sampled and delayed fuzzy output feedback stabilization for uncertain nonlinear systems

IF 3.2 1区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS
Zebin Wu , Meng Li , Chenguang Wu , Yanjun Shen
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引用次数: 0

Abstract

In this paper, the problem of fuzzy adaptive output feedback stabilization is investigated for a class of uncertain nonlinear systems with sampled and delayed outputs. There also exists unknown measurement noise. An extended fuzzy nonlinear system is obtained on the basis of an output filter and the fuzzy logical approximation theory. Subsequently, a non-fragile sampled output feedback controller is constructed in the light of a non-fragile fuzzy extended observer. By the time delay method, the closed-loop system is transformed into a continuous and delayed nonlinear system. Sufficient conditions are given to ensure the stability performance of the closed-loop system. Finally, the effectiveness of the proposed scheme is validated through a dynamic model of a single linkage robotic arm.
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来源期刊
Fuzzy Sets and Systems
Fuzzy Sets and Systems 数学-计算机:理论方法
CiteScore
6.50
自引率
17.90%
发文量
321
审稿时长
6.1 months
期刊介绍: Since its launching in 1978, the journal Fuzzy Sets and Systems has been devoted to the international advancement of the theory and application of fuzzy sets and systems. The theory of fuzzy sets now encompasses a well organized corpus of basic notions including (and not restricted to) aggregation operations, a generalized theory of relations, specific measures of information content, a calculus of fuzzy numbers. Fuzzy sets are also the cornerstone of a non-additive uncertainty theory, namely possibility theory, and of a versatile tool for both linguistic and numerical modeling: fuzzy rule-based systems. Numerous works now combine fuzzy concepts with other scientific disciplines as well as modern technologies. In mathematics fuzzy sets have triggered new research topics in connection with category theory, topology, algebra, analysis. Fuzzy sets are also part of a recent trend in the study of generalized measures and integrals, and are combined with statistical methods. Furthermore, fuzzy sets have strong logical underpinnings in the tradition of many-valued logics.
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