On the local quadratic stability of T–S fuzzy systems in the vicinity of the origin

IF 3.2 1区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS
Donghwan Lee , Do Wan Kim
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引用次数: 0

Abstract

The main goal of this paper is to introduce new local stability conditions for continuous-time Takagi-Sugeno (T-S) fuzzy systems. These stability conditions are based on linear matrix inequalities (LMIs) in combination with quadratic Lyapunov functions. Moreover, they integrate information on the membership functions at the origin and effectively leverage the linear structure of the underlying nonlinear system in the vicinity of the origin. As a result, the proposed conditions are proved to be less conservative compared to existing methods using fuzzy Lyapunov functions. Moreover, we establish that the proposed methods offer necessary and sufficient conditions for the local exponential stability of T-S fuzzy systems. Discussions on the inherent limitations associated with fuzzy Lyapunov approaches are also given. To illustrate the theoretical results, we provide comprehensive examples that demonstrate the core concepts and validate the efficacy of the proposed conditions.

论 T-S 模糊系统在原点附近的局部二次稳定性
本文的主要目的是为连续时间高木-菅野(Takagi-Sugeno,T-S)模糊系统引入新的局部稳定性条件。这些稳定条件以线性矩阵不等式(LMI)为基础,并与二次型 Lyapunov 函数相结合。此外,它们整合了原点处的成员函数信息,并有效利用了原点附近底层非线性系统的线性结构。结果证明,与使用模糊 Lyapunov 函数的现有方法相比,所提出的条件不那么保守。此外,我们还证明了所提出的方法为 T-S 模糊系统的局部指数稳定性提供了必要且充分的条件。我们还讨论了与模糊 Lyapunov 方法相关的固有局限性。为了说明理论结果,我们提供了全面的示例,以展示核心概念并验证所提条件的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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