基于故障调节的故障导数变换模糊系统综合静态输出反馈控制

IF 3.2 1区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Fuzzy Sets and Systems Pub Date : 2025-03-28 DOI:10.1016/j.fss.2025.109396

Hong-Jun Wang , Sheng-Juan Huang

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引用次数: 0

摘要

本文研究了由执行器故障困扰的Takagi-Sugeno （T-S）模糊系统的基于故障导数变换的综合静态输出反馈控制的容错问题。为了更有效地实现故障调节，引入故障导数变换技术，设计了具有更宽松参数的综合观测器结构。在稳定性分析过程中，采用基于线性变换矩阵（LTM）因子的Lyapunov函数对导出的矩阵不等式中的耦合项进行消去，从而得到基于线性矩阵不等式（LMI）的稳定性条件。此外，提出了一种改进的不等式标度方法，以降低基于lmi的稳定性条件的保守性。以T-S模糊模型为代表的两个数值算例验证了所设计的综合控制策略。

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Synthetic static output feedback control for fuzzy systems under fault derivative transformation based fault accommodation

This work examines the problem of synthetic static output feedback control embedded fault derivative transformation based fault accommodation for Takagi-Sugeno (T-S) fuzzy systems beset by actuator faults. To implement the fault accommodation more effectively, a fault derivative transformation technique is introduced to design a synthetic observer structure with more relaxed parameters. In the process of stability analysis, a linear transformation matrix (LTM) factor based Lyapunov function is employed to eliminate the coupling terms in the derived matrix inequalities, so as to obtain the linear matrix inequality (LMI) based stability conditions. Furthermore, an improved inequality scaling method is proposed to reduce the conservatism of the LMI-based stability conditions. Two numerical examples represented by T-S fuzzy models test the designed synthetic control strategy.

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来源期刊

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.