Mixed H∞/passive fuzzy control for singularly perturbed semi-Markov jump systems under weighted try-once-discard protocol

IF 2.7 1区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Fuzzy Sets and Systems Pub Date : 2025-09-16 DOI:10.1016/j.fss.2025.109593

Hao Shen , Chao Jiang , Jing Wang , Jinde Cao

引用次数: 0

Abstract

In this paper, the output feedback control problem is addressed for fuzzy singularly perturbed semi-Markov jump systems with a weighted try-once-discard protocol. To ensure effective information transmission, a weighted try-once-discard protocol is employed to conserve communication resources under limited bandwidth. Additionally, the Kronecker delta function is utilized to model the rate of information updates. By analyzing the expected value of the Lyapunov function at the virtual point, the mean square exponential stability criterion is derived. Next, the mixed performance of the discrete-time systems is systematically evaluated. Then, a static output feedback controller based on the weighted try-once-discard protocol is designed. Finally, two simulation examples are presented to demonstrate the effectiveness and practicality of the designed controller.

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加权尝试一次丢弃协议下奇异摄动半马尔可夫跳变系统的混合H∞/被动模糊控制

本文研究了模糊奇摄动半马尔可夫跳变系统的输出反馈控制问题，该系统采用加权“尝试一次丢弃”协议。为了保证信息的有效传输，在有限的带宽下，采用加权的try-once-discard协议来节约通信资源。此外，利用Kronecker函数对信息更新速率进行建模。通过分析虚点处李雅普诺夫函数的期望值，推导出均方指数稳定性判据。其次，系统地评价了离散时间系统的混合性能。然后，设计了基于加权尝试一次丢弃协议的静态输出反馈控制器。最后给出了两个仿真实例，验证了所设计控制器的有效性和实用性。

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 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.