具有多节点轮循协议和丢包的区间 2 型模糊奇异扰动系统的 H∞ 控制

IF 3.2 1区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS
Yue Hu , Oh-Min Kwon , Seung-Hoon Lee
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引用次数: 0

摘要

本文研究了具有数据包丢失的区间型-2(IT2)模糊奇异扰动系统(SPS)的 H∞ 控制。为了减少数据碰撞,本文采用了多节点循环协议(RRP)来决定传感器和控制器之间的通信顺序。在多节点轮循协议的帮助下,每一瞬间只有部分传感器节点有访问网络信道的优先权。同时,采用伯努利丢包模型进一步分析数据传输过程中丢包现象的发生。本文的主要研究成果和贡献包括两部分:(1)首先,结合多节点 RRP、伯努利丢包模型和错配成员函数,设计了一种依赖于调度状态信息的 IT2 模糊控制器,在此控制器下,系统的控制性能可以得到有效提升。(2) 其次,通过建立与奇异扰动参数相关的新 Lyapunov 函数,可以获得系统稳定性条件,并缓解了无条件问题。最后,以 Van der Pol 电路系统为例,验证了所开发方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
H∞ control for interval type-2 fuzzy singularly perturbed systems with multi-node round-robin protocol and packet dropouts
The H control of the interval type-2 (IT2) fuzzy singularly perturbed systems (SPSs) with packet losses is studied in this paper. To alleviate data collision, a multi-node round-robin protocol (RRP) is adopted to decide the communication sequences between the sensors and controller. With the help of multi-node RRP, only partial sensor nodes have the priority to access the network channels at each instant. Meanwhile, a Bernoulli packet dropout model is used to further analyze the occurrence of packet dropouts during data transmission. This article's main research results and contributions consist of two parts: (1) Firstly, by combining multi-node RRP, Bernoulli packet loss model and mismatched membership functions, an IT2 fuzzy controller dependent on scheduling state information is designed, under which the control performance of the system can be effectively enhanced. (2) Secondly, by establishing a new Lyapunov functional related to singular perturbation parameter, the system stability conditions can be obtained and ill-conditioned issue is alleviated. Eventually, the feasibility of developed method is validated by the Van der Pol circuit system example.
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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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