具有丢包和时延的网络T-S模糊系统的周期性事件触发控制

IF 2.7 1区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS
Xiuxiu Zhang , Jumei Wei , Xun-Lin Zhu , Mouquan Shen , Jiuxiang Dong
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引用次数: 0

摘要

在本文中,我们讨论了网络Takagi-Sugeno (T-S)模糊系统的动态周期性事件触发控制(PETC)问题,该系统具有丢包和无确认传输延迟。为了有效地节省网络资源,我们在传感器-控制器(S-C)和控制器-执行器(C-A)通道上采用两种独立的异步事件触发机制(etm)来确定测量输出和控制输入是否需要通过网络传输。首先,我们引入辅助变量并设计适当的跳跃规则来监测网络误差的变化过程。随后,我们为每个触发器的触发函数和动态变量提供了设计条件,并利用相应触发器的本地可用信息表示这些条件。然后,我们提出了一种基于仿真方法的PETC策略,该策略可以保证系统的渐近稳定性,并且只需要定期检查触发函数。最后通过数值算例验证了理论结果的可行性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Periodic event-triggered control for networked T-S fuzzy systems with packet losses and delays
In this paper, we address the problem of dynamic periodic event-triggered control (PETC) for networked Takagi-Sugeno (T-S) fuzzy systems subject to packet losses and transmission delays without acknowledgments. To save network resources efficiently, we adopt two independent and asynchronous event-triggering mechanisms (ETMs) on the sensor-controller (S-C) and controller-actuator (C-A) channels to determine whether the measurement output and control input need to be transmitted over the networks. Firstly, we introduce auxiliary variables and design appropriate jump rules to monitor the change process of the network-induced errors. Subsequently, we provide design conditions for the triggering function and dynamic variables of each trigger, and represent these conditions by using the locally available information of the corresponding trigger. Then, we propose a PETC strategy based on the emulation method, which can ensure the asymptotic stability of the system and only needs to check the triggering functions periodically. Finally, we give a numerical example to validate the feasibility and effectiveness of the theoretical results.
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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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