State-Observer-Based Adaptive Fuzzy Event-Triggered Formation Control for Nonlinear Multiagent System

IF 5.3 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-01 DOI:10.1109/TETCI.2024.3377254

Shuai Sui;Dongyu Shen;Shaocheng Tong;C. L. Philip Chen

引用次数: 0

Abstract

This study examined the problemof event-triggered formation control for nonlinear multiagent systems (MASs) with unmeasured states. First, by applying fuzzy logic systems (FLSs), the identification of unknown nonlinearities could be achieved. To save communication resources, we introduce an event-triggered mechanism. And use the triggered output signal to construct the fuzzy state observer. Then, a formation control algorithm based on event-triggered is proposed through dynamic surface control (DSC) technology and adaptive backstepping control technology, combined with two new event-triggered conditions. Finally, using the Lyapunov theory, it can be shown that all closed-loop signals are bounded. The validity of the proposed scheme can be demonstrated through simulation verification.

查看原文本刊更多论文

基于状态观测器的非线性多代理系统自适应模糊事件触发编队控制

本研究探讨了具有不可测量状态的非线性多代理系统（MAS）的事件触发编队控制问题。首先，通过应用模糊逻辑系统（FLS），可以实现未知非线性的识别。为了节省通信资源，我们引入了事件触发机制。并利用触发输出信号构建模糊状态观测器。然后，通过动态表面控制（DSC）技术和自适应反步进控制技术，结合两种新的事件触发条件，提出了一种基于事件触发的编队控制算法。最后，利用 Lyapunov 理论，可以证明所有闭环信号都是有界的。通过仿真验证，可以证明所提方案的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE Transactions on Emerging Topics in Computational Intelligence Mathematics-Control and Optimization

CiteScore

10.30

自引率

7.50%

发文量

147

期刊介绍： The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys. TETCI is an electronics only publication. TETCI publishes six issues per year. Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.