Practical Fixed-Time Consensus Tracking for Second-Order Multi-Agent Systems With Mismatched Disturbances

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-10-27 DOI:10.1109/TSIPN.2023.3328276

Jiayi Gong;Fuyong Wang;Zhongxin Liu;Zengqiang Chen

引用次数: 0

Abstract

This article focuses on the practical fixed-time consensus tracking problem of second-order multi-agent systems (MASs) with mismatched disturbances and matched disturbances under directed graph. Actually, the leader can be a virtual signal or an actual agent. Considering these two situations, followers can track the leader in a finite time with uniform bound. By using the adding-a-power-integrator method, two fixed-time control protocols are proposed, thus the practical fixed-time tracking consensus is achieved in finite time, and settling time is independent of initial states. Finally, several simulations are given to further illustrate the effectiveness of the theory.

查看原文本刊更多论文

具有不匹配扰动的二阶多智能体系统的实用定时一致性跟踪

研究了有向图下具有不匹配扰动和匹配扰动的二阶多智能体系统的固定时间一致性跟踪问题。实际上，领导者可以是一个虚拟信号，也可以是一个实际的代理。考虑到这两种情况，follower可以在有限时间内以一致的边界跟踪leader。采用功率积分器的方法，提出了两种固定时间控制协议，从而在有限时间内实现了实际的固定时间跟踪一致性，并且稳定时间与初始状态无关。最后，通过仿真进一步说明了该理论的有效性。

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 Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.