Hybrid-Triggered Output Feedback Containment Control for Multi-Agent Systems With Missing Measurements

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

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-01-18 DOI:10.1109/TSIPN.2024.3355747

Arumugam Parivallal;Sangwoon Yun;Yoon Mo Jung

引用次数: 0

Abstract

In this paper, we investigate the output feedback containment control problem for multi-agent systems with missing measurements. The primary objective is to design a hybrid-triggered controller that not only reduces the unwanted data transmission but also ensures the required control performance. The proposed hybrid-triggered controller is developed by combining the time-triggered and event-triggered schemes using a Bernoulli random variable. Utilizing Lyapunov stability theory, we derive sufficient conditions to ensure the containment control of the considered multi-agent system. Finally, we verify the derived theoretical results through two numerical examples.

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具有缺失测量的多代理系统的混合触发输出反馈遏制控制

在本文中，我们研究了具有缺失测量的多代理系统的输出反馈遏制控制问题。主要目标是设计一种混合触发控制器，它不仅能减少不必要的数据传输，还能确保所需的控制性能。所提出的混合触发控制器是通过使用伯努利随机变量将时间触发和事件触发方案结合起来而开发的。利用 Lyapunov 稳定性理论，我们推导出了确保对所考虑的多代理系统进行遏制控制的充分条件。最后，我们通过两个数值实例验证了推导出的理论结果。

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

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.