基于事件的随机lyapunov型定理及其在领导-跟随多智能体系统中的应用

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences Pub Date : 2025-08-26 DOI:10.1016/j.ins.2025.122622

Zunjie Yu , Weihai Zhang , Qingpeng Meng , Chao Guo

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引用次数: 0

摘要

基于事件触发机制，建立了随机脉冲系统的随机lyapunov型定理，并讨论了该系统的概率稳定性。特别是，这种机制不需要实时监控系统行为，从而大大节省了网络资源，减少了网络负担。此外，即使对于需要实时系统行为监控的触发机制，这个定理仍然有效。作为应用，我们研究了基于事件触发脉冲控制的随机领导-跟随多智能体系统的随机一致性问题。最后给出了一个算例，证明了该协议的实用性。

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Event-based stochastic Lyapunov-type theorem and its application to leader-following multi-agent systems

Based on the event-triggered mechanism, we establish a stochastic Lyapunov-type theorem for stochastic impulsive systems and discuss stability in probability of such systems. In particular, this mechanism does not require real-time monitoring of system behavior, resulting in significant savings in network resources and reducing the network burden. Furthermore, this theorem remains valid even for triggering mechanisms that require real-time system behavior monitoring. As an application, we explore the stochastic consensus of a stochastic leader-following multi-agent system based on event-triggered impulsive control. Finally, an example is given to prove that the proposed protocol is practical.

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来源期刊

Information Sciences 工程技术-计算机：信息系统

CiteScore

14.00

自引率

17.30%

发文量

1322

审稿时长

10.4 months

期刊介绍： Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.