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基于事件触发随机采样的半马尔可夫跳跃多智能体系统的均方一致性

Mean-square consensus of a semi-Markov jump multi-agent system based on event-triggered stochastic sampling.

作者信息

Zhao Duoduo, Gao Fang, Cao Jinde, Li Xiaoxin, Ma Xiaoqin

机构信息

School of Big Data and Artificial Intelligence, Chizhou University, Chizhou 247000, Anhui, China.

School of Mathematics, Southeast University, Nanjing 211189, Jiangsu, China.

出版信息

Math Biosci Eng. 2023 Jun 28;20(8):14241-14259. doi: 10.3934/mbe.2023637.

Abstract

This paper focuses on achieving leader-follower mean square consensus in semi-Markov jump multi-agent systems. To effectively reduce communication costs and control updates, we propose an event-triggered protocol based on stochastic sampling. The stochastic sampling interval randomly switches between finite given values, while the event-triggered function depends on the stochastic sampled data from neighboring agents. Using the event-triggered strategy, we present sufficient conditions to ensure mean square consensus. Finally, we provide a numerical example demonstrating the effectiveness of the theoretical results.

摘要

本文聚焦于在半马尔可夫跳跃多智能体系统中实现领导者-跟随者均方一致性。为有效降低通信成本和控制更新,我们提出一种基于随机采样的事件触发协议。随机采样间隔在有限给定值之间随机切换,而事件触发函数依赖于来自相邻智能体的随机采样数据。利用事件触发策略,我们给出确保均方一致性的充分条件。最后,我们提供一个数值例子来证明理论结果的有效性。

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