间歇通信下合作多智能体网络的群集动力学

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xiaoyu Shi , Eber J. Ávila-Martínez , Yixiao Li , Lei Shi
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引用次数: 0

摘要

在这项工作中,我们研究了cucker - small (C-S)模型中涉及间歇通信的领导者-追随者群体控制问题。人们认识到,在控制器、传感器和执行器之间的通道中,间歇性通信是随机发生的。为了评估C-S模型的群集行为,本研究提出了一种基于次随机矩阵乘积的技术。该方法建立了集群行为的充分条件,这些条件取决于智能体的权函数、拓扑结构和初始状态。与以前的结果只适用于特定形式的正和递减权函数相反,我们的发现更普遍,可以应用于任何下界非零的正和递减权函数。此外,通过对误差系统的分析,保证了所有个体的速度最终趋于一致,并收敛在一个有上界的凸包内。最后,通过仿真算例验证了结果的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Flocking dynamics for cooperation multi-agent networks subject to intermittent communication
In this work, we investigate the leader-follower flocking control issue of the Cucker-Smale(C-S) model that involves intermittent communication. It is recognized that intermittent communication occurred randomly in the channels between controllers, sensors, and actuators. In order to assess the flocking behavior of the C-S model, this research suggests a technique based on the product of sub-stochastic matrices. This method establishes the sufficient conditions for flocking behavior, which are contingent on the agent’s weight function, topological structure, and initial state. In contrast to previous results that only apply to specific forms of positive and decreasing weight functions, our findings are more generic and can be applied to any positive and decreasing weight functions with non-zero lower bounds. In addition, through the analysis of the error system, it is ensured that the speeds of all individuals eventually tend to be consistent and converge within a convex hull with an upper bound. Eventually, the validity of our results is verified through simulation examples.
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来源期刊
Expert Systems with Applications
Expert Systems with Applications 工程技术-工程:电子与电气
CiteScore
13.80
自引率
10.60%
发文量
2045
审稿时长
8.7 months
期刊介绍: Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.
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