Multi-Agent Bipartite Flocking Control Over Cooperation-Competition Networks With Asynchronous Communications

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-04-03 DOI:10.1109/TSIPN.2024.3384817

Zhuangzhuang Ma;Lei Shi;Kai Chen;Jinliang Shao;Yuhua Cheng

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Abstract

In this contribution, the bipartite flocking control problem of a set of autonomous mobile agents over cooperation-competition networks is investigated. Two kinds of asynchronous communication scenarios are considered, where each agent communicates with the neighbors only at certain time instants determined by its own clock, but not at other time instants. In addition, each agent adjusts the control input at all time instants in the first asynchronous scenario, and adjusts the control input only at its communication time instants in the second asynchronous scenario. Nonlinear positive and negative weight functions are designed to describe the effect of the distance between agents on the cooperation/competition degree in real interaction scenarios, where the farther (closer) the distance, the weaker (stronger) the cooperation/competition degree. With the help of signed graph theory and sub-stochastic matrix, the dynamic models under different asynchronous scenarios are analyzed, and the algebraic conditions for achieving bipartite flocking control are established separately. At last, the effectiveness of algebraic conditions is verified through numerical simulations.

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具有异步通信功能的合作-竞争网络上的多代理双方成群控制

本文研究了一组自主移动代理在合作-竞争网络上的双向成群控制问题。本文考虑了两种异步通信情况，即每个代理只在由自身时钟决定的特定时间点与邻居通信，而不在其他时间点与邻居通信。此外，在第一种异步情况下，每个代理在所有时间时刻调整控制输入，而在第二种异步情况下，每个代理仅在其通信时间时刻调整控制输入。设计非线性正负权重函数是为了描述真实交互场景中代理之间的距离对合作/竞争程度的影响，距离越远（越近），合作/竞争程度越弱（越强）。借助符号图论和子随机矩阵，分析了不同异步场景下的动态模型，并分别建立了实现双方成群控制的代数条件。最后，通过数值模拟验证了代数条件的有效性。

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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%

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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.