Bipartite formation control of second-order multi-agent systems with antagonistic interactions under dynamic event-triggered adaptive schemes

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS Information Sciences Pub Date : 2024-10-31 DOI:10.1016/j.ins.2024.121606

Zhenwei Liang , Ze Tang , Dong Ding , Jianwen Feng

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Abstract

This article examines the adaptive leader-following bipartite formation (LBF) of second-order nonlinear multi-agent systems (MASs) with antagonistic interactions over a directed signed graph. To save the scarce communication resources efficiently, a novel distributed controller is designed for the LBF of the MASs, where a dynamic event-triggered (DET) control protocol with dynamic parameters is proposed. Meanwhile, the optimal control gains for realizing adaptive bipartite formation are obtained in accordance with the designed adaptive updating laws. By employing the DET mechanism and the Lyapunov functionals in conjunction, sufficient criteria for the adaptive LBF of the second-order MASs with antagonistic interactions and a directed signed graph are obtained. Furthermore, it has been demonstrated that Zeno phenomenon doesn't exist. Finally, the efficacy of the theoretical outcomes and the control protocol is validated through a numerical simulation.

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动态事件触发自适应方案下具有拮抗相互作用的二阶多代理系统的两方形成控制

本文研究了在有向符号图上具有拮抗交互作用的二阶非线性多代理系统（MAS）的自适应领导-跟随双方阵（LBF）。为了有效节省稀缺的通信资源，本文设计了一种新型分布式控制器，其中提出了具有动态参数的动态事件触发（DET）控制协议。同时，根据所设计的自适应更新规律，获得了实现自适应双方阵的最优控制增益。通过结合使用 DET 机制和 Lyapunov 函数，获得了具有拮抗相互作用和有向符号图的二阶 MAS 的自适应 LBF 的充分标准。此外，还证明了芝诺现象并不存在。最后，通过数值模拟验证了理论成果和控制协议的有效性。

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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.