Prescribed-Time Sampled-Data Control for the Bipartite Consensus of Linear Multi-Agent Systems in Singed Networks

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Cognitive Computation Pub Date : 2024-07-06 DOI:10.1007/s12559-024-10319-8
Mengke Liu, Wenbing Zhang, Guanglei Wu
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Abstract

This article examines the prescribed-time sampled-data control problem for multi-agent systems in signed networks. A time-varying high gain-based protocol is devised to solve the prescribed-time bipartite consensus problem of the linear multi-agent systems with the control gain matrix being resolved through the utilization of the parametric Lyapunov equation. By using the method of scalarization, sufficient conditions are attained to ensure the prescribed-time bipartite consensus of linear multi-agent systems, where the maximum allowable sampling interval (MASI) ensuring the prescribed-time consensus is determined by the initial values of the system state, the linear dynamics of the system, and the maximum eigenvalue of the Laplacian matrix. Specifically, the MASI is inversely proportional to the maximum eigenvalue of the Laplacian matrix. Finally, the validity of the conclusion is ensured through numerical simulation.

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针对单一网络中线性多代理系统的两方共识的规定时间采样数据控制
本文研究了签名网络中多代理系统的规定时间采样数据控制问题。本文设计了一种基于时变高增益的协议,以解决线性多代理系统的规定时间两端共识问题,并利用参数 Lyapunov 方程解决了控制增益矩阵问题。通过使用标量化方法,获得了确保线性多代理系统的规定时间双向共识的充分条件,其中确保规定时间共识的最大允许采样间隔(MASI)由系统状态的初始值、系统的线性动力学和拉普拉斯矩阵的最大特征值决定。具体来说,MASI 与拉普拉斯矩阵的最大特征值成反比。最后,通过数值模拟确保了结论的正确性。
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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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