非线性多智能体系统基于性能障碍的事件触发领导-追随者共识控制

IF 6.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Song Gao , Jin-Liang Wang , Shun-Yan Ren , Bei Peng
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引用次数: 0

摘要

本文提出了一种基于性能障碍的事件触发控制机制,研究了一类非线性多智能体系统(MASs)的leader-follower一致性问题。首先,提出了一种具有导出的质量稳定性条件的领导-追随者共识控制律,并集成了基于性能障碍的事件触发机制,以减少控制更新,同时保证Lyapunov函数的期望收敛性。随后,分析确定了最小事件间时间(MIET)的存在,从而加强了所提出方法在现实场景中的实际可行性。此外,还引入了一种动态平均共识算法,将该策略扩展到分布式质量。仿真结果表明,该控制协议有效地达到了规定的收敛性能,具有较强的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance-barrier-based event-triggered leader–follower consensus control for nonlinear multi-agent systems
This paper investigates the leader–follower consensus issue for a class of nonlinear multi-agent systems (MASs) by putting forth a novel performance-barrier-based event-triggered control mechanism. First, a leader–follower consensus control law is proposed with a derived stability condition for the MASs, and a performance-barrier-based event-triggering mechanism is integrated to reduce control updates while guaranteeing the desired convergence of the Lyapunov function. Subsequently, the presence of the minimum inter-event time (MIET) is analytically established, reinforcing the practical feasibility of the proposed approach in real-world scenarios. In addition, a dynamic average consensus algorithm is incorporated to extend the strategy to distributed MASs. Finally, simulation results verify that the developed control protocol effectively achieves the prescribed convergence performance with strong robustness.
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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