NNs-Based Event-Triggered Consensus Control of Nonlinear Multi-Agent Systems With Uncertain Dynamics

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• 英文论文题名：NNs-Based Event-Triggered Consensus Control of Nonlinear Multi-Agent Systems With Uncertain Dynamics
• 论文作者：YANG Yang ; YUE Dong ; XIE Xiangpeng ; ZHOU Yingjiang ; GE Hui
• 英文论文作者：YANG Yang ; YUE Dong ; XIE Xiangpeng ; ZHOU Yingjiang ; GE Hui ; College of Automation ; Nanjing University of Posts and Telecommunications ; Institute of Advanced Technology ; Nanjing University of Posts and Telecommunications
• 年：2017
• 作者机构：College of Automation,Nanjing University of Posts and Telecommunications;Institute of Advanced Technology,Nanjing University of Posts and Telecommunications;
• 英文论文关键词：Event-triggered Control ; Consensus ; Nonlinear Multi-agent Systems ; Neural Networks
• 会议召开时间：2017-07-26
• 会议录名称：第36届中国控制会议论文集（A）
• 英文会议录名称：Proceedings of the 36th Chinese Control Conference（A）
• 语种：英文
• 分类号：TP13;TP18
• 学会代码：KZLL
• 会议名称：第36届中国控制会议
• 会议地点：中国辽宁大连
• 主办单位：中国自动化学会控制理论专业委员会
• 学会名称：中国自动化学会控制理论专业委员会
• 页数：6
• 文件大小：224k
• 原文格式：D
• 会议级别：全国

摘要

In this paper, we consider the consensus problem for a class of uncertain nonlinear systems under an undirected communication topology via event-triggered approaches. The centralized and distributed event-triggered control schemes are presented utilizing neural networks(NNs) and event-driven mechanisms, where the advantages of the proposed control laws lie that they remove the requirement for exact priori knowledge about parameters of individual agents by taking advantage of NNs approximators and they save computing and communication resources since control tasks only execute at certain instants with respect to predefined threshold functions. It is proven that all signals in the closed-loop system are bounded and Zeno behavior is excluded.
In this paper, we consider the consensus problem for a class of uncertain nonlinear systems under an undirected communication topology via event-triggered approaches. The centralized and distributed event-triggered control schemes are presented utilizing neural networks(NNs) and event-driven mechanisms, where the advantages of the proposed control laws lie that they remove the requirement for exact priori knowledge about parameters of individual agents by taking advantage of NNs approximators and they save computing and communication resources since control tasks only execute at certain instants with respect to predefined threshold functions. It is proven that all signals in the closed-loop system are bounded and Zeno behavior is excluded.

引文

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