通过双终端事件触发机制实现单边利普斯奇茨多代理系统的分布式领导者-追随者双方共识。

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Networks Pub Date : 2024-10-19 DOI:10.1016/j.neunet.2024.106808
Yanjun Zhao , Haibin Sun , Xiangyu Wang , Dong Yang , Ticao Jiao
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引用次数: 0

摘要

本文通过双终端事件触发输出反馈控制方法,分析了单边 Lipschitz 多代理系统的领导者-追随双方共识。文章设计了一个分布式观测器,利用触发时点的相对输出信息来估计未知系统状态,然后提出了一个事件触发输出反馈控制器。在传感器-观测器信道和控制器-执行器信道中提出了双端动态事件触发机制,这在很大程度上节省了通信资源,并排除了芝诺行为。提出了一种新的广义单边 Lipschitz 条件来处理非线性项并实现两方共识。还提出了一些稳定性条件,以保证领导者-跟随者的两方共识。最后,介绍了单链机器人操纵器系统,以证明所设计方案的可用性。结果表明,机器人操纵器的代理可以双向跟踪参考轨迹,并在传感器-观察者和控制器-执行器通道上分别有效减少了 61.22% 和 68.04% 的通信资源。
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Distributed leader-following bipartite consensus for one-sided Lipschitz multi-agent systems via dual-terminal event-triggered mechanism
This article analyses leader-following bipartite consensus for one-sided Lipschitz multi-agent systems by dual-terminal event-triggered output feedback control approach. A distributed observer is designed to estimate unknown system states by employing relative output information at triggering time instants, and then an event-triggered output feedback controller is proposed. Dual-terminal dynamic event-triggered mechanisms are proposed in sensor–observer channel and controller–actuator channel, which can save communication resources to a great extent, and the Zeno behavior is ruled out. A new generalized one-sided Lipschitz condition is proposed to handle the nonlinear term and achieve bipartite consensus. Some stability conditions are presented to guarantee leader-following bipartite consensus. Finally, one-link robot manipulator systems are introduced to demonstrate the availability of the designed scheme. The results demonstrate that the agents of the robot manipulators can track the reference trajectories bi-directionally, and effectively reduce communication resources by 61.22% and 68.04% at the sensor–observer and controller–actuator channels, respectively.
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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
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