Distributed Control in Uncertain Nonlinear Multiagent Systems Under Event-Triggered Communication and General Directed Graphs

IF 3 3区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-07-03 DOI:10.1109/TSIPN.2024.3422878
Gang Wang;Zongyu Zuo;Peng Li
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Abstract

Designing a consensus algorithm for multiagent systems within an event-triggered communication setting is challenging due to the discontinuous and inaccurate interaction information caused by event-triggering mechanisms. Currently, most related results require an undirected or balanced directed graph. To avoid such restrictive requirements and consider general directed graphs with a spanning tree, we first investigate the perturbed consensus problem of first-order dynamics. Then, we extend our findings to address the consensus problem of the uncertain nonlinear multiagent systems described in Lagrangian dynamics, Brunovsky dynamics, and strict-feedback dynamics under event-triggered communication. We develop three distributed consensus protocols that consider the unique characteristics of these systems and assign different reference signals accordingly. Our proposed schemes ensure that consensus errors either converge to zero or to a small adjustable neighborhood around zero without Zeno behavior while preserving signal boundedness in the closed-loop system. Finally, we conduct extensive simulations to further illustrate the efficiency of our theoretical results.
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事件触发通信和一般有向图条件下不确定非线性多代理系统的分布式控制
由于事件触发机制导致交互信息不连续、不准确,因此在事件触发通信环境下为多代理系统设计共识算法具有挑战性。目前,大多数相关成果都需要无向图或平衡有向图。为了避免这种限制性要求,并考虑具有生成树的一般有向图,我们首先研究了一阶动力学的扰动共识问题。然后,我们将研究结果扩展到解决拉格朗日动力学、布鲁诺夫斯基动力学和事件触发通信下的严格反馈动力学中描述的不确定非线性多代理系统的共识问题。我们开发了三种分布式共识协议,考虑到了这些系统的独特性,并相应地分配了不同的参考信号。我们提出的方案可确保共识误差要么趋近于零,要么趋近于零点附近的一个可调整的小邻域,而不会出现泽诺行为,同时保持闭环系统中的信号有界性。最后,我们进行了大量仿真,以进一步说明我们理论结果的效率。
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来源期刊
IEEE Transactions on Signal and Information Processing over Networks
IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications
CiteScore
5.80
自引率
12.50%
发文量
56
期刊介绍: The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.
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