Distributed‐filter‐based double event‐triggered formation control for stochastic nonlinear MASs under switching topologies

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Yonghua Peng, Guohuai Lin, Hongru Ren, Hongyi Li
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引用次数: 0

Abstract

This article investigates a formation control method for stochastic nonlinear multi‐agent systems (MASs) under switching topologies. To reduce the communication bandwidth occupancy, two event‐triggered mechanisms of sensor‐to‐controller and controller‐to‐actuator network channels are proposed. Taking advantage of the neural networks approximation capability, a dynamic high‐gain observer is introduced to estimate unmeasured states and tackle the non‐differentiable issue of triggered output signal. Furthermore, it should be noted that a distributed filter is employed to handle the discontinuous local reference signal resulting from switching topologies. By using discontinuous topology information, the distributed filter generates a differentiable signal to design a virtual controller. Concomitantly, a first‐order filter is implemented to avoid the problem of “explosion of complexity.” Through stability analysis, it is proven that the designed formation controller achieves boundedness in probability for all signals in the stochastic nonlinear MASs. Ultimately, a simulation is performed to confirm the viability of the control approach.
开关拓扑下随机非线性 MAS 的基于分布式滤波器的双事件触发编队控制
本文研究了开关拓扑结构下随机非线性多代理系统(MAS)的编队控制方法。为了减少通信带宽占用,提出了传感器到控制器和控制器到执行器网络通道的两种事件触发机制。利用神经网络的近似能力,引入了动态高增益观测器来估计未测量的状态,并解决了触发输出信号的不可分问题。此外,值得注意的是,还采用了分布式滤波器来处理开关拓扑结构导致的不连续局部参考信号。利用不连续拓扑信息,分布式滤波器生成可微分信号,从而设计出虚拟控制器。同时,为了避免 "复杂性爆炸 "问题,采用了一阶滤波器。通过稳定性分析,证明所设计的编队控制器对随机非线性 MAS 中的所有信号都实现了概率有界。最后,还进行了仿真,以确认控制方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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