基于观测器的多智能体系统自适应二部时变队形跟踪

IF 3.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Robust and Nonlinear Control Pub Date : 2025-05-02 DOI:10.1002/rnc.7982

Xinyu Zhang, Xisheng Zhan, Jie Wu, Tao Han, Huaicheng Yan

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引用次数: 0

摘要

研究了具有有界输入和未知输入的线性多智能体系统（MASs）的二部时变队形约束（BTVFC）跟踪问题。本文提出了一种基于双部控制的适合MASs的自适应BTVFC跟踪协议。研究表明，利用李雅普诺夫稳定性，真实领导者可以实现预期的形成并跟踪虚拟领导者的轨迹，追随者可以进入领导者穿过的凸包络。此外，本文还设计了状态观测器来观察系统中难以测量的agent的状态。最后，通过数值模拟验证了本文主要研究结果的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Observer-Based Adaptive Bipartite Time-Varying Formation-Containment Tracking for Multi-Agent Systems

This article investigates the bipartite time-varying formation-containment (BTVFC) tracking problem of linear multi-agent systems (MASs) with bounded and unknown inputs for the virtual leader. This article proposes adaptive BTVFC tracking protocols suitable for MASs based on bipartite control. It has been demonstrated that real leaders can realize the expected formation and track the virtual leaders' trajectory, and followers can enter the convex envelope that the leaders cross by utilizing Lyapunov stability. In addition, this article also designs the state observer to observe the states of agents that are difficult to measure in the system. Finally, the validity of the main results in the article was verified through numerical simulations.

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来源期刊

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.