Distributed bipartite time‐varying formation tracking control with prescribed‐time convergence for heterogeneous Euler‐Lagrange systems

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

International Journal of Robust and Nonlinear Control Pub Date : 2024-09-03 DOI:10.1002/rnc.7620

Huimin Zhong, Tao Han, Bo Xiao, Xi‐Sheng Zhan, Huaicheng Yan

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Abstract

This paper focuses on the study of the prescribed‐time bipartite time‐varying formation tracking (BTVFT) problem for multiple heterogeneous Euler‐Lagrange systems (HELSs) with external disturbances under directed signed graphs. Considering the fact that the states of leader cannot be accurately obtained, a set of observers which can estimate the states of the leader within a prescribed time, is firstly established according to the time‐varying function. Subsequently, a prescribed‐time distributed control protocol is proposed by employing a terminal sliding surface, aiming to achieve the BTVFT under digraphs within a prescribed time. The theoretical proof utilizes suitable Lyapunov functions to demonstrate that the control law can guide the state trajectories of each follower to the corresponding terminal sliding surface within a predefined time, thus ensuring the solution to the BTVFT problem of HELSs. Finally, simulation results are provided to validate the effectiveness of the derived findings.

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为异构欧拉-拉格朗日系统提供具有规定时间收敛性的分布式双方时变编队跟踪控制

本文主要研究有向符号图下具有外部干扰的多异质欧拉-拉格朗日系统（HELS）的规定时间双方位时变编队跟踪（BTVFT）问题。考虑到无法精确获得领导者的状态，首先根据时变函数建立一组能在规定时间内估计领导者状态的观测器。随后，通过使用终端滑动面，提出了一种规定时间分布式控制协议，旨在规定时间内实现数字图下的 BTVFT。理论证明利用合适的 Lyapunov 函数证明了控制法则能在预定时间内引导每个跟随者的状态轨迹到达相应的终端滑动面，从而确保解决 HELS 的 BTVFT 问题。最后，模拟结果验证了推导结论的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.