Bipartite tracking for Euler–Lagrange systems via prescribed-time hierarchical control based on the matrix-weighted signed digraphs

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Robust and Nonlinear Control Pub Date : 2024-08-05 DOI:10.1002/rnc.7585
Ren-Jie Gu, Tao Han, Bo Xiao, Xi-Sheng Zhan, Huaicheng Yan
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Abstract

The goal of this article is to investigate the bipartite tracking control problem using a prescribed-time convergence method for Euler–Lagrange systems (ELSs) with external disturbances. Agent interactions are represented by a matrix-weighted signed directed graph. There are not only cooperative but also adversarial interactions. An essential component of the system, the prescribed-time hierarchical control (PTHC) algorithm, including a more general time-varying function, is newly developed to guarantee that all agents converge to the same leader state but with opposite signs within a prescribed time. The salient feature of the introduced method lies in the fact that the convergence time of the control objective can be prespecified by the user. The state transformation, the property of the matrix-weighted Laplacian, and the generalized Lyapunov stability argument are employed to theoretically validate the proposed algorithms. Finally, the effectiveness of the algorithm is confirmed through the execution of numerical examples.

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通过基于矩阵加权符号数字图的规定时间分层控制实现欧拉-拉格朗日系统的双向跟踪
本文旨在研究具有外部扰动的欧拉-拉格朗日系统(ELS)的双方位跟踪控制问题,采用了一种规定时间收敛方法。代理交互由矩阵加权有符号有向图表示。不仅存在合作性交互,还存在对抗性交互。该系统的一个重要组成部分,即规定时间分层控制(PTHC)算法,包括一个更通用的时变函数,是新开发的,以保证所有代理在规定时间内收敛到相同的领导状态,但符号相反。该方法的显著特点在于控制目标的收敛时间可以由用户预先设定。利用状态变换、矩阵加权拉普拉斯的特性和广义 Lyapunov 稳定性论证,从理论上验证了所提出的算法。最后,通过执行数值示例证实了算法的有效性。
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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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