Globally Exponential Synchronization of Delayed Complex Dynamic Networks With Average Impulsive Delay-Gain

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Robust and Nonlinear Control Pub Date : 2024-12-04 DOI:10.1002/rnc.7753

Kangping Gao, Yishu Wang, Jianquan Lu, Jürgen Kurths

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Abstract

In this article, we investigate globally exponential synchronization problems in delayed complex dynamic networks (DCDNs) characterized by both time-varying impulsive delay and gain (TIDG). Our research is grounded on the Halanay inequality, which serves as the keystone of our analysis. Adopting the method of average impulsive delay-gain (AIDG), we formulate criteria for globally exponential synchronization dependent on the overall impulsive disturbances. Our criteria reveal the negative effect of AIDG on synchronization, which hinders the synchronization process. Additionally, we refine the concept of average impulsive gain to enhance its applicability. Furthermore, our results demonstrate that even in the simultaneous presence of desynchronizing and synchronizing impulses, along with time-varying impulsive delays, DCDNs are able to maintain the original synchronization under appropriate conditions, irrespective of whether the average impulsive interval is finite or not. Finally, we validate our theoretical findings by applying them to network examples.

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具有平均脉冲延迟增益的延迟复杂动态网络的全局指数同步

本文研究了具有时变脉冲时延和增益的时滞复杂动态网络（dcdn）的全局指数同步问题。我们的研究基于Halanay不等式，这是我们分析的基石。采用平均脉冲延迟增益（AIDG）方法，给出了依赖于整体脉冲扰动的全局指数同步判据。我们的标准揭示了AIDG对同步的负面影响，它阻碍了同步进程。此外，我们改进了平均脉冲增益的概念，以提高其适用性。此外，我们的研究结果表明，即使在同时存在非同步和同步脉冲以及时变脉冲延迟的情况下，无论平均脉冲间隔是否有限，dcdn都能够在适当的条件下保持原始同步。最后，我们通过网络实例验证了我们的理论发现。

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.