高维异质Kuramoto振子网络的全局同步:钉住脉冲方法

IF 10.5 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Cybernetics Pub Date : 2025-02-13 DOI:10.1109/TCYB.2025.3531105
Shanshan Peng;Jianquan Lu;Tingwen Huang;Jürgen Kurths
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引用次数: 0

摘要

对于连续时间演化的高维异质Kuramoto振子网络(HDHKONs)来说,实现全局同步是极具挑战性的,在许多情况下,对于各种网络配置(包括柏拉图体和阿基米德体)或大规模网络来说,实现全局同步似乎是不可能的。在此基础上,提出了一种固定脉冲控制方法,在不施加初始相位分布约束的情况下实现单位球上的全局同步。利用这种方法,通过脉冲控制一小部分振子,整个网络全局稳定在一个客观的轨迹上。此外,还提供了几个同步标准,以确保全局同步过程成功。这些标准很容易验证,并阐明了网络参数、受控振荡器的百分比、脉冲强度和脉冲频率之间的相互作用。最后,通过两个算例验证了理论结果。
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Global Synchronization of High-Dimensional Heterogeneous Kuramoto Oscillator Networks: Pinning Impulsive Approach
For high-dimensional heterogeneous Kuramoto oscillator networks (HDHKONs) evolving in continuous time, achieving global synchronization can be exceedingly challenging, and in many cases, it may even appear impossible for various network configurations, including Platonic solids and Archimedean solids, or large-scale networks. Herein, a pinning impulsive control approach for HDHKONs is developed to attain global synchronization on the unit sphere without imposing constraints on initial phase distributions. With this approach, the entire network is globally stabilized onto an objective trajectory by impulsively controlling only a small fraction of oscillators. Furthermore, several synchronization criteria are provided to ensure that the global synchronization procedure is successful. These criteria are easily verified and shed light on the interplay among network parameters, the percentage of controlled oscillators, impulsive intensity, and impulsive frequency. Finally, two examples are implemented to validate the theoretical results.
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来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
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