Fixed-time event-triggered pinning synchronization of complex network via aperiodically intermittent control

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Junru Zhang, Jian-An Wang, Jie Zhang, Mingjie Li, Zhicheng Zhao, Xinyu Wen
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引用次数: 0

Abstract

This paper studies the fixed-time event-triggered pinning synchronization problem for complex network using aperiodic intermittent control. A novel fixed-time stability lemma with aperiodic intermittent characteristic is first proposed. By designing appropriate event-triggered aperiodic intermittent pinning controller (ETAIPC) based on the average control rate, several conditions are derived to ensure the fixed-time synchronization. The upper bound of setting-time is independent of any initial values and only concerns with design parameters, network size and node dimension. A simple to execute selection algorithm is adopted to renew the pinning node set. The Zeno behavior is also excluded through a rigorous theoretical analysis. Simulation examples are employed to demonstrate the efficacy of the obtained method.
通过非周期性间歇控制实现复杂网络的固定时间事件触发引脚同步
本文研究了使用非周期性间歇控制的复杂网络的固定时间事件触发引脚同步问题。首先提出了一个具有非周期性间歇特性的新型固定时间稳定性定理。通过基于平均控制率设计适当的事件触发非周期性间歇引脚控制器(ETAIPC),得出了几个确保固定时间同步的条件。设定时间的上限与任何初始值无关,只与设计参数、网络大小和节点尺寸有关。采用了一种易于执行的选择算法来更新引脚节点集。通过严格的理论分析,还排除了 Zeno 行为。仿真实例证明了所获方法的有效性。
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
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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