Dynamic periodic event-triggered control of stochastic complex networks with time-varying delays

IF 6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks Pub Date : 2025-06-06 DOI:10.1016/j.neunet.2025.107659

Xuetao Yang , Anjie Li , Quanxin Zhu

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Abstract

This paper focuses on the exponential stabilization of stochastic complex network systems with time-varying delays. A novel dynamic periodic event-triggered control (ETC) with the graph theory and the Lyapunov–Razumikhin method is proposed. First, different from continuous ETCs, periodic sampling inherently prevents the occurrence of Zeno phenomenon. Second, compared with the traditional static ETCs, our dynamic ETC can reduce the update frequency of the controller and save communication resources by designing a proper dynamic function. Moreover, the graph theory is employed to handle the coupling relationships among nodes in complex networks and the Lyapunov–Razumikhin method is employed to address the difficulties caused by time delays in stochastic complex systems. Then, the mean-square exponential stabilization for stochastic complex network systems is obtained. Finally, a numerical example of single-link robot arm with multiple nodes is performed in a stochastic complex network system to verify the effectiveness of theoretical results.

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时变时滞随机复杂网络的动态周期事件触发控制

研究了具有时变时滞的随机复杂网络系统的指数镇定问题。利用图论和Lyapunov-Razumikhin方法，提出了一种新的动态周期事件触发控制（ETC）。首先，与连续ETCs不同，周期性采样本质上防止了芝诺现象的发生。其次，与传统的静态ETC相比，通过设计适当的动态函数，我们的动态ETC可以降低控制器的更新频率，节省通信资源。此外，利用图论处理复杂网络中节点间的耦合关系，利用Lyapunov-Razumikhin方法解决随机复杂系统中时间延迟带来的困难。然后，得到了随机复杂网络系统的均方指数镇定性。最后，以随机复杂网络系统中的多节点单连杆机械臂为例，验证了理论结果的有效性。

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

Neural Networks 工程技术-计算机：人工智能

CiteScore

13.90

自引率

7.70%

发文量

425

审稿时长

67 days

期刊介绍： Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.