Exponential Synchronization of Reaction-Diffusion Systems on Networks via Asynchronous Intermittent Control

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-12-01 DOI:10.1109/TSIPN.2023.3338452

Jian Liu;Yan Yang;Yongbao Wu;Seaar Al-Dabooni;Lei Xue;Donald C. Wunsch

引用次数: 0

Abstract

In this article, the exponential synchronization (ES) of the reaction-diffusion systems on networks is studied under an asynchronous aperiodic intermittent control strategy. Different from the preceding studies, the control strategy of each node is different, which is more general and challenging. Meanwhile, to address the asynchrony problem of the asynchronous intermittent control, a new asynchronous average control rate (ACR) is constructed, which is different for each node. The ACR is greater than the lower bound of the control rate in the existing literature, which makes the results of this article less conservative. Then, by constructing the Lyapunov function and adopting the graph theory, some ES criteria are given for the reaction-diffusion systems on networks. Finally, the effectiveness of the algorithms is verified by the numerical simulations.

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通过异步间歇控制实现网络上反应扩散系统的指数同步

本文研究了异步非周期间歇控制策略下网络反应扩散系统的指数同步问题。与以往的研究不同，每个节点的控制策略不同，更具一般性和挑战性。同时，为了解决异步间歇控制的异步性问题，构造了一个新的异步平均控制率(ACR)，该ACR在每个节点上都是不同的。该ACR大于现有文献中控制率的下界，使得本文的结果不那么保守。然后，通过构造Lyapunov函数并采用图论给出了网络上反应扩散系统的ES准则。最后，通过数值仿真验证了算法的有效性。

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

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.