Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/134045
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Type: Journal article
Title: Synchronization of complex dynamical networks subject to noisy sampling interval and packet loss
Author: Hu, Z.
Ren, H.
Shi, P.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2022; 33(8):3216-3226
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2022
ISSN: 2162-237X
2162-2388
Statement of
Responsibility: 
Zhipei Hu, Hongru Ren, Peng Shi
Abstract: This article focuses on the sampled-data synchronization issue for a class of complex dynamical networks (CDNs) subject to noisy sampling intervals and successive packet losses. The sampling intervals are subject to noisy perturbations, and categorical distribution is used to characterize the sampling errors of noisy sampling intervals. By means of the input delay approach, the CDN under consideration is first converted into a delay system with delayed input subject to dual randomness and probability distribution characteristic. To verify the probability distribution characteristic of the delayed input, a novel characterization method is proposed, which is not the same as that of some existing literature. Based on this, a unified framework is then established. By recurring to the techniques of stochastic analysis, a probability-distribution-dependent controller is designed to guarantee the mean-square exponential synchronization of the error dynamical network. Subsequently, a special model is considered where only the lower and upper bounds of delayed input are utilized. Finally, to verify the analysis results and testify the effectiveness and superiority of the designed synchronization algorithm, a numerical example and an example using Chua's circuit are given.
Keywords: Complex dynamical networks (CDNs); noisy sampling interval; successive packet losses; synchronization control
Description: Date of publication 22 January 2021; date of current version 4 August 2022.
Rights: © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
DOI: 10.1109/TNNLS.2021.3051052
Grant ID: http://purl.org/au-research/grants/arc/DP170102644
Published version: http://dx.doi.org/10.1109/tnnls.2021.3051052
Appears in Collections:Electrical and Electronic Engineering publications

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