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Fuzzy Sampled-Data Control for Synchronization of T-S Fuzzy Reaction-Diffusion Neural Networks with Additive Time-Varying Delays

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Ruimei Zhang, Deqiang Zeng, Ju H. Park, Hak Keung Lam, Xiangpeng Xie

Original languageEnglish
Article number9113402
Pages (from-to)2384-2397
Number of pages14
JournalIEEE Transactions on Cybernetics
Volume51
Issue number5
DOIs
PublishedMay 2021

Bibliographical note

Funding Information: Manuscript received March 4, 2020; accepted May 18, 2020. Date of publication June 10, 2020; date of current version April 15, 2021. The work of Ju H. Park was supported by the National Research Foundation of Korea grant funded by the Korea Government (MSIT) under Grant 2020R1A2B5B02002002. The work of Xiangpeng Xie was supported by the Jiangsu Natural Science Foundation for Distinguished Young Scholars under Grant BK20190039. This article was recommended by Associate Editor C.-F. Juang. (Corresponding author: Ju H. Park.) Ruimei Zhang is with the College of Cybersecurity, Sichuan University, Chengdu 610065, China (e-mail: ruimeizhang163@163.com). Publisher Copyright: © 2013 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

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Abstract

This article focuses on the exponential synchronization problem of T-S fuzzy reaction-diffusion neural networks (RDNNs) with additive time-varying delays (ATVDs). Two control strategies, namely, fuzzy time sampled-data control and fuzzy time-space sampled-data control are newly proposed. Compared with some existing control schemes, the two fuzzy sampled-data control schemes cannot only tolerate some uncertainties but also save the limited communication resources for the considered systems. A new fuzzy-dependent adjustable matrix inequality technique is proposed. According to different fuzzy plant and controller rules, different adjustable matrices are introduced. In comparison with some traditional estimation techniques with a determined constant matrix, the fuzzy-dependent adjustable matrix approach is more flexible. Then, by constructing a suitable Lyapunov-Krasovskii functional (LKF) and using the fuzzy-dependent adjustable matrix approach, new exponential synchronization criteria are derived for T-S fuzzy RDNNs with ATVDs. Meanwhile, the desired fuzzy time and time-space sampled-data control gains are obtained by solving a set of linear matrix inequalities (LMIs). In the end, some simulations are presented to verify the effectiveness and superiority of the obtained theoretical results.

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