Finite-time non-fragile passivity control for neural networks with time-varying delay

被引：82

作者：

Rajavel, S. ^{[1
]}

Samidurai, R. ^{[1
]}

Cao, Jinde ^{[2
,3
,4
]}

Alsaedi, Ahmed ^{[5
]}

Ahmad, Bashir ^{[5
]}

机构：

[1] Thiruvalluvar Univ, Dept Math, Vellore 632115, Tamil Nadu, India

[2] Southeast Univ, Dept Math, Nanjing 210096, Jiangsu, Peoples R China

[3] Southeast Univ, Res Ctr Complex Syst & Network Sci, Nanjing 210096, Jiangsu, Peoples R China

[4] King Abdulaziz Univ, Dept Math, Fac Sci, Jeddah 21589, Saudi Arabia

[5] King Abdulaziz Univ, Dept Math, Nonlinear Anal & Appl Math NAAM Res Grp, Jeddah 21589, Saudi Arabia

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2017年 / 297卷

关键词：

Finite-time; Passivity; Non-fragile; Linear matrix inequality; Lyapunov-Krasovskii functional; H-INFINITY CONTROL; DISTRIBUTED DELAYS; STABILITY ANALYSIS; OBSERVER DESIGN; SYSTEMS; DISCRETE; BOUNDEDNESS; INPUT; STATE;

D O I：

10.1016/j.amc.2016.10.038

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, the problem of finite-time non-fragile passivity control for neural networks with time-varying delay is studied. We construct a new Lyapunov-Krasovskii function with triple and four integral terms and then utilizing Wirtinger-type inequality technique. The sufficient conditions for finite-time boundedness and finite-time passivity are derived. Furthermore, a non-fragile state feedback controller is designed such that the closed-loop system is finite-time passive. Moreover, the proposed sufficient conditions can be simplified into the form of linear matrix inequalities (LMIs) using Matlab LMI toolbox. Finally, three numerical examples are presented to illustrate the effectiveness of the proposed criteria. (C) 2016 Elsevier Inc. All rights reserved.

引用

页码：145 / 158

页数：14

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