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
收藏
页码：145 / 158
页数：14
相关论文
共 50 条
  • [1] Non-fragile finite-time dissipative piecewise control for time-varying system with time-varying delay
    Chen, Menghua
    Sun, Jian
    IET CONTROL THEORY AND APPLICATIONS, 2019, 13 (03): : 321 - 332
  • [2] Non-fragile finite-time H∞ state estimation of neural networks with distributed time-varying delay
    Ali, M. Syed
    Saravanan, S.
    Zhu, Quanxin
    JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2017, 354 (16): : 7566 - 7584
  • [3] Non-fragile mixed H∞ and passivity control for neural networks with successive time-varying delay components
    Rajavel, Sathasivam
    Samidurai, Rajendran
    Kilbert, Sebastiyan Anthuvan Jerome
    Cao, Jinde
    Alsaedi, Ahmed
    NONLINEAR ANALYSIS-MODELLING AND CONTROL, 2018, 23 (02): : 159 - 181
  • [4] Finite-time passivity of neural networks with time varying delay
    Xiao, Jian
    Zeng, Zhigang
    JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2020, 357 (04): : 2437 - 2456
  • [5] Finite-Time Non-Fragile Extended Dissipative Control of Periodic Piecewise Time-Varying Systems
    Liu, Yun
    Deng, Zuolin
    Li, Panshuo
    Zhang, Bin
    IEEE ACCESS, 2020, 8 : 136512 - 136523
  • [6] Non-fragile control protocol for finite-time consensus of stochastic multi-agent systems with input time-varying delay
    B. Kaviarasan
    R. Sakthivel
    Y. Li
    D. Zhao
    Y. Ren
    International Journal of Machine Learning and Cybernetics, 2020, 11 : 325 - 337
  • [7] Non-fragile control protocol for finite-time consensus of stochastic multi-agent systems with input time-varying delay
    Kaviarasan, B.
    Sakthivel, R.
    Li, Y.
    Zhao, D.
    Ren, Y.
    INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2020, 11 (02) : 325 - 337
  • [8] New delay-dependent conditions for finite-time extended dissipativity based non-fragile feedback control for neural networks with mixed interval time-varying delays
    Zamart, Chantapish
    Botmart, Thongchai
    Weera, Wajaree
    Charoensin, Suphachai
    MATHEMATICS AND COMPUTERS IN SIMULATION, 2022, 201 : 684 - 713
  • [9] Finite time non-fragile dissipative control for uncertain TS fuzzy system with time-varying delay
    Ma, Yuechao
    Chen, Menghua
    NEUROCOMPUTING, 2016, 177 : 509 - 514
  • [10] The passivity of neural networks with time-varying delay
    Luo, Jinnan
    Tian, Wenhong
    Zhong, Shouming
    Liao, Daixi
    Cheng, Jun
    Shi, Kaibo
    2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019), 2019, : 2183 - 2188
12345