A novel event-triggered asynchronous H∞ control for T-S fuzzy Markov jump systems under hidden Markov switching topologies

被引:20
作者
Xie, Wenqian [1 ,3 ]
Nguang, Sing Kiong [2 ]
Zhu, Hong [3 ]
Zhang, Yuping [3 ]
Shi, Kaibo [4 ,5 ]
机构
[1] Zhejiang Gongshang Univ, Sch Informat & Elect Engn, Sussex Artificial Intelligence Inst, Hangzhou 310018, Peoples R China
[2] Univ Auckland, Dept Elect Comp & Software Engn, Auckland 1142, New Zealand
[3] Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu 611731, Peoples R China
[4] Chengdu Univ, Sch Elect Informat & Elect Engn, Chengdu 610106, Peoples R China
[5] Univ Elect Sci & Technol China Guangdong, Inst Elect & Informat Engn, Dongguan 523808, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Fuzzy Markov jump systems; General transition rates; Event-triggered control; Asynchronous control; Hidden Markov model; NEURAL-NETWORKS; STABILIZATION; STABILITY; DESIGN;
D O I
10.1016/j.fss.2021.09.014
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
This paper investigates the network-based H-infinity control problem for Takagi-Sugeno fuzzy Markov jump systems with communication delays. Firstly, we propose a novel mode-dependent event-triggered communication scheme (ETCS) based on aperiodically sampled data, which largely reduce the data transmission. Secondly, to obtain a more applicable result, an asynchronous controller based on a hidden Markov model is designed for the first time to stabilize the fuzzy Markov jump systems with general transition rates. Thirdly, a less restrictive Lyapunov-Krasovskii functional, which is discontinuous and only required to be positive definite at endpoints of each subinterval of the holding intervals, is newly introduced for event-triggered asynchronous control issue. Based on the above methods, sufficient conditions with less conservatism are obtained to ensure the stochastic stability of the resulting closed-loop systems with a prescribed H(infinity )performance. A co-design method of the desired controller and ETCS is then presented. Finally, truck-trailer model and mass-spring-damper model are provided to demonstrate the effectiveness and practicability of the developed results. (C) 2021 Elsevier B.V. All rights reserved.
引用
收藏
页码:258 / 282
页数:25
相关论文
共 41 条
  • [1] Robust H∞ control for uncertain delayed T-S fuzzy systems with stochastic packet dropouts
    Cai, Xiao
    Zhong, Shouming
    Wang, Jun
    Shi, Kaibo
    [J]. APPLIED MATHEMATICS AND COMPUTATION, 2020, 385
  • [2] Nonstationary Control for T-S Fuzzy Markovian Switching Systems With Variable Quantization Density
    Cheng, Jun
    Shan, Yaonan
    Cao, Jinde
    Park, Ju H.
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2021, 29 (06) : 1375 - 1385
  • [3] Quantized Nonstationary Filtering of Networked Markov Switching RSNSs: A Multiple Hierarchical Structure Strategy
    Cheng, Jun
    Park, Ju H.
    Zhao, Xudong
    Karimi, Hamid Reza
    Cao, Jinde
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2020, 65 (11) : 4816 - 4823
  • [4] An Asynchronous Operation Approach to Event-Triggered Control for Fuzzy Markovian Jump Systems With General Switching Policies
    Cheng, Jun
    Park, Ju H.
    Zhang, Lixian
    Zhu, Yanzheng
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2018, 26 (01) : 6 - 18
  • [5] Finite-time H∞ fuzzy control of nonlinear Markovian jump delayed systems with partly uncertain transition descriptions
    Cheng, Jun
    Park, Ju H.
    Liu, Yajuan
    Liu, Zhijun
    Tang, Liming
    [J]. FUZZY SETS AND SYSTEMS, 2017, 314 : 99 - 115
  • [6] New result on reliable H∞ performance state estimation for memory static neural networks with stochastic sampled-data communication
    Dong, Shiyu
    Zhu, Hong
    Zhong, Shouming
    Shi, Kaibo
    Cheng, Jun
    Kang, Wei
    [J]. APPLIED MATHEMATICS AND COMPUTATION, 2020, 364
  • [7] Dynkin E.B., 1965, FIZMATGIZ MOSKOW, VI
  • [8] Gu K., 2003, STABILITY TIME DELAY
  • [9] Stabilization of Neural-Network-Based Control Systems via Event-Triggered Control With Nonperiodic Sampled Data
    Hu, Songlin
    Yue, Dong
    Xie, Xiangpeng
    Ma, Yong
    Yin, Xiuxia
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 29 (03) : 573 - 585
  • [10] Finite-time stability for fractional-order complex-valued neural networks with time delay
    Hu, Taotao
    He, Zheng
    Zhang, Xiaojun
    Zhong, Shouming
    [J]. APPLIED MATHEMATICS AND COMPUTATION, 2020, 365 (365)