Variable-sampling-period dependent global stabilization of delayed memristive neural networks based on refined switching event-triggered control  

作　　者：Zhilian YAN Xia HUANG Jinde CAO 

机构地区：[1]College of Electrical Engineering and Automation,Shandong University of Science and Technology,Qingdao 266590,China [2]School of Mathematics,Southeast University,Nanjing 210096,China

出　　处：《Science China(Information Sciences)》2020年第11期151-166,共16页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant Nos.61973199,61473178,61573008)。

摘　　要：This paper studies the stabilization problem of delayed memristive neural networks under eventtriggered control.A refined switching event-trigger scheme that switches between variable sampling and continuous event-trigger can be designed by introducing an exponential decay term into the threshold function.Compared with the existing mechanisms,the proposed scheme can enlarge the interval between two successively triggered events and therefore can reduce the amount of triggering times.By constructing a time-dependent and piecewise-defined Lyapunov functional,a less-conservative criterion can be derived to ensure global stability of the closed-loop system.Based on matrix decomposition,equivalent conditions in linear matrix inequalities form of the above stability criterion can be established for the co-design of both the trigger matrix and the feedback gain.A numerical example is provided to demonstrate the effectiveness of the theoretical analysis and the advantages of the refined switching event-trigger scheme.

关 键 词：event-triggered control delayed memristive neural networks global stabilization time-dependent Lyapunov functional variable sampling 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]