Finite-time synchronization of reaction-diffusion memristive neural networks: A gain-scheduled integral sliding mode control scheme.

Journal: ISA transactions
Published Date: Aug 20, 2022

Abstract

The finite-time synchronization issue of reaction-diffusion memristive neural networks (RDMNNs) is studied in this paper. To better synchronize the parameter-varying drive and response systems, an innovative gain-scheduled integral sliding mode control scheme is proposed, where the 2 controller gains can be scheduled and an integral switching surface function that contains a discontinuous term is involved. Moreover, by constructing a novel Lyapunov-Krasovskii functional and combining reciprocally convex combination (RCC) method, a less conservative finite-time synchronization criterion for RDMNNs is derived in the form of linear matrix inequalities (LMIs). Finally, three numerical simulations are exploited to illustrate the effectiveness, superiority and practicability of this paper.

Authors

  • Jingtao Man
    School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China. Electronic address: 180318050247@stu.haust.edu.cn.
  • Xiaona Song
    School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China. Electronic address: xiaona97@haust.edu.cn.
  • Shuai Song
    School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China. Electronic address: shuaisong@njust.edu.cn.
  • Junwei Lu
    School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210042, China.

Keywords

External Resources

Popular Topics
Recent Journals