Finite-time cluster synchronization for complex dynamical networks under FDI attack: A periodic control approach.

Journal: Neural networks : the official journal of the International Neural Network Society
Published Date:

Abstract

In this paper, the finite-time cluster synchronization problem is addressed for complex dynamical networks (CDNs) with cluster characteristics under false data injection (FDI) attacks. A type of FDI attack is taken into consideration to reflect the data manipulation that controllers in CDNs may suffer. In order to improve the synchronization effect while reducing the control cost, a new periodic secure control (PSC) strategy is proposed in which the set of pinning nodes changes periodically. The aim of this paper is to derive the gains of the periodic secure controller such that the synchronization error of the CDN remains at a certain threshold in finite time with the presence of external disturbances and false control signals simultaneously. Through considering the periodic characteristics of PSC, a sufficient condition is obtained to guarantee the desired cluster synchronization performance, based on which the gains of the periodic cluster synchronization controllers are acquired by resolving an optimization problem proposed in this paper. A numerical case is carried out to validate the cluster synchronization performance of the PSC strategy under cyber attacks.

Authors

  • Jun-Yi Li
    Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, 510006, Guangzhou, China; Pazhou Lab, 510330, Guangzhou, China. Electronic address: jun-yi-li@foxmail.com.
  • Yang-Cheng Huang
    Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, 510006, Guangzhou, China. Electronic address: yangcheng-huang@foxmail.com.
  • Hong-Xia Rao
    Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, 510006, Guangzhou, China. Electronic address: raohxia@163.com.
  • Yong Xu
    Department of Psychiatry, The First Hospital of Shanxi Medical University, Taiyuan, China.
  • Renquan Lu