Practical Fixed-Time Bipartite Synchronization of Uncertain Coupled Neural Networks Subject to Deception Attacks via Dual-Channel Event-Triggered Control

• Published in: IEEE transactions on cybernetics Vol. 54; no. 6; pp. 3615 - 3625
• Main Authors: Chen, Xiangyong, Jia, Tianyuan, Wang, Zhanshan, Xie, Xiangpeng, Qiu, Jianlong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuators; Automation; Biological neural networks; Bipartite synchronization; Channels; Communication channels; Consumption; Controllers; Cyberattack; deception attacks (DAs); dual-channel event-triggered control (DCETC); Event triggered control; fixed-time stability; Graphical representations; Laplace equations; Neural networks; Synchronization; Time synchronization; Uncertainty
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2023.3338165

This article investigates the practical fixed-time synchronization of uncertain coupled neural networks via dual-channel event-triggered control. Contrary to some previous studies, the bipartite synchronization of signed graphs representing cooperative and antagonistic interactions is studied. The communication channel is introduced into deception attacks, which are described by Bernoulli's stochastic variables. Based on the concept of two channels, event-triggered mechanisms are designed for sensor-to-controller and controller-to-actuator channels to reduce communication consumption and controller update consumption as much as possible. Lyapunov and comparison theories are used to derive synchronization criteria and explicit expression of settling time. An example of Chua's circuit system is presented to demonstrate the feasibility of the obtained theoretical results.