Event-Triggered Output Feedback Synchronization of Master-Slave Neural Networks Under Deception Attacks

The problem of event-triggered synchronization of master-slave neural networks is investigated in this article. It is assumed that both communication channels from the sensor to controller and from controller to actuator are subject to stochastic deception attacks modeled by two independent Markov processes. Two discrete event-triggered mechanisms are introduced for both channels to reduce the number of data transmission through the communication channels. To comply with practical point of view, static output feedback is utilized. By employing the Lyapunov-Krasovskii functional method, some sufficient conditions on the synchronization of master-slave neural networks are derived in terms of linear matrix inequalities, which make it easy to design suitable output feedback controllers. Finally, a numerical example is presented to show the effectiveness of the proposed method.

Automated summary

This article investigates the problem of event-triggered synchronization of master-slave neural networks, assuming stochastic deception attacks on the communication channels. Two discrete event-triggered mechanisms and static output feedback are introduced to reduce data transmission. By using the Lyapunov-Krasovskii functional method, sufficient conditions for synchronization are derived in terms of linear matrix inequalities, facilitating the design of suitable controllers. A numerical example is presented to demonstrate the effectiveness of the proposed method.