Event-triggered control for uncertain delayed neural networks with actuator saturation against deception attack

Abstract

In this paper, event-triggered control for uncertain delayed neural networks (DNNs) with actuator saturation against deception attack is discussed. We propose a novel framework into which an event-triggered mechanism (ETM), actuator saturation, system uncertainty, and deception attack are combined. In the framework, the discrete ETM is employed to determine whether the sampled signal should be transmitted to controller so as to save network bandwidth, a random occurrence deception attack model satisfying Bernoulli distribution is introduced to construct the system robust, and actuator saturation is considered due to the actual complex network environment. Based on the framework, we discussed the stochastic stability of a novel delayed neural network model in a closed-loop system. By resorting to the appropriate Lyapunov–Krasovskii functional (LKF), we derive some new sufficient conditions for the stochastically stable of the system and obtain the gain of the system controller using efficient linear matrix inequality (LMI) method. Finally, a numerical example, in the end, demonstrates the effectiveness of the proposed method.

Keywords:

• Event-triggered mechanism (ETM)
• delayed neural networks (DNNs)
• deception attack
• Bernoulli distribution
• actuator saturation

Disclosure statement

The authors declare that there is no conflict of interest regarding the publication of this paper.

Data availability statement

All data generated or analysed during this study are included in this published article.

Additional information

Funding

This research was supported by the National Natural Science Foundation of China [grant number 61502284].