Adaptive Neural Security Control for Networked Singular Systems Under Deception Attacks

Abstract

This paper studies the issue of the adaptive neural security controller design for uncertain networked singular systems in the presence of deception attacks. Considering that the attack signal is unknown, the neural networks technique is exploited to approximate the attack signal, which eliminates the assumption that the attack signal has a known upper bound. By combining the state feedback with the estimated information of the attack, the impact of the attack is effectively compensated. Furthermore, a novel Lyapunov function, including the decomposed state vector and the weight matrix estimation error, is established to evaluate the bounded area of the system state. Finally, a numerical example substantiates the validity of the theoretical results.INDEX TERMS Networked singular systems, neural networks, security control, deception attacks.

Adaptive neural control for delayed discrete-time switched systems under deception attacks

“…Based on the sufficient condition (11) and the Schur complement of sufficient condition (12), we can deduce that…”

“…Therefore, the deception attack signals can be identified and estimated by applying neural network (NN) techniques, and the approximation function can be employed to design the controller in this article. Currently, the applications of NN techniques to approximate nonlinear functions have yielded numerous research results [11] . In nonlinear systems, these techniques were employed to approximate nonlinear functions that construct adaptive models to save communication resources and address stabilization problems.…”