Neural-Network-Adaptive Event-Triggered Control for Stochastic Nonlinear Systems With Sensor Attacks

This article studies the adaptive neural network (NN) event-triggered secure control issue for stochastic nonlinear systems subject to sensor attacks. NNs are adopted to identify unknown nonlinear dynamics, and an NN state estimator is established to address the issue resulting from unmeasurable states. An NN observer is proposed to estimate unknown sensor attack signals. To save limited communication resources and reduce the number of controller updates, an event-triggered control (ETC) scheme is introduced. Then, an adaptive NN event-triggered secure control algorithm is designed by backstepping control method. The results demonstrate the stability of the control system and its consistent convergence in tracking errors under sensor attacks. Finally, simulations are shown to verify the effectiveness of the investigated theory.