Event-Triggered Adaptive NN Control for a Class of Unknown Stochastic Nonlinear Systems under DoS Attacks

Abstract

In this paper, a resilient control problem is investigated for a class of stochastic nonlinear systems under denial-of-service attacks. First, the unknown nonlinear noise strength is approximated by a neural network. Then, an event-triggering mechanism is designed to save resources and a sufficient condition is given to guarantee the system state semi-global mean-square uniformly ultimately bounded. Finally, the effectiveness of the proposed control strategy is demonstrated by a numerical example.