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.

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