Observer-based event-triggered impulsive control of delayed reaction-diffusion neural networks

In this paper, we present a novel design of an observer-based event-triggered impulsive control strategy for delayed reaction-diffusion neural networks subject to impulsive perturbation. The impulsive instants of impulsive control are determined in an event-triggered way, and the control strength is designed by the sampling output of an impulsive observer. Several criteria with Lyapunov conditions and linear matrix inequalities are established for the global exponential stability of delayed reaction-diffusion neural networks. It inherits the advantages of event-triggered impulsive control such as low triggering frequency and high efficiency, and is applicable for networks with unmeasurable states. Finally, the effectiveness of theoretical results is verified by a numerical example.