Boundary intermittent stabilization for delay reaction–diffusion cellular neural networks

Exponential stability is considered for delay reaction–diffusion cellular neural networks (DRDCNNs) under two cases where the state information is fully available and not fully available. When the state information of controlled system is fully available, an aperiodically intermittent boundary controller is designed to stabilize the controlled system. When the state information is not fully available, we propose an observer based on the boundary output to estimate the system state, and an observer-based aperiodically intermittent boundary controller is designed. Employing the Lyapunov functional method and Poincaré’s inequality, we obtain a criterion to ensure DRDCNNs achieve the exponential stabilization. Based on our obtained results, the influence of diffusion coefficient matrix, control gains, time-delays and control proportion on the stability are studied. To illustrate the effectiveness of our theoretical results, at last, numerical examples are given.