Sampled-data exponential synchronization of time-delay neural networks subject to random controller gain perturbations

Abstract

• A non-fragile sampled-data control method is used to study the exponential synchronization of neural networks with mixed delays. • A sufficient exponential stability condition is developed by using a new two-sided looped Lyapunov functional. • A design scheme of the non-fragile sampled-data controller is proposed by using some decoupling techniques. In this paper, a non-fragile sampled-data control method is used to investigate the exponential synchronization of neural networks with discrete and distributed delays. The occurrence of controller gain perturbations is assumed to be random, which is described by a stochastic variable with the Bernoulli distribution. An extended two-sided looped Lyapunov functional is constructed, which efficiently utilizes available state information of the sampled instants. By using the two-sided looped Lyapunov functional and introducing suitable free weighting matrices, a sufficient condition is derived under which the resulting synchronization-error system is exponentially stable. Then, a design scheme of the non-fragile sampled-data controller is proposed with the aid of some decoupling techniques. At last, a numerical example is provided to illustrate the effectiveness and superiority of the proposed sampled-data control method.