Model reference adaptive control using neural networks for synchronization of discrete-time chaotic systems

This paper presents a model reference adaptive control (MRAC) approach based on neural networks (NN) for the synchronization of a discrete-time chaotic systems. The input of reference model system is chosen using the output of master system and the slave system is the discrete-time chaotic system. We design the adaptive controller using NN so that the controlled slave system achieves asymptotic synchronization with the reference system given that master system and slave system with different conditions and/or different type of model. The parameters of controller which can stabilize the error equation are updated via a projection algorithm. Simulation examples are given to demonstrate the validity of our proposed adaptive method.