Adaptive neural network based stabilization and trajectory tracking control of discrete-time chaotic systems

This paper investigates the stability and tracking performance of discrete-time chaotic systems in the presence of external disturbance and noise. For this purpose, a neural network control scheme is developed on the basis of a novel adaptive learning rate to stabilize the chaotic motion of discrete-time chaotic systems to a fixed point as well as to track the desired reference trajectory. The effectiveness of the proposed method is investigated through simulation studies on 2 dimensional Lozi map and performance comparison has been made with well-known backstepping control strategy