Synchronization of a class of partially unknown chaotic system with adaptive nonlinear observer

A novel adaptive nonlinear observer with a compensator based on orthogonal neural networks is designed for a class of chaotic system where the nonlinear portion of the structure can not be evaluated. The adaptation laws of the network weight and the controller are derived from Lyapunov stability analysis to ensure the stability of the closed loop system. Numerical examples show that the synchronization of chaotic system can be achieved even if the structure of the chaotic system gets partially unknown. Moreover, the higher synchronizing rate and smaller control task verify the practicability of the proposed method.