同步一类结构部分未知混沌系统的自适应非线性观测器

针对一类结构部分未知混沌系统，设计了一种舍有补偿器的自适应正交神经网络非线性观测器．这种自适应非线性观测器以混沌系统的线性部分能准确复制为前提，利用正交神经网络的非线性逼近能力实现结构部分未知混沌系统在状态不可测情况下的同步．在所设计的观测器中，正交神经网络的权值和补偿控制器的自适应调节规律都是通过Lyapunov稳定性原理推导所得，保证了闭环系统的稳定性，

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.