非参数不确定性非线性系统的自适应观测器设计

本文考虑一类不确定非线性系统的自适应观测器设计问题.系统的不确定性不能参数化,这类非线性系统的观测器无法用传统方法设计.首先用神经网络对系统的不确定性进行逼近,然后利用神经网络的基函数向量对系统进行滤波变换,再由此构造自适应观测器.给出了观测误差估计.本文结果表明适当选定神经网络的逼近精度和调整观测器的设计参数可使观测误差任意地小.

Adaptive observer design for nonlinear systems with nonparametric uncertainties

Based on neural networks, we design an adaptive observer for a class of systems with nonparametric uncertainties, improving the traditional methods which fail in designing the adaptive observers for such a class of systems. First, a neural network is used to approximate the uncertainties; and then, a filtered transformation of the system is performed by using the vector of the basic functions of the neural network. Based on the transformed system, the observer for the original system is constructed. An estimation of the observation error is also given. The result of the paper shows that the observation error value can be made arbitrarily small by reducing the error in the approximate uncertainties and properly choosing the design parameters of the observer.