基于RBF神经网络的非线性观察器设计

提出了一种新的非线性观察器设计方法。与一般方法采用神经网络逼近整个非线性系统不同，该方法用RBF神经网络逼近系统的非线性项，故提高了状态估计的精度。基于李亚普诺夫方法，证明了状态估计误差渐近稳定且渐近收敛到零。仿真结果表明，所提出的非线性观察器设计方法具有良好的性能。在故障检测、状态估计等领域具有广泛的应用前景。

DESIGN OF NONLINEAR OBSERVER FOR NONLINEAR SYSTEM BASED ON RBF NEURAL NETWORKS

A new type of nonlinear observer for nonlinear systems is presented. Instead of approximating the entire nonlinear system with the neural network (NN), only the un-modeled part left over after the linearization is approximated. Compared with the conventional linear observer, the observer provides more accurate estimation of the state. The state estimation error is proved to asymptotically approach zero with the Lyapunov method. The simulation result shows that the proposed observer scheme is effective and has a potential application ability in the fault detection and identification (FDI), and the state estimation.