有输入未建模动态的非线性系统自适应逆设计

对于一类存在输人未建模动态的非线性系统,提出了一种基于RBF神经网络的自适应逆补偿器设计方法。首先用两个神经网络设计了补偿器,一个用来估计输入未建模动态,另一个用来作为未建模动态的自适应逆补偿器。该设计放宽了对未建模动态的一些苛刻的要求,如相对度为零,满足小增益条件等。文中仅要求D(u)逆稳和连续光滑。然后应用反演设计技术设计了控制器,并应用Lyapunov稳定性理论推导出神经网络权重向量的调节律,同时证明了闭环系统的渐近稳定性。最后给出的仿真研究证明了该设计方法的有效性。

Adaptive Inverse Design for Nonlinear Systems With Input Unmodeled Dynamics

A neural adaptive inverse compensator design method is proposed for a class of nonlinear systems with input ummodeled dynamics based on RBF neural networks. The compensator is designed using two neural networks, one to estimate the input unmodeled dynamics and another to provide adaptive inverse compensation to the input unmodeled dynamics. The method relaxes some rigorous demands to unmodeled dynamics such as relative degree zero, satisfying the small gain assumption and so on. In this paper we only assume that D(u) is minimum-phased, continuous and smooth. The controller is designed using backstepping control techniques. The Lyapunov stability theorem is used to derive the tuning laws for the weight vectors of the neural networks and prove that the close-loop system is gradually stable. Simulation studies are included to demonstrate the effectiveness of the proposed method.