采样非线性系统的动态神经网络稳定自适应控制

研究了一类采样数据非线性系统的动态神经网络稳定自适应控制方法.不同于静态 神经网络自适应控制,动态神经网络自适应控制中神经网络用于逼近整个采样数据非线性系 统,而不是动态系统中的非线性分量.系统的控制律由神经网络系统的动态逆、自适应补偿项 和神经变结构鲁棒控制项组成.神经变结构控制用于保证系统的全局稳定性,并加速动态神 经网络系统的适近速度.证明了动态神经网络自适应控制系统的稳定性,并得到了动态神经 网络系统的学习算法.仿真研究表明,基于动态神经网络的非线性系统稳定自适应控制方法 较基于静态神经网络的自适应方法具有更好的性能.

STABLE ADAPTIVE CONTROL FOR SAMPLED-DATA NONLINEAR SYSTEMS USING DYNAMIC NEURAL NETWORKS

A stable adaptive control approach using dynamic neural networks(DNN's) has been developed for a class of multi input multi output(MIMO) sampled data nonlinear systems with unknown dynamic nonlinearities. Unlike static NN's (SNN's) to approximate nonlinear components in the dynamic system, DNN's are used to approximate the whole dynamic system. The system control law is composed of the dynamic inversion of the DNN system, adaptive compensation and NN variable structure control(VSC) components. The NN variable structurecontrol is used to guarantee the stability of the controlled system and improve the system dynamic performance. The proof of complete stability and tracking error convergence is given by using Lyapunov stability theory, and the learning algorithm for the DNN system is obtained thereby. Simulations for a two link manipulator show that the stable adaptive control approach using DNN's has a better dynamic performance than that using SNN's.