基于神经网络的非线性系统复合内模控制

Nonlinear Internal Model Control Strategy Based on Neural Network

利用RBF神经网络逼近连续非线性系统的α阶积分逆系统,并对原非线性系统及其逆系统构成的伪线性系统采用内模控制方法进行复合控制,从理论上分析了滤波器对跟踪误差的影响.仿真结果表明,内模控制与逆系统方法相结合的复合控制方案是处理非线性问题比较有效的方法之一.

RBF neural network is used to approximate to the a -integral inverse system of nonlinear continuous systems in this paper. And then pseudo - linear systems, which are composed of nonlinear continuous systems and its a -integral inverse system, are combined by the internal model control method, and the effects of filter for tracking error are analyzed. Simulation results show that internal model control (IMC) method is one of available methods for nonlinear systems.