基于线性网络的一类带扰动线性对象的逆控制研究

将线性网络应用于一类带扰动的线性对象，提出了一种基于该线性网络的自适应逆控制方案，该方案由辨识器、控制器和扰动消除器三部分构成，合理选择三个线性网络的输入，通过辨识器的在线学习，同时更新控制器和扰动消除器的权值，文章研究了该方
案的收敛性和方案的跟踪性。根据可变步长权值收敛条件，设计了输入解相关变步长LMS算法调整辨识器权值方法。通过仿真研究了逆控制方法的有效性。
关键词：线性网络；LMS 算法；自适应逆控制
Abstract：The linear neural network is applied of a class linear plant with Disturbance, and a
LNN-based adaptive inverse control scheme is presented. The control scheme is composed of
three parts: identifier, controller and disturbance canceller. Three LLN’s respectively selected
inputs are so reasonable that when on-line training is only in the identifier, three networks’ weight values are updated at the same time. The scheme’s weight convergence is expatiated, and a desired reference can be tracked by the plant output. In according to the weight convergence condition of the variable step size least mean square algorithm, a new input-decorrelated variable step size least mean square algorithm is presented to make the scheme be applied in practice. A simulation examples are also presented to evaluate the scheme.
Key words：Linear Neural Network(LNN); Least Mean Square(LMS) Algorithm; Adaptive
Inverse Control(AIC)