基于过采样的Hammerstein模型最小方根辨识算法

Least squares identification of hammerstein model based on over-sampling scheme

针对Hammerstein模型提出了基于过采样原理的新的最小方根辨识算法,通过估计线性部分的中间输入,可以辨识出任意连续函数的未知非线性元件和线性部分传输函数。即使在非线性元件传递函数存在近似误差的情况下,线性部分的估计值也具有一致性。

Hammerstein model is a commonly used model in control field, which consists of a nonlinear element followed by a linear dynamic model. For this model, a new least squares type of identification algorithm based on an over-sampling scheme is proposed. By estimation the intermediate input to the linear part explicitly, we can identify an arbitrary continuous function type of nonlinear element as well as the unknown linear transfer function model. Furthermore, the consistency of the estimated parameters in the linear part can be assured unlike other previous methods even if there is an approximation error in the description of the nonlinear function.