Research of dynamic compensation method based on Hammerstein model for Wiener model sensor

MAF (Mass Air Flow) sensor has the characteristics of dynamic nonlinear. It is necessary to design corrective system so as to compensate static nonlinear error and improve dynamic performance indices. Dynamic nonlinear corrective system based on Hammerstein model is designed for Wiener model of MAF sensor. The corrective system consists of a static nonlinear calibration part cascaded with a dynamic linear compensation part. It makes use of least square polynomial regression analysis to design static nonlinear calibration part based on the static experimental data. The step signal with 50.05g/s amplitude is input and the result after the static nonlinear calibration of MAF sensor model's step response is output, then pseudo-linear model will be established according to aforementioned input and output. Reference model is basically designed on the pseudo-linear model. Make the results from static nonlinear calibration of MAF sensor model step response as input signal, reference model output as output signal, dynamic linear compensation model is designed based on prediction error method. Dynamic nonlinear corrective system is realized based on dSPACE the real-time simulation system. The experimental results approve that the corrective system has availability and practicability. The algorithm of corrective system is simple and easy to implement. It has strong capability of anti-interference and a good compensation effect.