热线式空气质量流量传感器Hammerstein模型结构辨识

利用稳态和动态校准信息,辨识了基于Hammerstein模型的热线式空气质量流量(MAF)传感器模型结构.模型辨识采用两步法,在用多项式逼近静态非线性特性的基础上,动态线性环节分别选取ARX模型、输出误差(OE)模型和Box-Jenkins(BJ)模型,采用交叉准则法进行参数估计和阶次选择,模型的残差分析和用验证数据对模型交叉检验的结果表明,最终输出误差(FOE)准则和最终预报误差(FPE)准则选择的阶次一致,基于预测误差法的3阶OE和BJ模型均可用于热线式MAF传感器Hammerstein模型动态线性环节的建模.

Structure Identification of Hammerstein Model of Hot Wire Type Mass Air Flow Sensor

The model structure of a hot wire type mass air flow(MAF) sensor based on Hammerstein model was identified using steady state and dynamic calibration information of the sensor.The two-step method was used for model identification.On the basis of approximating polynomial to static non-linear characteristics of the sensor,the linear dynamic part of the model was chosen respectively as auto-regression with exogenous variable(ARX) model,output error(OE) model and Box-Jenkins(BJ) model.The cross-criterion method was used in parameters estimation and order selection,and residual analysis and cross checking with validation data of these models were made.Results show that orders selected by final output error(FOE) criterion is in agreement with that by final prediction error(FPE) criterion,both OE model and BJ model with three orders based on prediction error method are adequate for modeling linear dynamic part of the Hammerstein model for the MAF sensor.