一种热线式MAF传感器分段块联模型辨识

采用静态非线性函数与动态线性环节的块连接模型来描述热线式空气质量流量（MAF）传感器的动态特性，非线性环节用多项式表示，动态线性环节采用OE模型结构．基于静、动态标定实验数据，分别建立了热线式MAF传感器在正、负阶跃激励下各校准点的Hammerstein模型，并利用非建模数据对其进行了相互验证．通过合理选择分段区间，确定出热线式MAF传感器各工作区域的最佳局部动态数学模型．模型检验结果表明：基于Hammerstein模型的分段模型比由任意一组动态数据所建模型具有更高的预测精度．

Piecewise Block-Connected Model Identification of a Hot-Wire Type MAF Sensor

A block-connected model structure based on a static nonlinear function and a dynamic linear part was introduced to describe the dynamic characteristics of a hot-wire type mass-air-flow （MAF） sensor. The nonlinear part was expressed by polynomial fitting and the linear part was modeled with an output error （OE） model. For each calibration point when the sensor worked both in positive-step and negative-step modes, Hammerstein models were established respectively based on the calibration experiment data and validated mutually using non-modeling data. The optimal local models of the MAF sensor were fitted in every operating region after decomposing properly the sensor operation range into several operating regions. The simulation and validation results show that the piecewise Hammerstein model has higher prediction accuracy than the model obtained by using a set of experiment data at arbitrary operating point.