基于改进型辅助变量法的压力传感器建模

针对经典压力传感器建模方法存在适应性较差、难以准确获取压力传感器动态特性的问题，提出了一种基于改进型辅助变量法的压力传感器动态建模方法。首先运用粒子群算法预估计模型参数，将预估计的参数代入差分方程构造辅助变量。然后利用输入、输出数据用辅助变量法辨识模型的参数，进行压力传感器系统仿真，在不同噪声模型及信噪比下得到系统输出，分别用提出的方法和现有方法建模，比较建模结果的差异。最后用激波管动态校准实验平台对压力传感器进行动态校准。根据输入、输出数据构造信息矩阵并对其正交分解确定模型阶次，再次用提出的方法和现有方法建模，验证仿真结果。通过压力传感器系统仿真及实验数据验证表明：在压力传感器建模中，改进型辅助变量法的辨识精度明显高于现有方法。

Pressure Sensor Modeling Based on Improved Auxiliary Variable Method

Aiming at the problem that the classical pressure sensor modeling method has poor adaptability and it is difficult to accurately obtain the dynamic characteristics of the pressure sensor, a dynamic modeling method of pressure sensor based on improved auxiliary variable method is proposed. Firstly, the particle swarm optimization algorithm was used to pre-estimate the model parameters, and the pre-estimated parameters were substituted into the difference equation to construct the auxiliary variables. Then the input and output data were used to identify the parameters of the model by the auxiliary variable method, The pressure sensor system was simulated, and the system output was obtained under different noise models and signal-to-noise ratios, The proposed method and the existing method were used to model and compare the differences of the modeling results. Finally, the pressure sensor was dynamically calibrated by the shock tube dynamic calibration experiment platform. The information matrix was constructed according to the input and output data and the model order was determined by orthogonal decomposition, The proposed method and the existing method were used again to verify the simulation result. Through the simulation of pressure sensor system and experimental data, it is shown that in the modeling of pressure sensor, the identification accuracy of the improved auxiliary variable method is significantly higher than the existing method.