一种MEMS压力传感器温度补偿方法

Temperature Compensation Method for MEMS Pressure Sensors

针对微机电系统(MEMS)压阻式压力传感器受环境温度影响产生温度漂移的问题,该文分析了常用的温度补偿方法,提出了一种基于粒子群优化-径向基函数(PSO-RBF)的压力传感器温度补偿模型,结合标定实验采集的样本数据,建立了标定压力同敏感元件输出电压和温度的非线性映射关系,实现了温度补偿效果。结果表明,与传统的最小二乘法、三次样条插值法、标准RBF、粒子群优化反向传输神经网络(PSO-BP)、核极限学习机(ELM)神经网络等方法相比,该算法具有更好的补偿预测效果,且对样本数据不需要归一化处理,具有良好的工程实践意义。

Aiming at the problem of temperature drift of micro-electro-mechanical system(MEMS)piezoresistive pressure sensor affected by ambient temperature,based on the analysis of the common temperature compensation methods,a temperature compensation model of pressure sensor based on the particle swarm optimization radial basis function(PSO-RBF)is proposed in this paper.Combined with the sample data collected in the calibration experiment,the nonlinear mapping relationship between the calibration pressure and the output voltage and temperature of the sensitive element is established,and the temperature compensation is realized.The results show that the proposed algorithm has better compensation prediction effect than the traditional least squares method,cubic spline interpolation method,standard RBF,PSO-BP,ELM neural network and other methods,and does not need to normalize the sample data,which has good engineering practice significance.