粒子群优化算法在MEMS压力传感器电桥电阻计算与零偏补偿中的应用

Application of Particle Swarm Optimization Algorithm in Bridge Resistance Calculation and Zero-Bias Compensation of MEMS Pressure Sensors

采用粒子群优化(PSO)算法对微机电系统(MEMS)压力传感器电桥电阻进行了优化设计和验证。基于PSO算法的基本原理,建立电桥参数模型,充分利用冗余的测量数据配置优化条件,通过迭代运算得到了传感器惠斯通电桥电阻数值解,并将其作为零偏补偿的依据。然后根据计算和测试结果进行了零偏补偿,对补偿后结果进行了验证。结果表明,计算输出零偏值与实测输出零偏值之间的相对误差<1%;补偿后传感器的零偏值控制在0.5mV以内,实测零偏满量程误差≤0.1%。研究证明采用该算法模型可有效计算出MEMS压力传感器的电桥电阻参数,基于其计算结果的补偿方案可提升传感器电桥精度。

The bridge resistances of micro-electromechanical systems(MEMS)pressure sensor were optimally designed and verified by the particle swarm optimization(PSO)algorithm.Based on the basic principle of PSO algorithm,the bridge parameter model was established,and the redundant measurement data were making full used to configure optimized conditions.Through iterative computations,the resistance numerical solutions of the Wheatstone bridge for the sensor were obtained and used as a basis for zero-bias compensation.Then the zero-bias compensation was carried out according to the calculation and test results,and the compensated results were verified.The results show that the relative error between the calculated output zero-bias and the actual measured output zero-bias is less than 1%.After compensation,the zero-bias of the sensor is controlled within 0.5 mV,and the actual measured zero-bias full-scale error isn't more than 0.1%.The research demonstrates that the bridge resistance parameters of MEMS pressure sensors are effectively calculated by the algorithm model,and the compensation scheme based on these calculation results can enhance the accuracy of the sensor bridge.