一种MEMS陀螺标度因数误差补偿方法

高动态、恶劣温度环境下,微小型飞行器(MAV)导航、制导与控制系统关键器件微机电系统(MEMS)陀螺受温度和转速耦合影响,其标度因数误差呈强非线性特点,常规方法无法精确补偿。通过分析MEMS陀螺标度因数误差的产生机理,建立了包含温度和转速非线性因素的标度因数误差模型,提出一种基于径向基(RBF)神经网络的标度因数非线性耦合误差补偿方法,解决了常规补偿方法精度差的问题。标定与补偿实验表明:在-10～+55℃温度范围、-150～+150(°)/s输入转速范围内,采用新方法补偿后MEMS陀螺输出平均精度比多项式拟合方法提高7倍;在-20～+20(°)/s低输入转速的误差强非线性区间内,精度提高近20倍,验证了本文方法的有效性和优越性。

A Compensation Method for MEMS Gyro Scale Factor Error

In the adverse application environment of micro air vehicle(MAV) with high dynamics and poor temperatures,a micro-electro-mechanical system(MEMS) gyro may exhibit very serious nonlinear scale factor error due to the coupling effect of temperature and input rotation,for which compensation by conventional method is no longer sufficient.By analyzing the mechanism of the scale factor error of MEMS gyro,a model with temperature and rotation factors is established and a compensation method is proposed for the coupled nonlinear scale factor error based on radial basis function (RBF) neural network.This method can deal suecessfully with the large error problem of the conventional method.The calibration and compensation test results show that using the new compensation method,in the temperature range of-10-+55℃ and the rotation conventional compensation method.And in the strong nonlinear error rotation rate range of-20-+20(°)/s,the accuracy is increased by nearly 20 times.Through the tests,the effectiveness and superiority of this method are proved.