基于神经网络的MEMS陀螺标定与补偿

针对微机电系统(MEMS)陀螺低转速区间上非线性性能很强,采用传统方法对其标定误差较大,满足不了实际应用的问题,因此,采用了一种基于误差反向传播(BP)的神经网络的标定与补偿方法。设计了一组基于优先数的速率点,利用三轴转台进行12组速率实验,用最小二乘法求出在传统数学模型下的待标定系数;将三轴MEMS陀螺的输出和转台的实际转速作为样本,对BP神经网络进行训练,得到神经网络的补偿模型,并对比两种方法的补偿效果。结果表明,传统方法和BP神经网络都对MEMS陀螺的输出进行了有效的补偿;但在低转速区间上,神经网络的补偿效果比传统方法提高了3倍左右。

Calibration and Compensation of MEMS Gyroscope With Neural Network

Aiming at the problem that the non linearity of MEMS gyroscope at low speed range is very strong, the conventional error calibration method cannot meet the practical application, a calibration and compensation method based on the back propagation (BP) neural network is proposed in this paper. A set of rate points based on the number of priority is designed and 12 groups of rate experiments are carried out with the three axis turntable. The least square method is used to calculate the elements of the traditional mathematical model for calibration. We use the outputs of three MEMS gyroscopes and the actual rate of the three axis turntable as the sample to train the BP neural network to build the model. Also, we compare the compensation effect of two methods. The results show that both the traditional method and the BP neural network can compensate the output of the MEMS gyroscope effectively. However, the compensation effect of the neural network is about 3 times higher than that of the traditional method at the low speed range.