Abstract
In this paper, the method for compensating the temperature drift of high-G MEMS accelerometer (HGMA) is proposed, including radial basis function neural network (RBF NN), RBF NN based on genetic algorithm (GA), RBF NN based on GA with Kalman filter (KF), and the RBF NN + GA + KF method compensated by the temperature drift model. First, this paper introduces an HGMA structure working principle, conducts a finite element analysis, and produces the results. The simulation results show that the HGMA working mode is the 1st order mode, and its resonant frequency is 408 kHz. The 2nd order mode resonant frequency is 667 kHz, and the gap with the first mode is 260 kHz, indicating that the coupling movement between the two modes is tiny, so the HGMA has good linearity. Then, a temperature experiment is performed to obtain the output value of HGMA. The output values of HGMA are analyzed and optimized by using the algorithms proposed in this paper. The processing results show that the RBF NN + GA + KF method compensated by the temperature drift model achieves the best denoing consequent. The processing results show that the temperature drift of the HGMA is effectively compensated. The final results show that acceleration random walking improved from 17130 g/h/Hz0.5 to 765.3 g/h/Hz0.5, and bias stability improved from 4720 g/h to 57.27 g/h, respectively. The results show that after using the RBF NN + GA + KF method, combined with the temperature drift model, the temperature drift trend and noise characteristics of HGMA are well optimized.
Highlights
A high-range, impact-resistant, high-G micro electro mechanical system (MEMS) accelerometer (HGMA) has important applications in navigation, defense, and impact measurement [1]
Simple mathematical model that makes the sensor output more robust at high temperatures, which was successfully applied to two low-cost quartz accelerometers [14]. He established an analytical model for the temperature drift of bias (TDB) and temperature drift of scale factor (TDSF), which is based on the analysis results of thermal deformation and stiffness temperature dependence
This paper studies the design, fabrication, and corresponding algorithms to compensate for the temperature drift in a new HGMA
Summary
A high-range, impact-resistant, high-G micro electro mechanical system (MEMS) accelerometer (HGMA) has important applications in navigation, defense, and impact measurement [1]. Simple mathematical model that makes the sensor output more robust at high temperatures, which was successfully applied to two low-cost quartz accelerometers [14]. He established an analytical model for the temperature drift of bias (TDB) and temperature drift of scale factor (TDSF), which is based on the analysis results of thermal deformation and stiffness temperature dependence. This model can effectively improve the accuracy of a silicon MEMS capacitive accelerometer [15]. 4 showsofthe temperature experiment; and Section 5are shows thein results data processing the analysis various algorithms
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