Abstract

This article suggested two methods to compensate for the temperature drift of the micro-electro-mechanical system gyroscopes, which are support vector machine method and C-means support vector machine. The output of X axis which was ranged from −40°C to 60°C based on the micro-electro-mechanical system gyroscope is reduced and analyzed in this article. The results showed the correctness of the two methods. The final results indicate that when the temperature is ranged from −40°C to 60°C, the factor of B is reduced from 0.424 [Formula: see text] to 0.02194 [Formula: see text], and when the temperature is ranged from 60°C to −40°C, the factor of B is reduced from 0.1056 [Formula: see text] to 0.0329 [Formula: see text], and the temperature drift trend and noise characteristics are improved clearly.

Highlights

  • The micro-electro-mechanical system (MEMS) gyroscope is an inertial sensor based on the technology of MEMS, which is used to sense the rotation rate of the carrier

  • The results show that the extreme learning machine (ELM) has the best modeling accuracy and shortest training time, which would be valuable for online temperature drift modeling and compensation

  • Li et al.[14] proposed an support vector machine (SVM) model to compensate for the temperature drift of the fiber optic gyroscope (FOG)

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Summary

Introduction

The micro-electro-mechanical system (MEMS) gyroscope is an inertial sensor based on the technology of MEMS, which is used to sense the rotation rate of the carrier. One way is using hardware methods to reduce the temperature drift of the MEMS gyroscope. Ou et al.[5] investigated a new stress-released structure to improve the performance of temperature stability of MEMS gyroscope.

Results
Conclusion
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