Bias Temperature Compensation Analysis of MEMS Gyroscope

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Bias Temperature Compensation Analysis of MEMS Gyroscope

Post by Kelly01 »

Temperature is one of the important factors affecting MEMS gyroscope, which will greatly affect the output of MEMS gyro. MEMS gyroscope belongs to the temperature sensitive period, and its material is semiconductor silicon. When the temperature changes, the thermal expansion coefficient, elastic modulus, and resonant frequency of the silicon wafer will change, which will affect the performance of the MEMS gyroscopes, mainly bias. Based on MEMS, the most temperature-dependent factors belong to the field of oil logging and drilling while, because of the harsh working environment in this field, downhole high temperature and other conditions, the accuracy of MEMS gyroscopes will be more affected. Therefore, it is very important to take compensation measures. The following will introduce common temperature compensation methods, temperature characteristic model analysis, application of Markov chain in MEMS gyro and temperature zero deviation compensation experiment.

Common MEMS gyro temperature compensation method

In order to make the accuracy of MEMS gyros not affected, we must take a method to compensate it. Currently, there are two commonly used methods: one is to install a constant temperature device outside the MEMS gyros, through the control circuit, so that the MEMS gyroscope works at a stable temperature, but it will make the system complicated and the volume becomes larger, and it is difficult to apply in the downhole drilling system; The second is to establish a temperature compensation model to compensate the output of MEMS gyro through software algorithm to reduce the error. Compared with the first method, this method is simpler to implement and less affected by the environment, and can achieve higher accuracy under the same hardware conditions. The common compensation algorithms include polynomial fitting, piecewise fitting and gray theory model compensation. The characteristics and comparison of these algorithms are shown in Table 1.
Table 1 Comparison of common temperature compensation algorithms

The above temperature bias compensation algorithms are more and more effective programs at present, but they all have more or less defects and need to be further improved. Based on the temperature characteristic model of MEMS gyros, a temperature compensation algorithm based on Markov process is proposed and compared with other algorithms. ... scope.html

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