Humidity Error Compensating of MEMS Gyros Based on A daptive Threshold Denoising and RBF Neural Network

The large random errors in a typical MEMS gyros under the ranges of relative humidity condition. In order to solve the large random error under the ranges of relative humidity, a method based on adaptive threshold denoising and neural network is proposed in this paper. Full relative humidity experim...

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Bibliographic Details
Published inChinese Control Conference pp. 4691 - 4696
Main Authors Xin-Wang, Wang, Xiao-Lin, Shen, Hui-Liang, Cao, Bo-Wen, Deng
Format Conference Proceeding
LanguageEnglish
Published Technical Committee on Control Theory, Chinese Association of Automation 01.07.2018
Subjects
adaptive threshold function
error compensations
Frequency modulation
Manganese
MEMS gyroscopes
radial basis function neural network
random error model
relative humidity
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Summary:The large random errors in a typical MEMS gyros under the ranges of relative humidity condition. In order to solve the large random error under the ranges of relative humidity, a method based on adaptive threshold denoising and neural network is proposed in this paper. Full relative humidity experiment is carried to test LSM6DS3 MEMS gyros. Then the adaptive threshold denoising is used to separate the drift error and drift data modeled with RBF neural network. The experimental results show the effectiveness of the method, and it is of great significance in improving the performance of MEMS gyros.
ISSN:1934-1768
DOI:10.23919/ChiCC.2018.8482841