scholarly journals A Compensation Algorithnm For Zero Drifting Error of MEMS Gyroscope

2016 ◽  
Author(s):  
Jin Du ◽  
Jie Li
Keyword(s):  
Mems Gyroscope

scholarly journals A Combined Method for MEMS Gyroscope Error Compensation Using a Long Short-Term Memory Network and Kalman Filter in Random Vibration Environments

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1181
Author(s):  
Chenhao Zhu ◽  
Sheng Cai ◽  
Yifan Yang ◽  
Wei Xu ◽  
Honghai Shen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Standard Deviation ◽  
Error Compensation ◽  
Random Vibration ◽  
Short Term Memory ◽  
Combined Method ◽  
Short Term ◽  
Mems Gyroscope ◽  
Long Short Term Memory ◽  
Lstm Network

In applications such as carrier attitude control and mobile device navigation, a micro-electro-mechanical-system (MEMS) gyroscope will inevitably be affected by random vibration, which significantly affects the performance of the MEMS gyroscope. In order to solve the degradation of MEMS gyroscope performance in random vibration environments, in this paper, a combined method of a long short-term memory (LSTM) network and Kalman filter (KF) is proposed for error compensation, where Kalman filter parameters are iteratively optimized using the Kalman smoother and expectation-maximization (EM) algorithm. In order to verify the effectiveness of the proposed method, we performed a linear random vibration test to acquire MEMS gyroscope data. Subsequently, an analysis of the effects of input data step size and network topology on gyroscope error compensation performance is presented. Furthermore, the autoregressive moving average-Kalman filter (ARMA-KF) model, which is commonly used in gyroscope error compensation, was also combined with the LSTM network as a comparison method. The results show that, for the x-axis data, the proposed combined method reduces the standard deviation (STD) by 51.58% and 31.92% compared to the bidirectional LSTM (BiLSTM) network, and EM-KF method, respectively. For the z-axis data, the proposed combined method reduces the standard deviation by 29.19% and 12.75% compared to the BiLSTM network and EM-KF method, respectively. Furthermore, for x-axis data and z-axis data, the proposed combined method reduces the standard deviation by 46.54% and 22.30% compared to the BiLSTM-ARMA-KF method, respectively, and the output is smoother, proving the effectiveness of the proposed method.

scholarly journals Design Approach for Reducing Cross-Axis Sensitivity in a Single-Drive Multi-Axis MEMS Gyroscope

Micromachines ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 902
Author(s):  
Hussamud Din ◽  
Faisal Iqbal ◽  
Byeungleul Lee
Keyword(s):  
Microelectromechanical Systems ◽  
Reduction Rate ◽  
Design Technique ◽  
Matching Method ◽  
Mems Gyroscope ◽  
Mode Split ◽  
The Cross ◽  
The Individual ◽  
Cross Axis ◽  
Ratio Matching

In this paper, a new design technique is presented to estimate and reduce the cross-axis sensitivity (CAS) in a single-drive multi-axis microelectromechanical systems (MEMS) gyroscope. A simplified single-drive multi-axis MEMS gyroscope, based on a mode-split approach, was analyzed for cross-axis sensitivity using COMSOL Multiphysics. A design technique named the “ratio-matching method” of drive displacement amplitudes and sense frequency differences ratios was proposed to reduce the cross-axis sensitivity. Initially, the cross-axis sensitivities in the designed gyroscope for x and y-axis were calculated to be 0.482% and 0.120%, respectively, having an average CAS of 0.301%. Using the proposed ratio-matching method and design technique, the individual cross-axis sensitivities in the designed gyroscope for x and y-axis were reduced to 0.018% and 0.073%, respectively. While the average CAS was reduced to 0.045%, showing a reduction rate of 85.1%. Moreover, the proposed ratio-matching method for cross-axis sensitivity reduction was successfully validated through simulations by varying the coupling spring position and sense frequency difference variation analyses. Furthermore, the proposed methodology was verified experimentally using fabricated single-drive multi-axis gyroscope.

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