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SPÁČIL, T. RAJCHL, M.
Original Title
Compensation of Linear Acceleration in Single-Mass MEMS Gyroscope
Type
conference paper
Language
English
Original Abstract
Single mass MEMS gyroscopes suffer from significant sensitivity to linear acceleration also known as gsensitivity. In the case of multi-axis inertia measurement unit (IMU), we could benefit from direct acceleration measurement to suppress the influence of linear acceleration on gyroscope output. In this paper, we will derive a gyroscope dynamic model, pointing out the influence of linear acceleration, evaluate the performance of common fusion algorithm and suggest a method for compensation of linear acceleration sensitivity using artificial neural network (ANN). The neural network was designed as a nonlinear autoregressive neural network with external input (NARX). The proposed method is experimentally tested on the real system with emphasis on tilt estimation. A comparison of tilt measurement against tilt estimator based on ANN and conventional fusion algorithm is made. Results suggest that the accuracy was improved with the proposed ANN.
Keywords
ANN; artificial neural network; gyroscope; gsensitivity; IMU; linear acceleration; MEMS; NARX; sensor fusion
Authors
SPÁČIL, T.; RAJCHL, M.
Released
23. 1. 2019
ISBN
978-80-214-5542-9
Book
PROCEEDINGS OF THE 2018 18TH INTERNATIONAL CONFERENCE ON MECHATRONICS - MECHATRONIKA (ME)
Pages from
338
Pages to
343
Pages count
6
BibTex
@inproceedings{BUT152522, author="Tomáš {Spáčil} and Matej {Rajchl}", title="Compensation of Linear Acceleration in Single-Mass MEMS Gyroscope", booktitle="PROCEEDINGS OF THE 2018 18TH INTERNATIONAL CONFERENCE ON MECHATRONICS - MECHATRONIKA (ME)", year="2019", pages="338--343", isbn="978-80-214-5542-9" }