Publication detail

Compensation of Linear Acceleration in Single-Mass MEMS Gyroscope

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"
}