Publication result 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

English Title

Compensation of Linear Acceleration in Single-Mass MEMS Gyroscope

Type

Paper in proceedings (conference paper)

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.

English 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

Key words in English

ANN; artificial neural network; gyroscope; gsensitivity; IMU; linear acceleration; MEMS; NARX; sensor fusion

Authors

SPÁČIL, T.; RAJCHL, M.

RIV year

2020

Released

23.01.2019

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"
}