Compensation of Linear Acceleration in Single-Mass MEMS Gyroscope

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F19%3APU130440" target="_blank" >RIV/00216305:26210/19:PU130440 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Compensation of Linear Acceleration in Single-Mass MEMS Gyroscope

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Compensation of Linear Acceleration in Single-Mass MEMS Gyroscope

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

PROCEEDINGS OF THE 2018 18TH INTERNATIONAL CONFERENCE ON MECHATRONICS - MECHATRONIKA (ME)

ISBN

978-80-214-5542-9

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

338-343

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

Brno

Datum konání akce

5. 12. 2018

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

000465104200053