Position control system based on inertia measurement unit sensor fusion with Kalman filter

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper proposes position control system based on measurements of an inertia measurement unit (IMU) sensor (composed of a gyro sensor and an acceleration sensor) attached on the tip position of a 2-link planar manipulator. To estimate joint angle from only one IMU sensor, velocity applied to end effector is required. However, it is difficult to measure accurate velocity from integration of measurements of the acceleration sensor due to noise, offset and drift error. Therefore, Kalman filter and sensor fusion with acceleration sensor and gyro sensor are introduced to estimate the velocity with high accuracy. In addition to that, Disturbance observer (DOB) is used in the position control system, and the estimated angular velocity information is utilized in DOB. To confirm the performance of proposed control system, 3 types of simulation of position control are conducted. Kalman filter can reduce the noise effect and position control is achieved by proposed control system.

Original languageEnglish
Title of host publication2016 IEEE 14th International Workshop on Advanced Motion Control, AMC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages153-159
Number of pages7
ISBN (Electronic)9781479984640
DOIs
Publication statusPublished - 2016 Jun 20
Event14th IEEE International Workshop on Advanced Motion Control, AMC 2016 - Auckland, New Zealand
Duration: 2016 Apr 222016 Apr 24

Publication series

Name2016 IEEE 14th International Workshop on Advanced Motion Control, AMC 2016

Other

Other14th IEEE International Workshop on Advanced Motion Control, AMC 2016
Country/TerritoryNew Zealand
CityAuckland
Period16/4/2216/4/24

ASJC Scopus subject areas

  • Control and Systems Engineering

Fingerprint

Dive into the research topics of 'Position control system based on inertia measurement unit sensor fusion with Kalman filter'. Together they form a unique fingerprint.

Cite this