Contribution to Robot System Identification: Noise Reduction using a State Observer

Bilal Tout, Jason Chevrie, Laurent Vermeiren, Antoine Dequidt, Antoine Dequidt

2022

Abstract

Conventional identification approach based on the inverse dynamic identification model using least-squares and direct and inverse dynamic identification techniques has been effectively used to identify inertial and friction parameters of robots. However these methods require a well-tuned filtering of the observation matrix and the measured torque to avoid bias in identification results. Meanwhile, the cutoff frequency of the low-pass filter fc must be well chosen, which is not always easy to do. In this paper, we propose to use a Kalman filter to reduce the noise of the observation matrix and the output torque signal of the PID controller.

Paper Citation

in Harvard Style

Tout B., Chevrie J., Vermeiren L. and Dequidt A. (2022). Contribution to Robot System Identification: Noise Reduction using a State Observer. In Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-585-2, pages 695-702. DOI: 10.5220/0011322600003271

in Bibtex Style

@conference{icinco22,
author={Bilal Tout and Jason Chevrie and Laurent Vermeiren and Antoine Dequidt},
title={Contribution to Robot System Identification: Noise Reduction using a State Observer},
booktitle={Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2022},
pages={695-702},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011322600003271},
isbn={978-989-758-585-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Contribution to Robot System Identification: Noise Reduction using a State Observer
SN - 978-989-758-585-2
AU - Tout B.
AU - Chevrie J.
AU - Vermeiren L.
AU - Dequidt A.
PY - 2022
SP - 695
EP - 702
DO - 10.5220/0011322600003271