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Authors: Bilal Tout 1 ; Jason Chevrie 1 ; Laurent Vermeiren 1 and Antoine Dequidt 2 ; 1

Affiliations: 1 Univ. Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201, F-59313 Valenciennes, France ; 2 INSA Hauts-de-France, F-59313 Valenciennes, France

Keyword(s): System Identification, Robot Dynamics, Kalman Filter, Least Squares Estimation.

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.

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Paper citation in several formats:
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 - ICINCO; ISBN 978-989-758-585-2; ISSN 2184-2809, SciTePress, pages 695-702. DOI: 10.5220/0011322600003271

@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 - ICINCO},
year={2022},
pages={695-702},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011322600003271},
isbn={978-989-758-585-2},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - ICINCO
TI - Contribution to Robot System Identification: Noise Reduction using a State Observer
SN - 978-989-758-585-2
IS - 2184-2809
AU - Tout, B.
AU - Chevrie, J.
AU - Vermeiren, L.
AU - Dequidt, A.
PY - 2022
SP - 695
EP - 702
DO - 10.5220/0011322600003271
PB - SciTePress

- Science and Technology Publications, Lda.
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