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Output error methods for robot identification

Brunot, Mathieu and Janot, Alexandre and Carrillo, Francisco Javier and Cheong, Joono and Noël, Jean-Philippe Output error methods for robot identification. (2019) Journal of Dynamic Systems, Measurement, and Control, 142 (3). ISSN 0022-0434

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Official URL: https://doi.org/10.1115/1.4045430

Abstract

Industrial robot identification is usually based on the inverse dynamic model (IDIM) that comes from Newton’s laws and has the advantage of being linear with respect to the parameters. Building the IDIM from the measurement signals allows the use of linear regression techniques like the least-squares (LS) or the instrumental variable (IV) for instance. Nonetheless, this involves a careful preprocessing to deal with sensor noise. An alternative in system identification is to consider an output error approach where the model’s parameters are iteratively tuned in order to match the simulated model’s output and the measured system’s output. This paper proposes an extensive comparison of three different output error approaches in the context of robot identification. One of the main outcomes of this work is to show that choosing the input torque as target identification signal instead of the output position may lead to a gain in robustness versus modeling errors and noise and in computational time. Theoretical developments are illustrated on a six degrees-of-freedom rigid robot

Item Type:Article
Additional Information:http://www.sherpa.ac.uk/romeo/search.php?source=journal&sourceid=12226&la=en&fIDnum=|&mode=simple
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
French research institutions > Office National d'Etudes et Recherches Aérospatiales - ONERA (FRANCE)
Other partners > Université de Liège (BELGIUM)
Other partners > Korea University (REPUBLIC OF KOREA)
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Deposited On:07 Apr 2020 15:41

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