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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(Document in English)
PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader 389kB |
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 |
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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 |
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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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