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A learning approach for feed-forward friction compensation
AstaZero, Sweden.
ABB AB, Sweden.
Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Automatic Control.
ABB AB, Sweden.
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2018 (English)In: Proceedings of the 12th IFAC Symposium on Robot Control, 2018, Vol. 51, p. 412-417, article id 22Conference paper, Published paper (Other academic)
Abstract [en]

An experimental comparison of two feed-forward based frictioncompensation methods is presented. The first method is based on theLuGre friction model, using identified friction model parameters, andthe second method is based on B-spline network, where the networkweights are learned from experiments. The methods are evaluated andcompared via experiments using a six axis industrial robot carryingout circular movements of different radii. The experiments show thatthe learning-based friction compensation gives an error reduction ofthe same magnitude as for the LuGre-based friction compensation.

Place, publisher, year, edition, pages
2018. Vol. 51, p. 412-417, article id 22
Series
IFAC-PapersOnLine, ISSN 2405-8963
Keywords [en]
friction, feed-forward compensation, splines, learning control, industrial ropots
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-150952DOI: 10.1016/j.ifacol.2018.11.578OAI: oai:DiVA.org:liu-150952DiVA, id: diva2:1245779
Conference
12th IFAC Symposium on Robot Control, Budapest, Hungary, August 27-30, 2018
Projects
LINK-SIC
Funder
VinnovaAvailable from: 2018-09-06 Created: 2018-09-06 Last updated: 2019-10-21

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Gunnarsson, Svante

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf