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Iterative Learning Control for Machining with Industrial Robots

Cano Marchal, Pablo ; Sörnmo, Olof LU ; Olofsson, Björn LU ; Robertsson, Anders LU ; Gómez Ortega, Juan and Johansson, Rolf LU orcid (2014) 19th IFAC World Congress, 2014 p.9327-9333
Abstract
We consider an iterative learning control (ILC) approach to machining with industrial robots. The robot and the milling process are modeled using system identification methods with a data-driven approach. Two different model-based ILC algorithms are proposed and subsequently experimentally verified in a milling scenario. The difference between the two approaches is the required sensors for acquiring relevant input data for the algorithms. The results from the experiments indicate that the proposed methods have the potential of significantly decreasing the position errors in robotic machining, up to 85% in the considered milling scenario.
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author
; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Proceedings of the 19th IFAC World Congress, 2014
pages
9327 - 9333
publisher
IFAC
conference name
19th IFAC World Congress, 2014
conference location
Cape Town, South Africa
conference dates
2014-08-24 - 2014-08-29
external identifiers
  • scopus:84929773679
project
LU Robotics Laboratory
SMErobotics
language
English
LU publication?
yes
id
92bc9103-61bc-4652-a9e0-2129999a24e7 (old id 4317026)
date added to LUP
2016-04-04 13:10:14
date last changed
2021-10-06 03:27:47
@inproceedings{92bc9103-61bc-4652-a9e0-2129999a24e7,
  abstract     = {We consider an iterative learning control (ILC) approach to machining with industrial robots. The robot and the milling process are modeled using system identification methods with a data-driven approach. Two different model-based ILC algorithms are proposed and subsequently experimentally verified in a milling scenario. The difference between the two approaches is the required sensors for acquiring relevant input data for the algorithms. The results from the experiments indicate that the proposed methods have the potential of significantly decreasing the position errors in robotic machining, up to 85% in the considered milling scenario.},
  author       = {Cano Marchal, Pablo and Sörnmo, Olof and Olofsson, Björn and Robertsson, Anders and Gómez Ortega, Juan and Johansson, Rolf},
  booktitle    = {Proceedings of the 19th IFAC World Congress, 2014},
  language     = {eng},
  month        = {08},
  pages        = {9327--9333},
  publisher    = {IFAC},
  title        = {Iterative Learning Control for Machining with Industrial Robots},
  url          = {https://lup.lub.lu.se/search/files/8475297/4611876.pdf},
  year         = {2014},
}