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Iterative Learning Control for Machining with Industrial Robots

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Author

Summary, in English

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.

Publishing year

2014

Language

English

Pages

9327-9333

Publication/Series

IFAC-PapersOnLine

Volume

47

Document type

Conference paper

Publisher

IFAC

Topic

  • Control Engineering

Conference name

19th IFAC World Congress, 2014

Conference date

2014-08-24 - 2014-08-29

Conference place

Cape Town, South Africa

Status

Published

Project

  • LU Robotics Laboratory
  • SMErobotics
  • LU Robotics Laboratory

Research group

  • LCCC
  • ELLIIT

ISBN/ISSN/Other

  • ISSN: 2405-8963