Iterative Learning Control for Machining with Industrial Robots

Pablo Cano Marchal, Olof Sörnmo, Björn Olofsson, Anders Robertsson, Juan Gómez Ortega, Rolf Johansson

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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.
Original languageEnglish
Title of host publication19th IFAC World Congress
PublisherIFAC
Pages9327-9333
Number of pages7
Volume47
Edition3
DOIs
Publication statusPublished - 2014
Event19th IFAC World Congress, 2014 - Cape Town, South Africa
Duration: 2014 Aug 242014 Aug 29
Conference number: 19

Publication series

NameIFAC-PapersOnLine
PublisherIFAC Secretariat
ISSN (Print)2405-8963

Conference

Conference19th IFAC World Congress, 2014
Country/TerritorySouth Africa
CityCape Town
Period2014/08/242014/08/29

Subject classification (UKÄ)

  • Control Engineering

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