Model-based iterative learning control applied to an industrial robot with elasticity

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In this paper model-based Iterative Learning Control (ILC) is applied to improve the tracking accuracy of an industrial robot with elasticity. The ILC algorithm iteratively updates the reference trajectory for the robot such that the predicted tracking error in the next iteration is minimised. The tracking error is predicted by a model of the closed-loop dynamics of the robot. The model includes the servo resonance frequency, the first resonance frequency caused by elasticity in the mechanism and the variation of both frequencies along the trajectory. Experimental results show that the tracking error of the robot can be reduced, even at frequencies beyond the first elastic resonance frequency.
Original languageUndefined
Title of host publicationProceedings of the 46th IEEE Conference on Decision and Control
Editors IEEE
Place of PublicationNew Orleans (LA), USA
ISBN (Print)9781424414970
Publication statusPublished - 12 Dec 2007
Event46th IEEE Conference on Decision and Control, CDC 2007 - Hilton New Orleans Riverside, New Orleans, United States
Duration: 12 Dec 200714 Dec 2007
Conference number: 46

Publication series



Conference46th IEEE Conference on Decision and Control, CDC 2007
Abbreviated titleCDC
Country/TerritoryUnited States
CityNew Orleans


  • industrial robot
  • Elasticity
  • IR-61487
  • model-based iterative learning control
  • trajectory tracking
  • closed-loop dynamics
  • METIS-240277
  • servo resonance frequency

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