Paper Info
Title
Model-Based Iterative Learning Control Applied To An Industrial Robot With Elasticity
Abstract
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.
Year
DOI
Venue
2007
10.1109/CDC.2007.4434366
PROCEEDINGS OF THE 46TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-14
Keywords
Field
DocType
tracking,resonant frequency,iterative methods,adaptive control,elasticity,iterative learning control
Servo,Computer science,Control theory,Iterative method,Industrial robot,Iterative learning control,Adaptive control,Robot,Trajectory,Tracking error
Conference
ISSN
Citations 
PageRank 
0743-1546
2
0.49
References 
Authors
7
4
Name
Order
Citations
PageRank
W. B. J. Hakvoort130.85
Ronald G. K. M. Aarts241.89
J. van Dijk320.49
J. B. Jonker430.85