Fast norm-optimal iterative learning control for industrial applications

Norm-optimal iterative learning control has potential to significantly increase the accuracy of many trajectory tracking tasks which can be found in industry. The algorithm can achieve very low levels of tracking error and the number of iterations required to reach minimal error is small compared to...

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Bibliographic Details
Main Authors: Ratcliffe, J., van Duinkerken, L., Lewin, P., Rogers, E., Hatonen, J., Harte, T., Owens, D.
Format: Conference Proceeding
Language:English
Subjects:
Control systems
Error correction
Frequency
Hardware
Industrial control
Iterative algorithms
Robotic assembly
Service robots
Systems engineering and theory
Trajectory
Online Access:Request full text
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Summary:Norm-optimal iterative learning control has potential to significantly increase the accuracy of many trajectory tracking tasks which can be found in industry. The algorithm can achieve very low levels of tracking error and the number of iterations required to reach minimal error is small compared to many other iterative learning control algorithms. However, in the current format, the algorithm is not attractive to industry because it requires a large number of calculations to be performed at each sample instant. This implies that control hardware must be very fast which is expensive, or that the sample frequency must be reduced which can result in reduced performance. To remedy these problems, a revised version, fast norm-optimal iterative learning control is proposed which is significantly simpler and faster to implement. The new version is tested both in simulation and in practice on a three axis industrial gantry robot.
ISSN:0743-1619
2378-5861
DOI:10.1109/ACC.2005.1470255