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

Wouter Hakvoort; Ronald G.K.M. Aarts; Johannes van Dijk; Jan B. Jonker

doi:10.1109/CDC.2007.4434366

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

Wouter Hakvoort, Ronald G.K.M. Aarts, Johannes van Dijk, Jan B. Jonker

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

12 Citations (Scopus)

269 Downloads (Pure)

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.

Original language	Undefined
Title of host publication	Proceedings of the 46th IEEE Conference on Decision and Control
Editors	IEEE
Place of Publication	New Orleans (LA), USA
Publisher	IEEE
Pages	4185-4190
ISBN (Print)	9781424414970
DOIs	https://doi.org/10.1109/CDC.2007.4434366
Publication status	Published - 12 Dec 2007
Event	46th IEEE Conference on Decision and Control, CDC 2007 - Hilton New Orleans Riverside, New Orleans, United States Duration: 12 Dec 2007 → 14 Dec 2007 Conference number: 46

Publication series

Name
Publisher	IEEE

Conference

Conference	46th IEEE Conference on Decision and Control, CDC 2007
Abbreviated title	CDC
Country/Territory	United States
City	New Orleans
Period	12/12/07 → 14/12/07

Keywords

2020 OA procedure
Elasticity
industrial robot
model-based iterative learning control
trajectory tracking
closed-loop dynamics
servo resonance frequency

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/CDC.2007.4434366

Hakvoort07modelFinal published version, 303 KBLicence: Taverne

Cite this

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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.",

keywords = "2020 OA procedure, Elasticity, industrial robot, model-based iterative learning control, trajectory tracking, closed-loop dynamics, servo resonance frequency",

author = "Wouter Hakvoort and Aarts, {Ronald G.K.M.} and {van Dijk}, Johannes and Jonker, {Jan B.}",

year = "2007",

month = dec,

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doi = "10.1109/CDC.2007.4434366",

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publisher = "IEEE",

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Hakvoort, W , Aarts, RGKM, van Dijk, J & Jonker, JB 2007, Model-based iterative learning control applied to an industrial robot with elasticity. in IEEE (ed.), Proceedings of the 46th IEEE Conference on Decision and Control. IEEE, New Orleans (LA), USA, pp. 4185-4190, 46th IEEE Conference on Decision and Control, CDC 2007, New Orleans, Louisiana, United States, 12/12/07. https://doi.org/10.1109/CDC.2007.4434366

Model-based iterative learning control applied to an industrial robot with elasticity. / Hakvoort, Wouter ; Aarts, Ronald G.K.M.; van Dijk, Johannes et al.
Proceedings of the 46th IEEE Conference on Decision and Control. ed. / IEEE. New Orleans (LA), USA: IEEE, 2007. p. 4185-4190.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Model-based iterative learning control applied to an industrial robot with elasticity

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AU - Aarts, Ronald G.K.M.

AU - van Dijk, Johannes

AU - Jonker, Jan B.

N1 - Conference code: 46

PY - 2007/12/12

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N2 - 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.

AB - 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.

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KW - Elasticity

KW - industrial robot

KW - model-based iterative learning control

KW - trajectory tracking

KW - closed-loop dynamics

KW - servo resonance frequency

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