Lifted system iterative learning control applied to an industrial robot

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

doi:10.1016/j.conengprac.2007.05.002

Lifted system iterative learning control applied to an industrial robot

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

Faculty of Engineering Technology

Research output: Contribution to journal › Article › Academic › peer-review

44 Citations (Scopus)

86 Downloads (Pure)

Abstract

This paper proposes a model-based iterative learning control algorithm for time-varying systems with a high convergence speed. The convergence of components of the tracking error can be controlled individually with the algorithm. The convergence speed of each error component can be maximised unless robustness for noise or unmodelled dynamics is needed. The learning control algorithm is applied to the industrial Stäubli RX90 robot. A linear time-varying model of the robot dynamics is obtained by linearisation of the non-linear dynamic equations. Experiments show that the tracking error of the robot joints can be reduced to the desired level in a few iterations.

Original language	Undefined
Pages (from-to)	377-391
Number of pages	15
Journal	Control engineering practice
Volume	16
Issue number	4
DOIs	https://doi.org/10.1016/j.conengprac.2007.05.002
Publication status	Published - 2008

Keywords

2020 OA procedure
Time-varying systems
Convergence analyses
Robot dynamics
Learning control
Singular value decomposition
Linearisation

UN SDGs

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

Access to Document

10.1016/j.conengprac.2007.05.002

Hakvoort08liftedFinal published version, 626 KBLicence: Taverne

Cite this

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title = "Lifted system iterative learning control applied to an industrial robot",

abstract = "This paper proposes a model-based iterative learning control algorithm for time-varying systems with a high convergence speed. The convergence of components of the tracking error can be controlled individually with the algorithm. The convergence speed of each error component can be maximised unless robustness for noise or unmodelled dynamics is needed. The learning control algorithm is applied to the industrial St{\"a}ubli RX90 robot. A linear time-varying model of the robot dynamics is obtained by linearisation of the non-linear dynamic equations. Experiments show that the tracking error of the robot joints can be reduced to the desired level in a few iterations.",

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AU - Aarts, Ronald G.K.M.

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AU - Jonker, Jan B.

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AB - This paper proposes a model-based iterative learning control algorithm for time-varying systems with a high convergence speed. The convergence of components of the tracking error can be controlled individually with the algorithm. The convergence speed of each error component can be maximised unless robustness for noise or unmodelled dynamics is needed. The learning control algorithm is applied to the industrial Stäubli RX90 robot. A linear time-varying model of the robot dynamics is obtained by linearisation of the non-linear dynamic equations. Experiments show that the tracking error of the robot joints can be reduced to the desired level in a few iterations.

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KW - Convergence analyses

KW - Robot dynamics

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KW - Singular value decomposition

KW - Linearisation

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