Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder

Max van Haren; Maurice Poot; Dragan Kostic; Robin van Es; Jim Portegies; Tom Oomen

doi:10.1109/AMC51637.2022.9729327

Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder

Max van Haren
, Maurice Poot
, Dragan Kostic
, Robin van Es
, Jim Portegies
, Tom Oomen

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

142 Downloads (Pure)

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Mechatronic systems have increasingly stringent performance requirements for
motion control, leading to a situation where many factors, such as position-dependency, cannot be neglected in feedforward control. The aim of this paper is to compensate for position-dependent effects by modeling feedforward parameters as a function of position. A framework to model and identify feedforward parameters as a continuous function of position is developed by combining Gaussian processes and feedforward parameter learning techniques. The framework results in a fully data-driven approach, which can be readily implemented for industrial control applications. The framework is experimentally validated and shows a significant performance increase on a commercial wire
bonder.

Originele taal-2	Engels
Titel	2022 IEEE 17th International Conference on Advanced Motion Control (AMC)
Uitgeverij	Institute of Electrical and Electronics Engineers
Pagina's	268-273
Aantal pagina's	6
ISBN van elektronische versie	978-1-7281-7711-3
DOI's	https://doi.org/10.1109/AMC51637.2022.9729327
Status	Gepubliceerd - 11 mrt. 2022
Evenement	17th IEEE International Conference on Advanced Motion Control, AMC 2022 - Padova, Italië Duur: 18 feb. 2022 → 20 feb. 2022 Congresnummer: 17 http://static.gest.unipd.it/AMC2022/

Congres

Congres	17th IEEE International Conference on Advanced Motion Control, AMC 2022
Verkorte titel	AMC 2022
Land/Regio	Italië
Stad	Padova
Periode	18/02/22 → 20/02/22
Internet adres	http://static.gest.unipd.it/AMC2022/

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10.1109/AMC51637.2022.9729327

AMC2022MaxvanHarenPureFinale auteursversie , 1,32 MBLicentie: CC BY

Citeer dit

van Haren, M., Poot, M., Kostic, D., van Es, R., Portegies, J., & Oomen, T. (2022). Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder. In 2022 IEEE 17th International Conference on Advanced Motion Control (AMC) (blz. 268-273). Artikel 9729327 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/AMC51637.2022.9729327

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title = "Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder",

abstract = "Mechatronic systems have increasingly stringent performance requirements for motion control, leading to a situation where many factors, such as position-dependency, cannot be neglected in feedforward control. The aim of this paper is to compensate for position-dependent effects by modeling feedforward parameters as a function of position. A framework to model and identify feedforward parameters as a continuous function of position is developed by combining Gaussian processes and feedforward parameter learning techniques. The framework results in a fully data-driven approach, which can be readily implemented for industrial control applications. The framework is experimentally validated and shows a significant performance increase on a commercial wire bonder.",

author = "\{van Haren\}, Max and Maurice Poot and Dragan Kostic and \{van Es\}, Robin and Jim Portegies and Tom Oomen",

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language = "English",

pages = "268--273",

booktitle = "2022 IEEE 17th International Conference on Advanced Motion Control (AMC)",

publisher = "Institute of Electrical and Electronics Engineers",

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note = "17th IEEE International Conference on Advanced Motion Control, AMC 2022, AMC 2022 ; Conference date: 18-02-2022 Through 20-02-2022",

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van Haren, M, Poot, M, Kostic, D, van Es, R, Portegies, J & Oomen, T 2022, Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder. in 2022 IEEE 17th International Conference on Advanced Motion Control (AMC)., 9729327, Institute of Electrical and Electronics Engineers, blz. 268-273, 17th IEEE International Conference on Advanced Motion Control, AMC 2022, Padova, Italië, 18/02/22. https://doi.org/10.1109/AMC51637.2022.9729327

Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder. / van Haren, Max; Poot, Maurice; Kostic, Dragan et al.
2022 IEEE 17th International Conference on Advanced Motion Control (AMC). Institute of Electrical and Electronics Engineers, 2022. blz. 268-273 9729327.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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T1 - Gaussian Process Position-Dependent Feedforward

T2 - 17th IEEE International Conference on Advanced Motion Control, AMC 2022

AU - van Haren, Max

AU - Poot, Maurice

AU - Kostic, Dragan

AU - van Es, Robin

AU - Portegies, Jim

AU - Oomen, Tom

N1 - Conference code: 17

PY - 2022/3/11

Y1 - 2022/3/11

N2 - Mechatronic systems have increasingly stringent performance requirements for motion control, leading to a situation where many factors, such as position-dependency, cannot be neglected in feedforward control. The aim of this paper is to compensate for position-dependent effects by modeling feedforward parameters as a function of position. A framework to model and identify feedforward parameters as a continuous function of position is developed by combining Gaussian processes and feedforward parameter learning techniques. The framework results in a fully data-driven approach, which can be readily implemented for industrial control applications. The framework is experimentally validated and shows a significant performance increase on a commercial wire bonder.

AB - Mechatronic systems have increasingly stringent performance requirements for motion control, leading to a situation where many factors, such as position-dependency, cannot be neglected in feedforward control. The aim of this paper is to compensate for position-dependent effects by modeling feedforward parameters as a function of position. A framework to model and identify feedforward parameters as a continuous function of position is developed by combining Gaussian processes and feedforward parameter learning techniques. The framework results in a fully data-driven approach, which can be readily implemented for industrial control applications. The framework is experimentally validated and shows a significant performance increase on a commercial wire bonder.

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DO - 10.1109/AMC51637.2022.9729327

M3 - Conference contribution

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PB - Institute of Electrical and Electronics Engineers

Y2 - 18 February 2022 through 20 February 2022

ER -

Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder

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