Model-free multi-variable learning control of a five axis nanopositioning stage

Thijs Sieswerda; Andrew J. Fleming; Tom Oomen

doi:10.1109/AIM46487.2021.9517342

Model-free multi-variable learning control of a five axis nanopositioning stage

Thijs Sieswerda
, Andrew J. Fleming
, Tom Oomen

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

Abstract

This article compares the performance of recently introduced learning control methods on a 5-axis nanopositioning stage. Of these methods, the Smoothed Model-Free Inversion-based Iterative Control (SMF-IIC) method requires no modeling effort for effective tracking of repetitive trajectories and is readily applicable to multi-variable systems. Experimental results show that the tracking performance of the SMF-IIC method is similar to traditional learning control methods when applied to a single axis of the nanopositioning stage. The SMF-IIC method is also found to be effective for reference tracking of two axes simultaneously.

Original language	English
Title of host publication	2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
Publisher	Institute of Electrical and Electronics Engineers
Pages	1190-1194
Number of pages	5
ISBN (Electronic)	9781665441391
DOIs	https://doi.org/10.1109/AIM46487.2021.9517342
Publication status	Published - 24 Aug 2021
Event	2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021 - Delft, Netherlands Duration: 12 Jul 2021 → 16 Jul 2021

Conference

Conference	2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021
Country/Territory	Netherlands
City	Delft
Period	12/07/21 → 16/07/21

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10.1109/AIM46487.2021.9517342

Cite this

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title = "Model-free multi-variable learning control of a five axis nanopositioning stage",

abstract = "This article compares the performance of recently introduced learning control methods on a 5-axis nanopositioning stage. Of these methods, the Smoothed Model-Free Inversion-based Iterative Control (SMF-IIC) method requires no modeling effort for effective tracking of repetitive trajectories and is readily applicable to multi-variable systems. Experimental results show that the tracking performance of the SMF-IIC method is similar to traditional learning control methods when applied to a single axis of the nanopositioning stage. The SMF-IIC method is also found to be effective for reference tracking of two axes simultaneously.",

author = "Thijs Sieswerda and Fleming, \{Andrew J.\} and Tom Oomen",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021 ; Conference date: 12-07-2021 Through 16-07-2021",

year = "2021",

month = aug,

day = "24",

doi = "10.1109/AIM46487.2021.9517342",

language = "English",

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booktitle = "2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)",

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Sieswerda, T, Fleming, AJ & Oomen, T 2021, Model-free multi-variable learning control of a five axis nanopositioning stage. in 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)., 9517342, Institute of Electrical and Electronics Engineers, pp. 1190-1194, 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021, Delft, Netherlands, 12/07/21. https://doi.org/10.1109/AIM46487.2021.9517342

Model-free multi-variable learning control of a five axis nanopositioning stage. / Sieswerda, Thijs; Fleming, Andrew J.; Oomen, Tom.
2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). Institute of Electrical and Electronics Engineers, 2021. p. 1190-1194 9517342.

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

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AU - Fleming, Andrew J.

AU - Oomen, Tom

PY - 2021/8/24

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N2 - This article compares the performance of recently introduced learning control methods on a 5-axis nanopositioning stage. Of these methods, the Smoothed Model-Free Inversion-based Iterative Control (SMF-IIC) method requires no modeling effort for effective tracking of repetitive trajectories and is readily applicable to multi-variable systems. Experimental results show that the tracking performance of the SMF-IIC method is similar to traditional learning control methods when applied to a single axis of the nanopositioning stage. The SMF-IIC method is also found to be effective for reference tracking of two axes simultaneously.

AB - This article compares the performance of recently introduced learning control methods on a 5-axis nanopositioning stage. Of these methods, the Smoothed Model-Free Inversion-based Iterative Control (SMF-IIC) method requires no modeling effort for effective tracking of repetitive trajectories and is readily applicable to multi-variable systems. Experimental results show that the tracking performance of the SMF-IIC method is similar to traditional learning control methods when applied to a single axis of the nanopositioning stage. The SMF-IIC method is also found to be effective for reference tracking of two axes simultaneously.

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DO - 10.1109/AIM46487.2021.9517342

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BT - 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

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Y2 - 12 July 2021 through 16 July 2021

ER -

Model-free multi-variable learning control of a five axis nanopositioning stage

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