Finite-time learning control using frequency response data with application to a nanopositioning stage

Robin de Rozario; Andrew Fleming; Tom Oomen

doi:10.1109/TMECH.2019.2931407

Finite-time learning control using frequency response data with application to a nanopositioning stage

Robin de Rozario (Corresponding author)
, Andrew Fleming
, Tom Oomen

Control Systems Technology

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

29 Citations (Scopus)

211 Downloads (Pure)

Abstract

Learning control enables significant performance improvement for systems that perform repeating tasks. Achieving high tracking performance by utilizing past error data typically requires noncausal learning that is based on a parametric model of the process. Such model-based approaches impose significant requirements on modeling and filter design. The aim of this paper is to reduce these requirements by developing a learning control framework that enables performance improvement through noncausal learning without relying on a parametric model. This is achieved by explicitly using the discrete Fourier transform to enable learning by using a nonparametric frequency response function model of the process. The effectiveness of the developed method is illustrated by application to a nanopositioning stage.

Original language	English
Article number	8777112
Pages (from-to)	2085-2096
Number of pages	12
Journal	IEEE/ASME Transactions on Mechatronics
Volume	24
Issue number	5
DOIs	https://doi.org/10.1109/TMECH.2019.2931407
Publication status	Published - 1 Oct 2019

Funding

Manuscript received July 30, 2018; revised March 18, 2019; accepted July 10, 2019. Date of publication July 26, 2019; date of current version October 15, 2019. Recommended by Technical Editor Q. Zou. This work was supported in part by Océ Technologies and in part by the Netherlands Organization for Scientific Research through Research Program VIDI under Project 15698. (Corresponding author: Robin de Rozario.) R. de Rozario and T. Oomen are with the Faculty of Mechanical Engineering, Control Systems Technology, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands (e-mail:, [email protected]; [email protected]).

Keywords

frequency response
Iterative learning control
motion control
nanopositioning

Access to Document

10.1109/TMECH.2019.2931407

publishers versionFinal published version, 4.96 MBLicence: TAVERNE

Cite this

@article{280c0c683ffc446abf548fb18489e350,

title = "Finite-time learning control using frequency response data with application to a nanopositioning stage",

abstract = "Learning control enables significant performance improvement for systems that perform repeating tasks. Achieving high tracking performance by utilizing past error data typically requires noncausal learning that is based on a parametric model of the process. Such model-based approaches impose significant requirements on modeling and filter design. The aim of this paper is to reduce these requirements by developing a learning control framework that enables performance improvement through noncausal learning without relying on a parametric model. This is achieved by explicitly using the discrete Fourier transform to enable learning by using a nonparametric frequency response function model of the process. The effectiveness of the developed method is illustrated by application to a nanopositioning stage.",

keywords = "frequency response, Iterative learning control, motion control, nanopositioning",

author = "\{de Rozario\}, Robin and Andrew Fleming and Tom Oomen",

year = "2019",

month = oct,

day = "1",

doi = "10.1109/TMECH.2019.2931407",

language = "English",

volume = "24",

pages = "2085--2096",

journal = "IEEE/ASME Transactions on Mechatronics",

issn = "1083-4435",

publisher = "Institute of Electrical and Electronics Engineers",

number = "5",

}

TY - JOUR

T1 - Finite-time learning control using frequency response data with application to a nanopositioning stage

AU - de Rozario, Robin

AU - Fleming, Andrew

AU - Oomen, Tom

PY - 2019/10/1

Y1 - 2019/10/1

N2 - Learning control enables significant performance improvement for systems that perform repeating tasks. Achieving high tracking performance by utilizing past error data typically requires noncausal learning that is based on a parametric model of the process. Such model-based approaches impose significant requirements on modeling and filter design. The aim of this paper is to reduce these requirements by developing a learning control framework that enables performance improvement through noncausal learning without relying on a parametric model. This is achieved by explicitly using the discrete Fourier transform to enable learning by using a nonparametric frequency response function model of the process. The effectiveness of the developed method is illustrated by application to a nanopositioning stage.

AB - Learning control enables significant performance improvement for systems that perform repeating tasks. Achieving high tracking performance by utilizing past error data typically requires noncausal learning that is based on a parametric model of the process. Such model-based approaches impose significant requirements on modeling and filter design. The aim of this paper is to reduce these requirements by developing a learning control framework that enables performance improvement through noncausal learning without relying on a parametric model. This is achieved by explicitly using the discrete Fourier transform to enable learning by using a nonparametric frequency response function model of the process. The effectiveness of the developed method is illustrated by application to a nanopositioning stage.

KW - frequency response

KW - Iterative learning control

KW - motion control

KW - nanopositioning

UR - https://www.scopus.com/pages/publications/85074247547

U2 - 10.1109/TMECH.2019.2931407

DO - 10.1109/TMECH.2019.2931407

M3 - Article

AN - SCOPUS:85074247547

SN - 1083-4435

VL - 24

SP - 2085

EP - 2096

JO - IEEE/ASME Transactions on Mechatronics

JF - IEEE/ASME Transactions on Mechatronics

IS - 5

M1 - 8777112

ER -

Finite-time learning control using frequency response data with application to a nanopositioning stage

Abstract

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this