Multivariable learning using frequency response data: a robust iterative inversion-based control approach with application

Robin de Rozario, Juliana Langen, Tom Oomen

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Learning control methods enable significant performance improvements for systems that operate repetitively. Typical methods rely on a parametric plant model to achieve fast and robust convergence. The aim of this paper is to develop a framework for multivariable systems that enables fast and robust learning without requiring a parametric plant model. This is achieved by connecting nonparametric frequency response function identification and robust control, which enables synthesis on a frequency-by-frequency basis. A nonconservative approach is obtained by ensuring that the identified uncertainty is directly compatible with the developed synthesis framework. Application to a multivariable benchmark motion system confirms the potential of the developed framework.

LanguageEnglish
Title of host publication2019 American Control Conference, ACC 2019
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages2215-2220
Number of pages6
ISBN (Electronic)978-1-5386-7926-5
StatePublished - 1 Jul 2019
Event2019 American Control Conference, ACC 2019 - Philadelphia, United States
Duration: 10 Jul 201912 Jul 2019
http://acc2019.a2c2.org

Conference

Conference2019 American Control Conference, ACC 2019
Abbreviated titleACC2019
CountryUnited States
CityPhiladelphia
Period10/07/1912/07/19
Internet address

Fingerprint

Frequency response
Multivariable systems
Robust control
Identification (control systems)
Uncertainty

Cite this

de Rozario, R., Langen, J., & Oomen, T. (2019). Multivariable learning using frequency response data: a robust iterative inversion-based control approach with application. In 2019 American Control Conference, ACC 2019 (pp. 2215-2220). [8814971] Piscataway: Institute of Electrical and Electronics Engineers.
de Rozario, Robin ; Langen, Juliana ; Oomen, Tom. / Multivariable learning using frequency response data : a robust iterative inversion-based control approach with application. 2019 American Control Conference, ACC 2019. Piscataway : Institute of Electrical and Electronics Engineers, 2019. pp. 2215-2220
@inproceedings{94630cbe75ad4958a3096143859747c2,
title = "Multivariable learning using frequency response data: a robust iterative inversion-based control approach with application",
abstract = "Learning control methods enable significant performance improvements for systems that operate repetitively. Typical methods rely on a parametric plant model to achieve fast and robust convergence. The aim of this paper is to develop a framework for multivariable systems that enables fast and robust learning without requiring a parametric plant model. This is achieved by connecting nonparametric frequency response function identification and robust control, which enables synthesis on a frequency-by-frequency basis. A nonconservative approach is obtained by ensuring that the identified uncertainty is directly compatible with the developed synthesis framework. Application to a multivariable benchmark motion system confirms the potential of the developed framework.",
author = "{de Rozario}, Robin and Juliana Langen and Tom Oomen",
year = "2019",
month = "7",
day = "1",
language = "English",
pages = "2215--2220",
booktitle = "2019 American Control Conference, ACC 2019",
publisher = "Institute of Electrical and Electronics Engineers",
address = "United States",

}

de Rozario, R, Langen, J & Oomen, T 2019, Multivariable learning using frequency response data: a robust iterative inversion-based control approach with application. in 2019 American Control Conference, ACC 2019., 8814971, Institute of Electrical and Electronics Engineers, Piscataway, pp. 2215-2220, 2019 American Control Conference, ACC 2019, Philadelphia, United States, 10/07/19.

Multivariable learning using frequency response data : a robust iterative inversion-based control approach with application. / de Rozario, Robin; Langen, Juliana; Oomen, Tom.

2019 American Control Conference, ACC 2019. Piscataway : Institute of Electrical and Electronics Engineers, 2019. p. 2215-2220 8814971.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

TY - GEN

T1 - Multivariable learning using frequency response data

T2 - a robust iterative inversion-based control approach with application

AU - de Rozario,Robin

AU - Langen,Juliana

AU - Oomen,Tom

PY - 2019/7/1

Y1 - 2019/7/1

N2 - Learning control methods enable significant performance improvements for systems that operate repetitively. Typical methods rely on a parametric plant model to achieve fast and robust convergence. The aim of this paper is to develop a framework for multivariable systems that enables fast and robust learning without requiring a parametric plant model. This is achieved by connecting nonparametric frequency response function identification and robust control, which enables synthesis on a frequency-by-frequency basis. A nonconservative approach is obtained by ensuring that the identified uncertainty is directly compatible with the developed synthesis framework. Application to a multivariable benchmark motion system confirms the potential of the developed framework.

AB - Learning control methods enable significant performance improvements for systems that operate repetitively. Typical methods rely on a parametric plant model to achieve fast and robust convergence. The aim of this paper is to develop a framework for multivariable systems that enables fast and robust learning without requiring a parametric plant model. This is achieved by connecting nonparametric frequency response function identification and robust control, which enables synthesis on a frequency-by-frequency basis. A nonconservative approach is obtained by ensuring that the identified uncertainty is directly compatible with the developed synthesis framework. Application to a multivariable benchmark motion system confirms the potential of the developed framework.

UR - http://www.scopus.com/inward/record.url?scp=85072293124&partnerID=8YFLogxK

M3 - Conference contribution

SP - 2215

EP - 2220

BT - 2019 American Control Conference, ACC 2019

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

ER -

de Rozario R, Langen J, Oomen T. Multivariable learning using frequency response data: a robust iterative inversion-based control approach with application. In 2019 American Control Conference, ACC 2019. Piscataway: Institute of Electrical and Electronics Engineers. 2019. p. 2215-2220. 8814971.