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.

Original 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
DOIs
Publication statusPublished - 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

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