Input-output data-driven control through dissipativity learning

Wentao Tang, Prodromos Daoutidis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Data-driven control offers an alternative to traditional model-based. Most present data-driven control strategies either involve model identification or need to assume availability of state information. In this work, we develop an input-output data-driven control method through dissipativity learning. Specifically, the learning of the subsystems' dissipativity property using one-class support vector machine (OC-SVM) is combined with the controller design to minimize an upper bound of the L-{2} -gain. The data-driven controller synthesis problem is then formulated as quadratic-semidefinite programming with linear and multilinear constraints, solved via the alternating direction method of multipliers (ADMM). The proposed method is illustrated with a polymerization reactor.

Original languageEnglish (US)
Title of host publication2019 American Control Conference, ACC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4217-4222
Number of pages6
ISBN (Electronic)9781538679265
DOIs
StatePublished - Jul 2019
Event2019 American Control Conference, ACC 2019 - Philadelphia, United States
Duration: Jul 10 2019Jul 12 2019

Publication series

NameProceedings of the American Control Conference
Volume2019-July
ISSN (Print)0743-1619

Conference

Conference2019 American Control Conference, ACC 2019
Country/TerritoryUnited States
CityPhiladelphia
Period7/10/197/12/19

Bibliographical note

Funding Information:
* This work is supported by NSF-CBET and Doctoral Dissertation Fellowship of University of Minnesota.

Publisher Copyright:
© 2019 American Automatic Control Council.

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