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 language | English (US) |
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| Title of host publication | 2019 American Control Conference, ACC 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 4217-4222 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538679265 |
| DOIs | |
| State | Published - Jul 2019 |
| Event | 2019 American Control Conference, ACC 2019 - Philadelphia, United States Duration: Jul 10 2019 → Jul 12 2019 |
Publication series
| Name | Proceedings of the American Control Conference |
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| Volume | 2019-July |
| ISSN (Print) | 0743-1619 |
Conference
| Conference | 2019 American Control Conference, ACC 2019 |
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| Country/Territory | United States |
| City | Philadelphia |
| Period | 7/10/19 → 7/12/19 |
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.