Data-driven control of infinite dimensional systems : Application to a continuous crystallizer
(2021) 2021 American Control Conference, ACC 2021 In Proceedings of the American Control Conference 2021-May. p.1438-1443- Abstract
Controlling infinite dimensional models remains a challenging task for many practitioners since they are not suitable for traditional control design techniques or will result in a high-order controller too complex for implementation. Therefore, the model or the controller need to be reduced to an acceptable dimension, which is time-consuming, requires some expertise and may introduce numerical error. This paper tackles the control of such a system, namely a continuous crystallizer, and compares two different data-driven strategies: the first one is a structured robust technique while the other one, called L-DDC, is based on the Loewner interpolatory framework. Model/Controller reduction, Stability of linear systems, Control... (More)
Controlling infinite dimensional models remains a challenging task for many practitioners since they are not suitable for traditional control design techniques or will result in a high-order controller too complex for implementation. Therefore, the model or the controller need to be reduced to an acceptable dimension, which is time-consuming, requires some expertise and may introduce numerical error. This paper tackles the control of such a system, namely a continuous crystallizer, and compares two different data-driven strategies: the first one is a structured robust technique while the other one, called L-DDC, is based on the Loewner interpolatory framework. Model/Controller reduction, Stability of linear systems, Control applications.
(Less)
- author
- Kergus, Pauline LU
- organization
- publishing date
- 2021-05-25
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2021 American Control Conference, ACC 2021
- series title
- Proceedings of the American Control Conference
- volume
- 2021-May
- article number
- 9483117
- pages
- 6 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2021 American Control Conference, ACC 2021
- conference location
- Virtual, New Orleans, United States
- conference dates
- 2021-05-25 - 2021-05-28
- external identifiers
-
- scopus:85111912382
- ISSN
- 0743-1619
- ISBN
- 9781665441971
- DOI
- 10.23919/ACC50511.2021.9483117
- language
- English
- LU publication?
- yes
- id
- c4d9fa50-0435-4aaf-9d26-8f725b9a243e
- date added to LUP
- 2021-09-03 15:31:18
- date last changed
- 2022-04-27 03:36:58
@inproceedings{c4d9fa50-0435-4aaf-9d26-8f725b9a243e,
abstract = {{<p>Controlling infinite dimensional models remains a challenging task for many practitioners since they are not suitable for traditional control design techniques or will result in a high-order controller too complex for implementation. Therefore, the model or the controller need to be reduced to an acceptable dimension, which is time-consuming, requires some expertise and may introduce numerical error. This paper tackles the control of such a system, namely a continuous crystallizer, and compares two different data-driven strategies: the first one is a structured robust technique while the other one, called L-DDC, is based on the Loewner interpolatory framework. Model/Controller reduction, Stability of linear systems, Control applications.</p>}},
author = {{Kergus, Pauline}},
booktitle = {{2021 American Control Conference, ACC 2021}},
isbn = {{9781665441971}},
issn = {{0743-1619}},
language = {{eng}},
month = {{05}},
pages = {{1438--1443}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{Proceedings of the American Control Conference}},
title = {{Data-driven control of infinite dimensional systems : Application to a continuous crystallizer}},
url = {{http://dx.doi.org/10.23919/ACC50511.2021.9483117}},
doi = {{10.23919/ACC50511.2021.9483117}},
volume = {{2021-May}},
year = {{2021}},
}