Abstract
Process systems are characterized by nonlinearity, uncertainty, large scales, and also the need of pursuing both safety and economic optimality in operations. As a result they are difficult to control effectively. Data-driven techniques such as machine learning algorithms can provide complementary tools and insights to classical model-based control by enhancing the capability of modeling the dynamics of complex systems and the maintenance of control performance. Moreover, by learning the behavior of plants and controllers as black boxes, data-driven techniques can enable a completely model-free control paradigm. Hence, data-driven process control has the potential to mitigate the challenges of state-of-the-art control technology and yield generic, adaptive, and scalable strategies. This paper aims at providing an overview and conceptual classification of the main approaches in this emerging and promising field, and identifying current limitations and future directions.
| Original language | English (US) |
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| Title of host publication | 2022 American Control Conference, ACC 2022 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1048-1064 |
| Number of pages | 17 |
| ISBN (Electronic) | 9781665451963 |
| DOIs | |
| State | Published - 2022 |
| Event | 2022 American Control Conference, ACC 2022 - Atlanta, United States Duration: Jun 8 2022 → Jun 10 2022 |
Publication series
| Name | 2022 American Control Conference (ACC) |
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Conference
| Conference | 2022 American Control Conference, ACC 2022 |
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| Country/Territory | United States |
| City | Atlanta |
| Period | 6/8/22 → 6/10/22 |
Bibliographical note
Funding Information:*This work was supported by NSF-CBET. 1Wentao Tang is currently with Surface Operations, Projects and Technology, Shell Global Solutions (U.S.) Inc., Houston, TX 77082, USA [email protected] 2Prodromos Daoutidis with the Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455, USA [email protected]
Publisher Copyright:
© 2022 American Automatic Control Council.