Data-Driven Control: Overview and Perspectives

Wentao Tang, Prodromos Daoutidis

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

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 languageEnglish (US)
Title of host publication2022 American Control Conference, ACC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1048-1064
Number of pages17
ISBN (Electronic)9781665451963
DOIs
StatePublished - 2022
Event2022 American Control Conference, ACC 2022 - Atlanta, United States
Duration: Jun 8 2022Jun 10 2022

Publication series

Name2022 American Control Conference (ACC)

Conference

Conference2022 American Control Conference, ACC 2022
Country/TerritoryUnited States
CityAtlanta
Period6/8/226/10/22

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 wentao.tang@shell.com 2Prodromos Daoutidis with the Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455, USA daout001@umn.edu

Publisher Copyright:
© 2022 American Automatic Control Council.

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