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Event-triggered pulse control with model learning (if Necessary)
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. Max Planck Institute for Intelligent Systems, Stuttgart/Tübingen, Germany.
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0001-9940-5929
2019 (English)In: Proceedings of the American Control Conference, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 792-797, article id 8814333Conference paper, Published paper (Refereed)
Abstract [en]

In networked control systems, communication is a shared and therefore scarce resource. Event-triggered control (ETC) can achieve high performance control with a significantly reduced amount of samples compared to classical, periodic control schemes. However, ETC methods usually rely on the availability of an accurate dynamics model, which is oftentimes not readily available. In this paper, we propose a novel event-triggered pulse control strategy that learns dynamics models if necessary. In addition to adapting to changing dynamics, the method also represents a suitable replacement for the integral part typically used in periodic control.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 792-797, article id 8814333
Series
Proceedings of the American Control Conference, ISSN 0743-1619 ; 2019
National Category
Other Civil Engineering
Identifiers
URN: urn:nbn:se:kth:diva-262593Scopus ID: 2-s2.0-85072288337ISBN: 9781538679265 (print)OAI: oai:DiVA.org:kth-262593DiVA, id: diva2:1366046
Conference
2019 American Control Conference, ACC 2019; Philadelphia; United States; 10 July 2019 through 12 July 2019
Note

QC 20191028

Available from: 2019-10-28 Created: 2019-10-28 Last updated: 2019-10-28Bibliographically approved

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Johansson, Karl H.

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