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State estimation in networked control systems with delayed and lossy acknowledgments

Rosenthal, Florian ORCID iD icon 1; Noack, Benjamin 1; Hanebeck, Uwe D. 1
1 Institut für Anthropomatik und Robotik (IAR), Karlsruher Institut für Technologie (KIT)

Abstract:

In this paper, we consider state estimation in Networked Control Systems where both control inputs and measurements are transmitted via networks which are lossy and introduce random transmission delays. In contrast to the common notion of TCP-like communication, where successful transmissions are acknowledged instantaneously and without losses, we focus on the case where the acknowledgment packets provided by the actuator upon reception of applicable control inputs are also subject to delays and losses. Consequently, the estimator has only partial and belated knowledge on the actually applied control inputs, which results in additional uncertainty. We derive an estimator for the considered setup by generalizing an existing approach for UDP-like communication which integrates estimates of the applied control inputs into the overall state estimation. The presented estimator is assessed in terms of Monte Carlo simulations.


Postprint §
DOI: 10.5445/IR/1000080760
Veröffentlicht am 13.03.2026
Originalveröffentlichung
DOI: 10.1109/MFI.2017.8170359
Scopus
Zitationen: 4
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Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2017
Sprache Englisch
Identifikator ISBN: 978-1-5090-6064-1
KITopen-ID: 1000080760
Erschienen in Proceedings of the International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017, Daegu, South Korea, 16th - 18th November 2017
Veranstaltung IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017), Daegu, Südkorea, 16.11.2017 – 18.11.2017
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 435-441
Bemerkung zur Veröffentlichung Zgl. erschienen in: S. Lee (Ed.): Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System. Cham : Springer, 2017.
Vorab online veröffentlicht am 11.12.2017
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