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On the Relation Between Smooth Variable Structure and Adaptive Kalman Filter

This article is addressed to the topic of robust state estimation of uncertain nonlinear systems. In particular, the smooth variable structure filter (SVSF) and its relation to the Kalman filter is studied. An adaptive Kalman filter is obtained from the SVSF approach by replacing the gain of the original filter. Boundedness of the estimation error of the adaptive filter is proven. The SVSF approach and the adaptive Kalman filter achieve improved robustness against model uncertainties if filter parameters are suitably optimized. Therefore, a parameter optimization process is developed and the estimation performance is studied.

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Einordnung

Datum der Veröffentlichung:
27.11.2020
DOI:
10.3389/fams.2020.585439
URN:
urn:nbn:de:hbz:464-20210311-165748-3
Sprache:
Englisch
Ressourcentyp:
Text
Schlagwörter:
state estimation; nonlinear systems; stochastic systems; Kalman filtering; control
Kollektion:
E-Publikationen
Sachgruppen der Deutschen Nationalbibliographie:
620 Ingenieurwissenschaften und Maschinenbau
Einrichtung:
Fakultät für Ingenieurwissenschaften
Förderung:

The publication of this article was supported by the Publication Fund of the University of Duisburg-Essen.

Informationen zur Erstveröffentlichung:

Spiller M and Söffker D (2020) On the Relation Between Smooth Variable Structure and Adaptive Kalman Filter. Front. Appl. Math. Stat. 6:585439.
doi: 10.3389/fams.2020.585439

Published: 27 November 2020

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