Adaptive Unscented Kalman Filter for Online State, Parameter, and Process Covariance Estimation

verfasst von

Mauro Hernan Riva, Matthias Dagen, Tobias Ortmaier

Abstract

A novel observer for state, parameter and process covariance estimation is presented in this paper. The new observer estimates system states using a Square-Root Unscented Kalman Filter (SRUKF) and by employing the Recursive Prediction-Error (RPE) method, unknown parameters and covariances are identified online. Two experimental applications based on an underactuated planar robot are included to demonstrate the algorithm performance. Additionally, sensitivity models for the SRUKF are derived. Results show that the online process covariance estimation improves the observer convergence and reduces parameter estimation bias.

Organisationseinheit(en)

Institut für Mechatronische Systeme

Typ

Aufsatz in Konferenzband

Seiten

4513-4519

Anzahl der Seiten

7

Publikationsdatum

28.07.2016

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Elektrotechnik und Elektronik

Elektronische Version(en)

https://doi.org/10.1109/acc.2016.7526063 (Zugang: Geschlossen)