Sensitivity-based adaptive SRUKF for online state, parameter, and process covariance estimation

verfasst von

Mauro Hernan Riva, Mark Wielitzka, Tobias Ortmaier

Abstract

State estimation is applicable to almost all areas of engineering and science. Applications that include a physical-parametric model of a system are candidates for state estimation. These estimators reconstruct the system states based on a system model and information received from the system sensors. The most widely applied state estimators are the Kalman Filter (KF) derivatives. These filters use a parametric system model, system measurements and input information, and require knowledge about the noise statistics affecting the system. These noise statistics are often unknown and inaccurate filter tuning may lead to decreased filter performance or even filter divergence. These estimators can be extended to estimate parameters. However, insufficient system excitation can cause parameter estimation drifts. In this paper, a sensitivity-based adaptive Square-Root Un-scented Kalman Filter (SRUKF) is presented. This filter estimates system states, parameters and noise covariances online. Moreover, local sensitivity analysis is performed to prevent parameter estimation drifts during phases of insufficient system excitation. The filter is evaluated on two testbeds based on an axis serial mechanism and compared with the joint SRUKF.

Organisationseinheit(en)

Institut für Mechatronische Systeme

Typ

Aufsatz in Konferenzband

Seiten

1547-1553

Anzahl der Seiten

7

Publikationsdatum

28.06.2017

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Entscheidungswissenschaften (sonstige), Wirtschaftsingenieurwesen und Fertigungstechnik, Steuerung und Optimierung

Elektronische Version(en)

https://doi.org/10.1109/cdc.2017.8263871 (Zugang: Geschlossen)