Symplectic Discretiziation Methods for Parameter Estimation of a Nonlinear Mechanical System Using an Extended Kalman Filter

verfasst von

Daniel Beckmann, Matthias Dagen, Tobias Ortmaier

Abstract

This paper presents two symplectic discretization methods in the context of online parameter estimation for a nonlinear mechanical system. These symplectic approaches are compared to established discretization methods (e.g. Euler Forward and Runge Kutta) regarding accuracy and computational effort. In addition, the influence of the discretization method on the performance of an augmented Extended Kalman Filter (EKF) for parameter estimation is analyzed. The methods are compared with a nonlinear mechanical simulation model, based on a belt-drive system. The simulation shows improved accuracy using simplectic integrators in comparison to the conventional methods, with almost the same or lower computational cost. Parameter estimation based on the EKF in combination with the simplectic integration scheme leads to more accurate values.

Organisationseinheit(en)

Institut für Mechatronische Systeme

Typ

Aufsatz in Konferenzband

Seiten

327 - 334

Anzahl der Seiten

8

Publikationsdatum

07.2016

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Steuerungs- und Systemtechnik, Maschinelles Sehen und Mustererkennung, Artificial intelligence, Information systems

Elektronische Version(en)

https://doi.org/10.5220/0005973503270334 (Zugang: Offen)
https://doi.org/10.5220/0005973503270334 (Zugang: Unbekannt)