Authors:
Daniel Beckmann
;
Matthias Dagen
and
Tobias Ortmaier
Affiliation:
Leibniz Universität Hannover, Germany
Keyword(s):
Online Estimation, Kalman Filter, Discretization Methods, Mechanical System.
Related
Ontology
Subjects/Areas/Topics:
Force and Tactile Sensors
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
;
System Identification
;
System Modeling
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.