Details
| Original language | English |
|---|---|
| Title of host publication | ICINCO 2016 - Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics |
| Editors | Oleg Gusikhin, Dimitri Peaucelle, Kurosh Madani |
| Place of Publication | Lissabon, Portugal |
| Pages | 327 - 334 |
| Number of pages | 8 |
| ISBN (electronic) | 9789897581984 |
| Publication status | Published - Jul 2016 |
| Event | 13th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2016 - Lisbon, Portugal Duration: 29 Jul 2016 → 31 Jul 2016 |
Publication series
| Name | ICINCO 2016 - Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics |
|---|---|
| Volume | 1 |
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.
Keywords
- Discretization methods, Kalman filter, Mechanical system, Online estimation
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Information Systems
Cite this
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ICINCO 2016 - Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics. ed. / Oleg Gusikhin; Dimitri Peaucelle; Kurosh Madani. Lissabon, Portugal, 2016. p. 327 - 334 (ICINCO 2016 - Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics; Vol. 1).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Symplectic Discretiziation Methods for Parameter Estimation of a Nonlinear Mechanical System Using an Extended Kalman Filter
AU - Beckmann, Daniel
AU - Dagen, Matthias
AU - Ortmaier, Tobias
PY - 2016/7
Y1 - 2016/7
N2 - 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.
AB - 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.
KW - Discretization methods
KW - Kalman filter
KW - Mechanical system
KW - Online estimation
UR - http://www.scopus.com/inward/record.url?scp=85013040160&partnerID=8YFLogxK
U2 - 10.5220/0005973503270334
DO - 10.5220/0005973503270334
M3 - Conference contribution
AN - SCOPUS:85013040160
T3 - ICINCO 2016 - Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics
SP - 327
EP - 334
BT - ICINCO 2016 - Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics
A2 - Gusikhin, Oleg
A2 - Peaucelle, Dimitri
A2 - Madani, Kurosh
CY - Lissabon, Portugal
T2 - 13th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2016
Y2 - 29 July 2016 through 31 July 2016
ER -