Details
| Original language | English |
|---|---|
| Title of host publication | 2016 European Control Conference, ECC 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 364-369 |
| Number of pages | 6 |
| ISBN (electronic) | 9781509025916 |
| Publication status | Published - 2016 |
| Event | 2016 European Control Conference (ECC) - Aalborg, Denmark Duration: 29 Jun 2016 → 1 Jul 2016 |
Abstract
In this paper a comparison of three methods for online parameter estimation is presented. The analyzed algorithms are a well known recursive least squares method (RLS), an Extended Kalman Filter (EKF) in joint state form, and an adaptive Extended Kalman Filter (aEKF). The methods' performances regarding accuracy, respond time and computing time are compared using a commercial industrial testbed, consisting of a linear belt drive for positioning tasks, an industrial servo inverter and a programmable logic controller. In addition, the online state sensitivity w.r.t. the parameters provided by the aEKF is analyzed to check the parameter excitation. These signals can be used to stop the parameter estimation for insufficient excitation.
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Mathematics(all)
- Control and Optimization
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2016 European Control Conference, ECC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 364-369 7810312.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Comparison of online-parameter estimation methods applied to a linear belt drive system
AU - Beckmann, Daniel
AU - Riva, Mauro Hernán
AU - Dagen, Matthias
AU - Ortmaier, Tobias
PY - 2016
Y1 - 2016
N2 - In this paper a comparison of three methods for online parameter estimation is presented. The analyzed algorithms are a well known recursive least squares method (RLS), an Extended Kalman Filter (EKF) in joint state form, and an adaptive Extended Kalman Filter (aEKF). The methods' performances regarding accuracy, respond time and computing time are compared using a commercial industrial testbed, consisting of a linear belt drive for positioning tasks, an industrial servo inverter and a programmable logic controller. In addition, the online state sensitivity w.r.t. the parameters provided by the aEKF is analyzed to check the parameter excitation. These signals can be used to stop the parameter estimation for insufficient excitation.
AB - In this paper a comparison of three methods for online parameter estimation is presented. The analyzed algorithms are a well known recursive least squares method (RLS), an Extended Kalman Filter (EKF) in joint state form, and an adaptive Extended Kalman Filter (aEKF). The methods' performances regarding accuracy, respond time and computing time are compared using a commercial industrial testbed, consisting of a linear belt drive for positioning tasks, an industrial servo inverter and a programmable logic controller. In addition, the online state sensitivity w.r.t. the parameters provided by the aEKF is analyzed to check the parameter excitation. These signals can be used to stop the parameter estimation for insufficient excitation.
UR - http://www.scopus.com/inward/record.url?scp=85015024166&partnerID=8YFLogxK
U2 - 10.1109/ecc.2016.7810312
DO - 10.1109/ecc.2016.7810312
M3 - Conference contribution
AN - SCOPUS:85015024166
SP - 364
EP - 369
BT - 2016 European Control Conference, ECC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 European Control Conference (ECC)
Y2 - 29 June 2016 through 1 July 2016
ER -