Details
| Original language | English |
|---|---|
| Title of host publication | Motion and Vibration Control - Selected Papers from MOVIC 2008 |
| Publisher | Kluwer Academic Publishers |
| Pages | 41-51 |
| Number of pages | 11 |
| ISBN (print) | 9781402094378 |
| Publication status | Published - 2009 |
| Event | 9th International Conference on Motion and Vibration Control, MOVIC 2008 - Munich, Germany Duration: 15 Sept 2008 → 18 Sept 2008 |
Publication series
| Name | Motion and Vibration Control - Selected Papers from MOVIC 2008 |
|---|
Abstract
This paper presents an application of Iterative Learning Control (ILC) for optimizing the cutting process of an electromagnetically actuated punch (EAP). In contrast to mechanical presses, with the EAP it is possible to change the ram's kinematics freely and to optimize it online. During the contact of the ram with the work piece, high transient forces are excited and deteriorate the positioning accuracy of the ram. By using a Sliding-Mode-Control it is not possible to compensate this. Thanks to the cyclic nature of the cutting process, we apply ILC in order to increase the accuracy of the ram. In this work we present a comparison study of two linear approaches. The first one consists in a filtered and phase lead compensated integral learning. In contrast, the second approach exploits explicit knowledge of the system's experimentally identified transfer function and performs a contraction mapping during the learning process. The experimental results show that both algorithms are capable to reduce the positioning error and to increase the accuracy of the system, even at high dynamics.
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
Motion and Vibration Control - Selected Papers from MOVIC 2008. Kluwer Academic Publishers, 2009. p. 41-51 (Motion and Vibration Control - Selected Papers from MOVIC 2008).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Applying iterative learning control for accuracy improvement of an electromagnetically actuated punch
AU - Dagen, M.
AU - Abdellatif, H.
AU - Heimann, B.
PY - 2009
Y1 - 2009
N2 - This paper presents an application of Iterative Learning Control (ILC) for optimizing the cutting process of an electromagnetically actuated punch (EAP). In contrast to mechanical presses, with the EAP it is possible to change the ram's kinematics freely and to optimize it online. During the contact of the ram with the work piece, high transient forces are excited and deteriorate the positioning accuracy of the ram. By using a Sliding-Mode-Control it is not possible to compensate this. Thanks to the cyclic nature of the cutting process, we apply ILC in order to increase the accuracy of the ram. In this work we present a comparison study of two linear approaches. The first one consists in a filtered and phase lead compensated integral learning. In contrast, the second approach exploits explicit knowledge of the system's experimentally identified transfer function and performs a contraction mapping during the learning process. The experimental results show that both algorithms are capable to reduce the positioning error and to increase the accuracy of the system, even at high dynamics.
AB - This paper presents an application of Iterative Learning Control (ILC) for optimizing the cutting process of an electromagnetically actuated punch (EAP). In contrast to mechanical presses, with the EAP it is possible to change the ram's kinematics freely and to optimize it online. During the contact of the ram with the work piece, high transient forces are excited and deteriorate the positioning accuracy of the ram. By using a Sliding-Mode-Control it is not possible to compensate this. Thanks to the cyclic nature of the cutting process, we apply ILC in order to increase the accuracy of the ram. In this work we present a comparison study of two linear approaches. The first one consists in a filtered and phase lead compensated integral learning. In contrast, the second approach exploits explicit knowledge of the system's experimentally identified transfer function and performs a contraction mapping during the learning process. The experimental results show that both algorithms are capable to reduce the positioning error and to increase the accuracy of the system, even at high dynamics.
UR - http://www.scopus.com/inward/record.url?scp=84900608660&partnerID=8YFLogxK
U2 - 10.1007/978-1-4020-9438-5_5
DO - 10.1007/978-1-4020-9438-5_5
M3 - Conference contribution
AN - SCOPUS:84900608660
SN - 9781402094378
T3 - Motion and Vibration Control - Selected Papers from MOVIC 2008
SP - 41
EP - 51
BT - Motion and Vibration Control - Selected Papers from MOVIC 2008
PB - Kluwer Academic Publishers
T2 - 9th International Conference on Motion and Vibration Control, MOVIC 2008
Y2 - 15 September 2008 through 18 September 2008
ER -