Abstract
Friction is an undesired phenomenon in many mechanical systems. Feedforward of a suitable estimate of friction is an effective method to compensate for the friction-dependent position errors in the steady state. It is not easy, however, to make a precise friction model because of the complexity of static and dynamic characteristics of friction such as the Stribeck effect, the Dahl effect, stick-slip motion, and so on. In this paper, we propose an effective friction compensation method for the electromechanical drive systems. In the proposed method, neural networks are applied in parallel to a linear observer, for the electromechanical positioning system (EMPS) which is used at the Cologne Laboratory of Mechatronics (CLM) at the University of Applied Sciences Cologne for experimental investigation of position control schemes for compliant systems with friction.
| Original language | English |
|---|---|
| Pages | 1758-1762 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - Dec 1 2004 |
| Externally published | Yes |
| Event | IECON 2004 - 30th Annual Conference of IEEE Industrial Electronics Society - Busan, Korea, Republic of Duration: Nov 2 2004 → Nov 6 2004 |
Other
| Other | IECON 2004 - 30th Annual Conference of IEEE Industrial Electronics Society |
|---|---|
| Country | Korea, Republic of |
| City | Busan |
| Period | 11/2/04 → 11/6/04 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering
Cite this
Friction compensation of the electromechanical drive systems using neural networks. / Kiguchi, Kazuo; Henrichfreise, Hermann; Hesseler, Karl Peter.
2004. 1758-1762 Paper presented at IECON 2004 - 30th Annual Conference of IEEE Industrial Electronics Society, Busan, Korea, Republic of.Research output: Contribution to conference › Paper
}
TY - CONF
T1 - Friction compensation of the electromechanical drive systems using neural networks
AU - Kiguchi, Kazuo
AU - Henrichfreise, Hermann
AU - Hesseler, Karl Peter
PY - 2004/12/1
Y1 - 2004/12/1
N2 - Friction is an undesired phenomenon in many mechanical systems. Feedforward of a suitable estimate of friction is an effective method to compensate for the friction-dependent position errors in the steady state. It is not easy, however, to make a precise friction model because of the complexity of static and dynamic characteristics of friction such as the Stribeck effect, the Dahl effect, stick-slip motion, and so on. In this paper, we propose an effective friction compensation method for the electromechanical drive systems. In the proposed method, neural networks are applied in parallel to a linear observer, for the electromechanical positioning system (EMPS) which is used at the Cologne Laboratory of Mechatronics (CLM) at the University of Applied Sciences Cologne for experimental investigation of position control schemes for compliant systems with friction.
AB - Friction is an undesired phenomenon in many mechanical systems. Feedforward of a suitable estimate of friction is an effective method to compensate for the friction-dependent position errors in the steady state. It is not easy, however, to make a precise friction model because of the complexity of static and dynamic characteristics of friction such as the Stribeck effect, the Dahl effect, stick-slip motion, and so on. In this paper, we propose an effective friction compensation method for the electromechanical drive systems. In the proposed method, neural networks are applied in parallel to a linear observer, for the electromechanical positioning system (EMPS) which is used at the Cologne Laboratory of Mechatronics (CLM) at the University of Applied Sciences Cologne for experimental investigation of position control schemes for compliant systems with friction.
UR - http://www.scopus.com/inward/record.url?scp=20544443995&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=20544443995&partnerID=8YFLogxK
U2 - 10.1109/IECON.2004.1431848
DO - 10.1109/IECON.2004.1431848
M3 - Paper
AN - SCOPUS:20544443995
SP - 1758
EP - 1762
ER -