Friction compensation of the electromechanical drive systems using neural networks

Kazuo Kiguchi; Hermann Henrichfreise; Karl Peter Hesseler

doi:10.1109/IECON.2004.1431848

Friction compensation of the electromechanical drive systems using neural networks

Kazuo Kiguchi, Hermann Henrichfreise, Karl Peter Hesseler

Research output: Contribution to conference › Paper › peer-review

3 Citations (Scopus)

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	https://doi.org/10.1109/IECON.2004.1431848
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

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/IECON.2004.1431848

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title = "Friction compensation of the electromechanical drive systems using neural networks",

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.",

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AU - Kiguchi, Kazuo

AU - Henrichfreise, Hermann

AU - Hesseler, Karl Peter

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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.

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