Structure and Parameter Identification of Process Models with hard Non-linearities for Industrial Drive Trains by means of Degenerate Genetic Programming

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Tantau, M.; Perner, L.; Wielitzka, M.; Ortmaier, T.: Structure and Parameter Identification of Process Models with hard Non-linearities for Industrial Drive Trains by means of Degenerate Genetic Programming. In: Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics. Prague : SciTePress, 2019, S. 368-376. DOI:

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Sum total of downloads: 48

The derivation of bright-grey box models for electric drives with coupled mechanics, such as stacker cranes, robots and linear gantries is an important step in control design but often too time-consuming for the ordinary commissioning process. It requires structure and parameter identification in repeated trial and error loops. In this paper an automated genetic programming solution is proposed that can cope with various features, including highly non-linear mechanics (friction, backlash). The generated state space representation can readily be used for stability analysis, state control, Kalman filtering, etc. This, however, requires several special rules in the genetic programming procedure and an automated integration of features into the defining state space form. Simulations are carried out with industrial data to investigate the performance and robustness.
License of this version: CC BY-NC-ND 4.0 Unported
Document Type: bookPart
Publishing status: publishedVersion
Issue Date: 2019-07-29
Appears in Collections:Fakultät für Maschinenbau

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pos. country downloads
total perc.
1 image of flag of Germany Germany 21 43.75%
2 image of flag of United States United States 12 25.00%
3 image of flag of China China 4 8.33%
4 image of flag of Russian Federation Russian Federation 3 6.25%
5 image of flag of Korea, Republic of Korea, Republic of 2 4.17%
6 image of flag of Ireland Ireland 2 4.17%
7 image of flag of United Kingdom United Kingdom 2 4.17%
8 image of flag of Poland Poland 1 2.08%
9 image of flag of Canada Canada 1 2.08%

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