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Adaptive control of cold rolling system in electrical strips production system with online-offline predictors
Mälardalen University, School of Sustainable Development of Society and Technology.
Mälardalen University, School of Sustainable Development of Society and Technology.
Mälardalen University, School of Sustainable Development of Society and Technology.ORCID iD: 0000-0002-7233-6916
2010 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 50, no 9-12, p. 917-930Article in journal (Refereed) Published
Abstract [en]

One of the main concerns of strips producers is to measure strip thickness accurately as it is produced. Correct modelling of the sensitivity of output variables to input variables in a rolling mill model is one of the keys to obtaining more accurate data. An adaptive control system that uses an artificial neural network (ANN) creates a model of the process directly from measurement data. Using the model, the control system can predict how the process will react to control actions. The creation of the model and the computation of the control strategy are carried out automatically by the control system. The proportional-integral-derivative controller is used in this method to increase accuracy of final estimated variables and to increase accuracy of control of the system. To determine the correct tuning for thickness control, three control parameters are considered: the roll gap, and front and back tensions. A predictive model is used, based on the sensitivity equations of the process, where the sensitivity factors are computed by differentiating a previously trained neural network. Results of a case study in a real plant show that this online-offline model is effective in reducing thickness variations in produced strips.

Place, publisher, year, edition, pages
2010. Vol. 50, no 9-12, p. 917-930
Keywords [en]
Process, Ferromagnetic strip, Electrical machines, Thickness, Estimation, ANN, Control
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-20135DOI: 10.1007/s00170-010-2585-7ISI: 000282840700007Scopus ID: 2-s2.0-77957858232OAI: oai:DiVA.org:mdh-20135DiVA, id: diva2:638501
Available from: 2013-07-30 Created: 2013-07-05 Last updated: 2017-12-06Bibliographically approved

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Dahlquist, Erik

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf