Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Sliding mode SISO control of model parameters for implicit dynamic feedback estimation of industrial tracking simulation systems
VTT Technical Research Centre of Finland Ltd, Espoo.
Aalto University, Helsinki.
VTT Technical Research Centre of Finland .
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Electrical Engineering and Automation, Aalto University.ORCID iD: 0000-0002-9315-9920
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

A tracking simulator is an online simulation system that achieves a permanent state synchronization with the targeted process by dynamically calibrating the model state after comparing process measurements with model results. Tracking simulators are a powerful industrial application that can be utilized as a plant-wide virtual sensor for process monitoring and diagnosis as well as a predictive tool to provide production forecasts based on the current state of the plant. In a tracking simulator, the online calibration is performed by a dynamic estimation method. One of the most adopted dynamic estimaton methods is implicit dynamic feedback, which is based on the adjustment of model parameter using feedback controllers to align simulation results and process outputs. Thus far, PI controllers have been the most popular approach for the implementation of implicit dynamic feedback estimators. Other feedback control techniques could be employed to improve the reliability of applications based on this estimation method. This paper presents an implicit dynamic feedback estimation approach based on sliding mode controllers (SMC) for industrial tracking simulation systems. In contrast to PI controllers, SMC controllers can be more easily tuned and they are robust against uncertainties related to the simulation model behavior and measurement noise. In this work, the SMC-based approach for estimation of tracking simulation systems is described, implemented and tested using a representative laboratory-scale process.

Place, publisher, year, edition, pages
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 6927-6932
Series
IEEE Industrial Electronics Society, ISSN 1553-572X
National Category
Computer Systems
Research subject
Dependable Communication and Computation Systems
Identifiers
URN: urn:nbn:se:ltu:diva-68273DOI: 10.1109/IECON.2017.8217211ISI: 000427164806134ISBN: 9781538611272 (electronic)OAI: oai:DiVA.org:ltu-68273DiVA, id: diva2:1196579
Conference
43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017, Bejing, China, 29 October - 1 November 2017
Available from: 2018-04-10 Created: 2018-04-10 Last updated: 2018-04-19Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Search in DiVA

By author/editor
Vyatkin, Valeriy
By organisation
Computer Science
Computer Systems

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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