liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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
Comparing Different Approaches to Model Error Modeling in Robust Identification
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Dipartimento di Ingegneria dell'Informazione Università di Siena, Siena, Italy.
2000 (English)Report (Other academic)
Abstract [en]

Identification for robust control must deliver not only a nominal model, but also a reliable estimate of the uncertainty associated with the model. This paper addresses recent approaches to robust identification, that aim at dealing with contributions from the two main uncertainty sources: unmodeled dynamics and noise affecting the data. In particular, non-stationary Stochastic Embedding, Model Error Modeling based on prediction error methods and Set Membership Identification are considered. Moreover, we show how Set Membership Identification can be embedded into a Model Error Modeling framework. Model validation issues are easily addressed in the proposed framework. A discussion of asymptotic properties of all methods is presented. For all three methods, uncertainty is evaluated in terms of the frequency response, so that it can be handled by H8 control techniques. An example, where a nontrivial undermodeling is ensured by the presence of a nonlinearity in the system generating the data, is presented to compare these methods.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2000. , 24 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2293
Keyword [en]
Identification for robust control, Model error modeling, Model validation, Set membership estimation, Stochastic embedding, Unmodeled dynamics
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-55744ISRN: LiTH-ISY-R-2293OAI: oai:DiVA.org:liu-55744DiVA: diva2:316587
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-09-05Bibliographically approved

Open Access in DiVA

fulltext(643 kB)104 downloads
File information
File name FULLTEXT01.pdfFile size 643 kBChecksum SHA-512
0a3f41f49d18098cfbbd383ce46a7d5456a81bd0d84943999c8543b610f1423d764bceb80b939f6aa1ba00e95a2e0f4d734deeeb5a900660a89bf1a98e84e9e1
Type fulltextMimetype application/pdf

Authority records BETA

Ljung, Lennart

Search in DiVA

By author/editor
Ljung, Lennart
By organisation
Automatic ControlThe Institute of Technology
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 104 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 76 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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