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.
University of Siena, Italy.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2002 (English)In: Automatica, ISSN 0005-1098, Vol. 38, no 5, 787-803 p.Article in journal (Refereed) Published
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
Elsevier, 2002. Vol. 38, no 5, 787-803 p.
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-47024DOI: 10.1016/S0005-1098(01)00269-2OAI: oai:DiVA.org:liu-47024DiVA: diva2:267920
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2013-07-17

Open Access in DiVA

No full text

Other links

Publisher's full textRelated report

Authority records BETA

Ljung, Lennart

Search in DiVA

By author/editor
Ljung, Lennart
By organisation
Automatic ControlThe Institute of Technology
In the same journal
Automatica
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 48 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