Change search
ReferencesLink to record
Permanent link

Direct link
Subspace Identification and ARX Modeling
KTH, Superseded Departments, Signals, Sensors and Systems.ORCID iD: 0000-0002-6855-5868
2003 (English)In: IFAC Symp on System Identification, 2003Conference paper (Refereed)
Abstract [en]

In this paper we present a new identification method that points at the closerelationship between high order ARX modeling and subspace identification. A high orderARX model is utilized to obtain initial estimates of certain Markov parameters. Theseparameters are then used to restructure the data model used for subspace identification tofacilitate the estimation of the state sequence. Based on the estimated state sequence, thesystem parameters are estimated by linear regression. The method is shown to be competitiveto existing subspace methods by a simulation example. The method can also be used,without modification, on closed loop data in contrast to most previously published subspaceidentification methods

Place, publisher, year, edition, pages
Keyword [en]
multivariable, identification
National Category
Signal Processing
URN: urn:nbn:se:kth:diva-82680OAI: diva2:498504
13'th IFAC Symp on System Identification, Rotterdam, The Netherlands, Aug 27-29 , 2003.
NR 20140805Available from: 2012-02-12 Created: 2012-02-12 Last updated: 2012-02-12Bibliographically approved

Open Access in DiVA

No full text

Search in DiVA

By author/editor
Jansson, Magnus
By organisation
Signals, Sensors and Systems
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar
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

Total: 631 hits
ReferencesLink to record
Permanent link

Direct link