Subspace-based Identification of Infinite-dimensional Multivariable Systems from Frequency-response Data
1996 (English)In: Automatica, ISSN 0005-1098, Vol. 32, no 6, 885-902 p.Article in journal (Refereed) Published
A new identification algorithm which identifies low complexity models of infinite-dimensional systems from equidistant frequency-response data is presented. The new algorithm is a combination of the Fourier transform technique with the recent subspace techniques. Given noisefree data, finite-dimensional systems are exactly retrieved by the algorithm. When noise is present, it is shown that identified models strongly converge to the balanced truncation of the identified system if the measurement errors are covariance bounded. Several conditions are derived on consistency, illustrating the trade-offs in the selection of certain parameters of the algorithm. Two examples are presented which clearly illustrate the good performance of the algorithm.
Place, publisher, year, edition, pages
Elsevier, 1996. Vol. 32, no 6, 885-902 p.
System identification, Frequency-response data, Infinite-dimensional systems, State-space models
IdentifiersURN: urn:nbn:se:liu:diva-56358DOI: 10.1016/0005-1098(96)00022-2OAI: oai:DiVA.org:liu-56358DiVA: diva2:318591