Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/491
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dc.contributor.authorDate, P-
dc.contributor.authorVinnicombe, G-
dc.coverage.spatial8en
dc.date.accessioned2007-01-03T10:45:41Z-
dc.date.available2007-01-03T10:45:41Z-
dc.date.issued2004-
dc.identifier.citationAutomatica, 40: 995-1002, Mar 2004en
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/491-
dc.description.abstractThis paper considers two robustly convergent algorithms for the identification of a linear system from (possibly) noisy frequency response data. Both algorithms are based on the same principle; obtaining a good worst case fit to the data under a smoothness constraint on the obtained model. However they differ in their notions of distance and smoothness. The first algorithm yields an FIR model of a stable system and is optimal, in a certain sense for a finite model order. The second algorithm may be used for modelling unstable plants and yields a real rational approximation in the -gap. Given a model and a controller stabilising the true plant, a procedure for winding number correction is also suggested.en
dc.format.extent501717 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherElsevier Scienceen
dc.subjectRobust identificationen
dc.subjectIdentification for controlen
dc.subjectν-gap metricen
dc.titleAlgorithms for worst case identification in H-infinity and the nu-gap metricen
dc.typeResearch Paperen
dc.identifier.doihttp://dx.doi.org/10.1016/j.automatica.2004.01.019-
Appears in Collections:Computer Science
Dept of Mathematics Research Papers
Mathematical Sciences

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