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dc.contributor.authorGevers, Michelpt_BR
dc.contributor.authorBazanella, Alexandre Sanfelicipt_BR
dc.contributor.authorBombois, Xavierpt_BR
dc.contributor.authorMiskovic, Ljubisapt_BR
dc.date.accessioned2011-01-29T06:00:44Zpt_BR
dc.date.issued2009pt_BR
dc.identifier.issn0018-9286pt_BR
dc.identifier.urihttp://hdl.handle.net/10183/27625pt_BR
dc.description.abstractIn prediction error identification, the information matrix plays a central role. Specifically, when the system is in the model set, the covariance matrix of the parameter estimates converges asymptotically, up to a scaling factor, to the inverse of the information matrix. The existence of a finite covariance matrix thus depends on the positive definiteness of the information matrix, and the rate of convergence of the parameter estimate depends on its “size”. The information matrix is also the key tool in the solution of optimal experiment design procedures, which have become a focus of recent attention. Introducing a geometric framework, we provide a complete analysis, for arbitrary model structures, of the minimum degree of richness required to guarantee the nonsingularity of the information matrix. We then particularize these results to all commonly used model structures, both in open loop and in closed loop. In a closed-loop setup, our results provide an unexpected and precisely quantifiable trade-off between controller degree and required degree of external excitation.en
dc.format.mimetypeapplication/pdfpt_BR
dc.language.isoengpt_BR
dc.relation.ispartofIEEE transactions on automatic control. New York. Vol. 54, no. 12 (Dec. 2009), p. 2828-2840pt_BR
dc.rightsOpen Accessen
dc.subjectIdentifiabilityen
dc.subjectControle automáticopt_BR
dc.subjectInformation matrixen
dc.subjectInput richnessen
dc.subjectTransfer of excitationen
dc.titleIdentification and the information matrix : how to get just sufficiently rich?pt_BR
dc.typeArtigo de periódicopt_BR
dc.identifier.nrb000736439pt_BR
dc.type.originEstrangeiropt_BR


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