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Inverse Problems in Analytic Interpolation for Robust Control and Spectral EstimationPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2008 (English)Doctoral thesis, comprehensive summary (Other academic)
##### Abstract [en]

##### Place, publisher, year, edition, pages

Stockholm: KTH , 2008. , p. xii, 22
##### Series

Trita-MAT. OS, ISSN 1401-2294 ; 08:09
##### Keyword [en]

Nevanlinna-Pick Interpolation, Approximation, Model Reduction, Robust Control, Gap-robustness, Sensitivity Shaping, Entropy functional, Spectral Estimation, Weak*-topology, Monge-Kantorovic Transportation
##### National Category

Computational Mathematics
##### Identifiers

URN: urn:nbn:se:kth:diva-9248ISBN: 978-91-7415-125-1 (print)OAI: oai:DiVA.org:kth-9246DiVA, id: diva2:37749
##### Public defence

2008-10-31, F3, Lindstedtsvägen 26, KTH, Stockholm, 13:00 (English)
##### Opponent

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##### Supervisors

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#####

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##### Note

QC 20100817Available from: 2008-10-16 Created: 2008-10-13 Last updated: 2010-08-17Bibliographically approved
##### List of papers

This thesis is divided into two parts. The first part deals with theNevanlinna-Pick interpolation problem, a problem which occursnaturally in several applications such as robust control, signalprocessing and circuit theory. We consider the problem of shaping andapproximating solutions to the Nevanlinna-Pick problem in a systematicway. In the second part, we study distance measures between powerspectra for spectral estimation. We postulate a situation where wewant to quantify robustness based on a finite set of covariances, andthis leads naturally to considering the weak*-topology. Severalweak*-continuous metrics are proposed and studied in this context.In the first paper we consider the correspondence between weighted entropyfunctionals and minimizing interpolants in order to find appropriateinterpolants for, e.g., control synthesis. There are two basic issues that weaddress: we first characterize admissible shapes of minimizers bystudying the corresponding inverse problem, and then we developeffective ways of shaping minimizers via suitable choices of weights.These results are used in order to systematize feedback controlsynthesis to obtain frequency dependent robustness bounds with aconstraint on the controller degree.The second paper studies contractive interpolants obtained as minimizersof a weighted entropy functional and analyzes the role of weights andinterpolation conditions as design parameters for shaping theinterpolants. We first show that, if, for a sequence of interpolants,the values of the corresponding entropy gains converge to theoptimum, then the interpolants converge in H_2, but not necessarily inH-infinity. This result is then used to describe the asymptoticbehaviour of the interpolant as an interpolation point approaches theboundary of the domain of analyticity.A quite comprehensive theory of analytic interpolation with degreeconstraint, dealing with rational analytic interpolants with an apriori bound, has been developed in recent years. In the third paper,we consider the limit case when this bound is removed, and only stableinterpolants with a prescribed maximum degree are sought. This leadsto weighted H_2 minimization, where the interpolants areparameterized by the weights. The inverse problem of determining theweight given a desired interpolant profile is considered, and arational approximation procedure based on the theory is proposed. Thisprovides a tool for tuning the solution for attaining designspecifications. The purpose of the fourth paper is to study the topology and develop metricsthat allow for localization of power spectra, based on second-orderstatistics. We show that the appropriate topology is theweak*-topology and give several examples on how to construct suchmetrics. This allows us to quantify uncertainty of spectra in anatural way and to calculate a priori bounds on spectral uncertainty,based on second-order statistics. Finally, we study identification ofspectral densities and relate this to the trade-off between resolutionand variance of spectral estimates.In the fifth paper, we present an axiomatic framework for seekingdistances between power spectra. The axioms requirethat the sought metric respects the effects of additive andmultiplicative noise in reducing our ability to discriminate spectra.They also require continuity of statistical quantities withrespect to perturbations measured in the metric. We then present aparticular metric which abides by these requirements. The metric isbased on the Monge-Kantorovich transportation problem and iscontrasted to an earlier Riemannian metric based on theminimum-variance prediction geometry of the underlying time-series. Itis also being compared with the more traditional Itakura-Saitodistance measure, as well as the aforementioned prediction metric, ontwo representative examples.

1. The Inverse Problem of Analytic Interpolation With Degree Constraint and Weight Selection for Control Synthesis$(function(){PrimeFaces.cw("OverlayPanel","overlay337237",{id:"formSmash:j_idt519:0:j_idt523",widgetVar:"overlay337237",target:"formSmash:j_idt519:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. On Degree-Constrained Analytic Interpolation With Interpolation Points Close to the Boundary$(function(){PrimeFaces.cw("OverlayPanel","overlay336573",{id:"formSmash:j_idt519:1:j_idt523",widgetVar:"overlay336573",target:"formSmash:j_idt519:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

3. Stability-preserving rational approximation subject to interpolation constraints$(function(){PrimeFaces.cw("OverlayPanel","overlay335874",{id:"formSmash:j_idt519:2:j_idt523",widgetVar:"overlay335874",target:"formSmash:j_idt519:2:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

4. Localization of power spectra$(function(){PrimeFaces.cw("OverlayPanel","overlay344079",{id:"formSmash:j_idt519:3:j_idt523",widgetVar:"overlay344079",target:"formSmash:j_idt519:3:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

5. Metrics for Power Spectra: An Axiomatic Approach$(function(){PrimeFaces.cw("OverlayPanel","overlay344080",{id:"formSmash:j_idt519:4:j_idt523",widgetVar:"overlay344080",target:"formSmash:j_idt519:4:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

isbn
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