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Robust Experiment Design for System Identification via Semi-Infinite Programming Techniques
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. (System Identification Group)
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. (System Identification Group)ORCID iD: 0000-0003-0355-2663
The University of Newcastle, Australia.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. (System Identification Group)ORCID iD: 0000-0002-9368-3079
2012 (English)In: Proceedings of the 16th IFAC Symposium on System Identification (SYSID 2012), 2012, 680-685 p.Conference paper, Published paper (Refereed)
Abstract [en]

Robust optimal experiment design for dynamic system identification is cast as a minmax optimization problem, which is infinite-dimensional. If the input spectrum is discretized (either by considering a Riemmann approximation, or by restricting it to the span of a finite dimensional linear space), this problem falls within the class of semi-infinite convex programs. One approach to this optimization problem of infinite constraints is the so called "scenario approach", which is based on a probabilistic description of the uncertainty to deliver a finite program that attempts to approximate the optimal solution with a prescribed probability. In this paper, we propose as an alternative an exchange algorithm based on some recent advances in the field of semi-infinite programming to tackle the same problem. This method is compared with the scenario approach both from the aspects of accuracy and computational efficiency. Furthermore, the comparison includes the MATLAB semi-infinite solver fseminf to provide a general palette of methods approximating the robust optimal design problem.

Place, publisher, year, edition, pages
2012. 680-685 p.
Series
IFAC Proceedings Volumes (IFAC-PapersOnline), ISSN 1474-6670
Keyword [en]
Convex programs, Exchange algorithms, Experiment design, Finite-dimensional linear spaces, Min-max optimization, Optimal experiment design, Optimal solutions, Optimization problems, Prescribed probability, Probabilistic descriptions, Robust Optimal Design, Scenario approach, Semi infinite programming
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-72593DOI: 10.3182/20120711-3-BE-2027.00346Scopus ID: 2-s2.0-84867038570ISBN: 978-390282306-9 (print)OAI: oai:DiVA.org:kth-72593DiVA: diva2:488348
Conference
16th IFAC Symposium on System Identification (SYSID 2012) Universite Libre de Bruxelles; Bruxelles; Belgium; 11 July 2012 through 13 July 2012
Funder
ICT - The Next Generation
Note

QC 20130110

Available from: 2012-02-01 Created: 2012-01-31 Last updated: 2013-10-16Bibliographically approved

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Rojas, Cristian R.Hjalmarsson, Håkan

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