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On the estimation of initial conditions in kernel-based system identification
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. (System Identification)ORCID iD: 0000-0002-2831-2909
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. (System Identification)
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. (System Identification)ORCID iD: 0000-0002-9368-3079
2015 (English)In: 2015 54th IEEE Conference on Decision and Control (CDC), IEEE conference proceedings, 2015, 1120-1125 p.Conference paper (Refereed)
Abstract [en]

Recent developments in system identification have brought attention to regularized kernel-based methods, where, adopting the recently introduced stable spline kernel, prior information on the unknown process is enforced. This reduces the variance of the estimates and thus makes kernel-based methods particularly attractive when few input-output data samples are available. In such cases however, the influence of the system initial conditions may have a significant impact on the output dynamics. In this paper, we specifically address this point. We propose three methods that deal with the estimation of initial conditions using different types of information. The methods consist in various mixed maximum likelihood-a posteriori estimators which estimate the initial conditions and tune the hyperparameters characterizing the stable spline kernel. To solve the related optimization problems, we resort to the expectation-maximization method, showing that the solutions can be attained by iterating among simple update steps. Numerical experiments show the advantages, in terms of accuracy in reconstructing the system impulse response, of the proposed strategies, compared to other kernel-based schemes not accounting for the effect initial conditions.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015. 1120-1125 p.
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-186521DOI: 10.1109/CDC.2015.7402361ISBN: 978-1-4799-7884-7OAI: oai:DiVA.org:kth-186521DiVA: diva2:927538
Conference
2015 54th IEEE Conference on Decision and Control (CDC), 15-18 Dec. 2015, Osaka, Japan
Note

QC 20160520

Available from: 2016-05-12 Created: 2016-05-12 Last updated: 2016-05-20Bibliographically approved

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Risuleo, Riccardo SvenBottegal, GiulioHjalmarsson, Håkan
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