Identification of low-order models using rational orthonormal basis functions

Mikael Manngård, Hannu T. Toivonen

Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

Abstract

A method for complexity constrained output-error system identification using rational orthonormal basis functions is presented. The model is expanded in terms of a Hambo basis, which generalizes several well-known bases such as the natural basis, Laguerre and Kautz basis. Properties of the Hambo operator transform induced by the basis functions are used to constrain the model order in the operator domain. The identification problem is formulated as a rank-constrained least-squares minimization problem, which is relaxed using the nuclear-norm to form a convex optimization problem. We demonstrate on a numerical example that the proposed identification method can outperform other state-of-the-art methods which rely on model order reduction to obtain low-order models.
Original languageEnglish
Title of host publication2019 IEEE 58th Conference on Decision and Control (CDC)
PublisherIEEE
Pages1728-1733
ISBN (Electronic)9781728113982
DOIs
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventIEEE Conference on Decision & Control - 58th Conference on Decision and Control
Duration: 11 Dec 201913 Dec 2019

Conference

ConferenceIEEE Conference on Decision & Control
Period11/12/1913/12/19

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