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    Rights statement: This is the author’s version of a work that was accepted for publication in IFCA-PapersOnline. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in IFCA-PapersOnline, 51,15 2018 DOI: 10.1016/j.ifacol.2018.09.065

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Frequency Domain Subspace Identification of Multivariable Dynamical Systems for Robust Control Design

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<mark>Journal publication date</mark>2018
<mark>Journal</mark>IFAC-PapersOnLine
Issue number15
Volume51
Number of pages6
Pages (from-to)990-995
Publication StatusPublished
<mark>Original language</mark>English
Event18th IFAC Symposium on System Identification - Stockholm, Sweden
Duration: 9/07/201811/07/2018

Symposium

Symposium18th IFAC Symposium on System Identification
CountrySweden
CityStockholm
Period9/07/1811/07/18

Abstract

Black-box system identification is subjected to the modelling uncertainties that are propagated from the non-parametric model of the system in time/frequency-domain. Unlike classical H1/H2 spectral analysis, in the recent robust Local Polynomial Method (LPM), the modelling variances are separated to
noise contribution and nonlinear contribution while suppressing the transient noise. On the other hand, without an appropriate weighting on the objective function in the system identification methods, the acquired model is subjected to bias. Consequently, in this paper the weighted regression problem in subspace frequency-domain system identification is revisited in order to have an unbiased estimate of the frequency response matrix of a flexible manipulator as a multi-input multi-output lightly-damped system. Although the unbiased parametric model representing the best linear approximation (BLA) of the system in this combination is a reliable framework for the control design, it is limited for a specific signal-tonoise (SNR) ratio and standard deviation (STD) of the involved input excitations. As a result, in this paper, an additional step is carried out to investigate the sensitivity of the identified model w.r.t. SNR/STD in order to provide an uncertainty interval for robust control design.

Bibliographic note

This is the author’s version of a work that was accepted for publication in IFCA-PapersOnline. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in IFCA-PapersOnline, 51,15 2018 DOI: 10.1016/j.ifacol.2018.09.065