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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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Frequency Domain Subspace Identification of Multivariable Dynamical Systems for Robust Control Design
AU - Oveisi, Atta
AU - Nestorović, Tamara
AU - Montazeri, Allahyar
N1 - 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
PY - 2018
Y1 - 2018
N2 - 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 tonoise 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.
AB - 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 tonoise 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.
KW - System identification
KW - smart structure
KW - confidence interval
KW - vibration control
KW - uncertainty quantification
KW - subspace method
KW - Monte-Carlo simulation
U2 - 10.1016/j.ifacol.2018.09.065
DO - 10.1016/j.ifacol.2018.09.065
M3 - Journal article
VL - 51
SP - 990
EP - 995
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
SN - 2405-8963
IS - 15
T2 - 18th IFAC Symposium on System Identification
Y2 - 9 July 2018 through 11 July 2018
ER -