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• W2894768224 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-to-noise (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." @default.
• W2894768224 created "2018-10-12" @default.
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• W2894768224 date "2018-01-01" @default.
• W2894768224 modified "2023-10-17" @default.
• W2894768224 title "Frequency Domain Subspace Identification of Multivariable Dynamical Systems for Robust Control Design" @default.
• W2894768224 cites W2029905675 @default.
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• W2894768224 doi "https://doi.org/10.1016/j.ifacol.2018.09.065" @default.
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