Subspace methods for frequency domain data

McKelvey, Tomas

doi:10.23919/acc.2004.1383681

Cited by 22 publications

(25 citation statements)

References 13 publications

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“…Although Subspace methods based on the inputoutput DFT data have been already proposed [3,4], [4] which uses DFT data is not robust to the disturbance factors since singular value decomposition(SVD) is only a tool to eliminate the disturbance factors. In [3,4], subspace methods based on H1 estimator which is one of the frequency response function(FRF) have been proposed, those methods are robust to the disturbance factor since H1 estimator has a high-SNR in the condition where the input is choice carefully, and the input-output data can be measured for a long period.…”

Section: Introductionmentioning

confidence: 99%

“…In [3,4], subspace methods based on H1 estimator which is one of the frequency response function(FRF) have been proposed, those methods are robust to the disturbance factor since H1 estimator has a high-SNR in the condition where the input is choice carefully, and the input-output data can be measured for a long period. On the other hand, the concept of the proposed method is to eliminate the disturbance factor from the output data, it can be performed by orthogonal decomposition based on Wold's decomposition [5].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the concept of the proposed method is to eliminate the disturbance factor from the output data, it can be performed by orthogonal decomposition based on Wold's decomposition [5]. Thus, by employing MIMO system identification method based on the frequency domain data given by the discrete Fourier transform [4], the data implemented by the proposed method can derive more accurate model. LQ decomposition is the key tool in the proposed method, it actually computes the orthogonal decomposition of the frequency domain data instead of Wold's decomposition.…”

Section: Introductionmentioning

confidence: 99%

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Prefiltering of Frequency Domain Data Based on Wold's Decomposition for System Identification

Matsubara¹

2012

Stochastic Systems Theory and its Applications (SSS)

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In this paper, we propose a prefiltering method of frequency domain data for linear multiple-input multipleoutput time-invariant system identification. This paper only deals with the case where the frequency domain input-output data is given by discrete Fourier transform. The concept of the proposed method is to eliminate the disturbance factor from the output data, it can be performed by orthogonal decomposition based on Wold's decomposition. Thus, by employing MIMO system identification method based on the frequency domain data given by the discrete Fourier transform, the data implemented by the proposed method can derive more accurate model. LQ decomposition is the key tool in the proposed method, it actually computes the orthogonal decomposition of the frequency domain data instead of Wold's decomposition. The efficacy of the proposed method is shown in numerical experimental.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prefiltering of Frequency Domain Data Based on Wold's Decomposition for System Identification

Matsubara¹

2012

Stochastic Systems Theory and its Applications (SSS)

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“…The prediction error approaches (oe, pem etc) implement the routines of Section VI, while the subspace estimation command n4sid is described in [8] for frequency domain data. (See also [7]. )…”

Section: Estimation and Validationmentioning

confidence: 99%

State of the art in linear system identification: time and frequency domain methods

Ljung

2004

Proceedings of the 2004 American Control Conference

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Identi cation of time-invariant linear dynamic systems is a mature subject. In this contribution we focus on the interplay between methods that use time and frequency domain data, respectively. The frequency domain data could be either input/output Fourier transforms or frequency functions. We explain how these di erent kinds of data types are used to t models, and how closely related the methods are. Of special interest is how transients (initial conditions and deviations from periodic signals) are handled. Direct estimation of time-continuous models is also discussed, as well as software aspects.Keywords: identi cation State of the Art in Linear System Identification: Time and Frequency Domain Methods Lennart LjungAbstract-Identification of time-invariant linear dynamic systems is a mature subject. In this contribution we focus on the interplay between methods that use time and frequency domain data, respectively. The frequency domain data could be either input/output Fourier transforms or frequency functions. We explain how these different kinds of data types are used to fit models, and how closely related the methods are. Of special interest is how transients (initial conditions and deviations from periodic signals) are handled. Direct estimation of timecontinuous models is also discussed, as well as software aspects.

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“…The prediction error approaches (oe, pem etc) implement variants of the minimization problem (3), while the subspace estimation command n4sid is described in [12] for frequency domain data. (See also [11]. )…”

Section: Estimation and Validationmentioning

confidence: 96%

Frequency Domain Versus Time Domain Methods in System Identification – Revisited

Ljung

Lecture Notes in Control and Information Science

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Subspace methods for frequency domain data

Cited by 22 publications

References 13 publications

Prefiltering of Frequency Domain Data Based on Wold's Decomposition for System Identification

Prefiltering of Frequency Domain Data Based on Wold's Decomposition for System Identification

State of the art in linear system identification: time and frequency domain methods

Frequency Domain Versus Time Domain Methods in System Identification – Revisited

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