Q-Markov covariance equivalent realization and its application to flexible structure identification

Liu, Ketao; Skelton, Robert E.

doi:10.2514/3.21005

Cited by 46 publications

(13 citation statements)

References 12 publications

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“…The matrix 6 frorn (16) can be expressed as Theorem 2: Let G be a stable linear system of order n and let GI, be given by (8) where n k satisfies the assumptions (26) and (28). Let G:"(z) denote the transfer function obtained by Algorithm 1 with q > n and r 2 n using M + 1 data points.…”

Section: Pro08 Letmentioning

confidence: 99%

“…Recently, identification and control of large flexible structures have received considerable attention [151, [28], [29], [6], [24], [23]. This type of system is also frequently encountered in the modal analysis area of mechanical engineering.…”

mentioning

confidence: 99%

“…A new frequency domain approach proposed by Liu and coworkers [28] is a frequency domain counterpart of the timedomain subspace methods by De Moor and Vandewalle [ 131 and Liu and Skelton [29]. Their approach does not require the data to be uniformly spaced in frequencies and also offers some frequency weighting capabilities.…”

mentioning

confidence: 99%

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Subspace-based multivariable system identification from frequency response data

McKelvey

Akçay

Ljung

1996

IEEE Trans. Automat. Contr.

488

354

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Two noniterative subspace-based algorithms which identify linear, time-invariant MIMO (multi-inpuUmultioutput) systems from frequency response data are presented. The algorithms are related to the recent time-domain subspace identification techniques. The first algorithm uses equidistantly, in frequency, spaced data and is strongly consistent under weak noise assumptions. The second algorithm uses arbitrary frequency spacing and is strongly consistent under more restrictive noise assumptions. Promising results are obtained when the algorithms are applied to real frequency data originating from a large flexible structure.

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Section: Pro08 Letmentioning

confidence: 99%

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confidence: 99%

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Subspace-based multivariable system identification from frequency response data

McKelvey

Akçay

Ljung

1996

IEEE Trans. Automat. Contr.

488

354

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“…The problem of designing filters from covariances and Markov parameters has been studied before in numerous papers [17], [13], [14], [15], [16], [20], [21], [18]. Skelton et.…”

Section: Introductionmentioning

confidence: 99%

Generalizing the Markov and covariance interpolation problem using input-to-state filters

Enqvist

2007

2007 European Control Conference (ECC)

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In the Markov and covariance interpolation problem a transfer function W is sought that match the first coefficients in the expansion of W around zero and the first coefficients of the Laurent expansion of the corresponding spectral density W W ⋆ . Here we solve an interpolation problem where the matched parameters are the coefficients of expansions of W and W W ⋆ around various points in the disc. The solution is derived using input-to-state filters and is determined by simple calculations such as solving Lyapunov equations and generalized eigenvalue problems.

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“…The algorithms can be divided in two groups iterative methods and non-iterative methods. Among the iterative we nd the prediction-error methods 14] and among the noniterative we nd the more recent sub-space based algorithms 16,20,13]. The advantage of the non-iterative methods is the absence of a nonlinear parametric optimization.…”

Section: Introductionmentioning

confidence: 99%

An efficient frequency domain state-space identification algorithm

McKelvey

Akçay

Proceedings of 1994 33rd IEEE Conference on Decision and Control

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In this paper we present a n o vel non-iterative algorithm for identifying linear time-invariant discrete time state-space models from frequency response data. We show that the algorithm recover the true system of order n if n +2 noise-free frequency response measurements are given at uniformly spaced frequencies. The algorithm is demonstrated to be related to the recent timedomain subspace identi cation algorithms formulated in the frequency domain. The algorithm is applied to real frequency data, originating from a exible mechanical structure, with promising results. In a companion paper robustness and stochastic analysis is performed.

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Q-Markov covariance equivalent realization and its application to flexible structure identification

Cited by 46 publications

References 12 publications

Subspace-based multivariable system identification from frequency response data

Subspace-based multivariable system identification from frequency response data

Generalizing the Markov and covariance interpolation problem using input-to-state filters

An efficient frequency domain state-space identification algorithm

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