| 1. |
- Abrahamsson, Tomas, et al.
(författare)
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A Study of some Approaches to Vibration Data Analysis
- 1993
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Using data from extensive vibrational tests of the new aircraft Saab 2000 three different methods for vibration analysis are studied. These methods are ERA (eigensystem realization algorithm), N4SID (a subspace method) and PEM (prediction error approach). We find that both the ERA and N4SID methods give good initial model parameter estimates that can be further improved by the use of PEM. We also find that all methods give good insights into the vibrational modes.
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| 2. |
- Carrette, Pierre, et al.
(författare)
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Model Parameter Gradients in Prediction Identification of State-Space Systems
- 1998
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The paper is devoted to the study of the gradient computation related to procedures for identifying state-space systems in the prediction error sense. The knowledge of these gradients is needed when iteratively estimating a state-space model for the system on the basis of data measurements. In classical estimation algorithm, any gradient signal is evaluated by running these data through a state-space dynamics corresponding to the model differentiation with respect to the related parameter. In order to reduce the computation burden of this estimation, the paper put into light the structure of the state-space gradient signals and, as a by product, propose a new method for computing them. The obtained improvement is based on exploiting the properties of matrices that commute with the prediction model state-feedback matrix.
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| 3. |
- Forssell, Urban, et al.
(författare)
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Time-Domain Identification of Dynamic Errors-in-Variables Systems Using Periodic Excitation Signals
- 1998
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The use of periodic excitation signals in identification experiments is advocated. With periodic excitation it is possible to separate the driving signals and the disturbances, which for instance implies that the noise properties can be independently estimated. In the paper a non-parametric noise model, estimated directly from the measured data, is used in a compensation strategy applicable to both least squares and total least squares estimation. The resulting least squares and total least squares methods are applicable in the errors-in-variables situation and give consistent estimates regardless of the noise. The feasibility of the idea is illustrated in a simulation study.
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| 4. |
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| 5. |
- Helmersson, Anders, 1957-, et al.
(författare)
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A Subspace Approach for Approximation of Rational Matrix Functions to Sampled Data
- 1996
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Ingår i: Proceedings of the 35th IEEE Conference on Decision and Control. - Linköping : Linköping University Electronic Press. - 0780335902 ; , s. 3660-3661 vol.4
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Konferensbidrag (refereegranskat)abstract
- Algorithms for approximation of rational matrix factors to data is described. The method is based on a subspace based multivariable frequency domain state-space identification, canonical and spectral factorization and parametric optimization. The algorithms can be used for identifying spectral factors and factors of positive real functions from frequency data. The methods are directly applicable in the D-K algorithm for complex μ-synthesis and the Y-Z-K algorithm for mixed μ-synthesis.
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| 6. |
- Helmersson, Anders, 1957-, et al.
(författare)
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State-Space Parametrizations of Multivariable Linear Systems using Tridiagonal Matrix Forms
- 1996
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Ingår i: Proceedings of Reglermöte 1996. - Linköping : Linköping University Electronic Press. ; , s. 244-248, s. 3654-3659 vol.4
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Tridiagonal parametrizations of linear state-space models are proposed for multivariable system identification. The parametrizations are surjective, i.e. all systems up to a given order can be described. The parametrization is based on the fact that any real square matrix is similar to a real tridiagonal form as well as a compact tridiagonal form. These parametrizations has significantly fewer parameters compared to a full parametrization of the state-space matrices.
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| 7. |
- Ljung, Lennart, et al.
(författare)
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A Least Squares Interpretation of Sub-Space Methods for System Identification
- 1996
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Ingår i: Proceedings of the 35th IEEE Conference on Decision and Control. - Linköping : Linköping University Electronic Press. - 0780335902 ; , s. 335-342 vol.1
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- So called subspace methods for direct identification of linear models in state space form have drawn considerable interest. The algorithms consist of series of quite complex projections, and it is not so easy to intuitively understand how they work. They have also defied, so far, complete asymptotic analysis of their stochastic properties. This contribution describes an interpretation of how they work. It specifically deals with how consistent estimates of the dynamics can be achieved, even though correct predictors are not used. We stress how the basic idea is to focus on the estimation of the state-variable candidates-the k-step ahead output predictors.
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| 8. |
- Ljung, Lennart, 1946-, et al.
(författare)
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Interpretation of Subspace Methods : Consistency Analysis
- 1999
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- So called subspace methods for direct identification of linear state space models form a very useful alternative to maximum-likelihood type approaches, inthat they are non-iterative and offer efficient numerical implementations. The algorithms consist of series of quite complex projections, and it is not so easy to intuitively understand how they work. The asymptotic analysis of them is also complicated. This contribution describes an interpretation of how they work in terms of k-step ahead predictors of carefully chosen orders. It specifically deals how consistent estimates of the dynamics can be achieved, even though correct predictors are not used. This analysis gives some new angles of attack to the problem of asymptotic behavior ofthe subspace algorithms.
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| 9. |
- Ljung, Lennart, 1946-, et al.
(författare)
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Subspace Identification Methods for Closed Loop Input-Output Data
- 1995
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- So called subspace methods for direct identication of linear mod els in state space form have drawn considerable interest recently They have been found to work well in many cases but have one drawback they do not yield consistent estimates for data collected under out put feedback This contribution points to the reasons for this and also shows how to modify the basic algorithm to handle closed loop data We stress how the basic idea is to focus on the estimation of the statevariable candidates the kstep ahead output predictors By re computing these from a nonparametric or rather high order ARX onestep ahead predictor model closed loop data can be handled
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| 10. |
- McKelvey, Tomas
(författare)
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A Combined State-Space Identification Algorithm Applied to Data From a Modal Analysis Experiment on a Separation System
- 1994
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Ingår i: Proceedings of the 33rd IEEE Conference on Decision and Control. - Linköping : Linköping University. - 0780319680 ; , s. 2286-2287 vol.3
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This paper discusses identification of state-space models from impulse response or initial value experiments. Kung's geometrical realization algorithm is combined with classical nonlinear parametric optimization to improve the quality of the estimated state-space model. These ideas are applied on real data originating from a modal analysis experiment on a separation system. The results indicate that the parametric optimization step increases the model quality significantly compared with the initial model the realization algorithm provides.
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