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Söderström, TorstenORCID iD iconorcid.org/0000-0003-4619-8879
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Publications (10 of 211) Show all publications
Söderström, T. & Soverini, U. (2017). Errors-in-variables identification using maximum likelihood estimation in the frequency domain. Automatica, 79, 131-143.
Open this publication in new window or tab >>Errors-in-variables identification using maximum likelihood estimation in the frequency domain
2017 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 79, 131-143 p.Article in journal (Refereed) Published
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-322085 (URN)10.1016/j.automatica.2017.01.016 (DOI)000399513300017 ()
Available from: 2017-03-02 Created: 2017-05-16 Last updated: 2017-05-17Bibliographically approved
Kreiberg, D., Söderström, T. & Yang-Wallentin, F. (2016). Errors-in-variables system identification using structural equation modeling. Automatica, 66, 218-230.
Open this publication in new window or tab >>Errors-in-variables system identification using structural equation modeling
2016 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 66, 218-230 p.Article in journal (Refereed) Published
Abstract [en]

Errors-in-variables (EIV) identification refers to the problem of consistently estimating linear dynamic systems whose output and input variables are affected by additive noise. Various solutions have been presented for identifying such systems. In this study, EIV identification using Structural Equation Modeling (SEM) is considered. Two schemes for how EIV Single-Input Single-Output (SISO) systems can be formulated as SEMs are presented. The proposed formulations allow for quick implementation using standard SEM software. By simulation examples, it is shown that compared to existing procedures, here represented by the covariance matching (CM) approach, SEM-based estimation provide parameter estimates of similar quality.

Keyword
System identification; Errors-in-variables models; Linear systems; Structural equation models
National Category
Control Engineering
Research subject
Statistics
Identifiers
urn:nbn:se:uu:diva-277383 (URN)10.1016/j.automatica.2015.12.007 (DOI)000371099300025 ()
Funder
Swedish Research Council, 421-2011-1727
Available from: 2016-01-25 Created: 2016-02-19 Last updated: 2017-11-30Bibliographically approved
Söderström, T., Diversi, R. & Soverini, U. (2014). A unified framework for EIV identification methods when the measurement noises are mutually correlated. Automatica, 50(12), 3216-3223.
Open this publication in new window or tab >>A unified framework for EIV identification methods when the measurement noises are mutually correlated
2014 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 50, no 12, 3216-3223 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, the previously introduced Generalized Instrumental Variable Estimator (GIVE) is extended to the case of errors-in-variables models where the additive input and output noises are mutually correlated white processes. It is shown how many estimators proposed in the literature can be described as various special cases of a generalized instrumental variable framework. It is also investigated how to analyze the common situation where some of the equations that define the estimator are to hold exactly, and others to hold approximately in a least squares sense, providing a detailed study of the accuracy analysis. 

National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-244579 (URN)10.1016/j.automatica.2014.10.037 (DOI)000347760100027 ()
Available from: 2014-10-27 Created: 2015-02-18 Last updated: 2017-12-04Bibliographically approved
Söderström, T., Kreiberg, D. & Mossberg, M. (2014). Extended accuracy analysis of a covariance matching approach for identifying errors-in-variables systems. Automatica, 50(10), 2597-2605.
Open this publication in new window or tab >>Extended accuracy analysis of a covariance matching approach for identifying errors-in-variables systems
2014 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 50, no 10, 2597-2605 p.Article in journal (Refereed) Published
Abstract [en]

A covariance matching approach for identifying errors-in-variables systems is analyzed for the general case. The asymptotic covariance matrix of the jointly estimated system parameters, noise variances and auxiliary parameters is derived. An algorithm for how to compute this covariance matrix from given system descriptions is also provided. The results generalize previous known special cases. Using Monte Carlo analysis, we illustrate the proposed algorithm. The results suggest close agreement between the theoretical and empirical accuracy.

National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-238583 (URN)10.1016/j.automatica.2014.08.020 (DOI)000344207300018 ()
Available from: 2014-09-16 Created: 2014-12-14 Last updated: 2017-12-05Bibliographically approved
Söderström, T., Wang, L., Pintelon, R. & Schoukens, J. (2013). Can errors-in-variables systems be identified from closed-loop experiments?. Automatica, 49(2), 681-684.
Open this publication in new window or tab >>Can errors-in-variables systems be identified from closed-loop experiments?
2013 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 49, no 2, 681-684 p.Article in journal (Refereed) Published
Abstract [en]

Errors-in-variables (EIV) systems are known to be identifiable not generally, but under some specific conditions. These conditions are normally formulated for open-loop systems. This paper examines to what extent an EIV system can be identifiable from closed-loop experiments.

Keyword
Errors-in-variables models, Linear systems, System identification, Closed-loop experiments, Errors in variables, Open loop systems, Experiments, Identification (control systems), Errors
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-194899 (URN)10.1016/j.automatica.2012.11.017 (DOI)000315003100045 ()
Available from: 2013-02-20 Created: 2013-02-19 Last updated: 2017-12-06Bibliographically approved
Söderström, T. (2013). Comparing some classes of bias-compensating least squares methods. Automatica, 49(3), 840-845.
Open this publication in new window or tab >>Comparing some classes of bias-compensating least squares methods
2013 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 49, no 3, 840-845 p.Article in journal (Refereed) Published
Abstract [en]

Three different classes of bias-compensating least squares identification methods are compared, and shown to be identical. It is also discussed how the user parameters in the classes can be chosen to achieve optimal accuracy of the parameter estimates.

Keyword
System identification, Bias compensation, Errors-in-variables models, Linear systems
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-198924 (URN)10.1016/j.automatica.2013.01.003 (DOI)000316590300019 ()
Available from: 2013-04-29 Created: 2013-04-29 Last updated: 2017-12-06Bibliographically approved
Kreiberg, D., Söderström, T. & Yang-Wallentin, F. (2013). Errors-in-variables identification using covariance matching and structural equation modeling. In: Proc. 52nd Conference on Decision and Control: . Paper presented at CDC 2013, December 10-13, Florence, Italy (pp. 5852-5857). Piscataway, NJ: IEEE.
Open this publication in new window or tab >>Errors-in-variables identification using covariance matching and structural equation modeling
2013 (English)In: Proc. 52nd Conference on Decision and Control, Piscataway, NJ: IEEE , 2013, 5852-5857 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2013
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-218042 (URN)10.1109/CDC.2013.6760812 (DOI)978-1-4673-5714-2 (ISBN)
Conference
CDC 2013, December 10-13, Florence, Italy
Available from: 2014-03-07 Created: 2014-02-07 Last updated: 2014-03-28Bibliographically approved
Söderström, T. & Yuz, J. (2013). Model validation methods for errors-in-variables estimation. In: Proc. 52nd Conference on Decision and Control: . Paper presented at CDC 2013, December 10-13, Florence, Italy (pp. 3882-3887). Piscataway, NJ: IEEE.
Open this publication in new window or tab >>Model validation methods for errors-in-variables estimation
2013 (English)In: Proc. 52nd Conference on Decision and Control, Piscataway, NJ: IEEE , 2013, 3882-3887 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2013
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-218043 (URN)10.1109/CDC.2013.6760482 (DOI)978-1-4673-5714-2 (ISBN)
Conference
CDC 2013, December 10-13, Florence, Italy
Available from: 2014-03-07 Created: 2014-02-07 Last updated: 2014-03-11Bibliographically approved
Söderström, T., Irshad, Y., Mossberg, M. & Zheng, W. X. (2013). On the accuracy of a covariance matching method for continuous-time errors-in-variables identification. Automatica, 49(10), 2982-2993.
Open this publication in new window or tab >>On the accuracy of a covariance matching method for continuous-time errors-in-variables identification
2013 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 49, no 10, 2982-2993 p.Article in journal (Refereed) Published
Abstract [en]

An analysis of a covariance matching method for continuous-time errors-in-variables system identification from discrete-time data is made. In the covariance matching method, the noise-free input signal is not explicitly modeled and only assumed to be a stationary process. The asymptotic normalized covariance matrix, valid for a large number of data and a small sampling interval, is derived. This involves the evaluation of a covariance matrix of estimated covariance elements and estimated derivatives of such elements, and large parts of the paper are devoted to this task. The latter covariance matrix consists of two parts, where the first part contains integrals that are approximations of Riemann sums, and the second part depends on the measurement noise variances.

Keyword
Continuous-time systems, Errors-in-variables systems, Parameter estimation, Covariance matching, Accuracy analysis
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-209156 (URN)10.1016/j.automatica.2013.07.010 (DOI)000324447500005 ()
Available from: 2013-10-15 Created: 2013-10-15 Last updated: 2017-12-06Bibliographically approved
Norlander, H., Valdek, U., Lundberg, B. & Söderström, T. (2013). Parameter estimation from wave propagation tests on a tube perforated by helical slots. Mechanical systems and signal processing, 40(1), 385-399.
Open this publication in new window or tab >>Parameter estimation from wave propagation tests on a tube perforated by helical slots
2013 (English)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 40, no 1, 385-399 p.Article in journal (Refereed) Published
Abstract [en]

For a tube with doubly symmetric cross section and perforations by helical slots there is a coupling between extension and torsion. In this paper a one dimensional (1D) model structure for a tube with such a helical slot segment (HSS) is established, and parameters accounting for the coupling between extension and torsion are estimated from wave propagation experiments. In these experiments incident extensional waves were generated through axial impact by strikers of different lengths, causing reflected and transmitted waves of extensional and torsional type which were measured in terms of surface strains on either side of the HSS part of the tube. A statistical test on the experimental data shows that the output residuals (the difference between modeled and experimental output) cannot be explained by measurement noise alone. This is not surprising since the 1D model structure is based on some simplifying assumptions concerning the geometry of the HSS. Parameters for two different geometries of the HSS are estimated, and the models are assessed in terms of model fa, simulations and wave energy distribution. It turns out that for one case, where the geometrical assumptions are valid, the 1D model is adequate, while for another case, where the validity of the assumptions is questionable, it is not. It is concluded that the 1D model structure provides a simple and efficient description of the HSS if the geometrical assumptions are valid.

Keyword
Tube, Helical slots, Elastic wave, Extension, Torsion, Parameter estimation
National Category
Control Engineering Applied Mechanics
Research subject
Engineering science with specialization in Applied Mechanics
Identifiers
urn:nbn:se:uu:diva-208351 (URN)10.1016/j.ymssp.2013.04.011 (DOI)000323803100024 ()
Available from: 2013-10-01 Created: 2013-09-30 Last updated: 2017-12-06Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-4619-8879

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