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Söderström, TorstenORCID iD iconorcid.org/0000-0003-4619-8879
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Publications (10 of 217) Show all publications
Söderström, T. (2019). A user perspective on errors-in-variables methods in system identification. Control Engineering Practice, 89, 56-69
Open this publication in new window or tab >>A user perspective on errors-in-variables methods in system identification
2019 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 89, p. 56-69Article in journal (Refereed) Published
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-392575 (URN)10.1016/j.conengprac.2019.05.013 (DOI)000477785800005 ()
Available from: 2019-05-30 Created: 2019-09-09 Last updated: 2019-09-20Bibliographically approved
Soverini, U. & Söderström, T. (2018). 2D-frequency domain identification of complex sinusoids in the presence of additive noise. In: : . Paper presented at SYSID 2018, July 9–11, Stockholm, Sweden (pp. 820-825). (15)
Open this publication in new window or tab >>2D-frequency domain identification of complex sinusoids in the presence of additive noise
2018 (English)Conference paper, Published paper (Refereed)
Series
IFAC-PapersOnLine, ISSN 2405-8963 ; 51:15
National Category
Control Engineering Signal Processing
Identifiers
urn:nbn:se:uu:diva-366232 (URN)10.1016/j.ifacol.2018.09.125 (DOI)000446599200139 ()
Conference
SYSID 2018, July 9–11, Stockholm, Sweden
Available from: 2018-10-08 Created: 2018-11-18 Last updated: 2018-12-14Bibliographically approved
Söderström, T. (2018). Errors-in-Variables Methods in System Identification. Springer
Open this publication in new window or tab >>Errors-in-Variables Methods in System Identification
2018 (English)Book (Refereed)
Place, publisher, year, edition, pages
Springer, 2018
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-366234 (URN)10.1007/978-3-319-75001-9 (DOI)978-3-319-75000-2 (ISBN)
Available from: 2018-11-18 Created: 2018-11-18 Last updated: 2018-12-07Bibliographically approved
Soverini, U. & Söderström, T. (2018). Identification of two-dimensional complex sinusoids in white noise: a state-space frequency approach. In: : . Paper presented at SYSID 2018, July 9–11, Stockholm, Sweden (pp. 996-1001). (15)
Open this publication in new window or tab >>Identification of two-dimensional complex sinusoids in white noise: a state-space frequency approach
2018 (English)Conference paper, Published paper (Refereed)
Series
IFAC-PapersOnLine, ISSN 2405-8963 ; 51:15
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-366284 (URN)10.1016/j.ifacol.2018.09.064 (DOI)000446599200168 ()
Conference
SYSID 2018, July 9–11, Stockholm, Sweden
Available from: 2018-10-08 Created: 2018-11-19 Last updated: 2018-12-14Bibliographically approved
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, p. 131-143Article 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
Soverini, U. & Söderström, T. (2017). Frequency domain identification of ARX models in the presence of additive input–output noise. In: 20th IFAC World Congress: . Paper presented at 20th World Congress of the International-Federation-of-Automatic-Control (IFAC),Toulouse, France, July 09-14, 2017. (pp. 6226-6231). Elsevier, 50
Open this publication in new window or tab >>Frequency domain identification of ARX models in the presence of additive input–output noise
2017 (English)In: 20th IFAC World Congress, Elsevier, 2017, Vol. 50, p. 6226-6231Conference paper, Published paper (Refereed)
Abstract [en]

This paper describes a new approach for identifying ARX models from a finite number of measurements, in presence of additive and uncorrelated white noise. The proposed algorithm is based on some theoretical results concerning the so-called dynamic Frisch Scheme. As a major novelty, the proposed approach deals with frequency domain data. In some aspects, the method resembles the characteristics of other identification algorithms, originally developed in the time domain. The proposed method is compared with other techniques by means of Monte Carlo simulations. The benefits of filtering the data and using only part of the frequency domain is highlighted by means of a numerical example.

Place, publisher, year, edition, pages
Elsevier, 2017
Series
IFAC-PapersOnLine, E-ISSN 2405-8963 ; 50: 1
National Category
Signal Processing
Research subject
Electrical Engineering with specialization in Automatic Control
Identifiers
urn:nbn:se:uu:diva-334132 (URN)10.1016/j.ifacol.2017.08.1023 (DOI)000423964900038 ()
Conference
20th World Congress of the International-Federation-of-Automatic-Control (IFAC),Toulouse, France, July 09-14, 2017.
Available from: 2017-11-21 Created: 2017-11-21 Last updated: 2018-05-16Bibliographically approved
Soverini, U. & Söderström, T. (2017). Frequency domain identification of complex sinusoids in the presence of additive noise. In: 20th IFAC World Congress: . Paper presented at 20th World Congress of the International-Federation-of-Automatic-Control (IFAC),Toulouse,France,July 09-14, 2017 (pp. 6244-6250). Elsevier, 50
Open this publication in new window or tab >>Frequency domain identification of complex sinusoids in the presence of additive noise
2017 (English)In: 20th IFAC World Congress, Elsevier, 2017, Vol. 50, p. 6244-6250Conference paper, Published paper (Refereed)
Abstract [en]

his paper describes a new approach for identifying the parameters of complex sinusoids from a finite number of measurements, in presence of additive and uncorrelated white noise. The proposed approach deals with frequency domain data and as a major feature, it enables the estimation to be frequency selective. In many aspects the new method resembles the well known ESPRIT subspace algorithm, originally developed in the time domain. However, the sub band frequency selective feature allows a reduction of the computations and can improve the quality of the estimates. The properties of the proposed method are analyzed by means of Monte Carlo simulations and its performance is compared with those of other estimation algorithms.

Place, publisher, year, edition, pages
Elsevier, 2017
Series
IFAC-PapersOnLine, E-ISSN 2405-8963 ; 50:1
National Category
Signal Processing
Research subject
Electrical Engineering with specialization in Automatic Control
Identifiers
urn:nbn:se:uu:diva-334711 (URN)10.10164/j.facol.2017.08.848 (DOI)000423964900041 ()
Conference
20th World Congress of the International-Federation-of-Automatic-Control (IFAC),Toulouse,France,July 09-14, 2017
Available from: 2017-11-27 Created: 2017-11-27 Last updated: 2018-05-17Bibliographically 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, p. 218-230Article 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.

Keywords
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, p. 3216-3223Article 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, p. 2597-2605Article 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
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ORCID iD: ORCID iD iconorcid.org/0000-0003-4619-8879

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