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Glad, T., Ljung, L. & Enqvist, M. (2024). Reglerteknik: grundläggande teori (5ed.). Lund: Studentlitteratur AB
Open this publication in new window or tab >>Reglerteknik: grundläggande teori
2024 (English)Book (Other academic)
Abstract [sv]

Reglerteknik förekommer numera i de flesta tekniska system: motorstyrning, antisladdsystem och farthållare i bilar; effektstyrning för mobiltelefoner; banföljning i industrirobotar; styrautomater i flygplan; styrning av allehanda kvalitetsvariabler i processindustrin liksom många tillämpningar inom konsumentelektronik...[Bokinfo]

Place, publisher, year, edition, pages
Lund: Studentlitteratur AB, 2024. p. 258 Edition: 5
Keywords
Reglerteknik
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-200628 (URN)9789144182155 (ISBN)
Available from: 2024-02-02 Created: 2024-02-02 Last updated: 2024-02-02Bibliographically approved
Ljung, L., Glad, T. & Hansson, A. (2021). Modeling and identification of dynamic systems (2ed.). Lund: Studentlitteratur
Open this publication in new window or tab >>Modeling and identification of dynamic systems
2021 (English)Book (Other academic)
Abstract [en]

Mathematical models of real life systems and processes are essential in today’s industrial work. To be able to construct such models is therefore a fundamental skill in modern engineering...

Place, publisher, year, edition, pages
Lund: Studentlitteratur, 2021. p. 484 Edition: 2
Keywords
Matematiska modeller, Systemanalys
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-180214 (URN)9789144153452 (ISBN)
Available from: 2021-10-12 Created: 2021-10-12 Last updated: 2024-01-08Bibliographically approved
Aljanaideh, K. F., Bhattacharjee, D., Singh, R. & Ljung, L. (2021). New Features in the System Identification Toolbox - Rapprochements with Machine Learning. In: IFAC PAPERSONLINE: . Paper presented at 19th IFAC Symposium on System Identification (SYSID), Padova, ITALY, jul 13-16, 2021 (pp. 369-373). ELSEVIER, 54(7)
Open this publication in new window or tab >>New Features in the System Identification Toolbox - Rapprochements with Machine Learning
2021 (English)In: IFAC PAPERSONLINE, ELSEVIER , 2021, Vol. 54, no 7, p. 369-373Conference paper, Published paper (Refereed)
Abstract [en]

The R2021b release of the System Identification ToolboxTM for MATLAB contains new features that enable the use of machine learning techniques for nonlinear system identification. With this release it is possible to build nonlinear ARX models with regression tree ensemble and Gaussian process regression mapping functions. The release contains several other enhancements including, but not limited to, (a) online state estimation using the extended Kalman filter and the unscented Kalman filter with code generation capability; (b) improved handling of initial conditions for transfer functions and polynomial models; (c) a new architecture of nonlinear black-box models that streamlines regressor handling, reduces memory footprint and improves numerical accuracy; and (d) easy incorporation of identification apps in teaching tools and interactive examples by leveraging the Live Editor tasks of MATLAB. Copyright (C) 2021 The Authors.

Place, publisher, year, edition, pages
ELSEVIER, 2021
Keywords
MATLAB; System Identification Toolbox; system identification; machine learning
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-180311 (URN)10.1016/j.ifacol.2021.08.387 (DOI)000696396200064 ()
Conference
19th IFAC Symposium on System Identification (SYSID), Padova, ITALY, jul 13-16, 2021
Available from: 2021-10-15 Created: 2021-10-15 Last updated: 2024-01-08Bibliographically approved
Schoukens, J. & Ljung, L. (2019). Nonlinear System Identification: A User-oriented road map. IEEE CONTROL SYSTEMS MAGAZINE, 39(6), 28-99
Open this publication in new window or tab >>Nonlinear System Identification: A User-oriented road map
2019 (English)In: IEEE CONTROL SYSTEMS MAGAZINE, ISSN 1066-033X, Vol. 39, no 6, p. 28-99Article in journal (Refereed) Published
Abstract [en]

Nonlinear system identification is an extremely broad topic, since every system that is not linear is nonlinear. That makes it impossible to give a full overview of all aspects of the fi eld. For this reason, the selection of topics and the organization of the discussion are strongly colored by the personal journey of the authors in this nonlinear universe.

Place, publisher, year, edition, pages
IEEE, 2019
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-170874 (URN)10.1109/MCS.2019.2938121 (DOI)000497711500010 ()
Note

Funding agencies:This work was supported in part by the Flanders Fund for Scientific Research (FWO), the Vrije Universiteit Brussel (VUB), Brussels, Belgium, and the European Research Council Advanced Grant SNLSID, under contract 320378.

Available from: 2020-10-28 Created: 2020-10-28 Last updated: 2024-01-08
Schoukens, J. & Ljung, L. (2019). Nonlinear System Identification A USER-ORIENTED ROAD MAP. IEEE CONTROL SYSTEMS MAGAZINE, 39(6), 28-99
Open this publication in new window or tab >>Nonlinear System Identification A USER-ORIENTED ROAD MAP
2019 (English)In: IEEE CONTROL SYSTEMS MAGAZINE, ISSN 1066-033X, Vol. 39, no 6, p. 28-99Article in journal (Refereed) Published
Abstract [en]

n/a

Place, publisher, year, edition, pages
IEEE, 2019
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-170874 (URN)10.1109/MCS.2019.2938121 (DOI)000497711500010 ()
Note

Funding agencies:This work was supported in part by the Flanders Fund for Scientific Research (FWO), the Vrije Universiteit Brussel (VUB), Brussels, Belgium, and the European Research Council Advanced Grant SNLSID, under contract 320378.

Available from: 2020-10-28 Created: 2020-10-28 Last updated: 2024-01-08
Pan, W., Yuan, Y., Ljung, L., Goncalves, J. & Stan, G.-B. (2018). Identification of Nonlinear State-Space Systems from Heterogeneous Datasets. IEEE Transactions on Control of Network Systems, 5(2), 737-747
Open this publication in new window or tab >>Identification of Nonlinear State-Space Systems from Heterogeneous Datasets
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2018 (English)In: IEEE Transactions on Control of Network Systems, E-ISSN 2325-5870, Vol. 5, no 2, p. 737-747Article in journal (Refereed) Published
Abstract [en]

This paper proposes a new method to identify nonlinear state-space systems from heterogeneous datasets. The method is described in the context of identifying biochemical/gene networks (i.e., identifying both reaction dynamics and kinetic parameters) from experimental data. Simultaneous integration of various datasets has the potential to yield better performance for system identification. Data collected experimentally typically vary depending on the specific experimental setup and conditions. Typically, heterogeneous data are obtained experimentally through 1) replicate measurements from the same biological system or 2) application of different experimental conditions such as changes/perturbations in biological inductions, temperature, gene knock-out, gene over-expression, etc. We formulate here the identification problem using a Bayesian learning framework that makes use of “sparse group” priors to allow inference of the sparsest model that can explain the whole set of observed heterogeneous data. To enable scale up to large number of features, the resulting nonconvex optimization problem is relaxed to a reweighted Group Lasso problem using a convex-concave procedure. As an illustrative example of the effectiveness of our method, we use it to identify a genetic oscillator (generalized eight species repressilator). Through this example we show that our algorithm outperforms Group Lasso when the number of experiments is increased, even when each single time-series dataset is short. We additionally assess the robustness of our algorithm against noise by varying the intensity of process noise and measurement noise.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
biological system modeling, system identification
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-161502 (URN)10.1109/TCNS.2017.2758966 (DOI)000435505900005 ()2-s2.0-85030779537 (Scopus ID)
Available from: 2019-11-03 Created: 2019-11-03 Last updated: 2024-01-08Bibliographically approved
Mu, B., Chen, T. & Ljung, L. (2018). On asymptotic properties of hyperparameter estimators for kernel-based regularization methods. Automatica, 94, 381-395
Open this publication in new window or tab >>On asymptotic properties of hyperparameter estimators for kernel-based regularization methods
2018 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 94, p. 381-395Article in journal (Refereed) Published
Abstract [en]

The kernel-based regularization method has two core issues: kernel design and hyperparameter estimation. In this paper, we focus on the second issue and study the properties of several hyperparameter estimators including the empirical Bayes (EB) estimator, two Steins unbiased risk estimators (SURE) (one related to impulse response reconstruction and the other related to output prediction) and their corresponding Oracle counterparts, with an emphasis on the asymptotic properties of these hyperparameter estimators. To this goal, we first derive and then rewrite the first order optimality conditions of these hyperparameter estimators, leading to several insights on these hyperparameter estimators. Then we show that as the number of data goes to infinity, the two SUREs converge to the best hyperparameter minimizing the corresponding mean square error, respectively, while the more widely used EB estimator converges to another best hyperparameter minimizing the expectation of the EB estimation criterion. This indicates that the two SUREs are asymptotically optimal in the corresponding MSE senses but the EB estimator is not. Surprisingly, the convergence rate of two SUREs is slower than that of the EB estimator, and moreover, unlike the two SUREs, the EB estimator is independent of the convergence rate of Phi(T)Phi/N to its limit, where Phi is the regression matrix and N is the number of data. A Monte Carlo simulation is provided to demonstrate the theoretical results. (C) 2018 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD, 2018
Keywords
Kernel-based regularization; Empirical Bayes; Steins unbiased risk estimator; Asymptotic analysis
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-149840 (URN)10.1016/j.automatica.2018.04.035 (DOI)000437076500041 ()
Note

Funding Agencies|National Natural Science Foundation of China [61773329, 61603379]; central government of China; Shenzhen Science and Technology Innovation Council [Ji-20170189, Ji-20160207]; Chinese University of Hong Kong, Shenzhen [PF. 01.000249, 2014.0003.23]; Swedish Research Council [2014-5894]; National Key Basic Research Program of China (973 Program) [2014CB845301]; Presidential Fund of the Academy of Mathematics and Systems Science, CAS [2015-hwyxqnrc-mbq]

Available from: 2018-08-02 Created: 2018-08-02 Last updated: 2024-01-08
Chen, T., Ardeshiri, T., Carli, F. P., Chiuso, A., Ljung, L. & Pillonetto, G. (2016). Maximum entropy properties of discrete-time first-order stable spline kernel. Automatica, 66, 34-38
Open this publication in new window or tab >>Maximum entropy properties of discrete-time first-order stable spline kernel
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2016 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 66, p. 34-38Article in journal (Refereed) Published
Abstract [en]

The first order stable spline (SS-1) kernel (also known as the tunedcorrelated kernel) is used extensively in regularized system identification, where the impulse response is modeled as a zero-mean Gaussian process whose covariance function is given by well designed and tuned kernels. In this paper, we discuss the maximum entropy properties of this kernel. In particular, we formulate the exact maximum entropy problem solved by the SS-1 kernel without Gaussian and uniform sampling assumptions. Under general sampling assumption, we also derive the special structure of the SS-1 kernel (e.g. its tridiagonal inverse and factorization have closed form expression), also giving to it a maximum entropy covariance completion interpretation.

Keywords
System identification;Regularization method;Kernel structure;Maximum entropy
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-121618 (URN)10.1016/j.automatica.2015.12.009 (DOI)
Available from: 2015-09-28 Created: 2015-09-28 Last updated: 2024-01-08Bibliographically approved
Pillonetto, G., Chen, T., Chiuso, A., De Nicolao, G. & Ljung, L. (2016). Regularized linear system identification using atomic, nuclear and kernel-based norms: The role of the stability constraint. Automatica, 69, 137-149
Open this publication in new window or tab >>Regularized linear system identification using atomic, nuclear and kernel-based norms: The role of the stability constraint
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2016 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 69, p. 137-149Article in journal (Refereed) Published
Abstract [en]

Inspired by ideas taken from the machine learning literature, new regularization techniques have been recently introduced in linear system identification. In particular, all the adopted estimators solve a regularized least squares problem, differing in the nature of the penalty term assigned to the impulse response. Popular choices include atomic and nuclear norms (applied to Hankel matrices) as well as norms induced by the so called stable spline kernels. In this paper, a comparative study of estimators based on these different types of regularizers is reported. Our findings reveal that stable spline kernels outperform approaches based on atomic and nuclear norms since they suitably embed information on impulse response stability and smoothness. This point is illustrated using the Bayesian interpretation of regularization. We also design a new class of regularizers defined by "integral" versions of stable spline/TC kernels. Under quite realistic experimental conditions, the new estimators outperform classical prediction error methods also when the latter are equipped with an oracle for model order selection. (C) 2016 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD, 2016
Keywords
Linear system identification; Kernel-based regularization; Atomic and nuclear norms; Hankel operator; Lasso; Bayesian interpretation of regularization; Gaussian processes; Reproducing kernel Hilbert spaces
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-130057 (URN)10.1016/j.automatica.2016.02.012 (DOI)000377312800015 ()
Note

Funding Agencies|MIUR FIRB project [RBFR12M3AC]; Progetto di Ateneo [CPDA147754/14]; Linnaeus Center CADICS; Swedish Research Council; ERC advanced grant LEARN [267381]; European Research Council; Swedish Research Council (VR) [2014-5894]

Available from: 2016-07-06 Created: 2016-07-06 Last updated: 2024-01-08
Isaksson, A., Sjöberg, J., Tornqvist, D., Ljung, L. & Kok, M. (2015). Using horizon estimation and nonlinear optimization for grey-box identification. Journal of Process Control, 30, 69-79
Open this publication in new window or tab >>Using horizon estimation and nonlinear optimization for grey-box identification
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2015 (English)In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 30, p. 69-79Article in journal (Refereed) Published
Abstract [en]

An established method for grey-box identification is to use maximum-likelihood estimation for the nonlinear case implemented via extended Kalman filtering. In applications of (nonlinear) model predictive control a more and more common approach for the state estimation is to use moving horizon estimation, which employs (nonlinear) optimization directly on a model for a whole batch of data. This paper shows that, in the linear case, horizon estimation may also be used for joint parameter estimation and state estimation, as long as a bias correction based on the Kalman filter is included. For the nonlinear case two special cases are presented where the bias correction can be determined without approximation. A procedure how to approximate the bias correction for general nonlinear systems is also outlined. (C) 2015 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
Elsevier, 2015
Keywords
System identification; State estimation; Parameter estimation; Optimization; Nonlinear systems; Kalman filtering; Moving horizon estimation; Model predictive control
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-120061 (URN)10.1016/j.jprocont.2014.12.008 (DOI)000356196200007 ()
Note

Funding Agencies|Swedish Foundation for Strategic Research (SSF) - as part of the Process Industry Centre Linkoping (PIC-LI); Swedish Agency for Innovation Systems (VINNOVA) through the ITEA 2 project MODRIO; Linnaeus Center CADICS - Swedish Research Council; ERC advanced grant LEARN - European Research Council [similar to267381]

Available from: 2015-07-06 Created: 2015-07-06 Last updated: 2024-01-08
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4881-8955

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