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Publications (10 of 319) Show all publications
Ferizbegovic, M., Umenberger, J., Hjalmarsson, H. & Schon, T. B. (2020). Learning Robust LQ-Controllers Using Application Oriented Exploration. IEEE Control Systems Letters, 4(1), 19-24, Article ID 8732482.
Open this publication in new window or tab >>Learning Robust LQ-Controllers Using Application Oriented Exploration
2020 (English)In: IEEE Control Systems Letters, ISSN 2475-1456, Vol. 4, no 1, p. 19-24, article id 8732482Article in journal (Refereed) Published
Abstract [en]

This letter concerns the problem of learning robust LQ-controllers, when the dynamics of the linear system are unknown. First, we propose a robust control synthesis method to minimize the worst-case LQ cost, with probability 1-δ , given empirical observations of the system. Next, we propose an approximate dual controller that simultaneously regulates the system and reduces model uncertainty. The objective of the dual controller is to minimize the worst-case cost attained by a new robust controller, synthesized with the reduced model uncertainty. The dual controller is subject to an exploration budget in the sense that it has constraints on its worst-case cost with respect to the current model uncertainty. In our numerical experiments, we observe better performance of the proposed robust LQ regulator over the existing methods. Moreover, the dual control strategy gives promising results in comparison with the common greedy random exploration strategies.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2020
Keywords
Identification, machine learning, robust control, Budget control, Control system synthesis, Identification (control systems), Learning systems, Linear systems, Numerical methods, Uncertainty analysis, Application-oriented, Exploration budget, Exploration strategies, Model uncertainties, Numerical experiments, Robust control synthesis, Robust controllers, Robust LQ controller, Controllers
National Category
Control Engineering
Research subject
Information and Communication Technology
Identifiers
urn:nbn:se:kth:diva-263457 (URN)10.1109/LCSYS.2019.2921512 (DOI)2-s2.0-85067870073 (Scopus ID)
Funder
Knut and Alice Wallenberg Foundation
Note

QC 20191205

Available from: 2019-12-05 Created: 2019-12-05 Last updated: 2019-12-05Bibliographically approved
Wang, M., Jacobsen, E. W., Chotteau, V. & Hjalmarsson, H. (2019). A multi-step least-squares method for nonlinear rational models. In: Proceedings of the American Control Conference: . Paper presented at 2019 American Control Conference, ACC 2019; Philadelphia; United States; 10 July 2019 through 12 July 2019 (pp. 4509-4514). Institute of Electrical and Electronics Engineers (IEEE), Article ID 8814404.
Open this publication in new window or tab >>A multi-step least-squares method for nonlinear rational models
2019 (English)In: Proceedings of the American Control Conference, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 4509-4514, article id 8814404Conference paper, Published paper (Refereed)
Abstract [en]

Models rational in the parameters arise frequently in biosystems and other applications. As with all models that are non-linear in the parameters, direct parameter estimation, using e.g. nonlinear least-squares, can become challenging due to the issues of local minima and finding good initial estimates. Here we propose a multi-step least-squares method for a class of nonlinear rational models. The proposed method is applied to an extended Monod-type model. Numerical simulations indicate that the proposed method is consistent.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Series
Proceedings of the American Control Conference, ISSN 0743-1619
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-262599 (URN)2-s2.0-85072268828 (Scopus ID)9781538679265 (ISBN)
Conference
2019 American Control Conference, ACC 2019; Philadelphia; United States; 10 July 2019 through 12 July 2019
Note

QC 20191028

Available from: 2019-10-28 Created: 2019-10-28 Last updated: 2019-10-28Bibliographically approved
Risuleo, R. S., Lindsten, F. & Hjalmarsson, H. (2019). Bayesian nonparametric identification of Wiener systems. Automatica, 108, Article ID UNSP 108480.
Open this publication in new window or tab >>Bayesian nonparametric identification of Wiener systems
2019 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 108, article id UNSP 108480Article in journal (Refereed) Published
Abstract [en]

We propose a nonparametric approach for the identification of Wiener systems. We model the impulse response of the linear block and the static nonlinearity using Gaussian processes. The hyperparameters of the Gaussian processes are estimated using an iterative algorithm based on stochastic approximation expectation-maximization. In the iterations, we use elliptical slice sampling to approximate the posterior distribution of the impulse response and update the hyperparameter estimates. The same sampling is finally used to sample the posterior distribution and to compute point estimates. We compare the proposed approach with a parametric approach and a semi-parametric approach. In particular, we show that the proposed method has an advantage when a parametric model for the system is not readily available.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD, 2019
Keywords
Spline models, Nonparametric estimation, System identification
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-260160 (URN)10.1016/j.automatica.2019.06.032 (DOI)000483631400016 ()2-s2.0-85068777049 (Scopus ID)
Note

QC 20191001

Available from: 2019-10-01 Created: 2019-10-01 Last updated: 2019-10-01Bibliographically approved
Galrinho, M., Rojas, C. R. & Hjalmarsson, H. (2019). Estimating models with high-order noise dynamics using semi-parametric weighted null-space fitting. Automatica, 102, 45-57
Open this publication in new window or tab >>Estimating models with high-order noise dynamics using semi-parametric weighted null-space fitting
2019 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 102, p. 45-57Article in journal (Refereed) Published
Abstract [en]

Standard system identification methods often provide inconsistent estimates with closed-loop data. With the prediction error method (PEM), this issue is solved by using a noise model that is flexible enough to capture the noise spectrum. However, a too flexible noise model (i.e., too many parameters) increases the model complexity, which can cause additional numerical problems for PEM. In this paper, we consider the weighted null-space fitting (WNSF) method. With this method, the system is first modeled using a non-parametric ARX model, which is then reduced to a parametric model of interest using weighted least squares. In the reduction step, a parametric noise model does not need to be estimated if it is not of interest. Because the flexibility of the noise model is increased with the sample size, this will still provide consistent estimates in closed loop and asymptotically efficient estimates in open loop. In this paper, we prove these results, and we derive the asymptotic covariance for the estimation error obtained in closed loop, which is optimal for an infinite-order noise model. For this purpose, we also derive a new technical result for geometric variance analysis, instrumental to our end. Finally, we perform a simulation study to illustrate the benefits of the method when the noise model cannot be parametrized by a low-order model.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Closed-loop identification, Identification algorithms, Least squares, Non-parametric identification, Parameter identification, System identification
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-246463 (URN)10.1016/j.automatica.2018.12.039 (DOI)000461725600006 ()2-s2.0-85060237267 (Scopus ID)
Note

QC 20190326

Available from: 2019-03-26 Created: 2019-03-26 Last updated: 2019-04-09Bibliographically approved
Zhang, L., Wang, M., Castan, A., Stevenson, J., Chatzissavidou, N., Hjalmarsson, H., . . . Chotteau, V. (2019). Glycan Residues Balance Analysis: A novel model for the N-linked glycosylation of IgG produced by CHO cells.. Metabolic engineering, Article ID S1096-7176(19)30086-2.
Open this publication in new window or tab >>Glycan Residues Balance Analysis: A novel model for the N-linked glycosylation of IgG produced by CHO cells.
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2019 (English)In: Metabolic engineering, ISSN 1096-7176, E-ISSN 1096-7184, article id S1096-7176(19)30086-2Article in journal (Refereed) Epub ahead of print
Abstract [en]

The structure of N-linked glycosylation is a very important quality attribute for therapeutic monoclonal antibodies. Different carbon sources in cell culture media, such as mannose and galactose, have been reported to have different influences on the glycosylation patterns. Accurate prediction and control of the glycosylation profile are important for the process development of mammalian cell cultures. In this study, a mathematical model, that we named Glycan Residues Balance Analysis (GReBA), was developed based on the concept of Elementary Flux Mode (EFM), and used to predict the glycosylation profile for steady state cell cultures. Experiments were carried out in pseudo-perfusion cultivation of antibody producing Chinese Hamster Ovary (CHO) cells with various concentrations and combinations of glucose, mannose and galactose. Cultivation of CHO cells with mannose or the combinations of mannose and galactose resulted in decreased lactate and ammonium production, and more matured glycosylation patterns compared to the cultures with glucose. Furthermore, the growth rate and IgG productivity were similar in all the conditions. When the cells were cultured with galactose alone, lactate was fed as well to be used as complementary carbon source, leading to cell growth rate and IgG productivity comparable to feeding the other sugars. The data of the glycoprofiles were used for training the model, and then to simulate the glycosylation changes with varying the concentrations of mannose and galactose. In this study we showed that the GReBA model had a good predictive capacity of the N-linked glycosylation. The GReBA can be used as a guidance for development of glycoprotein cultivation processes.

Keywords
CHO cells, IgG, Mathematical modelling, N-linked glycosylation, Pseudo-perfusion
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-261092 (URN)10.1016/j.ymben.2019.08.016 (DOI)31539564 (PubMedID)
Note

QC 20191112

Available from: 2019-10-01 Created: 2019-10-01 Last updated: 2019-11-12Bibliographically approved
Abdalmoaty, M. . & Hjalmarsson, H. (2019). Linear Prediction Error Methods for Stochastic Nonlinear Models. Automatica, 105, 49-63
Open this publication in new window or tab >>Linear Prediction Error Methods for Stochastic Nonlinear Models
2019 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 105, p. 49-63Article in journal (Refereed) Published
Abstract [en]

The estimation problem for stochastic parametric nonlinear dynamical models is recognized to be challenging. The main difficulty is the intractability of the likelihood function and the optimal one-step ahead predictor. In this paper, we present relatively simple prediction error methods based on non-stationary predictors that are linear in the outputs. They can be seen as extensions of the linear identification methods for the case where the hypothesized model is stochastic and nonlinear. The resulting estimators are defined by analytically tractable objective functions in several common cases. It is shown that, under certain identifiability and standard regularity conditions, the estimators are consistent and asymptotically normal. We discuss the relationship between the suggested estimators and those based on second-order equivalent models as well as the maximum likelihood method. The paper is concluded with a numerical simulation example as well as a real-data benchmark problem.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Parameter estimation; System identification; Stochastic systems; Nonlinear models; Prediction error methods.
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-235340 (URN)10.1016/j.automatica.2019.03.006 (DOI)000476963500005 ()2-s2.0-85063614946 (Scopus ID)
Funder
Swedish Research Council, 2015-05285 : 2016-06079
Note

QC 20180921

Available from: 2018-09-21 Created: 2018-09-21 Last updated: 2019-08-12Bibliographically approved
Risuleo, R. S., Bottegal, G. & Hjalmarsson, H. (2019). Modeling and identification of uncertain-input systems. Automatica, 105, 130-141
Open this publication in new window or tab >>Modeling and identification of uncertain-input systems
2019 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 105, p. 130-141Article in journal (Refereed) Published
Abstract [en]

We present a new class of models, called uncertain-input models, that allows us to treat system-identification problems in which a linear system is subject to a partially unknown input signal. To encode prior information about the input or the linear system, we use Gaussian-process models. We estimate the model from data using the empirical Bayes approach: the hyperparameters that characterize the Gaussian-process models are estimated from the marginal likelihood of the data. We propose an iterative algorithm to find the hyperparameters that relies on the EM method and results in decoupled update steps. Because in the uncertain-input setting neither the marginal likelihood nor the posterior distribution of the unknowns is tractable, we develop an approximation approach based on variational Bayes. As part of the contribution of the paper, we show that this model structure encompasses many classical problems in system identification such as Hammerstein models, blind system identification, and cascaded linear systems. This connection allows us to build a systematic procedure that applies effectively to all the aforementioned problems, as shown in the numerical simulations presented in the paper.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Estimation algorithms, Gaussian processes, Nonlinear models, Nonparametric identification, System identification, Gaussian noise (electronic), Identification (control systems), Iterative methods, Linear systems, Religious buildings, Blind system identification, Empirical Bayes approach, Estimation algorithm, Non-linear model, Non-parametric identification, Posterior distributions, System identification problems, Gaussian distribution
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-252499 (URN)10.1016/j.automatica.2019.03.014 (DOI)000476963500013 ()2-s2.0-85063903295 (Scopus ID)
Note

QC 20190711

Available from: 2019-07-11 Created: 2019-07-11 Last updated: 2019-08-12Bibliographically approved
Morelli, F., Bombois, X., Hjalmarsson, H., Bako, L. & Colin, K. (2019). Optimal Experiment Design for the Identification of One Module in the Interconnection of Locally Controlled Systems. In: 2019 18TH EUROPEAN CONTROL CONFERENCE (ECC): . Paper presented at 18th European Control Conference (ECC), Naples, ITALY, JUN 25-28, 2019 (pp. 363-368). IEEE
Open this publication in new window or tab >>Optimal Experiment Design for the Identification of One Module in the Interconnection of Locally Controlled Systems
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2019 (English)In: 2019 18TH EUROPEAN CONTROL CONFERENCE (ECC), IEEE , 2019, p. 363-368Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we consider the problem of designing the least costly experiment that leads to a sufficiently accurate estimate of one module in a network of locally controlled systems. A module in such a network can be identified by exciting the corresponding local closed loop system. Such an excitation signal will not only perturb the input/output of the to-be-identified module, but also other modules due to the interconnection. Consequently, the cost of the identification can be expressed as the sum of the influence of the excitation signal on the inputs and outputs of all locally controlled systems. We develop a methodology to design the spectrum of the excitation signal in such a way that this cost is minimized while guaranteeing a certain accuracy for the identified model. We also propose an alternative identification configuration which can further reduce the propagation of the excitation signal to other modules and we make steps to robustify this optimal experiment design problem with respect to the cost of the identification.

Place, publisher, year, edition, pages
IEEE, 2019
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-263407 (URN)10.23919/ECC.2019.8795694 (DOI)000490488300059 ()2-s2.0-85071533287 (Scopus ID)
Conference
18th European Control Conference (ECC), Naples, ITALY, JUN 25-28, 2019
Note

QC 20191107

Available from: 2019-11-07 Created: 2019-11-07 Last updated: 2019-11-07Bibliographically approved
Galrinho, M., Rojas, C. R. & Hjalmarsson, H. (2019). Parametric Identification Using Weighted Null-Space Fitting. IEEE Transactions on Automatic Control, 64(7), 2798-2813
Open this publication in new window or tab >>Parametric Identification Using Weighted Null-Space Fitting
2019 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 64, no 7, p. 2798-2813Article in journal (Refereed) Published
Abstract [en]

In identification of dynamical systems, the prediction error method with a quadratic cost function provides asymptotically efficient estimates under Gaussian noise, but in general it requires solving a nonconvex optimization problem, which may imply convergence to nonglobal minima. An alternative class of methods uses a nonparametric model as intermediate step to obtain the model of interest. Weighted null-space fitting (WNSF) belongs to this class, starting with the estimate of a nonparametric ARX model with least squares. Then, the reduction to a parametric model is a multistep procedure where each step consists of the solution of a quadratic optimization problem, which can be obtained with weighted least squares. The method is suitable for both open- and closed-loop data, and can be applied to many common parametric model structures, including output-error, ARMAX, and Box-Jenkins. The price to pay is the increase of dimensionality in the nonparametric model, which needs to tend to infinity as function of the sample size for certain asymptotic statistical properties to hold. In this paper, we conduct a rigorous analysis of these properties: namely, consistency, and asymptotic efficiency. Also, we perform a simulation study illustrating the performance of WNSF and identify scenarios where it can be particularly advantageous compared with state-of-the-art methods.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019
Keywords
Least squares, system identification
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-255416 (URN)10.1109/TAC.2018.2877673 (DOI)000473489700011 ()2-s2.0-85055726363 (Scopus ID)
Note

QC 20190815

Available from: 2019-08-15 Created: 2019-08-15 Last updated: 2019-10-15Bibliographically approved
Weerts, H. H., Galrinho, M., Bottegal, G., Hjalmarsson, H. & den Hof, P. M. (2018). A sequential least squares algorithm for ARMAX dynamic network identification. IFAC-PapersOnLine, 51(15), 844-849
Open this publication in new window or tab >>A sequential least squares algorithm for ARMAX dynamic network identification
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2018 (English)In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 15, p. 844-849Article in journal (Refereed) Published
Abstract [en]

Identification of dynamic networks in prediction error setting often requires the solution of a non-convex optimization problem, which can be difficult to solve especially for large-scale systems. Focusing on ARMAX models of dynamic networks, we instead employ a method based on a sequence of least-squares steps. For single-input single-output models, we show that the method is equivalent to the recently developed Weighted Null Space Fitting, and, drawing from the analysis of that method, we conjecture that the proposed method is both consistent as well as asymptotically efficient under suitable assumptions. Simulations indicate that the sequential least squares estimates can be of high quality even for short data sets.

Place, publisher, year, edition, pages
Elsevier B.V., 2018
Keywords
dynamic networks, identification algorithm, least squares, System identification, Convex optimization, Identification (control systems), Large scale systems, Asymptotically efficient, Dynamic network, Identification algorithms, Least Square, Least squares algorithm, Least squares estimate, Nonconvex optimization, Single input single output, Least squares approximations
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-247491 (URN)10.1016/j.ifacol.2018.09.119 (DOI)000446599200143 ()2-s2.0-85054462289 (Scopus ID)
Note

QC20190412

Available from: 2019-04-12 Created: 2019-04-12 Last updated: 2019-04-12Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9368-3079

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