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Ghosh, A., Abdalmoaty, M., Chatterjee, S. & Hjalmarsson, H. (2024). DeepBayes—An estimator for parameter estimation in stochastic nonlinear dynamical models. Automatica, 159, Article ID 111327.
Open this publication in new window or tab >>DeepBayes—An estimator for parameter estimation in stochastic nonlinear dynamical models
2024 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 159, article id 111327Article in journal (Refereed) Published
Abstract [en]

Stochastic nonlinear dynamical systems are ubiquitous in modern, real-world applications. Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains a challenging problem. The majority of existing methods employ maximum likelihood or Bayesian estimation. However, these methods suffer from some limitations, most notably the substantial computational time for inference coupled with limited flexibility in application. In this work, we propose DeepBayes estimators that leverage the power of deep recurrent neural networks. The method consists of first training a recurrent neural network to minimize the mean-squared estimation error over a set of synthetically generated data using models drawn from the model set of interest. The a priori trained estimator can then be used directly for inference by evaluating the network with the estimation data. The deep recurrent neural network architectures can be trained offline and ensure significant time savings during inference. We experiment with two popular recurrent neural networks — long short term memory network (LSTM) and gated recurrent unit (GRU). We demonstrate the applicability of our proposed method on different example models and perform detailed comparisons with state-of-the-art approaches. We also provide a study on a real-world nonlinear benchmark problem. The experimental evaluations show that the proposed approach is asymptotically as good as the Bayes estimator.

Place, publisher, year, edition, pages
Elsevier Ltd, 2024
Keywords
Deep learning, Dynamical systems, Nonlinear system identification, Parameter estimation, Recurrent neural networks
National Category
Control Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering Probability Theory and Statistics
Identifiers
urn:nbn:se:kth:diva-339038 (URN)10.1016/j.automatica.2023.111327 (DOI)001161034600001 ()2-s2.0-85174673962 (Scopus ID)
Note

QC 20231129

Available from: 2023-11-29 Created: 2023-11-29 Last updated: 2024-05-02Bibliographically approved
Wang, Y., Pasquini, M., Chotteau, V., Hjalmarsson, H. & Jacobsen, E. W. (2024). Iterative learning robust optimization - with application to medium optimization of CHO cell cultivation in continuous monoclonal antibody production. Journal of Process Control, 137, Article ID 103196.
Open this publication in new window or tab >>Iterative learning robust optimization - with application to medium optimization of CHO cell cultivation in continuous monoclonal antibody production
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2024 (English)In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 137, article id 103196Article in journal (Refereed) Published
Abstract [en]

In the presence of uncertainty, the optimum obtained based on a nominal identified model can neither provide any performance guarantee nor ensure that critical constraints are satisfied, which is crucial for e.g., bioprocess applications characterized by a high degree of complexity combined with costly experiments. Hence, uncertainty should be considered in the optimization and, furthermore, experiments designed to reduce the uncertainty most important for optimization. Herein, we propose a general framework that combines model-based robust optimization with optimal experiment design. The proposed framework can take advantage of prior knowledge in the form of a mechanistic model structure, and the importance of this is demonstrated by comparing it to more standard black-box models typically employed in learning. Through optimal experiment design, we repeatedly reduce the uncertainty most relevant for optimization so as to maximize the potential for improving the worst-case performance by balancing between exploration and exploitation. This makes the proposed method an efficient model-based robust optimization framework, especially in cases with limited experimental resources. The main part of the paper focuses on the case with modeling uncertainty that can be reduced with the availability of more experimental data. Towards the end of the paper, we consider extending the method to also include inherent uncertainty, such as input uncertainty and unmeasured disturbances. The effectiveness of the method is illustrated through a realistic simulation case study of medium optimization of Chinese hamster ovary cell cultivation in continuous monoclonal antibody production, where the metabolic network consists of 23 extracellular metabolites and 126 reactions.

Place, publisher, year, edition, pages
Elsevier BV, 2024
Keywords
Experiment design, Identification for optimization, Learning, Parametric and implementation uncertainty, Perfusion, Robust optimization
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-344925 (URN)10.1016/j.jprocont.2024.103196 (DOI)2-s2.0-85188712513 (Scopus ID)
Note

QC 20240527

Available from: 2024-04-03 Created: 2024-04-03 Last updated: 2024-05-27Bibliographically approved
Parsa, J., Rojas, C. R. & Hjalmarsson, H. (2024). Transformation of Regressors for Low Coherent Sparse System Identification. IEEE Transactions on Automatic Control, 69(5), 2947-2962
Open this publication in new window or tab >>Transformation of Regressors for Low Coherent Sparse System Identification
2024 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 69, no 5, p. 2947-2962Article in journal (Refereed) Published
Abstract [en]

One of the main problems in sparse representation in order to achieve accurate parameter estimation is the potentially large correlation between the regressors. The maximum absolute value of this correlation is called mutual coherence. The inverse of the mutual coherence controls approximately the number of nonzero parameters that can be recovered exactly from noise-free observations. In system identification, the observations are constructed from time-series data, which leads to that the columns in the regressor matrix may be highly correlated, and therefore, the mutual coherence can be large. This implies that using standard sparse estimation algorithms may lead to large errors. In this article, we derive a bound on the mutual coherence, which shows that directly using popular algorithms such as the orthogonal matching pursuit (OMP) algorithm can lead to large estimation errors. The key idea in this article is to make a coordinate transformation so that the regressor matrix has low mutual coherence in the new coordinates. The proposed method involves solving a nonconvex optimization problem, which is done by alternating minimization and proximal methods. Additionally, we derive a bound on the mutual coherence of the regressor in the new coordinates, which can be used to ensure the correct estimation of the support of the parameters for OMP. For this method, we show also that the criterion used to compute the transformation maximizes a lower bound for the probability of correct estimation of the support. For a limited setting, we also show formally that the transformation can significantly increase the probability of correct estimation of the support. Finally, we show via simulations how the proposed method performs in comparison with state-of-the-art sparse estimation algorithms.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
Infinity norm, mutual coherence, proximal mapping, sparse estimation, system identification
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-348221 (URN)10.1109/TAC.2023.3299550 (DOI)2-s2.0-85166285638 (Scopus ID)
Note

QC 20240624

Available from: 2024-06-24 Created: 2024-06-24 Last updated: 2024-06-24Bibliographically approved
Parsa, J., Rojas, C. R. & Hjalmarsson, H. (2023). Application-Oriented Input Design With Low Coherence Constraint. IEEE Control Systems Letters, 7, 193-198
Open this publication in new window or tab >>Application-Oriented Input Design With Low Coherence Constraint
2023 (English)In: IEEE Control Systems Letters, E-ISSN 2475-1456, Vol. 7, p. 193-198Article in journal (Refereed) Published
Abstract [en]

In optimal input design input sequences are typically generated without paying attention to the correlations between the regressors of the model to be estimated. In fact, in many cases high correlations are beneficial. This is in contrast to the requirements in sparse estimation. Mutual coherence is the maximum of these correlations, and in case the parameter vector is known to be sparse, we need a low mutual coherence in order to estimate it accurately. This contribution proposes adding a constraint on the mutual coherence to the optimal input design problem to improve the accuracy of estimated sparse models. The proposed method can be combined with any sparse estimation algorithm to estimate the parameters of a model. However, we focus in particular on the bound on the mutual coherence required for Orthogonal Matching Pursuit (OMP), a well-known algorithm in sparse estimation. Furthermore, we analyze the effect of the proposed method on the required input power. Finally, we evaluate, in a numerical study, the performance of the proposed method compared to state-of-the-art algorithms for input design.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Coherence, Matching pursuit algorithms, Degradation, Optimization, Ellipsoids, Mathematical models, Costs, System identification, input design, sparse estimation, mutual coherence
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:kth:diva-315885 (URN)10.1109/LCSYS.2022.3187319 (DOI)000824789000003 ()2-s2.0-85133775635 (Scopus ID)
Note

QC 20220728

Available from: 2022-07-28 Created: 2022-07-28 Last updated: 2023-08-25Bibliographically approved
Parsa, J., Rojas, C. R. & Hjalmarsson, H. (2023). Coherence-Based Input Design for Nonlinear Systems. IEEE Control Systems Letters, 7, 2934-2939
Open this publication in new window or tab >>Coherence-Based Input Design for Nonlinear Systems
2023 (English)In: IEEE Control Systems Letters, E-ISSN 2475-1456, Vol. 7, p. 2934-2939Article in journal (Refereed) Published
Abstract [en]

Many off-the-shelf generic non-linear model structures have inherent sparse parametrizations. Volterra series and non-linear Auto-Regressive with eXogeneous inputs (NARX) models are examples of this. It is well known that sparse estimation requires low mutual coherence, which translates into input sequences with certain low correlation properties. This letter highlights that standard optimal input design methods do not account for this requirement which may lead to designs unsuitable for this type of model structure. To tackle this problem, this letter proposes incorporating a coherence constraint to standard input design problems. The coherence constraint is defined as the ratio between the diagonal and non-diagonal entries of the Fisher information matrix (FIM) and can be easily added to any input design problem for nonlinear systems, while the resulting problem remains convex. This letter provides a theoretical analysis of how the range of the optimal objective function of the original problem is affected by the coherence constraint. Additionally, this letter presents numerical evaluations of the proposed approach's performance on a Volterra series model in comparison to state-of-the-art algorithms.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
System identification, input design, nonlinear system, sparse estimation, mutual coherence
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-334291 (URN)10.1109/LCSYS.2023.3291230 (DOI)001033525300013 ()2-s2.0-85163782955 (Scopus ID)
Note

QC 20230818

Available from: 2023-08-18 Created: 2023-08-18 Last updated: 2023-08-23Bibliographically approved
Pasquini, M. & Hjalmarsson, H. (2023). E2-RTO: An Exploitation-Exploration Approach for Real Time Optimization. In: IFAC-PapersOnLine, 22nd IFAC World Congress: . Paper presented at 22nd IFAC World Congress, Jul 9 2023 - Jul 14 2023, Yokohama, Japan. (pp. 1423-1430). Elsevier BV, 56
Open this publication in new window or tab >>E2-RTO: An Exploitation-Exploration Approach for Real Time Optimization
2023 (English)In: IFAC-PapersOnLine, 22nd IFAC World Congress, Elsevier BV , 2023, Vol. 56, p. 1423-1430Conference paper, Published paper (Refereed)
Abstract [en]

In Real-Time Optimization, the problem of optimizing the operating conditions of a plant is solved through iterative methods that directly use plant measurements. In this paper, a novel Exploitation-Exploration approach to solve this problem is proposed. Differently from a previously proposed Two-Step approach, consisting in strict exploitation and consecutive model update, in the Exploitation-Exploration approach an additional exploration step is included, in which an exploration input is constructed so that the updated model would approximately minimize the expectation of the objective function value for the new exploitation step. The proposed algorithm is described and its validity is shown through a numerical example, where it is compared to some common Real-Time Optimization schemes.

Place, publisher, year, edition, pages
Elsevier BV, 2023
Keywords
Real-Time Optimization
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-343700 (URN)10.1016/j.ifacol.2023.10.1819 (DOI)2-s2.0-85184958399 (Scopus ID)
Conference
22nd IFAC World Congress, Jul 9 2023 - Jul 14 2023, Yokohama, Japan.
Note

Part of ISBN: 978-171387234-4

QC 20240228

Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2024-02-28Bibliographically approved
Andersson, M., Taghavian, H., Hjalmarsson, H., Klass, V. & Johansson, M. (2023). Informative battery charging: integrating fast charging and optimal experiments. In: : . Paper presented at 22nd IFAC World Congress, Yokohama, Japan, Jul 9 2023 - Jul 14 2023 (pp. 11160-11166). Elsevier BV
Open this publication in new window or tab >>Informative battery charging: integrating fast charging and optimal experiments
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2023 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents informative battery charging, a novel approach for battery model parameter estimation during fast charge. Our solution comprises three distinct contributions: first, we develop a semi-explicit solution to an optimal fast charging problem for equivalent circuit models with health-conscious voltage constraints; second, we design optimal experiments for battery model parameter estimation; and third, we suggest a strategy for how the fast charging and experimentation currents can be combined while still satisfying constraints and maintaining acceptable charging times. Numerical results show that model parameters can be identified with lower variance if an optimal experiment is added to the charging procedure.

Place, publisher, year, edition, pages
Elsevier BV, 2023
Keywords
Electric vehicles, Fast charging, Input and excitation design, Lithium-ion battery, Optimal control
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-349847 (URN)10.1016/j.ifacol.2023.10.835 (DOI)001196708400578 ()2-s2.0-85180774770 (Scopus ID)
Conference
22nd IFAC World Congress, Yokohama, Japan, Jul 9 2023 - Jul 14 2023
Note

Part of ISBN 9781713872344

QC 20240703

Available from: 2024-07-03 Created: 2024-07-03 Last updated: 2024-07-03Bibliographically approved
Wang, Y., Pasquini, M., Colin, K., Mäkinen, M., Schwarz, H., Chotteau, V., . . . Jacobsen, E. W. (2023). Model-based Medium Optimization Methodologies in High-cell Density Perfusion Culture. In: : . Paper presented at Cell Culture Engineering XVIII, Cancun, Mexico, April 23-28 2023.
Open this publication in new window or tab >>Model-based Medium Optimization Methodologies in High-cell Density Perfusion Culture
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2023 (English)Conference paper, Poster (with or without abstract) (Refereed)
Keywords
Perfusion cultures, Medium optimization, Model-based techniques
National Category
Control Engineering Bioprocess Technology
Identifiers
urn:nbn:se:kth:diva-329571 (URN)
Conference
Cell Culture Engineering XVIII, Cancun, Mexico, April 23-28 2023
Note

Yu Wang and Mirko Pasquini contributed equally to this work

QC 20230704

Available from: 2023-06-21 Created: 2023-06-21 Last updated: 2024-04-04Bibliographically approved
Bombois, X., Colin, K., Van den Hof, P. M. J. & Hjalmarsson, H. (2023). On the informativity of direct identification experiments in dynamical networks. Automatica, 148, 110742, Article ID 110742.
Open this publication in new window or tab >>On the informativity of direct identification experiments in dynamical networks
2023 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 148, p. 110742-, article id 110742Article in journal (Refereed) Published
Abstract [en]

Data informativity is a crucial property to ensure the consistency of the prediction error estimate. This property has thus been extensively studied in the open-loop and in the closed-loop cases, but has only been briefly touched upon in the dynamic network case. In this paper, we consider the prediction error identification of the modules in a row of a dynamic network using the full input approach. Our main contribution is to propose a number of easily verifiable data informativity conditions for this identification problem. Among these conditions, we distinguish a sufficient data informativity condition that can be verified based on the topology of the network and a necessary and sufficient data informativity condition that can be verified via a rank condition on a matrix of coefficients that are related to a full-order model structure of the network. These data informativity conditions allow to determine different situations (i.e., different excitation patterns) leading to data informativity. In order to be able to distinguish between these different situations, we also propose an optimal experiment design problem that allows to determine the excitation pattern yielding a certain pre-specified accuracy with the least excitation power.

Place, publisher, year, edition, pages
Elsevier BV, 2023
Keywords
Dynamic network identification, Data informativity, Optimal experiment design
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-324820 (URN)10.1016/j.automatica.2022.110742 (DOI)000928279800010 ()2-s2.0-85143489645 (Scopus ID)
Note

QC 20230317

Available from: 2023-03-17 Created: 2023-03-17 Last updated: 2023-03-17Bibliographically approved
Colin, K., Hjalmarsson, H. & Bombois, X. (2023). Optimal exploration strategies for finite horizon regret minimization in some adaptive control problems. In: 22nd IFAC World Congress Yokohama, Japan, July 9-14, 2023: . Paper presented at 22nd IFAC World Congress, Yokohama, Japan, Jul 9 2023 - Jul 14 2023 (pp. 2564-2569). Elsevier BV, 56
Open this publication in new window or tab >>Optimal exploration strategies for finite horizon regret minimization in some adaptive control problems
2023 (English)In: 22nd IFAC World Congress Yokohama, Japan, July 9-14, 2023, Elsevier BV , 2023, Vol. 56, p. 2564-2569Conference paper, Published paper (Refereed)
Abstract [en]

In this work, we consider the problem of regret minimization in adaptive minimum variance and linear quadratic control problems. Regret minimization has been extensively studied in the literature for both types of adaptive control problems. Most of these works give results of the optimal rate of the regret in the asymptotic regime. In the minimum variance case, the optimal asymptotic rate for the regret is log(T) which can be reached without any additional external excitation. On the contrary, for most adaptive linear quadratic problems, it is necessary to add an external excitation in order to get the optimal asymptotic rate of √T. In this paper, we will actually show from a theoretical study, as well as, in simulations that when the control horizon is pre-specified a lower regret can be obtained with either no external excitation or a new exploration type termed immediate.

Place, publisher, year, edition, pages
Elsevier BV, 2023
Series
IFAC-PapersOnLine, ISSN 2405-8963 ; 56
Keywords
adaptive control, linear quadratic regulator, linear systems, minimum variance controller, Regret minimization
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-343694 (URN)10.1016/j.ifacol.2023.10.1339 (DOI)2-s2.0-85184960468 (Scopus ID)
Conference
22nd IFAC World Congress, Yokohama, Japan, Jul 9 2023 - Jul 14 2023
Note

QC 20240222

Part of ISBN 9781713872344

Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2024-03-25Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0002-9368-3079

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