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Biography [eng]

I'm a postdoctoral researcher at the Decision and Control Systems department of KTH.

My interests lie mainly in the common denominator of systems control theory, dynamical systems, and mathematical optimization.

My current research focus is on "Learning stochastic nonlinear dynamical models".

Publications (10 of 16) Show all publications
Rodrigues, D., Abdalmoaty, M., Jacobsen, E. W., Chotteau, V. & Hjalmarsson, H. (2021). An Integrated Approach for Modeling and Identification of Perfusion Bioreactors via Basis Flux Modes. Computers and Chemical Engineering, 149, Article ID 107238.
Open this publication in new window or tab >>An Integrated Approach for Modeling and Identification of Perfusion Bioreactors via Basis Flux Modes
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2021 (English)In: Computers and Chemical Engineering, ISSN 0098-1354, E-ISSN 1873-4375, Vol. 149, article id 107238Article in journal (Refereed) Published
Abstract [en]

This paper discusses the challenges associated with the reliable and optimal operation of perfusion bioreactors and presents methods for modeling and identification of perfusion bioreactors as well as the vision for their integration. After presenting ageneric model of perfusion bioreactors, the paper shows how to use the concept of basis flux modes to uniquely compute reaction rates. The advantage of this concept with respect to elementary flux nodes and similar concepts in metabolic flux analysis is the reduced number of flux modes that need to be modeled. In addition, a procedure to identify the model and estimate the parameters for each reaction using Monod-type kinetics is presented. It is shown that the rational structure of these kinetic models results in optimization problems that are amenable to tractable computation of globally optimal parameter estimates. The methods are illustrated via examples with simulated or experimental data.

Place, publisher, year, edition, pages
Elsevier BV, 2021
Keywords
Flux modes; Parameter estimation; Modeling; Perfusion bioreactors;
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-289223 (URN)10.1016/j.compchemeng.2021.107238 (DOI)000657570600001 ()2-s2.0-85103977622 (Scopus ID)
Funder
Vinnova, 2016-05181Swedish Research Council, 2016-06079 (NewLEADS)
Note

QC 20210621

Available from: 2021-01-22 Created: 2021-01-22 Last updated: 2024-04-04Bibliographically approved
Abdalmoaty, M., Eriksson, O., Bereza-Jarocinski, R., Broman, D. & Hjalmarsson, H. (2021). Identification of Non-Linear Differential-Algebraic Equation Models with Process Disturbances. In: Proceedings The 60th IEEE conference on Decision and Control (CDC): . Paper presented at The 60th IEEE conference on Decision and Control (CDC), Dec. 13-17, 2021, Austin, Texas, USA. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Identification of Non-Linear Differential-Algebraic Equation Models with Process Disturbances
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2021 (English)In: Proceedings The 60th IEEE conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE) , 2021Conference paper, Published paper (Refereed)
Abstract [en]

Differential-algebraic equations (DAEs) arise naturally as a result of equation-based object-oriented modeling. In many cases, these models contain unknown parameters that have to be estimated using experimental data. However, often the system is subject to unknown disturbances which, if not taken into account in the estimation, can severely affect the model's accuracy. For non-linear state-space models, particle filter methods have been developed to tackle this issue. Unfortunately, applying such methods to non-linear DAEs requires a transformation into a state-space form, which is particularly difficult to obtain for models with process disturbances. In this paper, we propose a simulation-based prediction error method that can be used for non-linear DAEs where disturbances are modeled as continuous-time stochastic processes. To the authors' best knowledge, there are no general methods successfully dealing with parameter estimation for this type of model. One of the challenges in particle filtering  methods are random variations in the minimized cost function due to the nature of the algorithm. In our approach, a similar phenomenon occurs and we explicitly consider how to sample the underlying continuous process to mitigate this problem. The method is illustrated numerically on a pendulum example. The results suggest that the method is able to deliver consistent estimates.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2021
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
Keywords
Nonlinear Identification; Process Disturbance; Differential-Algebraic Equations; Parameter Estimation; Simulated PEM
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-292187 (URN)10.1109/CDC45484.2021.9683787 (DOI)000781990302018 ()2-s2.0-85126001444 (Scopus ID)
Conference
The 60th IEEE conference on Decision and Control (CDC), Dec. 13-17, 2021, Austin, Texas, USA
Funder
Swedish Research Council, 2019-04956 and 2016-06079 (the research environment NewLEADS)Swedish Foundation for Strategic Research, FFL15-0032
Note

Financially also supported by SRA ICT TNG (Digital Futures) and KTH.

Part of proceedings ISBN 978-1-6654-3659-5

QC 20210326

QC 20220705

Available from: 2021-03-26 Created: 2021-03-26 Last updated: 2022-07-05Bibliographically approved
Abdalmoaty, M. & Hjalmarsson, H. (2020). Identification of Stochastic Nonlinear Models Using Optimal Estimating Functions. Automatica, 119, Article ID 109055.
Open this publication in new window or tab >>Identification of Stochastic Nonlinear Models Using Optimal Estimating Functions
2020 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 119, article id 109055Article in journal (Refereed) Published
Abstract [en]

The first part of the paper examines the asymptotic properties of linear prediction error method estimators, which were recently suggested for the identification of nonlinear stochastic dynamical models. It is shown that their accuracy depends not only on the shape of the unknown distribution of the data, but also on how the model is parameterized. Therefore, it is not obvious in general which linear prediction error method should be preferred. In the second part, the estimating functions approach is introduced and used to construct estimators that are asymptotically optimal with respect to a specific class of estimators. These estimators rely on a partial probabilistic parametric models, and therefore neither require the computations of the likelihood function nor any marginalization integrals. The convergence and consistency of the proposed estimators are established under standard regularity and identifiability assumptions akin to those of prediction error methods. The paper is concluded by several numerical simulation examples.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
System identication; Parameter Estimation; Stochastic systems; Nonlinear models; Prediction error methods.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-266779 (URN)10.1016/j.automatica.2020.109055 (DOI)000551496400014 ()2-s2.0-85085269394 (Scopus ID)
Funder
Swedish Research Council, 2015-05285Swedish Research Council, 2016-06079
Note

QC 20200528

Available from: 2020-01-21 Created: 2020-01-21 Last updated: 2022-06-26Bibliographically approved
Abdalmoaty, M., Hjalmarsson, H. & Wahlberg, B. (2020). The Gaussian MLE versus the Optimally weighted LSE. IEEE signal processing magazine (Print), 37(6), 195-199
Open this publication in new window or tab >>The Gaussian MLE versus the Optimally weighted LSE
2020 (English)In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 37, no 6, p. 195-199Article in journal (Refereed) Published
Abstract [en]

In this note, we derive and compare the asymptotic covariance matrices of two parametric estimators: the Gaussian Maximum Likelihood Estimator (MLE), and the optimally weighted Least-Squares Estimator (LSE). We assume a general model parameterization where the model's mean and variance are jointly parameterized, and consider Gaussian and non-Gaussian data distributions.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2020
Keywords
Gaussian MLE; optimally weighted LSE; least squares; optimal weighting; semi-parametric models; parameter estimation; system identification
National Category
Signal Processing Control Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-273764 (URN)10.1109/MSP.2020.3019236 (DOI)000587684700018 ()2-s2.0-85096226071 (Scopus ID)
Funder
Swedish Research Council, 2016-06079 (NewLEADS), 2015-05285, and 2019-04956
Note

QC 20200529

Available from: 2020-05-28 Created: 2020-05-28 Last updated: 2022-06-26Bibliographically approved
Rodrigues, D., Abdalmoaty, M. & Hjalmarsson, H. (2020). Toward tractable global solutions to bayesian point estimation problems via sparse sum-of-squares relaxations. In: Proceedings American Control Conference, ACC 2020: . Paper presented at 2020 American Control Conference, ACC 2020, Denver, CO, USA, July 1-3, 2020 (pp. 1501-1506). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Toward tractable global solutions to bayesian point estimation problems via sparse sum-of-squares relaxations
2020 (English)In: Proceedings American Control Conference, ACC 2020, Institute of Electrical and Electronics Engineers (IEEE) , 2020, p. 1501-1506Conference paper, Published paper (Refereed)
Abstract [en]

Bayesian point estimation is commonly used for system identification owing to its good properties for small sample sizes. Although this type of estimator is usually non-parametric, Bayes estimates can also be obtained for rational parametric models, which is often of interest. However, as in maximum-likelihood methods, the Bayes estimate is typically computed via local numerical optimization that requires good initialization and cannot guarantee global optimality. In this contribution, we propose a computationally tractable method that computes the Bayesian parameter estimates with posterior certification of global optimality via sum-of-squares polynomials and sparse semidefinite relaxations. It is shown that the method is applicable to certain discrete-time linear models, which takes advantage of the rational structure of these models and the sparsity in the Bayesian parameter estimation problem. The method is illustrated on a simulation model of a resonant system that is difficult to handle when the sample size is small.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2020
Series
Proceedings of the American Control Conference, ISSN 0743-1619
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-292022 (URN)10.23919/ACC45564.2020.9147484 (DOI)000618079801080 ()2-s2.0-85089592745 (Scopus ID)
Conference
2020 American Control Conference, ACC 2020, Denver, CO, USA, July 1-3, 2020
Note

QC 20210324

Available from: 2021-03-24 Created: 2021-03-24 Last updated: 2022-06-25Bibliographically 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: 2022-06-26Bibliographically approved
Rodrigues, D., Abdalmoaty, M. & Hjalmarsson, H. (2019). Toward Tractable Global Solutions to Maximum-Likelihood Estimation Problems via Sparse Sum-of-Squares Relaxations. In: 2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC): . Paper presented at 58th IEEE Conference on Decision and Control (CDC), DEC 11-13, 2019, Nice, FRANCE (pp. 3184-3189). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Toward Tractable Global Solutions to Maximum-Likelihood Estimation Problems via Sparse Sum-of-Squares Relaxations
2019 (English)In: 2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC), Institute of Electrical and Electronics Engineers (IEEE) , 2019, p. 3184-3189Conference paper, Published paper (Refereed)
Abstract [en]

In system identification, the maximum-likelihood method is typically used for parameter estimation owing to a number of optimal statistical properties. However, in many cases, the likelihood function is nonconvex. The solutions are usually obtained by local numerical optimization algorithms that require good initialization and cannot guarantee global optimality. This paper proposes a computationally tractable method that computes the maximum-likelihood parameter estimates with posterior certification of global optimality via the concept of sum-of-squares polynomials and sparse semidefinite relaxations. It is shown that the method can be applied to certain classes of discrete-time linear models. This is achieved by taking advantage of the rational structure of these models and the sparsity in the maximum-likelihood parameter estimation problem. The method is illustrated on a simulation model of a resonant mechanical system where standard methods struggle.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-281198 (URN)10.1109/cdc40024.2019.9029890 (DOI)000560779002150 ()2-s2.0-85082474440 (Scopus ID)
Conference
58th IEEE Conference on Decision and Control (CDC), DEC 11-13, 2019, Nice, FRANCE
Note

Part of ISBN 978-1-7281-1398-2

QC 20201019

Available from: 2020-10-19 Created: 2020-10-19 Last updated: 2024-03-11Bibliographically approved
Abdalmoaty, M. & Hjalmarsson, H. (2018). Application of a Linear PEM Estimator to a Stochastic Wiener-Hammerstein Benchmark Problem. In: 18th IFAC Symposium on System Identification: . Paper presented at 18th IFAC Symposium on System Identification, July 9-11, 2018. Stockholm, Sweden.
Open this publication in new window or tab >>Application of a Linear PEM Estimator to a Stochastic Wiener-Hammerstein Benchmark Problem
2018 (English)In: 18th IFAC Symposium on System Identification, 2018Conference paper, Published paper (Refereed)
Abstract [en]

The estimation problem of stochastic Wiener-Hammerstein models is recognized to be challenging, mainly due to the analytical intractability of the likelihood function. In this contribution, we apply a computationally attractive prediction error method estimator to a real-data stochastic Wiener-Hammerstein benchmark problem. The estimator is defined using a deterministic predictor that is nonlinear in the input. The prediction error method results in tractable expressions, and Monte Carlo approximations are not necessary. This allows us to tackle several issues considered challenging from the perspective of the current mainstream approach. Under mild conditions, the estimator can be shown to be consistent and asymptotically normal. The results of the method applied to the benchmark data are presentedand discussed.

Series
IFAC-PapersOnLine
Keywords
Nonlinear system identication, Stochastic systems, Wiener-Hammerstein, Benchmark problem.
National Category
Control Engineering Signal Processing
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-233635 (URN)
Conference
18th IFAC Symposium on System Identification, July 9-11, 2018. Stockholm, Sweden
Funder
Swedish Research Council, 2015-05285Swedish Research Council, 2016-06079EU, European Research Council, 267381
Note

QC 20180828

Available from: 2018-08-27 Created: 2018-08-27 Last updated: 2022-06-26Bibliographically approved
Abdalmoaty, M. . & Hjalmarsson, H. (2018). Application of a Linear PEM Estimator to a Stochastic Wiener-Hammerstein Benchmark Problem⁎. IFAC-PapersOnLine, 51(15), 784-789
Open this publication in new window or tab >>Application of a Linear PEM Estimator to a Stochastic Wiener-Hammerstein Benchmark Problem⁎
2018 (English)In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 15, p. 784-789Article in journal (Refereed) Published
Abstract [en]

The estimation problem of stochastic Wiener-Hammerstein models is recognized to be challenging, mainly due to the analytical intractability of the likelihood function. In this contribution, we apply a computationally attractive prediction error method estimator to a real-data stochastic Wiener-Hammerstein benchmark problem. The estimator is defined using a deterministic predictor that is nonlinear in the input. The prediction error method results in tractable expressions, and Monte Carlo approximations are not necessary. This allows us to tackle several issues considered challenging from the perspective of the current mainstream approach. Under mild conditions, the estimator can be shown to be consistent and asymptotically normal. The results of the method applied to the benchmark data are presented and discussed.

Place, publisher, year, edition, pages
Elsevier B.V., 2018
Keywords
Benchmark problem, Nonlinear systems, Stochastic systems, System identification, Wiener-Hammerstein, Error analysis, Identification (control systems), Monte Carlo methods, Stochastic models, Bench-mark problems, Benchmark data, Estimation problem, Likelihood functions, Monte-carlo approximations, Prediction error method, Wiener-hammerstein models, Benchmarking
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-247494 (URN)10.1016/j.ifacol.2018.09.135 (DOI)000446599200133 ()2-s2.0-85054433381 (Scopus ID)
Note

QC 20190403

Available from: 2019-04-03 Created: 2019-04-03 Last updated: 2022-09-15Bibliographically approved
Abdalmoaty, M. & Hjalmarsson, H. (2018). Consistent Estimators of Stochastic MIMO Wiener Models based on Suboptimal Predictors. In: : . Paper presented at 57th IEEE Conference on Decision and Control, 17-19 Dec. 2018, Miami Beach, FL, USA. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Consistent Estimators of Stochastic MIMO Wiener Models based on Suboptimal Predictors
2018 (English)Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
Nonlinear system identification, Multiple-inputs multiple outputs, Wiener Model, Stochastic System, Consistency, Prediction Error Method
National Category
Control Engineering Signal Processing
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-233826 (URN)10.1109/CDC.2018.8618926 (DOI)000458114803091 ()2-s2.0-85062175030 (Scopus ID)
Conference
57th IEEE Conference on Decision and Control, 17-19 Dec. 2018, Miami Beach, FL, USA
Funder
Swedish Research Council, 2015-05285; 2016-06079
Note

QC 20180904

Available from: 2018-08-29 Created: 2018-08-29 Last updated: 2022-06-26Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-5474-7060

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