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Abdalmoaty, Mohamed R.ORCID iD iconorcid.org/0000-0001-5474-7060
Alternative names
Biography [eng]

I'm a PhD student at the Automatic Control 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 (9 of 9) Show all publications
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
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: 2018-08-29Bibliographically 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: 2019-05-20Bibliographically 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: 2019-04-04Bibliographically approved
Abdalmoaty, M. ., Rojas, C. R. & Hjalmarsson, H. (2018). Identication of a Class of Nonlinear Dynamical Networks. In: : . Paper presented at 18th IFAC Symposium on System Identification.
Open this publication in new window or tab >>Identication of a Class of Nonlinear Dynamical Networks
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Identifcation of dynamic networks has attracted considerable interest recently. So far the main focus has been on linear time-invariant networks. Meanwhile, most real-life systems exhibit nonlinear behaviors; consider, for example, two stochastic linear time-invariant systems connected in series, each of which has a nonlinearity at its output. The estimation problem in this case is recognized to be challenging, due to the analytical intractability of both the likelihood function and the optimal one-step ahead predictors of the measured nodes. In this contribution, we introduce a relatively simple prediction error method that may be used for the estimation of nonlinear dynamical networks. The estimator is defined using a deterministic predictor that is nonlinear in the known signals. The estimation problem can be defined using closed-form analytical expressions in several non-trivial cases, and Monte Carlo approximations are not necessarily required. We show, that this is the case for some block-oriented networks with no feedback loops and where all the nonlinear modules are polynomials. Consequently, the proposed method can be applied in situations considered challenging by current approaches. The performance of the estimation method is illustrated on a numerical simulation example.

Series
IFAC-PapersOnLine
Keywords
System Identication, Dynamical Networks, Stochastic Systems, Block-Oriented Models, Prediction Error Method.
National Category
Signal Processing Control Engineering
Research subject
Electrical Engineering; Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-233639 (URN)
Conference
18th IFAC Symposium on System Identification
Funder
EU, European Research Council, 267381Swedish Research Council, 2015-05285Swedish Research Council, 2016-06079
Note

QC 20180829

Available from: 2018-08-27 Created: 2018-08-27 Last updated: 2018-08-29Bibliographically approved
Abdalmoaty, M. ., Rojas, C. R. & Hjalmarsson, H. (2018). Identification of a Class of Nonlinear Dynamical Networks⁎. IFAC-PapersOnLine, 51(15), 868-873
Open this publication in new window or tab >>Identification of a Class of Nonlinear Dynamical Networks⁎
2018 (English)In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 15, p. 868-873Article in journal (Refereed) Published
Abstract [en]

Identification of dynamic networks has attracted considerable interest recently. So far the main focus has been on linear time-invariant networks. Meanwhile, most real-life systems exhibit nonlinear behaviors; consider, for example, two stochastic linear time-invariant systems connected in series, each of which has a nonlinearity at its output. The estimation problem in this case is recognized to be challenging, due to the analytical intractability of both the likelihood function and the optimal one-step ahead predictors of the measured nodes. In this contribution, we introduce a relatively simple prediction error method that may be used for the estimation of nonlinear dynamical networks. The estimator is defined using a deterministic predictor that is nonlinear in the known signals. The estimation problem can be defined using closed-form analytical expressions in several non-trivial cases, and Monte Carlo approximations are not necessarily required. We show, that this is the case for some block-oriented networks with no feedback loops and where all the nonlinear modules are polynomials. Consequently, the proposed method can be applied in situations considered challenging by current approaches. The performance of the estimation method is illustrated on a numerical simulation example.

Place, publisher, year, edition, pages
Elsevier B.V., 2018
Keywords
Block-Oriented Models, Dynamical Networks, Prediction Error Method, Stochastic Systems, System Identification, Estimation, Identification (control systems), Invariance, Linear systems, Numerical methods, Real time systems, Stochastic models, Time varying control systems, Analytical expressions, Block oriented model, Likelihood functions, Linear time invariant networks, Linear time invariant systems, Monte-carlo approximations, Nonlinear analysis
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-247495 (URN)10.1016/j.ifacol.2018.09.113 (DOI)000446599200147 ()2-s2.0-85054394061 (Scopus ID)
Note

QC 20190403

Available from: 2019-04-03 Created: 2019-04-03 Last updated: 2019-05-20Bibliographically approved
Abdalmoaty, M. & Hjalmarsson, H. (2016). A Simulated Maximum Likelihood Method for Estimation of Stochastic Wiener Systems. In: 2016 IEEE 55th Conference on Decision and Control, CDC 2016: . Paper presented at 55th IEEE Conference on Decision and Control, CDC 2016, ARIA Resort and Casino, Las Vegas, United States, 12 December 2016 through 14 December 2016 (pp. 3060-3065). Institute of Electrical and Electronics Engineers (IEEE), Article ID 7798727.
Open this publication in new window or tab >>A Simulated Maximum Likelihood Method for Estimation of Stochastic Wiener Systems
2016 (English)In: 2016 IEEE 55th Conference on Decision and Control, CDC 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 3060-3065, article id 7798727Conference paper, Published paper (Refereed)
Abstract [en]

This paper introduces a simulation-based method for maximum likelihood estimation of stochastic Wienersystems. It is well known that the likelihood function ofthe observed outputs for the general class of stochasticWiener systems is analytically intractable. However, when the distributions of the process disturbance and the measurement noise are available, the likelihood can be approximated byrunning a Monte-Carlo simulation on the model. We suggest the use of Laplace importance sampling techniques for the likelihood approximation. The algorithm is tested on a simple first order linear example which is excited only by the process disturbance. Further, we demonstrate the algorithm on an FIR system with cubic nonlinearity. The performance of the algorithm is compared to the maximum likelihood method and other recent techniques.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Signal Processing
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-186218 (URN)10.1109/CDC.2016.7798727 (DOI)000400048103040 ()2-s2.0-85010790149 (Scopus ID)978-1-5090-1837-6 (ISBN)
Conference
55th IEEE Conference on Decision and Control, CDC 2016, ARIA Resort and Casino, Las Vegas, United States, 12 December 2016 through 14 December 2016
Funder
Swedish Research Council, 2015-05285EU, European Research Council, 67381
Note

QC 20170614

Available from: 2016-05-04 Created: 2016-05-04 Last updated: 2017-12-04Bibliographically approved
Abdalmoaty, M. & Hjalmarsson, H. (2015). On Re-Weighting, Regularization Selection, and Transient in Nuclear Norm Based Identification. In: : . Paper presented at 17th IFAC Symposium on System Identification (pp. 92-97). Elsevier, 48
Open this publication in new window or tab >>On Re-Weighting, Regularization Selection, and Transient in Nuclear Norm Based Identification
2015 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this contribution, we consider the classical problem of estimating an Output Error model given a set of input-output measurements. First, we develop a regularization method based on the re-weighted nuclear norm heuristic. We show that the re-weighting improves the estimate in terms of better fit. Second, we suggest an implementation method that helps in eliminating the regularization parameters from the problem by introducing a constant based on a validation criterion. Finally, we develop a method for considering the effect of the transient when the initial conditions are unknown. A simple numerical example is used to demonstrate the proposed method in comparison to classical and another recent method based on the nuclear norm heuristic.

Place, publisher, year, edition, pages
Elsevier, 2015
Series
IFAC-PapersOnLine, ISSN 2405-8963 ; 48
National Category
Signal Processing
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-186218 (URN)10.1016/j.ifacol.2015.12.106 (DOI)2-s2.0-84988531107 (Scopus ID)
Conference
17th IFAC Symposium on System Identification
Note

QC 20160512

Available from: 2016-05-04 Created: 2016-05-04 Last updated: 2017-12-04Bibliographically approved
Abdalmoaty, M., Henrion, D. & Rodrigues, L. (2013). Measures and LMIs for optimal control of piecewise-affine systems. In: 2013 European Control Conference, ECC 2013: . Paper presented at 2013 12th European Control Conference, ECC 2013; Zurich; Switzerland; 17 July 2013 through 19 July 2013 (pp. 3173-3178). IEEE, Article ID 6669627.
Open this publication in new window or tab >>Measures and LMIs for optimal control of piecewise-affine systems
2013 (English)In: 2013 European Control Conference, ECC 2013, IEEE, 2013, p. 3173-3178, article id 6669627Conference paper, Published paper (Refereed)
Abstract [en]

This paper considers the class of deterministic continuous-time optimal control problems (OCPs) with piecewise-affine (PWA) vector field, polynomial Lagrangian and semialgebraic input and state constraints. The OCP is first relaxed as an infinite-dimensional linear program (LP) over a space of occupation measures. This LP is then approached by an asymptotically converging hierarchy of linear matrix inequality (LMI) relaxations. The relaxed dual of the original LP returns a polynomial approximation of the value function that solves the Hamilton-Jacobi-Bellman (HJB) equation of the OCP. Based on this polynomial approximation, a suboptimal policy is developed to construct a state feedback in a sample-and-hold manner. The results show that the suboptimal policy succeeds in providing a suboptimal state feedback law that drives the system relatively close to the optimal trajectories and respects the given constraints.

Place, publisher, year, edition, pages
IEEE, 2013
Keywords
Hamilton-Jacobi-Bellman equations, Input and state constraints, Linear matrix inequality (LMI) relaxation, Occupation measure, Optimal control problem, Optimal controls, Optimal trajectories, Piecewise affine systems
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-143131 (URN)10.23919/ECC.2013.6669627 (DOI)000332509703095 ()2-s2.0-84893292748 (Scopus ID)978-3-033-03962-9 (ISBN)
Conference
2013 12th European Control Conference, ECC 2013; Zurich; Switzerland; 17 July 2013 through 19 July 2013
Note

QC 20140318

Available from: 2014-03-18 Created: 2014-03-17 Last updated: 2018-09-03Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-5474-7060

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