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1. Abdalmoaty, Mohamed PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_0_j_idt584",{id:"formSmash:items:resultList:0:j_idt584",widgetVar:"widget_formSmash_items_resultList_0_j_idt584",onLabel:"Abdalmoaty, Mohamed ",offLabel:"Abdalmoaty, Mohamed ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, School of Electrical Engineering (EES), Automatic Control.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:0:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:0:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Learning Stochastic Nonlinear Dynamical Systems Using Non-stationary Linear Predictors2017Licentiate thesis, monograph (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_0_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:0:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_0_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The estimation problem of stochastic nonlinear parametric models is recognized to be very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the maximum likelihood estimator and the optimal mean-square error predictor using Monte Carlo methods. Albeit asymptotically optimal, these methods come with several computational challenges and fundamental limitations.

The contributions of this thesis can be divided into two main parts. In the first part, approximate solutions to the maximum likelihood problem are explored. Both analytical and numerical approaches, based on the expectation-maximization algorithm and the quasi-Newton algorithm, are considered. While analytic approximations are difficult to analyze, asymptotic guarantees can be established for methods based on Monte Carlo approximations. Yet, Monte Carlo methods come with their own computational difficulties; sampling in high-dimensional spaces requires an efficient proposal distribution to reduce the number of required samples to a reasonable value.

In the second part, relatively simple prediction error method estimators are proposed. They are based on non-stationary one-step ahead predictors which are linear in the observed outputs, but are nonlinear in the (assumed known) input. These predictors rely only on the first two moments of the model and the computation of the likelihood function is not required. Consequently, the resulting estimators are defined via analytically tractable objective functions in several relevant cases. It is shown that, under mild assumptions, the estimators are consistent and asymptotically normal. In cases where the first two moments are analytically intractable due to the complexity of the model, it is possible to resort to vanilla Monte Carlo approximations. Several numerical examples demonstrate a good performance of the suggested estimators in several cases that are usually considered challenging.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:0:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 2. Abdalmoaty, Mohamed R. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_1_j_idt584",{id:"formSmash:items:resultList:1:j_idt584",widgetVar:"widget_formSmash_items_resultList_1_j_idt584",onLabel:"Abdalmoaty, Mohamed R. ",offLabel:"Abdalmoaty, Mohamed R. ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_1_j_idt587",{id:"formSmash:items:resultList:1:j_idt587",widgetVar:"widget_formSmash_items_resultList_1_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, School of Electrical Engineering (EES), Automatic Control.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:1:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Hjalmarsson, HåkanKTH, School of Electrical Engineering (EES), Automatic Control.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:1:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Application of a Linear PEM Estimator to a Stochastic Wiener-Hammerstein Benchmark Problem2018In: 18th IFAC Symposium on System Identification, 2018Conference paper (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_1_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:1:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_1_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); 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.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:1:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 3. Abdalmoaty, Mohamed R. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_2_j_idt584",{id:"formSmash:items:resultList:2:j_idt584",widgetVar:"widget_formSmash_items_resultList_2_j_idt584",onLabel:"Abdalmoaty, Mohamed R. ",offLabel:"Abdalmoaty, Mohamed R. ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_2_j_idt587",{id:"formSmash:items:resultList:2:j_idt587",widgetVar:"widget_formSmash_items_resultList_2_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:2:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Hjalmarsson, HåkanKTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:2:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Application of a Linear PEM Estimator to a Stochastic Wiener-Hammerstein Benchmark Problem⁎2018In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 15, p. 784-789Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_2_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:2:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_2_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); 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.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:2:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 4. Abdalmoaty, Mohamed R. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_3_j_idt584",{id:"formSmash:items:resultList:3:j_idt584",widgetVar:"widget_formSmash_items_resultList_3_j_idt584",onLabel:"Abdalmoaty, Mohamed R. ",offLabel:"Abdalmoaty, Mohamed R. ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_3_j_idt587",{id:"formSmash:items:resultList:3:j_idt587",widgetVar:"widget_formSmash_items_resultList_3_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:3:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Hjalmarsson, HåkanKTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:3:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Consistent Estimators of Stochastic MIMO Wiener Models based on Suboptimal Predictors2018Conference paper (Refereed)5. Abdalmoaty, Mohamed R. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_4_j_idt584",{id:"formSmash:items:resultList:4:j_idt584",widgetVar:"widget_formSmash_items_resultList_4_j_idt584",onLabel:"Abdalmoaty, Mohamed R. ",offLabel:"Abdalmoaty, Mohamed R. ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_4_j_idt587",{id:"formSmash:items:resultList:4:j_idt587",widgetVar:"widget_formSmash_items_resultList_4_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:4:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Hjalmarsson, HåkanKTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:4:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Linear Prediction Error Methods for Stochastic Nonlinear Models2019In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 105, p. 49-63Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_4_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:4:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_4_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); 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.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:4:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); The full text will be freely available from 2021-04-01 16:05$(function(){PrimeFaces.cw("Tooltip","widget_formSmash_items_resultList_4_j_idt853_0_j_idt856",{id:"formSmash:items:resultList:4:j_idt853:0:j_idt856",widgetVar:"widget_formSmash_items_resultList_4_j_idt853_0_j_idt856",showEffect:"fade",hideEffect:"fade",target:"formSmash:items:resultList:4:j_idt853:0:fullTextSvg"});}); 6. Abdalmoaty, Mohamed R. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_5_j_idt584",{id:"formSmash:items:resultList:5:j_idt584",widgetVar:"widget_formSmash_items_resultList_5_j_idt584",onLabel:"Abdalmoaty, Mohamed R. ",offLabel:"Abdalmoaty, Mohamed R. ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_5_j_idt587",{id:"formSmash:items:resultList:5:j_idt587",widgetVar:"widget_formSmash_items_resultList_5_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:5:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Rojas, Cristian R.KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.Hjalmarsson, HåkanKTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:5:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Identication of a Class of Nonlinear Dynamical Networks2018Conference paper (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_5_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:5:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_5_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); 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.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:5:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 7. Abdalmoaty, Mohamed R. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_6_j_idt584",{id:"formSmash:items:resultList:6:j_idt584",widgetVar:"widget_formSmash_items_resultList_6_j_idt584",onLabel:"Abdalmoaty, Mohamed R. ",offLabel:"Abdalmoaty, Mohamed R. ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_6_j_idt587",{id:"formSmash:items:resultList:6:j_idt587",widgetVar:"widget_formSmash_items_resultList_6_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:6:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Rojas, Cristian R.KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.Hjalmarsson, HåkanKTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:6:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Identification of a Class of Nonlinear Dynamical Networks⁎2018In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 15, p. 868-873Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_6_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:6:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_6_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); 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.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:6:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 8. Abdalmoaty, Mohamed PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_7_j_idt584",{id:"formSmash:items:resultList:7:j_idt584",widgetVar:"widget_formSmash_items_resultList_7_j_idt584",onLabel:"Abdalmoaty, Mohamed ",offLabel:"Abdalmoaty, Mohamed ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_7_j_idt587",{id:"formSmash:items:resultList:7:j_idt587",widgetVar:"widget_formSmash_items_resultList_7_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, School of Electrical Engineering (EES), Automatic Control.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:7:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Henrion, D.Rodrigues, L.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:7:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Measures and LMIs for optimal control of piecewise-affine systems2013In: 2013 European Control Conference, ECC 2013, IEEE, 2013, p. 3173-3178, article id 6669627Conference paper (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_7_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:7:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_7_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); 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.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:7:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 9. Abdalmoaty, Mohamed PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_8_j_idt584",{id:"formSmash:items:resultList:8:j_idt584",widgetVar:"widget_formSmash_items_resultList_8_j_idt584",onLabel:"Abdalmoaty, Mohamed ",offLabel:"Abdalmoaty, Mohamed ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_8_j_idt587",{id:"formSmash:items:resultList:8:j_idt587",widgetVar:"widget_formSmash_items_resultList_8_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:8:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Hjalmarsson, HåkanKTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:8:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); A Simulated Maximum Likelihood Method for Estimation of Stochastic Wiener Systems2016In: 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 (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_8_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:8:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_8_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); 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.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:8:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 10. Abdalmoaty, Mohamed PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_9_j_idt584",{id:"formSmash:items:resultList:9:j_idt584",widgetVar:"widget_formSmash_items_resultList_9_j_idt584",onLabel:"Abdalmoaty, Mohamed ",offLabel:"Abdalmoaty, Mohamed ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_9_j_idt587",{id:"formSmash:items:resultList:9:j_idt587",widgetVar:"widget_formSmash_items_resultList_9_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, School of Electrical Engineering (EES), Automatic Control.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:9:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Hjalmarsson, HåkanKTH, School of Electrical Engineering (EES), Automatic Control.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:9:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); On Re-Weighting, Regularization Selection, and Transient in Nuclear Norm Based Identification2015Conference paper (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_9_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:9:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_9_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); 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.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:9:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 11. Abdalmoaty, Mohamed PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_10_j_idt584",{id:"formSmash:items:resultList:10:j_idt584",widgetVar:"widget_formSmash_items_resultList_10_j_idt584",onLabel:"Abdalmoaty, Mohamed ",offLabel:"Abdalmoaty, Mohamed ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_10_j_idt587",{id:"formSmash:items:resultList:10:j_idt587",widgetVar:"widget_formSmash_items_resultList_10_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH Royal Institute of Technology.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:10:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Hjalmarsson, HåkanKTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:10:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Simulated Pseudo Maximum Likelihood Identification of Nonlinear Models2017In: The 20th IFAC World Congress, Elsevier, 2017, Vol. 50, p. 14058-14063Conference paper (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_10_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:10:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_10_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Nonlinear stochastic parametric models are widely used in various fields. However, for these models, the problem of maximum likelihood identification is very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the analytically intractable likelihood function and compute either the maximum likelihood or a Bayesian estimator. These methods, albeit asymptotically optimal, are computationally expensive. In this contribution, we present a simulation-based pseudo likelihood estimator for nonlinear stochastic models. It relies only on the first two moments of the model, which are easy to approximate using Monte-Carlo simulations on the model. The resulting estimator is consistent and asymptotically normal. We show that the pseudo maximum likelihood estimator, based on a multivariate normal family, solves a prediction error minimization problem using a parameterized norm and an implicit linear predictor. In the light of this interpretation, we compare with the predictor defined by an ensemble Kalman filter. Although not identical, simulations indicate a close relationship. The performance of the simulated pseudo maximum likelihood method is illustrated in three examples. They include a challenging state-space model of dimension 100 with one output and 2 unknown parameters, as well as an application-motivated model with 5 states, 2 outputs and 5 unknown parameters.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:10:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 12. Rasheed-Hilmy Abdalmoaty, Mohamed PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_11_j_idt584",{id:"formSmash:items:resultList:11:j_idt584",widgetVar:"widget_formSmash_items_resultList_11_j_idt584",onLabel:"Rasheed-Hilmy Abdalmoaty, Mohamed ",offLabel:"Rasheed-Hilmy Abdalmoaty, Mohamed ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Luleå University of Technology.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:11:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:11:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Measures and LMIs for optimal control of piecewise-affine dynamical systems: Systematic feedback synthesis in continuous-time2012Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_11_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:11:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_11_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The project considers the class of deterministic continuous-time optimal control problems (OCPs) with piecewise-affine (PWA) vector fields and polynomial data. The OCP is relaxed as an infinite-dimensional linear program (LP) over space of occupation measures. The LP is then written as a particular instance of the generalized moment problem which is then approached by an asymptotically converging hierarchy of linear matrix inequality (LMI) relaxations. The relaxed dual of the original LP gives a polynomial approximation of the value function along optimal trajectories. Based on this polynomial approximation, a novel 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 stabilizing suboptimal state feedback law that drives the system relatively close to the optimal trajectories and respects the given constraints.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:11:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500});

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