Dissertations / Theses on the topic 'Estimation non-Asymptotique et robuste'
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Beltaief, Slim. "Algorithmes optimaux de traitement de données pour des systèmes complexes d'information et télécommunication dans un environnement incertain." Thesis, Normandie, 2017. http://www.theses.fr/2017NORMR056/document.
Full textThis thesis is devoted to the problem of non parametric estimation for continuous-time regression models. We consider the problem of estimating an unknown periodoc function S. This estimation is based on observations generated by a stochastic process; these observations may be in continuous or discrete time. To this end, we construct a series of estimators by projection and thus we approximate the unknown function S by a finite Fourier series. In this thesis we consider the estimation problem in the adaptive setting, i.e. in situation when the regularity of the fonction S is unknown. In this way, we develop a new adaptive method based on the model selection procedure proposed by Konev and Pergamenshchikov (2012). Firstly, this procedure give us a family of estimators, then we choose the best possible one by minimizing a cost function. We give also an oracle inequality for the risk of our estimators and we give the minimax convergence rate
Harti, Mostafa. "Estimation robuste sous un modèle de contamination non symétrique et M-estimateur multidimensionnel." Nancy 1, 1986. http://www.theses.fr/1986NAN10063.
Full textHarti, Mostafa. "Estimation robuste sous un modèle de contamination non symétrique et M-estimateur multidimensionnel." Grenoble 2 : ANRT, 1986. http://catalogue.bnf.fr/ark:/12148/cb375982387.
Full textWang, Zhibo. "Estimations non-asymptotiques et robustes basées sur des fonctions modulatrices pour les systèmes d'ordre fractionnaire." Electronic Thesis or Diss., Bourges, INSA Centre Val de Loire, 2023. http://www.theses.fr/2023ISAB0003.
Full textThis thesis develops the modulating functions method for non-asymptotic and robust estimations for fractional-order nonlinear systems, fractional-order linear systems with accelerations as output, and fractional-order time-delay systems. The designed estimators are provided in terms of algebraic integral formulas, which ensure non-asymptotic convergence. As an essential feature of the designed estimation algorithms, noisy output measurements are only involved in integral terms, which endows the estimators with robustness against corrupting noises. First, for fractional-order nonlinear systems which are partially unknown, fractional derivative estimation of the pseudo-state is addressed via the modulating functions method. Thanks to the additive index law of fractional derivatives, the estimation is decomposed into the fractional derivatives estimation of the output and the fractional initial values estimation. Meanwhile, the unknown part is fitted via an innovative sliding window strategy. Second, for fractional-order linear systems with accelerations as output, fractional integral estimation of the acceleration is firstly considered for fractional-order mechanical vibration systems, where only noisy acceleration measurements are available. Based on the existing numerical approaches addressing the proper fractional integrals of accelerations, our attention is primarily restricted to estimating the unknown initial values using the modulating functions method. On this basis, the result is further generalized to more general fractional-order linear systems. In particular, the behaviour of fractional derivatives at zero is studied for absolutely continuous functions, which is quite different from that of integer order. Third, for fractional-order time-delay systems, pseudo-state estimation is studied by designing a fractional-order auxiliary modulating dynamical system, which provides a more general framework for generating the required modulating functions. With the introduction of the delay operator and the bicausal generalized change of coordinates, the pseudo-state estimation of the considered system can be reduced to that of the corresponding observer normal form. In contrast to the previous work, the presented scheme enables direct estimation for the pseudo-state rather than estimating the fractional derivatives of the output and a bunch of fractional initial values. In addition, the efficiency and robustness of the proposed estimators are verified by numerical simulations in this thesis. Finally, a summary of this work and an insight into future work were drawn
Gomez-Quintero, Claudia. "Modélisation et estimation robuste pour un procédé boues activées en alternance de phases." Toulouse 3, 2002. http://www.theses.fr/2002TOU30001.
Full textLiu, Jie. "State Estimation for Linear Singular and Nonlinear Dynamical Systems Based on Observable Canonical Forms." Electronic Thesis or Diss., Bourges, INSA Centre Val de Loire, 2024. http://www.theses.fr/2024ISAB0002.
Full textThis thesis aims, on the one hand, to design estimators for linear singular systems usingthemethod of modulation functions. On the other hand, it aims to develop observersfor a class of nonlinear dynamical systems using the method of canonical formsof observers. For singular systems, the designed estimators are presented in the formof algebraic integral equations, ensuring non-asymptotic convergence. An essentialcharacteristic of the designed estimation algorithms is that noisy measurements of theoutputs are only involved in integral terms, thereby imparting robustness to the estimatorsagainst perturbing noises. For nonlinear systems, the main design idea is totransform the proposed systems into a simplified form that accommodates existingobservers such as the high-gain observer and the sliding-mode observer. This simpleformis called auxiliary output depending observable canonical form.For the linear singular systems, we transform the considered system into a formsimilar to the Brunovsky’s observable canonical form with the injection of the inputs’and outputs’ derivatives. First, for linear singular systems with single input and singleoutput, the observability condition is proposed. The system’s input-output differentialequation is derived based on the Brunovsky’s observable canonical form. Algebraicformulas with a sliding integration window are obtained for the variables in differentsituations without knowing the system’s initial condition. Second, for linear singular systemswith multiple input and multiple output, an innovative nonasymptotic and robust estimation method based on the observable canonical form by means of a set of auxiliary modulating dynamical systems is introduced. The latter auxiliary systems are given by the controllable observable canonical with zero initial conditions. The proposed method is applied to estimate the states and the output’s derivatives for linear singular system in noisy environment. By introducing a set of auxiliary modulating dynamical systems which provides a more general framework for generating the requiredmodulating functions, algebraic integral formulas are obtained both for the state variables and the output’s derivatives. After giving the solutions of the required auxiliary systems, error analysis in discrete noisy case is addressed, where the provided noise error bound can be used to select design parameters.For the nonlinear dynamical systems, we propose a family of "ready to wear" nonlineardynamical systemswith multiple outputs that can be transformed into the outputauxiliarydepending observer normal forms which can support the well-known slidingmode observer. For this, by means of the so-called dynamics extension method anda set of changes of coordinates (basic algebraic integral computations), the nonlinearterms are canceled by auxiliary dynamics or replaced by nonlinear functions of themultiple outputs. It is worth mentioning that this procedure is finished in a comprehensible way without resort to the tools of differential geometry, which is user-friendly for those who are not familiar with the computations of Lie brackets. In addition, the efficiency and robustness of the proposed observers are verified by numerical simulations in this thesis. Second, a larger class of "ready to wear" nonlinear dynamicalsystems with multiple inputs and multiple outputs are provided to further extend anddevelop the systems proposed in the first case. In a similar way, by means of the corresponding auxiliary dynamics and a set of changes of coordinates, the provided systems are converted into targeted nonlinear observable canonical forms depending on both the multiple outputs and auxiliary variables. Naturally, this procedure is still completed without resort to geometrical tools. Finally, conclusions are outlined with some perspectives
Romero, Ugalde Héctor Manuel. "Identification de systèmes utilisant les réseaux de neurones : un compromis entre précision, complexité et charge de calculs." Thesis, Paris, ENSAM, 2013. http://www.theses.fr/2013ENAM0001/document.
Full textThis report concerns the research topic of black box nonlinear system identification. In effect, among all the various and numerous techniques developed in this field of research these last decades, it seems still interesting to investigate the neural network approach in complex system model estimation. Even if accurate models have been derived, the main drawbacks of these techniques remain the large number of parameters required and, as a consequence, the important computational cost necessary to obtain the convenient level of the model accuracy desired. Hence, motivated to address these drawbacks, we achieved a complete and efficient system identification methodology providing balanced accuracy, complexity and cost models by proposing, firstly, new neural network structures particularly adapted to a very wide use in practical nonlinear system modeling, secondly, a simple and efficient model reduction technique, and, thirdly, a computational cost reduction procedure. It is important to notice that these last two reduction techniques can be applied to a very large range of neural network architectures under two simple specific assumptions which are not at all restricting. Finally, the last important contribution of this work is to have shown that this estimation phase can be achieved in a robust framework if the quality of identification data compels it. In order to validate the proposed system identification procedure, application examples driven in simulation and on a real process, satisfactorily validated all the contributions of this thesis, confirming all the interest of this work
Sow, Mohamedou. "Développement de modèles non paramétriques et robustes : application à l’analyse du comportement de bivalves et à l’analyse de liaison génétique." Thesis, Bordeaux 1, 2011. http://www.theses.fr/2011BOR14257/document.
Full textThe development of robust and nonparametric approaches for the analysis and statistical treatment of high-dimensional data sets exhibiting high variability, as seen in the environmental and genetic fields, is instrumental. Here, we model complex biological data with application to the analysis of bivalves’ behavior and to linkage analysis. The application of mathematics to the analysis of mollusk bivalves’behavior gave us the possibility to quantify and translate mathematically the animals’behavior in situ, in close or far field. We proposed a nonparametric regression model and compared three nonparametric estimators (recursive or not) of the regressionfunction to optimize the best estimator. We then characterized the biological rhythms, formalized the states of opening, proposed methods able to discriminate the behaviors, used shot-noise analysis to characterize various opening/closing transitory states and developed an original approach for measuring online growth.In genetics, we proposed a more general framework of robust statistics for linkage analysis. We developed estimators robust to distribution assumptions and the presence of outlier observations. We also used a statistical approach where the dependence between random variables is specified through copula theory. Our main results showed the practical interest of these estimators on real data for QTL and eQTL analysis
Ichalal, Dalil. "Estimation et diagnostic de systèmes non linéaires décrits par un modèle de Takagi-Sugeno." Electronic Thesis or Diss., Vandoeuvre-les-Nancy, INPL, 2009. http://www.theses.fr/2009INPL088N.
Full textThis thesis deals with state estimation, fault diagnosis and fault tolerant control of nonlinear systems represented by a Takagi-Sugeno model with unmeasurable premise variables. The problem of state estimation of nonlinear systems with T-S model with unmeasurable premise variable is explored. Algorithms for robust observers synthesis with respect to perturbations, modeling uncertainties and unknown inputs are afterward presented. These algorithms are based on four kinds of observers called proportional, unknown input observers (UIOs), proportional-integral (PI) and multiple-integral (PMI) . The application on model-based diagnosis is studied based on three strategies. The first one uses unknown input observer to decouple some faults and makes the observers insensitive to certain faults. This allows to detect and isolate faults by constructing observers banks. Due to strong structural conditions on designing UIOs decoupling the faults on the state estimation error is not possible. To avoid this problem, the second strategy uses PI and PMI observers in order to estimate simultaneously the state and the faults of the system. The third strategy uses the H8 formalism. This aims to minimize the influence of perturbations and to maximize the effects of faults on the residual signal. An adequate choice of the residual generator parameters allows to detect, to isolate and to estimate the faults affecting the system. Lastly, a fault tolerant control law is proposed by reference trajectory tracking based on the use of PI and PMI observers
Ichalal, Dalil. "Estimation et diagnostic de systèmes non linéaires décrits par un modèle de Takagi-Sugeno." Phd thesis, Institut National Polytechnique de Lorraine - INPL, 2009. http://tel.archives-ouvertes.fr/tel-00454793.
Full textAlcaraz, González Víctor. "Estimation et commande robuste non-linéaires des procédés biologiques de dépollution des eaux usées : application à la digestion anaérobie." Perpignan, 2001. http://www.theses.fr/2001PERP0446.
Full textThis PhD Thèsis is concenred with the development of robust nonlinear estimation and control strategies for the optimisation of biological wastewaters treatment plants in general and with their experimental application on anaerobic digestion processes in particular
Monge, Thierry. "Modélisation et commande multivariable non linéaire robuste des réacteurs chimiques discontinus - application à un procédé industriel." Rouen, 1996. http://www.theses.fr/1996ROUES069.
Full textSchreier, Gerhard. "Estimation de l'état de systèmes linéaires incertains et de systèmes non linéaires." Vandoeuvre-les-Nancy, INPL, 1997. http://www.theses.fr/1997INPL101N.
Full textOlteanu, Severus. "Contribution à l’estimation et au diagnostic robuste des piles à combustibles basse température." Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10115/document.
Full textThe thesis contributes to the observer and diagnosis design for Polymer Electrolyte Membrane Fuel Cells using Takagi-Sugeno theory. There are three research objectives in this thesis. First is focused on modeling, estimation and diagnostics. The dynamic nonlinear model of PEMFCs is proposed, which considers the auxiliary components. In terms of parameter estimation for PEMFCs, a nonlinear approach is developed to design observers based on the nonlinear Takagi-Sugeno model in order to achieve a more robust estimation. The observers can replace the mass flow sensors which results in getting rid of expensive and cumbersome to install instrumentation for measurement of mass flow rates. By using such observers to develop algorithms for diagnosis, the fuel cell stack’s life can be prolonged. A simple method of diagnostic based on PI observer for state and sensor fault detection has been investigated. The second topic on embedding nonlinear algorithms, acts upon the potential of using small scaled embedded systems for complex tasks, thus reducing cost and physical size of the automatic system. More precisely the use of the Takagi-Sugeno approach in embedded applications is investigated. Different solutions for embedded observers have been provided. The last topic was the testing of these embedded solutions for fuel cell system in a Hardware In the Loop architecture, based on the professional software AMESim and Matlab for a Windows operating system. A real Fuel Cell has been used in order to prove the effectiveness of our approach
Orjuela, Rodolfo. "Contribution à l'estimation d'état et au diagnostic des systèmes représentés par des multimodèles." Phd thesis, Institut National Polytechnique de Lorraine - INPL, 2008. http://tel.archives-ouvertes.fr/tel-00359631.
Full textLes travaux présentés dans cette thèse portent sur les problèmes soulevés par l'identification, l'estimation d'état et le diagnostic de systèmes non linéaires représentés à l'aide d'un multimodèle découplé. Ce dernier, composé de sous-modèles qui peuvent être de dimensions différentes, est doté d'un haut degré de généralité et de flexibilité et s'adapte particulièrement bien à la modélisation des systèmes complexes à structure variable. Cette caractéristique le démarque des approches multimodèles plus conventionnelles qui ont recours à des sous-modèles de même dimension.
Après une brève introduction à l'approche multimodèle, le problème de l'estimation paramétrique du multimodèle découplé est abordé. Puis sont présentés des algorithmes de synthèse d'observateurs d'état robustes vis-à-vis des perturbations, des incertitudes paramétriques et des entrées inconnues affectant le système. Ces algorithmes sont élaborés à partir de trois types d'observateurs dits à gain proportionnel, à gain proportionnel-intégral et à gain multi-intégral. Enfin, les différentes phases d'identification, de synthèse d'observateurs et de génération d'indicateurs de défauts sont illustrées au moyen d'un exemple académique de diagnostic du fonctionnement d'un bioréacteur.
Pled, Florent. "Vers une stratégie robuste et efficace pour le contrôle des calculs par éléments finis en ingénierie mécanique." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2012. http://tel.archives-ouvertes.fr/tel-00776633.
Full textCondomines, Jean-Philippe. "Développement d’un estimateur d’état non linéaire embarqué pour le pilotage-guidage robuste d’un micro-drone en milieu complexe." Thesis, Toulouse, ISAE, 2015. http://www.theses.fr/2015ESAE0002.
Full textThis thesis presents the study of an algorithmic solution for state estimation problem of unmanned aerial vehicles, or UAVs. The necessary resort to multiple miniaturized low-cost and low-performance sensors integrated into mini-RPAS, which are obviously subjected to hardspace requirements or electrical power consumption constraints, has led to an important interest to design nonlinear observers for data fusion, unmeasured systems state estimation and/or flight path reconstruction. Exploiting the capabilities of nonlinear observers allows, by generating consolidated signals, to extend the way mini-RPAS can be controlled while enhancing their intrinsic flight handling qualities.That is why numerous recent research works related to RPAS certification and integration into civil airspace deal with the interest of highly robust estimation algorithm. Therefore, the development of reliable and performant aided-INS for many nonlinear dynamic systems is an important research topic and a major concern in the aerospace engineering community. First, we have proposed a novel approach for nonlinear state estimation, named pi-IUKF (Invariant Unscented Kalman Filter), which is based on both invariant filter estimation and UKF theoretical principles. Several research works on nonlinear invariant observers have been led and provide a geometrical-based constructive method for designing filters dedicated to nonlinear state estimation problems while preserving the physical properties and systems symmetries. The general invariant observer guarantees a straightforward form of the nonlinear estimation error dynamics whose properties are remarkable. The developed pi-IUKF estimator suggests a systematic approach to determine all the symmetry-preserving correction terms, associated with a nonlinear state-space representation used for prediction, without requiring any linearization of the differential equations. The exploitation of the UKF principles within the invariant framework has required the definition of a compatibility condition on the observation equations. As a first result, the estimated covariance matrices of the pi-IUKF converge to constant values due to the symmetry-preserving property provided by the nonlinear invariant estimation theory. The designed pi-IUKF method has been successfully applied to some relevant practical problems such as the estimation of Attitude and Heading for aerial vehicles using low-cost AH reference systems (i.e., inertial/magnetic sensors characterized by low performances). In a second part, the developed methodology is used in the case of a mini-RPAS equipped with an aided Inertial Navigation System (INS) which leads to augment the nonlinear state space representation with both velocity and position differential equations. All the measurements are provided on board by a set of low-cost and low-performance sensors (accelerometers, gyrometers, magnetometers, barometer and even Global Positioning System (GPS)). Our designed pi-IUKF estimation algorithm is described and its performances are evaluated by exploiting successfully real flight test data. Indeed, the whole approach has been implemented onboard using a data logger based on the well-known Paparazzi system. The results show promising perspectives and demonstrate that nonlinear state estimation converges on a much bigger set of trajectories than for more traditional approaches
Deremetz, Mathieu. "Contribution à la modélisation et à la commande de robots mobiles autonomes et adaptables en milieux naturels." Thesis, Université Clermont Auvergne (2017-2020), 2018. http://www.theses.fr/2018CLFAC079/document.
Full textThis work is focused on the conceptualization, the modeling and the genericcontrol of mobile robots when moving in off-road contexts and facing slipperyterrains, especially for very accurate tracking and following applications. Thisthesis summarizes the proposed methods and the obtained results to addressthis research issue, first for path following applications (absolute localization)and then for edge and target tracking applications (relative localization). A finalsection of this thesis introduces an adaptive robotic concept and its associatedcontroller allowing the adaptation of the pose (position and orientation) of thechassis with respect to the environment topography.For each application, this thesis introduces a panel of innovative control algorithmsfor controlling skid-steering, two-wheel steering and four-wheel steeringmobile robots. Each algorithm of the panel is described, in this thesis, infour steps : modeling, estimation, control and experiments.The first main contribution of this thesis deals with the slippage estimation.The latter is adaptive and model-based. It also includes the extended kinematicmodeling only or together with the dynamic modeling of the mobile robot toensure a robust estimation of the slippage whatever the speed of the robot, encountereddynamic phenomena or even ground characteristics.The second main contribution deals with the design of a generic control approachfor mobile robots when path following and target tracking. The proposedstrategy is mostly based on a backstepping method and is illustrated inthis thesis via a panel of control laws. When combining this proposed controlapproach with the slippage estimation described above, significant improvedtracking and following performances are obtained (in term of stability, repeatability,accuracy and robustness) whatever the encountered context.All algorithms have been tested and validated through simulations and/orfull-scale experiments, indoor and off-road, with different mobile robots
Orjuela, Rodolfo. "Contribution à l'estimation d'état et au diagnostic des systèmes représentés par des multimodèles." Electronic Thesis or Diss., Vandoeuvre-les-Nancy, INPL, 2008. http://www.theses.fr/2008INPL060N.
Full textThe state estimation of a system, with the help of an observer, is largely used in many practical situations in order to cope with many classic problems arising in control engineering. The observer design needs an exploitable model able to give an accurate description of the dynamic behaviour of the system. However, system modelling and observer design can not easily be accomplished when the dynamic behaviour of the system must be described by non linear models. The multiple model approach can be used to tackle these difficulties. This thesis deals with black box modelling, state estimation and fault diagnosis of nonlinear systems represented by a decoupled multiple model. This kind of multiple model provides a high degree of generality and flexibility in the modelling stage. Indeed, the decoupled multiple model is composed of submodels which dimensions can be different. Thus, this feature is a significant difference between the decoupled multiple model and the classical used multiple model where all the submodels have the same dimension. After a brief introduction to the multiple model approach, the parametric identification problem of a decoupled multiple model is explored. Algorithms for robust observers synthesis with respect to perturbations, modelling uncertainties and unknown inputs are afterwards presented. These algorithms are based on three kinds of observers called proportional, proportional-integral and multiple-integral. Lastly, identification, observers synthesis and fault sensitivity signals generation are illustrated via a simulation example of a bioreactor
Souley, Ali Harouna. "Observateurs robustes d'ordre réduit pour les systèmes linéaires et bilinéaires incertains." Nancy 1, 2002. http://www.theses.fr/2002NAN10013.
Full textIn this dissertation, we proposed robust, reduced order unbiased functional filters for linear and bilinear systems, as well as an unknown inputs residual generator for singular bilinear systems. The functional filtering purpose is not to estimate the whole state vector of a system, but only a linear combination of this one ; the system can be affected by uncertainties. For the linear systems, we treated the continuous and discrete-time cases, whereas only the continuous case was considered in the bilinear one. Necessary and sufficient conditions for the unbiasedness were established through a rank condition. The functional filters we proposed are of minimal order : their order is equal to the dimension of the linear combination of the state which we wish to estimate. As for the residual generator, it is of full order: its order is the same that the dimension of the state vector of the singular bilinear system
Dinh, Ngoc Thach. "Observateur par intervalles et observateur positif." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112335/document.
Full textThis thesis presents new results in the field of state estimation based on the theory of positive systems. It is composed of two separate parts. The first one studies the problem of positive observer design for positive systems. The second one which deals with robust state estimation through the design of interval observers, is at the core of our work.We begin our thesis by proposing the design of a nonlinear positive observer for discrete-time positive time-varying linear systems based on the use of generalized polar coordinates in the positive orthant. For positive systems, a natural requirement is that the observers should provide state estimates that are also non-negative so they can be given a physical meaning at all times. The idea underlying the method is that first, the direction of the true state is correctly estimated in the projective space thanks to the Hilbert metric and then very mild assumptions on the output map allow to reconstruct the norm of the state. The convergence rate can be controlled.Later, the thesis is continued by studying the so-called interval observers for different families of dynamic systems in continuous-time, in discrete-time and also in a context "continuous-discrete" (i.e. a class of continuous-time systems with discrete-time measurements). Interval observers are dynamic extensions giving estimates of the solution of a system in the presence of various type of disturbances through two outputs giving an upper and a lower bound for the solution. Thanks to interval observers, one can construct control laws which stabilize the considered systems
Popescu, Andrei. "Approches de commande pour des objectifs d'estimation : application au courant tunnel et aux processus de lévitation magnétique." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAT062.
Full textThis PhD thesis gathers its main contributions in the field of observers for dynamical systems, originally motivated by applications in MEMS or NEMS (Micro or Nano Electromechanical Systems), with a more particular case related to tunneling current. It also happened to consider experiments with a magnetic levitation system.Contributions of this PhD thesis are of two types, according to its two main parts:1. Methodological part: designing different control strategies to obtain observers using the control-based paradigm. In particular, we focused on non-optimal approaches (like Proportional and Proportional-Integral), optimal ones (Linear Quadratic Regulator and Linear Quadratic Integrator) and sub-optimal methods (Hinf controller). Moreover, we focus on the main two ways to formulate a control (tracking) problem, namely Error feedback regulation problem and Full information regulation problem.2. Experimental part: Applying the obtained methods for improving the topographic imaging using a Scanning-Tunneling Microscope as well as to improve the disturbance estimation for a magnetic levitation process.More precisely, each part will take the form of two chapters:1. Chapter II, dedicated to a formal introduction and contributive discussion about the ’control based observer’ approach this PhD investigates, and Chapter III, focusing on the use of such an approach for the purpose of new robust observer design in particular within an Hinf framework.2. Chapter IV, related to STM application, and chapter V, presenting the MAGLEV case.A final chapter VI summarizes the main conclusions of this work as well as some perspectives
Do, Manh Hung. "Synthèse robuste d'observateurs pour systèmes singuliers linéaires à paramètres variants." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALT053.
Full textThis Thesis is focused on the study of state and fault estimation in Linear Parameter-Varying (LPV) systems. The Thesis considers two classes of systems: non-singular and singular systems. In specific, the proposed observers are synthesized to be robust against parametric uncertainties, input and output disturbances, measurement noise, Lipschitz nonlinearities, and time delays. The major contributions of this research are respectively: an integrated observer-controller design for uncertain LPV systems with a new methodology of disturbance attenuation called output frequency-shaping filter; the design and the development of unknown input (UI) observers for fault estimation under the existence of partially decoupled UIs; the synthesis of H∞ and H2 observers for the singular system with Lipschitz nonlinearity; and a H∞ observer design for time-delay LPV system. Finally, the performance of the proposed methods is justified by laboratory experiments with INOVE platform and numerical examples
Romero, ugalde Héctor manuel. "Identification de systèmes utilisant les réseaux de neurones : un compromis entre précision, complexité et charge de calculs." Phd thesis, Ecole nationale supérieure d'arts et métiers - ENSAM, 2013. http://pastel.archives-ouvertes.fr/pastel-00869428.
Full textBel, Haj Frej Ghazi. "Estimation et commande décentralisée pour les systèmes de grandes dimensions : application aux réseaux électriques." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0139/document.
Full textThis thesis focuses on the decentralized estimation and control for large scale systems. The objective is to develop software sensors that can produce a reliable estimate of the variables necessary for the interconnected nonlinear systems stability analysis. A decomposition of a such large system into a set of n interconnected subsystems is paramount for model simplification. Then, taking into account the nature of the subsystem as well as the interconnected functions, observer-based decentralized control laws have been synthesized. Each control law is associated with a subsystem which allows it to be locally stable, thus the stability of the overall system is ensured. The existence of an observer and a controller gain matrix stabilizing the system depends on the feasibility of an LMI optimization problem. The LMI formulation, based on Lyapunov approach, is elaborated by applying the DMVT technique on the nonlinear interconnection function, assumed to be bounded and uncertain. Thus, non-restrictive synthesis conditions are obtained. Observer-based decentralized control schemes have been proposed for nonlinear interconnected systems in the continuous and discrete time. Robust Hinfini decentralized controllers are provided for interconnected nonlinear systems in the presence of perturbations and parametric uncertainties. Effectiveness of the proposed schemes are verified through simulation results on a power systems with interconnected machines
Bel, Haj Frej Ghazi. "Estimation et commande décentralisée pour les systèmes de grandes dimensions : application aux réseaux électriques." Electronic Thesis or Diss., Université de Lorraine, 2017. http://www.theses.fr/2017LORR0139.
Full textThis thesis focuses on the decentralized estimation and control for large scale systems. The objective is to develop software sensors that can produce a reliable estimate of the variables necessary for the interconnected nonlinear systems stability analysis. A decomposition of a such large system into a set of n interconnected subsystems is paramount for model simplification. Then, taking into account the nature of the subsystem as well as the interconnected functions, observer-based decentralized control laws have been synthesized. Each control law is associated with a subsystem which allows it to be locally stable, thus the stability of the overall system is ensured. The existence of an observer and a controller gain matrix stabilizing the system depends on the feasibility of an LMI optimization problem. The LMI formulation, based on Lyapunov approach, is elaborated by applying the DMVT technique on the nonlinear interconnection function, assumed to be bounded and uncertain. Thus, non-restrictive synthesis conditions are obtained. Observer-based decentralized control schemes have been proposed for nonlinear interconnected systems in the continuous and discrete time. Robust Hinfini decentralized controllers are provided for interconnected nonlinear systems in the presence of perturbations and parametric uncertainties. Effectiveness of the proposed schemes are verified through simulation results on a power systems with interconnected machines
Huard, Benoît. "Contribution à la modélisation non-linéaire et à la commande d'un actionneur robotique intégré pour la manipulation." Thesis, Poitiers, 2013. http://www.theses.fr/2013POIT2262/document.
Full textThe realization of dexterous manipulation tasks requires a complexity in robotic hands design as well as in their control laws synthesis. A mecatronical optimization of these systems helps to answer for functional integration constraints by avoiding external force sensors. Back-drivable mechanics allows the free-space positioning determination of such system as far as the detection of its interaction with a manipulated object thanks to proprioceptives measures at electric actuator level. The objective of this thesis is to synthesize a control law adapted to object manipulation by taking into account these mechanical properties in a one degree-of-freedom case. The proposed method is based on a robust control according to structural non-linearities due to gravitational effects and dry frictions on the one hand and with regard to a variable rigidity of manipulated objects on the other hand. The chosen approach requires a precise knowledge of the system configuration at all time. A dynamic representation of its behavior enables a software sensor synthesis for the exteroceptives variables estimation in a control law application purpose. The different steps are experimentally validated in order to justify the chosen approach leading to object manipulation
Dalalyan, Arnak S. "Estimation non-paramétrique asymptotiquement efficace pour des processus de diffusion ergodiques." Le Mans, 2001. http://cyberdoc.univ-lemans.fr/theses/2001/2001LEMA1018.pdf.
Full textTwo problems of nonparametric curve estimation are considered. In both problems the observation is a continuous path of an ergodic diffusion process over the time interval [0,T]. The diffusion coefficient of this process is supposed to be known. Thus the only unknown parameter is the trend coefficient. We develop the Pinsker’s approach for the model of ergodic diffuston supposing that the parameter space is a subset of a Sobolev ball and the L2-type risk is used to measure the error of estimation. The first problem studied in this work is the estimation of the derivative of invariant density. The local and the global minimax risks are considered. In both cases the exact asymptotics of these risks are found and some asymptotically efficient estimators are constructed. A generalization of these results to the higher order derivative estimation problem is proposed. The second problem investigated in this work is the trend coefficient estimation. In this problem, a lower bound of the local minimax risk is obtained. Then, using asymptotically efficient estimators of the invariant density and its derivative, an estimator of the trend coefficient is constructed. This last estimator is proved to be asymptotically efficient in the local minimax sense
Lhéritier, Hugo. "Comportement asymptotique de certains estimateurs sur des modèles paramétriques et sous des conditions non standard." Orléans, 2003. http://www.theses.fr/2003ORLE2005.
Full textHarari-Kermadec, Hugo. "Vraisemblance empirique généralisée et estimation semi-paramétrique." Paris 10, 2006. http://www.theses.fr/2006PA100136.
Full textEmpirical likelihood is an estimation method inspired by the classical likelihood method, but without assuming any parametric model for the distribution of the data. The empirical likelihood method can be described as the maximization of the likelihood of a discrete distribution supported by the data. It can be used to build confidence regions, as long as the parameter of interest is defined by some moment constraints. In this thesis, we will generalize the empirical likelihood method to a wide family of empirical discrepancy methods. We give in particular non asymptotic results for some well-chosen discrepancies. We will also propose an extension of empirical likelihood to Markov chains. Those theoretical results will be used in two. The first one proposes to evaluate some risk index for the exposition to methyl-mercury via sea products consumption, by taking into account several data sources. The second one evaluates the effect of social norm on obesity
Durot, Cécile. "Asymptotique fine pour l'estimateur isotonique en régression et méthodes de jackknife : applications à la comparaison de courbes de croissance." Paris 11, 1997. http://www.theses.fr/1997PA112007.
Full textBrua, Jean-Yves. "Estimation non paramétrique pour des modèles de diffusion et de régression." Phd thesis, Université Louis Pasteur - Strasbourg I, 2008. http://tel.archives-ouvertes.fr/tel-00338286.
Full textPour un modèle de régression non paramétrique et hétéroscédastique, où l'écart-type du bruit dépend à la fois du régresseur et de la fonction de régression supposée appartenir à une classe höldérienne faible de régularité connue, nous montrons qu'un estimateur à noyau est asymptotiquement efficace. Lorsque la régularité de la fonction de régression est inconnue, nous obtenons la vitesse de convergence minimax adaptative des estimateurs sur une famille de classes höldériennes. Enfin, pour un modèle de diffusion où la dérive appartient à un voisinage höldérien faible centré en une fonction lipschitzienne, nous présentons la construction d'un estimateur à noyau asymptotiquement efficace.
Ferrani, Yacine. "Sur l'estimation non paramétrique de la densité et du mode dans les modèles de données incomplètes et associées." Electronic Thesis or Diss., Littoral, 2014. http://www.theses.fr/2014DUNK0370.
Full textThis thesis deals with the study of asymptotic properties of e kernel (Parzen-Rosenblatt) density estimate under associated and censored model. In this setting, we first recall with details the existing results, studied in both i.i.d. and strong mixing condition (α-mixing) cases. Under mild standard conditions, it is established that the strong uniform almost sure convergence rate, is optimal. In the part dedicated to the results of this thesis, two main and original stated results are presented : the first result concerns the strong uniform consistency rate of the studied estimator under association hypothesis. The main tool having permitted to achieve the optimal speed, is the adaptation of the Theorem due to Doukhan and Neumann (2007), in studying the term of fluctuations (random part) of the gap between the considered estimator and the studied parameter (density). As an application, the almost sure convergence of the kernel mode estimator is established. The stated results have been accepted for publication in Communications in Statistics-Theory & Methods ; The second result establishes the asymptotic normality of the estimator studied under the same model and then, constitute an extension to the censored case, the result stated by Roussas (2000). This result is submitted for publication
Delsol, Laurent. "Régression sur variable fonctionnelle : estimation, tests de structure et applications." Phd thesis, Université Paul Sabatier - Toulouse III, 2008. http://tel.archives-ouvertes.fr/tel-00449806.
Full textFerrani, Yacine. "Sur l'estimation non paramétrique de la densité et du mode dans les modèles de données incomplètes et associées." Thesis, Littoral, 2014. http://www.theses.fr/2014DUNK0370/document.
Full textThis thesis deals with the study of asymptotic properties of e kernel (Parzen-Rosenblatt) density estimate under associated and censored model. In this setting, we first recall with details the existing results, studied in both i.i.d. and strong mixing condition (α-mixing) cases. Under mild standard conditions, it is established that the strong uniform almost sure convergence rate, is optimal. In the part dedicated to the results of this thesis, two main and original stated results are presented : the first result concerns the strong uniform consistency rate of the studied estimator under association hypothesis. The main tool having permitted to achieve the optimal speed, is the adaptation of the Theorem due to Doukhan and Neumann (2007), in studying the term of fluctuations (random part) of the gap between the considered estimator and the studied parameter (density). As an application, the almost sure convergence of the kernel mode estimator is established. The stated results have been accepted for publication in Communications in Statistics-Theory & Methods ; The second result establishes the asymptotic normality of the estimator studied under the same model and then, constitute an extension to the censored case, the result stated by Roussas (2000). This result is submitted for publication
Delecroix, Michel. "Sur l'estimation et la prévision non paramétrique des processus ergodiquesCycles économiques et taches solaires." Lille 1, 1987. http://www.theses.fr/1987LIL10146.
Full textDubost, Stéphanie. "Estimation dans les modèles localement sinusoi͏̈daux et localement harmoniques, avec application au débruitage de signaux de parole." Paris, ENST, 2001. http://www.theses.fr/2001ENST0006.
Full textGendre, Xavier. "Estimation par sélection de modèle en régression hétéroscédastique." Phd thesis, Université de Nice Sophia-Antipolis, 2009. http://tel.archives-ouvertes.fr/tel-00397608.
Full textLa première partie de cette thèse consiste dans l'étude du problème d'estimation de la moyenne et de la variance d'un vecteur gaussien à coordonnées indépendantes. Nous proposons une méthode de choix de modèle basée sur un critère de vraisemblance pénalisé. Nous validons théoriquement cette approche du point de vue non-asymptotique en prouvant des majorations de type oracle du risque de Kullback de nos estimateurs et des vitesses de convergence uniforme sur les boules de Hölder.
Un second problème que nous abordons est l'estimation de la fonction de régression dans un cadre hétéroscédastique à dépendances connues. Nous développons des procédures de sélection de modèle tant sous des hypothèses gaussiennes que sous des conditions de moment. Des inégalités oracles non-asymptotiques sont données pour nos estimateurs ainsi que des propriétés d'adaptativité. Nous appliquons en particulier ces résultats à l'estimation d'une composante dans un modèle de régression additif.
Lévy-Leduc, Céline. "Estimation semi-paramétrique de la période de fonctions périodiques inconnues dans divers modèles statistiques : théorie et applications." Paris 11, 2004. http://www.theses.fr/2004PA112146.
Full textThis thesis is devoted to semiparametric period estimation of unknown periodic functions in various statistical models as well as the construction of nonparametric tests to detect a periodic signal in the midst of noise. In chapter 1, we propose asymptotically optimal estimators of the period of an unknown periodic function and of the periods of two periodic functions from their sum corrupted by Gaussian white noise. In chapter 2, we propose a practical implementation of the period estimation method based on the ideas developed in the first chapter that we test on simulated laser vlbrometry signals. This algorithm is used in chapter 3 on real musical data. In chapter 4, we propose an estimator of the period when the observations are those of a particular almost periodic function corrupted by Gaussian white noise as well as a practical implementation of the method. This algorithm has also been tested on laser vibrometry data. In chapter 5, we propose a test in order to detect periodic functions in the midst of noise when the period of the function and the variance of noise are unknown. It is proved to be adaptive in the minimax sense and has been tested on laser vibrometry data
Barbu, Vlad. "Estimation des chaînes semi-markoviennes et des chaînes semi-markoviennes cachées en vue d'applications en fiabilité et en biologie." Compiègne, 2005. http://www.theses.fr/2005COMP1568.
Full textThe first part of my thesis concerns the discrete time semi-Markov models and the associated nonparametric estimation. The obtained results are used for deriving estimators of the systems reliability and of the associated measures. The asymptotic properties of the estimators are studied. An example illustrates how to practically compute the reliability indicators. The second part of my thesis is devoted to the estimation of hidden semi-Markov models. The asymptotic properties of the estimators are studied and an EM algorithm is proposed. An application in genetics for detecting the CpG islands in a DNA sequence shows the interest of our researches
Degras, David. "Contribution à l'étude de la régression non paramétrique et à l'estimation de la moyenne d'un processus à temps continu." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2007. http://tel.archives-ouvertes.fr/tel-00201438.
Full textBalmand, Samuel. "Quelques contributions à l'estimation de grandes matrices de précision." Thesis, Paris Est, 2016. http://www.theses.fr/2016PESC1024/document.
Full textUnder the Gaussian assumption, the relationship between conditional independence and sparsity allows to justify the construction of estimators of the inverse of the covariance matrix -- also called precision matrix -- from regularized approaches. This thesis, originally motivated by the problem of image classification, aims at developing a method to estimate the precision matrix in high dimension, that is when the sample size $n$ is small compared to the dimension $p$ of the model. Our approach relies basically on the connection of the precision matrix to the linear regression model. It consists of estimating the precision matrix in two steps. The off-diagonal elements are first estimated by solving $p$ minimization problems of the type $ell_1$-penalized square-root of least-squares. The diagonal entries are then obtained from the result of the previous step, by residual analysis of likelihood maximization. This various estimators of the diagonal entries are compared in terms of estimation risk. Moreover, we propose a new estimator, designed to consider the possible contamination of data by outliers, thanks to the addition of a $ell_2/ell_1$ mixed norm regularization term. The nonasymptotic analysis of the consistency of our estimator points out the relevance of our method
Mahiou, Ramdane. "Sur l'estimation d'une fonction-frontière et l'enveloppe convexe d'un échantillon." Paris 6, 1988. http://www.theses.fr/1988PA066379.
Full textKnefati, Muhammad Anas. "Estimation non-paramétrique du quantile conditionnel et apprentissage semi-paramétrique : applications en assurance et actuariat." Thesis, Poitiers, 2015. http://www.theses.fr/2015POIT2280/document.
Full textThe thesis consists of two parts: One part is about the estimation of conditional quantiles and the other is about supervised learning. The "conditional quantile estimate" part is organized into 3 chapters. Chapter 1 is devoted to an introduction to the local linear regression and then goes on to present the methods, the most used in the literature to estimate the smoothing parameter. Chapter 2 addresses the nonparametric estimation methods of conditional quantile and then gives numerical experiments on simulated data and real data. Chapter 3 is devoted to a new conditional quantile estimator, we propose. This estimator is based on the use of asymmetrical kernels w.r.t. x. We show, under some hypothesis, that this new estimator is more efficient than the other estimators already used. The "supervised learning" part is, too, with 3 chapters: Chapter 4 provides an introduction to statistical learning, remembering the basic concepts used in this part. Chapter 5 discusses the conventional methods of supervised classification. Chapter 6 is devoted to propose a method of transferring a semiparametric model. The performance of this method is shown by numerical experiments on morphometric data and credit-scoring data
Sart, Mathieu. "Estimation par tests." Phd thesis, Université Nice Sophia Antipolis, 2013. http://tel.archives-ouvertes.fr/tel-00931868.
Full textMestrah, Yasser. "Systèmes de communication robustes dans des environnements inconnus An unsupervised llr estimation with unknown noise distribution." Thesis, Reims, 2019. http://www.theses.fr/2019REIMS026.
Full textFuture networks will become more dense and heterogeneous due to the inevitable increase in the number of communicated devices and the coexistence of numerous independent networks. One of the consequences is the significant increase in interference. Many studies have shown the impulsive nature of such an interference that is characterized by the presence of high amplitudes during short time durations. In fact, this undesirable phenomenon cannot be captured by the Gaussian model but more properly by heavy-tailed distributions. Beyond networks, impulsive noises are also found in other contexts. They can be generated naturally or be man-made. Systems lose their robustness when the environment changes, as the design takes too much into account the specificities of the model. The problem is that most of the communication systems implemented are based on the Gaussian assumption.Several techniques have been developed to limit the impact of interference, such as interference alignment at the physical layer or simultaneous transmission avoidance techniques like CSMA at the MAC layer. Finally, other methods try to suppress them effectively at the receiver as the successive interference cancellation (SIC). However, all these techniques cannot completely cancel interference. This is all the more true sincewe are heading towards dense networks such as LoRa, Sigfox, 5G or in general the internet of things (IoT) networks without centralized control or access to theradio resources or emission powers. Therefore, taking into account the presence of interference at the receiver level becomes a necessity, or even an obligation.Robust communication is necessary and making a decision at the receiver requires an evaluation of the log-likelihood ratio (LLR), whose derivation depends on the noise distribution. In the presence of additive white Gaussian noise (AWGN) the performance of digital communication schemes has been widely studied, optimized and simply implemented thanks to the linear-based receiver. In impulsive noise, the LLR is not linear anymore and it is computationally prohibitive or even impossible when the noise distribution is not known. Besides, the traditional linear behaviour of the optimal receiver exhibits a significant performance loss. In this study, we focus on designing a simple, adaptive and robust receiver that exhibits a near-optimal performance over Gaussian and non-Gaussian environments. The receiver must strive for universality by adapting automatically and without assistance in real conditions.We prove in this thesis that a simple module between the channel output and the decoder input allows effectively to combat the noise and interference that disrupt point-to-point (P2P) communications in a network. This module can be used as a front end of any LLR-based decoder and it does not require the knowledge of the noise distribution including both thermal noise and interference. This module consists of a LLR approximation selected in a parametric family of functions, flexible enough to be able to represent many communication contexts (Gaussian or non-Gaussian).Then, the judicious use of an information theory criterion allows to search effectively for the LLR approximation function that matches the channel state. Two different methods are proposed and investigated for this search, either using supervised learning or with an unsupervised approach. We show that it is even possible to use such a scheme for short packet communications with a performance close to the true LLR, which is computationally prohibitive. Overall, we believe that our findings can significantly contribute to many communication scenarios and will be desired in different networks wireless or wired, point to point or dense networks
Bouhadjera, Feriel. "Estimation non paramétrique de la fonction de régression pour des données censurées : méthodes locale linéaire et erreur relative." Thesis, Littoral, 2020. http://www.theses.fr/2020DUNK0561.
Full textIn this thesis, we are interested in developing robust and efficient methods in the nonparametric estimation of the regression function. The model considered here is the right-hand randomly censored model which is the most used in different practical fields. First, we propose a new estimator of the regression function by the local linear method. We study its almost uniform convergence with rate. We improve the order of the bias term. Finally, we compare its performance with that of the classical kernel regression estimator using simulations. In the second step, we consider the regression function estimator, based on theminimization of the mean relative square error (called : relative regression estimator). We establish the uniform almost sure consistency with rate of the estimator defined for independent and identically distributed observations. We prove its asymptotic normality and give the explicit expression of the variance term. We conduct a simulation study to confirm our theoretical results. Finally, we have applied our estimator on real data. Then, we study the almost sure uniform convergence (on a compact set) with rate of the relative regression estimator for observations that are subject to a dependency structure of α-mixing type. A simulation study shows the good behaviour of the studied estimator. Predictions on generated data are carried out to illustrate the robustness of our estimator. Finally, we establish the asymptotic normality of the relative regression function estimator for α-mixing data. We construct the confidence intervals and perform a simulation study to validate our theoretical results. In addition to the analysis of the censored data, the common thread of this modest contribution is the proposal of two alternative prediction methods to classical regression. The first approach corrects the border effects created by classical kernel estimators and reduces the bias term. While the second is more robust and less affected by the presence of outliers in the sample
Kabui, Ali. "Value at risk et expected shortfall pour des données faiblement dépendantes : estimations non-paramétriques et théorèmes de convergences." Phd thesis, Université du Maine, 2012. http://tel.archives-ouvertes.fr/tel-00743159.
Full textCucala, Lionel. "ESPACEMENTS BIDIMENSIONNELS ET DONNÉES ENTACHÉES D'ERREURS DANS L'ANALYSE DES PROCESSUS PONCTUELS SPATIAUX." Phd thesis, Université des Sciences Sociales - Toulouse I, 2006. http://tel.archives-ouvertes.fr/tel-00135890.
Full textEl, Waled Khalil. "Estimations paramétriques et non-paramétriques pour des modèles de diffusions périodiques." Thesis, Rennes 2, 2015. http://www.theses.fr/2015REN20042/document.
Full textIn this thesis, we consider a drift estimation problem of a certain class of stochastic periodic processes when the length of observation goes to infinity. Firstly, we deal with the linear periodic signal plus noise model dζt = f (t, θ)dt + σ(t)dWt, ;and we study the parametric estimation from a continuous and discrete observation of the process f_tg throughout the interval [0; T]. Using the maximum likelihood method we show the existence of an estimator θˆT which is consistent, asymptotically normal and asymptotically efficient in the sens minimax. When f(t; _) = _f(t), the expression of ^_T is explicit and we obtain the mean square convergence in the both continuous and discrete observation cases. In addition, we deduce the strong consistency in the case of continuous observation.Secondly, we consider the nonparametric estimation problem of the function f(_) for the next two periodic models of type signal plus noise and Ornstein-Uhlenbeckd_t = f(t)dt + _(t)dWt; d_t = f(t)_tdt + dWt:For the signal plus noise model, we build a kernel estimator, the convergence in mean square uniformly over [0; P] and almost sure convergence are established, as well as the asymptotic normality. For the Ornstein-Uhlenbeck model, we prove the convergence uniformly over [0; P] of the bias and the mean square convergence. Moreover, we study the speed of these convergences