Cette thèse porte sur l'estimation simultanée du coefficient de diffusion et du terme source régissant le modèle de transport de la température dans les plasmas chauds. Ce phénomène physique est décrit par une équation différentielle partielle (EDP) linéaire, parabolique du second-ordre et non-homogène, où le coefficient de diffusion est distribué et le coefficient de réaction est constant. Ce travail peut se présenter en deux parties. Dans la première, le problème d'estimation est traité en dimension finie ("Early lumping approach"). Dans la deuxième partie, le problème d'estimation est traité dans le cadre initial de la dimension infinie ("Late lumping approach"). Pour l'estimation en dimension finie, une fois le modèle établi, la formulation de Galerkin et la méthode d'approximation par projection sont choisies pour convertir l'EDP de transport en un système d'état linéaire, temps-variant et à entrées inconnues. Sur le modèle réduit, deux techniques dédiées à l'estimation des entrées inconnues sont choisies pour résoudre le problème. En dimension infinie, l'estimation en-ligne adaptative est adoptée pour apporter des éléments de réponse aux contraintes et limitations dues à la réduction du modèle. Des résultats de simulations sur des données réelles et simulées sont présentées dans ce mémoire. / This work deals with the diffusion and source term estimation in a heat transport model for tokamaks plasma . This phenomenon is described by a second-order linear parabolic partial differential equation (PDE) with distributed diffusion parameter and input. Both "Early lumping" and "Late lumping" approaches are considered in this thesis. First, once the heat model is chosen, the Galerkin formulation and the parameter projection method are combined to convert the PDE to a set of ordinary differential equations (ODEs). Then, two estimation methods able to give optimal estimates of the inputs are applied on the reduced model to identify simultaneously the source term and the diffusion coefficient. In the infinite dimensional method, the adaptive estimation technique is chosen in order to reconstruct "freely" the unknown parameters without the constraints due to the model reduction method. Simulation results on both simulated and real data are provided to attest the performance of the proposed methodologies.

Le travail porte sur la problématique de l’observation des systèmes — la reconstruction de l’état complet d’un système dynamique à partir d'une mesure partielle de cet état. Nous considérons spécifiquement les systèmes non linéaires. Le filtre de Kalman étendu (EKF) est l’un des observateurs les plus utilisés à cette fin. Il souffre cependant d’une performance moindre lorsque l'état estimé n’est pas dans un voisinage de l'état réel. La convergence de l’observateur dans ce cas n’est pas prouvée. Nous proposons une solution à ce problème : l’EKF à grand gain adaptatif. La théorie de l’observabilité fait apparaître l’existence de représentations caractérisant les systèmes dit observables. C’est la forme normale d’observabilité. L’EKF à grand gain est une variante de l’EKF que l’on construit à base d’un paramètre scalaire. La convergence de cet observateur pour un système sous sa forme normale d’observabilité est démontrée pour toute erreur d’estimation initiale. Cependant, contrairement à l’EKF, cet algorithme est très sensible au bruit de mesure. Notre objectif est de combiner l’efficacit´e de l’EKF en termes de lissage du bruit, et la r´eactivit´e de l’EKF grand-gain face aux erreurs d’estimation. Afin de parvenir à ce résultat nous rendons adaptatif le paramètre central de la méthode grand gain. Ainsi est constitué l’EKF à grand gain adaptatif. Le processus d’adaptation doit être guidé par une mesure de la qualité de l’estimation. Nous proposons un tel indice et prouvons sa pertinence. Nous établissons une preuve de la convergence de notre observateur, puis nous l’illustrons à l’aide d’une série de simulations ainsi qu’une implémentation en temps réel dur. Enfin nous proposons des extensions au résultat initial : dans le cas de systèmes multi-sorties et dans le cas continu-discret. / The work concerns the “observability problem”—the reconstruction of a dynamic process’s full state from a partially measured state— for nonlinear dynamic systems. The Extended Kalman Filter (EKF) is a widely-used observer for such nonlinear systems. However it suffers from a lack of theoretical justifications and displays poor performance when the estimated state is far from the real state, e.g. due to large perturbations, a poor initial state estimate, etc. . . We propose a solution to these problems, the Adaptive High-Gain (EKF). Observability theory reveals the existence of special representations characterizing nonlinear systems having the observability property. Such representations are called observability normal forms. A EKF variant based on the usage of a single scalar parameter, combined with an observability normal form, leads to an observer, the High-Gain EKF, with improved performance when the estimated state is far from the actual state. Its convergence for any initial estimated state is proven. Unfortunately, and contrary to the EKF, this latter observer is very sensitive to measurement noise. Our observer combines the behaviors of the EKF and of the high-gain EKF. Our aim is to take advantage of both efficiency with respect to noise smoothing and reactivity to large estimation errors. In order to achieve this, the parameter that is the heart of the high-gain technique is made adaptive. Voila, the Adaptive High-Gain EKF. A measure of the quality of the estimation is needed in order to drive the adaptation. We propose such an index and prove the relevance of its usage. We provide a proof of convergence for the resulting observer, and the final algorithm is demonstrated via both simulations and a real-time implementation. Finally, extensions to multiple output and to continuous-discrete systems are given.

Ce mémoire est consacré à la modélisation des condensateurs électrolytiques aluminium dédiés à des applications à hautes températures ainsi qu’à la compréhension de leur vieillissement lors d’utilisations réalistes. En effet, dans le cas d’onduleur de traction de véhicule électrique, les sollicitations, notamment en température, peuvent être parfois très variables. Or, il se trouve que pour ce type d’applications, ces derniers sont la plupart du temps de type électrolytiques aluminium, technologie étant parmi les plus fragiles. Par conséquent, ce manuscrit propose tout d’abord une nouvelle modélisation électrique s’appuyant des phénomènes de diffusion permettant d’obtenir une représentation très précise de l’impédance de ces condensateurs. Compte-tenu de leur forte dépendance en température, la modélisation thermique couplée au modèle électrique est également traitée. Le but premier est de développer un outil permettant d’estimer précisément les pertes à chaque instant pour permettre au contrôleur d’ajuster la température de ce dernier par une modification de la stratégie MLI. Une méthode d’identification en ligne est alors proposée par l’utilisation de filtres de Kalman conjoints avec de très bons résultats obtenus en simulation. Le dimensionnement ainsi que la création d’un banc de cyclage accéléré est développé et une comparaison du vieillissement obtenu après 12 000 heures entre des composants cyclés thermiquement et d’autres non cyclés est donnée. Les résultats montrent une très bonne tenue dans le temps des condensateurs étudiés que ce soit au niveau de l’impédance ou bien visuellement avec néanmoins un impact du cyclage thermique non négligeable / This thesis is devoted to the modeling of aluminum electrolytic capacitors dedicatedto high temperatures. The purpose is also to understand their ageing while submitted to realistic use. Indeed, in the case of electric vehicle traction inverter, solicitations like temperature can vary a lot. This type of stress has already been studied for active components, but not yet on passive ones such as decoupling capacitors. However, it turns out that for this kind of application, they are most of the time aluminum electrolytic capacitors which is among the weakest technology. Consequently, this manuscript proposes at first a new electric model based on a diffusion phenomena which leads to a very accurate impedance description. It permits also a better understanding of the physical phenomena involved in these components. Because of their important temperature dependence, thermal modeling coupled to the electric model is also discussed. The very first purpose is to develop a tool that is able to estimate losses accurately at every moment. Thanks to it, the controller could so change the PWM strategy in order to act on the temperature. An online identification method is then proposed with the use of joint Kalman filters which led to very good results in simulation. The design and the creation of an accelerated cycling bench is developed and comparisons about the ageing obtained after 12 000 hours between thermally cycled components and others non-cycled are given. Results show a very good stability over time of the studied capacitors (PEG225MF470Q Kemet©) either on the impedance or visually. Nevertheless a significant impact can be observed on the cycled ones.

A new suboptimal guidance law design approach for aerospace vehicles is proposed in this thesis, followed by an advanced control design for supersonic air-breathing engines. The guidance law is designed using the newly developed Generalized Model Predictive Static Programming (G-MPSP), which is based on the continuous time nonlinear optimal control framework. The key feature of this technique is one-time backward propagation of a small-dimensional weighting matrix dynamics, which is used to update the entire control history. This key feature, as well as the fact that it leads to a static optimization problem, lead to its computational efficiency. It has also been shown that the existing model predictive static programming (MPSP), which is based on the discrete time framework, is a special case of G-MPSP. The G-MPSP technique is further extended to incorporate ‘input inequality constraints’ in a limited sense using the penalty function philosophy. Next, this technique has been developed also further in a ‘flexible final time’ framework to converge rapidly to meet very stringent final conditions with limited number of iterations. Using the G-MPSP technique in a flexible final time and input inequality constrained formulation, a suboptimal guidance law for a solid motor propelled carrier launch vehicle is successfully designed for a hypersonic mission. This guidance law assures very stringent final conditions at the injection point at the end of the guidance phase for successful beginning of the hypersonic vehicle operation. It also ensures that the angle of attack and structural load bounds are not violated throughout the trajectory. A second-order autopilot has been incorporated in the simulation studies to mimic the effect of the inner-loops on the guidance performance. Simulation studies with perturbations in the thrust-time behaviour, drag coefficient and mass demonstrate that the proposed guidance can meet the stringent requirements of the hypersonic mission. The G-MPSP technique in a fixed final time and input inequality constrained formulation has also been used for optimal guidance of an aerospace vehicle propelled by supersonic air-breathing engine, where the resulting thrust can be manipulated by managing the fuel flow and nozzle area (which is not possible in solid motors). However, operation of supersonic air-breathing engines is quite complex as the thrust produced by the engine is a result of very complex nonlinear combustion dynamics inside the engine. Hence, to generate the desired thrust, accounting for a fairly detailed engine model, a dynamic inversion based nonlinear state feedback control design has been carried out. The objective of this controller is to ensure that the engine dynamically produces the thrust that tracks the commanded value of thrust generated from the guidance loop as closely as possible by regulating the fuel flow rate. Simultaneously, by manipulating throat area of the nozzle, it also manages the shock wave location in the intake for maximum pressure recovery with sufficient margin for robustness. To filter out the sensor and process noises and to estimate the states for making the control design operate based on output feedback, an extended Kalman filter (EKF) based state estimation design has also been carried out and the controller has been made to operate based on estimated states. Moreover, independent control designs have also been carried out for the actuators so that their response can be faster. In addition, this control design becomes more challenging to satisfy the imposed practical constraints like fuel-air ratio and peak combustion temperature limits. Simulation results clearly indicate that the proposed design is quite successful in assuring the desired performance of the air-breathing engine throughout the flight trajectory, i.e., both during the climb and cruise phases, while assuring adequate pressure margin for shock wave management.

Master's thesis is dealing with desgin of a measuring unit incorporating inertial sensors, used for analysis in canoe sprint. Data from a three-axis accelerometer and a three-axis gyroscope were combined using an extended Kalman filter, yielding speed, roll, pitch and yaw of the boat and stroke rate. Calculated values were verified by a GPS. Furthermore, parameters describing dynamic behaviour of the system were identified, allowing an inclusion of dynamic quantities like force and power into the analysis.

This Ph.D. thesis deals with problems about voltage dips and short voltage interruptions, generally named as voltage events, which origin in power supply networks and have a negative influence for all connected electric appliances. In this thesis single phase appliances are considered. These problems closely relate with area of electromagnetic compatibility which solve all questions about correct operation of different types of electric appliances during electromagnetic disturbances impact. Voltage events are ones of the many types of electromagnetic disturbances. The connection between disturbance sources and sensitive electric appliances is created by power supply lines. The immunity of electric appliances to voltage dips and short interruptions is currently tested via voltage dips with strictly defined parameters which are intended by class of electromagnetic environment in which the usage of electric appliance is recommended. During immunity tests the rectangular shape of voltage dips is preferred. The main descriptive parameters of testing voltage events are the residual voltage and the event time duration. But voltage dips and short interruptions defined by this way do not closely relate with parameters of real voltage dips and interruptions occurred in public supply system where parameters of voltage dips are variable. Moreover in the power supply system there are many of others voltage parameters which can have a significant influence to immunity level of connected electric appliances. This Ph.D. thesis also deals with finding more voltage event parameters. Although the voltage events occurrence in the power supply system is not limited and voltage events are considered only as informative voltage parameter it is important to monitor voltage events occurrence. The monitoring device has to be able to operate for ling time period and it has to detect parameters of voltage events with adequate accuracy. The accuracy of detected event parameters and the detection delay depends on the detection algorithm characteristics. That is why the part of this thesis relates with a comparison of several detection algorithms and their abilities to correct detection of voltage event parameters. The main purpose of this thesis is the proposal of connection between classification of voltage dips and short interruptions occurred in power supply system with the classification of electric appliances immunity to these voltage events. On the base of many of provided electric appliances immunity tests and also on the base of long time period voltage events monitoring the special compatibility levels are proposed in this thesis. The observation of proposed compatibility levels will bring the increasing level of reliable operation of all connected electric appliances.

Les Éoliennes à Entraînement Direct (ÉED) sont équipées de Générateurs Syn- chrones à Aimants Permanents (GSAP). Leurs trois plus courantes défaillances sont la dé- magnétisation, l’excentricité (statique, dynamique et mixte) et le court-circuit inter-tour. L’analyse de la signature du courant de la machine est souvent utilisée pour rechercher des problèmes du générateur, car ces altérations introduisent des harmoniques supplémen- taires dans les courants générés. La Transformée de Fourier Rapide (TFR) est utilisée pour calculer le spectre des courants. Cependant, la TFR permet de calculer l’ensemble du spec- tre, tandis que le nombre de défauts possible et le nombre d’harmoniques introduites sont faibles. L’algorithme de Goertzel, mis en oeuvre sous forme de filtre (le filtre de Goertzel), est présenté comme une alternative plus efficace au TFR. Le spectre des courants change avec la vitesse du vent, ce qui rend la détection plus difficile. Le Filtre de Kalman Étendu (FKÉ) est proposé comme solution. Le spectre de résidus, calcule entre les courants estimés et les courants générés, est constant, quelle que soit la vitesse du vent. Cependant, l’effet des défauts est visible dans leur spectre. Lors de l’utilisation de l’FKÉ, un défi consiste à estime la matrice de covariance pour le bruit du processus. Une nouvelle méthode était développée pour ça, qui n’utilise aucune de maîtrise du filtre. Les ÉED sont placés soit dans des zones éloignées, soit dans des villes. Pour la surveillance des ÉED, des dizaines ou des centaines de kilomètres de câbles sont nécessaires. Les Réseaux de Capteurs sans Fil (RCF) sont bien adaptés pour être utilisés dans l’infrastructure de communication des ÉED. RCF ont des coûts initiaux et d’entretien plus faibles et leurs installations sont rapides. De plus, ils peuvent compléter les réseaux câblés. Différentes technologies sans fil sont comparées : les technologies à grande surface, ainsi que les technologies à courte portée qui supportent des débits de données élevés. / Direct Drive Wind Turbines (DDWTs) are equipped with Permanent Magnet Syn- chronous Generators (PMSGs). Their three most common failures are demagnetization, ec- centricity (static, dynamic and mixed) and inter-turn short circuit. Machine Current Signa- ture Analysis is often used to look for generator problems, as these impairments introduce additional harmonics into the generated currents. The Fast Fourier Transform (FFT) is utilized to compute the spectrum of the currents. However, the FFT calculates the whole spectrum, while the number of possible faults and the number of introduced harmonics is low. The Goertzel algorithm, implemented as a filter (the Goertzel filter), is presented as a more efficient alternative to the FFT. The spectrum of the currents changes with the wind speed, and thus the detection is made more difficult. The Extended Kalman Filter (EKF) is proposed as a solution. The spectrum of the residuals, computed between the estimated and the generated current, is constant, regardless of the wind speed. However, the effect of the faults is visible in the spectrum. When using the EKF, one challenge is to find out the covariance matrix of the process noise. A new method was developed in this regard, which does not use any of the matrices of the filter. DDWTs are either placed in remote areas or in cities. For the monitoring of a DDWT, tens or hundreds of kilometers of cables are necessary. Wireless Sensor Networks (WSNs) are suited to be used in the communication infrastructure of DDWTs. WSNs have lower initial and maintenance costs, and they are quickly installed. Moreover, they can complement wired networks. Different wireless technologies are com- pared - both wide area ones, as well as short range technologies which support high data rates.

Die Fahrzeuglokalisierung ist im Bereich der Fahrerassistenzsysteme von entscheidender Bedeutung und Voraussetzung fur verschiedene Anwendungen der Robotik, wie z.B. Navigation oder Kollisionsvermeidung fur fahrerlose Transportsysteme (FTS). In dieser Arbeit wird ein Verfahren zur Lokalisierung mittels Landmarken vorgestellt, die eine Orientierung bezuglich einer Karte ermoglichen. Dabei werden der Erweiterte- Kalman-Filter und der Partikel-Filter fur diese Aufgabe untersucht und verglichen. Ein Schwerpunkt dieser Betrachtungen stellt dabei der Partikel-Filter dar. Die besondere Problematik der Initialisierung wird ausfuhrlich fur beide Filter dargestellt. Simulationen und Versuche zeigen, dass sich der Partikel-Filter fur eine robuste Lokalisierung der Fahrzeugposition verwenden lasst. Im Vergleich dazu kann der Erweiterte-Kalman-Filter nur im begrenzten Maße eingesetzt werden. / The localization of vehicles is of vital importance in the field of driver assistance systems and a requirement of different applications for robotics, i.e. navigation or collision avoidance for automatic guided vehicle systems. In this thesis an approach for localization by means of landmarks is introduced, which enables an orientation regarding a map. The extended Kalman filter and the particle filter are analyzed and compared. The main focus for this consideration is on the particle filter. The problematic for initialization is discussed in detail for both filters. Simulations and tests prove that the particle filter is suitable for robust localization of the vehicle position. Compared to this, the extended Kalman filter can only be used to a certain extend.

<div>Stoichiometric air-fuel ratio (AFR) and air/EGR flow control are essential control problems in today’s advanced spark-ignited (SI) engines to enable effective application of the three-way-catalyst (TWC) and generation of required torque. External exhaust gas recirculation (EGR) can be used in SI engines to help mitigate knock, reduce enrichment and improve efficiency[1 ]. However, the introduction of the EGR system increases the complexity of stoichiometric engine-out lambda and torque management, particularly for high BMEP commercial vehicle applications. This thesis develops advanced frameworks for sensing and control architecture designs to enable robust air handling system management, stoichiometric cylinder air-fuel ratio (AFR) control and three-way-catalyst emission control.</div><div><br></div><div><div>The first work in this thesis derives a physically-based, control-oriented model for turbocharged SI engines utilizing cooled EGR and flexible VVA systems. The model includes the impacts of modulation to any combination of 11 actuators, including the throttle valve, bypass valve, fuel injection rate, waste-gate, high-pressure (HP) EGR, low-pressure (LP) EGR, number of firing cylinders, intake and exhaust valve opening and closing timings. A new cylinder-out gas composition estimation method, based on the inputs’ information of cylinder charge flow, injected fuel amount, residual gas mass and intake gas compositions, is proposed in this model. This method can be implemented in the control-oriented model as a critical input for estimating the exhaust manifold gas compositions. A new flow-based turbine-out pressure modeling strategy is also proposed in this thesis as a necessary input to estimate the LP EGR flow rate. Incorporated with these two sub-models, the control-oriented model is capable to capture the dynamics of pressure, temperature and gas compositions in manifolds and the cylinder. Thirteen physical parameters, including intake, boost and exhaust manifolds’ pressures, temperatures, unburnt and burnt mass fractions as well as the turbocharger speed, are defined as state variables. The outputs such as flow rates and AFR are modeled as functions of selected states and inputs. The control-oriented model is validated with a high fidelity SI engine GT-Power model for different operating conditions. The novelty in this physical modeling work includes the development and incorporation of the cylinder-out gas composition estimation method and the turbine-out pressure model in the control-oriented model.</div></div><div><br></div><div><div>The second part of the work outlines a novel sensor selection and observer design algorithm for linear time-invariant systems with both process and measurement noise based on <i>H</i>2 optimization to optimize the tradeoff between the observer error and the number of required sensors. The optimization problem is relaxed to a sequence of convex optimization problems that minimize the cost function consisting of the <i>H</i>2 norm of the observer error and the weighted <i>l</i>1 norm of the observer gain. An LMI formulation allows for efficient solution via semi-definite programing. The approach is applied here, for the first time, to a turbo-charged spark-ignited (SI) engine using exhaust gas recirculation to determine the optimal sensor sets for real-time intake manifold burnt gas mass fraction estimation. Simulation with the candidate estimator embedded in a high fidelity engine GT-Power model demonstrates that the optimal sensor sets selected using this algorithm have the best <i>H</i>2 estimation performance. Sensor redundancy is also analyzed based on the algorithm results. This algorithm is applicable for any type of modern internal combustion engines to reduce system design time and experimental efforts typically required for selecting optimal sensor sets.</div></div><div><br></div><div><div>The third study develops a model-based sensor selection and controller design framework for robust control of air-fuel-ratio (AFR), air flow and EGR flow for turbocharged stoichiometric engines using low pressure EGR, waste-gate turbo-charging, intake throttling and variable valve timing. Model uncertainties, disturbances, transport delays, sensor and actuator characteristics are considered in this framework. Based on the required control performance and candidate sensor sets, the framework synthesizes an H1 feedback controller and evaluates the viability of the candidate sensor set through analysis of the structured</div><div>singular value μ of the closed-loop system in the frequency domain. The framework can also be used to understand if relaxing the controller performance requirements enables the use of a simpler (less costly) sensor set. The sensor selection and controller co-design approach is applied here, for the first time, to turbo-charged engines using exhaust gas circulation. High fidelity GT-Power simulations are used to validate the approach. The novelty of the work in this part can be summarized as follows: (1) A novel control strategy is proposed for the stoichiometric SI engines using low pressure EGR to simultaneously satisfy both the AFR and air/EGR-path control performance requirements; (2) A parametrical method to simultaneously select the sensors and design the controller is first proposed for the internal combustion engines.</div></div><div><br></div><div><div>In the fourth part of the work, a novel two-loop estimation and control strategy is proposed to reduce the emission of the three-way-catalyst (TWC). In the outer loop, an FOS estimator consisting of a TWC model and an extended Kalman-filter is used to estimate the current TWC fractional oxygen state (FOS) and a robust controller is used to control the TWC FOS by manipulating the desired engine λ. The outer loop estimator and controller are combined with an existing inner loop controller. The inner loop controller controls the engine λ based on the desired λ value and the control inaccuracies are considered and compensated by the outer loop robust controller. This control strategy achieves good emission reduction performance and has advantages over the constant λ control strategy and the conventional two-loop switch-type control strategy.</div></div>

Micro air vehicles have recently gained popularity due to their potential as autonomous systems. Their future impact, however, will depend in part on how well they can navigate in GPS-denied and GPS-degraded environments. In response to this need, this dissertation investigates a potential solution for GPS-denied operations called relative navigation. The method utilizes keyframe-to-keyframe odometry estimates and their covariances in a global back end that represents the global state as a pose graph. The back end is able to effectively represent nonlinear uncertainties and incorporate opportunistic global constraints. The GPS-denied research community has, for the most part, neglected to consider fixed-wing aircraft. This dissertation enables fixed-wing aircraft to utilize relative navigation by accounting for their sensing requirements. The development of an odometry-like, front-end, EKF-based estimator that utilizes only a monocular camera and an inertial measurement unit is presented. The filter uses the measurement model of the multi-state-constraint Kalman filter and regularly performs relative resets in coordination with keyframe declarations. In addition to the front-end development, a method is provided to account for front-end velocity bias in the back-end optimization. Finally a method is presented for enabling multiple vehicles to improve navigational accuracy by cooperatively sharing information. Modifications to the relative navigation architecture are presented that enable decentralized, cooperative operations amidst temporary communication dropouts. The proposed framework also includes the ability to incorporate inter-vehicle measurements and utilizes a new concept called the coordinated reset, which is necessary for optimizing the cooperative odometry and improving localization. Each contribution is demonstrated through simulation and/or hardware flight testing. Simulation and Monte-Carlo testing is used to show the expected quality of the results. Hardware flight-test results show the front-end estimator performance, several back-end optimization examples, and cooperative GPS-denied operations.