L'objectif du travail réalisé dans le cadre de cette thèse est de montrer l'intérêt d'utiliser les FPGAs (Field Programmable Gate Array) comme support pour l'implantation d'algorithmes complexes dédiés à la commande de machines électriques. Pour ce faire, une commande sans capteur mécanique utilisant un filtre de Kalman étendu et basée sur FPGA est réalisée. Cette commande est destinée à piloter une machine synchrone à pôles saillants. Le modèle d-q de la machine basé sur l'approximation d'inertie infinie est implanté. L'ordre du Filtre de Kalman est donc égal à 4 et la complexité totale de la boucle de régulation est évaluée à près de 700 opérations arithmétiques (dont plus de 53% de multiplications). Les apports des solutions FPGAs en termes de performances de contrôle et en termes de capacité d'intégration sont quantifiés.En terme de performances de contrôle, il a été démontré qu'en utilisant de telles solutions matérielles, le temps de calcul est très réduit (de l'ordre de 5µs, 5% de la période d'échantillonnage). Cette rapidité de calcul permet d'avoir un contrôle quasi-instantané ce qui améliore la bande passante de la boucle de régulation. A ce sujet, une comparaison avec les performances obtenues avec une solution logicielle telle que le DSP est effectuée. Dans les deux cas, le comportement dynamique de la boucle de régulation s de vitesse ans capteur est quantifié.En termes de capacité d'intégration, il est possible de développer une architecture commune qui peut être adaptée à plusieurs systèmes. A titre d'exemple, il est possible de développer un filtre de Kalman sur un même FPGA capable d'estimer les grandeurs de plusieurs systèmes sans pour autant affecter les performances de contrôle. En outre, une méthodologie de développement dédiée à de tels algorithmes complexes est proposée. Il s'agit là d'une adaptation des méthodologies proposées dans des travaux de thèse précédents, [62] et [63]. En effet, une étape de spécification préliminaire du système ainsi que des procédures d'optimisation supplémentaires y sont introduites. Ces dernières sont particulièrement nécessaires dans le cas de commandes complexes et permettent une adéquation entre l'algorithme développé et l'architecture FPGA correspondante. De plus, cette méthodologie a été organisée de façon à distinguer l'étape du développement de l'algorithme et l'étape du développement de l'architecture FPGA. Un état de l'art sur les technologies FPGA est également proposé. La structure interne des FPGAs récents est décrite. Leur contribution dans le domaine de la commande des machines électriques est quantifiée. Les différentes étapes de la méthodologie de développement sont présentées. Le développement d'une commande numérique (basée sur FPGA) d'une machine synchrone à aimant permanent associée à un capteur de position Resolver est par la suite traité. Cette application s'inscrit dans un contexte avionique où l'objectif était d'avoir une solution FPGA hautement intégrée. Pour ce faire, le FPGA Actel Fusion est utilisé. Ce composant intègre un convertisseur analogique numérique. La commande, le traitement des signaux Resolver ainsi que la conversion analogique numériques sont implantés sur le même composant.En ce qui concerne la commande sans capteur basée sur le filtre de Kalman étendu, il a été décidé de structurer les chapitres correspondants à travers la méthodologie de développement proposée. Ainsi, la phase de spécification préliminaire du système, la phase du développement de l'algorithme, la phase du développement de l'architecture FPGA et la phase d'expérimentation sont séparément traitées. Durant la phase d'expérimentation, la procédure «Hardware-In-the-Loop (HIL)» est incluse afin de valider le fonctionnement de l'architecture développée une fois la phase d'implantation physique achevée. / The aim of this thesis is to present the interest of using Field Programmable Gate Array (FPGA) devices for the implementation of complex AC drive controllers. The case of a sensorless speed controller using the Extended Kalman Filter (EKF) has been chosen and applied to a Salient Synchronous Machine (SSM). The d-q model based on the infinite inertia hypothesis has been implemented. The corresponding EKF order is then equal to 4 and the complexity of the whole sensorless controller is equal to 700 arithmetic operations (more than 53% of multiplications). The contribution of FPGAs in this field has been quantified in terms of control performances and in terms of system integration. In terms of control performances, the proposed FPGA-based solution ensures a short execution time which is around 5µs (5% of the sampling period). This treatment fastness ensures a quasi-instantaneous control which improves the control bandwidth. To this purpose, a comparison with a software DSP-based solution is made. The dynamic behavior and the influence of the execution time, in both cases, on the control bandwidth have been quantified. In terms of integration capacity, it is possible to implement a generic FPGA architecture that can be adapted to the control of several systems. Thus, it is possible to develop a common EKF architecture that is able to estimate variables from many systems without affecting the control performances.In addition, a design methodology adapted to such complex controllers has been proposed. The particularity of this updated methodology, compared to the previous ones ([62], [63]), is to provide an enlarged set of steps starting from the preliminary system specification to the ultimate experimentation. Optimization procedures have also been introduced. These optimizations are necessary in case of complex controllers and lead to the adequation between the developed algorithm and the corresponding hardware FPGA architecture. A state of the art FPGA technology is also presented. The internal structure of the recent devices and their corresponding technology are discussed. Their contribution in the field of AC drive applications is quantified. An in-depth presentation of the proposed design methodology is made.Besides, the development of a fully integrated FPGA-based controller for a Permanent Magnet Synchronous Machine (PMSM) associated with a Resolver sensor is presented. This controller has been developed in for an aircraft application where the main objective was to develop a fully integrated FPGA solution. The Actel Fusion FPGA device has been used. This device integrates an Analog to Digital Converter (ADC). The current controller, the Resolver Processing Unit (RPU) and the analog to digital conversion are implemented within the same device. When it comes to the sensorless controller, the corresponding chapters have been structured according to the presented design methodology: the preliminary system specification, the algorithm development, the FPGA architecture development and finally the experimentation. The latter includes Hardware-In-the-Loop (HIL) tests and the final experimental validation.

A demanda por mais flexibilidade nas fábricas e serviços originou um aumento no volume de operações internas de carga e descarga, devido à maior diversidade dos elementos transportados. Logo, na busca por um fluxo de materiais mais eficiente, as empresas passaram a investir em soluções tecnológicas, entre elas, o uso de Automated Guided Vehicles (AGVs), por conta do custo mais atrativo e do avanço em relação aos primeiros AGVs, que até então dependiam de uma infraestrutura adicional para suportar a navegação. Muitos AGVs modernos possuem movimentação livre e são orientados por sistemas que utilizam sensores para interpretar o ambiente, sendo assim, tornar os AGVs autônomos despertou o interesse de pesquisadores na área de robótica móvel para o desenvolvimento de sistemas capazes de auxiliar e coordenar a navegação. Novas técnicas de localização, tal como a localização baseada em marcadores reflexivos, e a construção de armazéns com layouts estruturados para a navegação viabilizaram o uso de AGVs autônomos, entretanto sua utilização em armazéns existentes ainda é um desafio. Neste contexto, o Laboratório de Robótica Móvel (LabRom) do Grupo de Mecatrônica da EESC/USP, através do projeto do Armazém Inteligente, tem pesquisado os problemas de: roteamento, gerenciamento das baterias, navegação e auto-localização. Robôs autônomos precisam de um sistema de auto-localização eficiente e preciso para navegar com segurança, o qual depende de um mapa e da interpretação do ambiente utilizando sensores embarcados. Para alcançar esse objetivo este trabalho propõe um Sistema de Auto-localização baseado no Extended Kalman Filter (EKF) como solução. O sistema, desenvolvido em linguagem C, interage com outros dois sistemas: roteamento e navegação e foi implementado em um armazém simulado utilizando o software Player/Stage, mostrando ser confiável no fornecimento de uma estimativa de localização baseada em odometria e landmarks com localização conhecida. O sistema foi novamente testado utilizando a odometria de um robô móvel Pioneer P3-AT e os valores de um sensor de medição laser 2D SICK LMS200 extraídos de um ambiente indoor real. Para este teste foi construído um feature-based map a partir de um desenho de planta baixa no formato CAD e utilizou-se o algoritmo de segmentação Iterative End-Point Fit (IEPF) para interpretar o ambiente. Os resultados mostraram que as vantagens oferecidas pelas características padronizadas de um ambiente indoor, semelhante a um armazém, podem viabilizar o uso do Sistema de Auto-localização em armazéns existentes. / The demand for more flexibility in factories and services led to an increase in the volume of internal operations of loading and unloading, due to the greater diversity of elements transported. Hence, in the search for a more efficient materials flow, companies went to invest in technology solutions, among them, the use of Automated Guided Vehicles (AGVs), on account of the more attractive cost and improvement over the first AGVs, which hitherto depended of an additional infrastructure to support navigation. Many modern AGVs have free movement and are guided by systems that use sensors to interpret the environment, thus make AGVs autonomous aroused the interest of researchers in the mobile robotics field to development of systems able to assist and coordinate the navigation. New localization techniques, such as localization based on reflective markers, and the construction of warehouses with structured layouts for navigation did feasible the use of autonomous AGVs, however its use in existing warehouses is still a challenge. In this context, the Mobile Robotics Lab (LabRom) of the Mechatronics Group of EESC/USP, through the Intelligent Warehouse Project, has researched the problems: routing, battery management, navigation and self-localization. Autonomous robots need an efficient and accurate self-localization system to safely navigate, which depends on one map and of the interpretation of the environment using embedded sensors. To achieve this goal, this work proposes a Self-Localization System based on the Extended Kalman Filter (EKF) as a solution. The system, developed in C language, interacts with two other systems: routing and navigation and was implemented in a simulated warehouse using the Player/Stage software, showing to be reliable in providing an estimative of localization based on odometry and landmarks with known localization. The system was again tested using the odometry of mobile robot Pioneer P3-AT and the values of a 2D Laser Rangefinder SICK LMS200 extracted from a real indoor environment. For this test was built a feature-based map from a floor plan design in CAD format and was used the segmentation algorithm Iterative End-Point Fit (IEPF) to interpret the environment. The results showed that the advantages offered by the standard features of indoor environment, like a warehouse, can enable the use of the Self-Localization System on the existing warehouses.

Nous concentrons notre intérêt sur l'Équation du Bilan de la Population (PBE). Cette équation décrit l'évolution, au fil du temps, des systèmes de particules en fonction de sa fonction de densité en nombre (NDF) où des processus d'agrégation et de rupture sont impliqués. Dans la première partie, nous avons étudié la formation de groupes de particules et l'importance relative des variables dans la formation des ces groupes en utilisant les données dans (Vlieghe 2014) et des techniques exploratoires comme l'analyse en composantes principales, le partitionnement de données et l'analyse discriminante. Nous avons utilisé ce schéma d'analyse pour la population initiale de particules ainsi que pour les populations résultantes sous différentes conditions hydrodynamiques. La deuxième partie nous avons étudié l'utilisation de la PBE en fonction des moments standard de la NDF, et les méthodes en quadrature des moments (QMOM) et l'Extrapolation Minimale Généralisée (GME), afin de récupérer l'évolution, d'un ensemble fini de moments standard de la NDF. La méthode QMOM utilise une application de l'algorithme Produit- Différence et GME récupère une mesure discrète non-négative, étant donnée un ensemble fini de ses moments standard. Dans la troisième partie, nous avons proposé un schéma de discrétisation afin de trouver une approximation numérique de la solution de la PBE. Nous avons utilisé trois cas où la solution analytique est connue (Silva et al. 2011) afin de comparer la solution théorique à l'approximation trouvée avec le schéma de discrétisation. La dernière partie concerne l'estimation des paramètres impliqués dans la modélisation des processus d'agrégation et de rupture impliqués dans la PBE. Nous avons proposé une méthode pour estimer ces paramètres en utilisant l'approximation numérique trouvée, ainsi que le Filtre Étendu de Kalman. La méthode estime interactivement les paramètres à chaque instant du temps, en utilisant un estimateur de Moindres Carrés non-linéaire. / We center our interest in the Population Balance Equation (PBE). This equation describes the time evolution of systems of colloidal particles in terms of its number density function (NDF) where processes of aggregation and breakage are involved. In the first part, we investigated the formation of groups of particles using the available variables and the relative importance of these variables in the formation of the groups. We use data in (Vlieghe 2014) and exploratory techniques like principal component analysis, cluster analysis and discriminant analysis. We used this scheme of analysis for the initial population of particles as well as in the resulting populations under different hydrodynamics conditions. In the second part we studied the use of the PBE in terms of the moments of the NDF, and the Quadrature Method of Moments (QMOM) and the Generalized Minimal Extrapolation (GME), in order to recover the time evolution of a finite set of standard moments of the NDF. The QMOM methods uses an application of the Product-Difference algorithm and GME recovers a discrete non-negative measure given a finite set of its standard moments. In the third part, we proposed an discretization scheme in order to find a numerical approximation to the solution of the PBE. We used three cases where the analytical solution is known (Silva et al. 2011) in order to compare the theoretical solution to the approximation found with the discretization scheme. In the last part, we proposed a method for estimate the parameters involved in the modelization of aggregation and breakage processes in PBE. The method uses the numerical approximation found, as well as the Extended Kalman Filter. The method estimates iteratively the parameters at each time, using an non- linear Least Square Estimator.

<p>Robust localization is a prerequisite for mobile robot autonomy. In many situations the GPS signal is not available and thus an additional localization system is required. A simple approach is to apply localization based on dead reckoning by use of wheel encoders but it results in large estimation errors. With exteroceptive sensors such as a laser range finder natural landmarks in the environment of the robot can be extracted from raw range data. Landmarks are extracted with the Hough transform and a recursive line segment algorithm. By applying data association and Kalman filtering along with process models the landmarks can be used in combination with wheel encoders for estimating the global position of the robot. If several robots can cooperate better position estimates are to be expected because robots can be seen as mobile landmarks and one robot can supervise the movement of another. The centralized Kalman filter presented in this master thesis systematically treats robots and extracted landmarks such that benefits from several robots are utilized. Experiments in different indoor environments with two different robots show that long distances can be traveled while the positional uncertainty is kept low. The benefit from cooperating robots in the sense of reduced positional uncertainty is also shown in an experiment. </p><p>Except for localization algorithms a typical autonomous robot task in the form of change detection is solved. The change detection method, which requires robust localization, is aimed to be used for surveillance. The implemented algorithm accounts for measurement- and positional uncertainty when determining whether something in the environment has changed. Consecutive true changes as well as sporadic false changes are detected in an illustrative experiment.</p>

A technique for real time object tracking in a mobile computing environment and its application to video see-through Augmented Reality (AR) has been designed, verified through simulation, and implemented and validated on a mobile computing device. Using position based visual position and orientation (POSE) methods and the Extended Kalman Filter (EKF), it is shown how this technique lends itself to be flexible to tracking multiple objects and multiple object models using a single monocular camera on different mobile computing devices. Using the monocular camera of the mobile computing device, feature points of the object(s) are located through image processing on the display. The relative position and orientation between the device and the object(s) is determined recursively by an EKF process. Once the relative position and orientation is determined for each object, three dimensional AR image(s) are rendered onto the display as if the device is looking at the virtual object(s) in the real world. This application and the framework presented could be used in the future to overlay additional informational onto displays in mobile computing devices. Example applications include robotic aided surgery where animations could be overlaid to assist the surgeon, in training applications that could aid in operation of equipment or in search and rescue operations where critical information such as floor plans and directions could be virtually placed onto the display. Current approaches in the field of real time object tracking are discussed along with the methods used for video see-through AR applications on mobile computing devices. The mathematical framework for the real time object tracking and video see-through AR rendering is discussed in detail along with some consideration to extension to the handling of multiple AR objects. A physical implementation for a mobile computing device is proposed detailing the algorithmic approach along with design decisions. The real time object tracking and video see-through AR system proposed is verified through simulation and details around the accuracy, robustness, constraints, and an extension to multiple object tracking are presented. The system is then validated using a ground truth measurement system and the accuracy, robustness, and its limitations are reviewed. A detailed validation analysis is also presented showing the feasibility of extending this approach to multiple objects. Finally conclusions from this research are presented based on the findings of this work and further areas of study are proposed.

The goal of this project was to make a shirt with three embedded IMU sensors (Inertial Measurement Unit) that can measure a person’s movements throughout an entire workday. This can provide information about a person’s daily routine movements and aid in finding activities which can lead to work-related injuries in order to prevent them. The objective was hence to construct a sensor fusion framework that could retrieve the measurements from these three sensors and to create an estimate of the human body orientation and to estimate the angular movements of the arms. This was done using an extended Kalman filter which uses the accelerometer and magnetometer values to retrieve the direction of gravity and north respectively, thus providing a coordinate system that can be trusted in the long term. Since this method is sensitive to quick movements and magnetic disturbance, gyroscope measurements were used to help pick up quick movements. The gyroscope measurements need to be integrated in order to get the angle, which means that we get accumulated errors. This problem is reduced by the fact that we retrieve a correct long-term reference without accumulated errors from the accelerometer and magnetometer measurements. The Kalman filter estimates three quaternions describing the orientation of the upper body and the two arms. These quaternions were then translated into Euler angles in order to get a meaningful description of the orientations. The measurements were stored on a memory card or broadcast on both the local net and the Internet. These data were either used offline in Matlab or shown in real-time in the program Unity 3D. In the latter case the user could see that a movement gives rise to a corresponding movement on a skeleton model on the screen.

Orthogonal Frequency Division Multiplexing (OFDM) is an efficient multi- carrier modulation scheme, which has been adopted for several wireless stan- dards. Systems employing this scheme at the physical layer are sensitive to frequency offsets and that causes Inter Carrier Interference (ICI) and degra- dation in overall system performance of OFDM systems. In this thesis work, an investigation on impairments of OFDM systems will be carried out. Anal- ysis of previous schemes for cancellation of the ICI will be done and a scheme for estimating and compensating the frequency offset based on Unscented Ka- man Filter (UKF) and Extended Kaman Filter (EKF) will be implemented. Analysis on how the UKF improves the Signal to Noise Ratio (SNR); and how well it tracks the frequency offset estimation under Additive White Gaussian Noise (AWGN) channel and flat fading Rayleigh channel will be carried on.

Robust localization is a prerequisite for mobile robot autonomy. In many situations the GPS signal is not available and thus an additional localization system is required. A simple approach is to apply localization based on dead reckoning by use of wheel encoders but it results in large estimation errors. With exteroceptive sensors such as a laser range finder natural landmarks in the environment of the robot can be extracted from raw range data. Landmarks are extracted with the Hough transform and a recursive line segment algorithm. By applying data association and Kalman filtering along with process models the landmarks can be used in combination with wheel encoders for estimating the global position of the robot. If several robots can cooperate better position estimates are to be expected because robots can be seen as mobile landmarks and one robot can supervise the movement of another. The centralized Kalman filter presented in this master thesis systematically treats robots and extracted landmarks such that benefits from several robots are utilized. Experiments in different indoor environments with two different robots show that long distances can be traveled while the positional uncertainty is kept low. The benefit from cooperating robots in the sense of reduced positional uncertainty is also shown in an experiment. Except for localization algorithms a typical autonomous robot task in the form of change detection is solved. The change detection method, which requires robust localization, is aimed to be used for surveillance. The implemented algorithm accounts for measurement- and positional uncertainty when determining whether something in the environment has changed. Consecutive true changes as well as sporadic false changes are detected in an illustrative experiment.

A white Gaussian noise measurement model is widely used in target tracking problem formulation. In practice, the measurement noise may not be white. This phenomenon is due to the scintillation of the target. In many radar systems, the measurement frequency is high enough so that the correlation cannot be ignored without degrading tracking performance. In this thesis, target tracking problem with correlated measurement noise is considered. The correlated measurement noise is modeled by a first-order Markov model. The effect of correlation is thought as interference, and Optimum Decoding Based Smoothing Algorithm is applied. For linear models, the estimation performances of Optimum Decoding Based Smoothing Algorithm are compared with the performances of Alpha-Beta Filter Algorithm. For nonlinear models, the estimation performances of Optimum Decoding Based Smoothing Algorithm are compared with the performances of Extended Kalman Filter by performing various simulations.

This thesis presents the design of Kalman filter based attitude determination algorithms for a hypothetical LEO satellite and for a satellite attitude control test setup. For the hypothetical LEO satellite, an Extended Kalman Filter based attitude determination algorithms are formed with a multi-mode structure that employs the different sensor combinations and as well as online switching between these combinations depending on the sensor availability. The performance of these different attitude determination modes are investigated through Monte Carlo simulations. New attitude determination algorithms are prepared for the satellite attitude control test setup by considering the constraints on the selection of the suitable sensors. Here, performances of the Extended Kalman Filter and Unscented Kalman Filter are investigated. It is shown that robust and sufficiently accurate attitude estimation for the test setup is achievable by using the Unscented Kalman Filter.