Nesta dissertação é apresentada a comparação do desempenho de três estimadores - o Filtro de Kalman Estendido, o Filtro de Kalman Unscented e o Filtro de Partículas - aplicados para estimar a postura de um robô diferencial. Uma câmera foi fixa no teto para cobrir todo o campo operacional do robô durante os experimentos, a fim de extrair o mapa e gerar o ground truth. Isso permitiu realizar uma análise do erro de forma precisa a cada instante de tempo. O desempenho de cada um dos estimadores foi avaliado sistematicamente e numericamente para duas trajetórias. Os resultados desse primeiro experimento demonstram que os filtros proporcionam grandes melhorias em relação à odometria e que o modelo dos sensores é crítico para obter esse desempenho. O Filtro de Partículas mostrou um desempenho melhor em relação aos demais nos dois percursos. No entanto, seu elevado custo computacional dificulta sua implementação em uma aplicação de tempo real. O Filtro de Kalman Unscented, por sua vez, mostrou um desempenho semelhante ao Filtro de Kalman Estendido durante a primeira trajetória. Porém, na segunda trajetória, a qual possui uma quantidade maior de curvas, o Filtro de Kalman Unscented mostrou uma melhora significativa em relação ao Filtro de Kalman Estendido. Foi realizado um segundo experimento, em que o robô planeja e executa duas trajetórias. Os resultados obtidos mostraram que o robô consegue chegar a um determinado local com uma precisão da mesma ordem de grandeza do que a obtida durante a estimação de estados do robô. / In this dissertation, the performance of three nonlinear-model based estimators - the Extended Kalman Filter, the Unscented Kalman Filter and the Particle Filter - applied to pose estimation of a differential drive robot is compared. A camera was placed above the operating field of the robot to record the experiments in order to extract the map and generate the ground truth so the evaluation of the error can be done at each time step with high accuracy. The performance of each estimator is assessed systematically and numerically for two robot trajectories. The first experimental results showed that all estimators provide large improvements with respect to odometry and that the sensor modeling is critical for their performance. The particle filter showed a better performance than the others on both experiments, however, its high computational cost makes it difficult to implement in a real-time application. The Unscented Kalman Filter showed a similar performance to the Extended Kalman Filter during the first trajectory. However, during the second one (a curvier path) the Unscented Kalman Filter showed a significant improvement over the Extended Kalman Filter. A second experiment was carried out where the robot plans and executes a trajectory. The results showed the robot can reach a predefined location with an accuracy of the same order of magnitude as the obtained during the robot pose estimation.

Design of filters used in grid-connected inverter applications involves multiple constraints. The filter requirements are driven by tight filtering tolerances of standards such as IEEE 519-1992–IEEE Recommended Practices and Requirements for Harmonic Control in Electrical Power Systems and IEEE 1547.2-2008–IEEE Application Guide for IEEE Std 1547, IEEE Standard for Interconnecting Distributed Resources with Electric Power Systems. Higher order LCL filters are essential to achieve these regulatory standard requirements at compact size and weight. This objective of this thesis report is to evaluate design procedures for such higher order LCL filters. The initial configuration of the third order LCL filter is decided by the frequency response of the filter. The design equations are developed in per-unit basis so results can be generalized for different applications and power levels. The frequency response is decided by IEEE specifications for high frequency current ripple at the point of common coupling. The appropriate values of L and C are then designed and constructed. Power loss in individual filter components is modeled by analytical equations and an iterative process is used to arrive at the most efficient design. Different combinations of magnetic materials (ferrite, amorphous, powder) and winding types (round wire, foil) are designed and tested to determine the most efficient design. The harmonic spectrum, power loss and temperature rise in individual filter components is predicted analytically and verified by actual tests using a 3 phase 10 kW grid connected converter setup. Experimental results of filtering characteristics show a good match with analysis in the frequency range of interconnected inverter applications. The design process is stream-lined for the above specified core and winding types. The output harmonic current spectrum is sampled and it is established that the harmonics are within the IEEE recommended limits. The analytical equations predicting the power loss and temperature rise are verified by experimental results. Based on the findings, new LCL filter combinations are formulated by varying the net Lpu to achieve the highest efficiency while still meeting the recommended IEEE specifications. Thus a design procedure which can enable an engineer to design the most efficient and compact filter that can also meet the recommended guidelines of harmonic filtering for grid-connected converter applications is established.

This thesis deals with estimation of states and parameters in nonlinear and non-Gaussian dynamic systems. Sequential Monte Carlo methods are mainly used to this end. These methods rely on models of the underlying system, motivating some developments of the model concept. One of the main reasons for the interest in nonlinear estimation is that problems of this kind arise naturally in many important applications. Several applications of nonlinear estimation are studied. The models most commonly used for estimation are based on stochastic difference equations, referred to as state-space models. This thesis is mainly concerned with models of this kind. However, there will be a brief digression from this, in the treatment of the mathematically more intricate differential-algebraic equations. Here, the purpose is to write these equations in a form suitable for statistical signal processing. The nonlinear state estimation problem is addressed using sequential Monte Carlo methods, commonly referred to as particle methods. When there is a linear sub-structure inherent in the underlying model, this can be exploited by the powerful combination of the particle filter and the Kalman filter, presented by the marginalized particle filter. This algorithm is also known as the Rao-Blackwellized particle filter and it is thoroughly derived and explained in conjunction with a rather general class of mixed linear/nonlinear state-space models. Models of this type are often used in studying positioning and target tracking applications. This is illustrated using several examples from the automotive and the aircraft industry. Furthermore, the computational complexity of the marginalized particle filter is analyzed. The parameter estimation problem is addressed for a relatively general class of mixed linear/nonlinear state-space models. The expectation maximization algorithm is used to calculate parameter estimates from batch data. In devising this algorithm, the need to solve a nonlinear smoothing problem arises, which is handled using a particle smoother. The use of the marginalized particle filter for recursive parameterestimation is also investigated. The applications considered are the camera positioning problem arising from augmented reality and sensor fusion problems originating from automotive active safety systems. The use of vision measurements in the estimation problem is central to both applications. In augmented reality, the estimates of the camera’s position and orientation are imperative in the process of overlaying computer generated objects onto the live video stream. The objective in the sensor fusion problems arising in automotive safety systems is to provide information about the host vehicle and its surroundings, such as the position of other vehicles and the road geometry. Information of this kind is crucial for many systems, such as adaptive cruise control, collision avoidance and lane guidance.

This research work is devoted to the analysis, design and implementation of a shunt connected Hybrid Active Power Filter by the use of a lower rated voltage source PWM converter, and a series connected LC passive filter. In recent years, voltage and current harmonics have become a serious problem both in transmission and distribution systems, due to the widespread usage of non-linear loads such as diode/thyristor rectifiers, electric arc furnaces and motor drives. In order to obtain a better performance than those of the conventional passive filter solutions, active power filters (APF) have been worked on and developed. Among various configurations listed in the literature, conventional shunt connected voltage source active power filter is widely used in industrial applications. Unfortunately, for large power applications, the losses and the rating of the APF increase considerably. As a result, various hybrid filter topologies have been developed which combine the advantages of both passive and active filters. In this thesis, a shunt connected hybrid active power filter is developed by combining a 4.7 kVA voltage source converter and a 30kVAR 7th harmonic passive filter. The developed system has been implemented to eliminate the most dominant 5th, 7th and 11th current harmonic components existing at 400V low voltage bus of TUBITAK SPACE Technologies Institute. The theoretical and experimental results have shown that the DC link voltage of the converter and the rating of APF are minimized while keeping the filtering performance satisfactory.

Die dynamische Schätzung der Fahrzeugposition durch Sensordatenfusion ist eine der grundlegenden Aufgaben für moderne Verkehrsanwendungen wie zum Beispiel fahrerlose Transportsysteme oder Pre-Crash-Sicherheitssysteme. In dieser Arbeit wird ein Verfahren zur dezentralen kooperativen Fahrzeuglokalisierung vorgestellt, das auf einer allgemeinen Methode zur Fusion von Informationen mehrerer Teilnehmer beruht. Sowohl die lokale als auch die übertragene Schätzung wird durch Partikel dargestellt. Innerhalb einer Simulation wird gezeigt, dass sich die Positionsschätzung der einzelnen Teilnehmer im Netzwerk im Vergleich zu einer reinen GPS-basierten Lösung verbessert.

Nesta dissertação é apresentada a comparação do desempenho de três estimadores - o Filtro de Kalman Estendido, o Filtro de Kalman Unscented e o Filtro de Partículas - aplicados para estimar a postura de um robô diferencial. Uma câmera foi fixa no teto para cobrir todo o campo operacional do robô durante os experimentos, a fim de extrair o mapa e gerar o ground truth. Isso permitiu realizar uma análise do erro de forma precisa a cada instante de tempo. O desempenho de cada um dos estimadores foi avaliado sistematicamente e numericamente para duas trajetórias. Os resultados desse primeiro experimento demonstram que os filtros proporcionam grandes melhorias em relação à odometria e que o modelo dos sensores é crítico para obter esse desempenho. O Filtro de Partículas mostrou um desempenho melhor em relação aos demais nos dois percursos. No entanto, seu elevado custo computacional dificulta sua implementação em uma aplicação de tempo real. O Filtro de Kalman Unscented, por sua vez, mostrou um desempenho semelhante ao Filtro de Kalman Estendido durante a primeira trajetória. Porém, na segunda trajetória, a qual possui uma quantidade maior de curvas, o Filtro de Kalman Unscented mostrou uma melhora significativa em relação ao Filtro de Kalman Estendido. Foi realizado um segundo experimento, em que o robô planeja e executa duas trajetórias. Os resultados obtidos mostraram que o robô consegue chegar a um determinado local com uma precisão da mesma ordem de grandeza do que a obtida durante a estimação de estados do robô. / In this dissertation, the performance of three nonlinear-model based estimators - the Extended Kalman Filter, the Unscented Kalman Filter and the Particle Filter - applied to pose estimation of a differential drive robot is compared. A camera was placed above the operating field of the robot to record the experiments in order to extract the map and generate the ground truth so the evaluation of the error can be done at each time step with high accuracy. The performance of each estimator is assessed systematically and numerically for two robot trajectories. The first experimental results showed that all estimators provide large improvements with respect to odometry and that the sensor modeling is critical for their performance. The particle filter showed a better performance than the others on both experiments, however, its high computational cost makes it difficult to implement in a real-time application. The Unscented Kalman Filter showed a similar performance to the Extended Kalman Filter during the first trajectory. However, during the second one (a curvier path) the Unscented Kalman Filter showed a significant improvement over the Extended Kalman Filter. A second experiment was carried out where the robot plans and executes a trajectory. The results showed the robot can reach a predefined location with an accuracy of the same order of magnitude as the obtained during the robot pose estimation.

Nesta dissertação é apresentada a comparação do desempenho de três estimadores - o Filtro de Kalman Estendido, o Filtro de Kalman Unscented e o Filtro de Partículas - aplicados para estimar a postura de um robô diferencial. Uma câmera foi fixa no teto para cobrir todo o campo operacional do robô durante os experimentos, a fim de extrair o mapa e gerar o ground truth. Isso permitiu realizar uma análise do erro de forma precisa a cada instante de tempo. O desempenho de cada um dos estimadores foi avaliado sistematicamente e numericamente para duas trajetórias. Os resultados desse primeiro experimento demonstram que os filtros proporcionam grandes melhorias em relação à odometria e que o modelo dos sensores é crítico para obter esse desempenho. O Filtro de Partículas mostrou um desempenho melhor em relação aos demais nos dois percursos. No entanto, seu elevado custo computacional dificulta sua implementação em uma aplicação de tempo real. O Filtro de Kalman Unscented, por sua vez, mostrou um desempenho semelhante ao Filtro de Kalman Estendido durante a primeira trajetória. Porém, na segunda trajetória, a qual possui uma quantidade maior de curvas, o Filtro de Kalman Unscented mostrou uma melhora significativa em relação ao Filtro de Kalman Estendido. Foi realizado um segundo experimento, em que o robô planeja e executa duas trajetórias. Os resultados obtidos mostraram que o robô consegue chegar a um determinado local com uma precisão da mesma ordem de grandeza do que a obtida durante a estimação de estados do robô. / In this dissertation, the performance of three nonlinear-model based estimators - the Extended Kalman Filter, the Unscented Kalman Filter and the Particle Filter - applied to pose estimation of a differential drive robot is compared. A camera was placed above the operating field of the robot to record the experiments in order to extract the map and generate the ground truth so the evaluation of the error can be done at each time step with high accuracy. The performance of each estimator is assessed systematically and numerically for two robot trajectories. The first experimental results showed that all estimators provide large improvements with respect to odometry and that the sensor modeling is critical for their performance. The particle filter showed a better performance than the others on both experiments, however, its high computational cost makes it difficult to implement in a real-time application. The Unscented Kalman Filter showed a similar performance to the Extended Kalman Filter during the first trajectory. However, during the second one (a curvier path) the Unscented Kalman Filter showed a significant improvement over the Extended Kalman Filter. A second experiment was carried out where the robot plans and executes a trajectory. The results showed the robot can reach a predefined location with an accuracy of the same order of magnitude as the obtained during the robot pose estimation.

This master’s thesis deals with the design of tuneable frequency second order filter in CMOS technology. The thesis describes the design of a transconductor and its utilization for tunable gm-C filter. The design and all simulations were made in Cadence Spectre and Virtuoso software. Limitedly Orcad Pspice and SNAP were also used.

This master thesis studied methods for tracking and localising a moving target from an autonomous seeker drone. Feasible methods for automatic control of the seeker drone and different antenna configurations were explored as well. Two different tracking filters and two different controllers were tested for this purpose. The algorithm was developed in Python and MATLAB. The evaluation of the filters and controllers was done both theoretically with simulations but also practically with flight tests. Performance and robustness were measured by examining the estimated target position and the smoothness of the seeker path. Both filters performed satisfactorily, the same conclusion could be made for the automatic controllers as well. Regardless of the sufficient results, for future work there are several aspects which can be improved. The communication with the drone’s motors, the noise models and one of the automatic controllers are all examples of areas which canbe improved further.

Arbetet utfördes med hjälp av Micropower groups i Växjö. För att få ett förståelse för hur Micropowers batteriladdare beter sig och hur stora övertoner de skapar. Då 5 kW laddaren ej klarade SS-EN IEC 61000-3-12[1] standarden, konstruerades det passiva filter för att reducera övertonerna. Det paralleldämpade filtret klarade att reducera dessa övertoner till rätt nivå. 26 KW laddaren klarar standarden, men det lånades in ett elektroniskt filter av Comsys. Filtret testades för att reducera övertoner till den bästa nivån i standarden och hur många laddare filtret kan hantera. Efter detta gjordes det en enkel beräkning för att se hur stor ekonomisk vinst det är på att införskaffa ett filter istället för att bygga individuella filter för varje laddare.  Nyckelord: Batteriladdare, Filter, Övertoner, Passivt filter, Elektroniskt filter.