This thesis studies algorithms to estimate an aircraft’s position with different information from various radio stations. Because aircrafts both civilian and military are heavily dependant on GNSS signals, it can be interfered from hostile sources. The aircraft shall then be able to navigate without the GNSS signals. This thesis focuses on three radio navigation systems, DME,VOR and TACAN. With the measurements from these three radio stations and measurements from the inertial navigation system one can estimate a position with an estimation filter. In this thesis two types of filters will be used, the linear Kalman filter and the Extended Kalman filter. The linear Kalman filter will be used when converting the TACAN measurements to a pseudo position and the Extended Kalman filter will be used for the DME,VOR and TACAN measurements. The results shows that the converted TACAN measurements and TACAN measurements estimates very well in both north and east direction. When using only DME measurements the filter estimates the position fairly well in the direction towards the station and poorly in the orthogonal direction. For the VOR measurements the filter estimates the position quite poorly in the direction of the radio station and well in the orthogonal direction. In conclusion the converted TACAN measurement and TACAN measurement algorithm can be used for navigation purposes by its own measurements. However, the DME and VOR measurement algorithms need to be combined or using multiple stations at different locations to get better estimates in both directions. All of the filter could use some better tuning to get the optimal filter, but it is not necessary.

The topic of force reconstruction has been studied quite extensively but most of the existing research work that has been done are in the domain of structural and civil engineering construction like bridges and beams. Considerable work in force reconstruction has also being done in fabrication of machines and structures like aircrafts, gear boxes etc. The topic of force reconstruction of the cutting forces during a machining process like turning or milling machines is a recent line of research to suffice the requirement of proactive monitoring of forces generated during the operation of the machine tool. The forces causing vibrations while machining if detected and monitored can enhance system productivity and efficiency of the process. The objective of this study was to investigate the algorithms available in literature for inverse force reconstruction and apply for reconstruction of cutting forces while machining on a computer numerically controlled (CNC) machine. This study has applied inverse force reconstruction technique algorithms 1) Deconvolution method, 2) Kalman filter recursive least square and 3) augmented Kalman filter for inverse reconstruction of forces for multi degree of freedom systems. Results from experiments conducted as part of this thesis work shows the effectiveness of the methods of force reconstruction to monitor the forces generated during the machining process on machine tools in real time without employing dynamometers which are expensive and complex to set-up. This study for developing a cost-effective method of force reconstruction will be instrumental in applications for improving machining efficiency and proactive preventive maintenance. / Graduate

A permanent magnet synchronous motor is a type of motor that is used in several different application areas, not least in an autonomous robots where it is the motor that drives the wheels. Today, many actors choose simulation as a tool to save money and time when product tests are performed. This thesis covers both the process of modeling a permanent magnet synchronous motor and regulating it at low speeds, in a simulation environment. As previously mentioned, the motor is a permanent magnet synchronous motor and is a direct-driven outrunner, which means that the motor and the wheel are combined and that the rotor is spinning outside the stator. On current robots in production, there is a gear ratio between the motor and wheels to be able to regulate the motor at higher speeds and thus generate a torque. The gearing contributes to losses and is an extra cost, so the examination of a direct-drive motor is interesting. The direct-drive motor has a lower working speed and is therefore by some reasons more difficult to regulate when applying torque load to the motor. The motor is equipped with current sensors and a position sensor, which has a certain resolution. The position sensor is speed-dependent in the sense that at lower RPMs fewer measurements are obtained, which is a problem when regulating the motor. The thesis examines two different control strategies, one of which is a more classic PI control that is often used on the market in various systems and the other is model predictive control (MPC). The latter is an online optimization where, with the help of information about the system, an optimal input signal is calculated and applied. Two different non-linear Kalman filters are also examined, which are implemented with the two different control strategies, to estimate the speed with the help of the measurements from current and the position sensor. The conclusion is an ideal motor model that mimics the physical motor. MPC is able to regulate the motor between 0-50 RPM, both with and without applied torque and even better with speed estimation from a Kalman filter. The PI controller is not able to regulate the motor at 2 RPM but for speeds at 10 RPM and greater, however with over-/undershoot after an acceleration.

Le suivi de la mise en place du magma à faible profondeur et de sa migration vers la surface est crucial pour prévoir les éruptions volcaniques.Avec les progrès récents de l'imagerie SAR et le nombre croissant de réseaux GNSS continus sur les volcans, il est maintenant possible de fournir une évolution continue et spatialement étendue des déplacements de surface pendant les périodes inter-éruptives. Pour les volcans basaltiques, ces mesures combinées à des modèles dynamiques simples peuvent être exploitées pour caractériser et contraindre la mise en pression d'un ou de plusieurs réservoirs magmatiques, ce qui fournit une meilleure information prédictive sur l'emplacement du magma à faible profondeur. L'assimilation de données—un processus séquentiel qui combine au mieux les modèles et les observations, en utilisant parfois une information a priori basée sur les statistiques des erreurs, pour prédire l'état d'un système dynamique—a récemment gagné en popularité dans divers domaines des géosciences. Dans cette thèse, je présente la toute première application de l'assimilation de données en volcanologie en allant des tests synthétiques à l’utilisation de données géodésiques réelles.La première partie de ce travail se concentre sur le développement de stratégies afin d'évaluer le potentiel de l’assimilation de données. En particulier, le Filtre de Kalman d'Ensemble a été utilisé avec un modèle dynamique simple à deux chambres et de données géodésiques synthétiques pour aborder les points suivants : 1) suivi de l'évolution de la pression magmatique en profondeur et des déplacements de surface et estimation des paramètres statiques incertains du modèle, 2) assimilation des données GNSS et InSAR, 3) mise en évidence des avantages ou des inconvénients de l'EnKF par rapport à une technique d'inversion bayésienne. Les résultats montrent que l’EnKF fonctionne de manière satisfaisante et que l'assimilation de données semble prometteuse pour la surveillance en temps réel des volcans.La deuxième partie de la thèse est dédiée à l'application de la stratégie mise au point précédemment à l’exploitation des données GNSS inter-éruptives enregistrées de 2004 à 2011 au volcan Grímsvötn en Islande, afin de tester notre capacité à prédire la rupture d'une chambre magmatique en temps réel. Nous avons introduit ici le concept de ``niveau critique'' basé sur l’estimation de la probabilité d'une éruption à chaque pas de temps. Cette probabilité est définie à partir de la proportion d'ensembles de modèles qui dépassent un seuil critique, initialement assigné selon une distribution donnée. Nos résultats montrent que lorsque 25 +/- 1 % des ensembles du modèle ont dépassé la surpression critique une éruption est imminente. De plus, dans ce chapitre, nous élargissons également les tests synthétiques précédents en améliorant la stratégie EnKF d'assimilation des données géodésiques pour l'adapter à l’utilisation de données réelles en nombre limité. Les outils de diagnostiques couramment utilisés en assimilation de données sont mis en oeuvre et présentés.Enfin, je démontre qu'en plus de son intérêt pour prédire les éruptions volcaniques, l'assimilation séquentielle de données géodésiques basée sur l'utilisation de l'EnKF présente un potentiel unique pour apporter une information sur l'alimentation profonde du système volcanique. En utilisant le modèle dynamique à deux réservoirs pour le système de plomberie de Grímsvötn et en supposant une géométrie fixe et des propriétés magmatiques invariantes, nous mettons en évidence que l'apport basal en magma sous Grímsvötn diminue de 85 % au cours des 10 mois précédant le début de l'événement de rifting de Bárdarbunga. La perte d'au moins 0.016 km3 dans l'approvisionnement en magma de Grímsvötn est interprétée comme une conséquence de l'accumulation de magma sous Bárdarbunga et de l'alimentation consécutive de l'éruption Holuhraun à 41 km de distance. / Tracking magma emplacement at shallow depth as well as its migration towards the Earth's surface is crucial to forecast volcanic eruptions.With the recent advances in Interferometric Synthetic Aperture Radar (InSAR) imaging and the increasing number of continuous Global Navigation Satellite System (GNSS) networks recorded on volcanoes, it is now possible to provide continuous and spatially extensive evolution of surface displacements during inter-eruptive periods. For basaltic volcanoes, these measurements combined with simple dynamical models can be exploited to characterise and to constrain magma pressure building within one or several magma reservoirs, allowing better predictive information on the emplacement of magma at shallow depths. Data assimilation—a sequential time-forward process that best combines models and observations, sometimes a priori information based on error statistics, to predict the state of a dynamical system—has recently gained popularity in various fields of geoscience (e.g. ocean-weather forecasting, geomagnetism and natural resources exploration). In this dissertation, I present the very first application of data assimilation in volcanology from synthetic tests to analyzing real geodetic data.The first part of this work focuses on the development of strategies in order to test the applicability and to assess the potential of data assimilation, in particular, the Ensemble Kalman Filter (EnKF) using a simple two-chamber dynamical model (Reverso2014) and artificial geodetic data. Synthetic tests are performed in order to address the following: 1) track the magma pressure evolution at depth and reconstruct the synthetic ground surface displacements as well as estimate non-evolving uncertain model parameters, 2) properly assimilate GNSS and InSAR data, 3) highlight the strengths and weaknesses of EnKF in comparison with a Bayesian-based inversion technique (e.g. Markov Chain Monte Carlo). Results show that EnKF works well with the synthetic cases and there is a great potential in utilising data assimilation for real-time monitoring of volcanic unrest.The second part is focused on applying the strategy that we developed through synthetic tests in order to forecast the rupture of a magma chamber in real time. We basically explored the 2004-2011 inter-eruptive dataset at Grímsvötn volcano in Iceland. Here, we introduced the concept of “eruption zones” based on the evaluation of the probability of eruption at each time step estimated as the percentage of model ensembles that exceeded their failure overpressure values initially assigned following a given distribution. Our results show that when 25 +/- 1% of the model ensembles exceeded the failure overpressure, an actual eruption is imminent. Furthermore, in this chapter, we also extend the previous synthetic tests by further enhancing the EnKF strategy of assimilating geodetic data in order to adapt to real world problems such as, the limited amount of geodetic data available to monitor ice-covered active volcanoes. Common diagnostic tools in data assimilation are presented.Finally, I demonstrate that in addition to the interest of predicting volcanic eruptions, sequential assimilation of geodetic data on the basis of EnKF shows a unique potential to give insights into volcanic system roots. Using the two-reservoir dynamical model for Grímsvötn 's plumbing system and assuming a fixed geometry and constant magma properties, we retrieve the temporal evolution of the basal magma inflow beneath Grímsvötn that drops up to 85% during the 10 months preceding the initiation of the Bárdarbunga rifting event. The loss of at least 0.016 km3 in the magma supply of Grímsvötn is interpreted as a consequence of magma accumulation beneath Bárdarbunga and subsequent feeding of the Holuhraun eruption 41 km away.

Rocket Factory Augsburg (RFA) a German New Space Startup is developing a three-stage rocket launcher aiming at LEO/SSO orbits. A fundamental responsibility of the GNC team is the development of the rocket navigation algorithm to estimate the attitude, position, and velocity allowing the guidance and control loops to autonomously steer the rocket. This thesis focuses on the analysis and design of a Hybrid Navigation system able to satisfy the various necessities of a launch vehicle, such as delay compensation and GNSS outages. The navigation architecture was chosen to be a Closed Loop, Loosely Coupled, Delayed Error State Kalman Filter thanks to the proven capability of COTS receivers to autonomously provide a consistent PVT solution throughout the flight. A preliminary analysis used a reference trajectory to evaluate the effect of the sensor grade on inertial performances and choose an appropriate integration scheme. The filter’s system model was explored using approximate analytical results on observability. The developed navigation module was then tested within a Monte Carlo simulation environment by perturbating the sensor parameter in accordance with the sensor datasheet. As a further verification, the modeled IMU output was compared to the engineering model, to assure that the simulation result would yield conservative errors. Due to concern over the visibility of GNSS satellites during flight, a simplified Almanac-based GPS model has been developed, proving that enough satellite visibility is available along the trajectory. The estimation error was compared with the filter’s estimated covariance and found well within the bounds. Through the study of the covariance evolution, it was determined that given the reference dynamics, the sensor misalignments are the least observable states. Realistic signal outages were introduced in the most critical flight intervals. The filter was indeed found to be robust and the tuning proved to be adequate to capture the dead reckoning drift. Finally, the entire navigation module was deployed onto the avionics engineering model, including the flight computer, IMU, GNSS, and antennas, in a configuration equivalent to flight. The navigation module was then tested to ensure that the execution was in performance under severe multipath errors and prolonged GNSS outages with the covariance estimates correctly covering the uncertainty. / Rocket Factory Augsburg (RFA), ett tyskt nystartat rymdföretag, utvecklar en trestegsraket som siktar på LEO/SSO-banor. Ett grundläggande ansvar för GNC-teamet är utvecklingen av raketnavigationsalgoritmen för att uppskatta attityd, position och hastighet så att styr- och kontrollslingorna kan styra raketen autonomt. Avhandlingen fokuserar på analys och design av ett hybridnavigeringssystem som kan uppfylla de olika krav som ställs på en bärraket, såsom kompensation för fördröjningar och GNSS-avbrott. Navigationsarkitekturen valdes att vara ett Closed Loop, Loosely Coupled, Delayed Error State Kalman Filter tack vare den bevisade förmågan hos COTS-mottagare att autonomt tillhandahålla en konsekvent PVT-lösning under hela flygningen. En preliminär analys använde en referensbana för att utvärdera effekten av sensorkvaliteten på tröghetsprestanda och välja ett lämpligt integrationsschema. Filtrets systemmodell undersöktes med hjälp av approximativa analytiska resultat om observerbarhet. Den utvecklade navigeringsmodulen testades sedan i en Monte Carlo-simuleringsmiljö genom att störa sensorparametern i enlighet med sensorns datablad. Som en ytterligare verifiering jämfördes den modellerade IMU-utgången med den tekniska modellen, för att säkerställa att simuleringsresultatet skulle ge konservativa fel. På grund av oro över GNSS-satelliternas synlighet under flygning har en förenklad Almanac-baserad GPS-modell utvecklats, som bevisar att tillräcklig satellitsikt finns tillgänglig längs banan. Uppskattningsfelet jämfördes med filtrets uppskattade kovarians och låg väl inom gränserna. Genom att studera kovariansutvecklingen fastställdes det att givet referensdynamiken är sensorernas feljusteringar de minst observerbara tillstånden. Realistiska signalavbrott infördes i de mest kritiska flygintervallen. Filtret visade sig verkligen vara robust och inställningen visade sig vara tillräcklig för att fånga upp dödberäkningens drift. Slutligen installerades hela navigeringsmodulen på den flygtekniska modellen, inklusive flygdator, IMU, GNSS och antenner, i en konfiguration som motsvarar en flygning. Navigationsmodulen testades sedan för att säkerställa att utförandet var i prestanda under allvarliga multipath-fel och långvariga GNSS-avbrott med kovariansuppskattningarna som korrekt täcker osäkerheten.

Faulty manufactured alternators lead to commercial and safety concerns when installed in vehicles. Alternators have a major role in the Electrical Power Generation System (EPGS) of vehicles, and a defective alternator will lead to damaging of the battery and other important electric accessories. Therefore, fault detection and diagnosis of alternators can be implemented to quickly and accurately determine the health of an alternator during end of line testing, and not let faulty components leave the manufacturer. The focus of this research is to develop a Model Based Fault Detection and Diagnosis (FDD) strategy for detecting alternator faults during end of line testing. The proposed solution uses Extended Kalman Smooth Variable Structure Filter (EK-SVSF) to detect common alternator faults. A solution using the Dual Extended Kalman Filter (DEKF) is also discussed. The alternator faults were programmatically simulated on alternator measurements. The experimental results prove that both the EK-SVSF and DEKF strategies were very effective in alternator modeling and detecting open diode faults, shorted diode faults, and stator imbalance faults. / Thesis / Master of Applied Science (MASc)

Ziel der Arbeit ist die anschauliche Demonstration der Leistungsfähigkeit von Hardware- Systemen zur Regelung instabiler Systeme am Beispiel des Inversen Pendels. Dabei handelt es sich um das Balancieren eines Stabes, einem Standard-Problem der Regelungstechnik. Es wird die Konzeption und Implementierung einer Hardware-Regelung in einem FPGA-Prototypenboard zur Realisierung dieser Aufgabe beschrieben. Die Regelung basiert mit LQR-Entwurf und Kalman-Filter auf klassischen Methoden der Regelungstechnik. Zur Demonstration der Regelung wurde ein mechanischer Aufbau vorgenommen, an dem die Funktionsfähigkeit des Inversen Pendels praktisch gezeigt wurde.

Submitted by Bruna Rodrigues (bruna92rodrigues@yahoo.com.br) on 2016-10-04T14:03:27Z No. of bitstreams: 1 DissDPL.pdf: 3012901 bytes, checksum: 29c2df84e5e59e8e598fae154f0983d2 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-14T14:18:34Z (GMT) No. of bitstreams: 1 DissDPL.pdf: 3012901 bytes, checksum: 29c2df84e5e59e8e598fae154f0983d2 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-14T14:18:43Z (GMT) No. of bitstreams: 1 DissDPL.pdf: 3012901 bytes, checksum: 29c2df84e5e59e8e598fae154f0983d2 (MD5) / Made available in DSpace on 2016-10-14T14:18:52Z (GMT). No. of bitstreams: 1 DissDPL.pdf: 3012901 bytes, checksum: 29c2df84e5e59e8e598fae154f0983d2 (MD5) Previous issue date: 2016-03-04 / Não recebi financiamento / Kalman filters are rooted in the technical literature, as a way of predicting new states in nonlinear systems providing a recursive solution to the problem of linear optimal filtering. Therefore, 56 years after its discovery, many modifications have been proposed in order to obtain better accuracy and speed. Some of these changes are used in this work; these being the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Kalman Filter Cubature (CKF). This work , divided into three distinct parts: Implementation / Comparative analysis of prediction of Kalman filters in complex systems (Series), qualitative analysis of the possible uses of the Kalman filter variants for neural network training and position and velocity determination a displaced object on a simulated plane with some trajectories Having these analyzes key role in fostering the studies cited in the scientific literature , proving the possibility of such algorithms and methods are used for positioning in unstructured environments / Filtros de Kalman estão consagrados na literatura técnica, como uma das formas de prever novos estados em sistemas não-lineares, fornecendo uma solução recursiva para o problema da filtragem ideal linear. Após 56 anos de sua descoberta, muitas modificações e melhorias foram propostas, procurando obter uma maior precisão e velocidade na predição de novos estados. Algumas dessas mudanças são utilizadas neste trabalho; sendo elas o Filtro de Kalman Estendido (EKF), Unscented Kalman Filter (UKF) e Filtro de Kalman de Cubagem Esférica Radial (CKF).O objetivo deste trabalho, divido em três partes distintas, porém complementares: Implementação/Análise comparativa da predição dos Filtros de Kalman em sistemas complexos (Series), Análise qualitativa das possíveis utilizações das variantes do Filtro de Kalman para treinamento de Redes Neurais e Determinação de posição e velocidade de um objeto deslocado sobre um plano simulado. Possuindo essas análises papel fundamental na fomentação dos estudos citados na literatura científica durante o trabalho, e comprovando a possibilidade desses algoritmos/ métodos serem utilizados em tarefas de posicionamento em ambientes não estruturados.

This thesis deals with the filter algorithm design, implementing mathematical model to improve algorithm performance. Designed algorithms are implemented in a control unit of the experimental vehicle (filters signal used in the closed-loop controller). The improvement of the position estimation using Kalman Filter is demonstrated on the experimental vehicle. In the next part the design process of algorithm developing for dsPIC microcontroller using Matlab is described.

The master’s thesis deals with a design of the computational method for the analysis of swimmers training with the use of an inertial measurement unit. The developed algorithm uses quaternion-based Unscented Kalman filter and merges accelerometer and gyroscope measurements. The proposed method enables analysis of velocity, acceleration and inclination of a swimmer. Verification of the method was based on an underwater video camera capturing and a tethered velocity meter.