This thesis presents different state estimation techniques for speed sensorlees field oriented control of induction motors. The theoretical basis of each algorithm is explained in detail and its performance is tested with simulations and experiments individually. First, a stochastical nonlinear state estimator, Extended Kalman Filter (EKF) is presented. The motor model designed for EKF application involves rotor speed, dq-axis rotor fluxes and dq-axis stator currents. Thus, using this observer the rotor speed and rotor fluxes are estimated simultaneously. Different from the widely accepted use of EKF, in which it is optimized for either steady-state or transient operations, here using adjustable noise level process algorithm the optimization of EKF has been done for both states / the steady-state and the transient-state of operations. Additionally, the measurement noise immunity of EKF is also investigated. Second, Unscented Kalman Filter (UKF), which is an updated version of EKF, is proposed as a state estimator for speed sensorless field oriented control of induction motors. UKF state update computations, different from EKF, are derivative free and they do not involve costly calculation of Jacobian matrices. Moreover, variance of each state is not assumed Gaussian, therefore a more realistic approach is provided by UKF. In this work, the superiority of UKF is shown in the state estimation of induction motor. Third, Model Reference Adaptive System is studied as a state estimator. Two different methods, back emf scheme and reactive power scheme, are applied to MRAS algorithm to estimate rotor speed. Finally, a flux estimator and an open-loop speed estimator combination is employed to observe stator-rotor fluxes, rotor-flux angle and rotor speed. In flux estimator, voltage model is assisted by current model via a closed-loop to compensate voltage model&rsquo / s disadvantages.

One of Saab Space’s products is the S19 guidance system for sounding rockets.Today this system is based on an inertial navigation system that blindly calculatesthe position of the rocket by integrating sensor readings with unknown bias. Thepurpose of this thesis is to integrate a Global Positioning System (GPS) receiverinto the guidance system to increase precision and robustness. There are mainlytwo problems involved in this integration. One is to integrate the GPS with sensorfusion into the existing guidance system. The seconds is to get the GPS satellitetracking to work under extremely high dynamics. The first of the two problems issolved by using an Extended Kalman filter (EKF) with two different linearizations.One of them is uses Euler angles and the other is done with quaternions. Theintegration technique implemented in this thesis is a loose integration between theGPS receiver and the inertial navigation system. The main task of the EKF isto estimate the bias of the inertial navigation system sensors and correct it toeliminate drift in the position. The solution is verified by computing the positionof a car using a GPS and an inertial measurement unit. Different solutions to theGPS tracking problem are proposed in a pre-study. / En av Saab Space produkter är navigationssystemet S19 som styr sondraketer.Fram till idag har systemet varit baserat på ett tröghetsnavigeringssystem somblint räknar ut position genom att integrera tröghetsnavigerinssystemets sensorermed okända biaser. Syftet med detta exjobb är att integrera en GPS med tröghetsnavigeringsystemetför att öka robusthet och precision. Det kan i huvudsak delasupp i två problem; att integrera en GPS-mottagare med det befintliga navigationsystemetmed användning utav sensorfusion, och att få satellitföljningen attfungera under extremt höga dynamiska förhållanden. Det första av de två problemenlöses genom ett Extended Kalman filter (EKF) med två olika linjäriseringar.Den första linjäriseringen är med Eulervinklar och är välbeprövad. Den andra ärmed kvaternioner. Integrationstekniken som implementeras i detta Examensarbeteär en lös integration mellan GPS-mottagaren och tröghetsnavigeringssystemet. Huvudsyftetmed EKF:en är att estimera bias i tröghetsnavigeringsystemets sensoreroch korrigera dem för att eliminera drifter i position. Lösningen verifieras genomatt räkna ut positionen för en bil med GPS och en inertiell mätenhet. Olika lösningartill satellitföljningen föreslås i en förstudie.

The purpose of this report is to evaluate different methods for identifying states in robot models. Both linear and non-linear filters exist among these methods and are compared to each other. Advantages, disadvantages and problems that can occur during tuning and running are presented. Additional measurements from accelerometers are added and their use with above mentioned methods for state estimation is evaluated. The evaluation of methods in this report is mainly based on simulations in Matlab, even though some experiments have been performed on laboratory equipment. The conclusion indicates that simple non-linear models with few states can be more accurately estimated with a Kalman filter than with an extended Kalman filter, as long as only linear measurements are used. When non-linear measurements are used an extended Kalman filteris more accurate than a Kalman filter. Non-linear measurements are introduced through accelerometers with non-linear measurement equations. Using accelerometers generally leads to better state estimation when the measure equations have a simple relation to the model.

<p>One of Saab Space’s products is the S19 guidance system for sounding rockets.Today this system is based on an inertial navigation system that blindly calculatesthe position of the rocket by integrating sensor readings with unknown bias. Thepurpose of this thesis is to integrate a Global Positioning System (GPS) receiverinto the guidance system to increase precision and robustness. There are mainlytwo problems involved in this integration. One is to integrate the GPS with sensorfusion into the existing guidance system. The seconds is to get the GPS satellitetracking to work under extremely high dynamics. The first of the two problems issolved by using an Extended Kalman filter (EKF) with two different linearizations.One of them is uses Euler angles and the other is done with quaternions. Theintegration technique implemented in this thesis is a loose integration between theGPS receiver and the inertial navigation system. The main task of the EKF isto estimate the bias of the inertial navigation system sensors and correct it toeliminate drift in the position. The solution is verified by computing the positionof a car using a GPS and an inertial measurement unit. Different solutions to theGPS tracking problem are proposed in a pre-study.</p> / <p>En av Saab Space produkter är navigationssystemet S19 som styr sondraketer.Fram till idag har systemet varit baserat på ett tröghetsnavigeringssystem somblint räknar ut position genom att integrera tröghetsnavigerinssystemets sensorermed okända biaser. Syftet med detta exjobb är att integrera en GPS med tröghetsnavigeringsystemetför att öka robusthet och precision. Det kan i huvudsak delasupp i två problem; att integrera en GPS-mottagare med det befintliga navigationsystemetmed användning utav sensorfusion, och att få satellitföljningen attfungera under extremt höga dynamiska förhållanden. Det första av de två problemenlöses genom ett Extended Kalman filter (EKF) med två olika linjäriseringar.Den första linjäriseringen är med Eulervinklar och är välbeprövad. Den andra ärmed kvaternioner. Integrationstekniken som implementeras i detta Examensarbeteär en lös integration mellan GPS-mottagaren och tröghetsnavigeringssystemet. Huvudsyftetmed EKF:en är att estimera bias i tröghetsnavigeringsystemets sensoreroch korrigera dem för att eliminera drifter i position. Lösningen verifieras genomatt räkna ut positionen för en bil med GPS och en inertiell mätenhet. Olika lösningartill satellitföljningen föreslås i en förstudie.</p>

Les applications de la robotique mobile autonome en environnements extérieurs sont nombreuses : surveillance de site à la recherche d'anomalies, campagne d'acquisition de données, exploration, recherche de victimes sur des lieux de catastrophes, etc, et l'intérêt de la robotique pour ces applications est d'autant plus grand que les environnements peuvent être dangereux ou risqués pour l'homme. La localisation des robots est une fonction clé dans ces contextes car elle est indispensable à de nombreuses autres fonctions, particulièrement la construction de modèles d'environnement, l'exécution des trajectoires, ou la supervision des missions. Ces travaux présentent la construction d'une solution de localisation pour des robots autonomes, conçue pour être à la fois un outil générique de recherche et un outil opérationnel pour localiser nos robots lors de leurs missions de navigation autonome, capable de gérer de fortes dynamiques de mouvement. En partant d'une solution de localisation et cartographie simultanées (SLAM) basée sur l'utilisation d'une simple caméra, différentes solutions sont successivement construites en ajoutant progressivement des capteurs afin de pallier les difficultés rencontrées lors des évaluations, et ce jusqu'à obtenir un système robuste et précis combinant plusieurs caméras, une centrale inertielle et l'odométrie, et ayant en outre la possibilité d'intégrer des estimations de positions absolues quand elles peuvent être produites (par un récepteur GPS ou un algorithme exploitant une carte initiale). Une analyse profonde des capacités et limitations des différents systèmes est systématiquement effectuée, en considérant notamment l'intérêt d'estimer en ligne les calibrages extrinsèques et biais des capteurs. Un accent particulier est mis sur l'exécution temps réel des algorithmes à bord du robot et sur leur robustesse : cela implique la résolution de nombreux problèmes, portant notamment sur les aspects temporels de la gestion des données. Une large évaluation sur différents jeux de données réalistes permet d'évaluer et de valider les différents développements proposés tout au long du manuscrit.

High precision control of a truck and trailer system requires accurate and robust state estimation of the system. This thesis work explores the possibility of estimating the states with high accuracy from sensors solely mounted on the truck. The sensors used are a LIDAR sensor, a rear-view camera and a RTK-GNSS receiver. Information about the angles between the truck and the trailer are extracted from LIDAR scans and camera images through deep learning and through model-based approaches. The estimates are fused together with a model of the dynamics of the system in an Extended Kalman Filter to obtain high precision state estimates. Training data for the deep learning approaches and data to evaluate and compare these methods with the model-based approaches are collected in a simulation environment established in Gazebo. The deep learning approaches are shown to give decent angle estimations but the model-based approaches are shown to result in more robust and accurate estimates. The flexibility of the deep learning approach to learn any model given sufficient training data has been highlighted and it is shown that a deep learning approach can be viable if the trailer has an irregular shape and a large amount of data is available. It is also shown that biases in measured lengths of the system can be remedied by estimating the biases online in the filter and this improves the state estimates.

This thesis describes and investigates different approaches to indoor mapping and navigation. A system capable of mapping large indoor areas with a stereo camera and/or a laser camera mounted to e.g. a robot or a human is developed. The approaches investigated in this report are either based on Simultaneous Lo- calisation and Mapping (SLAM) techniques, e.g. Extended Kalman Filter-SLAM (EKF-SLAM) and Smoothing and Mapping (SAM), or registration techniques, e.g. Iterated Closest Point (ICP) and Normal Distributions Transform (NDT).In SLAM, it is demonstrated that the laser camera can contribute to the stereo camera by providing accurate distance estimates. By combining these sensors in EKF-SLAM, it is possible to obtain more accurate maps and trajectories compared to if the stereo camera is used alone.It is also demonstrated that by dividing the environment into smaller ones, i.e. submaps, it is possible to build large maps in close to linear time. A new approach to SLAM based on EKF-SLAM and SAM, called Submap Joining Smoothing and Mapping (SJSAM), is implemented to demonstrate this.NDT is also implemented and the objective is to register two point clouds from the laser camera to each other so that the relative motion can be obtained. The NDT implementation is compared to ICP and the results show that NDT performs better at estimating the angular difference between the point clouds.

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.

In this thesis two methods were developed for precise maritime landing of an autonomous multirotor aircraft based on real-time kinematic (RTK) Global Navigation Satellite System (GNSS). The first method called RTK-localized method (RLM) uses RTK GNSS measurements to localize a sea vessel and execute the landing. RLM was demonstrated outdoors in hardware and landed on a physically simulated boat called a mock-boat with an average landing error of 9.7 cm. The mock-boat was actuated to have boat-like motion and a forward velocity of ~2 m/s. This method showed that accurate landing is possible with RTK GNSS as the primary means of localizing a sea vessel. The localization was unaided by non-GNSS sensors or an estimator, but lacked full attitude estimation and measurement smoothing. The second method was called RTK-Estimation Method (REM) and provides a more complete and robust solution, particularly at sea. It includes a base (landing pad) estimator to fuse RTK GNSS measurements with a dynamic model of a sea vessel. In contrast to RLM, the estimator provides full attitude estimation and measurement smoothing. The base estimator consists of an EKF in conjunction with a complimentary filter and estimates the relative position, attitude, and velocity of a moving target using RTK GNSS and inertial measurements alone. REM was demonstrated outdoors in hardware for 18 flight tests. The same mock-boat from RLM was used as a substitute for a sea vessel, and the boat motion varied between tests. These dynamics were recorded and performances were compared. The rate of success was high given moderate mock-boat motion and degraded with more aggressive motion. Tests were conducted with forward velocities from 0 to 3 m/s and moderate to high wave like motion. Over all tests for REM, the multirotor landed with an average accuracy of 12.7 cm. The methods described depart from common methods given that the only sensors involved for tracking the sea vessel were RTK GNSS receivers and inertial measurement units. Most current methods rely on computer vision, and can fail in poor lighting conditions, in the presence of ocean spray, and other scenarios. The given solutions do not fail under such conditions. The multirotor was equipped with a standard off-the-shelf autopilot, PX4, and the methods function with common control and estimation schemes. The two methods are capable of landing on relatively small landing pads, on the order of 1 m by 1 m, at sea using measurements from satellites thousands of kilometers away.

In recent years, the interest in flying multiple Unmanned Aerial Vehicles (UAVs) in formation has increased. One challenging aspect of achieving this is the relative positioning within the swarm. This thesis evaluates two different methods for estimating the relative position and orientation between two fixed wing UAVs by fusing range measurements from Ultra-wideband (UWB) sensors and orientation estimates from Inertial Measurement Units (IMUs). To investigate the problem of estimating the relative position and orientation using range measurements, the performance of the UWB nodes regarding the accuracy of the measurements is evaluated. The resulting information is then used to develop a simulation environment where two fixed wing UAVs fly in formation. In this environment, the two estimation solutions are developed. The first solution to the estimation problem is based on the Extended Kalman Filter (EKF) and the second solution is based on Factor Graph Optimization (FGO). In addition to evaluating these methods, two additional areas of interest are investigated: the impact of varying the placement and number of UWB sensors, and if using additional sensors can lead to an increased accuracy of the estimates. To evaluate the EKF and the FGO solutions, multiple scenarios are simulated at different distances, with different amounts of changes in the relative position, and with different accuracies of the range measurements. The results from the simulations show that both solutions successfully estimate the relative position and orientation. The FGO-based solution performs better at estimating the relative position, while both algorithms perform similarly when estimating the relative orientation. However, both algorithms perform worse when exposed to more realistic range measurements. The thesis concludes that both solutions work well in simulation, where the Root Mean Square Error (RMSE) of the position estimates are 0.428 m and 0.275 m for the EKF and FGO solutions, respectively, and the RMSE of the orientation estimates are 0.016 radians and 0.013 radians respectively. However, to perform well on hardware, the accuracy of the UWB measurements must be increased. It is also concluded that by adding more sensors and by placing multiple UWB sensors on each UAV, the accuracy of the estimates can be improved. In simulation, the lowest RMSE is achieved by fusing barometer data from both UAVs in the FGO algorithm, resulting in an RMSE of 0.229 m for the estimated relative position.