This report presents the first steps of development aiming towards making, the open-source aeroelastic code, GEBTAero flight dynamics capable. The implementation was done partly in the Fortran code and part in the GEBTAero Python API with the objective of reusing as much of the existing code as possible with as little substantial architecture modification. The added capacities include the widening of the purview of the software to take into account beam assembly arranged in a plane-like structure, a trim function for the steady level flight was also implemented and the twelve degree of freedom flight mechanics system of equations was introduced in the algorithm. In this short time, unfortunately, few tests were performed fully but important foundation work giving preliminary results was carried out. This includes the verification of the structural modes simulation as well as several bug and inacuracy fixes. / Den här rapporten presenterar de första utvecklingsstegen som syftar till att göra, öppen käll aeroelastisk koden, GEBTAero flygdynamik kapabel. Implementeringen gjordes dels i Fortran-koden och dels i GEBTAero Python API med målet att återanvända så mycket av den befintliga koden som möjligt med så lite väsentlig arkitekturändring. De tillagda kapaciteterna inkluderar utvidgningen av programvaransräckvidd för att ta hänsyn till strålmontering anordnad i en flygplansliknande struktur, en “trim” funktion för jämn nivåflygning implementerades också och de tolv frihetsgraderna flygmekanik system av ekvationer introducerades i algoritmen. Under denna korta tid utfördes tyvärr få tester helt men viktigt grundarbete med preliminära resultat utfördes. Detta inkluderar verifiering av strukturlägen simulering samt flera korrigeringar av fel och felaktigheter.

The primary objective of this thesis is to evaluate the prospect of machine learning methods being used to classify flying qualities based on simulator data (with the focus being on pitch maneuvers). If critical flying qualities could be identified earlier in the verification process, they can be further invested in and focused on with less cost for design changes of the flight control system. Information from manned simulations with given flying quality levels are used to create a replication of the performed pitch maneuver in a desktop simulator. The generated flight data is represented by different measures in the classification to separately train and test the machine learning models against the given flying quality level. The models used are Logistic Regression, Support Vector Machines with radial basis functions (RBF), linear and polynomial kernels along with Artificial Neural Networks.  The results show that the classifiers correctly identify at least 80% of cases with critical flying qualities. The classification shows that the statistical measures of the time signals and first order time derivatives of pitch, roll and yaw rates are enough for classification within the scope of this thesis. The different machine learning models show no significant difference in performance in the scope of this thesis. In conclusion, machine learning methods show good potential for classification of flying qualities, and could become an important tool for evaluating flying qualities of large amounts of simulations, in addition to manned simulations. / Huvuduppgiften med detta examensarbete är att utvärdera huruvida maskininlärning kan användas för att klassificera flygkvaliteter från simulatordata (där fokus ligger på att utvärdera tippmanövrar). Om kritiska flygkvaliteter kan identifieras tidigare i verifikationsprocessen, kan resurser fokuseras för att åtgärda problemet tidigt med mindre kostnader för ändringar av styrsystemet. Information från bemannade simuleringar där flygkvalitetsnivåer har angetts av pilot används för att återskapa tippmanövern i skrivbordssimulatorn. Den genererade flygdatan representeras av olika mått i klassificeringen för att separat träna och testa maskininlärningsmodellerna mot den givna flygkvalitetsnivån. De modeller som används i rapporten är logistisk regression, stödvektormaskiner med radiella basfunktioner (RBF), linjär och polynomisk kärna samt artificiella neurala nätverk. Resultaten visar att klassificerarna korrekt identifierar över 80% av fallen med kritiska flygkvaliteter. Klassificeringen visar att statistiska mått av tidssignalen och första ordningens tidsderivator i tipp, roll och gir är tillräckligt för klassificering inom gränserna av detta examensarbete. De olika maskininlärningsmodellerna visar inga signifikanta skillnader i prestanda med datan som används. Sammanfattningsvis kan maskininlärningsmodellerna anses ha god potential för klassificering av flygkvaliteter, och kan vara ett viktigt verktyg för att klassificera flygkvaliteter för stora mängder flygdata, som komplement till bemannade simuleringar.

In this thesis, energy optimal route of an unmanned solar powered air vehicle is obtained for the given mission constraints in order to sustain the maximum energy balance. The mission scenario and the constraints of the solar powered UAV are defined. Equations of motion are obtained for the UAV with respect to the chosen structural properties and aerodynamic parameters to achieve the given mission. Energy income and loss equations that state the energy balance, up to the position of the UAV inside the atmosphere are defined. The mathematical model and the cost function are defined according to the mission constraints, flight mechanics and energy balance equations to obtain the energy optimal path of the UAV. An available optimal control technique is chosen up to the mathematical model and the cost function in order to make the optimization. Energy optimal path of the UAV is presented with the other useful results. Optimal route and the other results are criticized by checking them with the critical positions of the sun rays.

This thesis describes the automatic flight control systems designed for a conventional and an over actuated unmanned air vehicle (UAV). A nonlinear simulation model including the flight mechanics equations together with the interpolated nonlinear aerodynamics, environmental effects, mass-inertia properties, thrust calculations and actuator dynamics is created / trim and linearization codes are developed. Automatic flight control system of the conventional UAV is designed by using both classical and robust control methods. Performances of the designs for full autonomous flight are tested through nonlinear simulations for different maneuvers in the presence of uncertainties and disturbances in the aircraft model. The fault tolerant control of an over actuated UAV is the main concern of the thesis. The flight control system is designed using classical control techniques. Two static control allocation methods are examined: Moore-Penrose pseudo inverse and blended inverse. For this purpose, an aircraft with three sets of ailerons is employed. It is shown that with redundant control surfaces, fault tolerant control is possible. Although both of the static control allocation methods are found to be quite successful to realize the maneuvers, the new blended inverse algorithm is shown to be more effective in controlling the aircraft when some of the control surfaces are lost. It is also demonstrated that, with redundant control surfaces it is possible to recover the aircraft during a maneuver even some of the control surfaces are damaged or got stuck at a particular deflection.

When operating an aircraft, inaccurate measurements can have devastating consequences. For example, when measuring airspeed using a pitot tube, icing effects and other faults can result in erroneous measurements. Therefore, this master thesis aims to create an alternative method which utilizes known flight mechanical equations and sensor fusion to create an estimate of the airspeed during flight. For validation and generation of flight data, a simulation model developed by SAAB AB, called ARES, is used.  Two models are used to describe the aircraft behavior. One of which is called the dynamic model and utilizes forces acting upon the aircraft body in the equations of motion. The other model, called the kinematic model, instead describes the motion with accelerations of the aircraft body. The measurements used are the angle of attack (AoA), side-slip angle (SSA), GPS velocities, and angular rates from an inertial measurement unit (IMU). The dynamic model assumes that engine thrust and aerodynamic coefficients are already estimated to calculate resulting forces, meanwhile the kinematic model instead uses body fixed accelerations from the IMU. These models are combined with filters to create estimations of the airspeed. The filters used are the extended Kalman filter (EKF) and unscented Kalman filter (UKF). These are combined with the two models to create in total four methods to estimate the airspeed.  The results show no major difference in the performance between the filters except for computational time, for which the EKF has the fastest. Further, the result show similar airspeed estimation performance between the models, but differences can be seen. The kinematic model manages to estimate the wind with higher details and to converge faster, compared to the dynamic model. Both models suffer from an observability problem. This problem entails that the aircraft needs to be maneuvered to excite the AoA and SSA in order for the estimation methods to evaluate the wind, which is crucial for accurate airspeed estimation. The robustness of the dynamic model regarding errors in engine thrust and aerodynamic coefficients are also researched, which shows that the model is quite robust against errors in these values.

Flight mechanical simulators play an important role in the design steps during development of a new aircraft. To be able to simulate and evaluate flight mechanical characteristics during development it is important to minimize development time and cost while keeping flight safety high during early flights. The aim of the project presented in this thesis is to develop a method for validating a flight mechanical simulator against flight test data from dynamic maneuvering. An important part in this thesis is about how deviations in the result data can be found and analyzed, for example deviations between aircraft individuals or store configurations. The work presented here results in a good model for comparison of a big amount of data where it is easy to backtrace where the deviation occurs. / Flygmekaniska simulatorer är av stor betydelse under utvecklingen av ett nytt stridsflygplan. Möjligheten att simulera och utvärdera under tidens gång har stor betydelse både ur tid- och kostnadsbesparings perspektiv men även ur flygsäkerhetsperspektiv när det är dags för första flygning. Syftet med det här projektet är att utveckla en metod för jämförelse mellan simulering och flygprov för att validera hur bra den flygmekaniska simulatorn kan förutspå flygplansbeteende. En viktig del i projektet syftar till hur skillnader i resultaten kan hittas och analyseras, till exempel skillnader mellan olika flygplansindivider eller lastkonfigurationer. Arbetet presenterat här har resulterat i en modell som är bra för jämförelse av en stor mängd data där det är enkelt att spåra var skillnaderna har uppstått.

Simulation of maneuvers with multibody models of rotorcraft vehicles is an important research area due to its complexity. During the maneuvering flight, some important design limitations are encountered such as maximum loads and maximum turning rates near the proximity of the flight envelope. This increases the demand on high fidelity models in order to define appropriate controls to steer the model close to the desired trajectory while staying inside the boundaries. A framework based on the hierarchical decomposition of the problem is used for this study. The system should be capable of generating the track by itself based on the given criteria and also capable of piloting the model of the vehicle along this track. The generated track must be compatible with the dynamic characteristics of the vehicle. Defining the constraints for the maneuver is of crucial importance when the vehicle is operating close to its performance boundaries. In order to make the problem computationally feasible, two models of the same vehicle are used where the reduced model captures the coarse level flight dynamics, while the fine scale comprehensive model represents the plant. The problem is defined by introducing planning layer and control layer strategies. The planning layer stands for solving the optimal control problem for a specific maneuver of a reduced vehicle model. The control layer takes the resulting optimal trajectory as an optimal reference path, then tracks it by using a non-linear model predictive formulation and accordingly steers the multibody model. Reduced models for the planning and tracking layers are adapted by using neural network approach online to optimize the predictive capabilities of planner and tracker. Optimal neural network architecture is obtained to augment the reduced model in the best way. The methodology of adaptive learning rate is experimented with different strategies. Some useful training modes and algorithms are proposed for these type of applications. It is observed that the neural network increased the predictive capabilities of the reduced model in a robust way. The proposed framework is demonstrated on a maneuvering problem by studying an obstacle avoidance example with violent pull-up and pull-down.

Dans cette thèse, nous proposons de construire de meilleurs modèles Processus Gaussiens (GPs) en intégrant les connaissances antérieures avec des données expérimentales. En raison du coût élevé de l’exécution d’expériences sur les systèmes physiques, les modèles numériques deviennent un moyen évident de concevoir des systèmes physiques. Traditionnellement, ces modèles ont été construits expérimentalement et itérativement; une méthode plus rentable de construction de modèles consiste à utiliser des algorithmes d’apprentissage automatique. Nous démontrons comment créer des modèles en intégrant une connaissance antérieure en modifiant les fonctions de covariance. Nous proposons des modèles GP pour différents phénomènes physiques en mécanique des fluides.De même, les lois physiques entre plusieurs sorties peuvent être appliquées en manipulant les fonctions de covariance. Pour chaque application, nous comparons le modèle proposé avec le modèle de l’état de l’art et démontrons les gains de coût ou de performance obtenus. / In this thesis, we propose to build better Gaussian Process (GP) modelsby integrating the prior knowledge of Aircraft design with experimental data. Due tothe high cost of performing experiments on physical systems, models become an efficientmeans to designing physical systems. We demonstrate how to create efficient models byincorporating the prior information from engineering design, mainly by changing the covariancefunctions of the GP.We propose GP models to detect onset of non-linearity, detectmodal parameters and interpolate position of shock in aerodynamic experiments. Similarly,physical laws between multiple outputs can be enforced by manipulating the covariancefunctions, we propose to integrate flight-mechanics to better identify loads using thesemodels. For each application we compare the proposed model with the state-of-the-artmodel and demonstrate the cost or performance gains achieved.

La stabilisation et l’automatisation du vol de tout véhicule aérien nécessite la mise en oeuvre d’algorithmes de commande. La synthèse et la simulation des lois de commande reposent sur un modèle mathématique du véhicule, qui doit être de complexité et de précision appropriées. Cette thèse présente une méthodologie complète d’identification appliquée à un hélicoptère coaxialminiature. L’étude théorique de son comportement en vol permet d’établir plusieurs modèles basés sur la mécanique du vol, qui diffèrent par les phénomènes aérodynamiques pris en compte. Ils sont identifiés, comparés et validés grâce à des données de vol, mettant en évidence l’importance de certains phénomènes dans la précision du modèle. Différentes lois de commande sont alors étudiées et évaluées en simulation puis par des expérimentations sur un prototype. Les résultats obtenus sont conformes aux simulations numériques, validant ainsi l’ensemble de la démarche. / Control algorithms are at the heart of the stability and automatic flight capabilities of any aerial vehicle. Synthesis and simulation of control laws are based on a mathematicalmodel of the vehicle, which must be a trade-off between simplicity and accuracy. This work presents a complete system identification methodology applied on a miniature coaxial helicopter. Based on flight mechanics and aerodynamics, several models are built. They differ in the aerodynamic phenomena taken into account. They are identified, compared and validated thanks to flight data, highlighting important phenomena in the accuracy of the model. Several flight control strategies are then studied and evaluated through simulations and experiments with a prototype. The results are in accordance with numerical simulations, thus validating the whole approach.

This work deals with flight game development on Android platform. Firstly the possibilities of native development and development using Libgdx library will be discussed. Then flight mechanics of a real aircraft and simplified mechanics used in flight games will be explained. The work will also summarize current trends in mobile flight game controls and will propose a new control method based on touch input. Using this method a flight game will be designed and implemented. In the end of this work the process of testing and publishing will discussed, as well as possibilities of further development.