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The aim of this project was to design a flightcontrol system with the purpose of safely guiding a glidertoward a given GPS location over a distance of at least 50km. More specifically, the aim was to develop a control systemfor autonomous gliding and implement it on a given data hubcontaining sensors, GPS-module, microcontroller and a FieldProgrammable Gate Array (FPGA). A SIMULINK simulationenvironment has been developed for simulating flight dynamicsand the digital implementation of some of the on-board hardware.The simulation environment also serves as a platform to tunecontrollers and implement most of the necessary logic forthe control system. For the reference heading, a trigonometricformula is used along with latitude/longitude coordinates tocalculate the turn-angle necessary to travel along the shortestpath between two points. Four negative feedback control loopsare used to track the reference heading and achieve maximumglide ratio. The project has been conducted with mixed success,where the implementation part of the project has suffered greatdrawbacks mainly due to problems in developing the simulationenvironment. In spite of this, the open loop simulation outputspromising results where the glider behaves as expected and isconsidered realistic enough to be a suitable environment in whichto develop a flight control system. In addition, given that thegliders geometry offers reasonable aerodynamic stability, it isshown in this thesis that the proposed control system architectureand heading reference system is sufficient to steer the glider tothe given location under calm atmospheric conditions. / M°alet med detta projekt var att utveckla ettstyrsystem för att på ett säkert sätt styra ett segelflygplan mot engiven GPS-position över ett avstånd på minst 50 km. Mer specifiktvar målet att utveckla ett styrsystem för autonom glidningoch implementera det på en given datahubb som innehållersensorer, GPS-modul, mikrokontroller och programmerbar logik(FPGA). En SIMULINK-simuleringsmiljö har utvecklats föratt simulera flygdynamiken och för digital implementering avnågra av de givna hårdvarukomponenterna. Simuleringsmiljönagerar också som en plattform för att justera regulatorer ochimplementera det mesta av den nödvändiga logiken för styrsystemet.För referensriktning används en trigonometrisk formeltillsammans med latitud/longitud koordinater för att beräknaden sväng-vinkel som krävs för att färdas längs den kortastevägen mellan två punkter. Fyra regulatorer används till attfölja rätt kompassriktining samt maximera flygtid. Projektethar genomförts med blandad framgång, där genomförandet avprojektet har blivit lidande främst på grund av problem medatt utveckla simuleringsmiljön. Trots detta ger simulersmiljönlovande resultat där segelflygplanet beter sig som förväntat ochanses därmed vara en realistisk nog plattform för att utveckla ettkontrollsystem i. Dessutom, givet att geometrin av segelflygplanetger rimlig aerodynamisk stabilitet, framgår det i denna rapportatt den föreslagna styrsystemarkitekturen och referensriktningslogikenär tillräcklig för att styra segelflygplanet till den givnapositionen under lugna atmosfäriska förhållanden. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm

<p>Model based design has had a large impact on the process of software development in many different industries. A lack of formality in these environments can lead to incorrect software and does not facilitate the formal analysis of created models. A formal tool known as tabular expressions have been successfully used in developing safety critical systems, however insufficient tool support has hampered their wider adoption. To address this shortfall we have developed the Tabular Expression Toolbox for Matlab/Simulink.</p> <p>We have developed an intuitive user interface that allows users to easily create, modify and check the completeness and disjointness of tabular expressions using the theorem prover PVS or SMT solver CVC3. The tabular expressions are translated to m-functions allowing their seamless use with Matlab's simulation and code generation. We present a method of generating counter examples for incorrect tables and a means of effectively displaying this information to the user. We provide support for modelling inputs as floating point numbers, through subtyping a user can show the properness of a table using a more concrete representation of data. The developed tools and processes have been used in the modelling of a nuclear shutdown system as a case study of the practicality and usefulness of the tools.</p> / Master of Applied Science (MASc)

<p dir="ltr">The automotive industry has witnessed a significant transformation in recent years, largely driven by the emergence of electric powertrains. These systems offer a cleaner and more efficient alternative to traditional internal combustion engines, marking a pivotal shift towards sustainability in the transportation sector. At the heart of electric vehicles (EVs) lies the powertrain, a complex assembly of components tasked with converting electrical energy into mechanical power to propel the vehicle. In the context of electric race cars, the design and optimization of the powertrain are of utmost importance to achieve high performance on the track. The powertrain typically consists of four major components: the motor, inverter, battery, and gearbox. Each of these components plays a critical role in ensuring the efficient conversion and utilization of electrical energy to drive the vehicle forward. The process of designing an electric race car powertrain begins with a thorough understanding and explanation of each component's function and contribution to overall performance. This foundational understanding serves as the basis for subsequent analysis and optimization efforts. Central to the design process is the selection and configuration of the motor and battery, two key components that heavily influence the vehicle's performance characteristics. To facilitate this decision-making process, engineers leverage specialized software tools such as OptimumLap, MATLAB, and Simulink. OptimumLap allows engineers to input relevant parameters of the race car, such as its drag coefficient and frontal area, to gain insights into its aerodynamic performance. By conducting simulations on specific race tracks, such as the Adelaide circuit, engineers can generate valuable data representing the vehicle's performance in terms of lap times and speed. MATLAB's Grabit tool is then utilized to extract velocity data from the simulation results, providing crucial input for further analysis. This data is used to create a comprehensive table of values representing the vehicle's velocity profile under different conditions. Finally, engineers develop a Simulink model to simulate the operation of the electric powertrain under various scenarios. This model allows for the extraction of critical performance metrics and parameters, enabling engineers to optimize the motor and battery configuration to meet the specific requirements and constraints of the race car.</p>

The suspension system is one of the most sensitive systems of a vehicle as it affects the dynamic behavior of the vehicle with even minor changes. These systems are designed to carry out multiple tasks such as isolating the vehicle body from the road/tire vibrations as well as achieving desired ride and handling performance levels in both steady state and limit handling conditions. The damping coefficient of the damper plays a crucial role in determining the overall frequency response of the suspension system. Considerable research has been carried out on semi active damper systems as the damping coefficient can be varied without the system requiring significant external power giving them advantages over both passive and fully active suspension systems. Dampers behave as non-linear systems at higher frequencies and hence it has been difficult to develop accurate models for its full range of motion. This study aims to develop a velocity sensitive damper model using artificial neural networks and essentially provide a 'black-box' model which encapsulates the non-linear behavior of the damper. A feed-forward neural network was developed by testing a semi active damper on a shock dynamometer at CenTiRe for multiple frequencies and damping ratios. This data was used for supervised training of the network using MATLAB Neural Network Toolbox. The developed NN model was evaluated for its prediction accuracy. Further, the developed damper model was analyzed for feasibility of use for simulations and controls by integrating it in a Simulink based quarter car model and applying the well-known skyhook control strategy. Finally, effects on ride and handling dynamics were evaluated in Carsim by replacing the default damper model with the proposed model. It was established that this damper modeling technique can be used to help evaluate the behavior of the damper on both component as well as vehicle level without needing to develop a complex physics based model. This can be especially beneficial in the earlier stages of vehicle development. / Master of Science / The suspension system is one of the most sensitive systems of a vehicle as it affects the dynamic behavior of the vehicle with even minor changes. These systems are designed to carry out multiple tasks such as absorbing shocks from the road as well as improving the handling of the vehicle for a smoother and safer drive. The level of firmness of the shock absorber/damper plays a crucial role in determining the overall behavior of the suspension system. Considerable research has been carried out on semi active damper systems as the damper stiffness can be varied quickly and easily as compared to other passive and fully active damper systems. Dampers are complex systems to model especially for high speed operations and hence it has been difficult to develop accurate mathematical models for its full range of motion. This study aims to develop an accurate mathematical model for a semi active damper using artificial neural networks. A semi active damper was fabricated and tested on a shock dynamometer at CenTiRe for multiple speeds and stiffness values. Thistest data obtained was used for training of the mathematical model using the computer software MATLAB. The developed model was evaluated for its accuracy and further analyzed for feasibility of use in computer simulations. It was established that this damper modeling technique can be used to help evaluate the behavior of the damper with high accuracy while still running the simulations relatively quickly whereas in current simulations compromise has to be made on at least the accuracy of the model or the simulation speed. This can be especially beneficial in the earlier stages of vehicle development.