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Senast publicerade

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    Model-Based Robust Control of an Ultralight Fixed-Wing Tailsitter
    (2026) Edman, Gunnar; Ohlsson, Robin
    Development of fixed-wing VTOL UAVs is an active area of research, enabling increased range and top-speed far surpassing conventional quad-rotors, while preserving the landing flexibility. A subset of this research is the fixed-wing tailsitter, which achieves vertical landing by pitching the entire airframe up into hover. This thesis investigates model-based robust control of an ultralight 250 gram tailsitter UAV which aims to autonomously land vertically in real-world conditions. A 6-DOF model adapted for the unique challenges of tailsitters at hover was developed, extracting aerodynamic coefficients through CFD simulation. By incorporating propwash dynamics, the model ensures control surfaces maintain authority as airspeed approaches zero, enabling continuous simulation from cruise to hover. To account for the significant nonlinearities during transition, an LPV model was developed, scheduled over a grid of airspeed and angle of attack to cover the flight envelope. From this, a MIMO H∞ controller was developed to stabilize the vehicle’s coupled dynamics. Simulation results demonstrate successful trajectory tracking through transition and descent, showcasing that the LPV captures the nonlinear dynamics. Furthermore, wind gust simulations utilizing the Dryden wind model were used to evaluate the operational limits of the aircraft. These tests revealed that the gain-scheduled LQR baseline demonstrated higher overall performance and superior disturbance rejection. Ultimately, the results indicate the aircraft can only handle low to moderate wind conditions, which is attributed to its low inertia and control authority.
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    Explainable AI in Healthcare: Physicians Perspectives and Technical Evaluation of AI-Based Decision Support and Explainability Methods
    (2026) Tomasson, Moa; Westerkull, Saga
    Artificial intelligence (AI) is increasingly integrated into healthcare, particularly in clinical decision support and predictive modeling. However, the limited interpretability of many machine learning models remains a major challenge for clinical implementation, motivating growing interest in explainable artificial intelligence (XAI). This thesis investigates XAI in healthcare from both technical and clinical perspectives. The clinical perspective was explored through qualitative interviews with physicians focusing on AI-based decision support systems and the role of explainable AI in clinical practice. The findings revealed a cautiously optimistic view of AI-supported decision-making, while emphasizing that clinically useful explanations should be concise, intuitive, and seamlessly integrated into existing workflows. The technical part of the study investigated XAI methods for survival prediction in lymphoma patients using both tabular clinical data and medical imaging data. Multiple survival modelling approaches, including Cox regression, DeepSurv, and convolutional neural network models, were implemented and evaluated using several post-hoc explainability methods across the different data modalities. While both modalities demonstrated strong predictive performance, the tabular models achieved slightly stronger results with more stable, interpretable explanations. Furthermore, different XAI approaches highlighted complementary but inconsistent patterns, illustrating challenges related to the robustness and reliability of post-hoc explanations. Overall, the findings demonstrated that successful clinical integration of AI depends as much on providing reliable, clinically meaningful explanations as it does on achieving strong predictive performance.
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    Optimization of a Megawatt Truck Charging Station with Local Battery Storage - A study on cost optimization and peak load management in megawatt charging systems
    (2026) Danielsson, Filip; Karlsson, Hugo; Pettersson, Oscar; Zayton, Oliver; Åhman, Axel; Öberg, Simon
    This thesis evaluates the design and economic feasibility of a megawatt charging station using a local battery energy storage system with a limited grid connection. The study investigates when local battery storage should be used, how the number of chargers should be dimensioned and which parameters have the largest impact on investment decisions. The conclusions are based on a linear optimization model, which minimizes system cost by sizing the system using connection and BESS, a simulation model that verifies the optimization and determines the number of chargers needed and an investment calculation that calculates the financial viability of the charging station. The linear optimization results show that a higher battery cost leads to a smaller battery system and a larger grid connection as battery energy capacity and power capacity decrease. Additionally, with greater electricity price variation, it becomes more beneficial to have a larger battery capacity. For the base case, the optimal configuration under assumptions used in this study consisted of seven MCS chargers, two power distribution centers, a grid connection of approximately 642 kW, a battery energy capacity of 647 kWh and a battery power capacity of 264 kW. The simulation showed that eliminating queue time is not economically justified, since the additional reduction in waiting cost from installing another charger does not outweigh the additional investment cost. The investment calculation indicates that the charging station is profitable under the assumptions made in this study, with a positive NPV of 18.2 MSEK and a DPP below the assumed 10-year battery lifetime. Therefore, the results indicate that a local battery storage system is more beneficial when there are high charging peaks and a variation in electricity prices present. However, profitability is sensitive to demand level, battery cost, electricity price and retail price.
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