Searched for: contributor%3A%22Van+Kampen%2C+E.+%28mentor%29%22
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Menor de Oñate, Adrian (author)
Exploring planetary bodies using robot swarms can potentially increase the value of the exploration missions; enabling the execution of novel measurements and explorations previously deemed impractical or unattainable. Despite its potential, the technology readiness level of planetary swarms is not very mature. This work uses multi-agent...
master thesis 2024
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Jansen, Hidde (author)
Reinforcement Learning applied to flight control has shown to have several benefits over classical, linear flight controllers, as it eliminates the need for gain scheduling and it could provide fault-tolerance. The application to civil aviation in practice, however, is non-existent as there are multiple safety concerns. This research...
master thesis 2024
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Homola, Marek (author)
In the rapidly evolving aviation sector, the quest for safer and more efficient flight operations has historically relied on traditional Automatic Flight Control Systems (AFCS) based on high-fidelity models. However, such models not only incur high development costs but also struggle to adapt to new, complex aircraft designs and unexpected...
master thesis 2024
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Vieira dos Santos, Lucas (author)
The critical challenge for employing autonomous control systems in aircraft is ensuring robustness and safety. This study introduces an intelligent and fault-tolerant controller that merges two Reinforcement Learning (RL) algorithms in a hybrid approach: the Distributional Soft Actor-Critic (DSAC) and the Incremental Dual Heuristic Programming ...
master thesis 2023
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Herren Aguillar van de Laar, Thomas (author)
master thesis 2023
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Hummel, Jesse (author)
Power output during flight operation of multi-megawatt airborne wind energy systems is substantially affected by the mass of the airborne subsystem, resulting in power fluctuations. In this paper, an approach to control the tether force using the airborne subsystem is presented that improves the quality of the power output. This kite tether...
master thesis 2023
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Tzanetos Alevras, Tzortzis (author)
This research presents a comprehensive modeling approach for the flight dynamics of a hybrid compound helicopter, employing classical mechanics methods. The derived non-linear mathematical model encompasses the individual components of the aircraft, including the rotor, propellers, wings, fuselage, and empennage, which are then integrated into a...
master thesis 2023
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Elffers, Peter (author)
This paper investigates the performance of an autonomous navigation system to navigate a spacecraft in the proximity of a binary asteroid system using optical and laser ranging measurements. The knowledge about the binary asteroid is limited to its orbital parameters and ellipsoid shape models. The accelerometer bias random walk is included in...
master thesis 2023
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Gatti, Giulia (author)
master thesis 2023
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Maurer, Maarten (author)
Control of a helicopter with a deployed dipping sound navigation and ranging (SONAR) is no trivial task due to the complex dynamics of the suspension cable. The cable can change shape, which influences the effect of water, wind and the motion of the helicopter itself. To control such a system, a control method is needed that can cope with these...
master thesis 2023
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Gavra, Vlad (author)
Recent research in bio-inspired artificial intelligence potentially provides solutions to the challenging problem of designing fault-tolerant and robust flight control systems. The current work proposes SERL, a novel Safety-informed Evolutionary Reinforcement Learning algorithm, which combines Deep Reinforcement Learning (DRL) and neuro...
master thesis 2023
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Seres, Peter (author)
With the recent increase in the complexity of aerospace systems and autonomous operations, there is a need for an increased level of adaptability and model-free controller synthesis. Such operations require the controller to maintain safety and performance without human intervention in non-static environments with partial observability and...
master thesis 2022
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Casals Sadlier, Juliette (author)
The implementation of a model-free, off-policy, actor-critic deep reinforcement learning algorithm consistent of two separate agents to a six-degree-of freedom spacecraft docking maneuver to develop a control policy is carried out in the research presented in this article. Reinforcement learning has the ability to learn without instruction, this...
master thesis 2022
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Stougie, Jurian (author)
To reduce the environmental impact of aircraft, technological innovations are required. The Flying-V could be one of these technical innovations, as research shows it could be up to 20 % more efficient than regular aircraft of the same size. The Flying-V however has low lateral control authority, and pitch<br/>break-up could occur for high...
master thesis 2022
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Ferreira Lemos, André (author)
Even though Deep Reinforcement Learning (DRL) techniques have proven their ability to solve highly complex control tasks, the opaqueness and inexplicability associated with these solutions many times stops them from being applied to real flight control applications. In this research, reward decomposition explanations are used to tackle this...
master thesis 2022
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Teirlinck, Casper (author)
Recent advancements in fault-tolerant flight control have involved model-free offline and online Reinforcement Learning algorithms in order to provide robust and adaptive control to autonomous systems. Inspired by recent work on Incremental Dual Heuristic Programming (IDHP) and Soft Actor-Critic (SAC), this research proposes a hybrid SAC-IDHP...
master thesis 2022
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Völker, Willem (author)
Recent research on the Flying V - a flying-wing long-range passenger aircraft - shows that its airframe design is 25% more aerodynamically efficient than a conventional tube-and-wing airframe. The Flying V is therefore a promising contribution towards reduction in climate impact of long-haul flights. However, some design aspects of the Flying V...
master thesis 2022
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de Haro Pizarroso, Gabriel (author)
Reinforcement Learning is being increasingly applied to flight control tasks, with the objective of developing truly autonomous flying vehicles able to traverse highly variable environments and adapt to unknown situations or possible failures. However, the development of these increasingly complex models and algorithms further reduces our...
master thesis 2022
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van Zijl, Job (author)
Deep Reinforcement Learning (DRL) shows great potential for flight control, due to its adaptability, fault-tolerance, and as it does not require an accurate system model. However, these techniques, like many machine learning applications, are considered black-box as their inner workings are hidden. This paper aims to break open the black box of...
master thesis 2022
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Kloosterman, Luc (author)
In the future, Air Traffic Controllers are expected to work together with more advanced computer-based automation that can automatically take action. The main challenge is then how to design computer-based tools such that they foster acceptance among air traffic controllers. One possible approach to foster acceptance is by matching the automated...
master thesis 2022
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