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Ledzian, Patrick (author)
Decentralized control and estimation are both active research areas in the field of systems and control. A new approach to these topics utilizes graph theory to characterize inter-agent communication as a graph that, in this thesis, can have time-varying topology. This approach has been named "network-decentralized" and the use of network...
master thesis 2019
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Fris, Rein (author)
Deep Reinforcement Learning (DRL) enables us to design controllers for complex tasks with a deep learning approach. It allows us to design controllers that are otherwise cumbersome to design with conventional control methodologies. Often, an objective for RL is binary in nature. However, exploring in environments with sparse rewards is a problem...
master thesis 2020
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Solanki, Prashant (author)
Quadcopters are becoming increasingly popular across diverse sectors such as mapping, photography, or surveillance. Since rotor damages occur frequently, it is essential to improve the attitude estimation and thus ultimately the ability to control a damaged quadcopter. The Control and Simulation group of TU Delft developed a quadcopter...
master thesis 2020
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Kemmeren, Max (author)
As the application areas of Unmanned Aerial Vehicles (UAVs) keep expanding, new flight areas are encountered more often. Small UAVs, named Micro Air Vehicles (MAVs), even fly in areas like sewage pipes. These areas introduce new difficulties such as aerodynamic effects caused by the ground and/or ceiling. In this paper two main contributions are...
master thesis 2021
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Bello, Riccardo (author)
The demand of adding fault tolerance to quadcopter control systems has significantly increased with the rise of adoption of UAVs in numerous sectors. This work proposes and demonstrates the use of Hierarchical Reinforcement Learning to control a quadcopter subject to severe actuator fault. State-of-the-art algorithms are implemented, and a...
master thesis 2021
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