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Vermeer, Kaz (author)
Advanced tools such as machine learning are slowly finding their way into the modern scientist’s toolbox . In the design of mechanical systems however hardly any machine learning applications are being used. Research into the viability of such an application is therefore necessary.<br/>We have performed such research, using a specific type of...
master thesis 2017
document
Heyer, Stefan (author)
In recent years Adaptive Critic Designs (ACDs) have been applied to adaptive flight control of uncertain, nonlinear systems. However, these algorithms often rely on representative models as they require an offline training stage. Therefore, they have limited applicability to a system for which no accurate system model is available, nor readily...
master thesis 2019
document
Scholten, Jan (author)
Deep Reinforcement Learning enables us to control increasingly complex and high-dimensional problems. Modelling and control design is longer required, which paves the way to numerous in- novations, such as optimal control of evermore sophisticated robotic systems, fast and efficient scheduling and logistics, effective personal drug dosing...
master thesis 2019
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Goedhart, Menno (author)
Flight control of the DelFly is challenging, because of its complex dynamics and variability due to manufacturing inconsistencies. Machine Learning algorithms can be used to tackle these challenges. A Policy Gradient algorithm is used to tune the gains of a Proportional-Integral controller using Reinforcement Learning. Furthermore, a novel...
master thesis 2017
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Arnaoutis, Vasos (author)
Deep Learning performance dependents on the application and methodology. Neural Networks with convolutional layers have been a great success in multiple tasks trained under Supervised Learning algorithms. For higher dimensional problems, the selection of a deep network architecture can significantly improve the accuracy of the network, however...
master thesis 2019
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van Dam, Geart (author)
This research investigates and proposes a new method for obstacle detection and avoidance on quadrotors. One that does not require the addition of any sensors, but relies solely on measurements from the accelerometer and rotor controllers. The detection of obstacles is based on the principle that the airflow around a quadrotor changes when the...
master thesis 2019
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Ravi, Siddharth (author)
This project addresses a fundamental problem faced by many reinforcement learning agents. Commonly used reinforcement learning agents can be seen to have deteriorating performances at increasing frequencies, as they are unable to correctly learn the ordering of expected returns for actions that are applied. We call this the disappearing...
master thesis 2017
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