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Reinforcement Learning for Flight Control: Evaluating Handling Qualities and Stability Properties of the PH-LAB

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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Autonomous Sailing with Sim-to-Real Reinforcement Learning

Bink, Kiki (author)

Facing the critical challenge of reducing greenhouse gas (GHG) emissions in the maritime industry, this thesis explores the potential of smart control systems using Reinforcement Learning (RL) for autonomous sailing. Traditional controls for sailing fall short in navigating the complex, dynamic conditions of maritime environments. RL has shown...

master thesis 2024

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Dynamic Target Time Management with Reinforcement Learning: A case study on Zurich short-haul regulated arrivals

Caranti, Leonardo (author)

This Master Thesis investigates the possible improvements to the Target Time Management concept to optimize the arrival flows for SWISS International Airlines. The aim is to improve operational performance based on the current model used, as well as prove that Target Time Management constitutes a valuable system to improve operations in a...

master thesis 2024

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A Hybrid Reinforcement Learning and Tree Search Approach for Network Topology Control

Meppelink, Geert jan (author)

The growing demand for electricity, driven by widespread adoption of heat pumps, electric vehicles, and industrial electrification, strains power grids and introduces challenges for a reliable and secure supply amidst intermittent renewable energy integration. Network topology control offers flexibility, altering connections to redirect power...

master thesis 2023

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Home Energy Management System: A Machine Learning Approach

Deivamani, Karthikeyan (author)

The increasing adoption of renewable energy sources, particularly photovoltaic (PV) systems in residential sectors has raised important energy balancing challenges due to the intermittent nature of energy generation. To address these challenges and prioritize cost savings for residential consumers, this research investigates the integration of...

master thesis 2023

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Control of Dynamical Systems via Deep Kernel Learning

Tan, Martin (author)

In the field of Systems and Control, optimal control problem-solving for complex systems is a core task. The development of accurate mathematical models to represent these systems’ dynamics is often difficult. This complexity comes from potential uncertainties, complex non-linearities, or unknown factors that might affect the system. Because of...

master thesis 2023

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Spiking Neural Networks Based Data Driven Control: An Illustration Using Cart-Pole Balancing Example

Liu, Yuxiang (author)

Machine learning can be effectively applied in control loops to robustly make optimal control decisions. There is increasing interest in using spiking neural networks (SNNs) as the apparatus for machine learning in control engineering, because SNNs can potentially offer high energy efficiency and new SNN-enabling neuromorphic hardwares are being...

master thesis 2023

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Explainable Artificial Intelligence Techniques for the Analysis of Reinforcement Learning in Non-Linear Flight Regimes

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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Deep Reinforcement Learning for Rapid Communication Network Recovery with Multiple Repair Crews

Vilhjálmsson, Ingimundur (author)

Natural disasters can destroy communication network components, potentially leading to severe losses in connectivity. During those devastating events, network connectivity is crucial for rescue teams as well as anyone in need of assistance. Therefore, swift network restoration following a disaster is vital. However, post-disaster network...

master thesis 2021

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Reinforcement Learning for Quadcopter Fault Tolerance: Analyzing Hierarchical and Online Deep Reinforcement Learning for Quadcopter Fault Tolerant Control

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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Reinforcement learning for order distribution in self-organizing logistics

de Vries, Yorick (author)

With the increasing global demand for logistics, supply chains have grown a lot in volume over the last decades. To be able to operate effectively within the capacity constraints of the carriers, proper collaboration and optimization of order allocation is required. Van Berkel Logistics facilitates the transport of containers by trucks from sea...

master thesis 2021

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Reinforcement Learning approach for decision-making in driver control shifting for semi-autonomous driving

Latoškinas, Evaldas (author)

Semi-autonomous driving innovations aim to bridge the gap to fully autonomous driving by co-operating with human drivers to lead to optimal choices on who should drive in different scenarios by offering different automation levels. However, in the present day, known semi-autonomous driving solutions do not generalise to every complex case of...

bachelor thesis 2021

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Deep Reinforcement Learning for Flight Control: Fault-Tolerant Control for the PH-LAB

Dally, Killian (author)

Fault-tolerant flight control faces challenges as developing a model-based controller for each unexpected failure is unrealistic, and online learning methods can handle limited system complexity due to their low sample efficiency. In this research, a model-free coupled-dynamics flight controller for a jet aircraft able to withstand multiple...

master thesis 2021

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Safe Curriculum Learning for Linear Systems With Unknown Dynamics in Primary Flight Control

De Buysscher, Diego (author)

Safe Curriculum Learning constitutes a collection of methods that aim at enabling Rein- forcement Learning (RL) algorithms on complex systems and tasks whilst considering the safety and efficiency aspect of the learning process. On the one hand, curricular reinforce- ment learning approaches divide the task into more gradual complexity stages to...

master thesis 2021

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Reinforcement Learning for Profiled Side-Channel Analysis: Applications of Q-Learning in the SCA Domain

Rijsdijk, Jorai (author)

Side-channel attacks (SCA), which use unintended leakage to retrieve a secret cryptographic key, have become more sophisticated over time. With the recent successes of machine learning (ML) and especially deep learning (DL) techniques against cryptographic implementations even in the presence of dedicated countermeasures, various methods have...

master thesis 2020

document

Artificial Potential Fields for Safe Reinforcement Learning: in Flight Control Systems

Miloševiċ, Stevan (author)

Reinforcement Learning (RL) methods have become a topic of interest for performing guidance and navigation tasks, due to potential adaptability and autonomy improvements within dynamic systems. Nevertheless, a core component of RL is an agent exploring the environment it finds itself in, resulting in an intrinsic violation of the agent's safety....

master thesis 2020

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Design of Reinforcement Learning based Incremental Flight Control Laws for the Cessna Citation II(PH-LAB) Aircraft

Konatala, Ramesh (author)

Online Adaptive Flight Control is interesting in the context of growing complexity of aircraft systems and their adaptability requirements to ensure safety. An Incremental Approximate Dynamic Programming (iADP) controller combines reinforcement learning methods, optimal control and Online identified incremental model to achieve optimal adaptive...

master thesis 2020

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Learning variable selection rules for the branch-and-bound algorithm using reinforcement learning

Scavuzzo Montana, Lara (author)

Mixed Integer Linear Programming (MILP) is a generalization of classical linear programming where we restrict some (or all) variables to take integer values. Numerous real-world problems can be modeled as MILPs, such as production planning, scheduling, network design optimization and many more. MILPs are, in fact, NP-hard. State-of-the-art...

master thesis 2020

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Safe Reinforcement Learning Applications

Monteiro Nunes, Tiago (author)

Reinforcement Learning (RL) focuses on maximizing the returns (discounted rewards) throughout the episodes, one of the main challenges when using it is that it is inadequate for safety-critical tasks due to the possibility of transitioning into critical states while exploring. Safe Reinforcement Learning (SafeRL) is a subset of RL that focuses...

master thesis 2019

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Deep vs Shallow Reinforcement Learning for low dimensional continuous control tasks

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