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

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...

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

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...

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

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...