From flight dynamics to unmanned aerial vehicles, from simulation to real flight tests - we analyse, test and develop innovations that will shape the flying of the future. Autonomous flight control increasingly relies on reinforcement learning (RL) to develop high-performance control strategies directly from data and interactions. RL can adapt online to new conditions and is particularly well suited for mission-level objectives that are difficult to design explicitly (efficiency, flight envelope, comfort).
In this context, a master’s thesis is offered to ensure the safe operation of an RL-based flight controller. Approaches from machine learning (including Gaussian processes) will be investigated to estimate uncertainties.
The thesis will be jointly supervised by DLR and Prof. Mayank Shekhar Jha from the Research Center for Automatic Control (CRAN), Centres Internationaux de Recherche (CNRS), Université de Lorraine, France.
Strong programming skills, particularly in Python or C++