Reinforcement Learning for Flight Control of the Flying V

Reinforcement Learning for Flight Control of the Flying V

Völker, Willem (TU Delft Aerospace Engineering)

van Kampen, E. (mentor)
Li, Y. (graduation committee)

Delft University of Technology

Aerospace Engineering

2022-07-04

Recent research on the Flying V - a flying-wing long-range passenger aircraft - shows that its airframe design is 25% more aerodynamically efficient than a conventional tube-and-wing airframe. The Flying V is therefore a promising contribution towards reduction in climate impact of long-haul flights. However, some design aspects of the Flying V still remain to be investigated, one of which is automatic flight control. Due to the unconventional airframe shape of the Flying V, aerodynamic modelling cannot rely on validated aerodynamic-modelling tools and the accuracy of the aerodynamic model is uncertain. Therefore, this contribution investigates how an automatic flight controller that is robust to aerodynamic-model uncertainty can be developed, by utilising Twin-Delayed Deep Deterministic Policy Gradient (TD3) - a recent deep-reinforcement-learning algorithm. The results show that an offline-trained single-loop altitude controller that is fully based on TD3 can track a given altitude-reference signal and is robust to aerodynamic-model uncertainty of more than 25%.

Reinforcement Learning
Flying V
Deep Deterministic Policy Gradients
TD3
Robust Control
flight control
Automatic Flight Control System
offline learning
fixed-wing
flying wing
altitude control
autopilot

http://resolver.tudelft.nl/uuid:a6b645d2-8d47-44d3-a4ad-1d5a6024f13f

Student theses

master thesis

© 2022 Willem Völker