Guidance & Control Networks for Time-Optimal Quadcopter Flight

Guidance & Control Networks for Time-Optimal Quadcopter Flight

Origer, Sebastien (TU Delft Aerospace Engineering)

de Wagter, C. (mentor)
de Croon, G.C.H.E. (mentor)
Izzo, Dario (mentor)
Ferede, R. (mentor)

Delft University of Technology

Aerospace Engineering

2023-04-05

Reaching fast and autonomous flight requires computationally efficient and robust algorithms. To this end, we train Guidance & Control Networks to approximate optimal control policies ranging from energy-optimal to time-optimal flight. We show that the policies become more difficult to learn the closer we get to the time-optimal ’bang-bang’ control profile. We also assess the importance of knowing the maximum angular rotor velocity of the quadcopter and show that over- or underestimating this limit leads to less robust flight. We propose an algorithm to identify the current maximum angular rotor velocity onboard and a network that adapts its policy based on the identified limit. Finally, we extend previous work on Guidance & Control Networks by learning to take consecutive waypoints into account. We fly a 4×3m track in similar lap times as the differential-flatness-based minimum snap benchmark controller while benefiting from the flexibility that Guidance & Control Networks offer.

http://resolver.tudelft.nl/uuid:efa9ee47-b200-4a52-b61d-d2c8e5b6fb78

2024-04-10

Student theses

master thesis

© 2023 Sebastien Origer