NeuroBEM: Hybrid Aerodynamic Quadrotor Model
Leonard Bauersfeld, Elia Kaufmann, Philipp Foehn, Sihao Sun, Davide Scaramuzza
Abstract
Long-exposure images depicting quadrotor trajectory tracking at speeds up to 65 km/h in a large-scale motion-capture system. The captured data is used to fit a hybrid quadrotor model combining blade-element-momentum (BEM) theory with a neural network compensating residual dynamics. This hybrid model reproduces the flown trajectories in simulation with a positional RMSE error reduction of over 50% compared to state-of-the-art.
Topics & Concepts
AerodynamicsComputer scienceControl theory (sociology)ResidualThrustArtificial neural networkAerodynamic forceTrajectoryTorqueControl engineeringController (irrigation)Artificial intelligenceSimulationEngineeringAerospace engineeringAlgorithmControl (management)AstronomyBiologyThermodynamicsPhysicsAgronomyModel Reduction and Neural NetworksAerospace and Aviation TechnologyFluid Dynamics and Turbulent Flows