Implementation of a neural network for non-linearities estimation in a
  tail-sitter aircraft
  The control of a tail-sitter aircraft is a challenging task, especially
during transition maneuver where the lift and drag forces are highly nonlinear.
In this work, we implement a Neural Network (NN) capable of estimate such
nonlinearities. Once they are estimated, one can propose a control scheme where
these forces can correctly feed-forwarded. Our implementation of the NN has
been programmed in C++ on the PX4 Autopilot an open-source autopilot for
drones. To ensure that this implementation does not considerably affect the
autopilot's performance, the coded NN must be of a light computational load.
With the aim to test our approach, we have carried out a series of realistic
simulations in the Software in The Loop (SITL) using the PX4 Autopilot. These
experiments demonstrate that the implemented NN can be used to estimate the
tail-sitter aerodynamic forces, and can be used to improve the control
algorithms during all the flight phases of the tail-sitter aircraft: hover,
cruise flight, and transition.
