  The ability to adapt to changing conditions is a key feature of a successful
autonomous system. In this work, we use the Recursive Gaussian Processes (RGP)
for identification of the quadrotor air drag model online, without the need of
training data. The identified drag model then augments a physics-based model of
the quadrotor dynamics, which allows more accurate quadrotor state prediction
with increased ability to adapt to changing conditions. This data-augmented
physics-based model is utilized for precise quadrotor trajectory tracking using
the suitably modified Model Predictive Control (MPC) algorithm. The proposed
modelling and control approach is evaluated using the Gazebo simulator and it
is shown that the proposed approach tracks a desired trajectory with a higher
accuracy compared to the MPC with the non-augmented (purely physics-based)
model.
