  In this paper we present a learning-based tracking controller based on
Gaussian processes (GP) for a fault-tolerant hexarotor in a recovery maneuver.
In particular, to estimate certain uncertainties that appear in a hexacopter
vehicle with the ability to reconfigure its rotors to compensate for failures.
The rotors reconfiguration introduces disturbances that make the dynamic model
of the vehicle differ from the nominal model. The control algorithm is designed
to learn and compensate the amount of modeling uncertainties after a failure in
the control allocation reconfiguration by using GP as a learning-based model
for the predictions. In particular the presented approach guarantees a
probabilistic bounded tracking error with high probability. The performance of
the learning-based fault-tolerant controller is evaluated through experimental
tests with an hexarotor UAV.
