  In this paper, a guidance and tracking control strategy for fixed-wing
Unmanned Aerial Vehicle (UAV) autopilots is presented. The proposed control
exploits recent results on sample-based stochastic Model Predictive Control,
which allow coping in a computationally efficient way with both parametric
uncertainty and additive random noise. Different application scenarios are
discussed, and the implementability of the proposed approach are demonstrated
through software-in-the-loop simulations. The capability of guaranteeing
probabilistic robust satisfaction of the constraint specifications represents a
key-feature of the proposed scheme, allowing real-time tracking of the designed
trajectory with guarantees in terms of maximal deviation with respect to the
planned one. The presented simulations show the effectiveness of the proposed
control scheme.
