Sample-based SMPC for tracking control of fixed-wing UAV

In this paper, a guidance and tracking control strategy for fixed-wing unmanned aerial vehicle autopilots is presented. The proposed control exploits recent results on samplebased stochastic model predictive control, which allows 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 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.