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In this paper, a guidance and tracking control strategy for fixed-wingUnmanned Aerial Vehicle (UAV) autopilots is presented. The proposed controlexploits recent results on sample-based stochastic Model Predictive Control,which allow coping in a computationally efficient way with both parametricuncertainty and additive random noise. Different application scenarios arediscussed, and the implementability of the proposed approach are demonstratedthrough software-in-the-loop simulations. The capability of guaranteeingprobabilistic robust satisfaction of the constraint specifications represents akey-feature of the proposed scheme, allowing real-time tracking of the designedtrajectory with guarantees in terms of maximal deviation with respect to theplanned one. The presented simulations show the effectiveness of the proposedcontrol scheme.

Sample-based SMPC for tracking control of fixed-wing UAV: multi-scenario mapping