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.
@article{arxiv.1805.05879,
title = {Sample-based SMPC for tracking control of fixed-wing UAV: multi-scenario mapping},
author = {Martina Mammarella and Elisa Capello and Fabrizio Dabbene},
journal= {arXiv preprint arXiv:1805.05879},
year = {2018}
}