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arXiv · 1805.05879

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

Martina Mammarella·Elisa Capello·Fabrizio Dabbene
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Abstract

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.

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Martina Mammarella, Elisa Capello, Fabrizio Dabbene. 2018-05-15. Sample-based SMPC for tracking control of fixed-wing UAV: multi-scenario mapping. https://arxiv.org/abs/1805.05879

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