Sample-based SMPC for tracking control of fixed-wing UAV: multi-scenario mapping
This work addresses the need for robust real-time trajectory tracking in fixed-wing UAVs, but the results are incremental as they apply existing methods to a specific domain without quantitative SOTA comparisons.
The paper presents a guidance and tracking control strategy for fixed-wing UAV autopilots using sample-based stochastic Model Predictive Control, demonstrating through software-in-the-loop simulations that it can handle parametric uncertainty and additive noise while guaranteeing probabilistic constraint satisfaction.
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.