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

TL;DR

This paper introduces a sample-based stochastic Model Predictive Control method for fixed-wing UAVs, enabling real-time trajectory tracking with probabilistic guarantees across multiple scenarios.

Contribution

It presents a novel control strategy that efficiently handles uncertainty and noise, ensuring robust constraint satisfaction and trajectory tracking in UAV autopilots.

Findings

Successful software-in-the-loop simulations demonstrate effectiveness.

Guarantees probabilistic constraint satisfaction.

Achieves real-time trajectory tracking with bounded deviation.

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.