Robust Co-design Optimisation for Agile Fixed-Wing UAVs

TL;DR

This paper introduces a robust co-design framework for agile fixed-wing UAVs that incorporates uncertainties and disturbances into the optimization process, resulting in more resilient and effective designs for real-world environments.

Contribution

It presents a novel bi-level robust co-design approach that jointly optimizes physical design and control strategies considering stochastic disturbances.

Findings

Outperforms deterministic baselines in three flight missions.

Tailors aerodynamic features for optimal trade-off between performance and robustness.

Demonstrates improved disturbance rejection through integrated design and control.

Abstract

Co-design optimisation of autonomous systems has emerged as a powerful alternative to sequential approaches by jointly optimising physical design and control strategies. However, existing frameworks often neglect the robustness required for autonomous systems navigating unstructured, real-world environments. For agile Unmanned Aerial Vehicles (UAVs) operating at the edge of the flight envelope, this lack of robustness yields designs that are sensitive to perturbations and model mismatch. To address this, we propose a robust co-design framework for agile fixed-wing UAVs that integrates parametric uncertainty and wind disturbances directly into the concurrent optimisation process. Our bi-level approach optimises physical design in a high-level loop while discovering nominal solutions via a constrained trajectory planner and evaluating performance across a stochastic Monte Carlo ensemble using feedback LQR control. Validated across three agile flight missions, our strategy consistently outperforms deterministic baselines. The results demonstrate that our robust co-design strategy inherently tailors aerodynamic features, such as wing placement and aspect ratio, to achieve an optimal trade-off between mission performance and disturbance rejection.