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This paper introduces a robust co-design framework for agile fixed-wing UAVs that integrates parametric uncertainty and wind disturbances into the concurrent optimization of physical design and control strategies. A bi-level approach optimizes physical design in a high-level loop, while a constrained trajectory planner discovers nominal solutions and evaluates performance using feedback LQR control across a stochastic Monte Carlo ensemble. Validated across three agile flight missions, the robust co-design strategy consistently outperforms deterministic baselines by tailoring aerodynamic features for an optimal trade-off between mission performance and disturbance rejection.
Robust co-design optimization can significantly improve the performance of agile UAVs in real-world environments by directly incorporating uncertainty and disturbances into the design process.
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.
