Robust Co-design Optimisation for Agile Fixed-Wing UAVs

2026-03-11

AI Summary

This work addresses the sensitivity of agile fixed-wing unmanned aerial vehicles to parametric uncertainties and wind disturbances under existing co-design approaches, which often lack robustness in real-world environments. The paper proposes a novel robust co-design framework that explicitly integrates both parametric uncertainty and wind perturbations for the first time in this domain. The approach employs a bilevel optimization strategy to jointly optimize aerodynamic configuration and control policy: the upper level tunes airframe geometric parameters, while the lower level combines constrained trajectory planning with LQR-based feedback control. Performance under stochastic disturbances is evaluated via Monte Carlo simulations. Validated across three agile maneuvering tasks, the method consistently outperforms deterministic baselines by adaptively optimizing wing placement and aspect ratio, thereby significantly enhancing mission performance and stability in the presence of disturbances.

Technology Category

• Intelligent Robots: State Estimation
• Multiagent Systems: Multiagent Systems under Uncertainty
• Planning, Routing, and Scheduling: Planning under Uncertainty

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• robust co-design
• agile fixed-wing UAVs
• parametric uncertainty
• wind disturbances
• model mismatch

Innovation

• robust co-design
• agile fixed-wing UAVs
• parametric uncertainty
• Monte Carlo ensemble
• trajectory planning