Global nonlinear aerodynamic modeling via automated and cost-effective flight testing

The development of aircraft is constrained by aerodynamic models, making the acquisition of high-fidelity aerodynamic data a critical task. Nonlinear characteristics in aerodynamic modeling cannot be neglected; however, numerical simulations and wind tunnel experiments often struggle to fully reproduce real flight conditions, or do so at considerable cost. Flight testing, traditionally performed later in the development process, is not only indispensable to the development process but also capable of capturing the nonlinear and dynamic features of actual flight. When combined with modern system identification techniques, flight data provides a direct and efficient pathway to refine or augment existing aerodynamic models. However, most existing flight tests rely heavily on manual operation, which limits random error, consistency, and test efficiency. To overcome these challenges, this study proposes an autonomous excitation control algorithm that injects signals in a modular manner, enabling repeatable, robust and fully automated system-identification flight tests. For aerodynamic modeling, a structure incorporating nonlinear terms is adopted, and a complete modeling workflow is established to enhance model rationality and generalization capability. Validation experiments are conducted using a small flying-wing UAV. The results demonstrate that, in the tested flight envelope, the proposed method accurately captures nonlinear and dynamic aerodynamic characteristics with good repeatability. The proposed framework serves as a complementary, cost-effective solution for rapid model validation and refinement.