Luderer, OliverOliverLudererThielecke, FrankFrankThielecke2024-07-192024-07-192024-01American Institute of Aeronautics and Astronautics, AIAA SciTech Forum 2024978-1-6241-0711-5https://hdl.handle.net/11420/48453The hybrid loads observer has consistently demonstrated its effectiveness in precisely estimating structural loads and wind disturbances in prior applications. This level of precision is attained through the combination of a high-fidelity physical, nonlinear flight dynamics model with a data-driven correction model. To mitigate the typically high computational effort, the nonlinear model of the loads observer is substituted in this work with a linear parameter-varying (LPV) system while preserving a comparable level of accuracy of the hybrid observer. For this purpose, the model is approximated by scheduled Linear Time-Invariant (LTI) models derived from Jacobian linearization of the nonlinear model. The robustness of this linear parameter-varying hybrid approach against parameter uncertainties is evaluated through virtual flight test studies using the subscale test aircraft Wingfinity-BL as an application example. It is demonstrated that increased model uncertainties lead to a reduction in wind estimation accuracy of the hybrid loads observer. Additionally, increased parameter uncertainties adversely affect the quality of structural loads estimation within the model-based (physical) approach of the observer. Nonetheless, this loss of accuracy can be effectively compensated by the data-driven correction model, leading to a high degree of loads estimation accuracy. Finally, experimental data from a wind tunnel test campaign, utilizing a 1-DOF representative test wing of the aircraft, confirms the high estimation accuracy of the LPV-based hybrid loads observer. Despite employing low-fidelity models, achieving high accuracy is feasible while maintaining the characteristic low complexity of the correction model.enTechnology::690: Building, ConstructionTechnology::620: EngineeringConference Paper10.2514/6.2024-2486Conference Paper