Aircraft Load Estimation Using Linear Parameter-Varying System-Based Hybrid Observers

The hybrid loads observer has demonstrated its precision in estimating structural loads and wind disturbances in prior applications. This level of precision is attained by combining a high-fidelity physical, nonlinear flight dynamics model with a data-driven correction model. To mitigate the typically high computational effort, the nonlinear model is substituted in this work with a linear parameter-varying (LPV) system. Therefore, the nonlinear model is approximated by scheduled Linear Time-Invariant models derived from Jacobian linearization. The robustness of this novel approach against parameter uncertainties is evaluated through simulated flight test studies using a subscale test aircraft as an example. It is demonstrated that increased model uncertainties lead to wind estimation accuracy reduction. Additionally, increased parameter uncertainties adversely affect the accuracy of structural load estimation within the model-based (physical) part of the observer. Nonetheless, this loss of accuracy can be compensated by the correction model, leading to the typical high load estimation accuracy of the hybrid loads observer. Finally, experimental data from wind-tunnel tests, utilizing a 1-degree-of-freedom representative test wing, 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.