Flexible aircraft flight dynamics and loads model identification from flight test data including unsteady aerodynamic effects

Abstract

To reduce emissions and improve efficiency, modern aircraft designs are moving towards higher aspect ratios and lighter materials. While these design choices enhance performance, they also result in more flexible aircraft structures. This flexibility leads to greater interaction between rigid body motion and structural dynamics. Accurate modelling of these interactions is critical for evaluating aircraft handling qualities, predicting structural loads, tuning control laws for stability and performance, and developing simulators for pilot training.
System identification techniques provide a means to derive these models from flight test measurements. State-of-the-art system identification methods successfully capture the effects of structural dynamics. However, they rely on the assumption of (quasi-) steady aerodynamics. In steady aerodynamic models, changes in parameters such as angle of attack or control surface deflections are assumed to result in instantaneous changes in aerodynamic forces and moments. In reality, due to wake effects from unsteady aerodynamics, these forces and moments take time to develop, introducing delays in the response. Accurately capturing these delays is crucial for correctly predicting and modelling the aircraft’s dynamic behaviour. Failure to account for unsteady aerodynamics can lead to errors in load predictions, degraded handling quality assessments, and suboptimal control law design.
This dissertation develops a methodology for identifying a parametric flight dynamics and loads model from flight test measurements for a flexible aircraft that also include the effects of structural dynamics and unsteady aerodynamics. A two-step approach was adopted where the identification procedure consists of separate state estimation and parameter estimation steps. This allowed to perform model parameter estimation using a linear least squares approach. In contrast, alternative methods such as the output-error approach perform state estimation and model parameter estimation in a single nonlinear optimisation process. While this method can provide accurate results, it requires accurate initial parameter estimates to achieve convergence and a good fit, and it imposes a significantly higher computational load, making it less efficient for larger and more complex models.
A scaled Diana 2 glider unmanned aerial vehicle (UAV) was used as the flight test platform in this research. Using a UAV allowed to conduct flight testing with much fewer rules and regulations compared to full-scale aircraft testing, while also significantly lowering costs. A glider configuration was selected due to its high aspect ratio and flexible structure, making it well-suited for studying aeroelastic effects. Furthermore, the flight tests could be conducted at airspeeds and reduced frequencies corresponding to unsteady aerodynamic conditions...