Aeroservoelastic Model Uncertainty Bound Estimation from Flight Data

Uncertainty modeling is a critical element in the estimation of robust stability margins for stability boundary prediction and robust flight control system development. There has been a serious deficiency to date in aeroservoelastic data analysis with attention to uncertainty modeling. Uncertainty can be estimated from flight data using both parametric and nonparametric identification techniques. The model validation problem addressed here is to identify aeroservoelastic models with associated uncertainty structures from a limited amount of controlled excitation inputs over an extensive flight envelope. The challenge is to update analytical models from flight data estimates while also deriving nonconservative uncertainty descriptions consistent with the flight data. Transfer function estimates are incorporated in a robust minimax estimation scheme to update models and get error bounds consistent with the data and model structure. Uncertainty estimates derived from the data in this manner provide an appropriate and relevant representation for model development and robust stability analysis. The method incorporates parametric and nonparamteric uncertainty into various uncertainty structures for quantitative measures of robust stability relating to parameter variations and unmodeled dynamics. This model-plus-uncertainty identification procedure is applied to aeroservoelastic flight data from the NASA Dryden Flight Research Center F-18 Systems Research Aircraft.