Abstract
A new method for global nonlinear aerodynamic model identification is presented. This new identification method uses multivariate splines inside a linear regression framework. The linear regression framework allows the use of standard parameter estimation techniques for estimating the parameters of the multivariate splines. The new identification method is used to identify a global nonlinear aerodynamic model of the F-16 fighter aircraft based on simulated flight test data from a NASA subsonic wind tunnel model of the F-16. The high approximation power of the multivariate splines allows the pilot to fly high amplitude, long duration maneuvers resulting in a globally valid, high quality aerodynamic model. The identified aerodynamic model is compared directly with the NASA wind tunnel model showing that the multivariate splines can accurately model both small scale and large scale nonlinear aerodynamic phenomena.
Published Version
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