In contrast to the conventional aerodynamic and high-order Taylor series models, the model of wavelet transform is defined. This model is a black box model and has a better efficiency in comparison to the conventional models. The wavelet model can be used as a function of high-order aerodynamic derivatives to estimate the aerodynamic behaviour of an aircraft. Different wavelet aerodynamic model structures are made and evaluated. The results show that these non-parametric models are efficient models, and in comparison to high-order Taylor aerodynamic derivatives, the number of terms and the complexity of the model are reduced.

A conceptual method based on the empirical mode decomposition algorithm is proposed to identify a high-fidelity full-flight envelope aerodynamic model, utilising flight data. The key idea is to recognise dominant phenomena of flight containing dissimilar amplitudes and frequencies by means of the empirical mode decomposition. Being distinguished and separated from each other, flight modes can be considered in the aerodynamic model, independently. To achieve the goal, an equation error method is utilised to identify six multi-input single-output systems for aerodynamic forces and moments. The inputs of the identification systems are intrinsic mode functions of flight parameters. The nonparametric identification problem uses the Hammerstein nonlinear ARMAX structure and estimates parameters by the least squares iterative algorithm. Flight tests of an unmanned aircraft in two complex manoeuvres are employed for the modelling and simulation. Several models with different nonlinear functions are introduced and trained by the first dataset. Then, to verify the identified model, the flight simulation is performed for the second dataset. Results indicate the appropriate performance of the identification method in nonlinear aerodynamics.