System identification consists of the development of techniques for model estimation from experimental data, demanding no previous knowledge of the process. Aeroelastic models are directly influence of the benefits of identification techniques, basically because of the difficulties related to the modelling of the coupled aero- and structural dynamics. In this work a comparative study of the bilinear dynamic identification of a helicopter blade aeroelastic response is carried out using artificial neural networks is presented. Two neural networks architectures are considered in this study. Both are variations of static networks prepared to accomodate the system dynamics. A time delay neural networks (TDNN) for response prediction and a typical recurrent neural networks (RNN) are used for the identification. The neural networks have been trained by Levemberg-Marquardt algorithm. To compare the performance of the neural networks models, generalization tests are produced where the aeroelastic responses of the blade in flapping and torsion motions at its tip due to noisy pitching angle are presented. An analysis in frequency of the signals from simulated and the emulated models are presented. In order to perform a qualitative analysis, return maps with the simulation results generated by the neural networks are presented.

A numerical model for the simulation of fixed wings aeroelastic response is presented. The methodology used in the work is to treat the aerodynamics and the structural dynamics separately and then couple them in the equations of motion. The dynamic characterization of the wing structure is done by the finite element method and the equations of motion are written in modal coordinates. The unsteady aerodynamic loads are predicted using the vortex lattice method. The exchange of information between the aerodynamic and structural meshes is done by the surface splines interpolation scheme, and the equations of motion are solved iteratively in the time domain, employing a predictor-corrector method. Numerical simulations are performed for a prototype aircraft wing. The aeroelastic response is represented by time histories of the modal coordinates for different airspeeds, and the flutter occurrence is verified when the time histories diverge (i.e. the amplitudes keep growing). Fast Fourier Transforms of these time histories show the coupling of frequencies typical of the flutter phenomenon.

This work deals with an hybrid PID+fuzzy logic controller applied to control the machine tool biaxial table motions. The non-linear model includes backlash and the axis elasticity. Two PID controllers do the primary table control. A third PID+fuzzy controller has a cross coupled structure whose function is to minimise the trajectory contour errors. Once with the three PID controllers tuned, the system is simulated with and without the third controller. The responses results are plotted and compared to analyse the effectiveness of this hybrid controller over the system. They show that the proposed methodology reduces the contour error in a proportion of 70:1.