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A new approach for the prediction of lift, drag and moment coefficients is presented. This approach is based on the Support Vector Machines methodology, and on a optimization algorithm, the Extended Great Deluge. The novelty of this approach is the combination between the SVM and the EGD algorithm. The EGD is used to optimize the SVM parameters to allow it to predict the aerodynamic coefficients of ATR 42 model. The training and validation of this new combination method is realized using the aerodynamic coefficients of an ATR-42 wing model with Xfoil software and experimental tests using the Price-Païdoussis wind tunnel. The results obtained with our approach are compared with the XFoil results, experimental results and XFLR5 software results for different flight cases, expressed as various combinations of angles of attack and Mach numbers. The main purpose of this methodology is to rapidly predict aircraft aerodynamic coefficients.

An airplane model is usually obtained from preliminary wind tunnel experiments and CFD analysis. These models are then tuned from flight test measurements using system identification, and are used for airplane stability assessment and control design. However, sometimes no or little preliminary data and documentation are available and flight test identification is the main mean to obtain the model needed for control system design. If so, the purpose of this paper is to identify the grey-box model of an airplane without initial data using a combination of the least square and output error estimation methods. A grey-box model identification is preferred because it gives aerodynamic parameter estimations of the airplane. Before flight test data are available, this method was applied to the Cessna Citation X business airplane's high fidelity simulations and carried out with human-in-the-loop on a professional level D flight dynamics simulator designed and manufactured by CAE Inc.

This article presents a structural analysis of the Unmanned Aerial System UAS-S4 ETHECATL. Mass, center of gravity position and mass moment of inertia are numerically determined and experimentally attested using the pendulum method. To determine the mass moment of inertia, a bifilar torsion-type pendulum is used for the Z-axis and a simple pendulum for the X and Y axes [14]. A nonlinear dynamic model is developed for the rotational motion about the center of gravity (Gs) of the tested system, including the effects of large-angle oscillations, aerodynamic drag, viscous damping and additional mass effects. MATLAB genetic algorithms are then used to obtain the values of mass moment of inertia that would validate the experimental data with the numerical results. The experiment used data gathered by three sensors: an accelerometer, a gyroscope and a magnetometer. Therefore, a method is used to calibrate these three sensors.

The main goal of this flight control system is to achieve good performance with acceptable flying quality within the specified flight envelope while ensuring robustness for model variations, such as mass variation due to fuel burn. The Cessna Citation X aircraft linear model is presented for different flight conditions to cover the aircraft's flight envelope, on which a robust controller is designed using the H-infinity method optimized by two heuristic algorithms. The optimal controller was used to achieve satisfactory dynamic characteristics for the longitudinal and lateral stability control augmentation systems with respect to this aircraft's flying quality requirements. The weighting functions of the H-infinity method were optimised by using both genetic and differential evolution algorithms. The evolutionary algorithms gave very good results.