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During an aircraft development, mathematical models are elaborated from its characteristics, physical laws and modeler prior knowledge of the system. Once the aircraft built, those models (mainly linear models) are tuned with flight test recorded data. Regulation authorities define the precision needed for such models. The purpose of this paper is to build an aircraft global model complying with regulation authorities' accuracy requirements with minimal prior knowledge of the system. A professional D level simulator has been used as a flight test aircraft. More than 1,000 experimental flight tests were made with numerous configurations in speed (140 to 240 kt), altitude (10,000 to 46,300 ft), mass (24,000 to 33,000 lb) and the center of gravity position (17 to 34 % of the mean aerodynamic chord). Aircraft's global model is built by identifying linear models at flight points within aircraft flight envelop and the center of gravity limits.

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.