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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.

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.