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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.

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.

During aircraft development, mathematical models are elaborated from our knowledge of fundamental physical laws. Those models are used to gain knowledge in order to make the best decisions at all development stages. Depending on the application, different models can be used to describe, in one way or another, the aircraft behavior. The goal of this paper is to develop a high-fidelity aircraft simulation model that is exceptionally capable, flexible and responsive to the needs of the researchers. The proposed model includes nonlinear aerodynamic coefficients, a generic engine model and a complete autopilot with auto-landing. The simulation model has been designed to help researchers develop and validate new algorithms for trajectory optimization, control design, stability analysis and parameter estimation. To make it easy to use, the simulation model also includes algorithms for stability and control analysis.