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The future solution for the increasing air traffic density appears to be the free flight for which each airplane will have the possibility to choose its own trajectory and to follow it with only minor ground air traffic controllers' guides. The present paper deals with global collision avoidance which aims at separating aircraft taking into consideration the global traffic in a given area, contrarily to most of the existing approaches which are concerned with local traffic alone. The paper proposes a modeling of the global collision avoidance problem in the free flight context, in the horizontal plane, as a constrained optimization problem. A simulation example is performed in the end which shows the suitability of the proposed approach.

The present paper deals with optimal flight collision avoidance in terminal areas. The collision avoidance problem is expressed as an optimization problem whose solution vector is composed of individual aircraft headings, velocities and flight levels as guidance information that corresponding aircraft should follow to automatically avoid collision and at the same time to converge to a specified landing procedure start point. Simulation results are presented and show that the proposed method is capable of ensuring collision avoidance in an optimal way.

Aircraft stability derivative estimation provides a framework for flight control systems design and improvement from their implementation to their operation lifetime. Estimating such parameters on-line has a special interest in establishing the accuracy of airborne simulations and enhancing the adaptive flight control systems. The present paper proposes an on-line estimation method based on a nonlinear flight model. The temporal axis is subdivided according to equidistant temporal landmarks, and the parameter estimation is done at the end of such subintervals with the measurements performed on the system during the corresponding subinterval. The optimization approach underlying the estimation method is based on a cyclical optimization along coordinate axes, a derivative-free technique that has shown to outperform standard derivative-free optimization algorithms.