Ground-based prediction of aircraft climb : point-mass model vs regression methods

Richard Alligier (1) , Mohammad Ghasemi Hamed (2) , David Gianazza (1) , Mathieu Serrurier (3)

1 DGAC - Direction Générale de l'Aviation Civile
2 MAIA-OPTIM - ENAC Equipe MAIAA-OPTIM
3 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage

Predicting aircraft trajectories with great accuracy is central to most operational concepts ([1], [2]) and automated tools that are expected to improve the air traffic management (ATM) in the near future. On-board flight management systems predict the aircraft trajectory using a point-mass model describing the forces applied to the center of gravity. This model is formulated as a set of differential algebraic equations that must be integrated over a time interval in order to predict the successive aircraft positions in this interval. The point-mass model requires knowledge of the aircraft state (mass, thrust, etc), atmospheric conditions (wind, temperature), and aircraft intent (target speed or climb rate, for example).

Domaines: Optimisation et contrôle [math.OC]

Richard Alligier, Mohammad Ghasemi Hamed, David Gianazza, Mathieu Serrurier. Ground-based prediction of aircraft climb : point-mass model vs regression methods. Complex World 2011, 1st Annual Complex World Conference, Jul 2011, Seville, Spain. pp xxxx. ⟨hal-00940961⟩