Neural Networks Modelling for Aircraft Flight Guidance Dynamics

Wen-Chi Lu (1) , Walid El-Moudani (2) , Téo Cerqueira Revoredo (3) , Felix Mora-Camino (4)

1 National Formosa University
2 Faculty of Business
3 UERJ - Universidade do Estado do Rio de Janeiro [Rio de Janeiro]
4 LAAS-DISCO - Équipe DIagnostic, Supervision et COnduite

The sustained increase of the air transportation sector over the last decades has led to traffic saturated situations, inducing higher costs for airlines and important negative impacts for airport surrounding communities. The efficient management of air traffic supposes that aircraft trajectories are fully mastered and their impacts can be accurately forecasted. Inversion of aircraft flight dynamics, which are essentially nonlinear, appears necessary. Aircraft flight dynamics is shown to be differentially flat, which is a property that has enabled the development of new numerical tools for the management of complex nonlinear dynamic systems. However, since in the case of aircraft flight dynamics this differential flatness property is implicit, a neural network is introduced to deal with its numerical inversion. Results related to the developed neural network training are displayed, while potential uses of the proposed tool are discussed.

Mots clés: neural networks; differential flatness; aircraft flight dynamics

Domaines: Sciences de l'ingénieur [physics]; Automatique / Robotique

Wen-Chi Lu, Walid El-Moudani, Téo Cerqueira Revoredo, Felix Mora-Camino. Neural Networks Modelling for Aircraft Flight Guidance Dynamics. Journal of Aerospace Technology and Management, 2012, 4 (2), pp 169-174. ⟨10.5028/jatm.v4i2.152⟩. ⟨hal-01002817⟩