Neural Network Based Icing Identification and Fault Tolerant Control of a 340 Aircraft


Caliskan F.

Conference of the World-Academy-of-Science-Engineering-and-Technology, Prague, Czech Republic, 27 - 29 July 2007, vol.22, pp.111-116 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 22
  • City: Prague
  • Country: Czech Republic
  • Page Numbers: pp.111-116

Abstract

This paper presents a Neural Network (NN) identification of icing parameters in an A340 aircraft and a reconfiguration technique to keep the A/C performance close to the performance prior to icing. Five aircraft parameters are assumed to be considerably affected by icing. The off-line training for identifying the clear and iced dynamics is based on the Levenberg-Marquard Backpropagation algorithm. The icing parameters are located in the system matrix. The physical locations of the icing are assumed at the right and left wings. The reconfiguration is based on the technique known as the control mixer approach or pseudo inverse technique. This technique generates the new control input vector such that the A/C dynamics is not much affected by icing. In the simulations, the longitudinal and lateral dynamics of an Airbus A340 aircraft model are considered, and the stability derivatives affected by icing are identified. The simulation results show the successful NN identification of the icing parameters and the reconfigured flight dynamics having the similar performance before the icing. In other words, the destabilizing icing affect is compensated.