Intelligent Autolanding Controller Based on Neural Netwolks

To expand the flight envelope of a typical jet transport and to minimize number of tests for the certification process, a design methodology has been proposed based on neural networks. The design procedure leads to an intelligent neuro-controller for landing phase that can handle different wind patterns. The procedure uses, a classical PID controller as the teaching mechanism of a neuro-controller. Finally, a hybrid neuro-PID controller which its inner loop is PID-based and its outer loop is neural-based has been proposed. Two wind patterns, Strong and Very Strong winds in comparison to JFK Airport Downburst, have been investigated to test the performance of the proposed controllers. To discuss the complexity of the controllers, three aspects have been considered. Simulation results show that the hybrid controller provides the necessary performance conditions in presence of Very Strong wind. Copyright © 2003 IFAC