Simulated flight control using a hybrid neural network/genetic algorithm architecture

Abstract

A controller for an agile, high-subsonic autonomous flight vehicle, incorporating neural network and genetic algorithm techniques, is presented. Simulated flight results for nominal and off-nominal vehicle configurations are reported. The results show that an inverse dynamic model neural network can offer better tracking performance and greater robustness than a conventional linear controller. However, the genetic algorithm technique employed here was found to offer no significant improvement in controller performance.<>