Nonlinear lateral command control using neural network for F-16 aircraft

A discrete time neural network based lateral controller design for an F-16 nonlinear model is presented. The controller is designed using model reference indirect adaptive control and the input output representation and control law for nonlinear model are established using system theory. The input-output representation and control law are approximated using neural networks with linear filters. The design takes into account the multi input multi output nature of the lateral model. Roll rate and side slip commands are used to generate reference signals and the neural networks are trained to follow the reference signals. Nonlinear simulation results are given to prove the effectiveness of the controller.