Performance comparison of different neural augmentation for the NASA Gen-2 IFCS F-15 control laws

This paper describes the results of a study focused on comparing the performance of three different neural augmentations of the dynamic inversion-based control laws used for fault tolerant purposes on the NASA IFCS F-15 aircraft. The performance of the specific neural algorithms, the extended minimal resource allocating networks algorithm, the single hidden layer neural network, and the SigmaPi neural network have been compared. The comparison has been conducted in terms of specific parameters relative to the tracking of desirable handling qualities following the injection of simulated failures on the actuators of the right stabilator and the left canard of the NASA F-15 aircraft. The simulation results have shown that all three neural networks have promising performance with the extended minimal resource allocating networks algorithm slightly outperforming the other algorithms.