Comparison of Different Neural Augmentations for the Fault Tolerant Control Laws of the WVU YF-22 Model Aircraft

A fault tolerant neurally augmented control scheme based on non-linear dynamic inversion is designed for the WVU YF-22 aircraft model. The parameters of the model following adaptive flight controller are determined at a single flight condition and a neural network is used to compensate for inversion errors and changes in aircraft dynamics, including actuator failures. Three different neural networks are used: the extended minimal resource allocating network, the single hidden layer network, and the sigma pi. Numerical simulations are performed at nominal flight conditions and failure conditions affecting the stabilator or the aileron. Performance assessment parameters are defined based on the angular rate tracking errors. The performance of the three neural networks is compared in terms of these parameters