Reconfigurable flight control using feedback linearization with online Neural Network adaption

This work suggests a design approach building on past results in the area of adaptive Neural Network (NN) based reconfigurable flight control which has been successfully utilized for a variety of aerospace applications, while incorporating recent advances in the areas of output feedback and NN adaptation algorithms. The paradigm is based on feedback linearization and synthesis of a fixed-gain dynamic compensator, whilst incorporating a neural network by using concurrent update information to compensate for model uncertainties, system faults and external disturbances. The concurrent learning networks update law is simplified and restructured for better and easy use during applications. The stability and reconfigurable ability of the improved control system based on the concurrent learning algorithm is validated with simulation performances of the nonlinear Machan UAV.