Adaptive Trajectory Controller for Generic Fixed-Wing Unmanned Aircraft

This work deals with the construction of a nonlinear adaptive trajectory controller, which is easily applicable to a multitude of fixed wing unmanned aircraft. Given a common signal interface, the adaptive trajectory controller is divided into a generic part, which is common for each vehicle, and into a part, which is unique. The generic part of the control architecture bases on a common inversion model which is used for feedback linearization. However, the dynamics of the aircraft and the inversion model differ, thus introducing model uncertainties to the feedback linearized system. The effect of modeling uncertainties is reduced by the application of a concurrent learning model reference adaptive controller, which uses neural networks in order to approximate the uncertainty. Leveraging instantaneous as well as stored data concurrently for adaptation ensures convergence of the adaptive parameters to a set of optimal weights, which minimize the approximation error. Performance and robustness against certain model uncertainties is shown through numerical simulation for two significantly different unmanned aircraft.