Adaptive Trajectory Controller for Generic FixedWing Unmanned Aircraft

This work deals with the construction of a nonlinear adaptive path con- troller, which is easily applicable to a multitude of fixed wing unmanned aircraft. Given a common signal interface, the adaptive flight path 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 feedback linearization. The dynamics of the aircraft and the inversion model differ, thus introducing model un- certainties to the feedback linearized system. The effect of modeling uncertainties is reduced by the application of a concurrent learning model reference adaptive con- troller, which uses neural networks in order to approximate the uncertainty. Lever- aging instantaneous as well as stored data concurrently for adaptation ensures con- vergence of the adaptive parameters to a set of optimal weights, which minimize the approximation error. Performance and robustness against certain model uncertain- ties is shown through numerical simulation for two significantly different unmanned aircraft