Disturbances Based Adaptive Neuro-Control for UAVs: A First Approach

Abstract

In this work an adaptive neuro-control is proposed to cope with some external disturbances that can affect unmanned aerial vehicles (UAV) dynamics, specifically: the variation of the system mass during logistic tasks and the influence of the wind. An intelligent control strategy based on a feedforward neural networks is applied. In particular, a variant of the generalized learning algorithm has been used. Simulation results show how the on-line learning increases the robustness of the controller, reducing the effects of the changes in mass and the effects of wind on the UAV stabilization, thus improving the system response. It has been compared with a PID controller obtaining better results.