Application Of Reinforcement Learning In Heading Control Of A Fixed Wing UAV Using X-Plane Platform

Abstract

Heading control of an Unmanned Aerial Vehicle, UAV is a vital operation of an autopilot system. It is executed by employing a design of control algorithms that control its direction and navigation. Most commonly available autopilots exploit Proportional-Integral-Derivative (PID) based heading controllers. In this paper we propose an online adaptive reinforcement learning heading controller. The autopilot heading controller will be designed in Matlab/Simulink for controlling a UAV in X-Plane test platform. Through this platform, the performance of the controller is shown using real time simulations. The performance of this controller is compared to that of a PID controller. The results show that the proposed method performs better than a well tuned PID controller.