UAV heading controller using reinforcement learning

Abstract

The control of heading of an Unmanned Aerial Vehicle is a vital operation. It is accomplished by employing a design of control algorithms that control its flying direction. The available autopilots exploit Proportional-Integral-Derivative (PID) based heading controllers. Here we propose an adaptive controller based on reinforcement learning. The heading controller will be designed in Matlab/Simulink for controlling a UAV in X-Plane test platform. Through this platform, the performance of the designed controller is compared with that of a well-tuned PID controller using real time simulations. The results show that the proposed method performs better.