Design of attitude and path tracking controllers for quad-rotor robots using reinforcement learning

Abstract

There is a lot of interest in using quad-rotor helicopters as Miniature Aerial Vehicles (MAVs) due to their simple mechanical construction and straightforward propulsion system. However, since these vehicles are highly unstable nonlinear dynamical systems, a suitable control system is required for their attitude stabilization and navigation. This article presents a simulation environment for the design and evaluation of attitude stabilization and path tracking controllers for quad-rotor aerial robots using Reinforcement Learning (RL). Firstly, the nonlinear mathematical model for a commercial X3D-BL quad-rotor robot from Ascending Technologies is introduced. The attitude stabilization and path tracking controllers for the quad-rotor robot are formulated. It is shown how the parameters of the controllers can be adjusted using a RL algorithm called Learning Automata. Next, the proposed simulation topology is presented and its main features are discussed. It employs 2 host computers where one host executes the control loops and the reinforcement learning algorithm using MATLAB/SIMULINK. The other host runs the quad-rotor robot model using the X-Plane Flight Simulator. The two hosts communicate using UDP (User Datagram Protocol) over a standard Ethernet wired network. Finally, some simulation cases are presented and the controllers adjusted by the RL algorithm are evaluated.