Abstract—This paper aims to solve the tracking problem and optimality effectiveness of an Unmanned Aerial Vehicle (UAV) by model-free data Reinforcement Learning (RL) algorithms in both sub-systems of attitude and position. First, a cascade UAV model structure is given to establish the control system diagram with two corresponding attitude and position control loops. Second, based on the computation of the time derivative of the Bellman function by two different methods, the combination of the Bellman function and the optimal control is adopted to maintain the control signal as time converges to infinity with the addition of a discount factor. Third, according to off policy technique, the two proposed model-free RL algorithms are designed for attitude and position sub-systems in UAV control structure with a discount factor, respectively. In particular, the designed algorithms not only solve the trajectory tracking problem but also guarantee the optimality performance. Finally, an illustrative system is used to verify the performance of the proposed model-free data RL algorithms in the UAV control system.

Keywords—data Reinforcement Learning (RL), Unmanned Aerial Vehicles (UAVs), quadrotor, Approximate/Adaptive Dynamic Programming (ADP), model-free based control

Cite: Ngoc Trung Dang and Phuong Nam Dao, "Data-Driven Reinforcement Learning Control for Quadrotor Systems" International Journal of Mechanical Engineering and Robotics Research, Vol. 13, No. 5, pp. 495-501, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.