To design a good control system for a 6 degree-of-freedom aircraft is not an easy work. The classic linear method such as PID can’t provide a good performance for complex nonlinear systems in some cases. In this paper, an end-to-end policy using Reinforcement Learning (RL) based on Proximal Policy Optimization (PPO) is proposed. This mothed is used to established a mapping from the observed states to the motor signals to control the attitude of quadrotor. In order to evaluate the performance of the end-to-end controller based on PPO, a RL-PID controller is designed to compare with the end-to-end controller in this paper. The result shows that the performance of the end-to-end controller based on PPO is better.
Attitude Control Based on Reinforcement Learning for Quadrotor
Lect. Notes Electrical Eng.
International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021
Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021) ; Kapitel : 33 ; 331-338
2022-03-18
8 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Attitude control of a quadrotor
Tema Archiv | 2009
|Attitude control of a quadrotor
IEEE | 2009
|Attitude Control of Quadrotor UAV Based on ADRC
IEEE | 2023
|