Automatic Gain Tuning Method of a Quad-Rotor Geometric Attitude Controller Using A3C

Abstract

In this paper, we address an automated gain tuning method using Asynchronous Advantage Actor-Critic (A3C) reinforcement learning approach. A quad-rotor Unmanned Aerial Vehicle (UAV) with nonlinear geometric tracking controller is introduced to test our approach. In the geometric controller, two attitude gains must be provided appropriately to achieve stable error dynamics. To ease the difficulties while optimizing the controller performances, such as minimizing tracking error together with reducing control energy, we made Reinforcement Learning (RL) agents to substitute the entire gain tuning process. By training the RL agents with multiple quad-rotor configurations, we were not only able to reduce our efforts putting into the gain tuning by the trial-and-error methods, but also able to deal with the parameter changes by constructing an adaptive structure.