The paper presents a technique using reinforcement learning (RL) to adapt the control gains of a quadcopter controller. Specifically, we employed Proximal Policy Optimization (PPO) to train a policy which adapts the gains of a cascaded feedback controller in-flight. The primary goal of this controller is to minimize tracking error while following a specified trajectory. The paper's key objective is to analyze the effectiveness of the adaptive gain policy and compare it to the performance of a static gain control algorithm, where the Integral Squared Error and Integral Time Squared Error are used as metrics. The results show that the adaptive gain scheme achieves over 40% decrease in tracking error as compared to the static gain controller.
@article{arxiv.2403.07216,
title = {Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control},
author = {Mike Timmerman and Aryan Patel and Tim Reinhart},
journal= {arXiv preprint arXiv:2403.07216},
year = {2024}
}