Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control

arXiv:2403.07216v1 Announce Type: cross
Abstract: 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 …

abstract adapt arxiv control cs.lg cs.ro cs.sy eess.sy feedback optimization paper policy ppo reinforcement reinforcement learning scheduling train type