強化学習によるクアッドコプター制御の適応ゲインスケジューリング
Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control
PPOを用いてクアッドコプターのカスケード制御器のゲインを飛行中に適応させ、静的ゲイン制御と比較して追従誤差を40%以上低減した。
著者: Mike Timmerman, Aryan Patel, Tim Reinhart
分類: eess.SY, cs.LG, cs.RO, cs.SY
原文アブストラクト
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.
