不確実な風条件下でのメタ学習に基づくロバスト適応飛行制御
Meta-Learning-Based Robust Adaptive Flight Control Under Uncertain Wind Conditions
深層ニューラルネットの出力を基底関数として適応制御に組み込み、メタ学習で風条件の変化に素早く適応する飛行制御手法を提案し、風洞実験で追従・予測誤差の改善を示した。
著者: Michael O'Connell, Guanya Shi, Xichen Shi, Soon-Jo Chung
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
Realtime model learning proves challenging for complex dynamical systems, such as drones flying in variable wind conditions. Machine learning technique such as deep neural networks have high representation power but is often too slow to update onboard. On the other hand, adaptive control relies on simple linear parameter models can update as fast as the feedback control loop. We propose an online composite adaptation method that treats outputs from a deep neural network as a set of basis functions capable of representing different wind conditions. To help with training, meta-learning techniques are used to optimize the network output useful for adaptation. We validate our approach by flying a drone in an open air wind tunnel under varying wind conditions and along challenging trajectories. We compare the result with other adaptive controller with different basis function sets and show improvement over tracking and prediction errors.
