DATT: クアッドロータ制御のための深層適応軌道追従
DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control
強化学習で訓練したフィードフォワード・フィードバック・適応制御構造とL1適応制御による外乱推定を組み合わせ、実世界の大きな外乱下でも任意の軌道を高精度に追従するクアッドロータ制御手法を提案。
著者: Kevin Huang, Rwik Rana, Alexander Spitzer, Guanya Shi, Byron Boots
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
Precise arbitrary trajectory tracking for quadrotors is challenging due to unknown nonlinear dynamics, trajectory infeasibility, and actuation limits. To tackle these challenges, we present Deep Adaptive Trajectory Tracking (DATT), a learning-based approach that can precisely track arbitrary, potentially infeasible trajectories in the presence of large disturbances in the real world. DATT builds on a novel feedforward-feedback-adaptive control structure trained in simulation using reinforcement learning. When deployed on real hardware, DATT is augmented with a disturbance estimator using L1 adaptive control in closed-loop, without any fine-tuning. DATT significantly outperforms competitive adaptive nonlinear and model predictive controllers for both feasible smooth and infeasible trajectories in unsteady wind fields, including challenging scenarios where baselines completely fail. Moreover, DATT can efficiently run online with an inference time less than 3.2 ms, less than 1/4 of the adaptive nonlinear model predictive control baseline
