ロバスト飛行制御のための微分可能な移動ホライズン推定
Differentiable Moving Horizon Estimation for Robust Flight Control
移動ホライズン推定のチューニングパラメータに対する解析的勾配をカルマンフィルタから効率的に求め、正解データなしで軌道追従誤差から学習するデータ効率の良い手法を提案し、クアッドロータの飛行実験で有効性を示した。
著者: Bingheng Wang, Zhengtian Ma, Shupeng Lai, Lin Zhao, Tong Heng Lee
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Estimating and reacting to external disturbances is of fundamental importance for robust control of quadrotors. Existing estimators typically require significant tuning or training with a large amount of data, including the ground truth, to achieve satisfactory performance. This paper proposes a data-efficient differentiable moving horizon estimation (DMHE) algorithm that can automatically tune the MHE parameters online and also adapt to different scenarios. We achieve this by deriving the analytical gradient of the estimated trajectory from MHE with respect to the tuning parameters, enabling end-to-end learning for auto-tuning. Most interestingly, we show that the gradient can be calculated efficiently from a Kalman filter in a recursive form. Moreover, we develop a model-based policy gradient algorithm to learn the parameters directly from the trajectory tracking errors without the need for the ground truth. The proposed DMHE can be further embedded as a layer with other neural networks for joint optimization. Finally, we demonstrate the effectiveness of the proposed method via both simulation and experiments on quadrotors, where challenging scenarios such as sudden payload change and flying in downwash are examined.
