Differentiable Moving Horizon Estimation for Robust Flight Control
This work addresses robust control for quadrotors by reducing tuning and data requirements, though it is incremental as it builds on existing moving horizon estimation methods.
The paper tackled the problem of robust flight control for quadrotors by proposing a differentiable moving horizon estimation algorithm that automatically tunes parameters online, achieving data-efficient adaptation to disturbances like sudden payload changes and downwash, with demonstrated effectiveness in simulations and experiments.
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.