Papers › Neural Moving Horizon Estimation for Robust Flight Control
Neural Moving Horizon Estimation for Robust Flight Control
Bingheng Wang, Zhengtian Ma, Shupeng Lai, Lin Zhao
Estimating and reacting to external disturbances is crucial for robust flight control of quadrotors. Existing estimators typically require significant tuning for a specific flight scenario or training with extensive ground-truth disturbance data to achieve satisfactory performance. In this paper, we propose a neural moving horizon estimator (NeuroMHE) that can automatically tune the key parameters modeled by a neural network and adapt to different flight scenarios. We achieve this by deriving the analytical gradients of the MHE estimates with respect to the weighting matrices, which enables a seamless embedding of the MHE as a learnable layer into neural networks for highly effective learning. Interestingly, we show that the gradients can be computed efficiently using a Kalman filter in a recursive form. Moreover, we develop a model-based policy gradient algorithm to train NeuroMHE directly from the quadrotor trajectory tracking error without needing the ground-truth disturbance data. The effectiveness of NeuroMHE is verified extensively via both simulations and physical experiments on quadrotors in various challenging flights. Notably, NeuroMHE outperforms the state-of-the-art neural network-based estimator with estimation error reductions of up to about 49.4% by using only a 2.5% amount of the neural network parameters. The proposed method is general and can be applied to robust adaptive control of other robotic systems.
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