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.
翻译:估计和应对外部扰动对于对振动器进行稳健的飞行控制至关重要。 现有的测算器通常要求对特定的飞行场景进行重大调整,或提供广泛的地面真实扰动数据培训,以取得令人满意的性能。 在本文件中,我们提议了一个神经移动地平线测深仪(NeuroMHE),可以自动调和由神经网络建模的关键参数,并适应不同的飞行场景。我们通过测算MHE对加权矩阵的估算的分析梯度来实现这一点,使MHE能够顺利地作为可学习层嵌入神经网络,以便进行高效的学习。有趣的是,我们显示梯度可以通过循环形式的Kalman过滤器进行高效的计算。此外,我们开发了一个基于模型的政策梯度算法,直接将NeuroMHEHE从一个神经轨轨迹跟踪错误的跟踪错误进行训练,而不需要地平流扰动数据。 NeroMHEHE的效能可以通过模拟和对具有挑战性的各种飞行的振动性层图进行物理实验得到广泛的验证。 很明显,NeuromMHHEHEHEH 将一个基于常规网络的网络的系统用于常规的系统测测算,而只是测测测算,只有基于常规的系统,只能测测算。