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 MHE 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 tuning parameters, which enable a seamless embedding of an MHE as a learnable layer into the neural network for highly effective learning. Most interestingly, we show that the gradients can be obtained efficiently from a Kalman filter in a recursive form. Moreover, we develop a model-based policy gradient algorithm to train NeuroMHE directly from the trajectory tracking error without the need for the ground-truth disturbance data. The effectiveness of NeuroMHE is verified extensively via both simulations and physical experiments on a quadrotor in various challenging flights. Notably, NeuroMHE outperforms the state-of-the-art estimator with force estimation error reductions of up to $49.4\%$ 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 for 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。 在本文中,我们提出一个神经移动地平线估计仪(NeuroMHE),可以自动调控由神经网络建模的MHE参数,并适应不同的飞行场景。我们通过计算MHE对调控参数的估计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,以便进行高度有效的学习。最有意思的是,我们表明可以从卡尔曼过滤器以循环形式高效率地获取梯度。此外,我们开发一个基于模型的政策梯度算法,直接从轨迹跟踪错误中对NeuroMHEE直接进行训练,而不需要地面稳定扰动数据。 NeroMHEHE的效能通过模拟和在具有挑战性的各种飞行中将一个可学习的地面图层图层图层图层实验得到广泛的验证。,Neurom$MHEHEHEHEHEDER值的系统只能用其他的正位方法对正数进行。