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Novel docking controller for autonomous aerial refueling with probe direct control and learning-based preview method

机译：具有探测器直接控制和基于学习的预览方法的新型自动空中加油对接控制器

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摘要

Autonomous aerial refueling (AAR) has always been a hot research area due to its significant application and complicated control problem. In order to improve the docking precision of AAR, a novel docking controller with probe direct control and learning-based preview method is proposed. Firstly, the controlled object is transformed from receiver barycenter to probe tactfully. Then, a suitable probe direct controller designed via the combination of reference-observer-based tracking control method and the high order sliding mode control method is proposed for the probe direct control. Furthermore, a learning-based preview method is introduced to solve the tracking lag problem. The prediction of drogue motion is considered in the reference signal. Then, a novel learning algorithm, named deep learning and reinforcement learning (DLRL), which combines deep learning (DL) and reinforcement learning (RL) spatially rather than structurally like deep reinforcement learning (DRL) is proposed to generate the preview time adaptively. And a novel preview index is proposed to adapt for it. Through the combination of probe direct controller and learning-based preview method, the proposed docking controller could improve the tracking precision largely. Effectiveness of the proposed method is demonstrated by the simulations. (C) 2019 Elsevier Masson SAS. All rights reserved.
机译：自主空中加油（AAR）由于其广泛的应用和复杂的控制问题一直是研究的热点。为了提高AAR的对接精度，提出了一种基于探针直接控制和基于学习的预览方法的新型对接控制器。首先，将受控对象从接收器重心转换为轻巧地进行探测。然后，提出了一种基于参考观察者的跟踪控制方法和高阶滑模控制方法相结合设计的探头直接控制器。此外，引入了一种基于学习的预览方法来解决跟踪滞后问题。在参考信号中考虑了锥套运动的预测。然后，提出了一种新颖的学习算法，称为深度学习和强化学习（DLRL），该算法将深度学习（DL）和强化学习（RL）在空间上结合在一起，而不是像深度强化学习（DRL）在结构上结合起来，以自适应地生成预览时间。并提出了一种新颖的预览指标来适应它。通过将探针直接控制器与基于学习的预览方法相结合，所提出的对接控制器可以大大提高跟踪精度。仿真结果证明了该方法的有效性。 （C）2019 Elsevier Masson SAS。版权所有。

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