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

Abstract

Abstract 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.