Trajectory tracking and anti-disturbance control for aerial recovery drogues without flow angle measurements

For the aerial docking problem, as one of the docking subjects, the active control of the drogue is very important. However, there are challenges such as multiple disturbances, inability to install accurate measurement sensors, and attitude constraints. Aiming at these problems, this paper proposes a trajectory anti-disturbance control method for the recovery drogue without flow angle measurements to effectively suppress the sensitivity of the drogue to multiple wind disturbances and provide favorable conditions for successful docking. First, a deep learning-based conversion method from flow angle to Euler angle under wind disturbance conditions is proposed. Then, an affine connection between the position loop and attitude loop is established, and the overall anti-disturbance control framework from attitude to position is formed to realize the high-precision control of drogue position under multiple disturbance. Furthermore, the appointed-time prescribed performance control method is developed to accurately constrain the Euler angles to meet the docking requirements. Finally, the stability of the proposed method is proven, and the effectiveness is demonstrated by abundant simulations.