Flexible prescribed performance-based approaching control for UAV aerial recovery under multiwind disturbances

This article investigates the approaching control for fixed-wing Unmanned Aerial Vehicle (UAV) aerial recovery in the presence of pre-specified performance requirements, complex airflows, maneuvering flight of transport aircraft, and different initial deviations. First, a novel control-oriented Six-Degree-Of-Freedom (6-DOF) UAV model considering airflow disturbances is established for better consistency with the actual UAV system. Then, to achieve satisfactory performance in the approaching process, a Flexible Appointed-time Prescribed Performance Control (FAPPC) algorithm, with the features of user-specified time convergence, no overshoot, independence from the initial value, and singularity-free, is proposed. Specifically, to solve the singularity issue encountered by the existing PPC methods in dealing with sudden disturbances, an adaptive adjustment signal is introduced in FAPPC to perceive the threat of increasing error and relax the preset boundaries appropriately. Moreover, minimum learning parameter-based neural network estimators are developed to approximate unknown lumped disturbances at a low computational cost. Finally, the stability of the closed system is analyzed via Lyapunov synthesis, and the effectiveness and advantages of the proposed control scheme are demonstrated via simulation and Hardware-In-the-Loop (HIL) experimental validation.