Rope-hook recovery system of fixed wing UAV based on FFT prediction algorithm and adaptive fuzzy PID control

The heterogeneous cross-domain collaboration is one of the effective means to solve the existing bottleneck problem of the unmanned equipment, and it is also the main operation mode of unmanned war in the future. The unmanned system composed of the fixed wing UAV and the unmanned ship is a typical heterogeneous cross domain collaboration system. The flight characteristics of the fixed wing UAV brought great technical challenges in the process of the autonomous recovery in the complex environment (such as wind disturbance on the water surface. The waves have strong time-varying and randomness. The high-frequency components of the waves will produce strong effects on the hardware and software equipment of the system, resulting in drift of displacement, and seriously decrease of the robustness of the system. As the conventional PID cannot adapt the online parameters, it is easy to lead to a series of problems such as slow tracking speed, poor interference immunity, and large overshoot. In this work, a control strategy combining adaptive FFT prediction algorithm with fuzzy PID to construct the rope-hook recovery system for the recovery of fixed wing UAVs was proposed. The fast Fourier transform (FFT) is used to decompose the relative pose of the aircraft and the recovery rope disturbance in real time, and Kalman filter is used to build an adaptive disturbance prediction algorithm; And the interference values of different frequencies are estimated online by Kalman filter. Secondly, the expected positions of the position and the attitude compensation mechanism the joints were calculated by the mathematical model of the recovery mechanism. Then, according to the different expected position, the PID control parameters of the motor are adjusted in real time by the adaptive fuzzy PID control. The effectiveness of the rope-hook recovery system and the corresponding algorithm were verified by the half physical simulation test platform of a recovery network system.