Twin-scenario driven autolanding control for fixed-wing UAVs
In this paper,a twin-scenario driven autonomous landing flight control optimization scheme is proposed to solve practical problems such as the difficulty in modeling low-attitude airflow disturbance during fixed-wing unmanned aerial vehicle(UAV)landing.Firstly,a high-fidelity scenario simulation system was constructed by introducing twin technology,based on which landing flight data under various wind disturbance conditions were collected.Then a trajectory tracking learning control algorithm is designed to resist the influence of low-level wind disturbance by mining the historical safe landing flight experience.The online adjustment strategy of the desired landing trajectory is designed to resist the violent disturbance of the position and attitude of the UAVs caused by wind gusts.Finally,the landing control law and system stability are given under wind disturbance.Multiple sorts landing flights of fixed wing UAVs were verified in the twin scenario.The effectiveness of the proposed control method is verified by comparing with the classical control scheme.
万方数据
维普
