孪生场景驱动的固定翼无人机进场自主着陆控制

Twin-scenario driven autolanding control for fixed-wing UAVs

本文针对固定翼无人机着陆过程中低空风扰难建模等实际问题,提出孪生场景驱动的自主着陆飞行控制优化方案.首先,引入孪生技术,构建高保真场景模拟系统,采集无人机多架次安全着陆飞行数据;进而,挖掘历史安全着陆飞行经验,设计轨迹跟踪学习控制算法来抵抗低空风扰影响,并设计期望着陆轨迹在线调整策略,抑制阵风引起的无人机位姿剧烈扰动;最后,给出风扰场景下的着陆控制律及系统稳定性证明.基于孪生场景开展固定翼无人机多架次着陆飞行验证,通过与经典控制方案对比,验证了本文所提控制方法的有效性.

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.