连续障碍环境下无人机实时避障控制  

Reactive MAV Control for Obstacle Avoidance in Continuous Obstacle Environment

作　　者：张乐 袁锁中[1] 黄永康 ZHANG Le;YUAN Suozhong;HUANG Yongkang(Key Laboratory of Navigation,Guidance and Health-Management Technologies of Advanced Aerocraft,Ministry of Industry and Information Technology,College of Automation Engineering Nanjing University of Aeronautics and Astronautics,Nanjing 211100 China)

机构地区：[1]南京航空航天大学自动化学院先进飞行器导航、控制与健康管理工业和信息化部重点实验室,南京211100

出　　处：《电光与控制》2020年第4期6-10,14,共6页Electronics Optics & Control

基　　金：国家自然科学基金(61273050);中央高校基本科研业务费资助(XCA18155)。

摘　　要：针对小型固定翼无人机在未知环境下,面对墙壁等连续型障碍物不能快速实时防撞的问题,提出了一种结合多幂次滑模趋近律和超螺旋观测器设计的无人机避障控制方法。首先通过空间位置关系和无人机运动学方程建立无人机避障系统数学模型,进而利用多幂次滑模趋近律快速收敛的特性设计了无人机避障制导律;同时考虑到传感器误差和系统建模等造成的不确定性,引入了超螺旋干扰观测器加以补偿,并对系统的收敛性加以证明。仿真结果表明,所提出的避障控制策略能够使无人机快速避开连续障碍,同时对传感器扰动有较强的鲁棒性。It’s difficult for fixed-wing Miniature Air Vehicles(MAV) in the unknown environment to avoid collision reactively when facing continuous obstacles such as the wall.To solve the problem,a MAV control method for obstacle avoidance is proposed,which combines multi-power reaching law of sliding mode control with super-twisting disturbance observer.First,the mathematical model of MAV collision avoidance system is established by using the spatial relationship and the MAV kinematics equation.Then,the guidance law of MAV collision avoidance is designed by using the characteristics of fast convergence of multi-power reaching law of sliding mode control.As to the uncertainty caused by sensor errors and system modeling errors,the super-twisting disturbance observer is introduced to compensate for the uncertainty.The convergence of the system is proved.Simulation results show that the proposed control method for collision avoidance enables the MAV to quickly avoid continuous obstacles,and has strong robustness to sensor disturbance.

关 键 词：固定翼无人机 实时避障 制导律 多幂次滑模趋近律 超螺旋干扰观测器 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]