摘要
在空间翻滚目标的最终逼近与跟踪任务段,追踪星对目标星的视觉相对导航会因进入盲区而失效,而基于间接相对导航的位置视觉伺服控制精度太差.针对该问题,基于图像视觉伺服思想,直接利用目标图像特征点坐标,建立了超近距离姿轨跟踪的成像运动模型,推导得到指数收敛的图像视觉伺服期望广义速度、相对位置估计修正量,进而引入基于双滑模面控制律的相对姿轨耦合控制闭环,从而提出了间接估计辅助的图像视觉伺服控制方法.数学仿真结果表明:该方法的相对位置和姿态控制精度显著提高,可为空间翻滚目标最终逼近与跟踪任务提供安全、准确、稳定的相对姿态和轨道条件.
During the process of final approaching and tracking tumbling target in space, the visual relative navigation from the chaser to the target may lose efficacy in the dead zone due to the ultra-short distance, thus, only indirect relative navigation information can be used in the position based visual servoing （PBVS）, which causes poor control precision. A novel image based visual servoing （IBVS） method is proposed to solve this problem, which directly uses several feature points’ coordinates from target’s image. The motion and imaging models are firstly established, then functions for the IBVS expected generalized velocities satisfying exponential convergence and the relative position estimation corrections are derived and presented. By introducing these velocities and corrections into the relative attitude and orbit coupled control close-loop based on dual sliding-mode surface control law, an indirect estimation assisted IBVS control method is proposed. Numerical simulations indicate that, the proposed method can dramatically promote the relative attitude and position control accuracies. Hence, it can provide much more safe, accurate, and stable relative attitude and orbit condition for final approaching and tracking the tumbling target in space.
作者
韩飞
段广仁
王兆龙
贺亮
武海雷
宋婷
HAN Fei;DUAN Guang-ren;WANG Zhao-long;HE Liang;WU Hai-lei;SONG Ting(School of Astronautics,Harbin Institute of Technology,Harbin Heilongjiang 150001,China;Shanghai Key Laboratory of Aerospace Intelligent Control Technology,Shanghai 201109,China)
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2018年第10期1473-1483,共11页
Control Theory & Applications
基金
国家自然科学基金重大项目(61690214)
国家自然科学基金面上项目(61374162)资助.
关键词
翻滚目标
最终逼近
滑模控制
图像视觉伺服
tumbling target
final approaching
sliding mode control
image based visual servoing
