摘要
针对航天器姿态稳定控制问题,设计一种迭代学习姿态控制器.将连续非周期运动的姿态跟踪过程分解为队列重复运动,采用前一周期的姿态跟踪误差修正后一周期的控制输入,分别对未知参数和干扰构建有界迭代学习律,给出航天器姿态稳定控制器,并从理论上分析了闭环系统的渐近稳定性和姿态跟踪误差的一致有界性.通过在轨捕获非合作目标过程中航天器姿态跟踪控制问题的数值仿真,验证了迭代学习控制器的鲁棒性和强抗干扰性.
A barrier alignment iterative learning controller is designed for spacecraft attitude stability control with both unbounded perturbation of moment of inertia and external disturbance. Under the condition that the expected attitude is const,the non-repetitive attitude tracking process is decomposed into periodic, continuous state alignment repetitive process, and a barrier alignment iterative learning controller is designed to guarantee the attitude stabilization. For the physical actuators saturation limiting, a barrier projection is utilized to estimate the unknown moment of inertia and external disturbance, and the asymptotic stability of the closed-loop system and the boundness of the attitude tracking error are analyzed. The simulation results show that the controller has the advantages of simple structure, strong robustness and bound attitude tracking error.
出处
《控制与决策》
EI
CSCD
北大核心
2016年第8期1493-1498,共6页
Control and Decision
关键词
姿态控制
迭代学习
障碍李雅普诺夫函数
在轨捕获
attitude control
iterative learning
barrier Lyapunov function
on-orbit capture
