摘要
分析了空间机器人本体质量变化和负载情况变化对系统动力学不确定性的影响,并针对空间机器人操作负载变化显著的特点,设计了一种自适应Terminal滑模控制方法。利用RBF神经网络在线学习系统的不确定性上界,系统状态始终保持在滑模面上,并能保证系统控制误差在有限时间内收敛。以两自由度空间机器人为对象,对不同负载情况的运动进行了仿真,结果表明这种滑模控制方法对系统负载变化不敏感,并能保证期望控制精度。
The dynamic uncertainty of space robot caused by the change of base mass or load mass is analyzed.According to the characteristic that the operation load of space robot varies significantly,an adaptive terminal sliding mode controller is designed,and the upper bound of the dynamic uncertainty is learnt using a RBF neural network.The designed controller ensures that the system states stay on the sliding surface all the time.Further,the control errors converge to zero in finite time.Simulation with different loads is carried out on a space robot with two DOF.Results show that the proposed method is robust to dynamic uncertainty.
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2010年第3期800-805,共6页
Journal of Jilin University:Engineering and Technology Edition
基金
'863'国家高技术研究发展计划项目(2005AA745060)
