存在环境约束的机器人自适应迭代学习控制
Adaptive iterative learning control of robot manipulators in the presence of environmental constraint
摘要点击 2291  全文点击 1533  投稿时间:2012-05-14  修订日期:2012-07-21
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DOI编号  10.7641/j.issn.1000-8152.2012.8.LCTA120513
  2012,29(8):1038-1042
中文关键词  受限机器人  自适应控制  迭代学习控制  外界干扰
英文关键词  constrained robots  adaptive control  iterative learning control  external disturbances
基金项目  This work was supported by the the National Natural Science Foundation of China (No. 61074054).
作者单位E-mail
何熊熊 浙江工业大学 信息工程学院 hxx@zjut.edu.cn 
秦贞华 浙江工业大学 信息工程学院  
张端 浙江工业大学 信息工程学院  
中文摘要
      针对存在不确定性和外界干扰的受限机器人系统提出一种自适应迭代学习控制律. 不确定性参数被估计在时间域内, 同时重复性外界干扰在迭代域内得到补偿. 通过引入饱和学习函数, 保证了闭环系统所有信号有界. 借助Lyapunov复合能量函数法, 证明了系统渐进收敛到期望轨迹的同时, 能够保证力跟踪误差有界可调.
英文摘要
      A novel adaptive iterative learning algorithm is proposed for a class of constraint robotic manipulators with uncertainties and external disturbances. The uncertain parameters are estimated in the time domain whereas the repetitive disturbances is compensated in the iteration domain. With the adoption of saturated learning, all the signals in the closed loop are guaranteed to be bounded. By constructing a Lyapunov-Krasovskii-like composite energy function, the states of the closed system is proved to be asymptotically convergent to the desired trajectory while ensuring the constrained force remains bounded. Simulation results show the effectiveness of the proposed algorithm.