中文摘要：

在反复的学习控制(ILC ) 的研究，起始的状态与需要的状态一致或起始的状态每重复被修理，这通常被假定。由考虑 ILC 法律是困难的在起始的状态的限制下面为操纵者适用于追踪的控制的问题，我们由还原剂顺序转变把操纵者系统的动态模型变成一个降低顺序的系统。为转变操纵者系统，有角度修正术语的一个开结束的循环 ILC 算法被建议，它使用错误信号和二个邻近的错误信号的偏差调整自己。与传统的 P 类型算法相比，这个算法做更好的使用节省并且当前的信息；当时与 PD 类型算法相比，它克服衍生物行动引起的不稳定性。同时，产量向量的角度关系被用作一个标准估计控制输入的质量，授于或惩罚算法的变化趋势。那么，快集中速度和优秀追踪效果两个都被认识到。当每个联合旋转角度的限制被考虑时，改进策略为上述算法被建议。最后，模拟结果验证控制计划的有效性。

英文摘要：

In the research of iterative learning control（ILC）,it is usually assumed that the initial states are consistent with the desired states or the initial states are fixed per iteration.By considering the problem that ILC law is difficult to apply to the tracking control for the manipulator under the restriction of initial states,we change the dynamic model of the manipulator system into a lower-order system by reduced-order transformations.For the transformed manipulator system,an open-closed loop ILC algorithm with angle correction term is proposed,which uses the error signal and the deviation of two adjacent error signals to adjust itself.Compared with traditional P-type algorithm,this algorithm makes better use of the saved and current information;while compared with PD-type algorithm,it overcomes the instability caused by the derivative action.Meanwhile,the angle relationship of output vectors is used as a standard to estimate the quality of the control inputs,＂awarding or punishing＂ the changing trend of the algorithm.So,a fast convergence speed and excellent tracking effect are both realized.Improved strategies are proposed for the above algorithm when the limitation of each joint rotating angle is considered.Finally,the simulation results verify the effectiveness of the control scheme.