We focus our attention on the ilc architecture of using feedback and feedforward actions in order to improve the robustness of the ilc scheme . this dissertation aims to develop new methodologies for robust ilc design that involves a tradeoff between rapid convergence and good tracking performance . these design methods are systematic to resolve the problem of choosing the parameters in learning law and enhancing the utilitarian of ilc 为了加强算法的鲁棒性，重点采用同时具有反馈与前馈作用的开闭环综合迭代学习控制结构，旨在给出同时兼顾收敛性和跟踪性能的鲁棒迭代学习控制律的设计方法，避免学习律参数选择的盲目性，拓宽迭代学习控制的应用范围，加强迭代学习控制的实用性。
