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中文摘要：
针对存在初态误差的情形,提出多变量非线性系统的变阶采样迭代学习控制方法.相对固定阶迭代学习算法,变阶算法可有效降低跟踪误差.对变阶采样迭代学习算法进行了收敛性分析,推导出收敛充分条件.给出了变阶学习的两种实现策略-DD（Direct division）和DIP（Division in phases）策略.数值仿真表明,基于DIP策略的变阶采样迭代学习算法在获得较高的控制精度的同时,具有较快的收敛速度.
英文摘要：
In this paper,the problem of sampled-data iterative learning control is addressed for a class of nonlinear MIMO systems in the presence of perturbed initial conditions.In contrast to the fixed-order learning algorithms,a varying-order learning algorithm is proposed,for enhancing tracking performance against repositioning errors.Sufficient convergence conditions of the proposed varying-order learning algorithm are given,by which the learning gain can be chosen.The proposed learning algorithm is shown to be a unified one,because it is applicable to the systems with arbitrary but well-defined relative degree.Two implementation schemes,sampled-data direct division（DD） and division in phases（DIP） schemes,are presented,and numerical results are given to demonstrate effectiveness of the proposed schemes.
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