现有的迭代学习控制研究关注于迭代学习控制系统在迭代批次方向的鲁棒收敛性，对控制系统在时间方向上的性能关注较少，没有展现出控制系统的动态特征。文中研究了有限频域范围线性连续重复过程在区域极点约束下的迭代学习控制问题。首先，结合二维系统理论，在时域中建立迭代学习控制系统的二维连续 Roesser 模型; 然后基于 Kalman-Yakubovich-Popov (KYP) 引理，分析迭代学习控制系统的跟踪性能和区域极点约束问题; 进而以线性矩阵不等式的形式给出控制器存在的充分条件，保证系统在批次方向和时间方向的性能，并将结果扩展到结构不确定性模型中; 最后，通过典型的跟踪伺服系统执行机构的控制仿真验证了设计方案的有效性和可行性。

Existing studies on iterative learning control focus on the robust convergence of the iterative learning control system along the trial direction，and pay little attention to the performance along the time direction，which fails to show the dynamic characteristics of the control system． Iterative learning control for linear differential repet-itive processes with regional pole constraints was studied． Firstly，combining with the two-dimensional (2D) sys-tem theory，the 2D continuous Roesser model was built under the time domain． Then，based on the Kalman-Yakubovich-Popov lemma，the tracking performance of iterative learning control systems and regional pole con-straints problems were analyzed． Moreover，the sufficient conditions of existence for the controller were derived in terms of linear matrix inequalities，which can guarantee the system performance along the trial direction and the time direction． Furthermore，the results are extended to structural uncertainty model as well． Finally，the simula-tions for a typical actuator of tracking servo system prove that the design is effective and feasible．