Abstract: For a class of discrete linear systems with non-repetitive uncertainties, a double-loops construction based robust indirect-type Iterative Learning Control (ILC) algorithm was proposed to track the batch-varying trajectories. In the inner-loop, a Proportional-Integral (PI) type feedback controller was designed to ensure the stability of the closed-loop system along the time axis, and the fast tracking in the first few batches was realized. In the outer-loop, by using a High-Order Internal Model (HOIM) to describe the pattern of varying trajectories, a HOIM based Proportional (P) type ILC controller was designed to improve the tracking performance along the batch axis, and the accurate tracking of the varying trajectories was realized. For the disturbance caused by non-repetitive uncertainties, a performance criterion function was designed and the system model was transformed into a repetitive process model under the effect of the indirect-type ILC controller. Based on the stability theory of repetitive process model, the condition of stability along the batch axis was transformed into Linear Matrix Inequality (LMI) conditions. Finally, the effectiveness of proposed algorithm is verified through the simulation of a class of permanent magnet linear motor.