For a class of discrete linear systems with non-repetitive uncertainties， a robust indirect-type Iterative Learning Control （ILC） algorithm based on dual-loop control structure 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， realizing the fast tracking in the first few batches. In the outer-loop， a High-Order Internal Model （HOIM） was used 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， realizing the accurate tracking of the varying trajectories. 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 conditions of stability along the batch axis were transformed into Linear Matrix Inequality （LMI） conditions. Finally， the effectiveness of proposed algorithm was verified through the simulation of a class of permanent magnet motor.