In this paper, several types of iterative learning control (ILC) schemes for hard disk drive (HDD) control, including previous cycle learning (PCL), current cycle learning (CCL) and their combinations, are designed and analyzed under the sampled-data environment. The feedback loop of the CCL stabilizes the system in time domain while the PCL guarantees the convergence along the iteration axis. Therefore, the combination of these two schemes is applied and the effectiveness of the scheme is illustrated through simulations and experiments.