Comparative studies on repeatable runout compensation using iterative learning control

Abstract

In this paper, two types of iterative learning control schemes, previous cycle learning (PCL) and current cycle learning (CCL), are used to eliminate repeatable run-out (RRO) disturbance in hard disk drive (HDD) servomechanism. The convergence conditions of two learning control schemes have been explored in details. The analysis shows that PCL must possess a relative degree of zero. The CCL scheme, on the other hand, allows a relative degree of one for certain range of frequencies to be tracked. To illustrate and compare the applicability and effectiveness of the two ILC schemes for HDD RRO problems, a number of simulations are conducted under a noise contaminated environment.