Fourier series-based repetitive learning variable structure control of hard disk drive servos

Abstract

The demand for larger capacity and higher track density of hard disk drives requires significant improvement of actuator track following control. We developed a new repetitive learning system which synthesizes variable structure control (VSC) and repetitive learning during track following to improve tracking accuracy. Proof shows it completely nullifies the tracking error caused by bias force and repeatable runout. The proposed learning scheme uses the past VSC signal for updating instead of the past tracking error, and hence achieves a fast convergence. The Fourier series-based implementation selectively attenuates disturbance at given frequencies, hence avoids the influence from noise and other nonrepeatable factors.