A New Optical Disk Driver Fine Seek Algorithm Based on Runout Learning

The eccentricity in the Optical Disk Drive (ODD) is the inevitable deviation of the geometric center of circular tracks from the rotating center of the disk. The resulted “runout” in the drive is thus periodic with disk rotation. To overcome the runout, conventional approach is for the pick-up head to go forward to the target track while shaking with the period runout during track accessing. This paper proposes an integration of the learning algorithm to learn the runout motion with an on-line observer to estimate the track runout during track accessing. The purpose is to allow for online computation of the target track kinematics so that the controller can adjust the accessing strategy to accommodate for the target track behavior. The proposed algorithm is demonstrated to be feasible through experiments applied to the fine jump control for a general optical storage opto-mechenical-electrical-control plant from OES in ITRI.