An iterative learning controller for reduction of repetitive runout in disk drives

This paper considers the design of an iterative learning controller for the reduction of repeatable runout (RRO) in disk drives. The advocated controller is an add-on controller that does not require modification to the existing track following controller. A design methodology is provided for the selection of the parameters of the update law to ensure convergent learning. It is shown that the update law to ensure convergent learning is equivalent to stabilization of a multi-input multi-output system using static output feedback. Experimental results using a 3.5-in Western Digital drive show that a 50% reduction in the sigma of the RRO is achieved in ten iterations when the levels of repetitive and nonrepeatable runout in the position error signal are comparable.