Adaptive disturbance rejection for disk drives using neural networks

This paper presents the experimental verification of an adaptive feedforward disturbance rejection scheme for hard disk drives. The control scheme is shown to reduce the error during track-following by as much as 11.8%. The adaptive disturbance rejector is added to a baseline controller and broken into two parts: a part to suppress the repeatable runout and another to attenuate the residual disturbance by using radial basis functions. Both parts are adapted online to ensure good disturbance rejection. The experimental results are included to demonstrate the effectiveness on a commercial hard disk drive.