An improved adaptive neural network compensation of pivot nonlinearity in hard disk drives

Abstract

In hard disk drives (HDDs), the movement of the read/write (R/V) head is driven by voice-coil-motor (VCM) which is supported by a pivot cartridge consisting of a pair of preloaded ball bearings. The pivot friction nonlinearity is defined as the frictional hysteresis that occurs at the bearing of the actuator pivot in the hard disk drives. This nonlinear effect can be observed as a large gain reduction especially in the low-frequency range and will cause large residual errors and/or high-frequency oscillations which in turn affect track-following and seeking performance. Basically, the linear techniques were usually used to suppress the influences by raising the low-frequency gain but were limited b.y the Bode's gain-phase relationship. In this paper, an improved adaptive neural network (NN) controller is designed to compensate for the pivot nonlinearity. To capture the time- varying uncertainties and nonlinearity, the adaptive tuning scheme is employed for the NN weights and the width in RBF functions. The proposed scheme has the advantages of fast convergence for parameter estimation and improvement of high hump of the magnitude of closed-loop sensitivity function. The simulation results show the effectiveness of the proposed scheme.