Neural network based control of a suspension assembly with self-sensing micro-actuator for dual-stage HDD

Abstract

This paper presents a system identification process and control system design of an artificial neural network based suspension assembly with self-sensing micro-actuator for hard disk drives. Artificial neural networks can be used effectively for the identification and control of nonlinear dynamical systems such as flexible micro-actuators and self-sensing systems. Three neural networks are developed for the self-sensing micro-actuator. The first for system identification, the second for inverse modeling for control using the signal from a laser sensor, and the third for inverse modeling for control using the signal from a self-sensing piezoelectric. We also use a neural network inverse model to control the suspension assembly which includes a micro-actuator pair. Simulation and experimental results show that good control performance can be achieved using artificial neural networks.