Affiliations: [a] Department of Human and Information Systems
Engineering, Gifu University, Gifu, Japan | [b] Department of Mechanical Engineering, Kangwon National
Univeristy, Chunchon, Korea | [c] Department of Mechanical and Systems Engineering, Gifu
University, Gifu, Japan
Correspondence:
[*]
Corresponding author. Department of Human and Information
Systems Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1193,
Japan. Tel.: +81 58 293 2541; Fax: +81 58 230 1892; E-mail:
[email protected]; http://www.ics.human.gifu-u.ac.jp
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.
Keywords: Neural network, identification and control, self-sensing, micro-actuator, hard disk drive, dual-stage actuation
