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Proceedings Paper

Neural network based control of a suspension assembly with self-sensing micro-actuator for dual-stage HDD
Author(s): Hiroyuki Yamada; Minoru Sasaki; Yoonsu Nam; Satoshi Ito
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Paper 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 dual-stage hard disk drive. Artificial neural networks can be used effectively for the identification and control of nonlinear dynamical systems such as a flexible micro-actuator and self-sensing system. Three neural networks are developed for the self-sensing micro-actuator, the first for system identification, the second for inverse model for control using laser sensor signal, and the third for inverse model for control using only self-sensing piezoelectric signal. And we use a neural network inverse model to control the suspension assembly which includes the micro-actuator pair. Simulation and experimental results show that good control performance can be achieved by using artificial neural networks approach.

Paper Details

Date Published: 2 May 2006
PDF: 6 pages
Proc. SPIE 6042, ICMIT 2005: Control Systems and Robotics, 60420S (2 May 2006); doi: 10.1117/12.664573
Show Author Affiliations
Hiroyuki Yamada, Gifu Univ. (Japan)
Minoru Sasaki, Gifu Univ. (Japan)
Yoonsu Nam, Kangwon National Univ. (South Korea)
Satoshi Ito, Gifu Univ. (Japan)

Published in SPIE Proceedings Vol. 6042:
ICMIT 2005: Control Systems and Robotics
Yunlong Wei; Kil To Chong; Takayuki Takahashi; Shengping Liu; Zushu Li; Zhongwei Jiang; Jin Young Choi, Editor(s)

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