2 May 2006 Neural network based control of a suspension assembly with self-sensing micro-actuator for dual-stage HDD
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Proceedings Volume 6042, ICMIT 2005: Control Systems and Robotics; 60420S (2006) https://doi.org/10.1117/12.664573
Event: ICMIT 2005: Merchatronics, MEMS, and Smart Materials, 2005, Chongqing, China
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.
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Hiroyuki Yamada, Hiroyuki Yamada, Minoru Sasaki, Minoru Sasaki, Yoonsu Nam, Yoonsu Nam, Satoshi Ito, Satoshi Ito, } "Neural network based control of a suspension assembly with self-sensing micro-actuator for dual-stage HDD", Proc. SPIE 6042, ICMIT 2005: Control Systems and Robotics, 60420S (2 May 2006); doi: 10.1117/12.664573; https://doi.org/10.1117/12.664573
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