Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

Feedforward Control Based on Neural Networks for Hard Disk Drives

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Xuemei Ren ; Dept. of Autom. Control, Beijing Inst. of Technol., Beijing ; Lewis, F.L. ; Jingliang Zhang ; Ge, S.S.

We present a novel feedforward control based on neural networks to attenuate the effect of external vibrations on the positioning accuracy of hard disk drives. The neural network compensator, which is an add-on function on top of nominal feedback control, uses the accelerometer signals obtained from a sensor to detect external vibrations. Our feedforward control can be regarded as a nonlinear finite impulse response (FIR) that corresponds to linear FIR when the basis function of the neural network is linear. By neural network learning, the tracking performance of hard disk drives can be improved with no information on disturbance dynamics or sensor model. We have analyzed the stability of the proposed scheme by the Lyapunov criterion. Here, we give simulation results to demonstrate that our control scheme can eliminate the effect of external disturbances on positioning accuracy.

Published in:

Magnetics, IEEE Transactions on  (Volume:45 ,  Issue: 7 )