Please use this identifier to cite or link to this item:
|Title:||Disturbance and friction compensations in hard disk drives using neural networks|
Hard disk drives (HDDs)
Neural networks (NNs)
|Citation:||Lai, C.Y., Lewis, F.L., Venkataramanan, V., Ren, X., Ge, S.S., Liew, T. (2010-02). Disturbance and friction compensations in hard disk drives using neural networks. IEEE Transactions on Industrial Electronics 57 (2) : 784-792. ScholarBank@NUS Repository. https://doi.org/10.1109/TIE.2009.2027257|
|Abstract:||In this paper, we show that by using two adaptive neural networks (NNs), each of which is tailored for a specific task, the tracking performance of the hard-disk-drive (HDD) actuator can be significantly improved. The first NN utilizes accelerometer signal to detect external vibrations and compensates for its effect on HDD position via feedforward action. The second NN is designed to compensate for pivot friction. The appealing advantage of the NN compensators is that the design does not involve any information on the plant, sensor, disturbance dynamics, and friction model. The stability of the proposed scheme is analyzed by the Lyapunov criterion. Experimental results show that the tracking performance of the HDDs can be improved significantly with the use of the NN compensators as compared to the case without compensation. © 2009 IEEE.|
|Source Title:||IEEE Transactions on Industrial Electronics|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on May 17, 2018
WEB OF SCIENCETM
checked on May 9, 2018
checked on May 4, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.