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Title: Disturbance and friction compensations in hard disk drives using neural networks
Authors: Lai, C.Y.
Lewis, F.L.
Venkataramanan, V.
Ren, X.
Ge, S.S. 
Liew, T.
Keywords: Disturbance feedforward
Friction compensation
Hard disk drives (HDDs)
Neural networks (NNs)
Issue Date: Feb-2010
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.
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
ISSN: 02780046
DOI: 10.1109/TIE.2009.2027257
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