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|Title:||Discrete linear control enhanced by adaptive neural networks in application to a HDD-servo-system|
Sam Ge, S.
|Keywords:||Bias and friction forces|
|Source:||Herrmann, G., Sam Ge, S., Guo, G. (2008-08). Discrete linear control enhanced by adaptive neural networks in application to a HDD-servo-system. Control Engineering Practice 16 (8) : 930-945. ScholarBank@NUS Repository. https://doi.org/10.1016/j.conengprac.2007.10.011|
|Abstract:||The performance of a linear, discrete high performance track following controller in a hard disk drive is improved for its disturbance rejection by the introduction of a discrete non-linear, adaptive neural network (NN) element. The NN-element is deemed to be particularly effective for rejection of bias forces and friction. Theoretical, simulation and experimental results have been obtained. It is shown theoretically that an NN-element is effective in counteracting these non-linear, system-specific, model-dependent disturbances. The disturbance, i.e. the bias and friction force, is assumed to be unknown, with the exception that the disturbance is known to be matched to the plant actuator input range and the disturbance is an (unknown) continuous function of the plant output measurements. For a non-linear simulation model and a laboratory HDD-servo-system, it is shown that the NN-control element improves performance and appears particularly effective for a reasonably small number of NN-nodes. © 2007 Elsevier Ltd. All rights reserved.|
|Source Title:||Control Engineering Practice|
|Appears in Collections:||Staff Publications|
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