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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/50031


    Title: A disturbance reduction scheme for linear small delay systems with modeling uncertainties
    Authors: Tsai,MH;Tung,PC
    Contributors: 機械工程學系
    Keywords: MODIFIED SMITH PREDICTOR;LONG DEAD-TIME;NEURAL NETWORKS;INTEGRATOR
    Date: 2010
    Issue Date: 2012-03-27 17:01:34 (UTC+8)
    Publisher: 國立中央大學
    Abstract: A disturbance reduction scheme for linear delay systems with modeling uncertainties is presented in this paper. The linear systems in this study are assumed to be nominally stable, minimum phase and relative degree one systems. The control structure is based on Astrom's modified Smith predictor with a disturbance reduction scheme and an artificial neural network (ANN). Unlike other disturbance rejection methods, the proposed scheme does not require information about unknown disturbance frequencies. The ANN is used to approximate a product of an inverse of a time delay and a nonnegative gain and to augment the robustness of the proposed approach against modeling uncertainties including a time-varying delay. Connective weights of the ANN are trained on-line using a back-propagation algorithm according to a disturbance estimation error function. Simulation results show the effectiveness of the presented disturbance reduction scheme for linear delay systems with modeling uncertainties, subjected to both periodic and non-periodic unknown load disturbances. (C) 2010 Elsevier Ltd. All rights reserved.
    Relation: JOURNAL OF PROCESS CONTROL
    Appears in Collections:[機械工程學系] 期刊論文

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