Disturbance compensation for servo-control applications using a discrete adaptive neural network feedforward method

G Herrmann, FL Lewis, SS Ge, J Zhang

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    This paper introduces a novel adaptive neural network compensator for feedforward compensation of external disturbances affecting a closed loop system. The neural network scheme is posed so that the nonlinear disturbance model for a measurable disturbance can be adapted for rejection of the disturbance affecting a closed loop system. The non-linear neural network approach has been particularly developed for mobile applications where the adaptation algorithm has to remain simple. For that reason, the theoretical framework justifies a very simple least-mean-square approach suggested in a mobile hard disk drive context. This approach is generalized to a non-linear adaptive neural network compensation scheme. In addition, usual assumptions are relaxed, so that it is sufficient to model the nonlinear disturbance model as a stable system avoiding strictly positive real assumptions. The output of the estimated disturbance model is assumed to be matched to the compensation signal for effectiveness, although for stability this is not necessary. Simulation examples show different features of the adaptation algorithm also considering a realistic hard disk drive simulation.
    Original languageEnglish
    Title of host publication2007 46th IEEE Conference on Decision and Control
    Number of pages8
    DOIs
    Publication statusPublished - Dec 2007

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