Intelligent Predictive Control of Nonlienar Processes Using

Peter Magnus Nørgård, Paul Haase Sørensen, Niels Kjølstad Poulsen, Ole Ravn, Lars Kai Hansen

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    Abstract

    This paper presents a novel approach to design of generalized predictive controllers (GPC) for nonlinear processes. A neural network is used for modelling the process and a gain-scheduling type of GPC is subsequently designed. The combination of neural network models and predictive control has frequently been discussed in the neural network community. This paper proposes an approximate scheme, the approximate predictive control (APC), which facilitates the implementation and gives a substantial reduction in the required amount of computations. The method is based on a technique for extracting linear models from a nonlinear neural network and using them in designing the control system. The performance of the controller is demonstrated in a simulation study of a pneumatic servo system
    Original languageEnglish
    Title of host publicationIntelligent Control, 1996., Proceedings of the 1996 IEEE International Symposium on
    Place of PublicationDearborn, Michigan, USA
    PublisherIEEE
    Publication date1996
    Pages301-306
    ISBN (Print)0-7803-2978-3
    DOIs
    Publication statusPublished - 1996
    Event11th IEEE International Symposium on Intelligent Control - Dearborn, Michigan, USA
    Duration: 1 Jan 1996 → …

    Conference

    Conference11th IEEE International Symposium on Intelligent Control
    CityDearborn, Michigan, USA
    Period01/01/1996 → …

    Bibliographical note

    Copyright: 1996 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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