Stable nonlinear receding horizon regulator using RBF neural network models

Zahir Ahmida, Abdelfatah Charef, Victor Becerra

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    The general stability theory of nonlinear receding horizon controllers has attracted much attention over the last fifteen years, and many algorithms have been proposed to ensure closed-loop stability. On the other hand many reports exist regarding the use of artificial neural network models in nonlinear receding horizon control. However, little attention has been given to the stability issue of these specific controllers. This paper addresses this problem and proposes to cast the nonlinear receding horizon control based on neural network models within the framework of an existing stabilising algorithm.
    Original languageEnglish
    Title of host publication14th Mediterranean Conference on Control and Automation, 2006
    Subtitle of host publicationMED '06
    Place of PublicationPiscataway
    PublisherIEEE
    Pages1-5
    ISBN (Electronic)0978672003
    ISBN (Print)0978672011
    DOIs
    Publication statusPublished - 2006

    Keywords

    • RBF neural network model, closed-loop stability, nonlinear receding horizon controller, stabilisation algorithm, stability theory, stable nonlinear receding horizon regulator

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