Real-time application of a constrained predictive controller based on dynamic neural networks with feedback linearization

Jiamei Deng, Victor Becerra, Richard Stobart, Shaohua Zhong

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

    Abstract

    This paper describes an experimental application of constrained predictive control and feedback linearisation based on dynamic neural networks. It also verifies experimentally a method for handling input constraints, which are transformed by the feedback linearisation mappings. A performance comparison with a PID controller is also provided. The experimental system consists of a laboratory based single link manipulator arm, which is controlled in real time using MATLAB/SIMULINK together with data acquisition equipment.
    Original languageEnglish
    Title of host publication18th IFAC World Congress, August 28 - September 2, 2011, Milano (Italy)
    EditorsSergio Bittanti, Angelo Cenedese, Sandro Zampieri
    PublisherInternational Federation of Automatic Control (IFAC)
    Pages6727-6732
    ISBN (Print)9783902661937
    DOIs
    Publication statusPublished - 1 Aug 2011

    Publication series

    NameWorld congress
    PublisherIFAC
    Number1
    Volume18
    ISSN (Print)1474-6670

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