Predictive control using feedback linearization based on dynamic neural models

Jiamei Deng, V. M. Becerra, R. Stobart

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


    This paper presents a hybrid control strategy integrating dynamic neural networks and feedback linearization into a predictive control scheme. Feedback linearization is an important nonlinear control technique which transforms a nonlinear system into a linear system using nonlinear transformations and a model of the plant. In this work, empirical models based on dynamic neural networks have been employed. Dynamic neural networks are mathematical structures described by differential equations, which can be trained to approximate general nonlinear systems. A case study based on a mixing process is presented.
    Original languageEnglish
    Title of host publicationIEEE International Conference on Systems, Man and Cybernetics (ISIC 2007)
    Place of PublicationPiscataway
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages2716 - 2721
    ISBN (Electronic)9781424409914
    ISBN (Print)9781424409907
    Publication statusPublished - 2007


    • predictive control, neural networks, nonlinear predictive control , NETWORKS, SYSTEMS


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