Investigation in to the Application of PLS in MPC Schemes

Oliver Onel, Ian David Lockhart Bogle (Editor)

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    Abstract

    Since its introduction in 1975, Partial Least Squares (PLS) has become a standard tool for developing regression models when data sets contain highly correlated variables. In this paper, the implementation of PLS models within a Model Predictive Control (MPC) scheme is investigated. A significant problem when developing an MPC application is generating suitable data to identify an accurate model of the process. To address issues that can result when the data available for identifying a model is not sufficiently exciting, several researchers have recently suggested using PLS, rather than more traditional identification algorithms when developing predictive models. This paper shows that caution should be exercised when using models identified with PLS in an MPC algorithm. It is shown that if the data available for modelling is not highly correlated then traditional techniques can produce models that provide improved predictive capabilities. However, when limited data is available then there are benefits in using standard PLS. Furthermore, the paper proposes several methods that can be applied to generate unbiased models with varying structures.
    Original languageEnglish
    Title of host publicationhost publication
    EditorsIan David Lockhart Bogle
    Publication statusPublished - Jun 2012
    Event22nd European Symposium on Computer Aided Process Engineering - University College London, Gower Street, London, UK, WC1E 6BT
    Duration: 17 Jun 201220 Jun 2012

    Conference

    Conference22nd European Symposium on Computer Aided Process Engineering
    CityUniversity College London, Gower Street, London, UK, WC1E 6BT
    Period17/06/1220/06/12

    Keywords

    • partial least squares, model predictive control, system identification, excitation, unbiased

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