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Lookup NU author(s): Dr Pino Baffi, Professor Elaine Martin
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The extension of model predictive control (MPC) to non-linear systems is proposed through dynamic non-linear Partial Least Squares (PLS) models. PLS has been shown to be an appropriate multivariate regression methodology for modelling noisy, correlated and/or collinear data. It has been applied extensively, within a 'static' framework, for the modelling and analysis of industrial process data. The contribution of this paper is the development of a non-linear dynamic PLS framework for applications in MPC. The non-linear dynamic PLS models make use of an error based non-linear partial least squares algorithm where the non-linear inner models are built within an AutoRegressive with eXogeneous inputs (ARX) framework. In particular, quadratic and feedforward neural network inner models are considered. The application of a dynamic PLS model within a MPC framework opens up the potential of using multivariate statistical projection based methods not only for process modelling, inferential estimation and performance monitoring, but also for model predictive control. A benchmark simulation of a pH neutralization system is used to demonstrate the application of a non-linear dynamic PLS framework for model predictive control.
Author(s): Baffi G, Morris J, Martin E
Publication type: Article
Publication status: Published
Journal: Chemical Engineering Research & Design
Year: 2002
Volume: 80
Issue: A1
Pages: 75-86
ISSN (print): 0263-8762
ISSN (electronic): 1744-3563
Publisher: Elsevier Ltd
URL: http://dx.doi.org/10.1205/026387602753393240
DOI: 10.1205/026387602753393240
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