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Non-linear model based predictive control through dynamic non-linear partial least squares

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.

Publication metadata

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


DOI: 10.1205/026387602753393240


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