Multi-loop nonlinear internal model controller design under nonlinear dynamic PLS framework using ARX-neural network model

Bin Hu

doi:10.1016/j.jprocont.2011.09.002

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Multi-loop nonlinear internal model controller design under nonlinear dynamic PLS framework using ARX-neural network model

2012-01-01

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Bin Hu

Zhao Zhao

Jun Liang *

DOI：10.1016/j.jprocont.2011.09.002

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Abstract

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In this paper, a novel multi-loop nonlinear internal model control (IMC) strategy for multiple-input multiple-output (MIMO) systems is presented under the partial least squares (PLS) framework, which automatically decomposes the system into several univariate subsystems in the latent space. To formulate a nonlinear dynamic PLS framework, we propose an ARX-neural network (ARX-NN) cascaded structure, and incorporate it into PLS inner model. A gradient-based optimization approach is then provided to identify the parameter sets of the ARX-NN PLS model so that the plant-model mismatch is minimized. Furthermore, with perfect model, we show that the response of the closed loop system can be reduced to a simple linear IMC filter with the original system delay. The simulation results of a methylcyclohexane (MCH) distillation column from Aspen Dynamic Module, demonstrate the effectiveness of our approach in terms of disturbance rejection and tracking performance. (C) 2011 Elsevier Ltd. All rights reserved.

Keywords:

Partial least squares

ARX-NN structure

Nonlinear IMC scheme

Aspen Dynamic Module

MCH distillation column

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