Multi-loop nonlinear internal model controller design under nonlinear dynamic PLS framework using ARX-neural network model. Hu, B., Zhao, Z., & Liang, J. In Journal of Process Control, volume 22, pages 207-217, 2012. doi abstract bibtex 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. © 2011 Elsevier Ltd. All Rights Reserved.
@inproceedings{
title = {Multi-loop nonlinear internal model controller design under nonlinear dynamic PLS framework using ARX-neural network model},
type = {inproceedings},
year = {2012},
keywords = {ARX-NN structure,Aspen Dynamic Module,MCH distillation column,Nonlinear IMC scheme,Partial least squares},
pages = {207-217},
volume = {22},
issue = {1},
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last_modified = {2021-04-09T16:14:43.220Z},
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abstract = {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. © 2011 Elsevier Ltd. All Rights Reserved.},
bibtype = {inproceedings},
author = {Hu, Bin and Zhao, Zhao and Liang, Jun},
doi = {10.1016/j.jprocont.2011.09.002},
booktitle = {Journal of Process Control}
}
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