Detection and Location of Model-Plant Mismatch in Multiple Input Multiple Output Systems under Model Predictive Controller Using Granger Causality Method

In closed-loop control systems, the model accuracy exerts large influences on the controllability, stability and quality of the whole process. Among all the faults that could affect the system performance, Model Plant Mismatch (MPM) is the one that not only directly threatens the system stability bu...

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Autores principales:	Ming Chen, Lei Xie, Hongye Su
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	model-plant mismatch granger causality analysis multiple-input multiple-output closed-loops detection and location Chemical technology TP1-1185 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/8a31b5b232374dcd869a80f024b78aa1
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id	oai:doaj.org-article:8a31b5b232374dcd869a80f024b78aa1
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spelling	oai:doaj.org-article:8a31b5b232374dcd869a80f024b78aa12021-11-25T18:51:07ZDetection and Location of Model-Plant Mismatch in Multiple Input Multiple Output Systems under Model Predictive Controller Using Granger Causality Method10.3390/pr91119762227-9717https://doaj.org/article/8a31b5b232374dcd869a80f024b78aa12021-11-01T00:00:00Zhttps://www.mdpi.com/2227-9717/9/11/1976https://doaj.org/toc/2227-9717In closed-loop control systems, the model accuracy exerts large influences on the controllability, stability and quality of the whole process. Among all the faults that could affect the system performance, Model Plant Mismatch (MPM) is the one that not only directly threatens the system stability but also deteriorates the controller performance. Meanwhile, MPM has a major influence on the qualities of outputs about industrial products. In this work, a new detection method based on Granger Causality is proposed to detect and locate the MPM in multiple input multiple output systems. Causality can reflect the relations between the mismatch fault and its negative effects on model predictive control(MPC) systems. With the assistance of disturbance transfer function models, the causality method can further be used to locate the mismatch positions and get the correct channels of each kind of mismatches. The proposed method was examined and validated in the Wood-Berry process in contrast to the decussation location method under model predictive controller.Ming ChenLei XieHongye SuMDPI AGarticlemodel-plant mismatchgranger causality analysismultiple-input multiple-output closed-loopsdetection and locationChemical technologyTP1-1185ChemistryQD1-999ENProcesses, Vol 9, Iss 1976, p 1976 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	model-plant mismatch granger causality analysis multiple-input multiple-output closed-loops detection and location Chemical technology TP1-1185 Chemistry QD1-999
spellingShingle	model-plant mismatch granger causality analysis multiple-input multiple-output closed-loops detection and location Chemical technology TP1-1185 Chemistry QD1-999 Ming Chen Lei Xie Hongye Su Detection and Location of Model-Plant Mismatch in Multiple Input Multiple Output Systems under Model Predictive Controller Using Granger Causality Method
description	In closed-loop control systems, the model accuracy exerts large influences on the controllability, stability and quality of the whole process. Among all the faults that could affect the system performance, Model Plant Mismatch (MPM) is the one that not only directly threatens the system stability but also deteriorates the controller performance. Meanwhile, MPM has a major influence on the qualities of outputs about industrial products. In this work, a new detection method based on Granger Causality is proposed to detect and locate the MPM in multiple input multiple output systems. Causality can reflect the relations between the mismatch fault and its negative effects on model predictive control(MPC) systems. With the assistance of disturbance transfer function models, the causality method can further be used to locate the mismatch positions and get the correct channels of each kind of mismatches. The proposed method was examined and validated in the Wood-Berry process in contrast to the decussation location method under model predictive controller.
format	article
author	Ming Chen Lei Xie Hongye Su
author_facet	Ming Chen Lei Xie Hongye Su
author_sort	Ming Chen
title	Detection and Location of Model-Plant Mismatch in Multiple Input Multiple Output Systems under Model Predictive Controller Using Granger Causality Method
title_short	Detection and Location of Model-Plant Mismatch in Multiple Input Multiple Output Systems under Model Predictive Controller Using Granger Causality Method
title_full	Detection and Location of Model-Plant Mismatch in Multiple Input Multiple Output Systems under Model Predictive Controller Using Granger Causality Method
title_fullStr	Detection and Location of Model-Plant Mismatch in Multiple Input Multiple Output Systems under Model Predictive Controller Using Granger Causality Method
title_full_unstemmed	Detection and Location of Model-Plant Mismatch in Multiple Input Multiple Output Systems under Model Predictive Controller Using Granger Causality Method
title_sort	detection and location of model-plant mismatch in multiple input multiple output systems under model predictive controller using granger causality method
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8a31b5b232374dcd869a80f024b78aa1
work_keys_str_mv	AT mingchen detectionandlocationofmodelplantmismatchinmultipleinputmultipleoutputsystemsundermodelpredictivecontrollerusinggrangercausalitymethod AT leixie detectionandlocationofmodelplantmismatchinmultipleinputmultipleoutputsystemsundermodelpredictivecontrollerusinggrangercausalitymethod AT hongyesu detectionandlocationofmodelplantmismatchinmultipleinputmultipleoutputsystemsundermodelpredictivecontrollerusinggrangercausalitymethod
_version_	1718410657796194304

Detection and Location of Model-Plant Mismatch in Multiple Input Multiple Output Systems under Model Predictive Controller Using Granger Causality Method

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