Model-plant Mismatch Detection of Nonlinear Processes Based on Multi-model LPV Model

The performance of model-based controller depends on the quality of the identified model. Accurate detection of the channel with model-plant mismatch can avoid re-identification of the entire multivariable system, thereby reducing the disturbance to normal production caused by identification test. A...

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Main Authors: Huang Jiangyin, Zhao Jing
Format: Article
Language:English
Published: EDP Sciences 2017-01-01
Series:MATEC Web of Conferences
Online Access:https://doi.org/10.1051/matecconf/201713900030
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spelling doaj-b74c9220e051471182b1ccaee58f3f612021-02-02T07:37:09ZengEDP SciencesMATEC Web of Conferences2261-236X2017-01-011390003010.1051/matecconf/201713900030matecconf_icmite2017_00030Model-plant Mismatch Detection of Nonlinear Processes Based on Multi-model LPV ModelHuang JiangyinZhao JingThe performance of model-based controller depends on the quality of the identified model. Accurate detection of the channel with model-plant mismatch can avoid re-identification of the entire multivariable system, thereby reducing the disturbance to normal production caused by identification test. A model-plant mismatch detection methodology for nonlinear systems based on LPV (Linear Parameter Varying) model was proposed in this work. The detection was performed only when the control performace becomes worse. Firstly, the LPV model based on multi-model interpolation was adopted to represent the nonlinear process. Then partial correlation coefficients between the model residuals and the inputs of the models at each of the operation points were analyzed to diagnose the model-plant mismatch of the local models. Finally, the LPV model was re-identified by updating the local mismatch models and re-estimating the model weighing parameters. The experimental results show that the partial correlation coefficient of the mismatch model is obviously larger than that of the exact model, which can point out the channel with model-plant mismatch correctly.The proposed method is suitable for the nonlinear processes which have relative steady states in their operating trajectorys.https://doi.org/10.1051/matecconf/201713900030
collection DOAJ
language English
format Article
sources DOAJ
author Huang Jiangyin
Zhao Jing
spellingShingle Huang Jiangyin
Zhao Jing
Model-plant Mismatch Detection of Nonlinear Processes Based on Multi-model LPV Model
MATEC Web of Conferences
author_facet Huang Jiangyin
Zhao Jing
author_sort Huang Jiangyin
title Model-plant Mismatch Detection of Nonlinear Processes Based on Multi-model LPV Model
title_short Model-plant Mismatch Detection of Nonlinear Processes Based on Multi-model LPV Model
title_full Model-plant Mismatch Detection of Nonlinear Processes Based on Multi-model LPV Model
title_fullStr Model-plant Mismatch Detection of Nonlinear Processes Based on Multi-model LPV Model
title_full_unstemmed Model-plant Mismatch Detection of Nonlinear Processes Based on Multi-model LPV Model
title_sort model-plant mismatch detection of nonlinear processes based on multi-model lpv model
publisher EDP Sciences
series MATEC Web of Conferences
issn 2261-236X
publishDate 2017-01-01
description The performance of model-based controller depends on the quality of the identified model. Accurate detection of the channel with model-plant mismatch can avoid re-identification of the entire multivariable system, thereby reducing the disturbance to normal production caused by identification test. A model-plant mismatch detection methodology for nonlinear systems based on LPV (Linear Parameter Varying) model was proposed in this work. The detection was performed only when the control performace becomes worse. Firstly, the LPV model based on multi-model interpolation was adopted to represent the nonlinear process. Then partial correlation coefficients between the model residuals and the inputs of the models at each of the operation points were analyzed to diagnose the model-plant mismatch of the local models. Finally, the LPV model was re-identified by updating the local mismatch models and re-estimating the model weighing parameters. The experimental results show that the partial correlation coefficient of the mismatch model is obviously larger than that of the exact model, which can point out the channel with model-plant mismatch correctly.The proposed method is suitable for the nonlinear processes which have relative steady states in their operating trajectorys.
url https://doi.org/10.1051/matecconf/201713900030
work_keys_str_mv AT huangjiangyin modelplantmismatchdetectionofnonlinearprocessesbasedonmultimodellpvmodel
AT zhaojing modelplantmismatchdetectionofnonlinearprocessesbasedonmultimodellpvmodel
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