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...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
EDP Sciences
2017-01-01
|
Series: | MATEC Web of Conferences |
Online Access: | https://doi.org/10.1051/matecconf/201713900030 |
id |
doaj-b74c9220e051471182b1ccaee58f3f61 |
---|---|
record_format |
Article |
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 |
_version_ |
1724299137693777920 |