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Parameter Estimation and an Extended Predictive-Based Tuning Method for a Lab-Scale Distillation Column
[Image: see text] The present study proposes a new PI controller tuning method using extended predictive control (EPC). The PI controller parameter values are calculated using the EPC controller output and its closed-loop response. This provides a simple and an effective tuning strategy which result...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical
Society
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6921618/ https://www.ncbi.nlm.nih.gov/pubmed/31867517 http://dx.doi.org/10.1021/acsomega.9b02713 |
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author | Yadav, Eadala Sarath Indiran, Thirunavukkarasu Priya, S. Shanmuga Fedele, Giuseppe |
author_facet | Yadav, Eadala Sarath Indiran, Thirunavukkarasu Priya, S. Shanmuga Fedele, Giuseppe |
author_sort | Yadav, Eadala Sarath |
collection | PubMed |
description | [Image: see text] The present study proposes a new PI controller tuning method using extended predictive control (EPC). The PI controller parameter values are calculated using the EPC controller output and its closed-loop response. This provides a simple and an effective tuning strategy which results in an improved closed-loop response compared to conventional tuning methods. The tuning methodology is applicable for single input single output and multi input multi output stable processes. Simulation and experimental results reveal the efficacy of the method under plant uncertainty conditions. |
format | Online Article Text |
id | pubmed-6921618 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | American Chemical
Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-69216182019-12-20 Parameter Estimation and an Extended Predictive-Based Tuning Method for a Lab-Scale Distillation Column Yadav, Eadala Sarath Indiran, Thirunavukkarasu Priya, S. Shanmuga Fedele, Giuseppe ACS Omega [Image: see text] The present study proposes a new PI controller tuning method using extended predictive control (EPC). The PI controller parameter values are calculated using the EPC controller output and its closed-loop response. This provides a simple and an effective tuning strategy which results in an improved closed-loop response compared to conventional tuning methods. The tuning methodology is applicable for single input single output and multi input multi output stable processes. Simulation and experimental results reveal the efficacy of the method under plant uncertainty conditions. American Chemical Society 2019-12-05 /pmc/articles/PMC6921618/ /pubmed/31867517 http://dx.doi.org/10.1021/acsomega.9b02713 Text en Copyright © 2019 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes. |
spellingShingle | Yadav, Eadala Sarath Indiran, Thirunavukkarasu Priya, S. Shanmuga Fedele, Giuseppe Parameter Estimation and an Extended Predictive-Based Tuning Method for a Lab-Scale Distillation Column |
title | Parameter Estimation
and an Extended Predictive-Based
Tuning Method for a Lab-Scale Distillation Column |
title_full | Parameter Estimation
and an Extended Predictive-Based
Tuning Method for a Lab-Scale Distillation Column |
title_fullStr | Parameter Estimation
and an Extended Predictive-Based
Tuning Method for a Lab-Scale Distillation Column |
title_full_unstemmed | Parameter Estimation
and an Extended Predictive-Based
Tuning Method for a Lab-Scale Distillation Column |
title_short | Parameter Estimation
and an Extended Predictive-Based
Tuning Method for a Lab-Scale Distillation Column |
title_sort | parameter estimation
and an extended predictive-based
tuning method for a lab-scale distillation column |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6921618/ https://www.ncbi.nlm.nih.gov/pubmed/31867517 http://dx.doi.org/10.1021/acsomega.9b02713 |
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