A comparison study of MPC strategies based on minimum variance control index performance

Model Predictive Control (MPC) is a useful tool when controlling processes that handle a large number of input and output variables. This study presents a comparison of different MPC strategies when they are subjected to control process variables directly. The strategies studied are IMC, GPC, MPC-D,...

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Autores:
BORRERO-SALAZAR, Alex A.
CARDENAS-CABRERA, Jorge M.
BARROS-GUTIERREZ, Daniel A.
JIMÉNEZ-CABAS, Javier A.
Tipo de recurso:
Article of journal
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/5002
Acceso en línea:
http://hdl.handle.net/11323/5002
https://repositorio.cuc.edu.co/
Palabra clave:
MPC design
Minimum variance control
FCOR
CSTR
Diseño MPC
Control de Mínima Varianza
Rights
openAccess
License
CC0 1.0 Universal
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/5002
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv A comparison study of MPC strategies based on minimum variance control index performance
dc.title.translated.spa.fl_str_mv Comparación de estrategias MPC basado en índice de mínima varianza
title A comparison study of MPC strategies based on minimum variance control index performance
spellingShingle A comparison study of MPC strategies based on minimum variance control index performance
MPC design
Minimum variance control
FCOR
CSTR
Diseño MPC
Control de Mínima Varianza
title_short A comparison study of MPC strategies based on minimum variance control index performance
title_full A comparison study of MPC strategies based on minimum variance control index performance
title_fullStr A comparison study of MPC strategies based on minimum variance control index performance
title_full_unstemmed A comparison study of MPC strategies based on minimum variance control index performance
title_sort A comparison study of MPC strategies based on minimum variance control index performance
dc.creator.fl_str_mv BORRERO-SALAZAR, Alex A.
CARDENAS-CABRERA, Jorge M.
BARROS-GUTIERREZ, Daniel A.
JIMÉNEZ-CABAS, Javier A.
dc.contributor.author.spa.fl_str_mv BORRERO-SALAZAR, Alex A.
CARDENAS-CABRERA, Jorge M.
BARROS-GUTIERREZ, Daniel A.
JIMÉNEZ-CABAS, Javier A.
dc.subject.spa.fl_str_mv MPC design
Minimum variance control
FCOR
CSTR
Diseño MPC
Control de Mínima Varianza
topic MPC design
Minimum variance control
FCOR
CSTR
Diseño MPC
Control de Mínima Varianza
description Model Predictive Control (MPC) is a useful tool when controlling processes that handle a large number of input and output variables. This study presents a comparison of different MPC strategies when they are subjected to control process variables directly. The strategies studied are IMC, GPC, MPC-D, MPC-DR, and DMC. Evaluation of the performance of the controlled loop was performed with the filtering and correlation analysis algorithm (FCOR). The methodology proposed is validated in a Continuous Stirred-Tank Reactor (CSTR) case study. Discrete predictive control demonstrated the best results in this study.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-07-12T20:23:36Z
dc.date.available.none.fl_str_mv 2019-07-12T20:23:36Z
dc.date.issued.none.fl_str_mv 2019-07
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.spa.fl_str_mv 0798-1015
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dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
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identifier_str_mv 0798-1015
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url http://hdl.handle.net/11323/5002
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
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dc.relation.references.spa.fl_str_mv Bauer, M., Horch, A., Xie, L., Jelali, M., & Thornhill, N. (2016). The current state of control loop performance monitoring--A survey of application in industry. Journal of Process Control, 38, 1–10. Bosgra, O. (2007). Multivariable Feedback Control-Analysis and Design (Skogestad, S. and Postlewaite, I.; 2005)[book review]. IEEE Control Systems, 27(1), 80–81. Brice, A. (2008). A guide to major chemical disasters worldwide. Camacho, E. F., & Bordons, C. (2007). Nonlinear model predictive control: An introductory review. In Assessment and future directions of nonlinear model predictive control (pp. 1– 16). Springer. CANO, S., BOTERO, L., & RIVERA, L. (2017). Evaluación del desempeño de Lean Construction. Revista ESPACIOS| Vol. 38 (No39) Año 2017, 38(39). Clarke, D. W., Mohtadi, C., & Tuffs, P. S. (1987). Generalized Predictive Control&Mdash;Part I. The Basic Algorithm. Automatica, 23(2), 137–148. https://doi.org/10.1016/0005- 1098(87)90087-2 dos SANTOS, F. F. P., & others. (2016). Tecnologia destinada a produção de biodiesel utilizando uma plataforma de baixo custo e multifuncionalidade: Reator multifuncional destinado a produção de biodiesel. Revista ESPACIOS| Vol. 37 (No22) Año 2016. Duarte, J., Garcia, J., Jiménez, J., Sanjuan, M. E., Bula, A., & González, J. (2017). Autoignition control in spark-ignition engines using internal model control structure. Journal of Energy Resources Technology, 139(2), 22201. Garcia, C. E., & Morari, M. (1982). Internal model control. A unifying review and some new results. Industrial & Engineering Chemistry Process Design and Development, 21(2), 308– 323. Harris, T. J., Seppala, C. T., & Desborough, L. D. (1999). A review of performance monitoring and assessment techniques for univariate and multivariate control systems. Journal of Process Control, 9(1), 1–17. Huang, B. (1998). Multivariate statistical methods for control loop performance assessment. University of Alberta Alberta, Edmonton, Canada. Huang, B., & Kadali, R. (2008). Dynamic modeling, predictive control and performance monitoring: a data-driven subspace approach. Springer. Jelali, M. (2012). Control performance management in industrial automation: assessment, diagnosis and improvement of control loop performance. Springer Science & Business Media. JORDÃO, R. V. D., Neto, J. A. S., & others. (2016). Estratégia e desenho do sistema de controle gerencial. Revista ESPACIOS| Vol. 37 (No04) Año 2016. Lindström, J., Kyösti, P., & Delsing, J. (2018). European roadmap for industrial process automation. Mauricio Johnny, L., & RODRIGUEZ, C. M. T. (2015). Mapeamento do Estado da Arte do tema Avaliação de Desempenho direcionado para a Logística Lean. Revista ESPACIOS| Vol. 36 (No14) Año 2015. Rawlings, J. B. (2000). Tutorial overview of model predictive control. IEEE Control Systems Magazine, 20(3), 38–52. https://doi.org/10.1109/37.845037 Rivera, J. R., Alzate, C. E. O., & Arias, J. A. T. (2015). Estudio preliminar de vigilancia tecnológica de emulsificantes usados en chocolatería. Espacios, 36(13). Sanjuan, M., Kandel, A., & Smith, C. A. (2006). Design and implementation of a fuzzy supervisor for on-line compensation of nonlinearities: An instability avoidance module. Engineering Applications of Artificial Intelligence, 19(3), 323–333. https://doi.org/10.1016/j.engappai.2005.09.003 Smith, C. A., & Corripio, A. B. (1985). Principles and practice of automatic process control (Vol. 2). Wiley New York. Wang, L. (2009). Model predictive control system design and implementation using MATLAB. Springer Science & Business Media. Zio, E., & Aven, T. (2013). Industrial disasters: Extreme events, extremely rare. Some reflections on the treatment of uncertainties in the assessment of the associated risks. Process Safety and Environmental Protection, 91(1), 31–45. https://doi.org/https://doi.org/10.1016/j.psep.2012.01.004
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institution Corporación Universidad de la Costa
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spelling BORRERO-SALAZAR, Alex A.0c0fa12efc58287d0419aba0e5a31d50CARDENAS-CABRERA, Jorge M.f34ef8008f99157d6997c5c17ef9e415BARROS-GUTIERREZ, Daniel A.13cb1d281cb5e5b0691c219343e136f2JIMÉNEZ-CABAS, Javier A.03745c4c9a95a0341118367786b354102019-07-12T20:23:36Z2019-07-12T20:23:36Z2019-070798-1015http://hdl.handle.net/11323/5002Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Model Predictive Control (MPC) is a useful tool when controlling processes that handle a large number of input and output variables. This study presents a comparison of different MPC strategies when they are subjected to control process variables directly. The strategies studied are IMC, GPC, MPC-D, MPC-DR, and DMC. Evaluation of the performance of the controlled loop was performed with the filtering and correlation analysis algorithm (FCOR). The methodology proposed is validated in a Continuous Stirred-Tank Reactor (CSTR) case study. Discrete predictive control demonstrated the best results in this study.El Control predictivo de modelos (MPC) es una herramienta útil para controlar procesos que manejan un gran número de variables de entrada y salida. Este estudio presenta una comparación de diferentes estrategias de MPC cuando son usadas para controlar directamente variables de proceso. Las estrategias estudiadas son IMC, GPC, MPC-D, MPC-DR y DMC. La evaluación del desempeño del lazo de control se realizó con el algoritmo de análisis de filtrado y correlación (FCOR). La metodología propuesta se valida en un caso de estudio tipo CSTR. El control predictivo discreto demostró los mejores resultados en este estudio.engEspacioshttp://www.revistaespacios.com/a19v40n20/19402012.htmlBauer, M., Horch, A., Xie, L., Jelali, M., & Thornhill, N. (2016). The current state of control loop performance monitoring--A survey of application in industry. Journal of Process Control, 38, 1–10. Bosgra, O. (2007). Multivariable Feedback Control-Analysis and Design (Skogestad, S. and Postlewaite, I.; 2005)[book review]. IEEE Control Systems, 27(1), 80–81. Brice, A. (2008). A guide to major chemical disasters worldwide. Camacho, E. F., & Bordons, C. (2007). Nonlinear model predictive control: An introductory review. In Assessment and future directions of nonlinear model predictive control (pp. 1– 16). Springer. CANO, S., BOTERO, L., & RIVERA, L. (2017). Evaluación del desempeño de Lean Construction. Revista ESPACIOS| Vol. 38 (No39) Año 2017, 38(39). Clarke, D. W., Mohtadi, C., & Tuffs, P. S. (1987). Generalized Predictive Control&Mdash;Part I. The Basic Algorithm. Automatica, 23(2), 137–148. https://doi.org/10.1016/0005- 1098(87)90087-2 dos SANTOS, F. F. P., & others. (2016). Tecnologia destinada a produção de biodiesel utilizando uma plataforma de baixo custo e multifuncionalidade: Reator multifuncional destinado a produção de biodiesel. Revista ESPACIOS| Vol. 37 (No22) Año 2016. Duarte, J., Garcia, J., Jiménez, J., Sanjuan, M. E., Bula, A., & González, J. (2017). Autoignition control in spark-ignition engines using internal model control structure. Journal of Energy Resources Technology, 139(2), 22201. Garcia, C. E., & Morari, M. (1982). Internal model control. A unifying review and some new results. Industrial & Engineering Chemistry Process Design and Development, 21(2), 308– 323. Harris, T. J., Seppala, C. T., & Desborough, L. D. (1999). A review of performance monitoring and assessment techniques for univariate and multivariate control systems. Journal of Process Control, 9(1), 1–17. Huang, B. (1998). Multivariate statistical methods for control loop performance assessment. University of Alberta Alberta, Edmonton, Canada. Huang, B., & Kadali, R. (2008). Dynamic modeling, predictive control and performance monitoring: a data-driven subspace approach. Springer. Jelali, M. (2012). Control performance management in industrial automation: assessment, diagnosis and improvement of control loop performance. Springer Science & Business Media. JORDÃO, R. V. D., Neto, J. A. S., & others. (2016). Estratégia e desenho do sistema de controle gerencial. Revista ESPACIOS| Vol. 37 (No04) Año 2016. Lindström, J., Kyösti, P., & Delsing, J. (2018). European roadmap for industrial process automation. Mauricio Johnny, L., & RODRIGUEZ, C. M. T. (2015). Mapeamento do Estado da Arte do tema Avaliação de Desempenho direcionado para a Logística Lean. Revista ESPACIOS| Vol. 36 (No14) Año 2015. Rawlings, J. B. (2000). Tutorial overview of model predictive control. IEEE Control Systems Magazine, 20(3), 38–52. https://doi.org/10.1109/37.845037 Rivera, J. R., Alzate, C. E. O., & Arias, J. A. T. (2015). Estudio preliminar de vigilancia tecnológica de emulsificantes usados en chocolatería. Espacios, 36(13). Sanjuan, M., Kandel, A., & Smith, C. A. (2006). Design and implementation of a fuzzy supervisor for on-line compensation of nonlinearities: An instability avoidance module. Engineering Applications of Artificial Intelligence, 19(3), 323–333. https://doi.org/10.1016/j.engappai.2005.09.003 Smith, C. A., & Corripio, A. B. (1985). Principles and practice of automatic process control (Vol. 2). Wiley New York. Wang, L. (2009). Model predictive control system design and implementation using MATLAB. Springer Science & Business Media. Zio, E., & Aven, T. (2013). Industrial disasters: Extreme events, extremely rare. Some reflections on the treatment of uncertainties in the assessment of the associated risks. Process Safety and Environmental Protection, 91(1), 31–45. https://doi.org/https://doi.org/10.1016/j.psep.2012.01.004CC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2MPC designMinimum variance controlFCORCSTRDiseño MPCControl de Mínima VarianzaA comparison study of MPC strategies based on minimum variance control index performanceComparación de estrategias MPC basado en índice de mínima varianzaArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionORIGINALA comparison study of MPC strategies based on minimum variance control index performance.pdfA comparison study of MPC strategies based on minimum variance control index performance.pdfapplication/pdf1170471https://repositorio.cuc.edu.co/bitstream/11323/5002/1/A%20comparison%20study%20of%20MPC%20strategies%20based%20on%20minimum%20variance%20control%20index%20performance.pdffd381863604ec1747c71ba3aade32fd3MD51open accessCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstream/11323/5002/2/license_rdf42fd4ad1e89814f5e4a476b409eb708cMD52open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstream/11323/5002/3/license.txt8a4605be74aa9ea9d79846c1fba20a33MD53open accessTHUMBNAILA comparison study of MPC strategies based on minimum variance control index performance.pdf.jpgA comparison study of MPC strategies based on minimum variance control index performance.pdf.jpgimage/jpeg89722https://repositorio.cuc.edu.co/bitstream/11323/5002/5/A%20comparison%20study%20of%20MPC%20strategies%20based%20on%20minimum%20variance%20control%20index%20performance.pdf.jpg21b1a95ef45b5b122d66465e097e0c84MD55open accessTEXTA comparison study of MPC strategies based on minimum variance control index performance.pdf.txtA comparison study of MPC strategies based on minimum variance control index performance.pdf.txttext/plain21010https://repositorio.cuc.edu.co/bitstream/11323/5002/6/A%20comparison%20study%20of%20MPC%20strategies%20based%20on%20minimum%20variance%20control%20index%20performance.pdf.txt77a4e228326af45cc7cd422a6cfaf186MD56open access11323/5002oai:repositorio.cuc.edu.co:11323/50022023-12-14 12:14:46.161CC0 1.0 Universal|||http://creativecommons.org/publicdomain/zero/1.0/open accessRepositorio Universidad de La Costabdigital@metabiblioteca.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