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A comparison study of MPC strategies based on minimum variance control index performance
| dc.contributor.author | BORRERO-SALAZAR, Alex A. | spa |
| dc.contributor.author | CARDENAS-CABRERA, Jorge M. | spa |
| dc.contributor.author | BARROS-GUTIERREZ, Daniel A. | spa |
| dc.contributor.author | JIMÉNEZ-CABAS, Javier A. | spa |
| dc.date.accessioned | 2019-07-12T20:23:36Z | |
| dc.date.available | 2019-07-12T20:23:36Z | |
| dc.date.issued | 2019-07 | |
| dc.identifier.issn | 0798-1015 | spa |
| dc.identifier.uri | http://hdl.handle.net/11323/5002 | spa |
| dc.description.abstract | 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. | spa |
| dc.description.abstract | 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. | spa |
| dc.language.iso | eng | |
| dc.publisher | Espacios | spa |
| dc.relation.ispartof | http://www.revistaespacios.com/a19v40n20/19402012.html | spa |
| dc.rights | CC0 1.0 Universal | spa |
| dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | spa |
| dc.subject | MPC design | spa |
| dc.subject | Minimum variance control | spa |
| dc.subject | FCOR | spa |
| dc.subject | CSTR | spa |
| dc.subject | Diseño MPC | spa |
| dc.subject | Control de Mínima Varianza | spa |
| dc.title | A comparison study of MPC strategies based on minimum variance control index performance | spa |
| dc.type | Artículo de revista | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.identifier.instname | Corporación Universidad de la Costa | spa |
| dc.identifier.reponame | REDICUC - Repositorio CUC | spa |
| dc.identifier.repourl | https://repositorio.cuc.edu.co/ | spa |
| dc.relation.references | 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 | spa |
| dc.title.translated | Comparación de estrategias MPC basado en índice de mínima varianza | spa |
| dc.type.coar | http://purl.org/coar/resource_type/c_6501 | spa |
| dc.type.content | Text | spa |
| dc.type.driver | info:eu-repo/semantics/article | spa |
| dc.type.redcol | http://purl.org/redcol/resource_type/ART | spa |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
| dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
| dc.rights.coar | http://purl.org/coar/access_right/c_abf2 | spa |
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