Control scheme selection and optimal tuning in industrial process control using factorial experiment design
In this study, a novel experimental approach for the optimal selection of an actuator-based control strategy is presented. The proposed approach is a two-stage method: first, a two-level factorial experiment design with n factors (2n) was applied to compare different control schemes. Schemes compari...
- Autores:
-
Acosta-Villamil, David Roberto
Noguera-Polania, José Fernando
Verdeza-Villalobos, Arnaldo
Foliaco-Romero, Blanca Luz
Rincón-Montenegro, Adriana Fernanda
Sanjuan-Mejía, Marco Enrique
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/6781
- Acceso en línea:
- https://hdl.handle.net/20.500.12442/6781
- Palabra clave:
- Optimal control tuning
Process control
Design of experiments
Factorial design
Sintonía óptima de control
Control de procesos
Diseño de experimentos
Diseño factorial
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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Control scheme selection and optimal tuning in industrial process control using factorial experiment design |
| dc.title.translated.spa.fl_str_mv |
Selección de estrategia y sintonización óptima de control industrial usando un diseño de experimento factorial |
| title |
Control scheme selection and optimal tuning in industrial process control using factorial experiment design |
| spellingShingle |
Control scheme selection and optimal tuning in industrial process control using factorial experiment design Optimal control tuning Process control Design of experiments Factorial design Sintonía óptima de control Control de procesos Diseño de experimentos Diseño factorial |
| title_short |
Control scheme selection and optimal tuning in industrial process control using factorial experiment design |
| title_full |
Control scheme selection and optimal tuning in industrial process control using factorial experiment design |
| title_fullStr |
Control scheme selection and optimal tuning in industrial process control using factorial experiment design |
| title_full_unstemmed |
Control scheme selection and optimal tuning in industrial process control using factorial experiment design |
| title_sort |
Control scheme selection and optimal tuning in industrial process control using factorial experiment design |
| dc.creator.fl_str_mv |
Acosta-Villamil, David Roberto Noguera-Polania, José Fernando Verdeza-Villalobos, Arnaldo Foliaco-Romero, Blanca Luz Rincón-Montenegro, Adriana Fernanda Sanjuan-Mejía, Marco Enrique |
| dc.contributor.author.none.fl_str_mv |
Acosta-Villamil, David Roberto Noguera-Polania, José Fernando Verdeza-Villalobos, Arnaldo Foliaco-Romero, Blanca Luz Rincón-Montenegro, Adriana Fernanda Sanjuan-Mejía, Marco Enrique |
| dc.subject.eng.fl_str_mv |
Optimal control tuning Process control Design of experiments Factorial design |
| topic |
Optimal control tuning Process control Design of experiments Factorial design Sintonía óptima de control Control de procesos Diseño de experimentos Diseño factorial |
| dc.subject.spa.fl_str_mv |
Sintonía óptima de control Control de procesos Diseño de experimentos Diseño factorial |
| description |
In this study, a novel experimental approach for the optimal selection of an actuator-based control strategy is presented. The proposed approach is a two-stage method: first, a two-level factorial experiment design with n factors (2n) was applied to compare different control schemes. Schemes comparison was carried out in terms of energy consumption and closed-loop performance. For the best relative scheme, a Central Composite Face-centered (CCF) design was completed obtaining the controller parameters that optimize the performance in terms of the Integral Absolute Error (IAE) while operating in a region of low energy consumption. The proposed approach was experimentally tested using real data obtained from a laboratory prototype plant. Some experimental tests illustrating the suitability of our method are shown at the end of this article. |
| publishDate |
2020 |
| dc.date.accessioned.none.fl_str_mv |
2020-11-10T22:03:11Z |
| dc.date.available.none.fl_str_mv |
2020-11-10T22:03:11Z |
| dc.date.issued.none.fl_str_mv |
2020 |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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Artículo científico |
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24222844 |
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https://hdl.handle.net/20.500.12442/6781 |
| dc.identifier.doi.none.fl_str_mv |
10.17533/udea.redin.20201010 |
| dc.identifier.url.none.fl_str_mv |
ttps://revistas.udea.edu.co/index.php/ingenieria/article/view/341251 |
| identifier_str_mv |
24222844 10.17533/udea.redin.20201010 ttps://revistas.udea.edu.co/index.php/ingenieria/article/view/341251 |
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https://hdl.handle.net/20.500.12442/6781 |
| dc.language.iso.eng.fl_str_mv |
eng |
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eng |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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http://purl.org/coar/access_right/c_abf2 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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pdf |
| dc.publisher.spa.fl_str_mv |
Universidad de Antioquia |
| dc.source.spa.fl_str_mv |
Revista Facultad de Ingeniería Universidad de Antioquia |
| dc.source.none.fl_str_mv |
Vol. XX, (2020) |
| institution |
Universidad Simón Bolívar |
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Acosta-Villamil, David Roberto9bfc6c07-4571-4f04-926b-6a04c1e416b2Noguera-Polania, José Fernando769a5997-a023-43e6-8a0b-2e4cdc97f9aeVerdeza-Villalobos, Arnaldo31d287c5-618e-4380-aab3-4ec1f0ee36c5Foliaco-Romero, Blanca Luzd36c208e-6bc8-48c6-b02c-24a5d7366554Rincón-Montenegro, Adriana Fernandaa4a2a602-ff4f-46d5-930f-153bd90a630aSanjuan-Mejía, Marco Enrique0e84e4f0-823b-4e0b-8976-d7a3c581eed62020-11-10T22:03:11Z2020-11-10T22:03:11Z202024222844https://hdl.handle.net/20.500.12442/678110.17533/udea.redin.20201010ttps://revistas.udea.edu.co/index.php/ingenieria/article/view/341251In this study, a novel experimental approach for the optimal selection of an actuator-based control strategy is presented. The proposed approach is a two-stage method: first, a two-level factorial experiment design with n factors (2n) was applied to compare different control schemes. Schemes comparison was carried out in terms of energy consumption and closed-loop performance. For the best relative scheme, a Central Composite Face-centered (CCF) design was completed obtaining the controller parameters that optimize the performance in terms of the Integral Absolute Error (IAE) while operating in a region of low energy consumption. The proposed approach was experimentally tested using real data obtained from a laboratory prototype plant. Some experimental tests illustrating the suitability of our method are shown at the end of this article.en este estudio se presenta un nuevo enfoque experimental para la selección óptima de una estrategia de control basada en el actuador. El enfoque propuesto es un método de dos etapas: primero se aplica un diseño de experimento factorial de dos niveles con n factores (2n) para comparar diferentes esquemas de control. La comparación de esquemas se lleva a cabo en términos de consumo de energía y rendimiento de circuito cerrado. Para el mejor esquema relativo, se completa un Diseño Central Compuesto Centrado en las Caras (CCF, por sus siglas en inglés) obteniendo parámetros de controlador que optimizan el rendimiento, en términos del Error absoluto integral (IAE, por sus siglas en inglés), mientras operan en una región de bajo consumo de energía. El enfoque propuesto se probó experimentalmente utilizando datos reales obtenidos de una planta prototipo de laboratorio. Algunas pruebas experimentales que ilustran la idoneidad de nuestro método se muestran al final de este artículo.pdfengUniversidad de AntioquiaAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Revista Facultad de Ingeniería Universidad de AntioquiaVol. XX, (2020)Optimal control tuningProcess controlDesign of experimentsFactorial designSintonía óptima de controlControl de procesosDiseño de experimentosDiseño factorialControl scheme selection and optimal tuning in industrial process control using factorial experiment designSelección de estrategia y sintonización óptima de control industrial usando un diseño de experimento factorialinfo:eu-repo/semantics/articleArtículo científicohttp://purl.org/coar/resource_type/c_2df8fbb1C. Knospe, “PID control,” IEEE Control Syst. Mag., vol. 26, no. 1, February 2006. [Online]. Available: https://doi.org/10.1109/MCS.2006.1580151M. L. Brisk, “Process control: Potential benefits and wasted opportunities,” Aust. J. Electr. Electron. Eng., vol. 2, no. 1, 2005. [Online]. Available: https://doi.org/10.1080/1448837X.2005.11464113P. Klán and R. Gorez, “PI controller design for actuator preservation,” IFAC Proc. Vol., vol. 41, no. 2, 2008. [Online]. Available: https://doi.org/10.3182/20080706-5-KR-1001.00981F. Castrillón and D. Castellanos, “New tuning rules for PID controllers based on IMC with minimum IAE for inverse response processes,” DYNA, vol. 82, no. 194, November 2015. [Online]. Available: http://dx.doi.org/10.15446/dyna.v82n194.46744J. Shen, “New tuning method for PID controller,” ISA Trans., vol. 41, no. 4, October 2002. [Online]. Available: https://doi.org/10.1016/S0019-0578(07)60103-7M. T. Özdemir and D. Öztürk and Ī. Eke and V. Çelik and K. Y. Lee, “Tuning of optimal classical and fractional order PID parameters for automatic generation control based on the bacterial swarm optimization,” IFAC-PapersOnLine, vol. 48, no. 30, 2015. [Online]. Available: https://doi.org/10.1016/j.ifacol.2015.12.429O. García, D. Acosta, and C. Diaz, “GPU-Implementation of a sequential monte carlo technique for the localization of an ackerman robot,” in International Conference on Applied Informatics ICAI 2018: Applied Informatics, Bogotá, Colombia, 2018, pp. 309–320.M. Bauer, A. Horch, L. Xie, M. Jelali, and N. Thornhill, “The current state of control loop performance monitoring – A survey of application in industry,” J. Process Control, vol. 38, February 2016. [Online]. Available: https://doi.org/10.1016/j.jprocont.2015.11.002K. L. Morales and H. D. Álvarez, “Determination and use of feasible operation region in flash distillation control,” Revista Facultad de Ingeniería Universidad de Antioquia, vol. 95, April 2020. [Online]. Available: http://dx.doi.org/10.17533/udea.redin.20190738D. C. Montgomery, Design and analysis of experiments, 9th ed. John wiley & sons, 2017.K. J. Åström and T. Hägglund, Advanced PID control. ISA-The Instrumentation, Systems, and Automation Society, 2006.C. A. Smith and A. B. Corripio, Principles and Practice of Automatic Process Control, 3rd ed. John wiley & sons, 2006.J. Duarte and et al, “Auto-ignition control in spark-ignition engines using internal model control structure,” J. Energy Resour. Technol., vol. 139, no. 2, March 2017. [Online]. Available: https://doi.org/10. 1115/1.4034026H. Zeng and K. Xiao, “A new design of multivariable decoupling internal model controller,” J. Theor. Appl. Inf. Technol., vol. 48, no. 1, pp. 417–422, Feb. 2013.A. Verdeza and L. Di Mare and M. Sanjuán and A. Bula, “Diseño de ecuaciones de sintonía para controladores PID (Proporcional-Integral-Derivativo) implementados en fotobiorreactores,” Inf. tecnológica, vol. 27, no. 4, 2016. [Online]. Available: http://dx.doi.org/10.4067/S0718-07642016000400013J. Duarte and W. Orozco, “Optimización de sintonización de controladores PID bajo el criterio IAE aplicados a procesos térmicos,” Revista Inge@UAN, vol. 5, no. 10, pp. 35–45, 2015.J. A. López, A. Duque, and A. F. Navas, “Sintonización de un controlador PID en un PLC haciendo uso de inteligencia de enjambres/auto-tuning of a PID controller implemented in a PLC using swarm intelligence,” Prospectiva, vol. 15, no. 1, January 2017. [Online]. Available: https://doi.org/10.15665/rp.v15i1.679A. S. Comas, A. T. Palacio, S. T. Mendoza, and D. N. Rodado, “Aplicación del diseño de experimentos taguchi para la identificación de factores de influencias en tiempos de impresión 3d con modelado por deposición fundida,” Int. J. Manag. Sci. Oper. Res., vol. 1, no. 1, pp. 43–48, Jan. 2016.J. C. Salazar and A. B. Zapata, “Análisis y diseño de experimentos aplicados a estudios de simulación,” DYNA, vol. 76, no. 159, pp. 249– 257, Sep. 2009.ORIGINALControl Scheme Selection and Optimal Tuning in Industrial.pdfControl Scheme Selection and Optimal Tuning in Industrial.pdfPDFapplication/pdf970122https://bonga.unisimon.edu.co/bitstreams/1b95708b-153c-4de8-ae23-addfb7e48026/download1bff664c7d3dfcd6fece7ec8a8859750MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://bonga.unisimon.edu.co/bitstreams/a354a50e-6565-4ad1-af12-3ba01f6b8ae6/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-8381https://bonga.unisimon.edu.co/bitstreams/c29692d8-18ba-415b-aec0-c1dfd8650467/download733bec43a0bf5ade4d97db708e29b185MD53TEXTControl Scheme Selection and Optimal Tuning in Industrial.pdf.txtControl Scheme Selection and Optimal Tuning in Industrial.pdf.txtExtracted texttext/plain35855https://bonga.unisimon.edu.co/bitstreams/f0070717-770a-4d18-8327-58d9f2f0d008/download04bfcf17ad1b6585c371cc73c1d2d8d4MD54THUMBNAILControl Scheme Selection and Optimal Tuning in Industrial.pdf.jpgControl Scheme Selection and Optimal Tuning in Industrial.pdf.jpgGenerated Thumbnailimage/jpeg1621https://bonga.unisimon.edu.co/bitstreams/a93d4b47-32b8-4efa-946a-812a5eda1d33/download76290c2e97bf14898b39a3c17cccd932MD5520.500.12442/6781oai:bonga.unisimon.edu.co:20.500.12442/67812021-04-06 11:43:18.195http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internacionalopen.accesshttps://bonga.unisimon.edu.coDSpace UniSimonbibliotecas@biteca.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 |