Predictive/Adaptive Control of Complex Systems Using Neural Engineering Techniques

The design and implementation of a predictive/adaptive control system is presented, using neural engineering techniques to control a non-linear MIMO system in order to control, at a later stage, the temperature and level in a non-linear conical plant. Preliminarily, conventional control structures w...

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Autores:
Gallardo Arancibia, José
Ayala Bravo, Claudio
Castro Castro, Rubén
Tipo de recurso:
Article of journal
Fecha de publicación:
2018
Institución:
Universidad de Medellín
Repositorio:
Repositorio UDEM
Idioma:
spa
OAI Identifier:
oai:repository.udem.edu.co:11407/5513
Acceso en línea:
http://hdl.handle.net/11407/5513
https://doi.org/10.22395/rium.v17n33a8
Palabra clave:
Neuronal engineering
Identification
Predictive control
Adaptive control
Non-linear MIMO systems
Engenharia neural
Identificação
Controle preditivo
Controle adaptativa
Sistemas MIMO não lineares
Ingeniería neuronal
Identificación
Control predictivo
Control adaptativo
Sistemas MIMO no lineales
Rights
License
http://creativecommons.org/licenses/by-nc-sa/4.0/
id REPOUDEM2_4e44d4af3f3b47a0ef844b3b592f46c4
oai_identifier_str oai:repository.udem.edu.co:11407/5513
network_acronym_str REPOUDEM2
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dc.title.eng.fl_str_mv Predictive/Adaptive Control of Complex Systems Using Neural Engineering Techniques
dc.title.por.fl_str_mv Controle preditivo/adaptativo de sistemas complexos utilizando técnicas de engenharia neural
dc.title.spa.fl_str_mv Control predictivo/adaptativo de sistemas complejos utilizando técnicas de ingeniería neuronal
title Predictive/Adaptive Control of Complex Systems Using Neural Engineering Techniques
spellingShingle Predictive/Adaptive Control of Complex Systems Using Neural Engineering Techniques
Neuronal engineering
Identification
Predictive control
Adaptive control
Non-linear MIMO systems
Engenharia neural
Identificação
Controle preditivo
Controle adaptativa
Sistemas MIMO não lineares
Ingeniería neuronal
Identificación
Control predictivo
Control adaptativo
Sistemas MIMO no lineales
title_short Predictive/Adaptive Control of Complex Systems Using Neural Engineering Techniques
title_full Predictive/Adaptive Control of Complex Systems Using Neural Engineering Techniques
title_fullStr Predictive/Adaptive Control of Complex Systems Using Neural Engineering Techniques
title_full_unstemmed Predictive/Adaptive Control of Complex Systems Using Neural Engineering Techniques
title_sort Predictive/Adaptive Control of Complex Systems Using Neural Engineering Techniques
dc.creator.fl_str_mv Gallardo Arancibia, José
Ayala Bravo, Claudio
Castro Castro, Rubén
dc.contributor.author.none.fl_str_mv Gallardo Arancibia, José
Ayala Bravo, Claudio
Castro Castro, Rubén
dc.subject.eng.fl_str_mv Neuronal engineering
Identification
Predictive control
Adaptive control
Non-linear MIMO systems
topic Neuronal engineering
Identification
Predictive control
Adaptive control
Non-linear MIMO systems
Engenharia neural
Identificação
Controle preditivo
Controle adaptativa
Sistemas MIMO não lineares
Ingeniería neuronal
Identificación
Control predictivo
Control adaptativo
Sistemas MIMO no lineales
dc.subject.por.fl_str_mv Engenharia neural
Identificação
Controle preditivo
Controle adaptativa
Sistemas MIMO não lineares
dc.subject.spa.fl_str_mv Ingeniería neuronal
Identificación
Control predictivo
Control adaptativo
Sistemas MIMO no lineales
description The design and implementation of a predictive/adaptive control system is presented, using neural engineering techniques to control a non-linear MIMO system in order to control, at a later stage, the temperature and level in a non-linear conical plant. Preliminarily, conventional control structures were tested, which gave rise to the need to test intelligent control structures that allow the control objectives to be met more effectively. The process begins with the experimentation of different neuronal control structures, and then escalates to a predictive/adaptive neuronal control system. The results achieved at the simulation level, testing the proposed design on mathematical models of non-linear MIMO systems, were satisfactory and met the control objectives established, therefore, in the next stage of the project, the experimentation is estimated in the real plant under study.
publishDate 2018
dc.date.created.none.fl_str_mv 2018-03-15
dc.date.accessioned.none.fl_str_mv 2019-11-07T15:03:03Z
dc.date.available.none.fl_str_mv 2019-11-07T15:03:03Z
dc.type.eng.fl_str_mv Article
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dc.type.local.spa.fl_str_mv Artículo científico
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dc.identifier.issn.none.fl_str_mv 1692-3324
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dc.identifier.doi.none.fl_str_mv https://doi.org/10.22395/rium.v17n33a8
dc.identifier.eissn.none.fl_str_mv 2248-4094
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https://doi.org/10.22395/rium.v17n33a8
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dc.relation.citationvolume.none.fl_str_mv 17
dc.relation.citationissue.none.fl_str_mv 33
dc.relation.citationstartpage.none.fl_str_mv 157
dc.relation.citationendpage.none.fl_str_mv 172
dc.relation.references.spa.fl_str_mv [1] A. Conradie, C. Aldrich, “Neurocontrol of a multi-effect batch distillation pilot plant based on evolutionary reinforcement learning,” Chemical Engineering Science, vol. 65, N.° 5, pp. 1627-1643, 2010.
[2] M. Bazaraa, H. Sherali, C.M. Shetty, Nonlinear programming: theory and Algorithms, 3.a ed., Nueva Jersey: Wiley Interscience, 2006, pp. 872.
[3] S. Chen, S. A. Billings, “Representations of non-linear systems: the NARMAX model,” International Journal of Control, vol. 49, N.° 3, pp. 1013-1032, 1988.
[4] H. González, M.S. Dutra, O. Lengerke, “Identification and modeling for non-linear dynamic system using neural networks type MLP,” presentado en Proceedings of the 2009 Euro American Conference on Telematics and Information Systems: New Opportunities to increase Digital Citizenship, Praga, junio 03-05, 2009.
[5] R. Hecht-Nielsen, Neurocomputing, Boston: Ed. Addison Wesley, 1988, pp. 433.
[6] J. Vojtesek, P. Dostal, “Adaptive control of water level in real model of water tank, Process Control (PC),” presentado en 20th International Conference on, Strbske Pleso, Eslovaquia, junio 9-12, 2015.
[7] A. U. Levin y K. Narendra, “Control of nonlinear dynamical systems using neural networks,” IEEE Neural Networks Council, vol.7, pp. 30-42, 1996.
[8] K. Narendra y K. Parthasarathy, “Identification and Control of Dynamical Systems Using Neural Networks,” IEEE Transactions on Neural Networks, vol. 7, N.° 1, 1996.
[9] H. M. Nguyen y N. Subbaram, “Advanced control strategies for wind energy systems: An overview”, presentado en IEEE/PES Power Systems Conference and Exposition, Phoenix, 2011.
[10] K.J. Nidhil, S. Sreeraj, B. Vijay y V. Bagyaveereswaran, “System identification using artificial neural network”, Circuit, Power and Computing Technologies (ICCPCT), presentado en 2015 International Conference, Nagercoil, 2015.
[11] M. Nørgaard, O. Ravn, NK. Poulsen y LK Hansen, Neural Networks for Modelling and Control of Dynamic Systems, Londres: Springer, 2000, pp. 246.
[12] K. Ogata, Ingeniería de control moderna, 4.a ed., Madrid: Prentice Hall, 2003, pp. 984.
[13] D. T. Pham y L. Xing, Neural Networks for identification, prediction and control, Londres: Springer, 2012, pp. 238.
[14] A. Kupin, “Application of neurocontrol principles and classification optimisation in conditions of sophisticated technological processes of beneficiation complexes”. Metallurgical y Mining Industry, vol. 6, pp. 16-24, 2014.
[15] R.J. Rajesh, R. Preethi, P. Mehata y B. Jaganatha Pandian, “Artificial neural network based inverse model control of a nonlinear process,” presentado en Computer, Communication and Control (IC4), International Conference, Indore, 2015.
[16] V.R. Ravi, M. Monica, S. Amuthameena, S.K. Divya, S. Jayashree y J. Varshini, “Sliding Mode Controller for Two Conical Tank Interacting Level System,” Applied Mechanics and Materials, vol. 573, pp. 273-278, 2014.
[17] A. M. Suárez, Nueva arquitectura de control predictivo para sistemas dinámicos no lineales usando redes neuronales, Tesis de Doctorado en Ciencias de la Ingeniería, Universidad de Chile, Santiago de Chile, 1998.
[18] D. Zhao, Z. Xia y D. Wang, “Model-Free Optimal Control for Affine Nonlinear Systems with Convergence Analysis”, IEEE Transactions on Automation Science and Engineering, vol. 12, pp. 1461-1468, 2015.
dc.relation.ispartofjournal.spa.fl_str_mv Revista Ingenierías Universidad de Medellín
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dc.rights.creativecommons.*.fl_str_mv Attribution-NonCommercial-ShareAlike 4.0 International
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Attribution-NonCommercial-ShareAlike 4.0 International
http://purl.org/coar/access_right/c_abf2
dc.format.extent.spa.fl_str_mv p. 157-172
dc.format.medium.spa.fl_str_mv Electrónico
dc.format.mimetype.none.fl_str_mv application/pdf
dc.coverage.none.fl_str_mv Lat: 06 15 00 N  degrees minutes  Lat: 6.2500  decimal degreesLong: 075 36 00 W  degrees minutes  Long: -75.6000  decimal degrees
dc.publisher.spa.fl_str_mv Universidad de Medellín
dc.publisher.faculty.spa.fl_str_mv Facultad de Ingenierías
dc.publisher.place.spa.fl_str_mv Medellín
dc.source.spa.fl_str_mv Revista Ingenierías Universidad de Medellín; Vol. 17 Núm. 33 (2018): Julio-Diciembre; 157-172
institution Universidad de Medellín
repository.name.fl_str_mv Repositorio Institucional Universidad de Medellin
repository.mail.fl_str_mv repositorio@udem.edu.co
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spelling Gallardo Arancibia, JoséAyala Bravo, ClaudioCastro Castro, RubénGallardo Arancibia, José; Universidad Católica del NorteAyala Bravo, Claudio; Universidad de AntofagastaCastro Castro, Rubén; Universidad Arturo Prat2019-11-07T15:03:03Z2019-11-07T15:03:03Z2018-03-151692-3324http://hdl.handle.net/11407/5513https://doi.org/10.22395/rium.v17n33a82248-4094reponame:Repositorio Institucional Universidad de Medellínrepourl:https://repository.udem.edu.co/instname:Universidad de MedellínThe design and implementation of a predictive/adaptive control system is presented, using neural engineering techniques to control a non-linear MIMO system in order to control, at a later stage, the temperature and level in a non-linear conical plant. Preliminarily, conventional control structures were tested, which gave rise to the need to test intelligent control structures that allow the control objectives to be met more effectively. The process begins with the experimentation of different neuronal control structures, and then escalates to a predictive/adaptive neuronal control system. The results achieved at the simulation level, testing the proposed design on mathematical models of non-linear MIMO systems, were satisfactory and met the control objectives established, therefore, in the next stage of the project, the experimentation is estimated in the real plant under study.Apresenta-se a criação e a implementação de um sistema de controle preditivo/adaptativo utilizando técnicas de engenharia neural para controlar um sistema MIMO não linear com o objetivo de controlar, em uma etapa posterior, a temperatura e o nível em uma planta não linear de tipo cônica. Preliminarmente, estruturas de controle convencional foram ensaiadas, o que fez com que surgisse a necessidade de testar estruturas e controle inteligente que permitissem cumprir os objetivos de controle de forma mais eficaz. O processo começa com a experimentação de diferentes estruturas de controle neural, para depois escalar em direção a um sistema de controle neural preditivo/adaptativo. Os resultados alcançados na simulação, ensaiando o desenho proposto sobre modelos matemáticos de sistemas MIMO não lineares, foram satisfatórios e cumpriram os objetivos de controle estabelecidos, portanto, na seguinte etapa do projeto, estima-se realizar a experimentação na planta real em estudo.Se presenta el diseño e implementación de un sistema de control predictivo/adaptativo, utilizando técnicas de ingeniería neuronal para controlar un sistema MIMO no lineal con el objeto de controlar, en una etapa posterior, la temperatura y el nivel en una planta no lineal de tipo cónica. Preliminarmente, se ensayaron estructuras de control convencional lo que hizo surgir la necesidad de probar estructuras de control inteligente que permitan cumplir más eficazmente con los objetivos de control. El proceso se inicia con la experimentación de diferentes estructuras de control neuronal, para luego escalar hacia un sistema de control neuronal predictivo/adaptativo. Los resultados logrados a nivel simulación, ensayando el diseño propuesto sobre modelos matemáticos de sistemas MIMO no lineales, fueron satisfactorios y cumplieron los objetivos de control establecidos, por tanto, en la siguiente etapa del proyecto, se estima la experimentación en la planta real en estudio.p. 157-172Electrónicoapplication/pdfspaUniversidad de MedellínFacultad de IngenieríasMedellínhttps://revistas.udem.edu.co/index.php/ingenierias/article/view/21961733157172[1] A. Conradie, C. Aldrich, “Neurocontrol of a multi-effect batch distillation pilot plant based on evolutionary reinforcement learning,” Chemical Engineering Science, vol. 65, N.° 5, pp. 1627-1643, 2010.[2] M. Bazaraa, H. Sherali, C.M. Shetty, Nonlinear programming: theory and Algorithms, 3.a ed., Nueva Jersey: Wiley Interscience, 2006, pp. 872.[3] S. Chen, S. A. Billings, “Representations of non-linear systems: the NARMAX model,” International Journal of Control, vol. 49, N.° 3, pp. 1013-1032, 1988.[4] H. González, M.S. Dutra, O. Lengerke, “Identification and modeling for non-linear dynamic system using neural networks type MLP,” presentado en Proceedings of the 2009 Euro American Conference on Telematics and Information Systems: New Opportunities to increase Digital Citizenship, Praga, junio 03-05, 2009.[5] R. Hecht-Nielsen, Neurocomputing, Boston: Ed. Addison Wesley, 1988, pp. 433.[6] J. Vojtesek, P. Dostal, “Adaptive control of water level in real model of water tank, Process Control (PC),” presentado en 20th International Conference on, Strbske Pleso, Eslovaquia, junio 9-12, 2015.[7] A. U. Levin y K. Narendra, “Control of nonlinear dynamical systems using neural networks,” IEEE Neural Networks Council, vol.7, pp. 30-42, 1996.[8] K. Narendra y K. Parthasarathy, “Identification and Control of Dynamical Systems Using Neural Networks,” IEEE Transactions on Neural Networks, vol. 7, N.° 1, 1996.[9] H. M. Nguyen y N. Subbaram, “Advanced control strategies for wind energy systems: An overview”, presentado en IEEE/PES Power Systems Conference and Exposition, Phoenix, 2011.[10] K.J. Nidhil, S. Sreeraj, B. Vijay y V. Bagyaveereswaran, “System identification using artificial neural network”, Circuit, Power and Computing Technologies (ICCPCT), presentado en 2015 International Conference, Nagercoil, 2015.[11] M. Nørgaard, O. Ravn, NK. Poulsen y LK Hansen, Neural Networks for Modelling and Control of Dynamic Systems, Londres: Springer, 2000, pp. 246.[12] K. Ogata, Ingeniería de control moderna, 4.a ed., Madrid: Prentice Hall, 2003, pp. 984.[13] D. T. Pham y L. Xing, Neural Networks for identification, prediction and control, Londres: Springer, 2012, pp. 238.[14] A. Kupin, “Application of neurocontrol principles and classification optimisation in conditions of sophisticated technological processes of beneficiation complexes”. Metallurgical y Mining Industry, vol. 6, pp. 16-24, 2014.[15] R.J. Rajesh, R. Preethi, P. Mehata y B. Jaganatha Pandian, “Artificial neural network based inverse model control of a nonlinear process,” presentado en Computer, Communication and Control (IC4), International Conference, Indore, 2015.[16] V.R. Ravi, M. Monica, S. Amuthameena, S.K. Divya, S. Jayashree y J. Varshini, “Sliding Mode Controller for Two Conical Tank Interacting Level System,” Applied Mechanics and Materials, vol. 573, pp. 273-278, 2014.[17] A. M. Suárez, Nueva arquitectura de control predictivo para sistemas dinámicos no lineales usando redes neuronales, Tesis de Doctorado en Ciencias de la Ingeniería, Universidad de Chile, Santiago de Chile, 1998.[18] D. Zhao, Z. Xia y D. Wang, “Model-Free Optimal Control for Affine Nonlinear Systems with Convergence Analysis”, IEEE Transactions on Automation Science and Engineering, vol. 12, pp. 1461-1468, 2015.Revista Ingenierías Universidad de Medellínhttp://creativecommons.org/licenses/by-nc-sa/4.0/Attribution-NonCommercial-ShareAlike 4.0 Internationalhttp://purl.org/coar/access_right/c_abf2Revista Ingenierías Universidad de Medellín; Vol. 17 Núm. 33 (2018): Julio-Diciembre; 157-172Neuronal engineeringIdentificationPredictive controlAdaptive controlNon-linear MIMO systemsEngenharia neuralIdentificaçãoControle preditivoControle adaptativaSistemas MIMO não linearesIngeniería neuronalIdentificaciónControl predictivoControl adaptativoSistemas MIMO no linealesPredictive/Adaptive Control of Complex Systems Using Neural Engineering TechniquesControle preditivo/adaptativo de sistemas complexos utilizando técnicas de engenharia neuralControl predictivo/adaptativo de sistemas complejos utilizando técnicas de ingeniería neuronalArticlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Artículo científicoinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85Comunidad Universidad de MedellínLat: 06 15 00 N  degrees minutes  Lat: 6.2500  decimal degreesLong: 075 36 00 W  degrees minutes  Long: -75.6000  decimal degrees11407/5513oai:repository.udem.edu.co:11407/55132021-05-14 14:29:48.52Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co

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