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Control de un sistema multivariable no lineal y en fase no mínima empleando un controlador predictivo neuronal

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Control de un sistema multivariable no lineal y en fase no mínima empleando un controlador predictivo neuronal

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dc.contributor.author Calle Chojeda, Elmer es_ES
dc.contributor.author Oliden Semino, José es_ES
dc.contributor.author Ipanaqué Alama, William es_ES
dc.date.accessioned 2023-01-17T08:55:51Z
dc.date.available 2023-01-17T08:55:51Z
dc.date.issued 2022-12-28
dc.identifier.issn 1697-7912
dc.identifier.uri http://hdl.handle.net/10251/191370
dc.description.abstract [EN] In this paper, a Neural Predictive Controller (ANN-MPC) is proposed to control a nonlinear quadruple tank system, which is complex to control due to the nonlinearity of its valves and the interaction between its controlled variables. In addition, the problem is aggravated since the process presents a transient response with inverse dynamics due to being in a non-minimum phase. The ANN-MPC employs a modular artificial neural network framework and the Levenberg-Marquardt training algorithm to more accurately and quickly estimate nonlinear process outputs and avoid model overfitting. Operational data was generated from the plant to train the neural network using Matlab. The performance of the ANN-MPC under reference changes was tested and compared with a linear MPC and a non-linear MPC. The simulation results showed that the ANN-MPC produced a shorter settling time than the linear MPC and generated RMSE values of the outputs similar to those of the NMPC. In addition, the computation time required to calculate the optimal control variable was reduced compared to the NMPC. es_ES
dc.description.abstract [ES] En este artículo se propone un Controlador Predictivo Neuronal (ANN-MPC) para controlar un sistema no lineal de tanque cuádruple, el cual es complejo de controlar debido a la no linealidad de sus válvulas y a la interacción entre sus variables controladas. Además, el problema se agrava ya que el proceso presenta una respuesta transitoria con dinámica inversa por estar en fase no mínima. El ANN-MPC emplea una estructura modular de red neuronal artificial y el algoritmo de entrenamiento Levenberg-Marquardt para estimar con mayor precisión y rapidez las salidas del proceso no lineal y evitar el sobreajuste del modelo. Se generaron datos operativos a partir de la planta para entrenar la red neuronal empleando Matlab. Se probó el rendimiento del ANN-MPC ante cambios de referencia y se comparó con un MPC lineal y un MPC no lineal. Los resultados de simulación mostraron que el ANN-MPC produjo un menor tiempo de establecimiento que el MPC lineal y generó valores RMSE de las salidas similares a los del NMPC. Además, se redujo el tiempo de cómputo requerido para calcular la variable de control óptima comparado con el NMPC. es_ES
dc.description.sponsorship E. Calle reconoce el apoyo financiero del Proyecto Concytec- Banco Mundial ”Mejoramiento y Ampliación de los Servicios del Sistema Nacional de Ciencia Tecnología e Innovación Tecnológica”8682-PE, a través de su unidad ejecutora Pro-Ciencia. [Contrato número 06-2018-FONDECYT /BM], para su trabajo de investigación denominado Control de un sistema multivariable no lineal y en fase no mínima empleando un controlador predictivo neuronal, ejecutado como parte del programa de Doctorado en Ingeniería con mención en Automatización, Control, y Optimización de Procesos, del Departamento de Ingeniería Mecánico-Eléctrica de la Universidad de Piura, Perú. es_ES
dc.language Español es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof Revista Iberoamericana de Automática e Informática industrial es_ES
dc.rights Reconocimiento - No comercial - Compartir igual (by-nc-sa) es_ES
dc.subject Model based predictive control es_ES
dc.subject Artificial neural networks es_ES
dc.subject MIMO systems es_ES
dc.subject Quadruple-tank system es_ES
dc.subject Control predictivo basado en modelo es_ES
dc.subject Redes neuronales artificiales es_ES
dc.subject Sistema MIMO es_ES
dc.subject Sistema de tanque cuádruple es_ES
dc.title Control de un sistema multivariable no lineal y en fase no mínima empleando un controlador predictivo neuronal es_ES
dc.title.alternative Control of a non-linear and non-minimum phase multivariable system using a neural predictive controller es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/riai.2022.17375
dc.relation.projectID info:eu-repo/grantAgreement/FONDECYT//06-2018-FONDECYT%2FBM/Control de un sistema multivariable no lineal y en fase no mínima empleando un controlador predictivo neuronal es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Gobierno del Perú//8682-PE/Mejoramiento y Ampliación de los Servicios del Sistema Nacional de Ciencia Tecnología e Innovación Tecnológica es_ES
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Calle Chojeda, E.; Oliden Semino, J.; Ipanaqué Alama, W. (2022). Control de un sistema multivariable no lineal y en fase no mínima empleando un controlador predictivo neuronal. Revista Iberoamericana de Automática e Informática industrial. 20(1):32-43. https://doi.org/10.4995/riai.2022.17375 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/riai.2022.17375 es_ES
dc.description.upvformatpinicio 32 es_ES
dc.description.upvformatpfin 43 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 20 es_ES
dc.description.issue 1 es_ES
dc.identifier.eissn 1697-7920
dc.relation.pasarela OJS\17375 es_ES
dc.contributor.funder Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica, Perú es_ES
dc.contributor.funder Fondo Nacional de Desarrollo Científico, Tecnológico y de Innovación Tecnológica, Perú es_ES
dc.contributor.funder World Bank Group es_ES
dc.contributor.funder Universidad de Piura es_ES
dc.contributor.funder Gobierno del Perú es_ES
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