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ICTACT Journal on Communication Technology

ISSNs 0976-0091 (impreso) 2229-6948 (en línea)

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Institución detectada	Período	Navegá	Descargá	Solicitá
No requiere	desde ene. 2010 / hasta may. 2024	Directory of Open Access Journals

Cobertura temática: Ingeniería eléctrica, electrónica e informática

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ICTACT Journal on Microelectronics

ISSNs 2395-1672 (impreso) 2395-1680 (en línea)

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Institución detectada	Período	Navegá	Descargá	Solicitá
No requiere	desde may. 2024 / hasta may. 2024	Directory of Open Access Journals

Cobertura temática: Ingeniería eléctrica, electrónica e informática

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ICTACT Journal on Soft Computing

ISSNs 0976-6561 (impreso) 2229-6956 (en línea)

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Institución detectada	Período	Navegá	Descargá	Solicitá
No requiere	desde ene. 2010 / hasta may. 2024	Directory of Open Access Journals

Cobertura temática: Ingeniería eléctrica, electrónica e informática

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Identificación y control predictivo

Autores/as: Germán Andrés Bustos ; Jacinto Luis Marchetti ; Juan Carlos Gómez ; Diego Feroldi ; José Luis Figueroa ; Alejandro Hernán González

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Institución detectada	Año de publicación	Navegá	Descargá	Solicitá
No requiere	2014	Biblioteca Virtual de la Universidad Nacional del Litoral (SNRD)

Cobertura temática: Ingeniería eléctrica, electrónica e informática

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Model-based predictive control (MPC) has become a technique control widely used today because of its ability to handle multivariable systems (MIMO) and restrictions. This technique makes use of models of the system to be controlled, so that the controller performance depends heavily on the accuracy of it. In the area of process control, the part that is dedicated to proposing methods for obtaining models is called, in general, system identification. In the specific framework of MPC, system identifacation has a special significance: on the one hand, as said, it is imperative to have accurate and updated models because the control technique bases its performance on the precision of these; on the other, the processes usually controlled by MPC can not be identified each time a new model is desired. An identification technique - independent of the control system to be used - that has proven to be an efficient alternative compared to classical identification methods, such as the prediction error method (PEM), is the subspace identification method (SID). This technique appears as promising in the framework of MPC, since it allows obtaining models in state space, multivariables, in a simple and direct way. For this reason, the objective of this Thesis is the study and development of identification methods that give answers to the typical problems of the implementation of MPC.

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