Maestría en Matemática Aplicada

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Subject: search.filters.subject.Control predictivo

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    Implementación de un sistema predictivo con redes neuronales para el control del comportamiento de la planta Festo MPS-PA
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Maestría en Matemática Aplicada, 2021) Soria Mejía, Daysi Maribel; Benalcázar Palacios, Freddy Geovanny
    In the present research work, the implementation of a Dynamic Matrix Predictive Controller (DMC) with Neural Networks was carried out for the level control process (liquid) of a Festo Compact Workstation plant of the hydraulics and pneumatics laboratory of the Technical University of Ambato. The dynamics of the plant was found through the training of a feedforward neural network, the training and testing data used were obtained by conducting an experiment that consists of applying different step inputs to the plant and the response of the system to said input. The algorithm implemented was that of a dynamic matrix predictive controller, for which it is necessary to know the mathematical model of the level process represented as a transfer function, said mathematical model was built using the exponential regression method by least squares.
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    Modelación matemática para un control robusto de la planta Festo MPS-PA Compact Workstation mediante la normativa IEC-61499
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Maestría en Matemática Aplicada, 2021) Bustos Pulluquitin, Sergio Patricio; García Sánchez, Marcelo Vladimir
    In the present research work, the implementation of a GPC control with restrictions was carried out in a FESTO MPS-PA CompactWorkstation level plant through the modeling of function blocks, thus obtaining an industrial control application with decentralized logic which is one of the objectives of the IEC 61499 standard. The IDE used compatible with IEC 61499 was the 4DIAC IDE and the runtime environment (runtime) 4DIAC FORTE, in addition the BBB card was used for reading and writing data from sensors and actuators respectively. The mathematical model of the level plant represented as a transfer function was found by two methods: the first using linear differential equations and the second using experimental data of the response to the step of a first order system. Three types of restrictions were implemented: in the control increment ( Delta u(t)) to avoid unnecessary efforts in the actuator and the output restrictions (y(t)) and control (u(t)) that will prevent damage to the plant, the GQPA algorithm was used to optimize the objective function.