Maestría en Electrónica y Automatización

Permanent URI for this collectionhttp://repositorio.uta.edu.ec/handle/123456789/35313

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    Diseño de un controlador multivariable utilizando herramientas de inteligencia artificial aplicado al proceso de incubación de embriones de Gallus Gallus Domesticus
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Maestría en Electrónica y Automatización, 2022) Balseca Chicaiza, Alvaro Bladimiro; Herrera Garzón, Marco Antonio
    This project presents, a multivariable control using fuzzy logic and genetic algorithms (GA) as Artificial Intelligence (AI) tools, applied to the hatching process of “Gallus gallus domesticus” embryos. The incubation process is a system with high interactions among its input and output variables. To reduce these interactions, a dynamic decoupling network is used through Relative Gain Array (RGA) analysis. The proportional integral (PI) controllers and the linear decoupler are designed from singlevariable control structures obtained from a parametric identification for systems that can be pproximated to first order and first order with delay (FOPDT) models. Performance of PI, PI-Fuzzy and PI-Fuzzy controllers tuned with Genetic Algorithms (GA), are evaluated through a comparison of the integral squared error (ISE), integral absolute error (IAE) and total variation control (TVu) through simulations in MATLAB® and experimental tests using the NODEMCU ESP-WROOM-32 embedded system.
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    Sistema electrónico de control y etiquetado de molduras para cuadros en tiempo-real mediante machine learning
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Maestría en Electrónica y Automatización, 2022) Chávez Pico, David Alejandro; Herrera Garzón, Marco Antonio
    In this work, a real-time system is presented that performs the detection of the different types of moldings made from the analysis of the image processing of their different surfaces, silhouettes and colors. The need to use this electronic system for the control and labeling of moldings for paintings in real time through machine learning is to reduce production times and store the number of moldings manufactured in a database in order to avoid downtime on the part of workers and thus increase productivity in the factory. In addition, it has the tools, software and hardware to be able to do it, in this case a device called NVIDIA's Jetson Nano will be used for image analysis with its respective camera, which allows artificial vision applications. On the other hand, this control system allows the moldings manufactured to be labeled with their specific characteristics by means of a QR code to make it functional and practical in the factory. This will benefit production since the amount of molding will increase, since costs will be reduced and there will be an increase in profits. Another important aspect is that with this system it is intended to have scalability for the future due to the fact that there are different branches where the moldings are transported and it will help to have a more exact control from the time the load leaves until the load arrives to avoid delays in counting as conventionally. It has been done in recent years, this data can be taken directly to the accounting department who are in charge of the amount of production that is carried out in the factory and the amount that is transported to the headquarters and each of the branches.