(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.
