Comparación de Estrategias de Control de Temperatura: Controlador PID y Redes Neuronales

Francisco Javier Carpio-Velasco; Gloria Margarita Garcés-Beltrán

doi:https://doi.org/10.69484/rcz/v4/n2/113

Comparación de Estrategias de Control de Temperatura: Controlador PID y Redes Neuronales

Journal

Revista Científica Zambos

ISSN

3028-8843

Date Issued

2025-05-31

Author(s)

Francisco Javier Carpio-Velasco

Gloria Margarita Garcés-Beltrán

DOI

https://doi.org/10.69484/rcz/v4/n2/113

URL

https://cris.ute.edu.ec/handle/123456789/2033

Abstract

Temperature control in industrial processes is essential to ensure quality and efficiency in production. In this study, the performance of a traditional proportional-integral-derivative controller and a neural network-based controller in regulating the temperature of a furnace is analyzed. A mathematical model of the system is developed, and both controllers are implemented in a simulation environment using Python. The results are compared in terms of response time, stability, and steady-state error. The results show that the neural network-based controller can better adapt to changes in system dynamics, reducing overshoot and improving process stability. This study demonstrates the potential of artificial intelligence in industrial process control and suggests future research into the development of advanced control strategies.