Diseño de un control de velocidad mediante redes neuronales y algoritmos genéticos para vehículos autónomos
- Argente-Mena, Javier 1
- Santos, Matilde 1
- Sierra García, Jesús Enrique 2
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1
Universidad Complutense de Madrid
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2
Universidad de Burgos
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- Ramón Costa Castelló (coord.)
- Manuel Gil Ortega (coord.)
- Óscar Reinoso García (coord.)
- Luis Enrique Montano Gella (coord.)
- Carlos Vilas Fernández (coord.)
- Elisabet Estévez Estévez (coord.)
- Eduardo Rocón de Lima (coord.)
- David Muñoz de la Peña Sequedo (coord.)
- José Manuel Andújar Márquez (coord.)
- Luis Payá Castelló (coord.)
- Alejandro Mosteo Chagoyen (coord.)
- Raúl Marín Prades (coord.)
- Vanesa Loureiro-Vázquez (coord.)
- Pedro Jesús Cabrera Santana (coord.)
Editorial: Servizo de Publicacións ; Universidade da Coruña
ISBN: 9788497498609
Año de publicación: 2023
Páginas: 121-126
Congreso: Jornadas de Automática (44. 2023. Zaragoza)
Tipo: Aportación congreso
Resumen
Autonomous Guided Vehicles (AGVs) are becoming increasingly popular in terms of internal factory logistics due to their ability to transport heavy loads and their high degree of autonomy. Nevertheless, the dynamics of these robots can undergo changes due to variations in their load and/or mechanical wear, which involves greater complexity in their speed control. Proportional Integral (PI) controllers are often used for this control. However, this controller requires fine tuning and lacks enough robustness against variations in working conditions. In order to improve the speed control performance, this article presents the design of a neuro-controller. Since finding optimal values for the learning hyperparameters can be difficult and requires multiple tests and adjustments, a Genetic Algorithm (GA) is used to find a valid solution among all the optimal.