Diseño de un control de velocidad mediante redes neuronales y algoritmos genéticos para vehículos autónomos

  1. Argente-Mena, Javier 1
  2. Santos, Matilde 1
  3. Sierra García, Jesús Enrique 2
  1. 1 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

  2. 2 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

Book:
XLIV Jornadas de Automática: libro de actas: Universidad de Zaragoza, Escuela de Ingeniería y Arquitectura, 6, 7 y 8 de septiembre de 2023, Zaragoza
  1. Ramón Costa Castelló (coord.)
  2. Manuel Gil Ortega (coord.)
  3. Óscar Reinoso García (coord.)
  4. Luis Enrique Montano Gella (coord.)
  5. Carlos Vilas Fernández (coord.)
  6. Elisabet Estévez Estévez (coord.)
  7. Eduardo Rocón de Lima (coord.)
  8. David Muñoz de la Peña Sequedo (coord.)
  9. José Manuel Andújar Márquez (coord.)
  10. Luis Payá Castelló (coord.)
  11. Alejandro Mosteo Chagoyen (coord.)
  12. Raúl Marín Prades (coord.)
  13. Vanesa Loureiro-Vázquez (coord.)
  14. Pedro Jesús Cabrera Santana (coord.)

Publisher: Servizo de Publicacións ; Universidade da Coruña

ISBN: 9788497498609

Year of publication: 2023

Pages: 121-126

Congress: Jornadas de Automática (44. 2023. Zaragoza)

Type: Conference paper

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Abstract

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.

Data source: Dialnet