Control de un laboratorio de control de temperatura mediante redes neuronales recurrentes

  1. Blanco Fernández, Cristian 1
  2. Sierra García, Jesús Enrique 2
  3. Santos, Matilde 3
  1. 1 UNED. Universidad Nacional de Educación a Distancia
  2. 2 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

  3. 3 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

Erakutsi afiliazioak +
Liburua:
XLIII Jornadas de Automática: libro de actas: 7, 8 y 9 de septiembre de 2022, Logroño (La Rioja)
  1. Carlos Balaguer Bernaldo de Quirós (coord.)
  2. José Manuel Andújar Márquez (coord.)
  3. Ramon Costa Castelló (coord.)
  4. Carlos Ocampo Martínez (coord.)
  5. Jesús Fernández Lozano (coord.)
  6. Matilde Santos Peñas (coord.)
  7. José Enrique Simó Ten (coord.)
  8. Montserrat Gil Martínez (coord.)
  9. Jose Luis Calvo Rolle (coord.)
  10. Raúl Marín Prades (coord.)
  11. Eduardo Rocón de Lima (coord.)
  12. Elisabet Estévez Estévez (coord.)
  13. Pedro Jesús Cabrera Santana (coord.)
  14. David Muñoz de la Peña Sequedo (coord.)
  15. José Luis Guzmán Sánchez (coord.)
  16. José Luis Pitarch Pérez (coord.)
  17. Oscar Reinoso García (coord.)
  18. Oscar Déniz Suárez (coord.)
  19. Emilio Jiménez Macías (coord.)
  20. Vanesa Loureiro Vázquez (coord.)

Argitaletxea: Servizo de Publicacións ; Universidade da Coruña

ISBN: 978-84-9749-841-8

Argitalpen urtea: 2022

Orrialdeak: 193-200

Biltzarra: Jornadas de Automática (43. 2022. Logroño)

Mota: Biltzar ekarpena

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Laburpena

Model Predictive Control (MPC) is an extended control strategy based on the resolution of an optimization problem in real time, which can be a computationally expensive process depending on the nature of the problem. To overcome this limitation, the use of neural networks already trained as a substitute for this type of controllers has been investigated. The underlying concept is that, for a sufficiently predictable system, a neural network can be trained, using data from an optimized controller, which can replace the MPC. With this approach the higher computational cost lies in the training of the network instead of the online operation of the system. This idea is explored using a temperature control lab. Two types of recurring neural networks are trained, and the performance and computational cost are compared with a conventional controller.

Datuen iturria: Dialnet