TY - JOUR
T1 - Un neuro-controlador estable en tiempo real para reducir el consumo de energía en una bomba centrífuga ante perturbaciones
AU - de Oca, Eduardo Yudho Montes
AU - Maya-Rodríguez, Mario Cesar
AU - Tolentino-Eslava, René
AU - Lozano-Hernández, Yair
N1 - Publisher Copyright:
© 2022 Universitat Politecnica de Valencia. All rights reserved.
PY - 2022
Y1 - 2022
N2 - In this paper, the application of an on-line tuning method based on neural networks for a PID controller was proposed to regulate the flow in a centrifugal pump. The implementation of a modified back-propagation algorithm stable in the sense of input-to-state stability was carried out to update the weights of a neural network. The energy consumed by the pump to maintain a certain flow in the pipeline of an experimental station as an indicator to assess the efficiency of the controller was chosen. Different experimental tests to show the performance of the proposed controller under different conditions were carried out such as non-disturbance, constant disturbances and time-dependent disturbances. A proportional valve was implemented to generate the disturbances in the system. The controller was compared with a classical PID controller and an on-line tuning method based on neural networks for a PID controller without back-propagation modification. The results showed that the on-line tuning method based on neural networks with a stable learning algorithm produced a lower energy consumption in the centrifugal pump.
AB - In this paper, the application of an on-line tuning method based on neural networks for a PID controller was proposed to regulate the flow in a centrifugal pump. The implementation of a modified back-propagation algorithm stable in the sense of input-to-state stability was carried out to update the weights of a neural network. The energy consumed by the pump to maintain a certain flow in the pipeline of an experimental station as an indicator to assess the efficiency of the controller was chosen. Different experimental tests to show the performance of the proposed controller under different conditions were carried out such as non-disturbance, constant disturbances and time-dependent disturbances. A proportional valve was implemented to generate the disturbances in the system. The controller was compared with a classical PID controller and an on-line tuning method based on neural networks for a PID controller without back-propagation modification. The results showed that the on-line tuning method based on neural networks with a stable learning algorithm produced a lower energy consumption in the centrifugal pump.
KW - Adaptive control by neural networks
KW - Neural Networks
KW - Process control
KW - Real-time control
KW - Water supply
KW - distribution systems
UR - http://www.scopus.com/inward/record.url?scp=85134558108&partnerID=8YFLogxK
U2 - 10.4995/RIAI.2022.16060
DO - 10.4995/RIAI.2022.16060
M3 - Artículo
AN - SCOPUS:85134558108
SN - 1697-7912
VL - 19
SP - 265
EP - 273
JO - RIAI - Revista Iberoamericana de Automatica e Informatica Industrial
JF - RIAI - Revista Iberoamericana de Automatica e Informatica Industrial
IS - 3
ER -