TY - JOUR
T1 - Neural network-based auto-tuning for PID controllers
AU - Rivas-Echeverría, F.
AU - Ríos-Bolívar, A.
AU - Casales-Echeverría, J.
PY - 2001
Y1 - 2001
N2 - PID controllers have become the most popular control strategy in industrial processes due to the versatility and tunning capabilities. The incorporation of auto-tunning tools have increased the use of this kind of controllers. In this paper we propose a neural network-based self-tunning scheme for on-line updating of PID parameters, which is based on integral error criteria (IAE, ISE, ITAE, ITSE).
AB - PID controllers have become the most popular control strategy in industrial processes due to the versatility and tunning capabilities. The incorporation of auto-tunning tools have increased the use of this kind of controllers. In this paper we propose a neural network-based self-tunning scheme for on-line updating of PID parameters, which is based on integral error criteria (IAE, ISE, ITAE, ITSE).
KW - Auto-tuning
KW - Integral error criteria
KW - Neural networks (NN)
KW - PID
UR - http://www.scopus.com/inward/record.url?scp=0034918546&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:0034918546
SN - 1210-0552
VL - 11
SP - 277
EP - 284
JO - Neural Network World
JF - Neural Network World
IS - 3
ER -