Neural network-based auto-tuning for PID controllers

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).