AUTOMATIC TUNING OF PARAMETERS CONTROLLER USING ARTIFICIAL NEURAL NETWORKS

Abstract

proportional, integral and derivative (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. The PID-controllers are often badly tuned, since it is too time consuming to calculate good PID-parameters at the time of deployment. This work investigates the applicability of artificial neural networks to control systems. The main properties of neural networks are identified as of major interest to this field: their ability to implement nonlinear mappings, their massively parallel structure and their capacity to adapt. This study suggests a certain technique to apply neural networks for the tuning of the PID controller‟s gains in a way human tune the gains depending on the environmental and systems requirements. Error Back-Propagation (BP) method is used as the tuning method for the controller which is also known as BP method and this method works on the local minima algorithm.