A hybrid intelligent system for PID controller using in a steel rolling process

Abstract

With the aim to improve the steel rolling process performance, this research presents a novel hybrid system for selecting the best parameters for tuning in open loop a PID controller. The novel hybrid system combines rule based system and Artificial Neural Networks. With the rule based system, it is modeled the existing knowledge of the PID controller tuning in open loop and, with Artificial Neural Network, it is completed the rule based model that allow to choose the optimal parameters for the controller. This hybrid model is tested with a long dataset to obtain the best fitness. Finally, the novel research is validated on a real steeling roll process applying the hybrid model to tune a PID controller which set the input speed in each of the gearboxes of the process.