ANN based Multi Model Predictive Control for pH-Control

Abstract

Artificial neural network (ANN) has many uses when non-linear behaviour is modelled. Here we are training a feedforward ANN that will mimic the behaviour of a Robust Model Predictive Controller (RMPC) for use in pH control. The training dataset were generated from running multiple tests on RMPC for different requirements and cases of pH-control. The training data focused on the control-inputs relating to the other process inputs. The training algorithm used in this neural network is Levenberg-Marquardt algorithm which is the most widely use algorithm in current machine learning industry. This neural network was trained by using the deep learning toolbox in Matlab®. Eight different cases is presented: four is for deploying neural network purpose, while the other four is for verification purpose. The result shows good control as long as the ANN-controller has been given a similar input and there are no multiplicity in the process input data.