ANN based Multi Model Predictive Control for pH-Control. Issue 1 (1st October 2022)

• Record Type: Journal Article
• Authors: Pua, ZY; Hermansson, AW; Lim, CH
• Is Part Of: IOP conference series. Volume 1257:Issue 1(2022)
• Publication Date: 2022-10-01
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/1257/1/012035
• Languages: English
• ISSNs: 1757-8981

Abstract

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.