ANN based Multi Model Predictive Control for pH-Control

Z.Y. Pua, Ayman William Hermansson, C. H. Lim

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publication33rd Symposium of Malaysian Chemical Engineers (SOMChE 2022)
PublisherIOP Publishing
Publication statusPublished - 21 Oct 2022
Event33rd Symposium of Malaysian Chemical Engineers 2022 -
Duration: 8 Aug 20229 Aug 2022


Conference33rd Symposium of Malaysian Chemical Engineers 2022
Abbreviated titleSOMChE 2022


  • Process control
  • ANN
  • MPC


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