• International Journal of Technology (IJTech)
  • Vol 6, No 3 (2015)

Min-Max Controller Output Configuration to Improve Multi-model Predictive Control when Dealing with Disturbance Rejection

Abdul Wahid, Arshad Ahmad

Corresponding email: wahid@che.ui.ac.id

Published at : 29 Jul 2015
Volume : IJtech Vol 6, No 3 (2015)
DOI : https://doi.org/10.14716/ijtech.v6i3.1139

Cite this article as:

Wahid, A., Ahmad, A., 2015. Min-Max Controller Output Configuration to Improve Multi-model Predictive Control when Dealing with Disturbance Rejection. International Journal of Technology. Volume 6(3), pp. 504-515

Abdul Wahid Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus Baru UI Depok, Depok 16424, Indonesia
Arshad Ahmad Department of Chemical Engineering, Faculty of Chemical Engineering, Universiti Teknologi Malaysia, Johor Baru, Johor 81310, Malaysia
Email to Corresponding Author


A Multiple Model Predictive Control (MMPC) approach is proposed to control a nonlinear distillation column. This control framework utilizes the best local linear models selected to construct the MMPC. The study was implemented on a multivariable nonlinear distillation column (Column A). The dynamic model of the Column A was simulated within MATLAB® programming and a SIMULINK® environment. The setpoint tracking and disturbance rejection performances of the proposed MMPC were evaluated and compared to a Proportional-Integral (PI) controller. Using three local models, the MMPC was proven more efficient in servo control of Column A compared to the PI controller tested. However, it was not able to cope with the disturbance rejection requirement. This limitation was overcome by introducing controller output configurations, as follows: Maximizing MMPC and PI Controller Output (called MMPCPIMAX). The controller output configurations of PI and single linear MPC (SMPC) have been proven to be able to improve control performance when the process was subjected to disturbance changes (F and zF). Compared to the PI controller, the first algorithm (MMPCPIMAX) provided better control performance when the disturbance sizes were moderate, but it was not able to handle a large disturbance of + 50% in zF.

Configuration, Control, Distillation, Multi-model, Predictive


Cutler, C.R., 1983. Dynamic Matrix Control – An Optimal Multivariable Control Algorithm with Constraints. Ph.D. Thesis, University of Houston

Dubay, M. Abu-Ayyad, R., 2006. MIMO Extended Predictive Control—Implementation and Robust Stability Analysis. ISA Transactions, Volume 45(4), pp. 545-561

Ellis, M., Christofides, Panagiotis, D., 2015. Real-time Economic Model Predictive Control of Nonlinear Process Systems. AIChE Journal, Volume 61(2), pp. 555-571

Fatihah, M.A., Farah, 2013. A Simulation Study on Model Predictive Control Application for Depropanizer using Aspen Hysys. Bachelor Thesis, Universiti Malaysia Pahang

Huang, H., Riggs, J.B., 2002a. Comparison of PI and MPC for Control of a Gas Recovery Unit. Journal of Process Control, Volume 12, pp. 163-173

Huang, H., Riggs, J.B., 2002b. Including Levels in MPC to Improve Distillation Control. Ind. Eng. Chem. Res., Volume 41, pp. 4048-4053

Hussain, M.A., 2013. A Dual-rate Model Predictive Controller for Fieldbus Based Distributed Control Systems. Master Thesis, The University of Western Ontario

Kalman, R.E., 1960. A New Approach to Linear Filtering and Prediction Problems. Transactions of ASME, Journal of Basic Engineering. Volume 87, pp. 35-45

Kozák, Š., 2014. State-of-the-art in Control Engineering. Journal of Electrical Systems and Information Technology, Volume 1, pp. 1-9

Magni, L., Raimondo, D.M., Allg?wer, F., (Eds.)., 2009. Nonlinear Model Predictive Control: Towards New Challenging Applications. Springer-Verlag Berlin Heidelberg

Morari, M., Lee, J.H., 1999. Model Predictive Control: Past, Current and Future. Computers and Chemical Engineering. Volume 23, pp. 667-682

Nikolaou, M., 1998. Model Predictive Controllers: A Critical Synthesis of Theory and Industrial Needs. Chemical Engineering Dept. University of Houston

Potts, A.S., Romano, R.A., Garcia, C., 2014. Improving Performance and Stability of MPC Relevant Identification Methods. Control Engineering Practice, Volume 22, pp. 20-33

Qin, S.J., Badgwell, T.A., 2003. A Survey of Industrial Model Predictive Control Technology. Control Engineering Practice, Volume 11, pp. 733-764

Rao, K.S., Misra, R., 2014. Comparative Study of P, PI and PID Controller for Speed Control of VSI-fed Induction Motor. International Journal of Engineering Development and Research. Volume 2(2), pp. 2740-2744

Richalet, J., Rault, A., Testud, J.L., Papon, J., 1978. Model Predictive Heuristic Control: Applications to Industrial Processes. Automatica, Volume 14(5), pp. 413-428

Skogestad, S., 2007. The Do’s and Don’ts of Distillation Column Control. Trans IChemE, Part A, Chemical Engineering Research and Design, Volume 85(A1), pp. 13-23

Singh, R., Marianthi, I., Rohit R., 2013. System-wide Hybrid MPC–PID Control of a Continuous Pharmaceutical Tablet Manufacturing Process via Direct Compaction. European Journal of Pharmaceutics and Biopharmaceutics, Volume 85, pp. 1164–1182

Wang, L., 2009. Model Predictive Control System Design and Implementation using MATLAB®. Springer-Verlag London Limited

Xi, Y-G., Li, D-W., Lin, S., 2013. Model Predictive Control – Status and Challenges. Acta Automatica Sinica, Volume 39(3), pp. 222-236