Min-Max Controller Output Configuration to Improve Multi-model Predictive Control when Dealing with Disturbance Rejection
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 dyna...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Indonesia
2015-07-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | http://ijtech.eng.ui.ac.id/article/view/1366 |
| _version_ | 1797966815699140608 |
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| author | Abdul Wahid Arshad Ahmad |
| author_facet | Abdul Wahid Arshad Ahmad |
| author_sort | Abdul Wahid |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T02:21:19Z |
| format | Article |
| id | doaj.art-3241dbfd60604848b150c00e8eea54bb |
| institution | Directory Open Access Journal |
| issn | 2086-9614 2087-2100 |
| language | English |
| last_indexed | 2024-04-11T02:21:19Z |
| publishDate | 2015-07-01 |
| publisher | Universitas Indonesia |
| record_format | Article |
| series | International Journal of Technology |
| spelling | doaj.art-3241dbfd60604848b150c00e8eea54bb2023-01-02T23:38:22ZengUniversitas IndonesiaInternational Journal of Technology2086-96142087-21002015-07-016350451510.14716/ijtech.v6i3.13661366Min-Max Controller Output Configuration to Improve Multi-model Predictive Control when Dealing with Disturbance RejectionAbdul Wahid0Arshad Ahmad1Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus Baru UI Depok, Depok 16424, IndonesiaDepartment of Chemical Engineering, Faculty of Chemical Engineering, Universiti Teknologi Malaysia, Johor Baru, Johor 81310, MalaysiaA 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.http://ijtech.eng.ui.ac.id/article/view/1366Configuration, Control, Distillation, Multi-model, Predictive |
| spellingShingle | Abdul Wahid Arshad Ahmad Min-Max Controller Output Configuration to Improve Multi-model Predictive Control when Dealing with Disturbance Rejection International Journal of Technology Configuration, Control, Distillation, Multi-model, Predictive |
| title | Min-Max Controller Output Configuration to Improve Multi-model Predictive Control when Dealing with Disturbance Rejection |
| title_full | Min-Max Controller Output Configuration to Improve Multi-model Predictive Control when Dealing with Disturbance Rejection |
| title_fullStr | Min-Max Controller Output Configuration to Improve Multi-model Predictive Control when Dealing with Disturbance Rejection |
| title_full_unstemmed | Min-Max Controller Output Configuration to Improve Multi-model Predictive Control when Dealing with Disturbance Rejection |
| title_short | Min-Max Controller Output Configuration to Improve Multi-model Predictive Control when Dealing with Disturbance Rejection |
| title_sort | min max controller output configuration to improve multi model predictive control when dealing with disturbance rejection |
| topic | Configuration, Control, Distillation, Multi-model, Predictive |
| url | http://ijtech.eng.ui.ac.id/article/view/1366 |
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