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 dynamic...
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| Format: | Article |
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Faculty of Engineering Universitas Indonesia
2015
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| _version_ | 1796860533838708736 |
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| author | Abdul Wahid, Abdul Wahid Ahmad, Arshad |
| author_facet | Abdul Wahid, Abdul Wahid Ahmad, Arshad |
| author_sort | Abdul Wahid, Abdul Wahid |
| collection | ePrints |
| 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 | 2024-03-05T19:42:48Z |
| format | Article |
| id | utm.eprints-58565 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:42:48Z |
| publishDate | 2015 |
| publisher | Faculty of Engineering Universitas Indonesia |
| record_format | dspace |
| spelling | utm.eprints-585652021-09-14T01:05:58Z http://eprints.utm.my/58565/ Min-max controller output configuration to improve multi-model predictive control when dealing with disturbance rejection Abdul Wahid, Abdul Wahid Ahmad, Arshad TP Chemical technology 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. Faculty of Engineering Universitas Indonesia 2015 Article PeerReviewed Abdul Wahid, Abdul Wahid and Ahmad, Arshad (2015) Min-max controller output configuration to improve multi-model predictive control when dealing with disturbance rejection. International Journal of Technology, 6 (3). pp. 504-515. ISSN 2086-9614 http://dx.doi.org/10.14716/ijtech.v6i3.1139 DOI:10.14716/ijtech.v6i3.1139 |
| spellingShingle | TP Chemical technology Abdul Wahid, Abdul Wahid Ahmad, Arshad Min-max controller output configuration to improve multi-model predictive control when dealing with disturbance rejection |
| 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 | TP Chemical technology |
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