Vol 6, No 3 (2015) > Chemical Engineering >

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

Abdul Wahid, Arshad Ahmad



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.

Keywords: Configuration; Control; Distillation; Multi-model; Predictive

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