Auto-Regressive with Exogenous Input Model Predictive Controller for Water Activity in Esterification

Abstract

In this work, the Auto-Regressive with Exogenous Input Model Predictive Controller (ARX-MPC) was designed and implemented to control the water activity of the lipase-catalysed esterification process. The empirical model, which was embedded in the MPC was developed using the Autoregressive with Exogenous input (ARX) model. The parameter estimation and model validation for the ARX model were carried out using the recursive least squares estimation (RLSE) system identification toolbox in MATLAB®. The capability of the models to capture the dynamics of the input and output variables was also verified. ARX models were solved using the quadratic programming (QP) method in the MPC toolbox in MATLAB®/Simulink. The ARX-MPC parameters were tuned to determine the best controller performance. Then, the performances of the best- tuned ARX-MPC were evaluated in terms of set point tracking and disturbance rejection. According to results, the ARX-MPC was considered suitable and reliable for tracking the set-point changes of the controlled process variable and able to eliminate the presence of disturbance in the process.