CLOSED-LOOP IDENTIFICATION OF MPC MODELS FOR MIMO PROCESSES USING GENETIC ALGORITHMS AND DITHERING ONE VARIABLE AT A TIME - APPLICATION TO AN INDUSTRIAL DISTILLATION TOWER

Model Predictive Controllers ( MPC) typically use step response models . The identification of these models is usually carried out under Open -Loop conditions, where large quantities of data are collected and/or large process perturbations are used. This makes the identification si mpler, but is costly in terms of personnel requirements and degraded p rocess performance during the test. This paper reports a methodology f or identifying Multiple-Input Multiple-Output ( MIMO ) step response m odels while the process is operating under multivariable control. The identification of MIMO step response models can be achieved by adding an external test signal to one variable at a time. The method has been successfully applied to a distillation tower in a petroleum refinery. During the test the tower was controlled by an existing MPC with the constraint handling active.