Application of Model Predictive Control suitable for closed-loop re-identification to a polymerization reactor

• We extend a recent MPC formulation suitable for closed-loop re-identification, to account for moderate-to-severe model mismatches.
• The formulation avoid the conflict between control and identification objectives.
• This way, the input and output data collected in closed-loop are uncorrelated, what significantly simplify the identification procedure.
• To test the proposed controller, a (nonlinear) polymerization reactor is selected.
• Suitable linear models were obtained under closed loop control, each model corresponding to different operating point.

Recently, a linear Model Predictive Control (MPC) suitable for closed-loop re-identification was proposed, which solves the potential conflict between the persistent excitation of the system (necessary to perform a suitable identification) and the control, and guarantees recursive feasibility and attractivity of an invariant region of the closed-loop. This approach, however, needs to be extended to account for a proper robustness to moderate-to-severe model mismatches, given that re-identifications are necessary when the system is not close to the operating point where the current linear model was identified. In this work, new results on robustness are presented, and an exhaustive application of the new MPC suitable for closed-loop re-identification to a nonlinear polymerization reactor simulator is made to explore the difficulties arising from a real life identification. Furthermore, several closed-loop re-identification are performed in order to clearly show that the proposed controller provides uncorrelated input–output data sets, which together with the guaranteed stability, constitute the main controller benefit.