Application of Model predictive control suitable for closed loop re-identification to a polymerization reactor

Abstract

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