Closed-loop identification for plants under model predictive control

Abstract Model predictive controllers incorporate step response models for pairings of independent and dependent variables. Motivated by the fact that it may be time-consuming to conduct open-loop experiments to identify the step response models, the paper assesses the performance of closed-loop system identification on MPC-equipped plants, using both simulated and actual plant data. Pure feedback closed-loop system identification is shown to be effective for an identifiable simulated system and an industrial hydrogen production plant. The use of closed-loop system identification as a mechanism for monitoring model quality in MPC implementations may enhance the long-term sustainability of the implementation.