Closed-loop identification for plants under model predictive control. (March 2018) - Esmaili, Ali; Li, Jianyi; Xie, Jinyu; Isom, Joshua D.
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. Highlights: Pure feedback closed-loop identification is effective for an identifiable simulated system. Pure feedback closed-loop identification is applied to an industrial hydrogen plant under MPC. Closed-loop identification may be used for monitoring of model quality in MPC implementations.