Control-Oriented Two-Dimensional Online System Identification for Batch Processes

Robust model predictive control (MPC) is widely applied to batch processes to handle its intrinsic plant-model mismatch by considering the worst-case scenario and thus leads to conservative performance. Identification methods that make use of system repetitiveness have been invented and combined with robust MPC to reduce conservativeness. In this paper, we show that in addition to the repetitiveness, time-wise continuity in system dynamics can also help to improve model accuracy and accelerate the convergence rate. Specifically, we propose a two-dimensional online identification method in the framework of set membership identification. The method is capable of (1) employing time-wise correlation and cycle-wise repetitiveness in system dynamics; (2) identifying both of the nominal value and the corresponding uncertainty set of the unknown parameters, thus being amenable to robust MPC schemes; and (3) guaranteeing convergence to the true parameters under mild conditions. Numerical examples are provided to illustrate its fast convergence and superiority in improving prediction accuracy and control performance.