Weighted similarity based just-in-time model predictive control for batch trajectory tracking

Abstract

Being different from the continuous process, batch processes in the practical industry have several distinct characteristics, such as the unsteady state, severe nonlinearity, and iterative operation. For tracking a reference trajectory of a batch process, data-driven model predictive controllers have been proposed with the progress of sensors and machine learning. Among them, the latent variable space model-based controllers (LV-MPC) have been applied to the batch processes for decades. When there exist time- and batch-varying trajectory and disturbance, however, utilization of a single model using the aggregate historical dataset may reduce the capability of the predictive model and the control performance. It is because maintaining a single global model can miss the details of process dynamics at the current state. To solve this problem, we propose to update the local model in the manner of just-in-time learning (JITL) and to use them to the predictive controller design at first. Then, two different weighted similarity methods based on principal component analysis (PCA) and partial least squares (PLS) are proposed to enhance the performance of sorting out the most relevant dataset able to explain the current state. A fed-batch bioreactor system, which has time- and batch-varying reference trajectory and disturbance, is simulated to verify the efficiency of the proposed methods. Simulation results show that weighted similarity based on PLS and its application to JITL latent variable space model predictive controller (LV-MPC) has an improved control performance as it sorts out the data with the useful information about the current dynamics.