Ensemble learning based latent variable model predictive control for batch trajectory tracking under concept drift

摘要

Industrial batch processes are characterized by unsteady state, multiple lines, and iterative operation. For tracking a reference trajectory varying batch-wisely, several latent variable based model predictive controllers have been proposed. In a concept drift condition where the internal dynamics of a batch change or an external factor causes a significant change in the process itself, however, maintaining a single latent variable model as in the conventional method can deteriorate the control performance. To solve this problem, we propose to combine an ensemble learning method with the latent variable model predictive control. Conventional on-line weighted ensemble learning is modified to apply to the multiple and iterative batch trajectory tracking problems. By using a total pool of local functions and historical data set, which evolves through the process and learning weights by ensemble algorithm, the detailed effects of concept drift on the process are reflected belter to the ensemble latent variable model than the conventional method. Multiple and iterative batch bioreactor system having arbitrarily different intermediate maintenance times and several concept drifts is simulated to verify the efficacy of the proposed method. Simulation results show that both predictive and control performances by the proposed method are improved compared to the ones of the conventional latent variable model predictive controller.