Tracking Control for Batch Processes through Integrating Batch-to-Batch Iterative Learning Control and within-Batch On-Line Control

Abstract

An integrated strategy for product quality trajectory tracking control in batch processes is proposed by combing batch-to-batch iterative learning control (ILC) with on-line shrinking horizon model predictive control (SHMPC) within a batch. Under batch-to-batch ILC based on a linear time varying perturbation model, the performance of future batch runs can be enhanced, and the convergence of batch-wise tracking error is guaranteed. But ILC cannot affect the performance of current batch run, and the correction to control policy is not made until the next batch run. On the other hand, on-line SHMPC within a batch can reduce the effects of disturbances and improve the performance of the current batch run. By combing two methods for tracking trajectories, the integrated control strategy can complement both methods to obtain good performance because on-line SHMPC can respond to disturbances immediately and batch-to-batch ILC can correct bias left uncorrected by the on-line controller. The proposed strategy is illustrated on a simulated batch polymerization process. The results demonstrate that the performance of tracking product qualities can be improved quite well under the integrated control strategy than under the simple batch-to-batch ILC, especially when disturbances exist.