Adaptive Predictive Control Algorithm for Batch Processes: Application to a Rotational Molding Process

The present manuscript addresses the problem of handling process nonlinearity in batch process operations and control via a re-identification-based subspace identification approach deployed within a model predictive control (MPC) framework. In contrast to existing re-identification algorithms for continuous and batch processes, where all of the recent and past experimental data is chosen to re-identify the model, the proposed approach is designed to use the most appropriate subset of the data. In particular, the data for re-identification is determined by first determining the equivalent of a "locator" index from the training data set, and only using the portion of training batches from the locator to batch termination. The idea is to try and build the model using data pertaining to the state-space region that the system is presently passing through. The proposed approach is implemented on a rotational molding lab-scale setup coordinated via an existing model monitoring technique deployed within the MPC, which detects the model mismatch and triggers the re-identification algorithm. Validation data sets are first used to demonstrate the improved model resulting from the proposed re-identification approach, followed by experimental results demonstrating improved closed-loop performance.