Batch to batch optimal control based on multiinput multioutput adaptive hinging hyperplanes prediction and Kalman filter correction

A batch to batch optimal control strategy based on multiinput multioutput adaptive hinging hyperplanes (MIMO AHH) prediction and Kalman filter correction is proposed for the products quality control of the batch process. The model of AHH is one kind of piecewise linear models and is extended to the MIMO case in this article. The MIMO AHH is then used to develop the predictive model of the batch process. Due to the model-plant mismatch and unknown disturbances, the optimal control policy calculated based on the MIMO AHH predictive model may not be optimal when applied to the true process. The Kalman filter is then utilized to correct the predictions of the current batch by considering the information of former batches. The effectiveness of the proposed strategy is verified through the simulation of a styrene batch polymerization reactor.