Constrained batch-to-batch optimal control for batch process based on kernel principal component regression model

A batch-to-batch optimal control method is presented in the paper for batch process control under input constraints. Generally it is very difficult to acquire an accurate mechanistic model for a batch process. Kernel principal component regression (KPCR) technique is a nonlinear modeling method that has a better ability to deal with nonlinear data. A KPCR model based batch-to-batch optimal control strategy is developed for end-point quality control of batch process. On the basis of the linearized KPCR model, the control input is obtained by minimising a quadratic objective function concerning the end-point product quality. To ensure the safe, smooth operations of batch process, certain input constraints are taken into considered. Furthermore, the KPCR model is updated from batch-to-batch to overcome the process variations or disturbances. Numerical simulation shows that the method can improve the end-point product qualities from batch to batch under input constraints. Based on updated KPCR model, the approach has better adaptability for process variations or disturbances than the policy based on updated PCR model has.