Author/Authors :
Jesus Flores-Cerrillo and John F. MacGregor، نويسنده ,
Title Of Article :
Latent variable MPC for trajectory tracking in batch processes
Latin Abstract :
A novel multivariate empirical model predictive control strategy (LV-MPC) for trajectory tracking and disturbance rejection for
batch processes is presented. The strategy is based on dynamic principal component analysis (PCA) models of the batch process. The
solution to the control problem is computed in the low dimensional latent variable space of the PCA model. The trajectories of all
variables over the future horizon are then computed from the latent variable solution of the controller. The excellent control performance
and the modest closed-loop data requirements for identification are illustrated for the temperature tracking in simulations
of an emulsion polymerization process, an exothermic chemical reaction system and for MIMO temperature and pressure tracking
in a nylon polymerization autoclave.
NaturalLanguageKeyword :
Principal component analysis , model based control , Batch processes
JournalTitle :
Studia Iranica
