A LV-MPC STRATEGY FOR TRAJECTORY TRACKING IN BATCH PROCESSES

J.R. VEGA; J. L. GODOY; J. L. MARCHETTI

Lamaca

Conferencia; IMAACA 2016 - 9th International Conference on Integrated Modeling and Analysis in Applied Control and Automation (part of the 13th International Mediterranean and Latin American Modelling Multiconference - I3M 2016); 2016

Aconstrained latent variable model predictive control (LV-MPC) technique isproposed for trajectory tracking in batch processes. The controller allows theincorporation of constraints on the process variables and is designed on thebasis of multi-way principal component analysis (MPCA) of a batch data array thatis rearranged by means of a regularized batch-wise unfolding approach. TheLV-MPC formulation includes a novel prediction stage and is offset-free. Thecontroller parameters are calculated on the basis of the identified latentmodel. The main advantages of LV-MPCover other MPC techniques are:(i) a relatively small dataset is required (e.g., around 10-20 batch runs),(ii) nonlinear processes can efficiently be handled algebraically through MPCA models, and (iii) the tuning procedureis simple. The proposed constrained LV-MPC technique is numerically testedthrough a benchmark process that has been used in previous LV-MPC formulations.