2016
IMAACA 2016 Proceeding
A LV-MPC strategy for trajectory tracking in batch processes
Authors: J. L. Godoy, J. L. Marchetti, J. R. Vega
Abstract
A constrained latent variable model predictive control (LV-MPC) technique is proposed for trajectory tracking in batch processes. The controller allows the incorporation of constraints on the process variables and is designed on the basis of multi-way principal component analysis (MPCA) of a batch data array that is rearranged by means of a regularized batch-wise unfolding approach. The LV-MPC formulation includes a novel prediction stage and is offset-free. The controller parameters are calculated on the basis of the identified latent model. The main advantages of LV- MPC over 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 procedure is simple. The proposed constrained LV-MPC technique is numerically tested through a benchmark process that has been used in previous LV- MPC formulations.
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