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Latent Variable Model Predictive Control (LV-MPC) for trajectory tracking in batch processes
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DOI：10.1016/j.jprocont.2010.01.007.png)
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Latent Variable Model Predictive Control (LV-MPC) algorithms are developed for trajectory tracking and disturbance rejection in batch processes. The algorithms are based on multi-phase PCA models developed using batch-wise unfolding of batch data arrays. Two LV-MPC formulations are presented, one based on optimization in the latent variable space and the other on direct optimization over a finite vector of future manipulated variables. In both cases prediction of the future trajectories is accomplished using statistical latent variable missing data imputation methods. The proposed LV-MPCs can handle constraints. Furthermore, due to the batch-wise unfolding approach selected in the modeling section, the nonlinear time-varying behavior of batch processes is captured by the linear LV models thereby yielding very simple and computationally fast nonlinear batch MPC. The methods are tested and compared on a simulated batch reactor case study. (C) 2010 Elsevier Ltd. All rights reserved.
Keyword:
Batch process
Latent variable models
MPC
PCA
Missing data imputation
Trajectory tracking
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Improved batch process monitoring and quality prediction based on multiphase statistical analysis
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