Latent Variable Model Predictive Control (LV-MPC) for trajectory tracking in batch processes |
| |
Authors: | Masoud Golshan John F MacGregor Mark-John Bruwer Prashant Mhaskar |
| |
Affiliation: | 1. Department of Chemical Engineering, McMaster University, Hamilton, ON, Canada L8S 4L7;2. ProSensus Inc., Hamilton, ON, Canada L8P 0A1 |
| |
Abstract: | 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. |
| |
Keywords: | |
本文献已被 ScienceDirect 等数据库收录! |
|