An improved multi-stage nonlinear model predictive control with application to semi-batch polymerization

Nonlinear model predictive control (NMPC) has been widely applied in the chemical industry for its performance of dealing with the multiple input multiple output problem (MIMO) and handling constraints. However, the performance of NMPC would be affected by the accuracy of the model. The NMPC controller has to be robust to uncertainties in the model. In this paper, the scenario-tree based on multi-stage NMPC approach has been applied to the semi-batch polymerization reactor. In this approach, in order to ensure the reasonableness of the uncertain variables and scenario tree number, a Monte Carlo-based second-order nonlinear model and K-means cluster algorithm have been proposed. The weights of each scenario branches are also considered into variable. The simulation results show that the performance of the improved method is better and the variable weights has a good ability of improving the performance of controller.