Application of Model Reduction for Model Predictive Control

In this paper model reduction methods are used to obtain a nonlinear process model for designing a model predictive controller (MPC). The corresponding controller and its closed-loop response is then compared with controllers that are determined from the original model and a linearized version of this model. The reduced dimensional nonlinear MPC controller performs almost as well as the nonlinear MPC controller that is based on the original model and considerably better than the linear MPC controller.