15th Triennial World Congress of the International Federation of Automatic Control
  Barcelona, 21–26 July 2002 
APPLICATION OF MODEL REDUCTION FOR MODEL PREDICTIVE CONTROL
Juergen Hahn1,3, Uwe Kruger2, and Thomas F. Edgar1
1 Department of Chemical Engineering
University of Texas at Austin
Austin, TX 78712-1062 U.S.A.
2 Intelligent Systems & Control Group
Queen’s University of Belfast
Belfast BT9 5AH United Kingdom
3 Lehrstuhl für Prozesstechnik
RWTH Aachen D-52056 Aachen Germany
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.
Keywords: Model-based control, Model reduction, Nonlinear systems, Controllability, Observability, System analysis
E-mail: hahn@che.utexas.edu
Session slot T-Th-E11: Nonlinear Model Predictive Control/Area code 7a : Chemical Process Control