Nonlinear model predictive control of an industrial four-stage evaporator system via simulation

Abstract

Issues related to applicability of model predictive control (MPC) to the nonlinear and integrating processes are addressed in this study. An industrial four-stage evaporator system is taken as an exemplary process, and two different models of this system are used as process and its prediction model in the controller. Unlike the past studies, where the necessity of stabilization is advocated prior to MPC implementation on an open-loop unstable process, nonlinear MPC without any pre-stabilization is successfully achieved by using process state variables for initialization, suitable prediction horizon and sampling period. However, steady state offset was observed. A useful offset removal technique is proposed and implemented successfully. Performance of NMPC is compared with that of decentralized controllers for the evaporator system, and the results show that both can provide comparable control. © 2002 Elsevier Science B.V. All rights reserved.