Stable Model Predictive Control for Integrating Systems with Optimizing Targets

This paper concerns the development of a stable model predictive controller (MPC) to be integrated with real time optimization (RTO) in the control structure of a process system with stable and integrating outputs. The real time process optimizer produces optimal targets for the system inputs and/or outputs that should be dynamically implemented by the MPC controller. This paper is based on a previous work (Comput. Chem. Eng. 2005, 29, 1089) where a nominally stable MPC was proposed for systems with the conventional control approach where only the outputs have set points. This work is also based on the work of Gonzalez et al. (J. Process Control 2009, 19, 110) where the zone control of stable systems is studied. The new controller is obtained by defining an extended control objective that includes input targets and zone control for the outputs. Additional decision variables are also defined to increase the set of feasible solutions to the control problem. The hard constraints resulting from the cancellation of the integrating modes at the end of the control horizon are softened, and the resulting control problem is made feasible to a large class of unknown disturbances and changes of the optimizing targets. The methods are illustrated with the simulated application of the proposed approaches to a distillation column of the oil refining industry.