Extending LP Optimizer Functionality for Model Predictive Control

The subject of this paper is linear programming (LP) optimizer application with Model Predictive Control (MPC). This extremely successful merger of two major control technologies is enabled as a consequence of the MPC feature of providing a prediction of the process outputs up to steady state, thus creating the required conditions for optimizer operation. However, the standard LP algorithm which finds a solution only within acceptable limits, does not perform properly when some of the predicted process outputs are out of limits. On the other hand, the optimizer applied with the MPC controller must always find a solution and thus there is a need to extend the original optimization formulation. This paper presents robust and reliable ways of handling optimized process outputs that are out of the limits. The technique is based on the priority structure, penalizing slack variables, and redefining the constraint model. In addition LP functionality is extended by defining one- or two-sided ranges around the control variables set points and preferred settling values for the manipulating variables. This technique has been implemented in an industrial control system and will be presented interactively by simulating the optimization and control of a distillation column.