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This paper presents a systematic method for the selection of the ModelPredictive Control (MPC) stage cost. We match the MPC feedback law to aproportional-integral (PI) controller, which we efficiently tune byhigh-performance Monte Carlo (MC) simulation. The PI tuning offers a wide rangeof tuning possibilities that is then inherited by the MPC design. The MCsimulation tuning of the PI controller is based on the minimization of twodifferent objectives; 1) the 2-norm tracking error, and 2) a bi-objectiveconsisting of the 2-norm tracking error and a 2-norm input rate of movementpenalty. We apply the method to design MPC for an exothermic chemical reactionconducted in an adiabatic continuous stirred tank reactor (CSTR). The processis of interest as the nonlinear dynamics result in a desired operating pointvery close to a constraint. Our MPC design includes stage costs automaticallydesigned to match the tuned PI controllers, hard input constraints, and a softoutput constraint. Stochastic simulation results show that both the PIcontroller and the MPC can track the desired operating point. However, the MPCshows reduced output constraint violation compared to the PI controller. Assuch, the MPC design method successfully combines the efficient tuning of thePI controller with the constraint handling properties of MPC.

Model Predictive Control Tuning by Monte Carlo Simulation and Controller Matching