On the Tuning of Nonlinear Model Predictive Control Algorithms

Nonlinear Model Predictive Controllers determine appropriate control actions by solving an on-line optimization problem. A nonlinear process model is utilized for online prediction, making such algorithms particularly appropriate for the control of chemical reactors. The algorithm presented in this paper incorporates an Extended Kalman Filter, which allows operations around unstable steady-state points. The paper proposes a formalization of the procedure for tuning the several parameters of the control algorithm. This is accomplished by specifying time-domain performance criteria and using an interactive multi-objective optimization package off-line to determine parameter values that satisfy these criteria. A reactor example is used to demonstrate the effectiveness of the proposed on-line algorithm and off-line tuning procedure.