A method for control of controlled variables (CV1, CV2) in a model predictive controller, comprising the steps of: specifying a setpoint for one or more controlled variables (CV1, CV2); specifying economic functions driving the controlled variables (CV1, CV2); specifying relative prioritization between control objectives; determining steady state target values for the controlled variables (CV1, CV2); determining one or more best performance values for the controlled variable each best performance value corresponds to a most vicinity point to a user-provided value or value of economically highest profit/lowest cost that is possible within specified limits for the controlled variables (CV1, CV2); detecting a disturbance, said disturbance comprising one or more of plant model mismatch, model uncertainty, unmodeled perturbations, unanticipated perturbations, sensor noise, measurement noise, change in temperature, change in quality measurements, change in a control, change in a level of inventory, and change in a volume of inventory; determining one or more dynamic tuning weights; and computing a dynamic plan of actions for manipulated variables; characterized by adjusting the one or more best performance values by the minimum needed to make the dynamic optimization problem consistent with the dynamic tuning weights and steady state target values so that the dynamic control solution converges to the desired steady state; wherein the dynamic tuning weights correspond to cost contours around each best performance value, and are based, at least in part, on one or more user specifications related to a desired dynamic speed of response for all of the controlled variables (CV1, CV2); and creating dynamic control objective using the adjusted one or more best performance values. specifying a setpoint for one or more controlled variables (CV1, CV2); specifying economic functions driving the controlled variables (CV1, CV2); specifying relative prioritization between control objectives; determining steady state target values for the controlled variables (CV1, CV2); determining one or more best performance values for the controlled variable each best performance value corresponds to a most vicinity point to a user-provided value or value of economically highest profit/lowest cost that is possible within specified limits for the controlled variables (CV1, CV2); detecting a disturbance, said disturbance comprising one or more of plant model mismatch, model uncertainty, unmodeled perturbations, unanticipated perturbations, sensor noise, measurement noise, change in temperature, change in quality measurements, change in a control, change in a level of inventory, and change in a volume of inventory; determining one or more dynamic tuning weights; and computing a dynamic plan of actions for manipulated variables; characterized by adjusting the one or more best performance values by the minimum needed to make the dynamic optimization problem consistent with the dynamic tuning weights and steady state target values so that the dynamic control solution converges to the desired steady state; wherein the dynamic tuning weights correspond to cost contours around each best performance value, and are based, at least in part, on one or more user specifications related to a desired dynamic speed of response for all of the controlled variables (CV1, CV2); and creating dynamic control objective using the adjusted one or more best performance values. <EOS>