POST-OPTIMALITY ANALYSIS OF STEADY-STATE LINEAR TARGET CALCULATION IN MODEL PREDICTIVE CONTROL

Abstract

Model predictive control (MPC) is an advanced process control strategy that is usually separated into two levels; steady-state target calculation and dynamic optimization. The existence of uncertainty in model parameters of target calculation can significantally affect the overall performance of the controller. Methods have been proposed to deal with model uncertainty using robust optimization. In this study, a new approach using post-optimality analysis is proposed to study the effect of uncertainty or variation in model parameters on the optimal solution of linear target calculation. This approach can compute the stability limits, for simultaneous variation in objective function coefficients or process limitations, before the optimal target or basis are changed.