Post-Optimality Approach to Prevent Cycling in Linear MPC Target Calculation

Abstract

Model predictive control (MPC) is an advanced process control strategy which is widely applied in many industries and it is often implemented in two levels; steady-state target calculation and dynamic optimization. Target calculation level computes the optimal steady-state targets of the process and sends them to dynamic optimization for implementation. The existence of uncertainty or variation in the parameters of the target calculation layer can lead to unstable or cyclic targets which can significatively affect the overall performance of the controller. Many 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 problem parameters on the optimal solution of linear target calculation. This approach can compute the stability limits, for simultaneous variations in objective function coefficients or process limitations, before the optimal target or basis are changed. It aims to improve the closed-loop robustness of MPC by assessing the required accuracy of parameter estimates and variation limits before the calculated target start cycling. The proposed approach offers a design-phase test for targets stability.