Robust disturbance modeling for model predictive controlwi th application to multivariable ill-conditioned processes

In this paper the disturbance model,used by MPC algorithms to achieve offset-free control,is optimally designed to enhance the robustness of single-model predictive controllers. The proposed methodology requires the off-line solution of a min-max optimization problem in which the disturbance model is chosen to guarantee the best closed-loop performance in the worst case of plant in a given uncertainty region. Application to a well-known ill-conditioned distillation column is presented to show that,for ill-conditioned processes,the use of a disturbance model that adds the correction term to the process inputs guarantees a robust performance, while the disturbance model that adds the correction term to the process outputs (used by industrial MPC algorithms) does not.