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. (C) 2003 Elsevier Ltd. All rights reserved.
|Autori interni:||PANNOCCHIA, GABRIELE|
|Titolo:||Robust Disturbance Modeling for Model Predictive Control with Application to Multivariable Ill-conditioned Processes|
|Anno del prodotto:||2003|
|Digital Object Identifier (DOI):||10.1016/S0959-1524(03)00134-8|
|Appare nelle tipologie:||1.1 Articolo in rivista|