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.
Robust Disturbance Modeling for Model Predictive Control with Application to Multivariable Ill-conditioned Processes
PANNOCCHIA, GABRIELE
2003-01-01
Abstract
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.File | Dimensione | Formato | |
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