Robust disturbance modeling for model predictive control with application to multivariable ill-conditioned processes |
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Authors: | Gabriele Pannocchia |
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Affiliation: | Department of Chemical Engineering, University of Pisa, Via Diotisalvi, 2-56126, Pisa, Italy |
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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. |
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Keywords: | Disturbance modeling MPC Ill-conditioned systems |
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