| Titre : | Nonlinear model predictive control : a self - adaptive approach (2010) |
| Auteurs : | Ivan Dones, Auteur ; Flavio Manenti, Auteur ; Heinz A. Preisig, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Industrial & engineering chemistry research (Vol. 49 N° 10, Mai 2010) |
| Article en page(s) : | pp. 4782–4791 |
| Note générale : | Industrial chemistry |
| Langues : | Anglais |
| Tags : | Dynamic models Predictive Control |
| Résumé : |
Model predictive control (MPC) is an online application based on dynamic models. Its application faces two major obstacles: (i) computational constraints and (ii) the need to accurately simulate the process by a model that properly predicts how the plant will behave in the future.
Implementation of MPC is not always possible in large-scale or industrial applications due to the computational complexity of MPC and to the dimensionality of the models. To facilitate MPC implementations, this paper proposes a self-adaptive approach based on simplified (or reduced-order) nonlinear models. The proposed methodology yields an MPC that adjusts the dimension of the model according to both the current process conditions and the control objectives. The self-adaptive approach is described and validated on an industrial case study, a C4-splitter. |
| ISSN : | 0888-5885 |
| En ligne : | http://pubs.acs.org/doi/abs/10.1021/ie901693w |
