URI | http://purl.tuc.gr/dl/dias/7E883FC5-F99D-40A3-AA63-998594436597 | - |
Αναγνωριστικό | https://doi.org/10.1002/aic.18172 | - |
Αναγνωριστικό | https://aiche.onlinelibrary.wiley.com/doi/10.1002/aic.18172 | - |
Γλώσσα | en | - |
Μέγεθος | 10 pages | en |
Τίτλος | Explicit model predictive control through robust optimization | en |
Δημιουργός | Pappas Iosif | en |
Δημιουργός | Diangelakis Nikolaos | en |
Δημιουργός | Διαγγελακης Νικολαος | el |
Δημιουργός | Pistikopoulos Stratos | en |
Εκδότης | Wiley | en |
Περίληψη | A strategy that calculates an explicit state feedback policy to regulate constrained uncertain discrete-time uncertain linear systems is presented. We consider uncertain processes, affected by box-bounded multiplicative uncertainty as well as bounded additive uncertainty with linear state and inputs constraints. The proposed method includes (i) the calculation of a terminal set constraint and (ii) the robust reformulation of state constraints in the prediction horizon. These features allow the derivation of the desired policy by solving a single multiparametric quadratic programming problem that guarantees feasible operation in the presence of uncertainty. Additionally, we employ variable and constraint elimination approaches to enhance the computational performance of the strategy. We demonstrate the steps and benefits of these developments with a numerical example and a chemical engineering case study. | en |
Τύπος | Peer-Reviewed Journal Publication | en |
Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
Άδεια Χρήσης | http://creativecommons.org/licenses/by-nc-nd/4.0/ | en |
Ημερομηνία | 2025-07-29 | - |
Ημερομηνία Δημοσίευσης | 2023 | - |
Θεματική Κατηγορία | Explicit model predictive control | en |
Θεματική Κατηγορία | Multiparametric programming | en |
Θεματική Κατηγορία | Optimization under uncertainty | en |
Θεματική Κατηγορία | Robust optimization | en |
Βιβλιογραφική Αναφορά | I. Pappas, N. A. Diangelakis and E. N. Pistikopoulos, “Explicit model predictive control through robust optimization,” AIChE J., vol. 69, no. 10, Oct. 2023, doi: 10.1002/aic.18172. | en |