| Title |
Output Feedback MPC based on Smoothed Projected Kinky Inference |
| Authors |
MANZANO CRESPO, JOSÉ MARÍA, LIMÓN MARRUEDO, DANIEL , MUÑOZ DE LA PEÑA SEQUEDO, DAVID , Calliess, Jan Peter |
| External publication |
Si |
| Means |
IET Contr. Theory Appl. |
| Scope |
Article |
| Nature |
Científica |
| JCR Quartile |
1 |
| SJR Quartile |
1 |
| JCR Impact |
3.34300 |
| SJR Impact |
1.35800 |
| Publication date |
01/01/2019 |
| ISI |
000464580000007 |
| DOI |
10.1049/iet-cta.2018.5522 |
| Abstract |
In this study, the authors propose a stabilising data-based model predictive controller for systems subject to constraints in which the prediction model is inferred from experimental data of the plant using a machine learning technique. The inference method is a modification of the kinky inference tailored for model predictive control. In particular, the modified method has a lower computational effort and provides smoother predictions than the original method. The controller formulation considers soft constraints in the outputs, hard constraints in the inputs and guarantees closed-loop robust stability as well as performance by means of the use of different control and prediction horizons and a weighted terminal cost. Under the assumption that the model of the system is Holder continuous, they prove that the closed-loop system is input-to-state stable with respect to the estimation errors. The results are demonstrated in a case study of a continuously stirred-tank reactor. |
| Universidad Loyola members |
|
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