| Título |
Robust learning-based MPC for nonlinear constrained systems |
| Autores |
MANZANO CRESPO, JOSÉ MARÍA, LIMÓN MARRUEDO, DANIEL , MUÑOZ DE LA PEÑA SEQUEDO, DAVID , Calliess, Jan |
| Publicación externa |
Si |
| Medio |
Automatica |
| Alcance |
Article |
| Naturaleza |
Científica |
| Cuartil JCR |
1 |
| Cuartil SJR |
1 |
| Impacto JCR |
5.944 |
| Impacto SJR |
3.132 |
| Web |
https://www.sciencedirect.com/science/article/pii/s0005109820301461 |
| Fecha de publicacion |
01/01/2020 |
| ISI |
000534593100004 |
| DOI |
10.1016/j.automatica.2020.108948 |
| Abstract |
This paper presents a robust learning-based predictive control strategy for nonlinear systems subject to both input and output constraints, under the assumption that the model function is not known a priori and only input-output data are available. The proposed controller is obtained using a nonparametric machine learning technique to estimate a prediction model. Based on this prediction model, a novel stabilizing robust predictive controller without terminal constraint is proposed. The design procedure is purely based on data and avoids the estimation of any robust invariant set, which is in general a hard task. The resulting controller has been validated in a simulated case study. (C) 2020 Elsevier Ltd. All rights reserved. |
| Palabras clave |
Predictive control; Learning control; Robust stability; Nonlinear systems; Lyapunov stability |
| Miembros de la Universidad Loyola |
|
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