| Título |
Online learning constrained model predictive controller based on double prediction |
| Autores |
MANZANO CRESPO, JOSÉ MARÍA, Munoz de la Pena, D. , Calliess, J. , Limon, D. |
| Publicación externa |
No |
| Medio |
Int. J. Robust Nonlinear Control |
| Alcance |
Article |
| Naturaleza |
Científica |
| Cuartil JCR |
1 |
| Cuartil SJR |
1 |
| Impacto JCR |
3.89700 |
| Impacto SJR |
1.55200 |
| Fecha de publicacion |
01/12/2021 |
| ISI |
000566563400001 |
| DOI |
10.1002/rnc.5124 |
| Abstract |
A data-based predictive controller is proposed, offering both robust stability guarantees and online learning capabilities. To merge these two properties in a single controller, a double-prediction approach is taken. On the one hand, a safe prediction is computed using Lipschitz interpolation on the basis of an offline identification dataset, which guarantees safety of the controlled system. On the other hand, the controller also benefits from the use of a second online learning-based prediction as measurements incrementally become available over time. Sufficient conditions for robust stability and constraint satisfaction are given. Illustrations of the approach are provided in a simulated case study. |
| Palabras clave |
data-based control; learning-based MPC; nonlinear MPC; robust control |
| Miembros de la Universidad Loyola |
|
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