| Título |
Input-to-state stable predictive control based on continuous projected kinky inference |
| Autores |
MANZANO CRESPO, JOSÉ MARÍA, Munoz de la Pena, David , Limon, Daniel |
| Publicación externa |
No |
| Medio |
Int. J. Robust Nonlinear Control |
| Alcance |
Article |
| Naturaleza |
Científica |
| Cuartil JCR |
1 |
| Cuartil SJR |
1 |
| Impacto JCR |
3.90000 |
| Impacto SJR |
1.40300 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144044501&doi=10.1002%2frnc.6525&partnerID=40&md5=c050853cf95ce1051bebbea9274fe041 |
| Fecha de publicacion |
13/12/2022 |
| ISI |
000898061600001 |
| Scopus Id |
2-s2.0-85144044501 |
| DOI |
10.1002/rnc.6525 |
| Abstract |
In this article, the authors propose a novel continuous projected kinky inference algorithm, which inherits the good properties of projected kinky inference in terms of prediction error bound and computational time while ensuring Lipschitz continuity. Based on this, a learning based MPC is presented which is demonstrated to be input-to-state stable by design. Illustrative examples are shown in a learning-based MPC framework. |
| Palabras clave |
inference algorithms; machine learning; predictive control; input-to-state stability; system identification |
| Miembros de la Universidad Loyola |
|
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