| Título | Output feedback MPC based on smoothed projected kinky inference |
|---|---|
| Autores | MANZANO CRESPO, JOSÉ MARÍA, Limon, Daniel , Munoz de la Pena, David , Calliess, Jan Peter |
| Publicación externa | No |
| Medio | IET Contr. Theory Appl. |
| Alcance | Article |
| Naturaleza | Científica |
| Cuartil JCR | 1 |
| Cuartil SJR | 1 |
| Impacto JCR | 3.343 |
| Impacto SJR | 1.358 |
| Fecha de publicacion | 16/04/2019 |
| 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. |
| Miembros de la Universidad Loyola |