| Título | Stability of Gaussian Process Learning Based Output Feedback Model Predictive Control |
|---|---|
| Autores | Maiworm, Michael , Limón, Daniel , MANZANO CRESPO, JOSÉ MARÍA, Findeisen, Rolf |
| Publicación externa | Si |
| Medio | IFAC PAPERSONLINE |
| Alcance | Conference Paper |
| Naturaleza | Científica |
| Cuartil SJR | 3 |
| Impacto SJR | 0.298 |
| Web | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056821769&doi=10.1016%2fj.ifacol.2018.11.047&partnerID=40&md5=0e7bbfb5c9186a5b1e8ebd31189a0b0a |
| Fecha de publicacion | 01/01/2018 |
| ISI | 000451092800070 |
| Scopus Id | 2-s2.0-85056821769 |
| DOI | 10.1016/j.ifacol.2018.11.047 |
| Abstract | We present an output feedback nonlinear model predictive control approach that uses a Gaussian process model for prediction. We show nominal stability assuming that the Gaussian process model is able to represent the real process and establish input-to-state stability assuming a bounded error between the real process and the Gaussian model approximation. These results are achieved using a predictive control formulation without terminal region. The approach is illustrated using a continuous stirred-tank reactor benchmark problem. © 2018 |
| Palabras clave | Convergence of numerical methods; Gaussian distribution; Gaussian noise (electronic); Model predictive control; Predictive control systems; Stability; Gaussian Processes; learning; Output feedback; Predictive control; robust; Feedback |
| Miembros de la Universidad Loyola |