| Title | Stability of Gaussian Process Learning Based Output Feedback Model Predictive Control |
|---|---|
| Authors | Maiworm, Michael , Limón, Daniel , MANZANO CRESPO, JOSÉ MARÍA, Findeisen, Rolf |
| External publication | Si |
| Means | IFAC PAPERSONLINE |
| Scope | Conference Paper |
| Nature | Científica |
| SJR Quartile | 3 |
| SJR Impact | 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 |
| Publication date | 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 |
| Keywords | 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 |
| Universidad Loyola members |