| Title | Robust Data-Based Model Predictive Control for Nonlinear Constrained Systems |
|---|---|
| Authors | MANZANO CRESPO, JOSÉ MARÍA, Limón, D. , Muñoz de la Peña, D. , Calliess, J. |
| 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-s2.0-85056884220&doi=10.1016%2fj.ifacol.2018.11.039&partnerID=40&md5=f062ff78346b3065f79b06cb56cf1c88 |
| Publication date | 01/01/2018 |
| ISI | 000451092800078 |
| Scopus Id | 2-s2.0-85056884220 |
| DOI | 10.1016/j.ifacol.2018.11.039 |
| Abstract | This paper presents stabilizing Model Predictive Controllers (MPC) to be applied to black-box systems subject to constraints in the inputs and the outputs. The prediction model of the controllers is inferred from experimental data of the inputs and outputs of the plant. Using a nonparametric machine learning technique called SPKI, the estimated (possibly nonlinear) model function is provided. Based on this, a predictive controller with stability guaranteed by design is proposed. Robust stability and recursive feasibility is ensured by using tightened constraints in the optimisation problem but without adding a terminal constraint on the optimisation problem. The proposed predictive controller has been validated in a simulation case study. © 2018 |
| Keywords | Constrained optimization; Controllers; Learning systems; Machine learning; Model predictive control; Predictive control systems; Constrained systems; Data based controls; Input-to-state stability; Machine learning techniques; Model predictive controllers; Optimisation problems; Predictive controller; Terminal constraint; Predictive analytics |
| Universidad Loyola members |