Name
Abstract | ||
|---|---|---|
Despite the proven advantages of scenario-based stochastic model predictive control for the operational control of water networks, its applicability is limited by its considerable computational footprint. In this paper, we fully exploit the structure of these problems and solve them using a proximal gradient algorithm parallelizing the involved operations. The proposed methodology is applied and validated on a case study: the water network of the city of Barcelona. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TCST.2017.2677741 | IEEE Trans. Contr. Sys. Techn. |
Keywords | Field | DocType |
Stochastic processes,Mathematical model,Uncertainty,Predictive control,Optimal control,Cost function,Signal processing algorithms | Mathematical optimization,Computer science,Model predictive control,Exploit,Operational control,Footprint,Stochastic model predictive control | Journal |
Volume | Issue | ISSN |
26 | 2 | 1063-6536 |
Citations | PageRank | References |
3 | 0.40 | 24 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ajay K. Sampathirao | 1 | 3 | 0.40 |
| Pantelis Sopasakis | 2 | 58 | 11.54 |
| Alberto Bemporad | 3 | 4353 | 568.62 |
| Panagiotis Patrinos | 4 | 268 | 31.71 |