GPU-Accelerated Stochastic Predictive Control of Drinking Water Networks

Ajay Kumar Sampathirao, Pantelis Sopasakis, Alberto Bemporad, Panagiotis Patrinos

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)
145 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)551-562
Number of pages12
JournalIEEE Transactions on Control Systems Technology
Issue number2
Publication statusPublished - 21 Mar 2017


  • Stochastic Model Predictive Control
  • Model predictive control
  • Drinking Water Networks
  • Numerical Optimization
  • Convex Optimization


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