GPU-accelerated stochastic predictive control of drinking water networks
IMT Institute for Advanced Studies Lucca, Piazza San Francesco 19, 55100 Lucca, Italy
arXiv:1604.01074 [math.OC], (4 Apr 2016)
@article{sampathirao2016gpuaccelerated,
title={GPU-accelerated stochastic predictive control of drinking water networks},
author={Sampathirao, Ajay K. and Sopasakis, Pantelis and Bemporad, Alberto and Patrinos, Panagiotis},
year={2016},
month={apr},
archivePrefix={"arXiv"},
primaryClass={math.OC}
}
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.
April 6, 2016 by hgpu
