GPU-Accelerated Stochastic Predictive Control of Drinking Water Networks

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

doi:10.1109/TCST.2017.2677741

GPU-Accelerated Stochastic Predictive Control of Drinking Water Networks

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

School of Electronics, Electrical Engineering and Computer Science

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

129 Downloads (Pure)

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.

Original language	English
Pages (from-to)	551-562
Number of pages	12
Journal	IEEE Transactions on Control Systems Technology
Volume	26
Issue number	2
DOIs	https://doi.org/10.1109/TCST.2017.2677741
Publication status	Published - 21 Mar 2017

Keywords

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

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10.1109/TCST.2017.2677741Licence: Unspecified

GPU-accelerated stochastic predictive control of drinking water networks
Copyright 2017 IEEE. This work is made available online in accordance with the publisher’s policies. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 0.98 MBLicence: Unspecified

Pantelis Sopasakis
- P.Sopasakisqub.acuk
- School of Electronics, Electrical Engineering and Computer Science - Lecturer
- Energy Power and Intelligent Control
Person: Academic

Cite this

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title = "GPU-Accelerated Stochastic Predictive Control of Drinking Water Networks",

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.",

keywords = "GPGPU, Stochastic Model Predictive Control, Model predictive control, Drinking Water Networks, Numerical Optimization, Convex Optimization",

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AU - Bemporad, Alberto

AU - Patrinos, Panagiotis

PY - 2017/3/21

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KW - Stochastic Model Predictive Control

KW - Model predictive control

KW - Drinking Water Networks

KW - Numerical Optimization

KW - Convex Optimization

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GPU-Accelerated Stochastic Predictive Control of Drinking Water Networks

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Pantelis Sopasakis

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