GPU-Accelerated Stochastic Predictive Control of Drinking Water Networks

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

doi:10.1109/tcst.2017.2677741

Cited by 34 publications

(44 citation statements)

References 52 publications

(79 reference statements)

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“…In particular, such a parallelization -assuming that full parallelization is supported by the hardware -equalizes the complexity of the scenario-based Riccati recursion to that of a deterministic one. A detailed exposition of the details of this procedure is available in [22,9].…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we present a software for the fast and efficient solution of such problems harnessing the immense computational capabilities of graphics processing units (GPU) building up on our previous work [9,21,22].…”

Section: State Of the Artmentioning

confidence: 99%

“…An SmpcController executes the accelerated proximal gradient algorithm to solve a stochastic optimal control problem at every time instant and compute the control actions to be applied to the water network. Certain functionality is delegated to Engine -a collection of utility methodswhich precomputes certain quantities which are associated with the Riccati-type recursion (details can be found in [9]) for the computation of the dual gradient and manages the associated memory on GPU.…”

Section: Controllerconfigjsonmentioning

confidence: 99%

See 2 more Smart Citations

Uncertainty-aware demand management of water distribution networks in deregulated energy markets

Sopasakis¹,

Sampathirao²,

Bemporad³

et al. 2018

Environmental Modelling & Software

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We present an open-source solution for the operational control of drinking water distribution networks which accounts for the inherent uncertainty in water demand and electricity prices in the day-ahead market of a volatile deregulated economy. As increasingly more energy markets adopt this trading scheme, the operation of drinking water networks requires uncertainty-aware control approaches that mitigate the effect of volatility and result in an economic and safe operation of the network that meets the consumers' need for uninterrupted water supply. We propose the use of scenario-based stochastic model predictive control: an advanced control methodology which comes at a considerable computation cost which is overcome by harnessing the parallelization capabilities of graphics processing units (GPUs) and using a massively parallelizable algorithm based on the accelerated proximal gradient method.

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Section: Methodsmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: Controllerconfigjsonmentioning

confidence: 99%

See 1 more Smart Citation

Uncertainty-aware demand management of water distribution networks in deregulated energy markets

Sopasakis¹,

Sampathirao²,

Bemporad³

et al. 2018

Environmental Modelling & Software

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“…As a result they scale gracefully with the problem dimension and they are applicable to large-scale and huge-scale problems as they are amenble to parallelization (such as on graphics processing units) [2]. Because of these advantages, they have attracted remarkable attention in signal processing [3]- [5].…”

Section: A Background and Motivationmentioning

confidence: 99%

A primal-dual line search method and applications in image processing

Sopasakis

Themelis

Suykens

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Operator splitting algorithms are enjoying wide acceptance in signal processing for their ability to solve generic convex optimization problems exploiting their structure and leading to efficient implementations. These algorithms are instances of the Krasnosel'skiȋ-Mann scheme for finding fixed points of averaged operators. Despite their popularity, however, operator splitting algorithms are sensitive to ill conditioning and often converge slowly. In this paper we propose a line search primaldual method to accelerate and robustify the Chambolle-Pock algorithm based on SuperMann: a recent extension of the Krasnosel'skiȋ-Mann algorithmic scheme. We discuss the convergence properties of this new algorithm and we showcase its strengths on the problem of image denoising using the anisotropic total variation regularization.

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“…The main benefit of stochastic model predictive control is that it can make predictions with the full use of the statistical information of disturbance. Stochastic model predictive control (SMPC) has been used in many fields, such as drinking water networks [7], microgrids [8,9], electric vehicles [10], and so forth [11,12]. Furthermore, the scenario-based stochastic model predictive control has rarely been applied to solve the optimal control problem of wind turbines under random wind speed [13].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Hybrid Estimator Based Fault Detection and Isolation for Wind Energy Conversion Systems with Unknown Fault Inputs

et al. 2018

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Abstract:In recent years, the wind energy conversion system (WECS) has been becoming the vital system to acquire wind energy. However, the high failure rate of WECSs leads to expensive costs for the maintenance of WECSs. Therefore, how to detect and isolate the faults of WECSs with stochastic dynamics is the pressing issue in the literature. This paper proposes a novel comprehensive fault detection and isolation (FDI) method for WECSs. First, a stochastic model predictive control (SMPC) controller is studied to construct the closed-loop system of the WECS. This controller is based on the Markov-jump linear model, which could precisely establish the stochastic dynamics of the WECS. Meanwhile, the SMPC controller has satisfied control performance for the WECS. Second, based on the closed-loop system with SMPC, the stochastic hybrid estimator (SHE) is designed to estimate the continuous and discrete states of the WECS. Compared with the existing estimators for WECSs, the proposed estimator is more suitable for WECSs since it considers both the continuous and discrete states of WECSs. In addition, the proposed estimator is robust to the fault input. Finally, with the proposed estimator, the comprehensive FDI method is given to detect and isolate the actuators' faults of the WECS. Both the system status and the actuators' faults can be detected by the FDI method and it can effectively quantify the actuators' fault by the fault residuals. The simulation results suggest that the SHE could effectively estimate the hybrid states of the WECS, and the proposed FDI method gives satisfied fault detection performance for the actuators of the WECS.

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GPU-Accelerated Stochastic Predictive Control of Drinking Water Networks

Cited by 34 publications

References 52 publications

Uncertainty-aware demand management of water distribution networks in deregulated energy markets

Uncertainty-aware demand management of water distribution networks in deregulated energy markets

A primal-dual line search method and applications in image processing

Stochastic Hybrid Estimator Based Fault Detection and Isolation for Wind Energy Conversion Systems with Unknown Fault Inputs

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