Paper Info
Title
Model Predictive Control using linearized Radial Basis Function Neural Models for Water Distribution Networks
Abstract
It is often the case that the main operation cost of Water Distribution Networks (WDN) is due to pump actuation. Although advanced control schemes are widely available, most water utilities still use on/off control. In this study, water networks with multiple flow inlets, storage tanks and several consumers are considered. Under mild assumptions on the consumption and hydraulic resistance of pipes, a reduced model is proposed with the aim of building its mathematical structure into a data-driven control design. For identification purposes we use Radial Basis Function Neural Networks (RBFNN). We show that linearization of the identified RBFNN model in the two peak points of the daily flow demand results in a control model with good prediction accuracy. Subsequently, this time-varying model is utilized in a standard economic Model Predictive Control (MPC) scheme, considering pump flows as inputs. A numerical case study on an EPANET4 model and experimental results on a test setup demonstrate the proposed method.
Year
DOI
Venue
2019
10.1109/CCTA.2019.8920627
2019 IEEE Conference on Control Technology and Applications (CCTA)
Keywords
Field
DocType
hydraulic resistance,pipes,EPANET4 model,MPC,storage tanks,WDN,linearized radial basis function neural models,standard economic model predictive control scheme,RBFNN model,data-driven control design,multiple flow inlets,water utilities,pump actuation,water distribution networks,time-varying model
Storage tank,Mathematical structure,Radial basis function neural,Computer science,Control theory,Flow (psychology),Distribution networks,Model predictive control,Artificial neural network,Linearization
Conference
ISBN
Citations 
PageRank 
978-1-7281-2768-2
1
0.40
References 
Authors
1
4
Name
Order
Citations
PageRank
Krisztian Mark Balla111.07
Tom Nørgaard Jensen210.40
Jan Dimon Bendtsen34622.56
Carsten Skovmose Kallesøe410.40