Reinforcement Learning Control for Water Distribution Networks with Periodic Disturbances

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

Abstrakt

Cost efficient management of Water Distribution Networks with storage units requires of extensive knowledge of the water network. However, the network models are not always available or the calibration costs are too high for most of small water utilities. This paper proposes a model-free control solution based on Q-learning methods that provides a policy for the operation of the network. This supervisory controller must guarantee the water supply despite of the uncertainty of the daily water consumption and reduce the operation cost. The function approximation proposed for the Q-learning controller uses Fourier Basis Functions which provide an accurate approximation of the periodic disturbances. This paper presents results of the control validation in a simulation framework as well as experimental evidence of the advantages and limitations of the proposed design.

OriginalsprogEngelsk
Titel2021 Annual American Control Conference, ACC 2021
Antal sider6
ForlagIEEE
Publikationsdato2021
Sider1010-1015
Artikelnummer9482787
ISBN (Trykt)978-1-7281-9704-3
ISBN (Elektronisk)978-1-6654-4197-1
DOI
StatusUdgivet - 2021
Begivenhed2021 American Control Conference (ACC) - New Orleans, USA
Varighed: 25 maj 202128 maj 2021

Konference

Konference2021 American Control Conference (ACC)
Land/OmrådeUSA
ByNew Orleans
Periode25/05/202128/05/2021
NavnAmerican Control Conference
ISSN0743-1619

Fingeraftryk

Dyk ned i forskningsemnerne om 'Reinforcement Learning Control for Water Distribution Networks with Periodic Disturbances'. Sammen danner de et unikt fingeraftryk.

Citationsformater