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.
|Title of host publication||2021 Annual American Control Conference, ACC 2021|
|Number of pages||6|
|Publication status||Published - 2021|
|Event||2021 American Control Conference (ACC) - New Orleans, United States|
Duration: 25 May 2021 → 28 May 2021
|Conference||2021 American Control Conference (ACC)|
|Period||25/05/2021 → 28/05/2021|
|Series||American Control Conference|
- Reinforcement Learning (RL)
- Optimal Control
- water distribution network
- Water Supply
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Smart Water Infrastructures Laboratory (SWIL)
Jorge Val Ledesma (Operator), Rafal Wisniewski (Manager), Carsten Kallesøe (Operator), Saruch Satishkumar Rathore (Manager), Rahul Misra (Manager), Vishal Sopan Sawant (Manager) & Abhijit Mazumdar (Manager)Department of Electronic Systems