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Control of urban drainage systems: optimal flow control and deep learning in action

Author
Ochoa, D.; Riaño-Briceño, G.; Quijano, N.; Ocampo-Martinez, C.
Type of activity
Presentation of work at congresses
Name of edition
2019 American Control Conference
Date of publication
2019
Presentation's date
2019
Book of congress proceedings
2019 American Control Conference (ACC)
First page
4826
Last page
4831
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
DOI
10.23919/ACC.2019.8814958
Project funding
DEOCS: Monitorización, diagnostico y control tolerante a fallos de sistemas ciberfísicos con métodos basados en datos
Unit of Excellence María de Maeztu
Repository
http://hdl.handle.net/2117/181345 Open in new window
URL
https://ieeexplore.ieee.org/document/8814958 Open in new window
Abstract
A hierarchical control strategy is proposed to solve the optimal drainage problem in sewer systems by combining an optimization technique known as minimum scaled consensus control (MSCC) with the deep deterministic policy gradient (DDPG) algorithm. The MSCC strategy operates at the global control level, and is used to determine the flows of the hydraulic structures of the drainage system, such that the water is optimally distributed, i.e., wastewater flows are controlled to minimize saturation o...
Citation
Ochoa, D. [et al.]. Control of urban drainage systems: optimal flow control and deep learning in action. A: American Control Conference. "2019 American Control Conference (ACC)". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 4826-4831.
Keywords
Deep deterministic policy gradients, Drainage networks, Flow control, Minimum scaled consensus
Group of research
CS2AC-UPC - Supervision, Safety and Automatic Control
SAC - Advanced Control Systems

Participants

  • Ochoa Tamayo, Daniel Esteban  (author and speaker )
  • Riaño Briceño, Gerardo  (author and speaker )
  • Quijano Silva, Nicanor  (author and speaker )
  • Ocampo Martinez, Carlos A.  (author and speaker )