Learning-Based Predictive Control with Gaussian Processes: An Application to Urban Drainage Networks

Krisztian Mark Balla*, Deividas Eringis, Mohamad Al Ahdab, Jan Dimon Bendtsen, Carsten Kallesøe, Carlos Ocampo-Martinez

*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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Many traditional control solutions in urban drainage networks suffer from unmodelled nonlinear effects such as rain and wastewater infiltrating the system. These effects are challenging and often too complex to capture through physical modelling without using a high number of flow sensors. In this article, we use level sensors and design a stochastic model predictive controller by combining nominal dynamics (hydraulics) with unknown nonlinearities (hydrology) modelled as Gaussian processes. The Gaussian process model provides residual uncertainties trained via the level measurements and captures the effect of the hydrologic load and the transport dynamics in the network. To show the practical effectiveness of the approach, we present the improvement of the closed-loop control performance on an experimental laboratory setup using real rain and wastewater flow data.
Original languageEnglish
Title of host publication2022 American Control Conference, ACC
Number of pages7
Publication dateSept 2022
ISBN (Print)978-1-6654-5197-0, 978-1-6654-9480-9
ISBN (Electronic)978-1-6654-5196-3
Publication statusPublished - Sept 2022
Event2022 American Control Conference (ACC) -
Duration: 8 Jun 202210 Jun 2022


Conference2022 American Control Conference (ACC)
SeriesAnnual American Control Conference (ACC)


  • Learning (artificial intelligence)
  • Gaussian Processes
  • Urban Drainage Network
  • Waste Water
  • Model Predictive Control
  • Gaussian process regression
  • Kernel function
  • Subset of Data
  • Water Management
  • Rain infiltration
  • Disturbances
  • Time delay systems
  • System identification
  • Training data
  • Closed-loop performance
  • Uncertainty propagation
  • Forecast


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