Publication

A nonlinear predictive control approach for urban drainage networks using data-driven models and moving horizon estimation

Journal Article (2022)

Journal

IEEE Transactions on Control Systems Technology

Pages

2147–2162

Volume

30

Number

5

Doc link

http://dx.doi.org/10.1109/TCST.2021.3137712

File

Download the digital copy of the doc pdf document

Abstract

Real-time control of urban drainage networks is a complex task where transport flows are non-pressurized and therefore impose flow-dependent time delays in the system. Unfortunately, the installation of flow sensors is economically out of reach at most utilities, although knowing volumes and flows are essential to optimize system operation. In this article, we formulate joint parameter and state estimation based on level sensors deployed inside manholes and basins in the network. We describe the flow dynamics on the main pipelines by the level variations inside manholes, characterized by a system of coupled partial differential equations. These dynamics are approximated with kinematic waves where the network model is established with the water levels being the system states. Moving horizon estimation is developed where the states and parameters are obtained via the levels and estimated flow data, utilizing the topological layout of the network. The obtained model complexity is kept within practically achievable limits, suitable for nonlinear predictive control. The effectiveness of the control and estimation method is demonstrated on a high-fidelity model of a drainage network, acting as virtual reality. We use real rain and wastewater flow data and test the controller against the uncertainty in the disturbance forecasts.

Categories

automation, control theory, optimisation.

Author keywords

Receding horizon control, transport delay, partial differential equation, urban drainage network

Scientific reference

K.M. Balla, C. Schou, J. Bendtsen, C. Ocampo-Martínez and C. Kallesoe. A nonlinear predictive control approach for urban drainage networks using data-driven models and moving horizon estimation. IEEE Transactions on Control Systems Technology, 30(5): 2147–2162, 2022.