TY - JOUR
T1 - A Nonlinear Predictive Control Approach for Urban Drainage Networks Using Data-Driven Models and Moving Horizon Estimation
AU - Balla, Krisztian Mark
AU - Schou, Christian
AU - Bendtsen, Jan Dimon
AU - Ocampo-Martinez, Carlos
AU - Kallesøe, Carsten
PY - 2022/9/1
Y1 - 2022/9/1
N2 - Real-time control (RTC) of urban drainage networks (UDNs) 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 (PDEs). These dynamics are approximated with kinematic waves where the network model is established with the water levels being the system states. Moving horizon estimation (MHE) 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.
AB - Real-time control (RTC) of urban drainage networks (UDNs) 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 (PDEs). These dynamics are approximated with kinematic waves where the network model is established with the water levels being the system states. Moving horizon estimation (MHE) 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.
KW - Receding horizon control
KW - Transport delays
KW - Partial Differential Equations
KW - Urban drainage networks
KW - data-driven modeling
KW - Real Time Control
KW - Moving horizon estimation
KW - Predictive Control
KW - structure-preserving modeling
KW - graph network
KW - high-fidelity model
KW - large-scale systems
KW - multiple shooting
KW - optimization
KW - System Identification
KW - Periodic disturbances
KW - nonlinear MPC
KW - State Estimation
KW - receding horizon control
KW - urban drainage network (UDN)
KW - Partial differential equation (PDE)
KW - transport delay
UR - http://www.scopus.com/inward/record.url?scp=85122569965&partnerID=8YFLogxK
U2 - 10.1109/TCST.2021.3137712
DO - 10.1109/TCST.2021.3137712
M3 - Journal article
SN - 1063-6536
VL - 30
SP - 2147
EP - 2162
JO - I E E E Transactions on Control Systems Technology
JF - I E E E Transactions on Control Systems Technology
IS - 5
ER -