Model Predictive Control of Urban Drainage Systems Considering Uncertainty

This brief contributes to the application of model predictive control (MPC) to address the combined sewer overflow (CSO) problem in urban drainage systems (UDSs) with uncertainty. In UDS, dealing with uncertainty in rain forecast and dynamic models is crucial due to the possible impact on the UDS control performance. Two different MPC approaches are considered: tube-based MPC (T-MPC) and chance-constrained MPC (CC-MPC), which represent uncertainty in deterministic and stochastic manners, respectively. This brief presents how to apply T-MPC to UDS, by establishing a mathematical relation with CC-MPC, and a rigorous mathematical comparison. Based on simulations using the Astlingen benchmark UDS, the strengths and weaknesses of the performance of T-MPC and CC-MPC in UDS were compared. Differences in the involved mathematical computations have also been analyzed. Moreover, the comparison in performance also indicates the applicability of each MPC approach in different uncertainty scenarios.

Automated summary

This paper investigates the application of model predictive control (MPC) to address the problem of combined sewer overflow (CSO) in urban drainage systems (UDSs) with uncertainty. Two different MPC approaches, tube-based MPC (T-MPC) and chance-constrained MPC (CC-MPC), are considered and compared based on simulations using the Astlingen benchmark UDS. The study provides insights into the strengths and weaknesses of each MPC approach in UDS and their applicability in different uncertainty scenarios.