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Output‐feedback control of combined sewer networks through receding horizon control with moving horizon estimation
Author(s) -
JosephDuran Bernat,
OcampoMartinez Carlos,
Cembrano Gabriela
Publication year - 2015
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2014wr016696
Subject(s) - sanitary sewer , horizon , model predictive control , control (management) , control theory (sociology) , combined sewer , moving horizon estimation , computer science , state (computer science) , time horizon , engineering , mathematical optimization , control engineering , kalman filter , environmental engineering , mathematics , extended kalman filter , algorithm , artificial intelligence , ecology , geometry , stormwater , surface runoff , biology
An output‐feedback control strategy for pollution mitigation in combined sewer networks is presented. The proposed strategy provides means to apply model‐based predictive control to large‐scale sewer networks, in‐spite of the lack of measurements at most of the network sewers. In previous works, the authors presented a hybrid linear control‐oriented model for sewer networks together with the formulation of Optimal Control Problems (OCP) and State Estimation Problems (SEP). By iteratively solving these problems, preliminary Receding Horizon Control with Moving Horizon Estimation (RHC/MHE) results, based on flow measurements, were also obtained. In this work, the RHC/MHE algorithm has been extended to take into account both flow and water level measurements and the resulting control loop has been extensively simulated to assess the system performance according different measurement availability scenarios and rain events. All simulations have been carried out using a detailed physically based model of a real case‐study network as virtual reality.