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Title: Robust model predictive control of water quality in drinking water distribution systems
Author: Chang, Tao
ISNI:       0000 0000 5507 0007
Awarding Body: University of Birmingham
Current Institution: University of Birmingham
Date of Award: 2003
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This thesis develops online feedback control of chlorine residuals performing at the lower level of a hierarchical structure of integrated quantity and quality control in drinking water distribution systems (DWDS), which provides a practical solution for online water quality control in DWDS. Input-output and state-space models of the chlorine residuals are developed from mathematical models of chlorine residual dynamics. The existing path analysis algorithm is extended and utilized to obtain the parameter structure. Joint parameter and model structure error estimation is developed using bounding approach based on a point-parametric model. The uncertainty radius of the system is outlined through robust output prediction, through which requirements for model accuracy from robust model predictive control (MFC) are explicitly imposed on model estimation. Hence, an integrated design of controller and model estimation is achieved. MFC is applied for chlorine residual control based on the set-bounded model. To fulfil output constraints under system uncertainties, safety zones are employed, which are designed from an online evaluation of the uncertainty scenarios of the system, to restrict the output constraints. The safety zones can be obtained by solving a nonlinear constrained optimization problem using a significantly simplified relaxation-gain algorithm. The resulting robust MFC (RMPC) is decentralized assuming communication among the decentralized RMPCs is available. The proposed methodology is verified by applying it to a simulated benchmark DWDS. Simulation study of model estimation and RMPC operation is presented and discussed.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available