Publication
Combining CSP and MPC for the operational control of water networks
Journal Article (2016)
Journal
Engineering Applications of Artificial Intelligence
Pages
126-140
Volume
49
Doc link
http://dx.doi.org/10.1016/j.engappai.2015.12.006
File
Abstract
This paper presents a control scheme which uses a combination of linear Model Predictive Control (MPC) and a Constraint Satisfaction Problem (CSP) to solve the non-linear operational optimal control of Drinking Water Networks (DWNs). The methodology has been divided into two functional layers: first, a CSP algorithm is used to transfer non-linear DWNs pressure equations into linear constraints on flows and tank volumes, which can enclose the feasible solution set of the hydraulic non-linear problem during the optimization process. Then, a linear MPC with tightened constraints produced in the CSP layer is solved to generate control strategies which optimize the control objectives. The proposed approach is simulated using Epanet to represent the real DWNs. Non-linear MPC is used for validation. To illustrate the performance of the proposed approach, a case study based on the Richmond water network is used and a realistic example, D-Town benchmark network, is added as a supplementary case study.
Categories
automation, control theory.
Scientific reference
C. Sun, V. Puig and G. Cembrano. Combining CSP and MPC for the operational control of water networks. Engineering Applications of Artificial Intelligence, 49: 126-140, 2016.
Follow us!