Publication
Output-feedback model predictive control of sewer networks through moving horizon estimation
Conference Article
Conference
IEEE Conference on Decision and Control (CDC)
Edition
53rd
Pages
1061-1066
Doc link
http://dx.doi.org/10.1109/CDC.2014.7039522
File
Abstract
Based on a simplified control-oriented hybrid linear delayed model, model predictive control (MPC) of a sewer network designed to reduce pollution during heavy rain events is presented. The lack of measurements at many parts of the system to update the initial conditions of the optimal control problems (OCPs) leads to the need for estimation techniques. A simple modification of the OCP used in the MPC iterations allows to formulate a state estimation problem (SEP) to reconstruct the full system state from a few measurements. Results comparing the system performance under the MPC controller using full-state measurements and a moving horizon estimation (MHE) strategy solving a finite horizon SEP at each time instant are presented. Closed-loop simulations are performed by using a detailed physically-based model of the network as virtual reality.
Categories
observability, optimisation, predictive control.
Author keywords
receding horizon control, moving horizon estimation, sewer networks, optimal control, state estimation.
Scientific reference
B. Joseph, C. Ocampo-Martínez and G. Cembrano. Output-feedback model predictive control of sewer networks through moving horizon estimation, 53rd IEEE Conference on Decision and Control, 2014, Los Angeles, pp. 1061-1066.
Follow us!