Publication
Stochastic model predictive control for water transport networks with demand forecast uncertainty
Book Chapter (2017)
Book Title
Real-Time Monitoring and Operational Control of Drinking-Water Systems
Publisher
Springer
Pages
269-290
Volume
26
Serie
Advances in Industrial Control
Doc link
http://dx.doi.org/10.1007/978-3-319-50751-4_14
File
Abstract
Two formulations of the stochastic model predictive control (SMPC) problem for the control of large-scale drinking water networks are presented in this chapter. The first formulation makes use of the assumption that the uncertain future water demands follows some known continuous probability distribution while at the same time we allocate certain risk (probability) for the state constraints to be violated. The second approach, namely, the tree-based stochastic MPC approach, does not require any assumptions to be taken on the probability distribution of the demand estimates, but brings about a complexity that is harder to handle by conventional computational tools and calls for more elaborate algorithms and the possible utilization of sophisticated devices.
Categories
automation, control theory, optimisation.
Author keywords
stochastic systems, predictive control, flow networks
Scientific reference
J.M. Grosso, C. Ocampo-Martínez and V. Puig. Stochastic model predictive control for water transport networks with demand forecast uncertainty. In Real-Time Monitoring and Operational Control of Drinking-Water Systems, 269-290. Springer, 2017.
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