Stochastic model predictive control based on Gaussian processes applied to drinking water networks
Cita com:
hdl:2117/89276
Tipus de documentArticle
Data publicació2016-05-16
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
:
Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
ProjecteOPERACION EFICIENTE DE INFRAESTRUCTURAS CRITICAS (MINECO-DPI2013-48243-C2-1-R)
CONTROL BASADO EN LA SALUD Y LA RESILIENCIA DE INFRAESTRUCTURAS CRITICAS Y SISTEMAS COMPLEJOS (MINECO-DPI2014-58104-R)
CONTROL BASADO EN LA SALUD Y LA RESILIENCIA DE INFRAESTRUCTURAS CRITICAS Y SISTEMAS COMPLEJOS (MINECO-DPI2014-58104-R)
CONTROL BASADO EN LA SALUD Y LA RESILIENCIA DE INFRAESTRUCTURAS CRITICAS Y SISTEMAS COMPLEJOS (MINECO-DPI2014-58104-R)
CONTROL BASADO EN LA SALUD Y LA RESILIENCIA DE INFRAESTRUCTURAS CRITICAS Y SISTEMAS COMPLEJOS (MINECO-DPI2014-58104-R)
Abstract
This study focuses on developing a stochastic model predictive control (MPC) strategy based on Gaussian processes (GPs) for propagating system disturbances in a receding horizon way. Using a probabilistic system representation, the state trajectories considering the influence of disturbances can be obtained through the uncertainty propagation by using GPs. This fact allows obtaining the confidence intervals for state evolutions over the MPC prediction horizon that are included into the MPC objective function and constraints. The feasibility of the proposed MPC strategy considering the incorporated results of disturbance forecasting is also discussed. Simulation results obtained from the application of the proposed approach to the Barcelona drinking water network taking real demand data into account are presented. The comparison with the well-known certainty-equivalent MPC shows the effectiveness of the proposed stochastic MPC approach.
CitacióWang, Y., Ocampo-Martinez, C.A., Puig, V. Stochastic model predictive control based on Gaussian processes applied to drinking water networks. "IET control theory and applications", 16 Maig 2016, vol. 10, núm. 8, p. 947-955.
ISSN1751-8644
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
1730-Stochastic ... rinking-Water-Networks.pdf | 417,3Kb | Visualitza/Obre |