Stochastic model predictive control based on Gaussian processes applied to drinking water networks
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...
| Authors: | ; ; |
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| Format: | Article |
| Publication date: | 2016 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/89276 |
| Online Access: | https://hdl.handle.net/2117/89276 https://dx.doi.org/10.1049/iet-cta.2015.0657 |
| Access Level: | Open access |
| Keyword: | automation control theory optimisation stochastic model predictive control Gaussian processes disturbance forecasting drinking water networks Classificació INSPEC::Control theory Àrees temàtiques de la UPC::Informàtica::Automàtica i control |
| Summary: | 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. |
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