Data-Enabled Predictive Control for Optimal Pressure Management
Recent developments in control theory coupled with the growing availability of real-time data have paved the way for improved data-driven control methodologies. This study explores the application of a data-enabled predictive control (DeePC) algorithm to optimize the operation of water distribution...
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MDPI AG
2024-08-01
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| Series: | Engineering Proceedings |
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| author | Gal Perelman Avi Ostfeld |
| author_facet | Gal Perelman Avi Ostfeld |
| author_sort | Gal Perelman |
| collection | DOAJ |
| description | Recent developments in control theory coupled with the growing availability of real-time data have paved the way for improved data-driven control methodologies. This study explores the application of a data-enabled predictive control (DeePC) algorithm to optimize the operation of water distribution systems (WDS). WDSs are characterized by inherent uncertainties and complex nonlinear dynamics. Hence, classic control strategies that involve physical model-based methods are often hard to implement and infeasible to scale. The DeePC method suggests a paradigm shift by utilizing a data-driven approach. This method employs real-time data to dynamically learn an unknown system’s behavior. It utilizes a finite set of input–output samples (control settings, and measured data) to derive optimal policies, effectively bypassing the need for an explicit mathematical model of the system. In this study, DeePC is applied to a pressure management case study and demonstrates superior performance compared to standard control strategies. |
| format | Article |
| id | doaj-art-2f7e3b892f454e8593d63f5b986783f9 |
| institution | OA Journals |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-2f7e3b892f454e8593d63f5b986783f92025-08-20T02:11:05ZengMDPI AGEngineering Proceedings2673-45912024-08-01691510.3390/engproc2024069005Data-Enabled Predictive Control for Optimal Pressure ManagementGal Perelman0Avi Ostfeld1Faculty of Civil and Environmental Engineering, Technion—Israel Institute of Technology, Haifa 32000, IsraelFaculty of Civil and Environmental Engineering, Technion—Israel Institute of Technology, Haifa 32000, IsraelRecent developments in control theory coupled with the growing availability of real-time data have paved the way for improved data-driven control methodologies. This study explores the application of a data-enabled predictive control (DeePC) algorithm to optimize the operation of water distribution systems (WDS). WDSs are characterized by inherent uncertainties and complex nonlinear dynamics. Hence, classic control strategies that involve physical model-based methods are often hard to implement and infeasible to scale. The DeePC method suggests a paradigm shift by utilizing a data-driven approach. This method employs real-time data to dynamically learn an unknown system’s behavior. It utilizes a finite set of input–output samples (control settings, and measured data) to derive optimal policies, effectively bypassing the need for an explicit mathematical model of the system. In this study, DeePC is applied to a pressure management case study and demonstrates superior performance compared to standard control strategies.https://www.mdpi.com/2673-4591/69/1/5water distribution systemspredictive controldata-drivenuncertaintyreal-time |
| spellingShingle | Gal Perelman Avi Ostfeld Data-Enabled Predictive Control for Optimal Pressure Management Engineering Proceedings water distribution systems predictive control data-driven uncertainty real-time |
| title | Data-Enabled Predictive Control for Optimal Pressure Management |
| title_full | Data-Enabled Predictive Control for Optimal Pressure Management |
| title_fullStr | Data-Enabled Predictive Control for Optimal Pressure Management |
| title_full_unstemmed | Data-Enabled Predictive Control for Optimal Pressure Management |
| title_short | Data-Enabled Predictive Control for Optimal Pressure Management |
| title_sort | data enabled predictive control for optimal pressure management |
| topic | water distribution systems predictive control data-driven uncertainty real-time |
| url | https://www.mdpi.com/2673-4591/69/1/5 |
| work_keys_str_mv | AT galperelman dataenabledpredictivecontrolforoptimalpressuremanagement AT aviostfeld dataenabledpredictivecontrolforoptimalpressuremanagement |