{"title":"基于残差自适应调制函数回归量的不确定管网泄漏大小解耦估计与定位","authors":"Italo Aranda-Cetraro;Gustavo Pérez-Zuñiga","doi":"10.1109/TCST.2025.3537820","DOIUrl":null,"url":null,"abstract":"Most solutions for detecting, estimating, and localizing leaks in water networks rely on complex banks of Kalman filters (BKFs) or advanced stand-alone Kalman filter (KF) algorithms to account for the network’s model uncertainty, requiring extra hardware, extensive calibration, and maintenance. This study proposes a modulating function (MF) regressor based on a lumped model with pressure-flow boundary conditions to detect and localize a single leak in a water network. The uncertainty of the lumped model is reduced by adapting the MF regressor via a Lyapunov-based adaptive law. A real water network system (WNS) test bench was employed to validate the effectiveness of the proposed regressor. Initially, an experimental phase was conducted to identify and analyze the primary sources of uncertainty of the plant models concerning the test setup. Subsequently, the proposed leak detection, estimation, and localization algorithm was tested and compared with the robust adaptive unscented Kalman Filter (RAUKF), showing promising results.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1479-1492"},"PeriodicalIF":3.9000,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive Residual-Based Modulating Function Regressor for Decoupled Estimation of Leak Size and Localization in Uncertain Water Network Systems\",\"authors\":\"Italo Aranda-Cetraro;Gustavo Pérez-Zuñiga\",\"doi\":\"10.1109/TCST.2025.3537820\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Most solutions for detecting, estimating, and localizing leaks in water networks rely on complex banks of Kalman filters (BKFs) or advanced stand-alone Kalman filter (KF) algorithms to account for the network’s model uncertainty, requiring extra hardware, extensive calibration, and maintenance. This study proposes a modulating function (MF) regressor based on a lumped model with pressure-flow boundary conditions to detect and localize a single leak in a water network. The uncertainty of the lumped model is reduced by adapting the MF regressor via a Lyapunov-based adaptive law. A real water network system (WNS) test bench was employed to validate the effectiveness of the proposed regressor. Initially, an experimental phase was conducted to identify and analyze the primary sources of uncertainty of the plant models concerning the test setup. Subsequently, the proposed leak detection, estimation, and localization algorithm was tested and compared with the robust adaptive unscented Kalman Filter (RAUKF), showing promising results.\",\"PeriodicalId\":13103,\"journal\":{\"name\":\"IEEE Transactions on Control Systems Technology\",\"volume\":\"33 5\",\"pages\":\"1479-1492\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-02-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Control Systems Technology\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10880680/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Control Systems Technology","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10880680/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Adaptive Residual-Based Modulating Function Regressor for Decoupled Estimation of Leak Size and Localization in Uncertain Water Network Systems
Most solutions for detecting, estimating, and localizing leaks in water networks rely on complex banks of Kalman filters (BKFs) or advanced stand-alone Kalman filter (KF) algorithms to account for the network’s model uncertainty, requiring extra hardware, extensive calibration, and maintenance. This study proposes a modulating function (MF) regressor based on a lumped model with pressure-flow boundary conditions to detect and localize a single leak in a water network. The uncertainty of the lumped model is reduced by adapting the MF regressor via a Lyapunov-based adaptive law. A real water network system (WNS) test bench was employed to validate the effectiveness of the proposed regressor. Initially, an experimental phase was conducted to identify and analyze the primary sources of uncertainty of the plant models concerning the test setup. Subsequently, the proposed leak detection, estimation, and localization algorithm was tested and compared with the robust adaptive unscented Kalman Filter (RAUKF), showing promising results.
期刊介绍:
The IEEE Transactions on Control Systems Technology publishes high quality technical papers on technological advances in control engineering. The word technology is from the Greek technologia. The modern meaning is a scientific method to achieve a practical purpose. Control Systems Technology includes all aspects of control engineering needed to implement practical control systems, from analysis and design, through simulation and hardware. A primary purpose of the IEEE Transactions on Control Systems Technology is to have an archival publication which will bridge the gap between theory and practice. Papers are published in the IEEE Transactions on Control System Technology which disclose significant new knowledge, exploratory developments, or practical applications in all aspects of technology needed to implement control systems, from analysis and design through simulation, and hardware.