Open AccessTakagi–Sugeno fuzzy sampled‐data model‐based less conservative stability criterion for a Francis hydropower unit's governing system
Author(s) -
Ma Teng,
Wang Bin,
Zhang Zhe,
Ai Bo
Publication year - 2020
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2020.0660
Subject(s) - control theory (sociology) , mathematics , controller (irrigation) , fuzzy logic , stability (learning theory) , fuzzy control system , lyapunov function , linear matrix inequality , sampling (signal processing) , computer science , mathematical optimization , nonlinear system , control (management) , artificial intelligence , physics , filter (signal processing) , quantum mechanics , machine learning , agronomy , computer vision , biology
In this study, the stability analysis and controller design of a hydropower unit's governing system (HUGS) were studied based on a Takagi–Sugeno (T–S) fuzzy model and constant sampled‐data control. First, according to the T–S fuzzy theory, the non‐linear Francis hydro‐turbine governing system under rigid water hammer is linearised and then the approximate linear system was obtained. Second, a fuzzy sampled‐data controller was designed in the case of periodic sampling, and the closed‐loop sampling system was discretised. After constructing the Lyapunov function and using the forward difference method, the stabilisation condition was given with less conservatism in a symmetrical linear matrix inequality form. Finally, the numerical simulation results showed that under four different sampling periods, the HUGS can quickly achieve stability and that it has different stability performance. In addition, the superiority of the controller was verified in comparison with traditional proportional–integral–derivative control and fuzzy control techniques.