DocumentCode
3477503
Title
Genetic algorithms-based fuzzy neural network sliding mode control for brushless doubly fed machine
Author
Shao, Zongkai
Author_Institution
Sch. of Hydropower & Inf. Eng., Huazhong Univ. of Sci. & Technol., Wuhan, China
Volume
3
fYear
2010
fDate
12-13 June 2010
Firstpage
467
Lastpage
475
Abstract
In this paper, because a genetic algorithms-based fuzzy neural network control is incorporated into the sliding mode control (SMC) to adaptively regulate the adaptive law of SMC, a genetic algorithm fuzzy neural network sliding mode controller (GAFNSMC) for brushless doubly fed machine (BDFM) adjustable speed system is presented. The proposed controller for BDFM eliminates the average chattering encountered by most SMC schemes, and employs the robustness and excellent static and dynamic performances of SMC. Simulation results show that the proposed control strategy is of the feasibility, correctness and effectiveness.
Keywords
AC motors; brushless machines; fuzzy neural nets; genetic algorithms; machine control; neurocontrollers; variable structure systems; velocity control; adjustable speed system; brushless doubly fed machine; fuzzy neural network; genetic algorithms; sliding mode control; Niobium; Robustness; brushless doubly fed machines (BDFM); dynamic model; fuzzy neural network control; genetic algorithms; sliding mode control;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Communication Technologies in Agriculture Engineering (CCTAE), 2010 International Conference On
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6944-4
Type
conf
DOI
10.1109/CCTAE.2010.5544317
Filename
5544317
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