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
Link To Document :
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