Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive

被引：195
|
作者：
Lin, FJ [1 ]
Lin, CH
Shen, PH
机构：
[1] Natl Dong Hwa Univ, Dept Elect Engn, Hualien 974, Taiwan
[2] Natl Lien Ho Inst Technol, Dept Elect Engn, Miaoli 360, Taiwan
来源：
IEEE TRANSACTIONS ON FUZZY SYSTEMS | 2001年 / 9卷 / 05期
关键词：
fuzzy neural network; gradient decent method; self-constructing; synchronous motor;
D O I：
10.1109/91.963761
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A self-constructing fuzzy neural network (SCFNN) which is suitable for practical implementation is proposed in this study. The structure and the parameter learning phases are performed concurrently and online in the SCFNN. The structure learning is based on the partition of input space and the parameter learning is based on the supervised gradient decent method using a delta adaptation law. Several simulation and experimental results are provided to demonstrate the effectiveness of the proposed SCFNN control stratagem with the implementation of a permanent-magnet synchronous motor (PMSM) speed drive. Moreover, the simulation results of time varying and nonlinear disturbances are given to show the dynamic characteristics of the proposed controller over a broad range of operating conditions.
引用
收藏
页码：751 / 759
页数：9
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