Re'current-fuzzy-neural-network-controlled linear induction motor servo drive using genetic algorithms

被引：62
作者：
Lin, Faa-Jeng [1 ]
Huang, Po-Kai
Chou, Wen-Der
机构：
[1] Natl Dong Hwa Univ, Dept Elect Engn, Hualien 974, Taiwan
[2] Lan Yang Inst Technol, Dept Informat Management, Ilan 261, Taiwan
来源：
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2007年 / 54卷 / 03期
关键词：
backpropagation algorithm; genetic algorithms (GAs); linear induction motor (LIM); recurrent fuzzy neural network (RFNN);
D O I：
10.1109/TIE.2007.892256
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A recurrent fuzzy neural network (RFNN) controller based on real-time genetic algorithms (GAs) is developed for a linear induction motor (LIM) servo drive in this paper. First, the dynamic model of an indirect field-oriented LIM servo drive I'S derived. Then, an online training RFNN with a backpropagation algorithm is introduced as the tracking controller. Moreover, to guarantee the global convergence of tracking error, a real-time GA is developed to search the optimal learning rates of the RFNN online. The GA-based RFNN control system is proposed to control the mover of the LIM for periodic motion. The theoretical analyses for the proposed GA-based RFNN controller are described in detail. Finally, simulated and experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance.
引用
收藏
页码：1449 / 1461
页数：13
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