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

页码：1449 / 1461

页数：13

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