@inproceedings{f1091057030745789168e49f25295770,
title = "Recurrent fuzzy neural network using genetic algorithm for linear induction motor servo drive",
abstract = "A recurrent fuzzy neural network (RFNN) using genetic algorithm (GA) is proposed to control the mover of a linear induction motor (LIM) servo drive for periodic motion in this paper. First, the dynamic model of an indirect field-oriented LIM servo drive is derived. Then, an on-line training RFNN with backpropagation algorithm is introduced as the tracking controller. Moreover, to guarantee the global convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. In addition, a real-time GA is developed to search the optimal weights between the membership layer and the rule layer of RFNN on-line. The theoretical analyses for the proposed RFNN using GA controller are described in detail. Finally, 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.",
author = "Lin, {F. J.} and Huang, {P. K.}",
year = "2006",
doi = "10.1109/ICIEA.2006.257084",
language = "???core.languages.en_GB???",
isbn = "078039514X",
series = "2006 1st IEEE Conference on Industrial Electronics and Applications",
booktitle = "2006 1st IEEE Conference on Industrial Electronics and Applications",
note = "2006 1st IEEE Conference on Industrial Electronics and Applications, ICIEA 2006 ; Conference date: 24-05-2006 Through 26-05-2006",
}