Application of a recurrent wavelet fuzzy-neural network in the positioning control of a magnetic-bearing mechanism
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- 作者：Syuan-Yi Chena ; chensy@ntnu.edu.tw" class="auth_mail" title="E-mail the corresponding author ; chensy0323@gmail.com" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Ying-Chih HungbAuthor Vitae ; Yi-Hsuan HungcAuthor Vitae ; Chien-Hsun WudAuthor Vitae
- 关键词：Fuzzy Neural Network (FNN) ; Magnetic Bearing (MB) ; Particle Swarm Optimization (PSO) ; Positioning control
- 刊名：Computers & Electrical Engineering
- 出版年：2016
- 出版时间：August 2016
- 年：2016
- 卷：54
- 期：Complete
- 页码：147-158
- 全文大小：2282 K
文摘
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A new recurrent wavelet fuzzy neural network (RWFNN) controller is proposed.
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RWFNN is adopted to control the rotor position of a thrust magnetic bearing (TMB).
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The online learning algorithm of RWFNN is derived using back-propagation method.
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The adaptive learning rates are performed via improved particle swarm optimization.
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Numerical simulations show the validity of TMB using the RWFNN controller.