Two-phase linear brushless machine control approach via recurrent fuzzy neural network theory

Jian Long Kuo*, Zen Shan Chang, Jiann Der Lee

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

1 Scopus citations

Abstract

One of the advantages of using linear brushless motor for high speed linear drives is that the position and speed of these moving machines can be precisely controlled based on the feedback control technique. This paper represents a recurrent fuzzy neural network-based control (RFNNC) approach for a two-phase linear brushless machine built with Neodymium-Iron-Boron (NdFeB) permanent magnets. The proposed RFNNC possesses four layers of recurrent neural network structure to perform the Takagi-Sugeno (T-S) fuzzy inference. The recurrence is formed by using the lagged membership-grade signals as the internal feedbacks to the membership layer, and it is expected to give the potential to trace the unknown system dynamics. The interconnection weights of the network can be on-line tuned by the gradient descent method to achieve satisfactory control performance. Verification shows that the proposed RFNNC is found to achieve good tracking performances since the on-line learning scheme is applied.

Original languageEnglish
Pages (from-to)725-730
Number of pages6
JournalWSEAS Transactions on Systems
Volume6
Issue number4
StatePublished - 04 2007

Keywords

  • Gradient descend algorithm
  • Recurrent fuzzy neural networks
  • Two-phase linear brushless machine

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