Recurrent radial basis function network-based fuzzy neural network control for permanent-magnet linear synchronous motor servo drive

Faa Jeng Lin, Po Hung Shen, Song Lin Yang, Po Huan Chou

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

We propse a recurrent radial basis function network-based (RBFN-based) fuzzy neural network (FNN) to control the position of the mover of a field-oriented control permanent-magnet linear synchronous motor (PMLSM) to track periodic reference trajectories. The proposed recurrent RBFN-based FNN combines the merits of self-constructing fuzzy neural network (SCFNN), recurrent neural network (RNN), and RBFN. Moreover, it performs the structure- and parameter-learning phases concurrently. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient descent method, using a delta adaptation law. Furthermore, all the control algorithms are implemented in a TMS320C32 DSP-based control computer. The simulated and experimental results due to periodic reference trajectories show that the dynamic behaviors of the proposed recurrent RBFN-based FNN control system are robust with regard to uncertainties.

Original languageEnglish
Article number1715679
Pages (from-to)3694-3705
Number of pages12
JournalIEEE Transactions on Magnetics
Volume42
Issue number11
DOIs
StatePublished - Nov 2006

Keywords

  • Gradient descent method
  • Permanent-magnet linear synchronous motor
  • Radial basis function network
  • Recurrent fuzzy neural network
  • Self-constructing

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