Hybrid control using recurrent fuzzy neural network for linear-induction motor servo drive

Faa Jeng Lin, Rong Jong Wai

Research output: Contribution to journalArticlepeer-review

98 Scopus citations

Abstract

In this paper, a hybrid control system using a recurrent-fuzzy-neural network (RFNN) is proposed to control a linear-induction motor (LIM) servo drive. First, the feedback linearization theory is used to decouple the thrust force and the flux amplitude of the LIM. Then, a hybrid control system is proposed to control the mover of the LIM for periodic motion. In the hybrid control system, the RFNN controller is the main tracking controller, which is used to mimic a perfect control law, and the compensated controller is proposed to compensate the difference between the perfect control law and the RFNN controller. Moreover, an on-line parameter training methodology, which is derived using the Lyapunov stability theorem and the gradient descent method, is proposed to increase the learning capability of the RFNN. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results. Furthermore, the advantages of the proposed control system are indicated in comparison with the sliding mode control system.

Original languageEnglish
Pages (from-to)102-115
Number of pages14
JournalIEEE Transactions on Fuzzy Systems
Volume9
Issue number1
DOIs
StatePublished - Feb 2001

Keywords

  • Feedback linearization
  • Hybrid control
  • Linear-induction motor (LIM)
  • Recurrent-fuzzy-neural network (RFNN)
  • Sliding mode control

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