A supervisory fuzzy neural network control system for tracking periodic inputs

Faa Jeng Lin*, Wen Jyi Hwang, Rong Jong Wai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

193 Citations (Scopus)


A supervisory fuzzy neural network (FNN) control system is designed to track periodic reference inputs in this study. The control system is composed of a permanent magnet (PM) synchronous servo motor drive with a supervisory FNN position controller. The supervisory FNN controller comprises a supervisory controller, which is designed to stabilize the system states around a defined bound region and an FNN sliding-mode controller, which combines the advantages of the sliding-mode control with robust characteristics and the FNN with on-line learning ability. The theoretical and stability analyses of the supervisory FNN controller are discussed in detail. Simulation and experimental results show that the proposed control system is robust with regard to plant parameter variations and external load disturbance. Moreover, the advantages of the proposed control system are indicated in comparison with the sliding-mode control system.

Original languageEnglish
Pages (from-to)41-52
Number of pages12
JournalIEEE Transactions on Fuzzy Systems
Issue number1
Publication statusPublished - 1999
Externally publishedYes


  • Fuzzy neural network
  • PM synchronous servo motor
  • Periodic inputs
  • Supervisory control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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