Recurrent functional-link-based fuzzy-neural-network-controlled induction-generator system using improved particle swarm optimization

Faa Jeng Lin, Li Tao Teng, Jeng Wen Lin, Syuan-Yi Chen

Research output: Contribution to journalArticle

84 Citations (Scopus)


A recurrent functional-link (FL)-based fuzzy-neural-network (FNN) controller with improved particle swarm optimization (IPSO) is proposed in this paper to control a three-phase induction-generator (IG) system for stand-alone power application. First, an indirect field-oriented mechanism is implemented for the control of the IG. Then, an ac/dc power converter and a dc/ac power inverter are developed to convert the electric power generated by a three-phase IG from variable frequency and variable voltage to constant frequency and constant voltage, respectively. Moreover, two online-trained recurrent FL-based FNNs are introduced as the regulating controllers for both the dc-link voltage of the ac/dc power converter and the ac line voltage of the dc/ac power inverter. Furthermore, IPSO is adopted to adjust the learning rates to improve the online learning capability of the recurrent FL-based FNNs. Finally, some experimental results are provided to demonstrate the effectiveness of the proposed recurrent FL-based FNN-controlled IG system.

Original languageEnglish
Pages (from-to)1557-1577
Number of pages21
JournalIEEE Transactions on Industrial Electronics
Issue number5
Publication statusPublished - 2009 Jun 3



  • Functional link neural network (FLNN)
  • Induction generator
  • Induction generator (IG)
  • Particle swarm optimization (PSO)
  • Power converter

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications

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