Hybrid controller using fuzzy neural networks for identification and control of induction servo motor drive

Rong Jong Wai, Hsin Hai Lin, Faa Jeng Lin

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

An induction servo motor drive with a hybrid controller, which combines the advantages of the integral-proportional (IP) position controller and the fuzzy neural network controller (FNNC), is introduced in this study. First, the IP position controller is designed according to the estimated plant model to match the time-domain command tracking specifications. Then, a compensated signal generated from FNNC is augmented to the control system to preserve a favorable model-following characteristic. The induction servo motor drive system is identified by a fuzzy neural network identifier (FNNI) to provide the sensitivity information of the drive system to the FNNC. A backpropagation algorithm is used to train both the FNNI and FNNC on line. Moreover, to guarantee the convergence of identification and tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the FNNs. In addition, the effectiveness of the induction servo motor drive system is demonstrated by some experimental results. Accurate tracking response can be obtained due to the powerful on-line learning capability of the FNNs. Furthermore, the influence of parameter variations and external disturbances on the induction servo motor drive system can be reduced effectively. (C) 2000 Elsevier Science B.V. All rights reserved.

Original languageEnglish
Pages (from-to)91-112
Number of pages22
JournalNeurocomputing
Volume35
Issue number1-4
DOIs
StatePublished - 2000

Keywords

  • Fuzzy neural network
  • Induction servo motor drive
  • Integral-proportional position controller
  • Sensitivity information

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