Fuzzy neural network speed estimation method for induction motor speed sensorless control

Chun Hao Lu, Cheng-Chi Tai, Tien Chi Chen, Wei Chung Wang

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

Field-oriented induction motor control presents high static, dynamic perfor¬mance.  Precise rotor speed information is critical for induction motor control to achieve speed loop feedback control. In the past encoders were widely used to obtain the speed information for the induction motor. However, a speed sensor would increase the cost of the entire system and reduce system reliability. In addition, for some special appli¬cations (such as very high speed motor drives) difficulties were encountered in mounting these speed sensors. Sensorless speed control would overcome these problems. This paper proposes a fuzzy neural network speed estimation method for induction motor speed sen¬sorless control. The speed estimation is based on rotor flux deduction and estimated rotor flux, calculated using a fuzzy neural network. The fuzzy neural network is a four-layer network. The steepest descent algorithm is used to adjust the fuzzy neural network pa¬rameters to minimize the error between the rotor flux and estimated rotor flux, enabling precise rotor speed estimation.

Original languageEnglish
Pages (from-to)433-446
Number of pages14
JournalInternational Journal of Innovative Computing, Information and Control
Volume11
Issue number2
Publication statusPublished - 2015 Jan 1

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Computational Theory and Mathematics

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