New adaptive vector control methods for induction motors with simpler structure and better performance

Kang Zhi Liu*, Masashi Yokoo, Keiichiro Kondo, Tadanao Zanma

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


This paper deals with the vector control, including both the direct vector control (DVC) and the indirect vector control (IdVC), of induction motors. It is well known that the estimation of rotor flux plays a fundamental role in the DVC and the estimation of rotor resistance is vital in the slip compensation of the IdVC. In these estimations, the precision is significantly affected by the motor resistances. Therefore, online estimation of motor resistances is indispensable in practice. For a fast estimation of motor resistances, it is necessary to slow down the convergence rate of the current estimate. On the other hand, for a fast estimation of the rotor flux, it is necessary to speed up its convergence rate. It is very difficult to realize such a trade-off in convergence rates in a full order observer. In this paper, we propose to decouple the current observer from the flux observer so as to realize independent convergence rates. Then, the resistance estimation algorithm is applied to both DVC and IdVC. In particular, in the application to IdVC the flux observer needs not be used, which leads to a simpler structure. Meanwhile, independent convergence rates of current observer and flux observer yield an improved performance. A superior performance in the torque and flux responses in both cases is verified by numerous simulations.

Original languageEnglish
Pages (from-to)173-183
Number of pages11
JournalControl Theory and Technology
Issue number2
Publication statusPublished - 2015 May 22
Externally publishedYes


  • Induction motor
  • current observer
  • direct vectorcontrol
  • flux observer
  • indirect vector control
  • parameter adaptation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Aerospace Engineering
  • Control and Optimization


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