• DocumentCode
    1794647
  • Title

    Neural learning algorithm based rotor resistance estimation for fuzzy logic based sensorlless IFOC of induction motor

  • Author

    Chandran, Saravanan

  • fYear
    2014
  • fDate
    6-11 Jan. 2014
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    This paper presents the MatlabbbSimulation of fuzzy logic based Sensorless sindirect vector control of induction motor with a rotor resistance adaptation scheme using Neural Learning Algorithm. Here the fuzzy controller offers superior transient performance when compared with the conventional control algorithms using PI controller. Rotor resistance of the motor changes significantly with temperature and frequency. This variation has a major influence on the field oriented control performance of an induction motor due to the deviation of slip frequency from the set value. This paper also uses neural learning algorithm for adaptation in a MRAS based rotor resistance estimator for making the robust against rotor resistance variation.
  • Keywords
    PI control; control engineering computing; electric resistance; fuzzy control; induction motors; machine vector control; neural nets; rotors; sensorless machine control; MRAS based rotor resistance estimator; PI controller; field oriented control performance; fuzzy controller; fuzzy logic based sensorless IFOC; induction motor; neural learning algorithm based rotor resistance estimation; sensorless indirect vector control; transient performance; Estimation; Fuzzy logic; Induction motors; Mathematical model; Resistance; Rotors; Torque; Fuzzy logic Controller; Indirect Field orientation control; MRAS Approach; Neural Learning Algorithm; Rotor Resistance estimation; Sensorless Contro Vector Control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Signals Control and Computations (EPSCICON), 2014 International Conference on
  • Conference_Location
    Thrissur
  • Print_ISBN
    978-1-4799-3611-3
  • Type

    conf

  • DOI
    10.1109/EPSCICON.2014.6887479
  • Filename
    6887479