Abstract: Field orientation control techniques of induction machine have permitted fast transient response by decoupled torque and flux control. Among these techniques, Indirect Field Oriented Control (IFOC) strategy is adopted as an effective one. The results showed that the technique can keep the rotor flux constant even during changes in load torque. This indicates that decoupling control of flux and torque has been obtained. However, field orientation detuning caused by parameter variation is a major difficulty for indirect field orientation control method. The decoupling that IFOC could perform is conditioned by the accuracy of slip calculation. The slip calculation depends on the rotor time constant, which varies continuously according to the operational conditions. A Fuzzy Logic Control (FLC) provides a systematic method to incorporate human experience and implement nonlinear algorithms, characterized by a series of linguistic statements, into the controller. Results of computer simulation showed that FL controller has potential to improve the closed-loop control performance and can outperform conventional PI controllers. An FLC scheme has been designed for an IFO induction machine drive system. Good performance of the IFO drive system is obtained in terms of overshoot, steady-state error, load disturbance rejection and variable speed tracking. The speed measurements are undesirable in a drive because they add cost and reliability problems, besides the need for a shaft extension and mounting arrangements. Therefore, this study has presented a state estimation technique for speed sensorless FOC of induction motors. A stochastically nonlinear state estimator, Extended Kalman Filter (EKF) is suggested for this purpose. Using this observer, the rotor speed and rotor fluxes are estimated simultaneously. The motor model designed for EKF application involves rotor speed, dq-axis stator currents. A number of simulations were carried out to verify the performance of EKF observer. The performance is tested under variable speed tracking, loaded conditions. Also in the implementation of the EKF different of measurement and state noise covariance matrices may be tried to detect the optimum research which increase performance of the EKF.