Sensorless backstepping control using a Luenberger observer for double-star induction motor

In this paper, we propose sensorless backstepping control of a double-star induction machine (DSIM). First, the backstepping approach is designed to steer the flux and speed variables to their references and to compensate uncertainties. Lyapunov"s theory is used and it demonstrates that the dynamic tracking of trajectories tracking is asymptotically stable. Second, unfortunately, this law control called sophisticated is a major problem which leads to the necessity of using a mechanical sensor (speed, load torque). This imposes an additional cost and increases the complexity of the montage. In practice, this variable is unknown and its measurement is expensive. To restrain this problem we estimate speed and load torque by using a Luenberger observer (LO). Simulation results are provided to illustrate the performance of the proposed approach in high and low variable speeds and load torque disturbance.