Zineb Kandoussi

This paper deals with sensolress induction machine (IM) based on Extended Kalman Filter(EKF) for sensorless speed and stator resistance estimation using measured stator currents, stator voltages and estimated fluxes. As known, the use of a speed sensor to obtain speed, the rotor position or both increase highly sensitivity to electromagnetic noise and system complexity. The extended Kalman filter gives very high performances for the estimation of the speed at very low speed even with noises, thanks to two matrices Q and R which guarantee convergences of the speed and the stator resistance. Performances of the proposed EKF have been validated through Hardware implementation using Matlab/Simulink and dSPACE system with DS1104 controller board. The obtained results confirm very satisfying performances of the introduced EKF algorithm in term of dynamic performance.

This paper describes a nonlinear control of the induction motor (IM) drive. The control strategy is based on backstepping approach in order to ensure a high performance of speed control and a better dynamic under uncertainties and external disturbances. The backstepping nonlinear control is developed using a non-linear law for currents, speed and flux, instead of vector control strategy. The performances of this control strategy have been examined through simulation and experimental results, using Matlab/Simulink and dSPACE system with DS1104 controller board. Obtained results show the robustness of the control algorithm for both load disturbances and speed variation.

Purpose: The purpose of this paper is to improve the performance of sensorless vector control of induction motor drives by developing a new sliding mode observer for rotor speed and fluxes estimation from measured stator currents and voltages and estimated stator currents. Design/methodology/approach: In the present paper, the discontinuity in the sliding mode observer is smoothed inside a thin boundary layer using fuzzy logic techniques instead of sign function to reduce efficiently the chattering phenomenon that affects the rotor speed. Findings: The feasibility of the proposed fuzzy sliding mode observer has been verified by experimentation. The experimental results are obtained with a 1 kW induction motor using a dSPACE system with DS1104 controller board showing clearly the effectiveness of the proposed approach in terms of dynamic performance compared to the classical sliding mode observer. Practical implications: The experimental results of the whole control structure highlights that this kind of sensorless induction motor drive can be used for variable speed drive in industrial applications such as oil drilling, electric vehicles, high speed trains (HSTs) and conveyers. Such drives may work properly at zero and low speed in both directions of rotation. Originality/value: Both the proposed speed observer and the classical sliding mode observer have been developed and implemented experimentally with other adaptive observers for detailed comparison under different operating conditions, such as parameter variation, no-load/load disturbances and speed variations in different speed operation regions.

In recent decades, several Induction Motor drives techniques without speed sensor have been proposed. In this paper, an improved Sliding Mode observer for rotor fluxes and speed estimation from measured stator terminal voltages and currents is presented. It introduces a Fuzzy logic algorithm instead on the sign function in order to reduce the chattering caused by the classical Sliding Mode observer. The stability of this observer is proved by the Lyapunov's theorem and the feasibility of the proposed method is verified by simulation under Matlab /Simulink.

In the last years, several Field Oriented Control (FOC) methods of induction motors without rational transducers have been proposed. The major drawback of most of these methods is there sensitivity to motor parameters variations especially the stator resistance that affects the drive performances at low speeds. Therefore, simultaneous estimation of rotor speed and stator resistance from measured stator terminal voltages and currents is proposed using Luenberger adaptive observer approach. The stability of this observer is proved by the Lyapunov's theorem and the feasibility of the proposed method is verified by simulation under Matlab /Simulink.