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This article presents a modified stator current-based model reference adaptive system (SC-MRAS) observer for sensorless speed control of induction motor (IM) drives. The proposed MRAS algorithm utilizes the actual value and the estimation error of the stator current in an adjustable model of IM. Sensitivity analysis for the IM resistance variations is also studied. To enhance the robustness of the proposed method, an adaptation mechanism employs a fuzzy logic controller (FLC) instead of a conventional PI controller. A detailed simulation study using MATLAB/Simulink is presented. Experimental validation using a DSP-DS1104-based laboratory prototype is provided. Analytical, simulation, and experimental studies proved that superior performance and accurate speed estimation of the proposed observer at very low speed under different operating conditions are achieved. [ABSTRACT FROM AUTHOR]

A speed control of sensorless induction motor (IM) drives at zero and very low frequencies is designed in this paper. A new adaptive sliding mode observer (SMO) to estimate the stator current, rotor flux, and rotor speed is proposed. To improve the robustness and accuracy of an adaptive SMO during very low frequency operation, the sliding mode flux observer uses independent gains of the correction terms. The gains of current and rotor flux SMOs are designed using Lyapunov stability theory to guarantee the stability and fast convergence of the estimated variables. A Lyapunov function candidate utilizing the error of rotor fluxes and speed estimation error is synthesized for speed estimation. Detailed simulations and experiments are given showing the operation of the sensorless speed control at very low frequency. The results prove the accuracy and robustness of the proposed adaptive SMO. Also, comparison results with the state-of-the-art methods prove that the proposed method shows excellent transient and steady-state speed estimation, particularly at very low and zero frequency operations. [ABSTRACT FROM PUBLISHER]

A novel separation procedure applied to overlap condition of saliencies in induction machine is presented. The machine's current reaction to voltage step excitation is used to obtain saliency signals. The proposed procedure is based on a digital signal processing implementation of the discrete sine and cosine transforms for extracting the slotting saliency signal at close and equal frequencies of the slotting and saturation saliencies under heavy load operation. The proposed algorithms are processed based on an online adaptive threshold. Using the proposed separation algorithm guarantees precise estimation of rotor/flux angle and efficient sensorless control of the induction machine. A sensorless induction motor drive is implemented in the laboratory. Extensive experimental results are presented. A performance comparison between the proposed algorithm and previous works is provided. The robustness and effectiveness of the applied signal processing algorithms for sensorless torque control are verified at low speed and heavy load. [ABSTRACT FROM PUBLISHER]