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MRAS state estimator for speed sensorless ISFOC induction motor drives with Luenberger load torque estimation

Abstract : This paper presents a novel method for estimating the load torque of a sensorless indirect stator flux oriented controlled (ISFOC) induction motor drive based on the model reference adaptive system (MRAS) scheme. As a matter of fact, this method is meant to inter-connect a speed estimator with the load torque observer. For this purpose, a MRAS has been applied to estimate the rotor speed with tuned load torque in order to obtain a high performance ISFOC induction motor drive. The reference and adjustable models, developed in the stationary stator reference frame, are used in the MRAS scheme in an attempt to estimate the speed of the measured terminal voltages and currents. The load torque is estimated by means of a Luenberger observer defined throughout the mechanical equation. Every observer state matrix depends on the mechanical characteristics of the machine taking into account the vicious friction coefficient and inertia moment. Accordingly, some simulation results are presented to validate the proposed method and to highlight the influence of the variation of the inertia moment and the friction coefficient on the speed and the estimated load torque. The experimental results, concerning to the sensorless speed with a load torque estimation, are elaborated in order to validate the effectiveness of the proposed method. The complete sensorless ISFOC with load torque estimation is successfully implemented in real time using a digital signal processor board DSpace DS1104 for a laboratory 3 kW induction motor.
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Contributor : William Domingues Vinhas Connect in order to contact the contributor
Submitted on : Tuesday, February 28, 2017 - 5:08:28 PM
Last modification on : Wednesday, November 3, 2021 - 4:03:06 AM



Youssef Agrebi Zorgani, Yassine Koubaa, Mohamed Boussak. MRAS state estimator for speed sensorless ISFOC induction motor drives with Luenberger load torque estimation. ISA Transactions, Elsevier, 2016, 61, pp.308-317. ⟨10.1016/j.isatra.2015.12.015⟩. ⟨hal-01479260⟩



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