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Article Dans Une Revue Neurocomputing Année : 2015

Adaptive fuzzy vector control for a doubly-fed induction motor

Résumé

Abstract This paper presents a new adaptive fuzzy vector controller (AFVC) to handle the torque and speed tracking problem of a doubly-fed induction motor (DFIM) as an alternative to classical PI based vector control method generally used for its simplicity. However, the control performance of DFIM is still influenced by the variations of the parameters, the external load disturbances and perturbations in practical applications. Then, it is difficult to achieve high control performances of DFIM by using conventional PI-type control techniques. The proposed AFVC scheme uses adaptive fuzzy systems to reasonably approximate the uncertain dynamics appearing in the DFIM, relaxing thereby the usual modeling requirement about the DFIM dynamics. Of fundamental interest, it is shown that all the closed-loop signals are bounded and the tracking errors exponentially converge to a residual set. Probing simulation results are given to emphasize the effectiveness of the proposed AFVC system with respect to the usual feedback linearization based vector control (FLVC) system.
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Dates et versions

hal-01136561 , version 1 (27-03-2015)

Identifiants

  • HAL Id : hal-01136561 , version 1

Citer

N. Bounar, Abdesselem Boulkroune, F. Boudjema, Mohammed M'Saad, Mondher Farza. Adaptive fuzzy vector control for a doubly-fed induction motor. Neurocomputing, 2015, 151 (2), pp.Pages 756-769. ⟨hal-01136561⟩
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