Proceedings of the Intelligent Vehicles '94 Symposium
DOI: 10.1109/ivs.1994.639560
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An error driven hybrid neuro-fuzzy torque/speed controller for electrical vehicle induction motor drive

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Cited by 2 publications
(3 citation statements)
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“…On the other hand, it is extremely tough to create a series of training data for neural networks that can handle all the operating modes. Neuro-FC, which have advantages of both FC and NNC are, therefore, adopted in some cases for inductionmotor control [29], [30].…”
Section: Ev and Hev Drives Controlmentioning
confidence: 99%
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“…On the other hand, it is extremely tough to create a series of training data for neural networks that can handle all the operating modes. Neuro-FC, which have advantages of both FC and NNC are, therefore, adopted in some cases for inductionmotor control [29], [30].…”
Section: Ev and Hev Drives Controlmentioning
confidence: 99%
“…Indeed, it should be noted that classical induction-motor control techniques, such as vector control, are not sufficient to achieve this goal. Therefore, control techniques that maximize the induction-motor efficiency are highly desirable for the fault-tolerant controller [8], [22], [29], [31]- [35].…”
Section: Ev and Hev Drives Controlmentioning
confidence: 99%
“…The ANN-based MPPT in PVHEVs was also used. An offline ANN, trained using the backpropagation and gradient descent momentum algorithm, can be used for online estimation of the reference voltage for the feed-forward loop [280]. In [281], it is proposed that one should use P&O algorithm when the vehicle is parked and voltage-based MPPT algorithm is suitable while driving.…”
Section: Mppt Algorithms Used In Hevsmentioning
confidence: 99%