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Neural network based estimation of feedback signals for a vector controlled induction motor drive

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2 Author(s)
M. G. Simoes ; Dept. of Electr. Eng., Tennessee Univ., Knoxville, TN, USA ; B. K. Bose

Neural networks are showing promise for application in power electronics and motion control systems. So far, they have been applied for a few cases, mainly in the control of converters and drives, but their application in estimation is practically new. The purpose of this paper is to demonstrate that such a technology can be applied for estimation of feedback signals in an induction motor drive with some distinct advantages when compared to DSP based implementation. A feedforward neural network receives the machine terminal signals at the input and calculates flux, torque, and unit vectors (cos θe and sin θe) at the output which are then used in the control of a direct vector-controlled drive system. The three-layer network has been trained extensively by Neural Works Professional II/Plus program to emulate the DSP-based computational characteristics. The performance of the estimator is good and is comparable to that of DSP-based estimation. The system has been operated in the wide torque and speed regions independently with a DSP-based estimator and a neural network-based estimator, and are shown to have comparable performance. The neural network estimator has the advantages of faster execution speed, harmonic ripple immunity, and fault tolerance characteristics compared to DSP-based estimator

Published in:

IEEE Transactions on Industry Applications  (Volume:31 ,  Issue: 3 )