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Robust control of induction motor with a neural-network load torque estimator and a neural-network identification

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3 Author(s)
Chich-Yi Huang ; Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan ; Tien-Chi Chen ; Ching-Lien Huang

This paper presents a control scheme for an induction motor drive which consists of a compensator, neural network identification (NNI), and neural network load torque estimator (NNLTE) based on the conventional proportional-integral controller. The NNI is a two-layer neural network which uses a projection algorithm to estimate the parameters of the induction motor and to regulate the gain of the compensator such that the response of the induction motor follows that of the nominal plant. The NNLTE is a two-layer neural network which uses the steepest descent algorithm to estimate the load disturbance and forward feed, resulting in equivalent control such that the speed response of the induction motor is robust against the load disturbance. Computer simulations and experimental results demonstrate that the proposed control scheme can obtain a robust speed control

Published in:

Industrial Electronics, IEEE Transactions on  (Volume:46 ,  Issue: 5 )

Date of Publication:

Oct 1999

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