Title :
Speed-sensorless vector control of an induction motor using neural network speed estimation
Author :
Kim, Seong-Hwan ; Park, Tae-Sik ; Yoo, Ji-Yoon ; Park, Gwi-Tae
Author_Institution :
Sch. of Electr. & Control Instrum. Eng., Mokpo Nat. Univ., Chonnam, South Korea
fDate :
6/1/2001 12:00:00 AM
Abstract :
In this paper, a novel speed estimation method of an induction motor using neural networks (NNs) is presented. The NN speed estimator is trained online by using the error backpropagation algorithm, and the training starts simultaneously with the induction motor working. The estimated speed is then fed back in the speed control loop, and the speed-sensorless vector drive is realized. The proposed NN speed estimator has shown good performance in the transient and steady states, and also at either variable-speed operation or load variation. The validity and the usefulness of the proposed algorithm are thoroughly verified with experiments on fully digitalized 2.2 kW induction motor drive systems
Keywords :
angular velocity control; backpropagation; electric machine analysis computing; induction motor drives; machine vector control; neural nets; parameter estimation; 2.2 kW; digitalized induction motor drive systems; error backpropagation algorithm; induction motor; load variation; neural network speed estimation; speed control loop; speed-sensorless vector control; speed-sensorless vector drive; steady state; training; transient state; variable-speed operation; Associate members; Backpropagation algorithms; Induction motors; Industrial training; Machine vector control; Mechanical sensors; Neural networks; Nonhomogeneous media; Robustness; Velocity control;
Journal_Title :
Industrial Electronics, IEEE Transactions on
