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Sensorless Speed Control System Using a Neural Network  

Huh Sung-Hoe (Dept. of Electrical Engineering, Korea University)
Lee Kyo-Beum (Dept. of Electrical Engineering, Korea University)
Kim Dong-Won (Dept. of Electrical Engineering, Korea University)
Choy Ick (Dept. of Information and Control Engineering, Kwangwoon University)
Park Gwi-Tae (Dept. of Electrical Engineering, Korea University)
Publication Information
International Journal of Control, Automation, and Systems / v.3, no.4, 2005 , pp. 612-619 More about this Journal
Abstract
A robust adaptive speed sensorless induction motor direct torque control (DTC) using a neural network (NN) is presented in this paper. The inherent lumped uncertainties of the induction motor DTC system such as parametric uncertainty, external load disturbance and unmodeled dynamics are approximated by the NN. An additional robust control term is introduced to compensate for the reconstruction error. A control law and adaptive laws for the weights in the NN, as well as the bounding constant of the lumped uncertainties are established so that the whole closed-loop system is stable in the sense of Lyapunov. The effect of the speed estimation error is analyzed, and the stability proof of the control system is also proved. Experimental results as well as computer simulations are presented to show the validity and efficiency of the proposed system.
Keywords
Neural network (NN); uncertainty observer; robust adaptive speed sensorless control; speed estimation error;
Citations & Related Records

Times Cited By Web Of Science : 3  (Related Records In Web of Science)
Times Cited By SCOPUS : 6
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