International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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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)
  • Published : 2005.12.01
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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

References

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