Unknown Parameter Identifier Design of Discrete-Time DC Servo Motor Using Artificial Neural Networks

  • Bae, Dong-Seog (Dept. of Automatic Production, Chang-Won Polytechnic Collage) ;
  • Lee, Jang-Myung (Dept. of Electronics Engineering, Pusan National University)
  • Published : 2000.09.01

Abstract

This paper introduces a high-performance speed control system based on artificial neural networks(ANN) to estimate unknown parameters of a DC servo motor. The goal of this research is to keep the rotor speed of the DC servo motor to follow an arbitrary selected trajectory. In detail, the aim is to obtain accurate trajectory control of the speed, specially when the motor and load parameters are unknown. By using an artificial neural network, we can acquire unknown nonlinear dynamics of the motor and the load. A trained neural network identifier combined with a reference model can be used to achieve the trajectory control. The performance of the identification and the control algorithm are evaluated through the simulation and experiment of nonlinear dynamics of the motor and the load using a typical DC servo motor model.

Keywords

References

  1. Proceedings of the fifth international symposium on artificial life and robotics v.2 Parameter identification and trajectory control of a DC motor using neural networks D. S. Bae
  2. 5th IEEE International Workshop on Robot and Human Communication Learning from humans strategies of motor contorl: a challenge for robotic systems design D. Miche
  3. IEEE Transation on Neural Network v.1 Identification & control of dynamical systems using neural network K. S. Narendra;K. Parthasarathy
  4. IEEE Transaction on Energy Conversion v.5 Adaptive tracking conrol for high performance DC drives S. Weerascoorija;M. A. El Sharkawi
  5. IEEE Transaction on Energy Conversion v.6 Identification and control of a DC motor using back-propagation neural network S. Weerasooriya;M. A. El Shakawi
  6. Introduction to Artificial Neural Systems J. M. Zurada
  7. Univ. of New South Wales. Proc. of the 1993 IEEE International Symposium on Intelligent Control Neural networks as complementary direct controller of nonlinear plants Mohammad Bahrami Keith E. Tate
  8. IEEE Transactions on Magnetics v.34 no.5 A neural network model of parametric non-linear hysteretic inductors Cincotti, S.;Marchesi, M.;Serri, M.
  9. IEEE Transations on Neural Networks v.10 no.2 Dynamic neural controllers for induction motor M. A. Brdys;G. J. Kulawski
  10. Neural Networks v.9 no.5 Single neuron with recurrent excitation: Effect of the transmission delay K. Pakdamam;J. F. Vibert;Eric Boussard;N. Azmy