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An adaptive recurrent-neural-network motion controller for X-Y table in CNC machine.

Faa-Jeng Lin1, Hsin-Jang Shieh, Po-Huang Shieh

  • 1Department of Electrical Engineering, National Dong Hwa University, Hualien 974, Taiwan. linfj@mail.ndhu.edu.tw

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|April 11, 2006
PubMed
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This study introduces an adaptive recurrent neural network (ARNN) for precise motion control in CNC machines. The ARNN system enhances position tracking and robustness against disturbances in permanent magnet synchronous motors.

Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence in Engineering
  • Mechatronics

Background:

  • Computer Numerical Control (CNC) machines rely on precise motion control for accuracy.
  • Permanent Magnet Synchronous Motors (PMSMs) are widely used in CNC due to their efficiency and performance.
  • Existing control systems face challenges with unknown dynamics and external disturbances.

Purpose of the Study:

  • To propose an adaptive recurrent neural network (ARNN) motion control system for biaxial mechanisms in CNC machines.
  • To enhance the position tracking performance and robustness of the control system.
  • To address uncertainties such as approximation errors, external disturbances, and friction torque.

Main Methods:

  • Utilizing a Recurrent Neural Network (RNN) with adaptive learning algorithms derived from Lyapunov stability theorem to approximate unknown system dynamics.

Related Experiment Videos

  • Developing a robust controller to handle system uncertainties and external disturbances.
  • Implementing an adaptive lumped uncertainty estimation law to relax controller requirements.

    • Main Results:

    • Substantial improvement in position tracking performance was achieved.
    • Demonstrated robustness against uncertainties including cross-coupled interference and friction torque.
    • Experimental results validated the effectiveness of the proposed ARNN control system for various reference contours.

    Conclusions:

    • The proposed ARNN control system offers superior motion control for CNC machines.
    • The adaptive and robust features ensure reliable performance in the presence of system uncertainties and disturbances.
    • The developed control strategy is suitable for practical applications requiring high-precision trajectory tracking.