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Neural network based dynamic controllers for industrial robots

S Y Oh1, W C Shin, H G Kim

  • 1Department of Electrical Engineering, Pohang University of Science and Technology, South Korea.

International Journal of Neural Systems
|September 1, 1995
PubMed
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This summary is machine-generated.

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This study introduces a novel neural network controller for industrial robots, enhancing dynamic performance and positioning accuracy at high speeds. The developed controller ensures system stability and shows excellent learning capabilities for practical applications.

Area of Science:

  • Robotics
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Industrial robot dynamic performance is critical, especially positioning accuracy at high speeds.
  • Accurate computation of robot dynamics at high servo rates is essential for system stability.
  • Existing controllers may not adequately address complex servo dynamics.

Purpose of the Study:

  • To develop a real-time dynamic controller for industrial robots utilizing neural networks.
  • To enhance the dynamic performance and high-speed positioning accuracy of industrial robots.
  • To improve system stability and reduce sensing requirements through specialized neural network tuning.

Main Methods:

  • Developed an efficient time-selectable hidden layer neural network architecture based on localized system dynamics.

Related Experiment Videos

  • Tuned the neural network architecture to specifically accommodate servo dynamics.
  • Implemented and tested the controller on an industrial robot system.

    • Main Results:

    • The novel neural network controller demonstrated excellent learning and control performance.
    • The controller achieved enhanced mapping accuracy and system stability.
    • Experimental results showed superior performance compared to a conventional controller, particularly when accounting for servo dynamics.

    Conclusions:

    • The developed neural network-based real-time dynamic controller is effective for industrial robots.
    • The controller's ability to learn and adapt to system dynamics, including servo dynamics, offers significant advantages.
    • This approach shows strong potential for practical implementation in industrial robotics for improved performance and accuracy.