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Repetitive learning control: a Lyapunov-based approach.

W E Dixon1, E Zergeroglu, D M Dawson

  • 1Robotics & Process Syst. Div., Oak Ridge Nat. Lab., TN.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 2, 2008
PubMed
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This study introduces a novel learning-based feedforward control to manage unknown periodic nonlinear dynamics. This approach enables hybrid control schemes for robust robotic system performance.

Area of Science:

  • Robotics
  • Control Theory
  • Machine Learning

Background:

  • Controlling systems with unknown nonlinear dynamics is challenging.
  • Periodic dynamics require specific compensation strategies.

Purpose of the Study:

  • Develop a learning-based feedforward term for systems with unknown periodic nonlinear dynamics.
  • Enable hybrid control schemes combining learning and adaptive techniques.

Main Methods:

  • A straightforward Lyapunov-like stability analysis to generate the learning-based feedforward term.
  • Design of hybrid adaptive/learning control schemes.
  • Application to a robot manipulator for position tracking.

Main Results:

  • Successful compensation for unknown periodic nonlinear dynamics.

Related Experiment Videos

  • Achieved global asymptotic link position tracking for a robot manipulator.
  • Demonstrated the effectiveness of hybrid control strategies.

    • Conclusions:

    • The proposed learning-based feedforward term effectively addresses periodic dynamics.
    • Hybrid control schemes offer a flexible framework for complex control problems.
    • This method enhances robotic system control accuracy and robustness.