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Summary
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This study introduces a hybrid control method for tendon-driven continuum robots, improving tracking accuracy and stability without prior data. The approach enhances real-time performance for adaptable robotic systems.

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Area of Science:

  • Robotics
  • Control Systems
  • Mechanical Engineering

Background:

  • Flexible continuum robots offer high adaptability but face challenges in accurate modeling and control due to nonlinearities.
  • Existing methods often require extensive data collection and training, limiting their real-time applicability.

Purpose of the Study:

  • To propose a hybrid model-based and online data-driven control method for tendon-driven continuum robots.
  • To enhance the accuracy and efficiency of continuum robot control without prior dataset requirements.

Main Methods:

  • A hybrid approach combining a piecewise constant curvature model with online Jacobian error compensation using a Kalman filter.
  • Implementing constraints on consecutive Jacobian estimates to ensure real-time stability and reduce fluctuations.

Main Results:

  • The proposed hybrid method significantly improves tracking accuracy for continuum robots.
  • The approach demonstrates robustness against external disturbances, validating its real-time effectiveness.
  • No prior dataset collection or offline training was necessary for implementation.

Conclusions:

  • The hybrid model-based and online data-driven control method is effective for tendon-driven continuum robots.
  • This approach offers a practical solution for enhancing the performance and robustness of adaptable robotic systems.
  • The method advances real-time control strategies for complex, nonlinear robotic systems.