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This study introduces a new dynamic sliding mode technique for robotic systems, improving online parameter identification. The method enhances torque prediction accuracy in robotic manipulators.

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • System Identification

Background:

  • Accurate dynamic parameter identification is crucial for robotic control.
  • Existing methods often struggle with real-time performance and noise sensitivity.
  • Online identification in closed-loop systems presents unique challenges.

Purpose of the Study:

  • To develop an online direct closed-loop identification method for robotic applications.
  • To improve the accuracy and reliability of dynamic parameter estimation.
  • To enhance the prediction of system input variables, such as torque.

Main Methods:

  • A novel dynamic sliding mode technique is proposed for direct closed-loop identification.
  • A robust differentiator based on higher-order sliding modes with dynamic gain is utilized.
  • Recursive least squares algorithm is employed for dynamic parameter estimation using sampled trajectory data.

Main Results:

  • The proposed method achieves accurate online estimation of dynamic parameters.
  • Experimental validation on a 2-DOF robot manipulator demonstrates effectiveness and reliability.
  • The new differentiator design significantly improves online parametric identification and torque prediction quality.

Conclusions:

  • The dynamic sliding mode technique offers a robust solution for online closed-loop system identification in robotics.
  • The choice of differentiator design critically impacts identification accuracy and predictive capabilities.
  • This approach provides a reliable method for enhancing robotic system modeling and control.