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Identification of Differential Drive Robot Dynamic Model Parameters.

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This study identifies mathematical model parameters for a two-wheeled mobile robot using offline and online methods. Combining offline and online identification enhances parameter accuracy for robot control systems.

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Mathematical Modeling

Background:

  • Accurate mathematical models are crucial for controlling mobile robots.
  • Parameter identification is essential for refining robot dynamics models.
  • Existing methods may require improvements in efficiency and accuracy.

Purpose of the Study:

  • To identify the mathematical model parameters of a differential-drive two-wheeled mobile robot.
  • To compare offline (Levenberg-Marquardt) and online (Recursive Least Squares) identification methods.
  • To propose a hybrid approach combining offline and online identification for improved accuracy.

Main Methods:

  • Offline parameter identification using the Levenberg-Marquardt algorithm.
  • Online parameter identification employing the Recursive Least Squares method.
  • Comparative analysis of identified parameters from both methods.

Main Results:

  • Successful identification of robot dynamics model parameters using both offline and online techniques.
  • Demonstrated that supporting Recursive Least Squares with offline results improves parameter accuracy.
  • Verified the correctness of parameter identification and control system performance through simulations and laboratory tests.

Conclusions:

  • The proposed hybrid identification approach enhances the accuracy of mobile robot models.
  • The validated model and control system ensure reliable robot trajectory tracking.
  • Error analysis confirmed the quality of regulation achieved by the proposed algorithm.