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Natural Frequencies Identification by FEM Applied to a 2-DOF Planar Robot and Its Validation Using MUSIC Algorithm.

Salvador Martínez-Cruz1, Juan P Amézquita-Sánchez1, Gerardo I Pérez-Soto2

  • 1Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, San Juan del Río, Querétaro 76807, Mexico.

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Summary
This summary is machine-generated.

This study validates a novel method for identifying robot natural frequencies (NFs) using the Multiple Signal Classification (MUSIC) algorithm, offering high resolution even in noisy conditions.

Keywords:
2-DOF planar robotMUSIC algorithmfinite element methodnatural frequenciesspectral analysis

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Area of Science:

  • Robotics
  • Mechanical Engineering
  • Signal Processing

Background:

  • Accurate identification of natural frequencies (NFs) is crucial for robot design and control.
  • Traditional methods may struggle with high-resolution identification, especially in noisy environments.

Purpose of the Study:

  • To present and validate a novel methodology for identifying the natural frequencies (NFs) of a two degrees-of-freedom (2-DOF) planar robot.
  • To compare the effectiveness of the Multiple Signal Classification (MUSIC) algorithm against the fast Fourier transform (FFT) method.

Main Methods:

  • Finite Element Method (FEM) analysis using ANSYS™ software for robot modeling.
  • Experimental validation using two distinct 2-DOF planar robots with varying materials and actuators.
  • Vibration signal analysis employing the Multiple Signal Classification (MUSIC) algorithm.

Main Results:

  • The proposed methodology successfully identifies NFs with high resolution, even for closely spaced frequencies.
  • The MUSIC algorithm demonstrates superior performance compared to FFT, particularly for signals with high noise levels.
  • High-frequency resolution was achieved with a short data set, showcasing efficiency.

Conclusions:

  • The MUSIC algorithm-based methodology provides accurate and high-resolution natural frequency identification for planar robots.
  • This approach is robust in noisy conditions and efficient in terms of data requirements.
  • Accurate NF identification aids in path planning and controller gain selection to prevent resonance and ensure stability.