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

Autor: Martínez-Cruz S; Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, San Juan del Río, Querétaro 76807, Mexico., Amézquita-Sánchez JP; Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, San Juan del Río, Querétaro 76807, Mexico., Pérez-Soto GI; Facultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Querétaro, Querétaro 76010, Mexico., Rivera-Guillén JR; Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, San Juan del Río, Querétaro 76807, Mexico., Morales-Hernández LA; Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, San Juan del Río, Querétaro 76807, Mexico., Camarillo-Gómez KA; Tecnológico Nacional de México/Instituto Tecnológico de Celaya, Celaya, Guanajuato 38010, Mexico.
Jazyk: angličtina
Zdroj: Sensors (Basel, Switzerland) [Sensors (Basel)] 2021 Feb 09; Vol. 21 (4). Date of Electronic Publication: 2021 Feb 09.
DOI: 10.3390/s21041209
Abstrakt: In this paper, the natural frequencies (NFs) identification by finite element method (FEM) is applied to a two degrees-of-freedom (2-DOF) planar robot, and its validation through a novel experimental methodology, the Multiple Signal Classification (MUSIC) algorithm, is presented. The experimental platforms are two different 2-DOF planar robots with different materials for the links and different types of actuators. The FEM is carried out using ANSYS™ software for the experiments, with vibration signal analysis by MUSIC algorithm. The advantages of the MUSIC algorithm against the commonly used fast Fourier transform (FFT) method are also presented for a synthetic signal contaminated by three different noise levels. The analytical and experimental results show that the proposed methodology identifies the NFs of a high-resolution robot even when they are very closed and when the signal is embedded in high-level noise. Furthermore, the results show that the proposed methodology can obtain a high-frequency resolution with a short sample data set. Identifying the NFs of robots is useful for avoiding such frequencies in the path planning and in the selection of controller gains that establish the bandwidth.
Databáze: MEDLINE