Mechanical Sciences
Mechanical Sciences
Mechanical Sciences
Articles | Volume 16, issue 1
Articles | Volume 16, issue 1
https://doi.org/10.5194/ms-16-51-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ms-16-51-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 1
Research article
 | 
24 Jan 2025
Research article |  | 24 Jan 2025

A predefined-time radial basis function (RBF) neural network tracking control method considering actuator faults for a new type of spraying robot

Jingang Zhao, Yinghui Li, Yiwen Li, Binbin Pei, Zhilong Yu, and Zehong Dong

Viewed

Total article views: 846 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
607 209 30 846 62 32 106
  • HTML: 607
  • PDF: 209
  • XML: 30
  • Total: 846
  • Supplement: 62
  • BibTeX: 32
  • EndNote: 106
Views and downloads (calculated since 24 Jan 2025)
Cumulative views and downloads (calculated since 24 Jan 2025)

Viewed (geographical distribution)

Total article views: 822 (including HTML, PDF, and XML) Thereof 822 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 Jan 2026
Download
Short summary
This paper focuses on the PRC method for unknown Euler–Lagrange systems with actuator faults and any bounded initial value. The attitude adjustment mechanism of the self-designed spraying robot is equivalent to a two-joint cooperative robot system. The convergence speed was improved to be within 1s. By comparing with traditional control methods, the improvement in control accuracy and convergence speed has been verified.
Read more
Share