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

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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.
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