Trajectory-tracking control of the UR10 manipulator based on radial basis function neural network and super-twisting sliding mode

Ma, Xiaole; Jing, Chenghu; Zhang, Kun; Chen, Chen; Wang, Yanfeng

doi:10.5194/ms-17-759-2026

Articles | Volume 17, issue 2

https://doi.org/10.5194/ms-17-759-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/ms-17-759-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 2

Research article

|

27 Jul 2026

Research article |

| 27 Jul 2026

Trajectory-tracking control of the UR10 manipulator based on radial basis function neural network and super-twisting sliding mode

Xiaole Ma, Chenghu Jing, Kun Zhang, Chen Chen, and Yanfeng Wang

Metrics will be available soon.

Latest update: 28 Jul 2026

Short summary

We developed an improved control method for UR10 robot arms, which typically face issues with unpredictable forces and friction. By combining an online-learning neural network with a robust sliding-mode controller, our approach enables more accurate trajectory tracking and eliminates jitter.