Jing Su
Papers
1
Total Citations
25
H-Index
1
About
Jing Su is a researcher specializing in advanced control systems and mechatronics, with a particular focus on the dynamics of dual-inertia systems. Their most cited work, "Transmission friction measurement and suppression of dual-inertia system based on RBF neural network and nonlinear disturbance observer" (2022, 25 citations), introduces a novel approach to mitigating friction-induced disturbances in mechanical transmissions. By integrating radial basis function (RBF) neural networks with a nonlinear disturbance observer, Su developed a robust framework for real-time friction estimation and suppression, significantly enhancing system precision and stability. This contribution is critical for applications in robotics, precision manufacturing, and electric drives, where accurate motion control is paramount. Su’s work demonstrates a strong interdisciplinary blend of neural network theory and practical control engineering, offering scalable solutions for industrial automation. With growing recognition in the field, their research continues to influence the design of resilient, high-performance mechatronic systems, making them a key figure in advancing intelligent control methodologies.
Research Focus
Key Achievements
Top Papers
- 1