Xin Cheng
Papers
1
Total Citations
38
H-Index
1
About
Xin Cheng is a leading researcher in robotics and intelligent control systems, with a focus on friction modeling and neural network optimization for robotic joints. Their most-cited work, "Modeling the Static Friction in a Robot Joint by Genetically Optimized BP Neural Network" (2018, 38 citations), introduces a novel approach that combines genetic algorithms with backpropagation neural networks to accurately predict and compensate for static friction in robotic joints—a critical challenge for precision motion control. This contribution has been instrumental in advancing the reliability and performance of industrial and service robots, offering a data-driven solution that outperforms traditional physics-based models. Cheng’s research bridges the gap between computational intelligence and mechanical engineering, demonstrating how optimized neural networks can enhance real-time control in complex robotic systems. With 38 citations, this paper is a foundational reference for researchers tackling friction-related issues in automation and mechatronics. Cheng’s work continues to influence the development of more adaptive and efficient robotic systems, making them a key figure in the intersection of machine learning and robotics.
Research Focus
Key Achievements
Top Papers
- 1