Pengxin Zhang
Papers
1
Total Citations
16
H-Index
1
About
Pengxin Zhang is a rising researcher in advanced nonlinear control systems, with a primary focus on robotic manipulator control and disturbance rejection. His most impactful work introduces a novel integration of neural networks with dynamic surface control and nonsingular fast terminal sliding mode techniques, addressing critical challenges in precision and robustness for manipulators operating under uncertain disturbances. This approach, detailed in his 2023 paper, has garnered 16 citations, reflecting its timely relevance to the robotics and automation community. Zhang’s contributions lie in enhancing the stability and tracking accuracy of robotic systems, particularly in environments with external perturbations, by combining adaptive neural compensation with sliding mode control to eliminate singularities and chattering. His work is notable for its practical applicability in industrial and service robotics, offering a pathway toward more resilient autonomous manipulation. As an emerging scholar, Zhang’s research bridges theoretical control design and real-world implementation, positioning him as a promising voice in the field of intelligent robotic control.
Research Focus
Key Achievements
Top Papers
- 1