Sheng-peng Li
Papers
2
Total Citations
18
H-Index
2
About
Sheng-peng Li is a researcher in robotics and intelligent control systems, with a focus on enhancing the dexterity and precision of robot hands. His work centers on developing advanced control algorithms that integrate bio-inspired and adaptive techniques to improve robotic manipulation. Li’s most cited paper, “Development of a GA-Fuzzy-Immune PID Controller with Incomplete Derivation for Robot Dexterous Hand” (2014, 16 citations), introduces a novel controller combining genetic algorithms, fuzzy logic, and immune mechanisms to optimize the performance of a robot dexterous hand, specifically modeling the index finger’s control system. This work demonstrates his ability to merge computational intelligence with mechanical design. In another study, “Improved adaptive neural network control for humanoid robot hand in workspace” (2014, 2 citations), Li proposes an adaptive neural network method using rival-penalized competitive learning and recursive orthogonal least-squares algorithms to boost learning capability and control accuracy. While his citation counts are modest, his contributions highlight a systematic approach to tackling complex control challenges in robotics, offering foundational insights for students and researchers interested in intelligent control, neural networks, and humanoid robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2