Papers
5
Total Citations
52
H-Index
4
About
Cheng Long is a researcher whose work sits at the intersection of robotics, control systems, and intelligent automation. His primary research areas include learning from demonstration (LfD), dynamic system stability, and adaptive control for manufacturing applications. Long’s most cited work, “Application of the redundant servomotor approach to design of path generator with dynamic performance improvement” (2011, 33 citations), demonstrates his early focus on enhancing robotic path generation through innovative motor control strategies. More recently, he has advanced adaptive impedance control for precision manufacturing, as seen in his 2024 paper on blade polishing using Kalman filter-based methods. A significant contribution is his work on learning stable dynamic systems with Lyapunov energy functions using neural networks, which addresses the critical challenge of balancing learning accuracy with system stability in LfD—a foundational problem in robotics. Long has also explored cross-disciplinary applications, including learning English writing skills from images, showcasing the versatility of his LfD algorithms. With a career spanning over a decade, his research continues to influence both theoretical developments in dynamic system learning and practical implementations in robotic manufacturing and skill acquisition.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4Neural Information Processing4 citations · 2018
- 5Learning English Writing Skills from Images2 citations · 2023