Dongdong Zheng
National University of Singapore, Beijing Institute of Technology
Papers
8
Total Citations
180
H-Index
6
About
Dongdong Zheng is a leading researcher in intelligent robotic control, with a focus on adaptive and learning-based systems that enhance precision and safety in complex environments. His work spans composite learning control, visual servoing, and active vibration suppression, addressing fundamental challenges in robotic manipulation and manufacturing. Zheng’s most cited paper, “Composite learning control of robotic systems: A least squares modulated approach” (2019, 105 citations), introduces a novel framework that combines online learning with deterministic convergence guarantees, significantly improving trajectory tracking accuracy. He has also pioneered prescribed performance control methods that adaptively adjust robot behavior under dynamic constraints, as seen in his 2023 paper on variable prescribed performance boundaries (13 citations). In recent years, Zheng has advanced visual servoing by integrating neural network estimators with spectral adaptive laws (2023, 15 citations), eliminating complex calibration steps. His 2024 work on six-axis acceleration feedback for active vibration control (19 citations) demonstrates practical impact in robotic grinding. With over 180 total citations, Zheng’s contributions are shaping next-generation adaptive systems for manufacturing, service robotics, and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7Adaptive control of robotic servo system with friction compensation6 citations · 2011
- 8Modeling and Control of a New Spherical Robot with Cable Transmission4 citations · 2023