Shuyang Chen
Papers
12
Total Citations
196
H-Index
8
About
Shuyang Chen is a robotics and control systems researcher whose work sits at the intersection of machine learning, motion control, and intelligent automation. His most significant contributions lie in advancing trajectory tracking control for industrial and flexible-joint robots, leveraging neural networks and iterative learning control (ILC) to overcome the limitations of closed proprietary servo systems. His 2021 paper on multi-layer neural network training via ILC has garnered 50 citations, establishing him as a notable voice in data-driven robot control. Complementing this, his work on adaptive neural control for flexible-joint manipulators addresses the real-world challenges faced by collaborative and space robots operating under uncertain dynamics. Beyond pure motion control, Chen has made meaningful contributions to applied robotics, including robotic deep rolling for industrial surface treatment, sensor-guided large-structure assembly, and medical robotics — notably a machine learning approach to respiratory motion tracking for abdominal radiation therapy and an ultrasound-guided prostate brachytherapy system. His earlier exploration of triple-state polymer actuators reveals a breadth of curiosity extending into soft robotics. With over 190 total citations across a diverse but cohesive portfolio, Chen represents an emerging researcher making tangible contributions to precision robotics across manufacturing, medical, and adaptive control domains.
Research Focus
Key Achievements
Top Papers
- 1
- 2Robotic Deep Rolling With Iterative Learning Motion and Force Control31 citations · 2020
- 3
- 4
- 5
- 6
- 7Sensor-Guided Assembly of Segmented Structures with Industrial Robots10 citations · 2021
- 8
- 9Software Framework for Robot-Assisted Large Structure Assembly7 citations · 2018
- 10Integration of a Low-Cost Three-Axis Sensor for Robot Force Control6 citations · 2018