Papers
26
Total Citations
319
H-Index
9
About
Silu Chen is a robotics and automation researcher whose work spans robot control, kinematic calibration, and intelligent manufacturing systems. His research has made meaningful contributions across several interconnected domains, accumulating over 230 citations that reflect both breadth and practical relevance. Chen's most influential work addresses cooperative robotic systems, particularly his 2021 paper on adaptive neural synchronized impedance control for manipulators operating under uncertain environments, which has garnered 58 citations and demonstrates his strength in bridging advanced control theory with real-world robustness challenges. His sustained focus on kinematic calibration is evident through multiple high-impact contributions, including self-calibration methods using position and distance constraints and unit dual quaternion-based approaches that improve parameter identification robustness for collaborative and articulated robots. Beyond control and calibration, Chen has pursued green manufacturing, developing practical energy consumption models for industrial robots that circumvent the difficulty of acquiring joint torque data directly. His work also extends into medical robotics — notably force estimation for ear surgical devices — modular reconfigurable robot modeling using graph theory, and vision-based sorting under complex lighting conditions. This diverse yet coherent portfolio positions Chen as a versatile researcher whose contributions meaningfully advance intelligent, precise, and sustainable robotic systems for both industrial and medical applications.
Research Focus
Key Achievements
Top Papers
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- 5A Feasible Method for Evaluating Energy Consumption of Industrial Robots20 citations · 2021
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- 9Precision motion control of a linear piezoelectric ultrasonic motor stage10 citations · 2013
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