Papers
3
Total Citations
14
H-Index
2
About
Nianfeng Shao is a robotics researcher whose work focuses on the control of robotic joints for safe and effective physical human-robot interaction. His primary research areas include adaptive control, impedance control, and the design of series elastic actuators. Shao’s major contributions lie in developing advanced control strategies to overcome fundamental performance limitations in robotic joint impedance rendering. His most cited work, “Adaptive Control of Robot Series Elastic Drive Joint Based on Optimized Radial Basis Function Neural Network” (2021, 11 citations), introduces a neural network-based approach to improve the precision and adaptability of elastic joint control. In subsequent work, Shao has tackled the challenging frequency-dependent nature of impedance control, demonstrating through LMI-based synthesis that impedance matching errors inevitably diverge at high frequencies due to the waterbed effect. His most recent study (2025) proposes a μ-synthesis-based robust impedance controller that explicitly accounts for structured uncertainties like dynamic parameter perturbations and sensor noise, ensuring stable interaction with passive environments. This work represents a significant step toward making robotic joints more reliable and safe for real-world applications, from collaborative manufacturing to assistive robotics.
Research Focus
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Top Papers
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