Papers
4
Total Citations
83
H-Index
3
About
Jialiang Fan is a robotics researcher whose work centers on the control of redundant manipulators—robotic arms with more degrees of freedom than needed for a given task, enabling greater flexibility and dexterity. His major contributions lie at the intersection of motion-force control, noise-tolerant algorithms, and neural dynamics, addressing fundamental challenges in both industrial and academic robotics. Fan’s most cited work, “Data-Driven Motion-Force Control Scheme for Redundant Manipulators: A Kinematic Perspective” (2021, 61 citations), introduces a kinematic approach to managing the complex interplay between motion and force in tasks requiring physical contact, such as assembly or polishing. He further advanced the field with a modified Newton integration algorithm that maintains performance even in noisy environments (2021, 18 citations), a critical improvement for real-world applications. More recently, Fan has explored quadratic programming-based control schemes and admittance-based learning for mobile manipulators, integrating neural dynamics to enable adaptive, compliant behavior. His research is notable for its practical emphasis on robustness and real-time implementation, making it highly relevant for students and engineers working on next-generation robotic systems.
Research Focus
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