Papers
48
Total Citations
2,409
H-Index
16
About
Jing Na is a leading researcher in adaptive control, robotics, and intelligent systems, with particular expertise in parameter estimation, finite-time convergence control, and neural network-based learning for robotic applications. His work has fundamentally advanced how robotic systems handle uncertainty, addressing the critical challenge of unknown kinematics and dynamics in real-world deployment. Among his most influential contributions is the development of adaptive parameter estimation frameworks that achieve both accuracy and rapid convergence — a combination previously underexplored in the field. His 2018 paper on finite-time convergence for robot manipulators has garnered over 460 citations, while his 2014 work on robust adaptive control for nonlinear robotic systems has accumulated nearly 390 citations, reflecting sustained community impact. Na has also made significant strides in dual-arm robot control, proposing adaptive fuzzy and composite learning methods to manage the complex coupled dynamics inherent in bimanual tasks. Beyond specific systems, his contributions extend to force sensorless admittance control using neural networks and unknown system dynamics estimation — practical tools that reduce hardware dependency while maintaining precision. With over 2,000 cumulative citations across his top works, Jing Na stands as a highly influential figure shaping modern intelligent robotic control.
Research Focus
Key Achievements
Top Papers
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- 2Robust adaptive finite‐time parameter estimation and control for robotic systems387 citations · 2014
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- 6Structural parameter identification for 6 DOF industrial robots133 citations · 2017
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