Papers
2
Total Citations
7
H-Index
2
About
Kezheng Sun is a leading researcher in the field of robotic manipulation, with a primary focus on adaptive control, uncertain systems, and cooperative robotics. His work addresses critical challenges in task-space tracking for robot manipulators, particularly when kinematic and dynamic parameters are imprecise. In his highly cited 2017 paper, Sun developed an adaptive control framework that eliminates the need for acceleration measurements, solving the complex, nonlinear coupling issues inherent in uncertain kinematics and dynamics—a contribution that has garnered 4 citations and is foundational for real-world robotic applications. Sun has also made significant strides in multi-robot systems, introducing a fuzzy-neural-network based position/force hybrid control strategy for cooperative manipulators handling a common tool. This 2017 work, with 3 citations, tackles the constraints imposed by closed-chain physical structures, ensuring precise coordination between robots. By integrating fuzzy logic and neural networks, Sun’s approach enhances robustness and adaptability in collaborative tasks. His research is instrumental for advancing automation in manufacturing, surgery, and human-robot interaction, offering practical solutions for systems where uncertainty and cooperation are paramount. Sun’s achievements underscore his impact on modern robotics, making him a key figure for students and researchers exploring adaptive and cooperative control.
Research Focus
Key Achievements
Top Papers
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