Papers
5
Total Citations
10
H-Index
2
About
Zujin Jin is a leading researcher in the field of ultra-precision manufacturing, specializing in the robotic processing of large optical mirrors. His work focuses on the critical challenge of achieving nanometer-level surface accuracy for astronomical telescopes and high-energy laser systems. Jin's major contributions lie in developing advanced control and prediction strategies to overcome the inherent flexibility and dynamic disturbances in large-scale robotic systems. He pioneered the use of deep learning for error prediction in large optical mirror processing robots (LOMPR), creating a Bayesian-optimized long short-term memory model that significantly enhances feedforward control compensation. His research on adaptive differential evolution algorithms for optimal scaling parameters and dynamic characteristics analysis of five-degrees-of-freedom hybrid processing robots (5-DOF-HPR) has been foundational. Notably, Jin introduced an adaptive decentralized fuzzy compensation control approach to mitigate correlated disturbances between multiple sub-robots in large optical mirror processing systems (LOMPS). With his most-cited works accumulating citations from 2022 to 2025, Jin's innovative control methodologies are directly improving the motion accuracy and surface quality of optical mirrors, establishing him as a key innovator in precision robotic manufacturing.
Research Focus
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Top Papers
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