Chenfu Yi

Jiangxi University of Science and Technology

Papers

Total Citations

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About

Chenfu Yi is a researcher whose work lies at the intersection of robotics, neural networks, and intelligent control systems. His primary research areas include multirobot coordination, kinematic control of redundant manipulators, and real-time matrix inversion using neural dynamics. Yi’s major contributions are centered on developing advanced controllers for complex robotic tasks. Notably, his 2018 paper on "Intelligent Controllers for Multirobot Competitive and Dynamic Tracking" (15 citations) introduced both centralized and distributed coordination models for target tracking using a subset of fittest robots, addressing critical challenges in multirobot systems with limited communication. In the same year, Yi proposed a nonlinearity activated noise-tolerant zeroing neural network (NANTZNN) for real-time varying matrix inversion (7 citations), a method with broad applications in image processing and robotics. His 2020 work on recurrent neural networks for kinematic control of redundant manipulators (4 citations) further demonstrates his expertise in motion planning for industrial robots. Yi’s research is notable for its practical focus on noise tolerance and real-time performance, making his algorithms suitable for deployment in dynamic, real-world environments. His work continues to influence the fields of robotic control and neural computation.