Papers
12
Total Citations
380
H-Index
8
About
Zexin Li is a computational intelligence and robotics researcher whose work bridges neural network theory and real-world robotic applications. His most significant contributions lie in zeroing neural networks (ZNN), recurrent neural network (RNN) architectures, and motion planning for redundant robot manipulators — areas in which he has established himself as a productive and influential voice. Li's foundational work on ZNN models for solving time-varying linear equations and inequality systems (101 citations) demonstrated how neural computation could address real-time mathematical challenges previously resistant to efficient solutions. Complementing this, his discrete-time RNN framework (46 citations) advanced online nonlinear equation solving with proven precision guarantees. In robotics, Li made notable strides in repetitive motion planning, developing noise-suppressive schemes (72 citations) and high-precision joint-angle repeatability methods (50 citations) that meaningfully elevated the reliability of redundant manipulator control under real-world conditions. More recently, Li has expanded into autonomous systems and real-time computing, exploring on-device deep reinforcement learning for adaptive robotics and systematic scheduling frameworks for dynamic robotic environments. With cumulative citations exceeding 370 across his published work, Li's research consistently connects rigorous mathematical modeling with practical engineering demands, making his contributions valuable reading for students and researchers working at the intersection of neural computation, control theory, and intelligent robotics.
Research Focus
Key Achievements
Top Papers
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- 3Repetitive Motion Planning of Robotic Manipulators With Guaranteed Precision50 citations · 2020
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- 6Acceleration-Level Configuration Adjustment Scheme for Robot Manipulators22 citations · 2020
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- 8New P-type RMPC Scheme for Redundant Robot Manipulators in Noisy Environment12 citations · 2019
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