Naimeng Cang
Papers
15
Total Citations
83
H-Index
5
About
Naimeng Cang is an emerging robotics and computational intelligence researcher whose work centers on neural network-based optimization, redundant robot manipulator control, and motion planning. Cang has made significant contributions to the development of zeroing neural network (ZNN) frameworks, advancing both continuous- and discrete-time formulations to solve time-dependent mathematical problems—including equality-constrained quadratic programs, nonlinear equations, and boundary-constrained linear equations—with enhanced robustness against real-world noise disturbances. Their most-cited work (2024, 20 citations) introduced a harmonic noise rejection ZNN specifically designed to maintain accuracy under challenging noise conditions, representing a meaningful step forward in practical neural network deployment for robotic applications. Cang has further extended these foundations to dual-arm robot systems and cooperative motion control, developing adaptive noise rejection strategies that address increasingly complex coordination demands. Their contributions to repetitive motion planning at both velocity and acceleration levels, kinematic modeling of mobile robot manipulators, and visual-inertial SLAM systems demonstrate a broad yet cohesive research vision spanning theoretical optimization and applied robotics. With a body of highly recent work collectively accumulating over 75 citations, Cang is establishing a strong early-career reputation as a versatile and prolific contributor to intelligent robotic systems research.
Research Focus
Key Achievements
Top Papers
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