Laicheng Yan

About

Laicheng Yan is a leading researcher in robotics and neural dynamics, with a focus on motion planning and kinematic control for redundant robot manipulators. His work is distinguished by pioneering contributions to noise-tolerant algorithms, addressing a critical challenge in real-world robotic systems: the presence of truncation, rounding, and model uncertainty. Yan’s most-cited paper, “The Application of Noise-Tolerant ZD Design Formula to Robots’ Kinematic Control via Time-Varying Nonlinear Equations Solving” (2017, 84 citations), introduced a novel Zhang dynamics (ZD) formula that enables robust control under noisy conditions. He further advanced the field with a pseudoinverse-based path-planning scheme incorporating PID characteristics (2017, 79 citations), which significantly improves precision in noisy environments. Yan also developed a noise-tolerant obstacle avoidance scheme (2018, 20 citations) and applied Li-function activated recurrent neural networks to acceleration-level control (2016, 14 citations). Collectively, his research has garnered over 197 citations, establishing him as a key innovator in making robotic manipulators more resilient and reliable. Yan’s work is essential reading for students and researchers exploring neural dynamics, time-varying problem-solving, and practical robot control.

Research Focus

Key Achievements

Top Papers

1. 1
   The Application of Noise-Tolerant ZD Design Formula to Robots’ Kinematic Control via Time-Varying Nonlinear Equations Solving

   84 citations · 2017
2. 2
   New Pseudoinverse-Based Path-Planning Scheme With PID Characteristic for Redundant Robot Manipulators in the Presence of Noise

   79 citations · 2017
3. 3
   A New Noise-Tolerant Obstacle Avoidance Scheme for Motion Planning of Redundant Robot Manipulators

   20 citations · 2018
4. 4
   The application of Li-function activated RNN to acceleration-level robots' kinematic control via time-varying matrix inversion

   14 citations · 2016

Key Collaborators

Contact & Links