Papers
6
Total Citations
120
H-Index
4
About
Linju Li is a leading researcher in neural dynamics and intelligent control systems, with a focus on zeroing neural networks (ZNN) and their applications in robotics and autonomous systems. Their major contributions include developing predefined-time and anti-noise ZNN models that dramatically improve convergence speed and robustness for solving time-varying complex Stein equations and matrix flows inversion problems, achieving up to 54 citations for their foundational 2023 work. Li’s innovative approach addresses the critical issue of conservatism in predefined-time ZNN models, as highlighted in their 2022 paper with 33 citations, which has become a key reference in the field. Their work extends to practical applications, including trajectory tracking for wheeled unmanned ground vehicles and uncalibrated visual servo control for robotic endoscopic surgery with remote center of motion constraints. Notably, Li’s 2024 paper on predefined-time adaptive ZNN for UR5 robot control demonstrates the real-world impact of their theories, while their 2025 publications on model-free predictive control and neural network-based surgical robotics showcase their ongoing leadership in advancing intelligent, noise-resistant control systems for complex engineering challenges.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6