Shuai Li
Hong Kong Polytechnic University, Swansea University, Stevens Institute of Technology, Lanzhou University, Guangdong Institute of Intelligent Manufacturing, Chongqing Institute of Green and Intelligent Technology, Oulu University of Applied Sciences, VTT Technical Research Centre of Finland, University of Oulu, University of Science and Technology of China, Hangzhou Academy of Agricultural Sciences, Rensselaer Polytechnic Institute, Institute of Intelligent Machines, Zaozhuang University, City University of Hong Kong, Finland University, State Grid Corporation of China (China), Peking University, Taiyuan University of Technology, Beijing Institute of Technology, Southern University of Science and Technology
Papers
168
Total Citations
8,394
H-Index
53
About
Shuai Li is a prominent researcher at the intersection of neural networks, robotic control, and computational intelligence, whose work has fundamentally advanced how intelligent systems solve complex real-time optimization problems. His research focuses primarily on neural network-based control of redundant robot manipulators, recurrent neural network (RNN) architectures for dynamic computation, and distributed multi-robot coordination. Li's most celebrated contributions include developing dynamic neural network frameworks that resolve long-standing challenges in robotic control, such as manipulability optimization and singularity avoidance — work that has garnered over 370 citations. His noise-tolerant Zhang Neural Network models for real-time matrix inversion and quadratic programming have become influential benchmarks, collectively exceeding 500 citations across two landmark papers. His 2018 survey on robot manipulator control using neural networks has rapidly become an essential reference in the field, reflecting his broad command of the discipline. Beyond individual robot control, Li has made significant strides in distributed multi-robot task allocation and cooperative control under limited communication, bridging theoretical neural dynamics with practical robotic systems. With his ten most-cited papers alone accumulating over 2,600 citations, Li's work represents a foundational body of knowledge for researchers and engineers designing next-generation intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Kinematic Control of Redundant Manipulators Using Neural Networks329 citations · 2016
- 3Robot manipulator control using neural networks: A survey312 citations · 2018
- 4
- 5
- 6
- 7Distributed Task Allocation of Multiple Robots: A Control Perspective242 citations · 2016
- 8
- 9Neural Dynamics for Cooperative Control of Redundant Robot Manipulators203 citations · 2018
- 10