Papers
5
Total Citations
24
H-Index
3
About
Ruiqing Luo is a robotics researcher whose work focuses on the dynamic modeling, control, and parameter identification of serial and modular robot joints, with a particular emphasis on friction compensation and trajectory optimization. His major contributions include the development of an adaptive neural computed torque control scheme that improves trajectory tracking accuracy by addressing nonlinear dynamics and time-varying effects like load and temperature—a paper that has garnered 9 citations since 2024. Luo also proposed a novel optimization method for designing exciting trajectories to identify dynamic parameters of serial robots, cited 7 times, which is critical for high-performance robotic tasks and realistic simulations. Additionally, he introduced a nonlinear friction model accounting for velocity and asymmetric load dependency, enhancing robust torque control. His work on differential modular robot joints, including a coupling friction model and the design of a nursing manipulator with a digital twin system, demonstrates practical applications in healthcare robotics. With a growing citation record, Luo’s research is advancing the precision and reliability of robot control systems, making significant strides in both theoretical modeling and real-world robotic implementations.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5