Z. Shi
Papers
1
Total Citations
2
H-Index
1
About
Z. Shi is a researcher whose work focuses on advancing the dynamic modeling and parameter estimation of industrial robotic systems. Their key contributions lie in developing innovative optimization techniques to improve the accuracy and efficiency of robot manipulator control. In their most cited work, Shi introduced a mutating particle swarm optimization (MuPSO) algorithm to estimate the unknown dynamic parameters of the first three joints of a six-degree-of-freedom industrial robot arm. By employing a finite Fourier series to design excitation trajectories, this approach significantly enhances the precision of dynamic models, which is critical for high-performance robotic applications. Although early in their citation impact, with this paper garnering 2 citations, Shi’s work represents a meaningful step toward more adaptive and robust industrial automation. Their research is particularly valuable for engineers and roboticists seeking to optimize manipulator performance in manufacturing settings. As Shi continues to refine these methods, their contributions hold promise for reducing computational complexity and improving real-time control in complex robotic environments.
Research Focus
Key Achievements
Top Papers
- 1