Zibo Guo
Papers
1
Total Citations
3
H-Index
1
About
Zibo Guo is a rising researcher in intelligent robotic control systems, with a primary focus on adaptive control, sliding mode control, and reinforcement learning for robotic manipulators. Their most-cited work, "Adaptive sliding mode control of robotic manipulator based on reinforcement learning" (2024), introduces a novel framework that integrates reinforcement learning with sliding mode control to address the challenges of time-varying, nonlinear, and coupled dynamics in robotic systems. This approach enables parameters' self-adaptive learning, significantly improving tracking performance under uncertainties such as parameter perturbations and external disturbances. While their citation count is currently modest at 3, the work represents a promising step toward more robust and autonomous robotic control. Guo’s research addresses a critical gap in traditional control algorithms, which often lack adaptive learning capabilities, and their work is gaining attention for its potential applications in advanced manufacturing and autonomous robotics. As an emerging scholar, Guo is contributing to the next generation of intelligent control systems that can dynamically adapt to complex, real-world environments.
Research Focus
Key Achievements
Top Papers
- 1