Yongshu Li
Papers
2
Total Citations
3
H-Index
1
About
Yongshu Li is a rising researcher in the field of robotic control systems, with a primary focus on advanced control strategies for robotic manipulators and flexible-joints robots. Their work centers on two key areas: event-triggered control mechanisms and reinforcement learning-based trajectory tracking. Li’s most-cited paper, “Event-Triggered Control for Hamiltonian-Based Flexible-Joints Robots” (2023), introduces a novel modular control strategy that leverages Hamiltonian theory and delay system approaches to ensure precise tracking of link and motor positions, addressing critical challenges in robotic flexibility and energy efficiency. This contribution has garnered 2 citations, establishing a foundation for further exploration. In their more recent work, “Trajectory tracking control for robotic manipulator with disturbances: a double-Q reinforcement learning method” (2025), Li pioneers the integration of double-Q learning to enhance robustness against disturbances, achieving 1 citation despite its novelty. While early in their career, Li’s innovative fusion of Hamiltonian dynamics and reinforcement learning signals a promising trajectory in intelligent robotics, with potential to advance autonomous systems in manufacturing and service applications. Their research exemplifies a commitment to bridging theoretical control theory with practical robotic performance.
Research Focus
Key Achievements
Top Papers
- 1Event-Triggered Control for Hamiltonian-Based Flexible-Joints Robots2 citations · 2023
- 2