Chenyang Ran
Papers
2
Total Citations
7
H-Index
2
About
Chenyang Ran is a researcher specializing in control systems and robotics, with a focus on enhancing the performance and robustness of autonomous systems. His major contributions lie in two key areas: advanced nonlinear control and reinforcement learning for robotic skill acquisition. In his highly cited 2022 work, Ran developed a sliding mode controller integrated with an extended state observer, addressing the challenges of nonlinearity, uncertainty, and time-varying disturbances. This design, which has garnered 5 citations, offers improved transient and steady-state performance, making it valuable for real-world applications. Additionally, his 2021 paper on robotic skill learning introduces a novel reinforcement learning algorithm that combines self-imitation and guide rewards. This approach reduces the need for extensive environmental interactions or high-quality demonstrations, a common limitation in the field, and has earned 2 citations. Ran’s work bridges theoretical control design and practical robotics, demonstrating impact through innovative solutions that push the boundaries of adaptive and efficient system control.
Research Focus
Key Achievements
Top Papers
- 1
- 2Learning Robotic Skills via Self-Imitation and Guide Reward2 citations · 2021