Guannan Lv
Papers
2
Total Citations
5
H-Index
2
About
Guannan Lv is a researcher advancing the frontier of intelligent robotic control, with a primary focus on addressing dynamical uncertainties in robot manipulators. His work centers on developing high-accuracy tracking control systems that can adapt to unknown and complex environments. Lv’s major contribution lies in pioneering the application of Gaussian Process (GP) models—specifically sparse online Gaussian processes (SOGP)—for real-time, model-free control. This innovative approach allows robots to learn and compensate for unmodeled dynamics, achieving superior tracking precision without requiring a pre-existing mathematical model of the system. His most cited paper, "High-accuracy tracking control for uncertain robot manipulators: a sparse online Gaussian process approach" (2025), has already garnered 3 citations, demonstrating early impact in this niche. A foundational earlier work, "Gaussian Process Based Tracking Control for Robot Manipulators with Dynamical Uncertainties" (2021), established the core methodology, earning 2 citations. Lv’s research is particularly notable for bridging machine learning and robust control theory, offering a practical path toward more autonomous and reliable robotic systems in manufacturing and service applications.
Research Focus
Key Achievements
Top Papers
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- 2