Lan Zheng
Papers
1
Total Citations
5
H-Index
1
About
Lan Zheng is a researcher whose work bridges the frontiers of intelligent control systems and neural network modeling, with a particular focus on mobile robotics and trajectory tracking. Their most-cited paper, "Predictive Control of the Mobile Robot under the Deep Long-Short Term Memory Neural Network Model" (2022), addresses a critical challenge in autonomous navigation: the degradation of system performance caused by network data packet loss in trajectory tracking control. By integrating deep long-short term memory (LSTM) neural networks with predictive control theory, Zheng proposed a novel framework that enhances the robustness and stability of mobile robot motion in real-world environments. This work has garnered 5 citations, reflecting its relevance to researchers tackling control system reliability in robotics. Zheng’s contributions lie at the intersection of deep learning and control engineering, offering practical solutions for improving autonomous system performance under adverse network conditions. Their research is particularly valuable for students and engineers working on intelligent transportation, warehouse automation, or any domain requiring precise robot navigation in uncertain communication environments.
Research Focus
Key Achievements
Top Papers
- 1