Fengqi Zhang
Papers
1
Total Citations
37
H-Index
1
About
Fengqi Zhang is a robotics researcher specializing in adaptive control systems, nonlinear dynamics, and intelligent autonomous navigation. His most-cited work, "Adaptive sliding mode attitude control of two-wheel mobile robot with an integrated learning-based RBFNN approach" (2022, 37 citations), introduces a novel hybrid framework combining sliding mode control with radial basis function neural networks to stabilize two-wheeled mobile robots under uncertain conditions. This contribution addresses critical challenges in real-time attitude regulation, enhancing robustness against model inaccuracies and external disturbances. Zhang’s research bridges classical control theory with machine learning, offering practical solutions for agile robotics in constrained environments. His work has been recognized for its potential in service robotics and autonomous systems, with the cited paper serving as a foundation for further studies in adaptive learning-based control. By integrating neural network adaptability with sliding mode robustness, Zhang advances the field of intelligent motion control, making his research valuable for engineers developing next-generation mobile robots.
Research Focus
Key Achievements
Top Papers
- 1