Songli Wu
Papers
1
Total Citations
16
H-Index
1
About
Dr. Songli Wu is a leading researcher in robotics and intelligent control systems, with a primary focus on the robust motion control of wheeled mobile robots. Her most cited work, "Wheeled Mobile Robot RBFNN Dynamic Surface Control Based on Disturbance Observer" (2014, 16 citations), addresses a critical challenge in autonomous navigation: maintaining stability and precision under uncertain parameters and external disturbances. In this study, Dr. Wu pioneered an adaptive neural network dynamic surface control (DSC) method that integrates a nonlinear disturbance observer with radial basis function neural networks (RBFNN). This innovative approach effectively compensates for unknown disturbances, significantly enhancing the robot's trajectory tracking performance in real-world environments. Her contributions are particularly valuable for applications in search-and-rescue, industrial automation, and autonomous logistics, where reliable control in unpredictable conditions is essential. By bridging neural network theory with practical disturbance rejection techniques, Dr. Wu has advanced the state of the art in mobile robotics, providing a foundation for more resilient and adaptive autonomous systems. Her work continues to inspire researchers developing intelligent control strategies for complex, dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1