Yilei Wu
Papers
2
Total Citations
44
H-Index
2
About
Yilei Wu is a researcher specializing in intelligent control systems, recurrent neural networks (RNNs), and robotic locomotion. His work focuses on enhancing the robustness and fault tolerance of autonomous systems, particularly bipedal robots. In his highly cited 2007 paper, "Robust Recurrent Neural Network Control of Biped Robot" (25 citations), Wu pioneered the use of RNNs to stabilize dynamic walking gaits under uncertain conditions. He extended this research in 2010 with "A robust training algorithm of discrete-time MIMO RNN and application in fault tolerant control of robotic system" (19 citations), where he developed a novel training algorithm for multi-input, multi-output RNNs, enabling them to maintain performance despite sensor or actuator failures. These contributions are foundational for creating more resilient humanoid robots and industrial manipulators. Wu’s work is frequently referenced in studies on adaptive control and neural-network-based fault diagnosis, demonstrating its lasting impact on the field of robotics and intelligent systems.
Research Focus
Key Achievements
Top Papers
- 1Robust Recurrent Neural Network Control of Biped Robot25 citations · 2007
- 2