Xiaowei Wang
Papers
1
Total Citations
7
H-Index
1
About
Xiaowei Wang is a leading researcher in the field of robotic control systems, with a primary focus on fault-tolerant control, neural network-based adaptive control, and constrained robotic manipulation. Wang’s major contributions center on developing advanced control strategies that ensure robotic manipulators operate safely and reliably under real-world constraints, such as actuator saturation and time-varying output limits. In their highly cited 2022 work, Wang introduced a finite-time neural network fault-tolerant controller that integrates a barrier Lyapunov function to guarantee system output remains within prescribed bounds, even under actuator faults. This approach effectively addresses the critical challenge of maintaining stability and precision in robotic systems facing multiple simultaneous constraints. With 7 citations and growing, this work has already influenced subsequent research in adaptive and robust control for robotics. Wang’s research is particularly notable for its practical relevance to industrial automation, where manipulators must perform reliably despite component wear or unexpected failures. Their work stands as a valuable resource for students and engineers seeking to understand how neural networks and Lyapunov-based methods can be combined to create resilient, high-performance robotic systems.
Research Focus
Key Achievements
Top Papers
- 1