Wenshi Chen

University of Science and Technology Beijing

Papers

Total Citations

233

H-Index

About

Wenshi Chen is a leading researcher in intelligent robotics and fault-tolerant control systems, whose work bridges the gap between theoretical control theory and practical robotic applications. His most influential contribution, "Neural Networks-Based Fault Tolerant Control of a Robot via Fast Terminal Sliding Mode" (2019, 149 citations), introduces a robust control scheme that uses Gaussian radial basis function neural networks to maintain system performance despite actuator failures and uncertainties—a critical advancement for reliable robotic operation in hazardous environments. Chen further advances the field with his work on "Dynamic Movement Primitives Based Robot Skills Learning" (2023, 84 citations), which develops a staged teaching framework that enables robots to learn and replicate complex human-like movements from demonstrations. This research has significant implications for industrial automation and assistive robotics, where robots must adapt to new tasks without explicit programming. With over 230 combined citations for these key papers, Chen's research is widely recognized for its practical impact on making robots more resilient and teachable. His work continues to influence next-generation robotic systems that can operate safely alongside humans in dynamic, unpredictable settings.