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About

Quanwei Wen is a rising researcher in the field of advanced robotics control, with a primary focus on intelligent control strategies for flexible-joint robotic manipulators. His work addresses critical challenges in robotic systems, including output constraints, matched and mismatched disturbances, and real-time performance optimization. Wen's most significant contribution is the development of a disturbance observer-based neural network integral sliding mode control for constrained flexible-joint robotic manipulators, which has garnered 17 citations since 2023. He has also pioneered a model-free sliding mode prescribed performance control method that employs an error-driven nonsingular fast terminal sliding mode to accelerate convergence rates while reducing real-time control torque. Additionally, his estimator and command filtering-based neural network controller for electrically driven flexible-joint manipulators demonstrates innovative approaches to handling system dynamics under disturbance. Though early in his career, Wen's work is gaining traction, with his papers collectively cited over 26 times, establishing him as a promising voice in robotic control theory and its practical applications.