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About

Vu Thi Yen is a prominent researcher specializing in intelligent control systems, with a particular focus on advanced control strategies for industrial robot manipulators. Her work sits at the intersection of fuzzy logic, wavelet theory, neural networks, and robust adaptive control, making significant contributions to the field of automation and robotics engineering. Yen's most celebrated contribution is her development of recurrent fuzzy wavelet neural network (RFWNN) architectures integrated with robust adaptive sliding mode control — a framework that has garnered over 131 citations since its 2018 publication, establishing it as a landmark reference in intelligent robot control. Her broader body of work systematically addresses critical real-world challenges in robotic systems, including nonlinear dynamics, uncertainty, and mechanical nonidealities such as dead-zone effects. By combining sliding mode control with biologically inspired neural network structures, she has delivered controllers that are both theoretically rigorous and practically resilient. Across her six most-cited publications, spanning 2017 to 2020 and accumulating over 329 citations collectively, Yen has demonstrated a sustained and progressive research trajectory — each study refining and expanding upon prior frameworks. Her research provides engineers and roboticists with powerful, adaptable tools for achieving precise, stable trajectory tracking in demanding industrial environments, cementing her reputation as a leading voice in intelligent control systems design.