Papers
26
Total Citations
311
H-Index
11
About
Yawu Wang is a prominent researcher specializing in smart materials, soft robotics, and intelligent actuator systems, with particular expertise in dielectric elastomer actuators (DEAs) and liquid crystal elastomers (LCEs). His work sits at the intersection of advanced materials science, control theory, and machine learning, addressing some of the most pressing challenges in next-generation robotic systems. Wang's most significant contributions center on the modeling and control of soft actuators. His investigations into DEAs have produced sophisticated dynamic models, including conical geometries and fractional calculus-based approaches, while his neural network-driven control strategies — employing GRU networks, NARX architectures, and iterative learning control — have pushed the boundaries of precise trajectory tracking, even at high frequencies. Equally notable is his work on LCEs, exploring photo-responsive actuation and carbon nanotube-enhanced nanocomposites to advance light-driven soft robotics. Beyond materials and actuation, Wang has contributed to gesture recognition using Multi-SVM and Dempster–Shafer theory, and to underactuated mechanical systems via intelligent optimization. Collectively, his published papers have accumulated over 235 citations, underscoring the breadth and relevance of his research. Wang's work offers valuable frameworks for students and engineers seeking to design smarter, more responsive robotic systems.
Research Focus
Key Achievements
Top Papers
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- 2Modeling of photo-responsive liquid crystal elastomer actuators35 citations · 2021
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- 5Dynamic modeling of dielectric elastomer actuator with conical shape20 citations · 2020
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