Papers
1
Total Citations
4
H-Index
1
About
Wanye Yao’s research lies at the intersection of intelligent control systems, robotics, and neural-fuzzy modeling, with a focus on enhancing the precision and adaptability of autonomous manipulators. His most-cited work, “Fuzzy-neural net based control strategy for robot manipulator trajectory tracking” (2004), introduced a pioneering hybrid control scheme that combines an improved backpropagation neural network as a feed-forward controller with a fuzzy logic feedback controller. This approach enables robot manipulators to achieve accurate trajectory tracking even when their dynamic models are unknown—a critical challenge in real-world automation. Though his citation count (4) modestly reflects the niche technical audience, the paper’s conceptual contribution to adaptive control has influenced subsequent research in intelligent robotics. Yao’s work exemplifies how integrating neural learning with fuzzy reasoning can bridge the gap between theoretical control methods and practical robotic applications, offering a foundation for more robust, model-free control strategies in manufacturing, prosthetics, and autonomous systems.
Research Focus
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Top Papers
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