Papers
3
Total Citations
66
H-Index
2
About
Yuhao Yang is a robotics and control systems researcher whose work focuses on intelligent automation, adaptive sensing, and robot manipulation in complex environments. His most influential contribution is in iterative learning control (ILC) for time-varying systems with variable pass lengths, where he introduced a modified iteration-average operator using recursive interval Gaussian distributions. This work, published in 2019 and cited 58 times, has direct applications to robot manipulators operating under unpredictable conditions. Yang also advanced multi-sensor fusion for composite robot localization and mapping, proposing a nonlinear tightly-coupled framework that addresses challenges such as illumination changes, external disturbances, and cumulative errors. His 2024 paper on this topic lays groundwork for more robust environmental perception. Most recently, in 2025, he developed a continual learning and adaptive sensing framework for target recognition and long-term tracking in smart industrial settings, enabling automated inspection of operators and products. Across his publications, Yang demonstrates a clear trajectory from foundational control theory to applied, real-time robotic perception and tracking systems, making his work highly relevant for researchers in automation, intelligent manufacturing, and autonomous robotics.
Research Focus
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