Yujuan Tao
Papers
2
Total Citations
128
H-Index
2
About
Yujuan Tao is a rising researcher in the field of networked control systems, with a focused expertise in iterative learning control (ILC) and communication-constrained signal processing. Her most impactful work, "Quantized iterative learning control of communication-constrained systems with encoding and decoding mechanism" (2024), has garnered 126 citations, establishing her as a key voice in addressing bandwidth limitations in network control systems (NCSs). Tao’s major contribution lies in developing a novel quantized ILC framework that integrates encoding-decoding mechanisms to mitigate data dropouts under high network loads—a critical challenge for real-world automation and remote control applications. In her earlier work (2023), she advanced this line of inquiry by formulating a gradient-based optimization approach for quantized ILC, constructing a mathematical cost function to derive an optimal control law for linear time-invariant systems. Together, these studies demonstrate Tao’s ability to bridge theoretical control theory with practical communication constraints, offering scalable solutions for modern industrial networks. Her research is particularly notable for its direct relevance to smart manufacturing, autonomous vehicles, and teleoperation systems, where reliable control under limited bandwidth is essential. With her recent high-impact publication, Tao is poised to become a leading figure in the intersection of control theory and networked communication.
Research Focus
Key Achievements
Top Papers
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