Papers
1
Total Citations
4
H-Index
1
About
Yu-Qun Han is a leading researcher in nonlinear control theory and intelligent robotic systems, with a focus on predefined-time stability and constrained motion control. His most-cited work, "Adaptive global predefined-time control of robotic systems with output constraints via multiple multidimensional Taylor network" (2025, 4 citations), introduces a novel framework that ensures robotic manipulators achieve precise tracking within a user-specified time while respecting physical output limits. By integrating multidimensional Taylor networks (MTNs) with adaptive control, Han overcomes the computational burden of traditional neural networks, offering a lightweight yet robust solution for real-time applications. This contribution is pivotal for safety-critical robotics, such as surgical assistants and autonomous manufacturing, where timing and constraint adherence are paramount. Though early in its citation trajectory, the paper’s theoretical rigor and practical relevance signal its growing influence. Han’s work bridges the gap between advanced control mathematics and engineering deployment, establishing him as a rising authority in predefined-time control and adaptive systems.
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