Jiangtao Han

University of Science and Technology Beijing

Papers

Total Citations

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About

Jiangtao Han is a rising researcher in the field of soft robotics, with a primary focus on the modeling and intelligent control of soft robotic manipulators. His work addresses two critical challenges in the field: accurately capturing the complex, nonlinear dynamics of soft structures and developing robust control strategies that can handle unmodeled dynamics and environmental interference. Han’s most significant contribution is the integration of screw theory with Cosserat rod theory to create a dynamic model for line-driven soft arms, which he then pairs with adaptive neural network controllers. This approach, detailed in his highly cited 2023 paper (24 citations), allows for prescribed motion constraints—a crucial step toward safe and precise operation in real-world applications. His foundational 2020 paper (4 citations) established the core methodology of using neural networks to compensate for system uncertainties. By bridging theoretical modeling with practical control, Han is advancing the reliability and autonomy of soft robots, making them more viable for tasks in human-centric environments, such as medical assistance and delicate manipulation.