Shengda Liu
Papers
8
Total Citations
62
H-Index
6
About
Shengda Liu is a leading researcher in rehabilitation robotics and intelligent control systems, with a focus on enhancing physical human–robot interaction for clinical applications. His major contributions include pioneering the concept of "drivable space" for rehabilitation robots—a framework that expands the smooth, user-driven workspace critical for effective therapy—and developing a multiposture robot capable of supporting the full cycle of lower-limb rehabilitation, from early mobilization to autonomous training. Liu’s work on Pythagorean-Hodograph curve-based trajectory planning for Delta robots has advanced pick-and-place precision in industrial settings, while his iterative learning control strategies for nonlinear differential inclusion systems address complex trajectory tracking in uncertain environments. With over 60 citations across his most-cited papers, his research on multi-mode adaptive control and Kalman filter-based torque estimation has improved safety and comfort for patients at varying recovery stages. Notably, his 2022 study on drivable space and his 2024 multiposture robot design represent key achievements, bridging theoretical control methods with practical rehabilitation devices. Liu’s interdisciplinary approach—merging adaptive control, nonlinear system theory, and robot design—positions him as a pivotal figure in making rehabilitation technology more responsive and effective for diverse clinical needs.
Research Focus
Key Achievements
Top Papers
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- 3Iterative learning control for nonlinear differential inclusion systems9 citations · 2020
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