Jinna Fu
Papers
2
Total Citations
20
H-Index
2
About
Jinna Fu is a rising scholar in nonlinear control systems and robotic manipulation, whose work focuses on achieving fast, accurate, and safe motion under physical constraints. Her research bridges planning and control, addressing the critical challenge of state constraints and input saturation that limit real-world robotic performance. In her 2024 paper on robotic manipulators, she developed an integrated planning-control framework that ensures both speed and precision while respecting joint limits and actuator saturation—a contribution that has already garnered 11 citations for its practical relevance. Her earlier 2022 work on optimization-based adaptive neural sliding mode control further advanced the field by combining neural network approximation with sliding mode techniques to deliver rapid, accurate responses for nonlinear systems under multiple constraints, earning 9 citations. Together, these papers establish Fu as a key contributor to constraint-aware control design, offering solutions that are both theoretically rigorous and implementable. Her work is particularly valuable for students and engineers developing next-generation autonomous systems, where safety and performance must coexist.
Research Focus
Key Achievements
Top Papers
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