Papers
10
Total Citations
2,297
H-Index
9
About
Yiting Dong is a prominent researcher specializing in intelligent control systems for robotic manipulators and exoskeletons, with particular expertise in adaptive neural network and fuzzy logic control, impedance control, and constraint handling. His work addresses some of the most challenging real-world problems in robotics, including input saturation, unknown system dynamics, state constraints, and human-robot interaction. Dong's most influential contribution, "Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation" (2015), has garnered an impressive 787 citations, establishing him as a leading voice in combining neural approximation techniques with impedance control frameworks. His follow-up work on adaptive fuzzy neural network control for constrained robots (638 citations) further cemented his reputation by elegantly integrating impedance learning with unknown environmental dynamics. Beyond manipulators, Dong has extended his methodologies to upper limb robotic exoskeletons, demonstrating a commitment to clinically relevant rehabilitation robotics. His repeated use of barrier Lyapunov functions, backstepping techniques, and radial basis function networks reflects a sophisticated and consistent theoretical toolkit. With over 2,000 cumulative citations across his body of work, Dong's research has meaningfully advanced safe, adaptive, and intelligent robotic control for both industrial and assistive applications.
Research Focus
Key Achievements
Top Papers
- 1Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation787 citations · 2015
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- 7UDE-Based Variable Impedance Control of Uncertain Robot Systems84 citations · 2017
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- 9Impedance Control for Coordinated Robots by State and Output Feedback26 citations · 2019
- 10Bounded control of robotic manipulators via fuzzy logic systems3 citations · 2017