Zi-Yuan Dong
Papers
4
Total Citations
30
H-Index
4
About
Zi-Yuan Dong is a rising figure in intelligent robotics and data-driven control, whose work centers on making robots safer, more adaptive, and more resilient. His primary research areas include fault-tolerant control, reinforcement learning, and physical human-robot interaction (pHRI). Dong’s most impactful contribution is a novel reinforcement learning-based model-free adaptive fault-tolerant control algorithm, applied to the Baxter robot, which tackles the notoriously difficult problem of controlling flexible multi-joint manipulators under faults—a paper that has already garnered 16 citations since 2023. He has further advanced the field with a data-driven anti-disturbance fault-tolerant control method for the Franka-Panda robot, addressing the critical challenge of sensor faults in the presence of measurement noise. Demonstrating a commitment to practical safety, Dong has also developed an adaptive-constrained admittance control scheme for pHRI, ensuring robots can interact with humans safely while respecting output constraints. His recent work on model-free adaptive iterative learning control for nonlinear systems with time-varying constraints (2025) continues to push boundaries. With a growing citation record and a focus on real-world robotic platforms, Dong is establishing himself as a key innovator in autonomous and human-assistive robotics.
Research Focus
Key Achievements
Top Papers
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