Yudong Zhao
Papers
7
Total Citations
104
H-Index
6
About
Yudong Zhao is a robotics researcher whose work centers on the control and coordination of mobile manipulators and multi-robot systems. His primary contributions lie in developing advanced control strategies—including sliding mode control, Lyapunov-based methods, and neural network compensation—to solve complex problems in formation control and manipulator dynamics. His most influential work, "Lyapunov and Sliding Mode Based Leader-follower Formation Control for Multiple Mobile Robots with an Augmented Distance-angle Strategy" (2019, 35 citations), introduces a robust approach for multi-robot coordination. In another highly cited paper, "PD Control of a Manipulator with Gravity and Inertia Compensation Using an RBF Neural Network" (2020, 30 citations), Zhao demonstrates how neural networks can enhance traditional control methods. His research also explores LQR control with center-of-gravity feedback for mobile manipulators and fuzzy-PD control for 3-DOF robotic arms. Across his publications, Zhao has accumulated over 100 citations, reflecting the practical significance of his work for autonomous systems, warehouse robotics, and human-robot collaboration. His integrated approach—combining kinematic and dynamic controllers with sensor feedback—offers valuable solutions for real-world robotic applications.
Research Focus
Key Achievements
Top Papers
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- 5LQR control for a Mobile Manipulator using COG feedback7 citations · 2015
- 6Control of 3-DOF Robotic Manipulator by Neural Network Based Fuzzy-PD6 citations · 2019
- 7Balancing control of mobile manipulator with sliding mode controller2 citations · 2015