Yanying Zou
Papers
6
Total Citations
36
H-Index
3
About
Yanying Zou is a rising researcher in the field of robotics and neural control, with a focus on advancing the motion planning and fault tolerance of both serial and parallel robotic systems. Her work centers on kinematic redundancy resolution, obstacle avoidance, and joint-limit avoidance for redundant manipulators and Gough–Stewart platforms. Zou’s major contributions include the design and comparison of novel zeroing neural networks (ZNNs) and gradient neural networks (GNNs) enhanced by cerebellar models, which provide non-iterative, real-time solutions to time-varying linear equations and quadratic programming problems. Her most-cited paper, “Novel Neural Controllers for Kinematic Redundancy Resolution of Joint-Constrained Gough–Stewart Robot” (2023, 13 citations), introduces three ZNN-based controllers that outperform traditional methods. Her 2024 paper on enhanced fault-tolerant kinematic control has already garnered 12 citations, reflecting the growing impact of her work. Zou’s research bridges theoretical neural network advances with practical robotic applications, offering safer, more reliable solutions for industrial and medical robotics. Her innovative integration of biological-inspired cerebellar models into neural controllers marks a notable achievement, positioning her as a promising voice in intelligent robotic control.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6