Anni Zhao
Papers
2
Total Citations
16
H-Index
2
About
Anni Zhao is an emerging researcher specializing in robotics control systems, with a particular focus on parallel robot mechanics, neural network applications, and advanced control algorithm design. Her work centers on the formidable challenges posed by Delta robots — highly nonlinear, over-actuated parallel systems widely used in industrial automation — where she has made meaningful contributions to both control theory and practical implementation. Zhao's most notable research explores how data-driven and machine learning approaches can address the complex mathematical demands of Delta robot operation. Her 2024 study on neural network-enhanced sliding mode control for trajectory tracking (9 citations) demonstrates how intelligent algorithms can improve the performance of classical control methods in highly nonlinear environments. Complementing this, her 2023 investigation into data-driven inverse kinematics approximation using stepper motors (7 citations) offers practical solutions to one of the field's most persistent computational challenges — accurately mapping motor angles to end-effector positions without relying solely on traditional analytical models. Together, these contributions signal Zhao's commitment to bridging theoretical robotics with real-world applicability. For students and researchers working at the intersection of machine learning and robotic control, her growing body of work represents a valuable and timely reference point.
Research Focus
Key Achievements
Top Papers
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