Zuriati Yusof
Papers
2
Total Citations
4
H-Index
2
About
Dr. Zuriati Yusof is a distinguished researcher in robotics and computational modeling, with a primary focus on inverse kinematics, neural network applications, and robotic arm control systems. Her work addresses critical challenges in industrial robotics, including end-effector positional accuracy, degree-of-freedom limitations, and trajectory tracking for complex real-world applications. Dr. Yusof’s major contributions include the development of trigonometric tangential mathematical models for inverse kinematic manipulation control and trajectory tracking of robotic arms, as well as innovative spreadsheet-based neural network modeling for training and predicting inverse kinematics. Her research has garnered attention in the field, with her most-cited papers accumulating citations that underscore their relevance to advancing robotic motion competency. Notably, she has pioneered the use of generalized reduced gradient algorithms in neural network training for two-degree-of-freedom planar robot arms, offering practical solutions for heavy payload lifting and long horizontal reach operations. Dr. Yusof’s work bridges theoretical modeling with applied robotics, making her a valuable contributor to the ongoing evolution of industrial automation and intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
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