Yuping Sun
Papers
1
Total Citations
124
H-Index
1
About
Yuping Sun is a leading researcher in robotics and neural network control, with a primary focus on solving complex kinematic challenges in redundant robot manipulators. Their most influential work, "A Varying-Parameter Convergent-Differential Neural Network for Solving Joint-Angular-Drift Problems of Redundant Robot Manipulators" (2018), has garnered 124 citations, establishing a novel framework that addresses the persistent issue of joint-angular drift. Sun’s major contribution lies in developing a varying-parameter convergent-differential neural network (VP-CDNN), which integrates a quadratic program-based feedback-considered joint-angular-drift-free (FC-JADF) scheme. This innovation enables real-time, precise control of robotic arms, significantly enhancing their stability and accuracy during repetitive tasks. By introducing dynamic parameter adjustments, Sun’s work overcomes limitations of traditional fixed-parameter neural networks, offering a robust solution for industrial automation and advanced robotics. Their research has profound implications for manufacturing, surgical robotics, and autonomous systems, where drift-free motion is critical. Sun’s achievements reflect a deep commitment to advancing computational intelligence and mechanical control, making them a respected figure in the field.
Research Focus
Key Achievements
Top Papers
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