Papers
1
Total Citations
3
H-Index
1
About
Ang Ma is a robotics researcher whose work centers on intelligent control systems for precision manufacturing, with a particular focus on improving robotic arm accuracy in industrial assembly tasks. His most cited paper, "End-Effector Impedance Control of Robotic Arm Based on Enhanced Neural Network RBF-PID-PSO" (2023), addresses a critical challenge in electrical power transformer calibration: the positional errors that occur when robotic arms rely solely on camera-based terminal positioning for wiring insertion and extraction. By integrating an enhanced Radial Basis Function neural network with PID control and Particle Swarm Optimization, Ma developed a novel impedance control framework that significantly improves end-effector precision, ensuring exact assembly of transformer terminals where conventional vision-guided approaches fail. This work has garnered 3 citations and represents a meaningful contribution to the intersection of adaptive control theory and practical industrial robotics. Ma's research demonstrates how advanced neural network architectures can be leveraged to overcome the limitations of traditional sensor-based robotic systems, offering tangible solutions for high-stakes manufacturing environments where millimeter-level accuracy is essential.
Research Focus
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Top Papers
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