Gengyao Wu

About

Gengyao Wu is a robotics researcher specializing in intelligent control systems for precision industrial automation. His primary research focuses on enhancing robotic arm manipulation through advanced impedance control, neural network optimization, and adaptive PID algorithms. Wu’s most cited work, "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 arising from camera-based terminal localization. By integrating Radial Basis Function neural networks with Particle Swarm Optimization-tuned PID control, his approach significantly improves the accuracy of wiring terminal insertion and extraction—a task where even millimeter-scale deviations can cause assembly failures. This work has accumulated 3 citations, establishing Wu as an emerging voice in the intersection of soft computing and industrial robotics. His contributions are particularly notable for tackling real-world manufacturing bottlenecks, offering a pathway toward more reliable automated assembly in high-stakes electrical infrastructure. As industries push toward full automation, Wu’s hybrid control methodology represents a practical step forward in bridging the gap between theoretical control systems and robust factory-floor performance.

Research Focus

Key Achievements

Top Papers

1. 1
   End-Effector Impedance Control of Robotic Arm Based on Enhanced Neural Network RBF-PID-PSO

   3 citations · 2023

Key Collaborators

Contact & Links