Xingchao Qu

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About

Xingchao Qu is a researcher in robotics and intelligent control systems, with a primary focus on manipulator dynamics and precision motion control. His most-cited work, "Control Method for Flexible Joints in Manipulator Based on BP Neural Network Tuning PI Controller" (2021, 9 citations), addresses a critical challenge in modern robotics: achieving high-performance control in compact, integrated joints. Qu developed a comprehensive model of an integrated joint motor servo system that accounts for gear angle error and frictional interference, building on the double inertia system framework. His key contribution lies in combining BP neural network tuning with traditional PI control, enabling adaptive, real-time compensation for nonlinear disturbances—a significant advance for flexible joint manipulators used in industrial automation and collaborative robotics. This work has been cited by researchers exploring neural network-enhanced control strategies, highlighting its practical relevance. Qu’s research bridges the gap between theoretical control design and real-world robotic applications, offering engineers a robust solution for improving positioning accuracy and stability in lightweight, space-constrained manipulators. His ongoing work continues to push the boundaries of intelligent actuation and servo system optimization.