Qinzhi Ji

Papers

Total Citations

H-Index

About

Qinzhi Ji is a leading researcher in the field of intelligent robotic machining, with a primary focus on enhancing the precision and stability of industrial manipulators. His work centers on advanced control theory and machine learning applications for manufacturing, particularly in the challenging domain of stone and material processing. Ji's most significant contribution is the development of a compensation sliding mode control for machining robotic manipulators (MRMs), which integrates a nonlinear disturbance observer to dramatically improve trajectory tracking accuracy and anti-interference capabilities—a critical advancement for high-efficiency, flexible automation. This work has garnered 20 citations, underscoring its impact on robust control design. Additionally, Ji pioneered an improved Quantum Particle Swarm Optimization (QPSO) algorithm to optimize Support Vector Machine (SVM) models for predicting milling forces in white marble, achieving a 6-citation milestone that highlights its value in predictive modeling for robot stone machining. By bridging theoretical control methods with practical machining challenges, Ji’s research directly addresses industry needs for higher quality and stability, establishing him as a key innovator in the intersection of robotics, nonlinear control, and intelligent manufacturing.