Tianqing Yuan
Papers
6
Total Citations
86
H-Index
3
About
Tianqing Yuan is a researcher specializing in advanced motor control, robotics servo systems, and intelligent control algorithms, with a particular focus on permanent magnet synchronous motors (PMSMs) and their applications in industrial robotics. His most significant contribution is a novel direct torque control (NDTC) scheme utilizing composite active vectors and discrete duty ratio modulation, which substantially reduces torque and flux ripple errors prevalent in conventional DTC approaches — a paper that has garnered 55 citations since its 2019 publication and stands as a landmark contribution to high-precision industrial robot servo control. Yuan has also made notable advances in sensorless motor control, proposing a Super-Twisting Sliding Mode Observer for rotor position estimation that circumvents the limitations of physical position sensors in robotic applications. His broader body of work encompasses SVM-based direct torque control for improved motion quality, radial basis function neural networks optimized with improved gravitational search algorithms for servo system modeling, adaptive notch filtering, and proportional-resonant current control strategies. Collectively, Yuan's research addresses critical challenges in precision, reliability, and performance within industrial robot drive systems, making him a meaningful contributor to the intersection of power electronics and intelligent robotics control.
Research Focus
Key Achievements
Top Papers
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- 4Modeling Method for Robot Servo System Based on IGSA-RBFNN3 citations · 2018
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