Papers
4
Total Citations
64
H-Index
4
About
Jieyu Tao is a leading researcher in advanced control systems for industrial robotics, with a focus on data-driven and model-free approaches to overcome the fundamental challenges of precision and robustness in manufacturing. Tao’s major contributions center on developing iterative and cascade control strategies that eliminate the need for complex, explicit system models. For instance, their most cited work (24 citations) introduces an iterative data-driven fractional model reference control for precise speed tracking in repetitive tasks, directly addressing unknown system dynamics. Complementing this, a robust cascade path-tracking method using constrained iterative feedback tuning (23 citations) tackles position control errors from disturbances and uncertainties. Further advancing the field, Tao has proposed data-based cascade control for permanent magnet synchronous motors (PMSM)—the core actuators in industrial robots—and developed an improved particle swarm optimization algorithm for dynamic modeling and load identification (8 citations). By pioneering these practical, high-performance control solutions, Tao’s research directly enhances the efficiency and precision of industrial automation, making a tangible impact on modern manufacturing.
Research Focus
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