Papers
1
Total Citations
4
H-Index
1
About
Xiang Xue’s research centers on advanced control systems, with a particular focus on nonlinear model predictive control and iterative learning control for robotic applications. Their most-cited work, “Nonlinear model predictive iterative learning control for robotic system” (2012), introduced a novel NMPILC framework that leverages a fuzzy model composed of local linear models to describe nonlinear plant dynamics. This approach enables the controller to utilize both past trajectory data and real-time measurements, significantly improving tracking performance and robustness in repetitive robotic tasks. While their citation count is modest, this contribution represents a foundational step in bridging model predictive control with iterative learning—a niche but impactful area for precision robotics. Xue’s work is particularly valuable for researchers exploring adaptive, data-driven control strategies in manufacturing and automation. By integrating fuzzy modeling with predictive control, they have provided a practical pathway for enhancing the efficiency and accuracy of robotic systems operating under nonlinear and uncertain conditions.
Research Focus
Key Achievements
Top Papers
- 1Nonlinear model predictive iterative learning control for robotic system4 citations · 2012