Phu-Nguyen Le
Papers
10
Total Citations
167
H-Index
6
About
Phu-Nguyen Le is a robotics and control systems researcher whose work centers on two interconnected frontiers: robot calibration and fault-tolerant control for industrial manipulators. He has made substantial contributions to enhancing the positional accuracy of robot manipulators through innovative hybrid calibration frameworks that merge model-based identification techniques with artificial intelligence-driven error compensation. His landmark 2019 paper — garnering 69 citations — introduced a groundbreaking method combining geometric error modeling with neural network compensation, setting a foundation for subsequent work that incorporated joint deflection models and advanced optimization algorithms such as Teaching Learning-Based Optimization and Invasive Weed Optimization. A recurring theme across his research is the intelligent selection of measurement poses using genetic algorithms to maximize calibration efficiency. Beyond accuracy enhancement, Le has expanded his expertise into robust control theory, developing adaptive non-singular fast terminal sliding mode controllers and disturbance observers that enable manipulators to maintain performance under faults and uncertainty — including scenarios without velocity measurements. With over 160 cumulative citations and a steadily growing publication record from 2019 onward, Le's work addresses pressing real-world challenges in industrial automation, making his research particularly valuable for engineers and scientists advancing precision robotics applications.
Research Focus
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