Pannatee Rakprayoon
Papers
1
Total Citations
4
H-Index
1
About
Pannatee Rakprayoon is a robotics researcher whose work centers on adaptive and repetitive control strategies for robotic manipulators, with a particular focus on real-time performance and computational efficiency. Her most cited paper, "Adaptive and repetitive controller for robotic manipulators with slowly updating scheme using B-spline shape function" (2011), addresses the challenge of compensating for unknown system parameters while minimizing memory usage. In this work, she applies the Desired Compensation Learning Law (DCLL), a control structure that combines a feedforward compensator with a slowly updating learning scheme, offering robustness against parameter uncertainties. Although her citation count is modest, her contribution lies in advancing practical control methods that balance adaptability with low computational overhead—a critical concern for real-world robotic applications. Rakprayoon’s research is particularly relevant for engineers developing manipulators that must operate reliably under varying conditions without requiring extensive onboard memory or processing power. Her work exemplifies the thoughtful integration of theoretical control principles with hardware-constrained implementation.
Research Focus
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Top Papers
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