Papers
11
Total Citations
101
H-Index
5
About
Thanh Quyen Ngo is a robotics and control systems researcher whose work centers on intelligent control strategies for robot manipulators, with a particular focus on adaptive, neural-fuzzy, and wavelet-based control architectures. Over more than a decade of research, Ngo has made significant contributions to the development of sophisticated controllers capable of achieving high-precision trajectory tracking in robotic systems operating under uncertainty and dynamic disturbances. His most-cited work, "Robust Adaptive Neural-Fuzzy Network Tracking Control for Robot Manipulator" (2014, 30 citations), exemplifies his expertise in combining neural networks and fuzzy logic to overcome the limitations of traditional model-based control. Complementary contributions, including adaptive iterative learning control and wavelet fuzzy CMAC systems, further demonstrate his commitment to bridging theoretical rigor with practical robotic applications. Notably, his research extends beyond industrial manipulators to real-world challenges, including de-icing robot systems and a sewerage cleaning robot designed for urban deployment in Ho Chi Minh City. More recently, Ngo has explored advanced architectures such as recurrent CMAC and type-2 fuzzy hybrid controllers, reflecting his continued evolution as a researcher. With a body of work accumulating nearly 100 citations, his contributions offer valuable tools for engineers and researchers tackling complex robotic control problems.
Research Focus
Key Achievements
Top Papers
- 1Robust Adaptive Neural-Fuzzy Network Tracking Control for Robot Manipulator30 citations · 2014
- 2An Adaptive Iterative Learning Control for Robot Manipulator in Task Space21 citations · 2014
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- 6An Application of Modified T2FHC Algorithm in Two-Link Robot Controller4 citations · 2023
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