Thai Dinh Kim
Papers
4
Total Citations
37
H-Index
2
About
Thai Dinh Kim is a rising researcher in advanced robotics and nonlinear control systems, with a focus on intelligent, adaptive control strategies for complex robotic platforms. His work centers on developing novel controllers that combine backstepping, sliding mode control, and neural network or fuzzy logic techniques to address the challenges of underactuated and nonlinear systems. A standout contribution is his 2023 paper on "Adaptive Backstepping Hierarchical Sliding Mode Control for 3-Wheeled Mobile Robots Based on RBF Neural Networks," which has already garnered 26 citations, demonstrating significant impact in the field. This work introduces a cooperative controller that integrates backstepping with hierarchical sliding mode and radial basis function neural networks, offering robust trajectory tracking for mobile robots. Kim has also advanced control for robotic manipulators, delta robots, and ball segways, the latter being a particularly challenging underactuated personal carrier robot. His 2022 paper on adaptive fuzzy hierarchical sliding mode control for ball segways showcases his ability to tackle complex balance and tracking problems. With a growing citation record and innovative contributions spanning from 2022 to 2025, Thai Dinh Kim is establishing himself as a key figure in intelligent robotic control.
Research Focus
Key Achievements
Top Papers
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- 4Adaptive Fuzzy Hierarchical Sliding Mode Control for Ball Segway2 citations · 2022