Kiem Nguyentien
Papers
1
Total Citations
7
H-Index
1
About
Dr. Kiem Nguyentien is a leading figure in intelligent control systems for mobile robotics, with a focused expertise in adaptive and neural network-based control strategies. His most impactful work addresses one of the most challenging problems in autonomous navigation: maintaining precise trajectory tracking under real-world uncertainties. His seminal 2014 paper, cited 7 times, introduces a novel neural network-based adaptive sliding mode control (ASMC) method for nonholonomic wheeled mobile robots (WMRs). This work is notable for its comprehensive handling of simultaneous unknown wheel slips, model uncertainties, and bounded external disturbances—a combination rarely addressed in a single framework. A key innovation is the use of Self-Recurrent Wavelet Neural Networks (SRWNN) to approximate unknown nonlinear dynamics online, enabling robust, adaptive performance without prior system knowledge. This contribution provides a practical, high-robustness solution for autonomous vehicles operating in slippery or unstructured environments, making it a valuable reference for researchers in robotics, nonlinear control, and intelligent systems. Dr. Nguyentien’s work stands out for its theoretical rigor and direct applicability to real-world autonomous navigation challenges.
Research Focus
Key Achievements
Top Papers
- 1