Papers
1
Total Citations
7
H-Index
1
About
Tuan Do is a leading researcher in intelligent control systems and robotics, with a primary focus on advancing the autonomy and robustness of nonholonomic wheeled mobile robots (WMRs). His most cited work, a 2014 study, introduces a groundbreaking neural network-based adaptive sliding mode control (ASMC) method that addresses critical challenges in robot tracking. By integrating self-recurrent wavelet neural networks (SRWNN), Do’s approach effectively compensates for unknown wheel slips, model uncertainties, and external disturbances—issues that traditionally hinder real-world robotic performance. This contribution has garnered 7 citations, reflecting its significance in the field. Do’s research bridges theoretical control theory and practical robotics, offering solutions that enhance stability and precision in dynamic environments. His work is particularly notable for its adaptive capabilities, enabling robots to maintain accurate trajectory tracking under unpredictable conditions. For students and researchers exploring robust control or mobile robotics, Do’s innovations provide a vital framework for developing resilient autonomous systems, marking him as a key figure in advancing intelligent motion control.
Research Focus
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Top Papers
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