Phuong Nam Dao
Papers
7
Total Citations
155
H-Index
6
About
Phuong Nam Dao is a robotics and control systems researcher whose work sits at the intersection of adaptive control, reinforcement learning, and mobile robotics. With a growing body of highly cited publications, Dao has established himself as a significant contributor to intelligent control strategies for wheeled mobile robots (WMRs) and related autonomous systems. His early influential work introduced a Gaussian wavelet network-based robust adaptive tracking controller for WMRs operating under unknown wheel slip conditions, demonstrating how neural network approximations can compensate for unmodeled nonlinear dynamics — a paper that has accumulated 40 citations since 2018. Dao has since advanced into reinforcement learning-based control, developing actor-critic and Q-learning frameworks for uncertain robotic systems, with recent papers attracting rapid citation uptake. His research on distributed model predictive control for multi-robot leader-follower formations reflects a broadening scope toward cooperative autonomy. Across his portfolio, Dao consistently addresses real-world challenges including system perturbations, input constraints, and incomplete dynamic knowledge. With over 150 cumulative citations spanning adaptive control, model predictive control, and data-driven learning, his contributions provide both theoretical rigor and practical relevance for researchers developing robust autonomous robotic systems.
Research Focus
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Top Papers
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