Papers
6
Total Citations
29
H-Index
3
About
Nguyen Duc Dien is a prominent researcher specializing in intelligent control systems, autonomous robotics, and reinforcement learning-based optimization. His work sits at the intersection of artificial intelligence and robotics engineering, with particular focus on mobile robot navigation and robot manipulator control in complex, real-world environments. Dien's most influential contribution, "Reinforcement Learning based Method for Autonomous Navigation of Mobile Robots in Unknown Environments" (2020, 12 citations), demonstrated how deep reinforcement learning can overcome the limitations of classical RL algorithms when operating in high-dimensional state spaces. Complementing this, his applied study on Deep Deterministic Policy Gradient (DDPG) for mobile robots bridged simulation and physical implementation, advancing practical autonomy in robotics. A significant thread of his research addresses the challenging problem of trajectory tracking for robot manipulators under nonlinear dynamics, input constraints, and external disturbances. Through adaptive sliding control integrating neural networks and fuzzy logic, H∞ optimal control via Approximate Dynamic Programming (ADP), and decentralized robust tracking frameworks, Dien has developed increasingly sophisticated solutions to real-world control constraints. His growing citation record reflects rising recognition within the robotics and intelligent systems community, positioning him as an emerging contributor to next-generation autonomous control technologies.
Research Focus
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