Nguyen Thien Thanh
Papers
3
Total Citations
42
H-Index
3
About
Nguyen Thien Thanh is a leading researcher in intelligent control systems, with a primary focus on reinforcement learning (RL) and adaptive control for autonomous mobile robotics. His most significant contributions lie in developing novel, model-free control algorithms that integrate kinematic and dynamic tracking for wheeled mobile robots. Thanh pioneered the use of actor-critic architectures and neural networks to design robust controllers that achieve optimality without requiring prior knowledge of a system’s drift dynamics—a major advancement in non-holonomic robot control. His work extends to multi-robot synchronization, where he proposed RL-based robust adaptive control laws for coordinated motion in communication networks. Among his highly cited works, his 2014 paper on RL-based intelligent tracking control for wheeled mobile robots has garnered 30 citations, demonstrating substantial impact in the field. Further notable achievements include his 2010 paper on robust RL-based tracking control, which introduced a policy iteration algorithm combined with a neural network to design an adaptive critic robust controller, incorporating H-infinity performance criteria. Thanh’s research bridges reinforcement learning and practical robotics, offering scalable, optimal solutions for autonomous navigation and multi-agent coordination.
Research Focus
Key Achievements
Top Papers
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