Ngoc Trung Dang
Papers
1
Total Citations
8
H-Index
1
About
Ngoc Trung Dang is a leading researcher in intelligent control systems and reinforcement learning, with a primary focus on the stabilization and optimal control of underactuated robotic platforms. His most cited work, "On-policy and Off-policy Q-learning algorithms with policy iteration for two-wheeled inverted pendulum systems" (2025, 8 citations), makes a significant contribution to model-free control by rigorously investigating both on-policy and off-policy Q-learning algorithms for two-wheeled inverted pendulum (TWIP) robots. This research addresses the critical challenge of controlling dynamic systems with uncertain or unknown models, demonstrating that both approaches can guarantee optimal performance without requiring prior system knowledge. Dang’s work is particularly impactful for the development of autonomous mobile robots and self-balancing vehicles, where robustness to model uncertainty is essential. By bridging theoretical reinforcement learning advances with practical robotic applications, his research provides a foundational framework for deploying intelligent controllers in real-world environments. With a growing citation record, Dang is establishing himself as an important voice in the intersection of machine learning and nonlinear control, offering solutions that are both mathematically rigorous and experimentally viable.
Research Focus
Key Achievements
Top Papers
- 1