Minh Hiep Phung
Papers
1
Total Citations
38
H-Index
1
About
Minh Hiep Phung is a researcher specializing in nonlinear control systems, reinforcement learning, and mobile robotics. His work focuses on developing robust control strategies for complex, perturbed robotic platforms, particularly those with mecanum wheels. Phung’s most-cited paper, "Nonlinear robust integral based actor–critic reinforcement learning control for a perturbed three-wheeled mobile robot with mecanum wheels" (2024, 38 citations), introduces a novel integration of actor-critic reinforcement learning with integral robust control to enhance trajectory tracking and disturbance rejection in wheeled robots. This contribution addresses critical challenges in real-world robotics, such as uncertainty and external perturbations, offering a scalable framework for autonomous navigation. While his citation count is still growing, the impact of this work is evident in its adoption by researchers exploring adaptive control for omnidirectional vehicles. Phung’s approach bridges model-free learning and robust control theory, paving the way for more resilient and intelligent robotic systems. His research holds promise for applications in warehouse automation, assistive robotics, and exploration, where precision and adaptability are paramount.
Research Focus
Key Achievements
Top Papers
- 1