Papers
3
Total Citations
13
H-Index
3
About
Dung Manh is a researcher focused on advanced control strategies for robotic manipulators, particularly in uncertain and disturbed environments. His work centers on adaptive and model-free approaches to enhance the precision and robustness of robotic systems. A key contribution is his 2023 paper on "Adaptive cooperation of optimal linear quadratic regulator and lumped disturbance rejection estimator-based tracking control for robotic manipulators," which has garnered 7 citations, demonstrating its impact in the field. This work integrates optimal control with disturbance rejection to improve tracking performance. Manh also proposed a model-free method for output regulation of uncertain 4-DOF serial robots, eliminating the need for a mathematical model while handling nonlinearities and disturbances. Additionally, his research on "Reinforcement Control for Planar Robot Based on Neural Network and Extended State Observer" (3 citations) explores combining reinforcement learning with neural networks and observers to address system uncertainties. These contributions highlight Manh’s dedication to developing practical, robust control solutions for real-world robotic applications, making his work valuable for students and researchers in robotics and control engineering.
Research Focus
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