Papers
3
Total Citations
13
H-Index
3
About
Duy Hoang is a researcher specializing in advanced control systems for robotic manipulators, with a focus on adaptive and model-free approaches. His work addresses critical challenges in robotics, including system uncertainties, nonlinearities, and external disturbances. Hoang’s most-cited paper, "Adaptive cooperation of optimal linear quadratic regulator and lumped disturbance rejection estimator-based tracking control for robotic manipulators" (2023, 7 citations), introduces a novel hybrid control strategy that combines optimal linear quadratic regulator (LQR) techniques with disturbance rejection estimators, significantly improving tracking accuracy and robustness in robotic systems. Another key contribution, "A Model-Free Approach for Output Regulation of uncertain 4 DOF Serial Robot with Disturbance" (2022, 3 citations), proposes a groundbreaking method that eliminates the need for a mathematical model of the robot, relying only on its degrees of freedom, while effectively handling nonlinearities, uncertainties, and input disturbances. Additionally, his work on "Reinforcement Control for Planar Robot Based on Neural Network and Extended State Observer" (2023, 3 citations) integrates reinforcement learning with neural networks and state observers, advancing adaptive control in dynamic environments. With a growing citation impact, Hoang’s research is paving the way for more intelligent, resilient, and model-independent robotic systems, offering practical solutions for real-world automation challenges.
Research Focus
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Top Papers
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