Papers
2
Total Citations
11
H-Index
2
About
Hoang Tran is a researcher specializing in advanced control systems for nonlinear and underactuated robotic platforms. His work primarily focuses on the intersection of adaptive control, reinforcement learning, and sliding mode techniques to solve complex motion control problems. Tran’s most cited paper, "An Efficient Approach for SIMO Systems using Adaptive Fuzzy Hierarchical Sliding Mode Control" (2021, 8 citations), introduces a novel hierarchical sliding mode control framework for challenging systems like inverted pendulums and overhead cranes, offering a robust solution to single-input, multiple-output (SIMO) dynamics. In a complementary vein, his work "Adaptive Dynamic Programming based Control Scheme for Uncertain Two-Wheel Robots" (2021, 3 citations) pioneers the use of reinforcement learning—specifically adaptive dynamic programming—to handle parameter uncertainties and nonlinearities in two-wheel robots, enabling more intelligent, self-optimizing control without requiring precise system models. Together, these contributions demonstrate Tran’s commitment to bridging theoretical control advances with practical robotic applications, making his research valuable for engineers tackling real-world instability and uncertainty in autonomous systems.
Research Focus
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Top Papers
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