Trung Thanh Cao
Papers
3
Total Citations
7
H-Index
2
About
Trung Thanh Cao is a rising researcher in the field of robotics and autonomous systems, with a focus on intelligent control, motion planning, and uncertainty compensation. His work addresses critical challenges in robotic manipulation and mobile robotics, particularly in developing robust control strategies that function without precise dynamic models. Cao’s most cited paper, "A Bayesian Neural Network-based Obstacle Avoidance Algorithm for an Educational Autonomous Mobile Robot Platform" (2023, 3 citations), introduces an affordable, AI-driven platform for technical education, blending neural network-based perception with real-time navigation. His 2025 paper on "Learning and Uncertainty Compensation in Robotic Motion Systems" (3 citations) explores Li-Slotine adaptive control and Iterative Learning Control (ILC), demonstrating how Lyapunov-based methods can stabilize robots under uncertainty while ensuring precision. Additionally, his work on "A model-free controller for uncertain robot manipulators with matched disturbances" (2023, 1 citation) proposes a digital control approach that bypasses the need for accurate dynamic models—a significant step toward practical, deployable robotic systems. Though early in his career, Cao’s contributions are already shaping accessible, low-cost robotics for education and industry, with a clear trajectory toward resilient, learning-enabled autonomy.
Research Focus
Key Achievements
Top Papers
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