Papers
1
Total Citations
2
H-Index
1
About
Yucong Cao is a researcher focused on advancing control systems for mobile robotics, with a particular emphasis on trajectory tracking and intelligent automation. Their most-cited work, "Research on trajectory tracking of wheeled mobile robots using fuzzy PID based on TD3" (2024), introduces a novel hybrid control strategy that integrates Twin Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning with fuzzy PID to address critical limitations in non-holonomic wheeled mobile robot (NWMR) navigation. This approach significantly improves tracking accuracy, reduces response delay, and enhances robustness and stability compared to traditional PID controllers. While this paper has garnered 2 citations in its early publication stage, it represents a promising contribution to the intersection of deep reinforcement learning and classical control theory. Cao’s work is particularly relevant for applications in autonomous vehicles and industrial robotics, where precise and adaptive trajectory following is essential. Their research demonstrates a commitment to solving real-world control challenges through innovative algorithmic fusion, positioning them as an emerging voice in the field of intelligent robotic systems.
Research Focus
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Top Papers
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