Meiqi Tang
Papers
1
Total Citations
15
H-Index
1
About
Meiqi Tang is a researcher specializing in robotics, adaptive control, and machine learning, with a particular focus on enhancing the autonomy and reliability of wheeled mobile robots. Their most-cited work, "Robust adaptive trajectory tracking for wheeled mobile robots based on Gaussian process regression" (2022, 15 citations), introduces a novel framework that integrates Gaussian process regression with adaptive control to achieve precise trajectory tracking under uncertain and dynamic environments. This contribution addresses critical challenges in mobile robotics, such as handling model inaccuracies and external disturbances, offering a robust solution that improves real-time performance and safety. Tang’s research bridges the gap between data-driven methods and classical control theory, demonstrating significant potential for applications in autonomous navigation, logistics, and service robotics. With a growing citation impact, their work is gaining recognition among peers for its practical relevance and theoretical depth. Tang’s achievements underscore a commitment to advancing intelligent systems that operate reliably in complex, real-world settings, making them a promising voice in the field of robotics and control.
Research Focus
Key Achievements
Top Papers
- 1