Guibing Yang
Papers
1
Total Citations
4
H-Index
1
About
Guibing Yang is a rising researcher at the forefront of data-driven control systems, specializing in the intersection of machine learning and adaptive control for complex, unknown dynamics. His most cited work, "Adaptive Tracking Control for Unknown Dynamics Systems with SINDYc-based Sparse Identification" (2023, 4 citations), tackles a critical bottleneck in modern control theory: the heavy data requirements and poor generalization of neural network-based approaches. Yang’s key contribution lies in integrating Sparse Identification of Nonlinear Dynamics (SINDYc) with adaptive control frameworks, enabling efficient, model-based control with significantly less training data. This work offers a more robust and interpretable alternative to black-box neural methods, promising safer and more reliable autonomous systems. Though early in his career, Yang’s focus on sparse identification for real-time adaptation marks a notable step toward practical, data-efficient control in robotics and aerospace. His research is particularly valuable for students and engineers seeking to bridge the gap between data science and classical control theory.
Research Focus
Key Achievements
Top Papers
- 1