Bingxiao Huang
Papers
1
Total Citations
8
H-Index
1
About
Dr. Bingxiao Huang is at the forefront of advancing safe and reliable learning-based control for complex nonlinear systems. Their research fundamentally bridges the gap between data-driven modeling and rigorous control theory, with a core focus on ensuring stability and safety guarantees in autonomous decision-making. In their highly influential work, "Learning-Based Modeling and Predictive Control for Unknown Nonlinear System With Stability Guarantees," Dr. Huang tackles a critical challenge: how to deploy learned dynamics models in real-world control without risking instability due to modeling errors. By proposing a novel learning-based scheme that explicitly imposes stability constraints during the modeling phase, they provide a principled framework that reconciles the flexibility of machine learning with the certifiable safety demanded by engineering applications. This contribution, which has already garnered significant early attention with 8 citations since its 2025 publication, is paving the way for more trustworthy AI-driven control in robotics, autonomous vehicles, and industrial automation. Dr. Huang’s work is essential reading for researchers seeking to build intelligent systems that are not only powerful but also provably safe.
Research Focus
Key Achievements
Top Papers
- 1