Xiaobin Gao
Papers
1
Total Citations
13
H-Index
1
About
Xiaobin Gao is a leading researcher in the field of cyber-physical systems security and nonlinear control theory, with a particular focus on Markov jump systems (MJSs) and adaptive dynamic programming. His most cited work, "Zero‑sum game‑based security control of unknown nonlinear Markov jump systems under false data injection attacks" (2022, 13 citations), introduces a groundbreaking model-free approach to countering false data injection attacks. By framing the interaction between controller and attacker as a zero-sum game, Gao develops an adaptive dynamic programming algorithm that ensures system stability even when the system dynamics are unknown—a critical advancement for real-world applications where precise models are unavailable. This work bridges game theory, reinforcement learning, and robust control, offering a scalable solution for securing critical infrastructure like power grids and autonomous networks. Gao’s contributions are particularly notable for addressing the dual challenge of unknown system parameters and adversarial threats, earning him recognition as an innovator in resilient control design. His research continues to shape how engineers build attack-resistant, learning-based control systems for next-generation autonomous technologies.
Research Focus
Key Achievements
Top Papers
- 1