Yabin Gao
Papers
8
Total Citations
480
H-Index
6
About
Yabin Gao is an accomplished control systems researcher whose work sits at the intersection of robust adaptive control, sliding mode theory, and intelligent learning-based methods, with a particular focus on uncertain robotic systems. His research has made substantial contributions to finite-time and fixed-time control frameworks, addressing critical practical challenges such as input saturation, actuator faults, singularity avoidance in terminal sliding-mode control, and disturbance rejection. Gao's most influential work introduces novel segmental sliding variables and adaptive anti-saturation mechanisms to achieve faster convergence in fixed-time controllers, a paper that has already garnered 146 citations since 2023. His integration of radial basis function neural networks (RBFNNs) and fuzzy neural architectures into trajectory tracking controllers has proven particularly impactful, with multiple papers exceeding 75 citations within just two years of publication. Notably, his 2021 research on stabilization at a prescribed instant breaks from conservative assumptions in classical fixed-time stability theory, offering a more precise and flexible stabilization paradigm. Collectively accumulating nearly 500 citations across his key works, Gao is emerging as a significant voice in intelligent robust control for next-generation robotic applications.
Research Focus
Key Achievements
Top Papers
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- 7Stabilization with Prescribed Instant for High-Order Integrator Systems2 citations · 2021
- 8Stabilization with Prescribed Instant for High-Order Integrator Systems2 citations · 2021