Sihua Zhang
Papers
8
Total Citations
49
H-Index
5
About
Sihua Zhang is an emerging researcher specializing in safety-critical control systems, control barrier functions (CBFs), and robotics, with a particular focus on bridging the gap between theoretical safety guarantees and real-world robotic applications. Zhang's most significant contributions address a fundamental challenge in modern robotics: ensuring safety when precise dynamic models and state measurements are unavailable. By developing innovative frameworks combining extended state observers (ESOs), Gaussian processes, and high-order control barrier functions, Zhang has advanced the field's ability to enforce safety constraints on systems with uncertain or unknown dynamics. Among Zhang's notable achievements is pioneering the concept of *prescribed-time safety*, which guarantees that systems can recover into safe operating regions within a user-defined timeframe — even when starting from unsafe initial states. This work has direct implications for robotic manipulators and other real-world systems. Zhang has also contributed to safe reinforcement learning through differential high-order CBF-based safety filters, connecting learning-based control with formal safety assurances. With papers accumulating over 40 citations since 2022 — including a 2023 paper already earning 14 citations — Zhang's work is gaining rapid recognition. Researchers interested in robust, uncertainty-aware safety-critical control will find Zhang's publications an invaluable resource.
Research Focus
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