Huijuan Fang

Papers

Total Citations

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About

Huijuan Fang’s research bridges the frontiers of intelligent control systems and brain-computer interfaces (BCIs), with a focus on enhancing human-machine collaboration. Her most cited work introduces a novel Petri net-based shared control method for BCI systems, which modularizes functional components and integrates control places to enable seamless cooperation between human intelligence and machine decision-making. This approach, detailed in her 2019 paper (6 citations), offers a structured framework for improving the reliability and adaptability of BCI-driven devices. Earlier, Fang contributed to robotics with a trajectory tracking sliding mode control scheme for multi-input/multi-output systems (2008, 2 citations), demonstrating finite-time convergence and robust performance against chattering—a common challenge in sliding mode designs. Her work showcases a dual expertise in theoretical control design and applied BCI system architecture, addressing critical issues in real-time coordination and stability. While her citation counts reflect a growing niche impact, Fang’s contributions are notable for their interdisciplinary ambition, laying groundwork for safer, more intuitive interfaces between humans and autonomous systems. Her research continues to inspire advancements in assistive robotics and neural control technologies.