Ming-Can Fan

Huazhong University of Science and Technology, Huizhou University

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About

Ming-Can Fan is a leading researcher in multi-agent systems, with a focus on flocking, synchronization, and formation control. Their work addresses fundamental challenges in collective motion, particularly in bounded spaces—a critical gap in conventional flocking theory. Fan’s 2013 paper on multi-agent flocking in confined environments, with 41 citations, provides algorithms and experiments that model natural and engineered systems where agents cannot maintain constant velocities without leaving the space. This contribution has been influential in robotics and swarm intelligence. Fan also introduced a novel approach to ultrafast network synchronization in 2018, using a Hankel matrix-based method to predict the final synchronized state from local observation of a single node, achieving results in far less time than the full process. This work, with 7 citations, offers a breakthrough for efficient network control. More recently, in 2022, Fan developed a Q-learning-based adaptive algorithm for formation tracking control of multi-mobile robot systems, integrating a linear extended state observer to handle model uncertainties and disturbances. This paper, with 4 citations, demonstrates Fan’s commitment to practical, intelligent control solutions. Through these contributions, Fan has advanced the theoretical and applied understanding of collective behavior in multi-agent systems, with lasting impact on robotics and network science.