2026年5月20日学术报告

报告题目    Adaptive Autonomous Synchronization of Multi-Agent Systems    

报告人    陈智勇 教授    

报告时间    2026年5月20日  星期三  9:00-10:00    

报告地点    江南大学自动化与智能科学学院C317    

邀请人    赵忠盖 教授    

报告简介：

In this talk, we introduce a new type of synchronization problem for heterogeneous multi-agent systems, called autonomous synchronization. Unlike traditional synchronization, neither the synchronized agent dynamics nor the synchronized states are predefined. Instead, they emerge autonomously based on the agents' inherent properties and initial states, providing greater adaptability and improving synchronization efficiency. To achieve this, we propose a novel control law and establish the necessary and sufficient conditions, particularly when the synchronized agent dynamics are unstable. Additionally, we present an adaptive, fully distributed control scheme that operates without relying on network topology. This approach is well-suited for tasks like deploying a swarm of drones over disaster areas to quickly locate survivors and assess damage. The exponentially diverging trajectories allow the swarm to cover large areas rapidly, while synchronization ensures reliable cooperation among drones.

报告人简介：

Zhiyong Chen received the B.E. degree in automation from the University of Science and Technology of China, Hefei, China, in 2000, and the M.Phil. and Ph.D. degrees in mechanical and automation engineering from the Chinese University of Hong Kong, in 2002 and 2005, respectively. He worked as a Research Associate at the University of Virginia, Charlottesville, VA, USA from 2005 to 2006. In 2006, he joined the University of Newcastle, Callaghan, NSW, Australia, where he is currently a Professor. He was also a Changjiang Chair Professor with Central South University, Changsha, China. His research interests include nonlinear systems and control, networked systems, and reinforcement learning. Dr. Chen is/was an Associate Editor of Automatica, IEEE Transactions on Automatic Control, IEEE Transactions on Neural Networks and Learning Systems, and IEEE Transactions on Cybernetics.